741
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This chapter should be cited as:
Flato, G., J. Marotzke, B. Abiodun, P. Braconnot, S.C. Chou, W. Collins, P. Cox, F. Driouech, S. Emori, V. Eyring, C.
Forest, P. Gleckler, E. Guilyardi, C. Jakob, V. Kattsov, C. Reason and M. Rummukainen, 2013: Evaluation of Climate
Models. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assess-
ment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor,
S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge,
United Kingdom and New York, NY, USA.
Coordinating Lead Authors:
Gregory Flato (Canada), Jochem Marotzke (Germany)
Lead Authors:
Babatunde Abiodun (South Africa), Pascale Braconnot (France), Sin Chan Chou (Brazil), William
Collins (USA), Peter Cox (UK), Fatima Driouech (Morocco), Seita Emori (Japan), Veronika
Eyring (Germany), Chris Forest (USA), Peter Gleckler (USA), Eric Guilyardi (France), Christian
Jakob (Australia), Vladimir Kattsov (Russian Federation), Chris Reason (South Africa), Markku
Rummukainen (Sweden)
Contributing Authors:
Krishna AchutaRao (India), Alessandro Anav (UK), Timothy Andrews (UK), Johanna Baehr
(Germany), Nathaniel L. Bindoff (Australia), Alejandro Bodas-Salcedo (UK), Jennifer Catto
(Australia), Don Chambers (USA), Ping Chang (USA), Aiguo Dai (USA), Clara Deser (USA),
Francisco Doblas-Reyes (Spain), Paul J. Durack (USA/Australia), Michael Eby (Canada), Ramon
de Elia (Canada), Thierry Fichefet (Belgium), Piers Forster (UK), David Frame (UK/New Zealand),
John Fyfe (Canada), Emiola Gbobaniyi (Sweden/Nigeria), Nathan Gillett (Canada), Jesus Fidel
González-Rouco (Spain), Clare Goodess (UK), Stephen Griffies (USA), Alex Hall (USA), Sandy
Harrison (Australia), Andreas Hense (Germany), Elizabeth Hunke (USA), Tatiana Ilyina (Germany),
Detelina Ivanova (USA), Gregory Johnson (USA), Masa Kageyama (France), Viatcheslav Kharin
(Canada), Stephen A. Klein (USA), Jeff Knight (UK), Reto Knutti (Switzerland), Felix Landerer
(USA), Tong Lee (USA), Hongmei Li (Germany/China), Natalie Mahowald (USA), Carl Mears
(USA), Gerald Meehl (USA), Colin Morice (UK), Rym Msadek (USA), Gunnar Myhre (Norway),
J. David Neelin (USA), Jeff Painter (USA), Tatiana Pavlova (Russian Federation), Judith Perlwitz
(USA), Jean-Yves Peterschmitt (France), Jouni Räisänen (Finland), Florian Rauser (Germany),
Jeffrey Reid (USA), Mark Rodwell (UK), Benjamin Santer (USA), Adam A. Scaife (UK), Jörg
Schulz (Germany), John Scinocca (Canada), David Sexton (UK), Drew Shindell (USA), Hideo
Shiogama (Japan), Jana Sillmann (Canada), Adrian Simmons (UK), Kenneth Sperber (USA),
David Stephenson (UK), Bjorn Stevens (Germany), Peter Stott (UK), Rowan Sutton (UK), Peter
W. Thorne (USA/Norway/UK), Geert Jan van Oldenborgh (Netherlands), Gabriel Vecchi (USA),
Mark Webb (UK), Keith Williams (UK), Tim Woollings (UK), Shang-Ping Xie (USA), Jianglong
Zhang (USA)
Review Editors:
Isaac Held (USA), Andy Pitman (Australia), Serge Planton (France), Zong-Ci Zhao (China)
Evaluation of
Climate Models
742
9
Table of Contents
Executive Summary ..................................................................... 743
9.1 Climate Models and Their Characteristics ............... 746
9.1.1 Scope and Overview of this Chapter ......................... 746
9.1.2 Overview of Model Types to Be Evaluated ................ 746
9.1.3 Model Improvements ................................................ 748
Box 9.1: Climate Model Development and Tuning ................ 749
9.2 Techniques for Assessing Model Performance ....... 753
9.2.1 New Developments in Model Evaluation
Approaches ............................................................... 753
9.2.2 Ensemble Approaches for Model Evaluation ............. 754
9.2.3 The Model Evaluation Approach Used in this
Chapter and Its Limitations ....................................... 755
9.3 Experimental Strategies in Support of Climate
Model Evaluation ............................................................ 759
9.3.1 The Role of Model Intercomparisons ......................... 759
9.3.2 Experimental Strategy for Coupled Model
Intercomparison Project Phase 5 ............................... 759
9.4 Simulation of Recent and Longer-Term Records
in Global Models ............................................................. 760
9.4.1 Atmosphere .............................................................. 760
Box 9.2: Climate Models and the Hiatus in Global Mean
Surface Warming of the Past 15 Years .................................... 769
9.4.2 Ocean ........................................................................ 777
9.4.3 Sea Ice ...................................................................... 787
9.4.4 Land Surface, Fluxes and Hydrology .......................... 790
9.4.5 Carbon Cycle ............................................................. 792
9.4.6 Aerosol Burdens and Effects on Insolation ................ 794
9.5 Simulation of Variability and Extremes .................... 795
9.5.1 Importance of Simulating Climate Variability ............ 795
9.5.2 Diurnal-to-Seasonal Variability .................................. 796
9.5.3 Interannual-to-Centennial Variability ........................ 799
9.5.4 Extreme Events ......................................................... 806
Box 9.3: Understanding Model Performance ......................... 809
9.6 Downscaling and Simulation of Regional-Scale
Climate ............................................................................... 810
9.6.1 Global Models ........................................................... 810
9.6.2 Regional Climate Downscaling ................................. 814
9.6.3 Skill of Downscaling Methods ................................... 814
9.6.4 Value Added through RCMs ...................................... 815
9.6.5 Sources of Model Errors and Uncertainties ............... 815
9.6.6 Relating Downscaling Performance to Credibility
of Regional Climate Information ............................... 816
9.7 Climate Sensitivity and Climate Feedbacks ............ 817
9.7.1 Equilibrium Climate Sensitivity, Idealized Radiative
Forcing, and Transient Climate Response in the
Coupled Model Intercomparison Project
Phase 5 Ensemble ..................................................... 817
9.7.2 Understanding the Range in Model Climate
Sensitivity: Climate Feedbacks .................................. 819
9.7.3 Climate Sensitivity and Model Performance .............. 820
9.8 Relating Model Performance to Credibility of
Model Applications ......................................................... 821
9.8.1 Synthesis Assessment of Model Performance ............ 821
9.8.2 Implications of Model Evaluation for Climate
Change Detection and Attribution ............................ 825
9.8.3 Implications of Model Evaluation for Model
Projections of Future Climate .................................... 825
References .................................................................................. 828
Appendix 9.A: Climate Models Assessed
in Chapter 9 .................................................................................. 854
Frequently Asked Questions
FAQ 9.1 Are Climate Models Getting Better, and How
Would We Know? ................................................... 824
743
Evaluation of Climate Models Chapter 9
9
1
In this Report, the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium, or high.
A level of confidence is expressed using five qualifiers: very low, low, medium, high, and very high, and typeset in italics, e.g., medium confidence. For a given evidence and
agreement statement, different confidence levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence (see
Section 1.4 and Box TS.1 for more details).
Executive Summary
Climate models have continued to be developed and improved
since the AR4, and many models have been extended into Earth
System models by including the representation of biogeochem-
ical cycles important to climate change. These models allow for
policy-relevant calculations such as the carbon dioxide (CO
2
) emissions
compatible with a specified climate stabilization target. In addition, the
range of climate variables and processes that have been evaluated has
greatly expanded, and differences between models and observations
are increasingly quantified using ‘performance metrics’. In this chapter,
model evaluation covers simulation of the mean climate, of historical
climate change, of variability on multiple time scales and of regional
modes of variability. This evaluation is based on recent internationally
coordinated model experiments, including simulations of historic and
paleo climate, specialized experiments designed to provide insight into
key climate processes and feedbacks and regional climate downscal-
ing. Figure 9.44 provides an overview of model capabilities as assessed
in this chapter, including improvements, or lack thereof, relative to
models assessed in the AR4. The chapter concludes with an assessment
of recent work connecting model performance to the detection and
attribution of climate change as well as to future projections. {9.1.2,
9.8.1, Table 9.1, Figure 9.44}
The ability of climate models to simulate surface temperature
has improved in many, though not all, important aspects rel-
ative to the generation of models assessed in the AR4. There
continues to be very high confidence
1
that models reproduce observed
large-scale mean surface temperature patterns (pattern correlation of
~0.99), though systematic errors of several degrees are found in some
regions, particularly over high topography, near the ice edge in the
North Atlantic, and over regions of ocean upwelling near the equa-
tor. On regional scales (sub-continental and smaller), the confidence
in model capability to simulate surface temperature is less than for
the larger scales; however, regional biases are near zero on average,
with intermodel spread of roughly ±3°C. There is high confidence that
regional-scale surface temperature is better simulated than at the time
of the AR4. Current models are also able to reproduce the large-scale
patterns of temperature during the Last Glacial Maximum (LGM), indi-
cating an ability to simulate a climate state much different from the
present. {9.4.1, 9.6.1, Figures 9.2, 9.6, 9.39, 9.40}
There is very high confidence that models reproduce the gener-
al features of the global-scale annual mean surface temperature
increase over the historical period, including the more rapid
warming in the second half of the 20th century, and the cooling
immediately following large volcanic eruptions. Most simulations
of the historical period do not reproduce the observed reduction in
global mean surface warming trend over the last 10 to 15 years. There
is medium confidence that the trend difference between models and
observations during 1998–2012 is to a substantial degree caused by
internal variability, with possible contributions from forcing error and
some models overestimating the response to increasing greenhouse
gas (GHG) forcing. Most, though not all, models overestimate the
observed warming trend in the tropical troposphere over the last 30
years, and tend to underestimate the long-term lower stratospheric
cooling trend. {9.4.1, Box 9.2, Figure 9.8}
The simulation of large-scale patterns of precipitation has
improved somewhat since the AR4, although models continue
to perform less well for precipitation than for surface tempera-
ture. The spatial pattern correlation between modelled and observed
annual mean precipitation has increased from 0.77 for models availa-
ble at the time of the AR4 to 0.82 for current models. At regional scales,
precipitation is not simulated as well, and the assessment remains dif-
ficult owing to observational uncertainties. {9.4.1, 9.6.1, Figure 9.6}
The simulation of clouds in climate models remains challeng-
ing. There is very high confidence that uncertainties in cloud processes
explain much of the spread in modelled climate sensitivity. However,
the simulation of clouds in climate models has shown modest improve-
ment relative to models available at the time of the AR4, and this has
been aided by new evaluation techniques and new observations for
clouds. Nevertheless, biases in cloud simulation lead to regional errors
on cloud radiative effect of several tens of watts per square meter.
{9.2.1, 9.4.1, 9.7.2, Figures 9.5, 9.43}
Models are able to capture the general characteristics of storm
tracks and extratropical cyclones, and there is some evidence of
improvement since the AR4. Storm track biases in the North Atlantic
have improved slightly, but models still produce a storm track that is
too zonal and underestimate cyclone intensity. {9.4.1}
Many models are able to reproduce the observed changes in
upper ocean heat content from 1961 to 2005 with the mul-
ti-model mean time series falling within the range of the avail-
able observational estimates for most of the period. The ability
of models to simulate ocean heat uptake, including variations imposed
by large volcanic eruptions, adds confidence to their use in assessing
the global energy budget and simulating the thermal component of
sea level rise. {9.4.2, Figure 9.17}
The simulation of the tropical Pacific Ocean mean state has
improved since the AR4, with a 30% reduction in the spurious
westward extension of the cold tongue near the equator, a per-
vasive bias of coupled models. The simulation of the tropical Atlan-
tic remains deficient with many models unable to reproduce the basic
east–west temperature gradient. {9.4.2, Figure 9.14}
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Chapter 9 Evaluation of Climate Models
9
Current climate models reproduce the seasonal cycle of Arctic
sea ice extent with a multi-model mean error of less than about
10% for any given month. There is robust evidence that the
downward trend in Arctic summer sea ice extent is better sim-
ulated than at the time of the AR4, with about one quarter of
the simulations showing a trend as strong as, or stronger, than
in observations over the satellite era (since 1979). There is a ten-
dency for models to slightly overestimate sea ice extent in the Arctic
(by about 10%) in winter and spring. In the Antarctic, the multi-model
mean seasonal cycle agrees well with observations, but inter-model
spread is roughly double that for the Arctic. Most models simulate a
small decreasing trend in Antarctic sea ice extent, albeit with large
inter-model spread, in contrast to the small increasing trend in obser-
vations. {9.4.3, Figures 9.22, 9.24}
Models are able to reproduce many features of the observed
global and Northern Hemispher (NH) mean temperature vari-
ance on interannual to centennial time scales (high confidence),
and most models are now able to reproduce the observed peak
in variability associated with the El Niño (2- to 7-year period)
in the Tropical Pacific. The ability to assess variability from millennial
simulations is new since the AR4 and allows quantitative evaluation of
model estimates of low-frequency climate variability. This is important
when using climate models to separate signal and noise in detection
and attribution studies (Chapter 10). {9.5.3, Figures 9.33, 9.35}
Many important modes of climate variability and intraseasonal
to seasonal phenomena are reproduced by models, with some
improvements evident since the AR4. The statistics of the global
monsoon, the North Atlantic Oscillation, the El Niño-Southern Oscilla-
tion (ENSO), the Indian Ocean Dipole and the Quasi-Biennial Oscilla-
tion are simulated well by several models, although this assessment is
tempered by the limited scope of analysis published so far, or by limited
observations. There are also modes of variability that are not simulated
well. These include modes of Atlantic Ocean variability of relevance
to near term projections in Chapter 11 and ENSO teleconnections
outside the tropical Pacific, of relevance to Chapter 14. There is high
confidence that the multi-model statistics of monsoon and ENSO have
improved since the AR4. However, this improvement does not occur in
all models, and process-based analysis shows that biases remain in the
background state and in the strength of associated feedbacks. {9.5.3,
Figures 9.32, 9.35, 9.36}
There has been substantial progress since the AR4 in the assess-
ment of model simulations of extreme events. Based on assess-
ment of a suite of indices, the inter-model range of simulated climate
extremes is similar to the spread amongst observationally based esti-
mates in most regions. In addition, changes in the frequency of extreme
warm and cold days and nights over the second half of the 20th centu-
ry are consistent between models and observations, with the ensemble
global mean time series generally falling within the range of observa-
tional estimates. The majority of models underestimate the sensitivity
of extreme precipitation to temperature variability or trends, especially
in the tropics, which implies that models may underestimate the pro-
jected increase in extreme precipitation in the future. Some high-res-
olution atmospheric models have been shown to reproduce observed
year-to-year variability of Atlantic hurricane counts when forced with
observed sea surface temperatures, though so far only a few studies of
this kind are available. {9.5.4, Figure 9.37}
An important development since the AR4 is the more wide-
spread use of Earth System models, which include an interac-
tive carbon cycle. In the majority of these models, the simulated
global land and ocean carbon sinks over the latter part of the
20th century fall within the range of observational estimates.
However, the regional patterns of carbon uptake and release are less
well reproduced, especially for NH land where models systematically
underestimate the sink implied by atmospheric inversion techniques
The ability of models to simulate carbon fluxes is important because
these models are used to estimate ‘compatible emissions’ (carbon
dioxide emission pathways compatible with a particular climate
change target; see Chapter 6). {9.4.5, Figure 9.27}
The majority of Earth System models now include an interac-
tive representation of aerosols, and make use of a consistent
specification of anthropogenic sulphur dioxide emissions. How-
ever, uncertainties in sulphur cycle processes and natural sources and
sinks remain and so, for example, the simulated aerosol optical depth
over oceans ranges from 0.08 to 0.22 with roughly equal numbers of
models over- and under-estimating the satellite-estimated value of
0.12. {9.1.2, 9.4.6, Table 9.1, Figure 9.29}
Time-varying ozone is now included in the latest suite of models,
either prescribed or calculated interactively. Although in some
models there is only medium agreement with observed changes in
total column ozone, the inclusion of time-varying stratospheric ozone
constitutes a substantial improvement since the AR4 where half of the
models prescribed a constant climatology. As a result, there is robust
evidence that the representation of climate forcing by stratospheric
ozone has improved since the AR4. {9.4.1, Figure 9.10}
Regional downscaling methods are used to provide climate
information at the smaller scales needed for many climate
impact studies, and there is high confidence that downscaling
adds value both in regions with highly variable topography and
for various small-scale phenomena. Regional models necessar-
ily inherit biases from the global models used to provide boundary
conditions. Furthermore, the ability to systematically evaluate region-
al climate models, and statistical downscaling schemes, is hampered
because coordinated intercomparison studies are still emerging. How-
ever, several studies have demonstrated that added value arises from
higher resolution of stationary features like topography and coastlines,
and from improved representation of small-scale processes like con-
vective precipitation. {9.6.4}
Earth system Models of Intermediate Complexity (EMICs) pro-
vide simulations of millennial time-scale climate change, and are
used as tools to interpret and expand upon the results of more
comprehensive models. Although they are limited in the scope and
resolution of information provided, EMIC simulations of global mean
surface temperature, ocean heat content and carbon cycle response
over the 20th century are consistent with the historical records and
with more comprehensive models, suggesting that they can be used to
provide calibrated projections of long-term transient climate response
745
Evaluation of Climate Models Chapter 9
9
and stabilization, as well as large ensembles and alternative, policy-rel-
evant, scenarios. {9.4.1, 9.4.2, 9.4.5, Figures 9.8, 9.17, 9.27}
The Coupled Model Intercomparison Project Phase 5 (CMIP5)
model spread in equilibrium climate sensitivity ranges from
2.1°C to 4.7°C and is very similar to the assessment in the AR4.
No correlation is found between biases in global mean surface tem-
perature and equilibrium climate sensitivity, and so mean temperature
biases do not obviously affect the modelled response to GHG forcing.
There is very high confidence that the primary factor contributing to
the spread in equilibrium climate sensitivity continues to be the cloud
feedback. This applies to both the modern climate and the LGM. There
is likewise very high confidence that, consistent with observations,
models show a strong positive correlation between tropospheric tem-
perature and water vapour on regional to global scales, implying a pos-
itive water vapour feedback in both models and observations. {9.4.1,
9.7.2, Figures 9.9, 9.42, 9.43}
Climate and Earth System models are based on physical princi-
ples, and they reproduce many important aspects of observed
climate. Both aspects contribute to our confidence in the
models’ suitability for their application in detection and attri-
bution studies (Chapter 10) and for quantitative future predic-
tions and projections (Chapters 11 to 14). In general, there is no
direct means of translating quantitative measures of past performance
into confident statements about fidelity of future climate projections.
However, there is increasing evidence that some aspects of observed
variability or trends are well correlated with inter-model differences
in model projections for quantities such as Arctic summertime sea
ice trends, snow albedo feedback, and the carbon loss from tropical
land. These relationships provide a way, in principle, to transform an
observable quantity into a constraint on future projections, but the
application of such constraints remains an area of emerging research.
There has been substantial progress since the AR4 in the methodol-
ogy to assess the reliability of a multi-model ensemble, and various
approaches to improve the precision of multi-model projections are
being explored. However, there is still no universal strategy for weight-
ing the projections from different models based on their historical per-
formance. {9.8.3, Figure 9.45}
746
Chapter 9 Evaluation of Climate Models
9
9.1 Climate Models and Their Characteristics
9.1.1 Scope and Overview of this Chapter
Climate models are the primary tools available for investigating the
response of the climate system to various forcings, for making climate
predictions on seasonal to decadal time scales and for making projec-
tions of future climate over the coming century and beyond. It is crucial
therefore to evaluate the performance of these models, both individu-
ally and collectively. The focus of this chapter is primarily on the models
whose results will be used in the detection and attribution Chapter 10
and the chapters that present and assess projections (Chapters 11 to
14; Annex I), and so this is necessarily an incomplete evaluation. In
particular, this chapter draws heavily on model results collected as part
of the Coupled Model Intercomparison Projects (CMIP3 and CMIP5)
(Meehl et al., 2007; Taylor et al., 2012b), as these constitute a set of
coordinated and thus consistent and increasingly well-documented cli-
mate model experiments. Other intercomparison efforts, such as those
dealing with Regional Climate Models (RCMs) and those dealing with
Earth System Models of Intermediate Complexity (EMICs) are also used.
It should be noted that the CMIP3 model archive has been extensively
evaluated, and much of that evaluation has taken place subsequent to
the AR4. By comparison, the CMIP5 models are only now being evalu-
ated and so there is less published literature available. Where possible
we show results from both CMIP3 and CMIP5 models so as to illustrate
changes in model performance over time; however, where only CMIP3
results are available, they still constitute a useful evaluation of model
performance in that for many quantities, the CMIP3 and CMIP5 model
performances are broadly similar.
The direct approach to model evaluation is to compare model output
with observations and analyze the resulting difference. This requires
knowledge of the errors and uncertainties in the observations, which
have been discussed in Chapters 2 through 6. Where possible, aver-
ages over the same time period in both models and observations are
compared, although for many quantities the observational record is
rather short, or only observationally based estimates of the climato-
logical mean are available. In cases where observations are lacking,
we resort to intercomparison of model results to provide at least some
quantification of model uncertainty via inter-model spread.
After a more thorough discussion of the climate models and meth-
ods for evaluation in Sections 9.1 and 9.2, we describe climate model
experiments in Section 9.3, evaluate recent and longer-term records as
simulated by climate models in Section 9.4, variability and extremes
in Section 9.5, and regional-scale climate simulation including down-
scaling in Section 9.6. We conclude with a discussion of model perfor-
mance and climate sensitivity in Section 9.7, and the relation between
model performance and the credibility of future climate projections in
Section 9.8.
9.1.2 Overview of Model Types to Be Evaluated
The models used in climate research range from simple energy balance
models to complex Earth System Models (ESMs) requiring state of the
art high-performance computing. The choice of model depends directly
on the scientific question being addressed (Held, 2005; Collins et al.,
2006d). Applications include simulating palaeo or historical climate,
sensitivity and process studies for attribution and physical understand-
ing, predicting near-term climate variability and change on seasonal
to decadal time scales, making projections of future climate change
over the coming century or more and downscaling such projections
to provide more detail at the regional and local scale. Computational
cost is a factor in all of these, and so simplified models (with reduced
complexity or spatial resolution) can be used when larger ensembles
or longer integrations are required. Examples include exploration of
parameter sensitivity or simulations of climate change on the millenni-
al or longer time scale. Here, we provide a brief overview of the climate
models evaluated in this chapter.
9.1.2.1 Atmosphere–Ocean General Circulation Models
Atmosphere–Ocean General Circulation Models (AOGCMs) were the
‘standard’ climate models assessed in the AR4. Their primary function
is to understand the dynamics of the physical components of the cli-
mate system (atmosphere, ocean, land and sea ice), and for making
projections based on future greenhouse gas (GHG) and aerosol forcing.
These models continue to be extensively used, and in particular are run
(sometimes at higher resolution) for seasonal to decadal climate pre-
diction applications in which biogeochemical feedbacks are not critical
(see Chapter 11). In addition, high-resolution or variable-resolution
AOGCMs are often used in process studies or applications with a focus
on a particular region. An overview of the AOGCMs assessed in this
chapter can be found in Table 9.1 and the details in Table 9.A.1. For
some specific applications, an atmospheric component of such a model
is used on its own.
9.1.2.2 Earth System Models
ESMs are the current state-of-the-art models, and they expand on
AOGCMs to include representation of various biogeochemical cycles
such as those involved in the carbon cycle, the sulphur cycle, or ozone
(Flato, 2011). These models provide the most comprehensive tools
available for simulating past and future response of the climate system
to external forcing, in which biogeochemical feedbacks play an impor-
tant role. An overview of the ESMs assessed in this chapter can be
found in Table 9.1 and details in Table 9.A.1.
9.1.2.3 Earth System Models of Intermediate Complexity
EMICs attempt to include relevant components of the Earth system,
but often in an idealized manner or at lower resolution than the
models described above. These models are applied to certain scientific
questions such as understanding climate feedbacks on millennial time
scales or exploring sensitivities in which long model integrations or
large ensembles are required (Claussen et al., 2002; Petoukhov et al.,
2005). This class of models often includes Earth system components
not yet included in all ESMs (e.g., ice sheets). As computing power
increases, this model class has continued to advance in terms of reso-
lution and complexity. An overview of EMICs assessed in this chapter
and in the AR5 WG1 is provided in Table 9.2 with additional details in
Table 9.A.2.
747
Evaluation of Climate Models Chapter 9
9
Table 9.1 | Main features of the Atmosphere–Ocean General Circulation Models (AOGCMs) and Earth System Models (ESMs) participating in Coupled Model Intercomparison
Project Phase 5 (CMIP5), and a comparison with Coupled Model Intercomparison Project Phase 3 (CMIP3), including components and resolution of the atmosphere and the ocean
models. Detailed CMIP5 model description can be found in Table 9.A.1 (* refers to Table 9.A.1 for more details). Official CMIP model names are used. HT stands for High-Top
atmosphere, which has a fully resolved stratosphere with a model top above the stratopause. AMIP stands for models with atmosphere and land surface only, using observed sea
surface temperature and sea ice extent. A component is coloured when it includes at least a physically based prognostic equation and at least a two-way coupling with another
component, allowing climate feedbacks. For aerosols, lighter shading means ‘semi-interactive’ and darker shading means ‘fully interactive’. The resolution of the land surface usually
follows that of the atmosphere, and the resolution of the sea ice follows that of the ocean. In moving from CMIP3 to CMIP5, note the increased complexity and resolution as well
as the absence of artificial flux correction (FC) used in some CMIP3 models.
748
Chapter 9 Evaluation of Climate Models
9
Significant advances in EMIC capabilities are inclusion of ice sheets
(UVic 2.9, Weaver et al., 2001; CLIMBER-2.4, Petoukhov et al., 2000;
LOVECLIM, Goosse et al., 2010) and ocean sediment models (DCESS,
Shaffer et al., 2008; UVic 2.9, Weaver et al., 2001; Bern3D-LPJ, Ritz
et al., 2011). These additional interactive components provide criti-
cal feedbacks involved in sea level rise estimates and carbon cycle
response on millennial time scales (Zickfeld et al., 2013). Further, the
flexibility and efficiency of EMICs allow calibration to specific climate
change events to remove potential biases.
9.1.2.4 Regional Climate Models
RCMs are limited-area models with representations of climate process-
es comparable to those in the atmospheric and land surface compo-
nents of AOGCMs, though typically run without interactive ocean and
sea ice. RCMs are often used to dynamically ‘downscale’ global model
simulations for some particular geographical region to provide more
detailed information (Laprise, 2008; Rummukainen, 2010). By contrast,
empirical and statistical downscaling methods constitute a range of
techniques to provide similar regional or local detail.
9.1.3 Model Improvements
The climate models assessed in this report have seen a number of
improvements since the AR4. Model development is a complex and
iterative task: improved physical process descriptions are developed,
new model components are introduced and the resolution of the
models is improved. After assembly of all model components, model
parameters are adjusted, or tuned, to provide a stable model climate.
Table 9.2 | Main features of the EMICs assessed in the AR5, including components and complexity of the models. Model complexityfor four componentsis indicated by colour
shading. Further detailed descriptions of the models are contained in Table 9.A.2.
The overall approach to model development and tuning is summarized
in Box 9.1.
9.1.3.1 Parameterizations
Parameterizations are included in all model components to represent
processes that cannot be explicitly resolved; they are evaluated both
in isolation and in the context of the full model. The purpose of this
section is to highlight recent developments in the parameterizations
employed in each model component. Some details for individual
models are listed in Table 9.1.
9.1.3.1.1 Atmosphere
Atmospheric models must parameterize a wide range of processes,
including those associated with atmospheric convection and clouds,
cloud-microphysical and aerosol processes and their interaction,
boundary layer processes, as well as radiation and the treatment of
unresolved gravity waves. Advances made in the representation of
cloud processes, including aerosol–cloud and cloud–radiation interac-
tions, and atmospheric convection are described in Sections 7.2.3 and
7.4.
Improvements in representing the atmospheric boundary layer since
the AR4 have focussed on basic boundary layer processes, the rep-
resentation of the stable boundary layer, and boundary layer clouds
(Teixeira et al., 2008). Several global models have successfully adopt-
ed new approaches to the parameterization of shallow cumulus con-
vection and moist boundary layer turbulence that acknowledge their
749
Evaluation of Climate Models Chapter 9
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Box 9.1 | Climate Model Development and Tuning
The Atmosphere–Ocean General Circulation Models, Earth System Models and Regional Climate Models evaluated here are based on
fundamental laws of nature (e.g., energy, mass and momentum conservation). The development of climate models involves several
principal steps:
1. Expressing the system’s physical laws in mathematical terms. This requires theoretical and observational work in deriving and sim-
plifying mathematical expressions that best describe the system.
2. Implementing these mathematical expressions on a computer. This requires developing numerical methods that allow the solution
of the discretized mathematical expressions, usually implemented on some form of grid such as the latitude–longitude–height grid
for atmospheric or oceanic models.
3. Building and implementing conceptual models (usually referred to as parameterizations) for those processes that cannot be rep-
resented explicitly, either because of their complexity (e.g., biochemical processes in vegetation) or because the spatial and/or
temporal scales on which they occur are not resolved by the discretized model equations (e.g., cloud processes and turbulence). The
development of parameterizations has become very complex (e.g., Jakob, 2010) and is often achieved by developing conceptual
models of the process of interest in isolation using observations and comprehensive process models. The complexity of each process
representation is constrained by observations, computational resources and current knowledge (e.g., Randall et al., 2007).
The application of state-of-the-art climate models requires significant supercomputing resources. Limitations in those resources lead to
additional constraints. Even when using the most powerful computers, compromises need to be made in three main areas:
1. Numerical implementations allow for a choice of grid spacing and time step, usually referred to as ‘model resolution’. Higher model
resolution generally leads to mathematically more accurate models (although not necessarily more reliable simulations) but also to
higher computational costs. The finite resolution of climate models implies that the effects of certain processes must be represented
through parameterizations (e.g., the carbon cycle or cloud and precipitation processes; see Chapters 6 and 7).
2. The climate system contains many processes, the relative importance of which varies with the time scale of interest (e.g., the carbon
cycle). Hence compromises to include or exclude certain processes or components in a model must be made, recognizing that an
increase in complexity generally leads to an increase in computational cost (Hurrell et al., 2009).
3. Owing to uncertainties in the model formulation and the initial state, any individual simulation represents only one of the possible
pathways the climate system might follow. To allow some evaluation of these uncertainties, it is necessary to carry out a number of
simulations either with several models or by using an ensemble of simulations with a single model, both of which increase compu-
tational cost.
Trade-offs amongst the various considerations outlined above are guided by the intended model application and lead to the several
classes of models introduced in Section 9.1.2.
Individual model components (e.g., the atmosphere, the ocean, etc.) are typically first evaluated in isolation as part of the model devel-
opment process. For instance, the atmospheric component can be evaluated by prescribing sea surface temperature (SST) (Gates et al.,
1999) or the ocean and land components by prescribing atmospheric conditions (Barnier et al., 2006; Griffies et al., 2009). Subsequently,
the various components are assembled into a comprehensive model, which then undergoes a systematic evaluation. At this stage, a small
subset of model parameters remains to be adjusted so that the model adheres to large-scale observational constraints (often global aver-
ages). This final parameter adjustment procedure is usually referred to as ‘model tuning’. Model tuning aims to match observed climate
system behaviour and so is connected to judgements as to what constitutes a skilful representation of the Earth’s climate. For instance,
maintaining the global mean top of the atmosphere (TOA) energy balance in a simulation of pre-industrial climate is essential to prevent
the climate system from drifting to an unrealistic state. The models used in this report almost universally contain adjustments to param-
eters in their treatment of clouds to fulfil this important constraint of the climate system (Watanabe et al., 2010; Donner et al., 2011; Gent
et al., 2011; Golaz et al., 2011; Martin et al., 2011; Hazeleger et al., 2012; Mauritsen et al., 2012; Hourdin et al., 2013).
With very few exceptions (Mauritsen et al., 2012; Hourdin et al., 2013) modelling centres do not routinely describe in detail how they
tune their models. Therefore the complete list of observational constraints toward which a particular model is tuned is generally not
(continued on next page)
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Chapter 9 Evaluation of Climate Models
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close mutual coupling. One new development is the Eddy-Diffusivi-
ty-Mass-Flux (EDMF) approach (Siebesma et al., 2007; Rio and Hour-
din, 2008; Neggers, 2009; Neggers et al., 2009; Rio et al., 2010). The
EDMF approach, like the shallow cumulus scheme of Park and Breth-
erton (2009), determines the cumulus-base mass flux from the statis-
tical distribution of boundary layer updraft properties, a conceptual
advance over the ad hoc closure assumptions used in the past. Realistic
treatment of the stable boundary layer remains difficult (Beare et al.,
2006; Cuxart et al., 2006; Svensson and Holtslag, 2009) with implica-
tions for modelling of the diurnal cycle of temperature even under clear
skies (Svensson et al., 2011).
Parameterizations of unresolved orographic and non-orographic gravi-
ty-wave drag (GWD) have seen only a few changes since the AR4 (e.g.,
Richter et al., 2010; Geller et al., 2011). In addition to new formula-
tions, the estimation of the parameters used in the GWD schemes has
recently been advanced through the availability of satellite and ground-
based observations of gravity-wave momentum fluxes, high-resolution
numerical modelling, and focussed process studies (Alexander et al.,
2010). Evidence from the Numerical Weather Prediction community
that important terrain-generated features of the atmospheric circu-
lation are better represented at higher model resolution has recently
been confirmed (Watanabe et al., 2008; Jung et al., 2012).
9.1.3.1.2 Ocean
Ocean components in contemporary climate models generally have
horizontal resolutions that are too coarse to admit mesoscale eddies.
Consequently, such models typically employ some version of the Redi
(Redi, 1982) neutral diffusion and Gent and McWilliams (Gent and
McWilliams, 1990) eddy advection parameterization (see also Gent
et al., 1995; McDougall and McIntosh, 2001). Since the AR4, a focus
has been on how parameterized mesoscale eddy fluxes in the ocean
interior interact with boundary layer turbulence (Gnanadesikan et al.,
2007; Danabasoglu et al., 2008; Ferrari et al., 2008, 2010). Another
focus concerns eddy diffusivity, with many CMIP5 models employing
flow-dependent schemes. Both of these refinements are important for
the mean state and the response to changing forcing, especially in
the Southern Ocean (Hallberg and Gnanadesikan, 2006; Boning et al.,
2008; Farneti et al., 2010; Farneti and Gent, 2011; Gent and Danabaso-
glu, 2011; Hofmann and Morales Maqueda, 2011).
In addition to mesoscale eddies, there has been a growing awareness
of the role that sub-mesoscale eddies and fronts play in restratifying
the mixed layer (Boccaletti et al., 2007; Fox-Kemper et al., 2008; Klein
and Lapeyre, 2009), and the parameterization of Fox-Kemper et al.
(2011) is now used in some CMIP5 models.
There is an active research effort on the representation of dianeutral
mixing associated with breaking gravity waves (MacKinnon et al.,
2009), with this work adding rigour to the prototype energetically con-
sistent abyssal tidal mixing parameterization of Simmons et al. (2004)
now used in several climate models (e.g., Jayne, 2009; Danabasoglu et
al., 2012). The transport of dense water down-slope by gravity currents
(e.g., Legg et al., 2008, 2009) has also been the subject of focussed
efforts, with associated parameterizations making their way into some
CMIP5 models (Jackson et al., 2008b; Legg et al., 2009; Danabasoglu
et al., 2010).
9.1.3.1.3 Land
Land surface properties such as vegetation, soil type and the amount
of water stored on the land as soil moisture, snow and groundwa-
ter all strongly influence climate, particularly through their effects on
surface albedo and evapotranspiration. These climatic effects can be
profound; for example, it has been suggested that changes in the state
of the land surface may have played an important part in the severity
and length of the 2003 European drought (Fischer et al., 2007), and
Box 9.1 (continued)
available. However, it is clear that tuning involves trade-offs; this keeps the number of constraints that can be used small and usually
focuses on global mean measures related to budgets of energy, mass and momentum. It has been shown for at least one model that
the tuning process does not necessarily lead to a single, unique set of parameters for a given model, but that different combinations
of parameters can yield equally plausible models (Mauritsen et al., 2012). Hence the need for model tuning may increase model uncer-
tainty. There have been recent efforts to develop systematic parameter optimization methods, but owing to model complexity they
cannot yet be applied to fully coupled climate models (Neelin et al., 2010).
Model tuning directly influences the evaluation of climate models, as the quantities that are tuned cannot be used in model evalua-
tion. Quantities closely related to those tuned will provide only weak tests of model performance. Nonetheless, by focusing on those
quantities not generally involved in model tuning while discounting metrics clearly related to it, it is possible to gain insight into model
performance. Model quality is tested most rigorously through the concurrent use of many model quantities, evaluation techniques, and
performance metrics that together cover a wide range of emergent (or un-tuned) model behaviour.
The requirement for model tuning raises the question of whether climate models are reliable for future climate projections. Models are
not tuned to match a particular future; they are tuned to reproduce a small subset of global mean observationally based constraints.
What emerges is that the models that plausibly reproduce the past, universally display significant warming under increasing green-
house gas concentrations, consistent with our physical understanding.
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Evaluation of Climate Models Chapter 9
9
that more than 60% of the projected increase in interannual summer
temperature variability in Europe is due to soil moisture–temperature
feedbacks (Seneviratne et al., 2006).
Land surface schemes calculate the fluxes of heat, water and momen-
tum between the land and the atmosphere. At the time of the AR4,
even the more advanced land surface schemes suffered from obvious
simplifications, such as the need to prescribe rather than simulate the
vegetation cover and a tendency to ignore lateral flows of water and
sub-gridscale heterogeneity in soil moisture (Pitman, 2003). Since the
AR4, a number of climate models have included some representation
of vegetation dynamics (see Sections 9.1.3.2.5 and 9.4.4.3), land–
atmosphere CO
2
exchange (see Section 9.4.5), sub-gridscale hydrology
(Oleson et al., 2008b) and changes in land use (see Section 9.4.4.4).
9.1.3.1.4 Sea ice
Most large-scale sea ice processes, such as basic thermodynamics and
dynamics, are well understood and well represented in models (Hunke
et al., 2010). However, important details of sea ice dynamics and defor-
mation are not captured, especially at small scales (Coon et al., 2007;
Girard et al., 2009; Hutchings et al., 2011). Currently, sea ice model
development is focussed mainly on (1) more precise descriptions of
physical processes such as microstructure evolution and anisotropy
and (2) including biological and chemical species. Many models now
include some representation of sub-grid-scale thickness variations,
along with a description of mechanical redistribution that converts
thinner ice to thicker ice under deformation (Hunke et al., 2010).
Sea ice albedo has long been recognized as a critical aspect of the
global heat balance. The average ice surface albedo on the scale of a
climate model grid cell is (as on land) the result of a mixture of surface
types: bare ice, melting ice, snow-covered ice, open water, etc. Many
sea ice models use a relatively simple albedo parameterization that
specifies four albedo values: cold snow; warm, melting snow; cold,
bare ice; and warm, melting ice, and the specific values may be subject
to tuning (e.g., Losch et al., 2010). Some parameterizations take into
account the ice and snow thickness, spectral band and surface melt
(e.g., Pedersen et al., 2009; Vancoppenolle et al., 2009). Solar radiation
may be distributed within the ice column assuming exponential decay
or via a more complex multiple-scattering radiative transfer scheme
(Briegleb and Light, 2007).
Snow model development for sea ice has lagged behind terrestrial
snow models. Lecomte et al. (2011) introduced vertically varying snow
temperature, density and conductivity to improve vertical heat con-
duction and melting in a 1D model intended for climate simulation,
but many physical processes affecting the evolution of the snow pack,
such as redistribution by wind, moisture transport (including flooding
and snow ice formation) and snow grain size evolution, still are not
included in most climate models.
Salinity affects the thermodynamic properties of sea ice, and is used
in the calculation of fresh water and salt exchanges at the ice–ocean
interface (Hunke et al., 2011). Some models allow the salinity to vary
in time (Schramm et al., 1997), while others assume a salinity profile
that is constant (e.g., Bitz and Lipscomb, 1999). Another new thrust is
the inclusion of chemistry and biogeochemistry (Piot and von Glasow,
2008; Zhao et al., 2008; Vancoppenolle et al., 2010; Hunke et al., 2011),
with dependencies on the ice microstructure and salinity profile.
Melt ponds can drain through interconnected brine channels when
the ice becomes warm and permeable. This flushing can effectively
clean the ice of salt, nutrients, and other inclusions, which affect the
albedo, conductivity and biogeochemical processes and thereby play a
role in climate change. Advanced parameterizations for melt ponds are
making their way into sea ice components of global climate models
(e.g., Flocco et al., 2012; Hunke et al., 2013).
9.1.3.2 New Components and Couplings: Emergence of
Earth System Modelling
9.1.3.2.1 Carbon cycle
The omission of internally consistent feedbacks among the physical,
chemical and biogeochemical processes in the Earth’s climate system
is a limitation of AOGCMs. The conceptual issue is that the physical
climate influences natural sources and sinks of CO
2
and methane (CH
4
),
the two most important long-lived GHGs. ESMs incorporate many of
the important biogeochemical processes, making it possible to sim-
ulate the evolution of these radiatively active species based on their
emissions from natural and anthropogenic sources together with their
interactions with the rest of the Earth system. Alternatively, when
forced with specified concentrations, a model can be used to diagnose
these sources with feedbacks included (Hibbard et al., 2007). Given the
large natural sources and sinks of CO
2
relative to anthropogenic emis-
sions, and given the primacy of CO
2
among anthropogenic GHGs, some
of the most important enhancements are the addition of terrestrial
and oceanic carbon cycles. These cycles have been incorporated into
many models (Christian et al., 2010; Tjiputra et al., 2010) used to make
projections of climate change (Schurgers et al., 2008; Jungclaus et al.,
2010). Several ESMs now include coupled carbon and nitrogen cycles
(Thornton et al., 2007; Gerber et al., 2010; Zaehle and Dalmonech,
2011) in order to simulate the interactions of nitrogen compounds with
ecosystem productivity, GHGs including nitrous oxide (N
2
O) and ozone
(O
3
), and global carbon sequestration (Zaehle and Dalmonech, 2011).
Oceanic uptake of CO
2
is highly variable in space and time, and is deter-
mined by the interplay between the biogeochemical and physical pro-
cesses in the ocean. About half of CMIP5 models make use of schemes
that partition marine ecosystems into nutrients, plankton, zooplankton
and detritus (hence called NPZD-type models) while others use a more
simplified representation of ocean biogeochemistry (see Table 9.A.1).
These NPZD-type models allow simulation of some of the important
feedbacks between climate and oceanic CO
2
uptake, but are limited by
the lack of marine ecosystem dynamics. Some efforts have been made
to include more plankton groups or plankton functional types in the
models (Le Quere et al., 2005) with as-yet uncertain implications for
Earth system response.
Ocean acidification and the associated decrease in calcification in
many marine organisms provides a negative feedback on atmospheric
CO
2
increase (Ridgwell et al., 2007a). New-generation models there-
fore include various parameterizations of calcium carbonate (CaCO
3
)
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Chapter 9 Evaluation of Climate Models
9
production as a function of the saturation state of seawater with
respect to calcite (Gehlen et al., 2007; Ridgwell et al., 2007a; Ilyina et
al., 2009) or partial pressure CO
2
(pCO
2
; Heinze, 2004). On centennial
to multi-millennial scales, deep-sea carbonate sediments neutralize
atmospheric CO
2
. Some CMIP5 models include the sediment carbon
reservoir, and progress has been made toward refined sediment rep-
resentation in the models (Heinze et al., 2009).
9.1.3.2.2 Aerosol particles
The treatment of aerosol particles has advanced since the AR4. Many
AOGCMs and ESMs now include the basic features of the sulphur
cycle and so represent both the direct effect of sulphate aerosol, along
with some of the more complex indirect effects involving cloud drop-
let number and size. Further, several AOGCMs and ESMs are currently
capable of simulating the mass, number, size distribution and mixing
state of interacting multi-component aerosol particles (Bauer et al.,
2008b; Liu et al., 2012b). The incorporation of more physically com-
plete representations of aerosol often improves the simulated climate
under historical and present-day conditions, including the mean pat-
tern and interannual variability in continental rainfall (Rotstayn et al.,
2010, 2011). However, despite the addition of aerosol–cloud interac-
tions to many AOGCMs and ESMs since the AR4, the representation
of aerosol particles and their interaction with clouds and radiative
transfer remains an important source of uncertainty (see Sections 7.3.5
and 7.4). Additional aerosol-related topics that have received attention
include the connection between dust aerosol and ocean biogeochemis-
try, the production of oceanic dimethylsulphide (DMS, a natural source
of sulphate aerosol), and vegetation interactions with organic atmos-
pheric chemistry (Collins et al., 2011).
9.1.3.2.3 Methane cycle and permafrost
In addition to CO
2
, an increasing number of ESMs and EMICs are also
incorporating components of the CH
4
cycle, for example, atmospheric
CH
4
chemistry and wetland emissions, to quantify some of the feed-
backs from changes in CH
4
sources and sinks under a warming climate
(Stocker et al., 2012). Some models now simulate the evolution of the
permafrost carbon stock (Khvorostyanov et al., 2008a, 2008b), and in
some cases this is integrated with the representation of terrestrial and
oceanic CH
4
cycles (Volodin, 2008b; Volodin et al., 2010).
9.1.3.2.4 Dynamic global vegetation models and wildland fires
One of the potentially more significant effects of climate change is the
alteration of the distribution, speciation and life cycle of vegetated
ecosystems (Bergengren et al., 2001, 2011). Vegetation has a signifi-
cant influence on the surface energy balance, exchanges of non-CO
2
GHGs and the terrestrial carbon sink. Systematic shifts in vegetation, for
example, northward migration of boreal forests, would therefore impose
biogeophysical feedbacks on the physical climate system (Clark et al.,
2011). In order to include these effects in projections of climate change,
several dynamic global vegetation models (DGVMs) have been devel-
oped and deployed in ESMs (Cramer et al., 2001; Sitch et al., 2008; Ostle
et al., 2009). Although agriculture and managed forests are not yet gen-
erally incorporated, DGVMs can simulate the interactions among natu-
ral and anthropogenic drivers of global warming, the state of terrestrial
ecosystems and ecological feedbacks on further climate change. The
incorporation of DGVMs has required considerable improvement in the
physics of coupled models to produce stable and realistic distributions of
flora (Oleson et al., 2008b). The improvements include better treatments
of surface, subsurface and soil hydrological processes; the exchange of
water with the atmosphere; and the discharge of water into rivers and
streams. Whereas the first DGVMs have been coupled primarily to the
carbon cycle, the current generation of DGVMs are being extended
to include ecological sources and sinks of other non-CO
2
trace gases
including CH
4
, N
2
O, biogenic volatile organic compounds (BVOCs) and
nitrogen oxides collectively known as NO
x
(Arneth et al., 2010). BVOCs
and NO
x
can alter the lifetime of some GHGs and act as precursors
for secondary organic aerosols (SOAs) and ozone. Disturbance of the
natural landscape by fire has significant climatic effects through its
impact on vegetation and through its emissions of GHGs, aerosols and
aerosol precursors. Because the frequency of wildland fires increases
rapidly with increases in ambient temperature (Westerling et al., 2006),
the effects of fires are projected to grow over the 21st century (Kloster
et al., 2012). The interactions of fires with the rest of the climate system
are now being introduced into ESMs (Arora and Boer, 2005; Pechony
and Shindell, 2009; Shevliakova et al., 2009).
9.1.3.2.5 Land use/land cover change
The impacts of land use and land cover change on the environment
and climate are explicitly included as part of the Representative Con-
centration Pathways (RCPs; cf. Chapters 1 and 12) used for climate
projections to be assessed in later chapters (Moss et al., 2010). Several
important types of land use and land cover change include effects of
agriculture and changing agricultural practices, including the poten-
tial for widespread introduction of biofuel crops; the management of
forests for preservation, wood harvest and production of woody bio-
fuel stock; and the global trends toward greater urbanization. ESMs
include increasingly detailed treatments of crops and their interaction
with the landscape (Arora and Boer, 2010; Smith et al., 2010a, 2010b),
forest management (Bellassen et al., 2010, 2011) and the interactions
between urban areas and the surrounding climate systems (Oleson et
al., 2008a).
9.1.3.2.6 Chemistry–climate interactions and stratosphere–
troposphere coupling
Important chemistry–climate interactions such as the impact of the
ozone hole and recovery on Southern Hemisphere (SH) climate or the
radiative effects of stratospheric water vapour changes on surface
temperature have been confirmed in multiple studies (SPARC-CCMVal,
2010; WMO, 2011). In the majority of the CMIP5 simulations strato-
spheric ozone is prescribed. The main advance since the AR4 is that
time-varying rather than constant stratospheric ozone is now generally
used. In addition, several CMIP5 models treat stratospheric chemistry
interactively, thus prognostically calculating stratospheric ozone and
other chemical constituents. Important chemistry–climate interactions
such as an increased influx of stratospheric ozone in a warmer climate
that results in higher ozone burdens in the troposphere have also been
identified (Young et al., 2013). Ten of the CMIP5 models simulate trop-
ospheric chemistry interactively whereas it is prescribed in the remain-
ing models (see Table 9.1 and Eyring et al. (2013)).
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Evaluation of Climate Models Chapter 9
9
It is now widely accepted that in addition to the influence of trop-
ospheric circulation and climate change on the stratosphere, strato-
spheric dynamics can in turn influence the tropospheric circulation and
its variability (SPARC-CCMVal, 2010; WMO, 2011). As a result, many
climate models now have the ability to include a fully resolved strato-
sphere with a model top above the stratopause, located at around 50
km. The subset of CMIP5 models with high-top configurations is com-
pared to the set of low-top models with a model top below the strat-
opause in several studies (Charlton-Perez et al., 2012; Hardiman et al.,
2012; Wilcox et al., 2012), although other factors such as differences
in tropospheric warming or ozone could affect the two sub-ensembles.
9.1.3.2.7 Land ice sheets
The rate of melt water release from the Greenland and Antarctic ice
sheets in response to climate change remains a major source of uncer-
tainty in projections of sea level rise (see Sections13.4.3 and13.4.4).
Until recently, the long-term response of these ice sheets to alterations
in the surrounding atmosphere and ocean has been simulated using
offline models. Several ESMs currently have the capability to have ice
sheet component models coupled to the rest of the climate system
(Driesschaert et al., 2007; Charbit et al., 2008; Vizcaino et al., 2008;
Huybrechts et al., 2011; Robinson et al., 2012) although these capabil-
ities are not exercised for CMIP5.
9.1.3.2.8 Additional features in ocean–atmosphere coupling
Several features in the coupling between the ocean and the atmos-
phere have become more widespread since the AR4. The bulk formulae
used to compute the turbulent fluxes of heat, water and momentum
at the air–sea interface, have been revised. A number of models now
consider the ocean surface current when calculating wind stress (e.g.,
Luo et al., 2005; Jungclaus et al., 2006). The coupling frequency has
been increased in some cases to include the diurnal cycle, which was
shown to reduce the SST bias in the tropical Pacific (Schmidt et al.,
2006; Bernie et al., 2008; Ham et al., 2010). Several models now repre-
sent the coupling between the penetration of the solar radiation into
the ocean and light-absorbing chlorophyll, with some implications on
the representation of the mean climate and climate variability (Murtu-
gudde et al., 2002; Wetzel et al., 2006). This coupling is achieved either
by prescribing the chlorophyll distribution from observations, or by
computing the chlorophyll distribution with an ocean biogeochemical
model (e.g., Arora et al., 2009).
9.1.3.3 Resolution
The typical horizontal resolution (defined here as horizontal grid spac-
ing) for current AOGCMs and ESMs is roughly 1 to 2 degrees for the
atmospheric component and around 1 degree for the ocean (Table 9.1).
The typical number of vertical layers is around 30 to 40 for the atmos-
phere and around 30 to 60 for the ocean (note that some ‘high-top’
models may have 80 or more vertical levels in the atmosphere). There
has been some modest increase in model resolution since the AR4,
especially for the near-term simulations (e.g., around 0.5 degree for
the atmosphere in some cases), based on increased availability of more
powerful computers. For the models used in long-term simulations
with interactive biogeochemistry, the resolution has not increased
substantially due to the trade-off against higher complexity in such
models. Since the AR4, typical regional climate model resolution has
increased from around 50 km to around 25 km (see Section 9.6.2.2),
and the impact of this has been explored with multi-decadal regional
simulations (e.g., Christensen et al., 2010). In some cases, RCMs are
being run at 10 km resolution or higher (e.g., Kanada et al., 2008;
Kusaka et al., 2010; van Roosmalen et al., 2010; Kendon et al., 2012).
Higher resolution can sometimes lead to a stepwise, rather than incre-
mental, improvement in model performance (e.g., Roberts et al., 2004;
Shaffrey et al., 2009). For example, ocean models undergo a transition
from laminar to eddy-permitting when the computational grid contains
more than one or two grid points per first baroclinic Rossby radius (i.e.,
finer than 50 km at low latitudes and 10 km at high latitudes) (Smith
et al., 2000; McWilliams, 2008). Such mesoscale eddy-permitting ocean
models better capture the large amount of energy contained in fronts,
boundary currents, and time dependent eddy features (e.g., McClean
et al., 2006). Models run at such resolution have been used for some
climate simulations, though much work remains before they are as
mature as the coarser models currently in use (Bryan et al., 2007; Bryan
et al., 2010; Farneti et al., 2010; McClean et al., 2011; Delworth et al.,
2012).
Similarly, atmospheric models with grids that allow the explicit rep-
resentation of convective cloud systems (i.e., finer than a few kilo-
metres) avoid employing a parameterization of their effects—a long-
standing source of uncertainty in climate models. For example, Kendon
et al. (2012) simulated the climate of the UK region over a 20-year
period at 1.5 km resolution, and demonstrated several improvements
of errors typical of coarser resolution models. Further discussion of this
is provided in Section 7.2.2.
9.2 Techniques for Assessing Model
Performance
Systematic evaluation of models through comparisons with observa-
tions is a prerequisite to applying them confidently. Several significant
developments in model evaluation have occurred since the AR4 and
are assessed in this section. This is followed by a description of the
overall approach to evaluation taken in this chapter and a discussion
of its known limitations.
9.2.1 New Developments in Model Evaluation
Approaches
9.2.1.1 Evaluating the Overall Model Results
The most straightforward approach to evaluate models is to compare
simulated quantities (e.g., global distributions of temperature, precip-
itation, radiation etc.) with corresponding observationally based esti-
mates (e.g., Gleckler et al., 2008; Pincus et al., 2008; Reichler and Kim,
2008). A significant development since the AR4 is the increased use of
quantitative statistical measures, referred to as performance metrics.
The use of such metrics simplifies synthesis and visualization of model
performance (Gleckler et al., 2008; Pincus et al., 2008; Waugh and
Eyring, 2008; Cadule et al., 2010; Sahany et al., 2012) and enables the
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Chapter 9 Evaluation of Climate Models
9
quantitative assessment of model improvements over time (Reichler
and Kim, 2008). Recent work has addressed redundancy of multiple
performance metrics through methods such as cluster analysis (Yokoi
et al., 2011; Nishii et al., 2012).
9.2.1.2 Isolating Processes
To understand the cause of model errors it is necessary to evaluate the
representation of processes both in the context of the full model and
in isolation. A number of evaluation techniques to achieve both pro-
cess and component isolation have been developed. One involves the
so-called ‘regime-oriented’ approach to process evaluation. Instead of
averaging model results in time (e.g., seasonal averages) or space (e.g.,
global averages), results are averaged within categories that describe
physically distinct regimes of the system. Applications of this approach
since the AR4 include the use of circulation regimes (Bellucci et al.,
2010; Brown et al., 2010b; Brient and Bony, 2012; Ichikawa et al.,
2012), cloud regimes (Williams and Brooks, 2008; Chen and Del Genio,
2009; Williams and Webb, 2009; Tsushima et al., 2013) and thermody-
namic states (Sahany et al., 2012; Su, 2012). The application of new
observations, such as vertically resolved cloud and water vapour infor-
mation from satellites (Jiang et al., 2012a; Konsta et al., 2012; Quaas,
2012) and water isotopes (Risi et al., 2012a; Risi et al., 2012b), has also
enhanced the ability to evaluate processes in climate models.
Another approach involves the isolation of model components or
parameterizations in off-line simulations, such as Single Column
Models of the atmosphere. Results of such simulations are compared
to measurements from field studies or to results of more detailed pro-
cess models (Randall et al., 2003). Numerous process evaluation data
sets have been collected since the AR4 (Redelsperger et al., 2006; Ill-
ingworth et al., 2007; Verlinde et al., 2007; May et al., 2008; Wood et
al., 2011) and have been applied to the evaluation of climate model
processes (Xie et al., 2008; Boone et al., 2009; Boyle and Klein, 2010;
Hourdin et al., 2010). These studies are crucial to test the realism of the
process formulations that underpin climate models.
9.2.1.3 Instrument Simulators
Satellites provide nearly global coverage, sampling across many mete-
orological conditions. This makes them powerful tools for model eval-
uation. The conventional approach has been to convert satellite-ob-
served radiation information to ‘model-equivalents’ (Stephens and
Kummerow, 2007), and these have been used in numerous studies
(Allan et al., 2007; Gleckler et al., 2008; Li et al., 2008; Pincus et al.,
2008; Waliser et al., 2009b; Li et al., 2011a, 2012a; Jiang et al., 2012a).
A challenge is that limitations of the satellite sensors demand various
assumptions in order to convert a satellite measurement into a ‘model
equivalent’ climate variable.
An alternative approach is to calculate ‘observation-equivalents’ from
models using radiative transfer calculations to simulate what the
satellite would provide if the satellite system were ‘observing’ the
model. This approach is usually referred to as an ‘instrument simula-
tor’. Microphysical assumptions (which differ from model to model)
can be included in the simulators, avoiding inconsistencies. A simulator
for cloud properties from the International Cloud Satellite Climatology
Project (ISCCP) (Yu et al., 1996; Klein and Jakob, 1999; Webb et al.,
2001) has been widely used for model evaluation since the AR4 (Chen
and Del Genio, 2009; Marchand et al., 2009; Wyant et al., 2009; Yoko-
hata et al., 2010), often in conjunction with statistical techniques to
separate model clouds into cloud regimes (e.g., Field et al., 2008; Wil-
liams and Brooks, 2008; Williams and Webb, 2009). New simulators for
other satellite products have also been developed and are increasingly
applied for model evaluation (Bodas-Salcedo et al., 2011). Although
often focussed on clouds and precipitation, the simulator approach has
also been used successfully for other variables such as upper tropo-
spheric humidity (Allan et al., 2003; Iacono et al., 2003; Ringer et al.,
2003; Brogniez et al., 2005; Brogniez and Pierrehumbert, 2007; Zhang
et al., 2008b; Bodas-Salcedo et al., 2011). Although providing an alter-
native to the use of model-equivalents from observations, instrument
simulators have limitations (Pincus et al., 2012) and are best applied in
combination with other model evaluation techniques.
9.2.1.4 Initial Value Techniques
To be able to forecast the weather a few days ahead, knowledge of the
present state of the atmosphere is of primary importance. In contrast,
climate predictions and projections simulate the statistics of weather
seasons to centuries in advance. Despite their differences, both weath-
er predictions and projections of future climate are performed with
very similar atmospheric model components. The atmospheric com-
ponent of climate models can be integrated as a weather prediction
model if initialized appropriately (Phillips et al., 2004). This allows
testing parameterized sub-grid scale processes without the compli-
cation of feedbacks substantially altering the underlying state of the
atmosphere.
The application of these techniques since the AR4 has led to some
new insights. For example, many of the systematic errors in the mod-
elled climate develop within a few days of simulation, highlighting
the important role of fast, parameterized processes (Klein et al., 2006;
Boyle et al., 2008; Xie et al., 2012). Errors in cloud properties for exam-
ple were shown to be present within a few days in a forecast in at least
some models (Williams and Brooks, 2008), although this was not the
case in another model (Boyle and Klein, 2010; Zhang et al., 2010b).
Other studies have highlighted the advantage of such methodologies
for the detailed evaluation of model processes using observations that
are available only for limited locations and times (Williamson and
Olson, 2007; Bodas-Salcedo et al., 2008; Xie et al., 2008; Hannay et al.,
2009; Boyle and Klein, 2010), an approach that is difficult to apply to
long-term climate simulations.
9.2.2 Ensemble Approaches for Model Evaluation
Ensemble methods are used to explore the uncertainty in climate
model simulations that arise from internal variability, boundary con-
ditions, parameter values for a given model structure or structural
uncertainty due to different model formulations (Tebaldi and Knutti,
2007; Hawkins and Sutton, 2009; Knutti et al., 2010a). Since the AR4,
techniques have been designed to specifically evaluate model per-
formance of individual ensemble members. Although this is typically
done to better characterize uncertainties, the methods and insights are
applicable to model evaluation in general. The ensembles are generally
755
Evaluation of Climate Models Chapter 9
9
of two types: Multi-model Ensembles (MMEs) and Perturbed Parameter
(or Physics) Ensembles (PPEs).
9.2.2.1 Multi-Model Ensembles
The MME is created from existing model simulations from multiple
climate modelling centres. MMEs sample structural uncertainty and
internal variability. However, the sample size of MMEs is small, and
is confounded because some climate models have been developed
by sharing model components leading to shared biases (Masson and
Knutti, 2011a). Thus, MME members cannot be treated as purely inde-
pendent, which implies a reduction in the effective number of inde-
pendent models (Tebaldi and Knutti, 2007; Jun et al., 2008; Knutti,
2010; Knutti et al., 2010a; Pennell and Reichler, 2011).
9.2.2.2 Perturbed-Parameter Ensembles
In contrast, PPEs are created to assess uncertainty based on a single
model and benefit from the explicit control on parameter perturba-
tions. This allows statistical methods to determine which parameters
are the main drivers of uncertainty across the ensemble (e.g., Rougier
et al., 2009). PPEs have been used frequently in simpler models such
as EMICs (Xiao et al., 1998; Forest et al., 2002, 2006, 2008; Stott and
Forest, 2007; Knutti and Tomassini, 2008; Sokolov et al., 2009; Loutre et
al., 2011) and are now being applied to more complex models (Murphy
et al., 2004; Annan et al., 2005; Stainforth et al., 2005; Collins et al.,
2006a, 2007; Jackson et al., 2008a; Brierley et al., 2010; Klocke et al.,
2011; Lambert et al., 2012). The disadvantage of PPEs is that they do
not explore structural uncertainty, and thus the estimated uncertainty
will depend on the underlying model that is perturbed (Yokohata et al.,
2010) and may be too narrow (Sakaguchi et al., 2012). Several stud-
ies (Sexton et al., 2012; Sanderson, 2013) recognize the importance
of sampling both parametric and structural uncertainty by combining
information from both MMEs and PPEs. However, even these approach-
es cannot account for the effect on uncertainty of systematic errors.
9.2.2.3 Statistical Methods Applied to Ensembles
The most common approach to characterize MME results is to calcu-
late the arithmetic mean of the individual model results, referred to
as an unweighted multi-model mean. This approach of ‘one vote per
model’ gives equal weight to each climate model regardless of (1) how
many simulations each model has contributed, (2) how interdependent
the models are or (3) how well each model has fared in objective eval-
uation. The multi-model mean will be used often in this chapter. Some
climate models share a common lineage and so share common biases
(Frame et al., 2005; Tebaldi and Knutti, 2007; Jun et al., 2008; Knutti,
2010; Knutti et al., 2010a, 2013; Annan and Hargreaves, 2011; Pennell
and Reichler, 2011; Knutti and Sedlácek, 2013). As a result, collections
such as the CMIP5 MME cannot be considered a random sample of
independent models. This complexity creates challenges for how best
to make quantitative inferences of future climate as discussed further
in Chapter 12 (Knutti et al., 2010a; Collins et al., 2012; Stephenson et
al., 2012; Sansom et al., 2013).
Annan and Hargreaves (2010) have proposed a ‘rank histogram’
approach to evaluate model ensembles as a whole, rather than
individual models, by diagnosing whether observations can be con-
sidered statistically indistinguishable from a model ensemble. Studies
based on this approach have suggested that MMEs (CMIP3/5) are ‘reli-
able’ in that they are not too narrow or too dispersive as a sample of
possible models, but existing single-model-based ensembles tend to
be too narrow (Yokohata et al., 2012, 2013). Although initial work has
analysed the current mean climate state, further work is required to
study the relationships between simulation errors and uncertainties in
ensembles of future projections (Collins et al., 2012).
Bayesian methods offer insights into how to account for model inad-
equacies and combine information from several metrics in both MME
and PPE approaches (Sexton and Murphy, 2012; Sexton et al., 2012),
but they are complex. A simpler strategy of screening out some model
variants on the basis of some observational comparison has been used
with some PPEs (Lambert et al., 2012; Shiogama et al., 2012). Edwards
et al. (2011) provided a statistical framework for ‘pre-calibrating’ out
such poor model variants. Screening techniques have also been used
with MMEs (Santer et al., 2009).
Additional Bayesian methods are applied to the MMEs so that past
model performance is combined with prior distributions to estimate
uncertainty from the MME (Furrer et al., 2007; Tebaldi and Knutti, 2007;
Milliff et al., 2011). Similar to Bayesian PPE methods, common biases
can be assessed within the MME to determine effective independence
of the climate models (Knutti et al., 2013) (see Section 12.2.2 for a
discussion of the assumptions in the Bayesian approaches).
9.2.3 The Model Evaluation Approach Used in this
Chapter and Its Limitations
This chapter applies a variety of evaluation techniques ranging from
visual comparison of observations and the multi-model ensemble
and its mean, to application of quantitative performance metrics (see
Section 9.2.2). No individual evaluation technique or performance
measure is considered superior; rather, it is the combined use of many
techniques and measures that provides a comprehensive overview of
model performance.
Although crucial, the evaluation of climate models based on past cli-
mate observations has some important limitations. By necessity, it
is limited to those variables and phenomena for which observations
exist. Table 9.3 provides an overview of the observations used in this
chapter. In many cases, the lack or insufficient quality of long-term
observations, be it a specific variable, an important processes, or a
particular region (e.g., polar areas, the upper troposphere/lower strat-
osphere (UTLS), and the deep ocean), remains an impediment. In addi-
tion, owing to observational uncertainties and the presence of internal
variability, the observational record against which models are assessed
is ‘imperfect’. These limitations can be reduced, but not entirely elim-
inated, through the use of multiple independent observations of the
same variable as well as the use of model ensembles.
The approach to model evaluation taken in the chapter reflects the
need for climate models to represent the observed behaviour of
past climate as a necessary condition to be considered a viable tool
for future projections. This does not, however, provide an answer to
756
Chapter 9 Evaluation of Climate Models
9
the much more difficult question of determining how well a model
must agree with observations before projections made with it can be
deemed reliable. Since the AR4, there are a few examples of emer-
gent constraints where observations are used to constrain multi-model
Quantity
CMIP5 Output
Variable Name
Observations
(Default/Alternates)
Reference Figure and Section Number(s)
ATMOSPHERE
Surface (2 m) Air
Temperature (°C)
Tas
(2 m)
ERA-Interim
1
NCEP-NCAR
1
ERA40
1
CRU TS 3.10
HadCRUT4
GISTEMP
MLOST
Dee et al. (2011)
Kalnay et al. (1996)
Uppala et al. (2005)
Mitchell and Jones (2005)
Morice et al. (2012)
Hansen et al. (2010)
Vose et al. (2012)
Figures 9.2, 9.3, 9.6
D
, 9.7
D
, Section 9.4.1;
Figures 9.38, 9.40, Section 9.6.1
Figures 9.6
A
, 9.7
A
, Section 9.4.1
Figure 9.38, Section 9.6.1
Figures 9.38, 9.39, Section 9.6.1
Figure 9.8, Section 9.4.1
Figure 9.8, Section 9.4.1
Figure 9.8, Section 9.4.1
Temperature (ºC) Ta
(200, 850 hPa)
ERA-Interim
1
NCEP-NCAR
1
Dee et al. (2011)
Kalnay et al. (1996)
Figure 9.9
D
Section 9.4.
Figure 9.9
A
Section 9.4.1
Zonal mean
wind ( m s
–1
)
Ua
(200, 850 hPa)
ERA-Interim
1
NCEP-NCAR
1
Dee et al. (2011)
Kalnay et al. (1996)
Figure 9.7
D
, Section 9.4.1
Figure 9.7
A
, Section 9.4.1
Zonal wind
stress ( m s
–1
)
Tauu QuikSCAT satellite measurements
NCEP/NCAR reanalysis
ERA-Interim
Risien and Chelton (2008)
Kalnay et al. (1996)
Dee et al. (2011)
Figures 9.19–9.20, Section 9.4.2
Figures 9.19–9.20, Section 9.4.2
Figures 9.19–9.20, Section 9.4.2
Meridional wind (m s
–1
) Va
(200, 850 hPa)
ERA-Interim
1
NCEP-NCAR
1
Dee et al. (2011)
Kalnay et al. (1996)
Figure 9.7
D
, Section 9.4.1
Figure 9.7
A
, Section 9.4.1
Geopotential
height (m)
Zg
(500 hPa)
ERA-Interim
1
NCEP-NCAR
1
Dee et al. (2011)
Kalnay et al. (1996)
Figure 9.7
D
, Section 9.4.1
Figure 9.7
A
, Section 9.4.1
TOA reflected short-
wave radiation (W m
–2
)
Rsut CERES EBAF 2.6
ERBE
Loeb et al. (2009)
Barkstrom (1984)
Figure 9.9
D
Section 9.4.1
Figure 9.9
A
, Section 9.4.1
TOA longwave
radiation (W m
–2
)
Rlut CERES EBAF 2.6
ERBE
Loeb et al. (2009)
Barkstrom (1984)
Figure 9.9
D
Section 9.4.1
Figure 9.9
A
, Section 9.4.1
Clear sky TOA short-
wave cloud radiative
effect (W m
–2
)
SW CRE
Derived from CMIP5
rsut and rsutcs
CERES EBAF 2.6
CERES ES-4 ERBE
Loeb et al. (2009)
Loeb et al. (2009)
Barkstrom (1984)
Figures 9.5
D
, 9.6
D
, 9.7
D
, Section 9.4.1
Figure 9.5
A
, Section 9.4.1
Figure 9.7
A
, Section 9.4.1
Clear sky TOA long-
wave cloud radiative
effect (W m
–2
)
LW CRE
Derived from CMIP5
rsut and rsutcs
CERES EBAF 2.6
CERES ES-4 ERBE
Loeb et al. (2009)
Loeb et al. (2009)
Figure 9.9
D
, Section 9.4.1
Figure 9.5
A
, Section 9.4.1
Total precipitation
(mm day
–1
)
Pr GPCP
CMAP
CRU TS3.10.1
Adler et al. (2003)
Xie and Arkin (1997)
Mitchell and Jones (2005)
Figures 9.4, 9.6
D
, 9.7
D
,
Section 9.4.1;
Figures 9.38, 9.40, Section 9.6.1
Figures 9.6
A
, 9.7
A
, Section 9.4.1;
Figures 9.38, 9.40, Section 9.6.1
Figures 9.38, 9.39, Section 9.6.1
ensemble projections. These examples, which are discussed further in
Section 9.8.3, remain part of an area of active and as yet inconclusive
research.
Table 9.3 | Overview of observations that are used to evaluate climate models in this chapter. The quantity and CMIP5 output variable name are given along with references
for the observations. Superscript (1) indicates this observations-based data set is obtained from atmospheric reanalysis. Superscript (D) indicates default reference; superscript (A)
alternate reference.
(continued on next page)
757
Evaluation of Climate Models Chapter 9
9
Quantity
CMIP5 Output
Variable Name
Observations
(Default/Alternates)
Reference Figure and Section Number(s)
ATMOSPHERE (continued)
Precipitable water PRW RSS V7 SSM/I
ERA-INTERIM
MERRA
JRA-25
Wentz et al. (2007)
Dee et al. (2011)
Rienecker et al. (2011)
Onogi et al. (2007)
Figure 9.9, Section 9.4.1
Lower-tropospheric
temperature
TLT RSS V3.3 MSU/AMSU
UAH V5.4 MSU/AMSU
ERA-INTERIM
MERRA
JRA-25
Mears et al. (2011)
Christy et al. (2007)
Dee et al. (2011)
Rienecker et al. (2011)
Onogi et al. (2007)
Figure 9.9, Section 9.4.1
Snow albedo
feedback (%/K)
tas, rsds, rsus Advanced Very High Resolu-
tion Radiometer (AVHRR), Polar
Pathfinder-x (APP-x), all-sky albedo
and ERA40 surface air temperature
Hall and Qu (2006); Fern-
andes et al. (2009)
Figure 9.43, Section 9.8.3
Reconstruction of bio-
climatic variables for
the mid-Holocene
and the Last Glacial
Maximum
Tas, pr,, tcold, twarm,
GDD5, alpha
Bartlein et al. (2010) Figure 9.11, Section 9.4.1
Figure 9.12, Section 9.4.1
OZONE and AEROSOLS
Aerosol optical depth aod MODIS
MISR
Shi et al. (2011)
Zhang and Reid (2010); Ste-
vens and Schwartz (2012)
Figures 9.28, 9.29, Section 9.4.6
Figure 9.29, Section 9.4.6
Total column ozone
(DU)
tro3 Ground-based measurements
NASA TOMS/OMI/SBUV(/2)
merged satellite data
NIWA combined total
column ozone database
Solar Backscatter Ultraviolet
(SBUV, SBUV/2) retrievals
DLR GOME/SCIA/GOME-2
updated from Fioletov et al. (2002)
Stolarski and Frith (2006)
Bodeker et al. (2005)
Updated from Miller et al. (2002)
Loyola et al. (2009); Loyola and
Coldewey-Egbers (2012)
Figure 9.10, Section 9.4.1
CARBON CYCLE
Atmospheric CO
2
(ppmv)
co2
Masarie and Tans (1995);
Meinshausen et al. (2011)
Figure 9.45, Section 9.8.3
Global Land Carbon
Sink (PgC yr
–1
)
NBP GCP Le Quere et al. (2009) Figure 9.26, 9.27, Section 9.4.5
Global Ocean Carbon
Sink (PgC yr
–1
)
fgCO2 GCP Le Quere et al. (2009) Figure 9.26, 9.27, Section 9.4.5
Regional Land Sinks
(PgC yr
–1
)
NBP JAM Gurney et al. (2003) Figure 9.27, Section 9.4.5
Regional Ocean
Sinks (PgC yr
–1
)
fgCO2 JAM
Gurney et al. (2003);
Takahashi et al. (2009)
Figure 9.27, Section 9.4.5
(continued on next page)
Table 9.3 (continued)
758
Chapter 9 Evaluation of Climate Models
9
Quantity CMIP5 Output
Variable Name
Observations
(Default/Alternates)
Reference Figure and Section Number(s)
OCEAN
Annual mean
temperature
thetao Levitus et al. (2009) Figure 9.13, Section 9.4.2
Annual mean salinity so Antonov et al. (2010) Figure 9.13, Section 9.4.2
Sea Surface
Temperature
tos HadISST1.1
HadCRU 4
ERA40
Rayner et al. (2003)
Jones et al. (2012)
Uppala et al. (2005)
Figure 9.14, Section 9.4.2
Figure 9.35, Section 9.5.3
Figure 9.36, Section 9.5.3
Global ocean heat
content (0 to 700 m)
OHC Levitus
Ishii
Domingues
Levitus et al. (2009)
Ishii and Kimoto, 2009)
Domingues et al. (2008)
Figure 9.17, Section 9.4.2
Dynamic Sea
surface height
SSH AVISO AVISO Figure 9.16, Section 9.4.2
Meridional heat
transport
hfnorth (1) Using surface and TOA heat fluxes:
NCEP/NCAR
ERA40
Updated NCEP reanalysis
(2) Direct estimates using
WOCE and inverse models
Trenberth and Fasullo (2008)
Large and Yeager (2009)
Kalnay et al. (1996)
Uppala et al. (2005)
Kistler et al. (2001)
Ganachaud and Wunsch (2003)
Figure 9.21, Section 9.4.2
Annual mean tem-
perature and salinity
Palaeoclimate reconstruction
of temperature and salinity
Adkins et al. (2002) Figure 9.18, Section 9.4.2
Total area (km
2
) of grid
cells where Sea Ice
Area Fraction (%) is
>15%. Boundary of sea
ice where Sea Ice Area
Fraction (%) is >15%
HadISST
NSIDC
NASA
Rayner et al. (2003)
Fetterer et al. (2002)
Comiso and Nishio (2008)
Figure 9.22, Section 9.4.3
Figure 9.23, Section 9.4.3
Figure 9.24, Section 9.4.3
MISC
Total area (km
2
) of grid
cells where Surface
Snow Area Fraction
(%) is 15% or Surface
Snow Amount (kg
m
–2
) is >5 kg m
–2
Robinson and Frei (2000) Figure 9.25, Section 9.4.4
3-hour precipitation
fields
15,000 stations and cor-
rected Ta from COADS (Dai
and Deser, 1999; Dai, 2001
Dai (2006) Figure 9.30, Section 9.5.2
Absolute value of
MJJAS minus NDJFM
precipitation exceeding
375 mm
GPCP
(Adler et al., 2003)
Wang et al. (2011a) Figure 9.32, Section 9.5.2
EXTREMES
Daily maximum and
minimum surface air
temperature fields (ºC)
Daily precipitation
fields (mm day
–1
)
for calculating
extremes indices
tas, precip ERA40
ERA-Interim,
NCEP/NCAR Reanalysis 1,
NCEP-DOE, Reanalysis 2
Uppala et al. (2005)
Dee et al. (2011)
Kistler et al. (2001)
Kanamitsu et al. (2002)
Calculation of indices is based
on Sillmann et al. (2013)
Figure 9.37, Section 9.5.4
Temperature extremes
indices based on
station observations
HadEX2 Donat et al. (2013) Figure 9.37, Section 9.5.4
Table 9.3 (continued)
Notes:
1
This observationally constrained data set is obtained from atmospheric reanalysis.
D
Default reference.
A
Alternate reference.
759
Evaluation of Climate Models Chapter 9
9
9.3 Experimental Strategies in Support of
Climate Model Evaluation
9.3.1 The Role of Model Intercomparisons
Systematic model evaluation requires a coordinated and well-doc-
umented suite of model simulations. Organized Model Intercompar-
ison Projects (MIPs) provide this via standard or benchmark experi-
ments that represent critical tests of a model’s ability to simulate
the observed climate. When modelling centres perform a common
experiment, it offers the possibility to compare their results not just
with observations, but with other models as well. This intercompari-
son enables researchers to explore the range of model behaviours, to
isolate the various strengths and weaknesses of different models in a
controlled setting, and to interpret, through idealized experiments, the
inter-model differences. Benchmark MIP experiments offer a way to
distinguish between errors particular to an individual model and those
that might be more universal and should become priority targets for
model improvement.
9.3.2 Experimental Strategy for Coupled Model
Intercomparison Project Phase 5
9.3.2.1 Experiments Utilized for Model Evaluation
CMIP5 includes a much more comprehensive suite of model experi-
ments than was available in the preceding CMIP3 results assessed in
the AR4 (Meehl et al., 2007). In addition to a better constrained speci-
fication of historical forcing, the CMIP5 collection also includes initial-
ized decadal-length predictions and long-term experiments using both
ESMs and AOGCMs (Taylor et al., 2012b) (Figure 9.1). The CO
2
forcing
additional predictions
Initialized in all years from
1960-present
prediction with
2010 Pinatubo-
like eruption
alternative
initialization
strategies
AMIP
30-year hindcast & prediction
ensembles: initialized 1960,
1980 & 2005
10-year hindcast & prediction
ensembles: initialized 1960,
1965, …, 2005
AMIP & historical
ensembles
Control,
AMIP &
historical
RCP4.5,
RCP8.5
E-driven Control
& historical
E-driven
RCP8.5
1%/yr CO
2
(140 yrs)
abrupt 4xCO
2
(150 yrs)
fixed SST with 1x &
4x CO
2
of these experiments is prescribed as a time series of either global
mean concentrations or spatially resolved anthropogenic emissions
(Section 9.3.2.2). The analyses of model performance in this chapter
are based on the concentration-based experiments with the exception
of the evaluation of the carbon cycle (see Section9.4.5).
Most of the model diagnostics are derived from the historical simula-
tions that span the period 1850– 2005. In some cases, these histori-
cal simulations are augmented by results from a scenario run, either
RCP4.5 or RCP8.5 (see Section 9.3.2.2), so as to facilitate comparison
with more recent observations. CMIP5/Paleoclimate Modelling Inter-
comparison Project version 3 (PMIP3) simulations for the mid-Holo-
cene and last glacial maximum are used to evaluate model response
to palaeoclimatic conditions. Historical emissions-driven simulations
are used to evaluate the prognostic carbon cycle. The analysis of global
surface temperature variability is based in part on long pre-industrial
control runs to facilitate calculation of variability on decadal to cen-
tennial time scales. Idealized simulations with 1% per year increases
in CO
2
are utilized to derive transient climate response. Equilibrium cli-
mate sensitivities are derived using results of specialized experiments,
with fourfold CO
2
increase, designed specifically for this purpose.
9.3.2.2 Forcing of the Historical Experiments
Under the protocols adopted for CMIP5 and previous assessments, the
transient climate experiments are conducted in three phases. The first
phase covers the start of the modern industrial period through to the
present day, years 1850–2005 (van Vuuren et al., 2011). The second
phase covers the future, 2006–2100, and is described by a collection
of RCPs (Moss et al., 2010). As detailed in Chapter 12, the third phase
is described by a corresponding collection of Extension Concentration
Figure 9.1 | Left: Schematic summary of CMIP5 short-term experiments with tier 1 experiments (yellow background) organized around a central core (pink background). (From
Taylor et al., 2012b, their Figure 2). Right: Schematic summary of CMIP5 long-term experiments with tier 1 experiments (yellow background) and tier 2 experiments (green back-
ground) organized around a central core (pink background). Green font indicates simulations to be performed only by models with carbon cycle representations, and ‘E-driven’
means ‘emission-driven’. Experiments in the upper semicircle either are suitable for comparison with observations or provide projections, whereas those in the lower semicircle are
either idealized or diagnostic in nature, and aim to provide better understanding of the climate system and model behaviour. (From Taylor et al., 2012b, their Figure 3.)
760
Chapter 9 Evaluation of Climate Models
9
Pathways (Meinshausen et al., 2011). The forcings for the historical
simulations evaluated in this section and are described briefly here
(with more details in Annex II).
In the CMIP3 20th century experiments, the forcings from radiatively
active species other than long-lived GHGs and sulphate aerosols were
left to the discretion of the individual modelling groups (IPCC, 2007).
By contrast, a comprehensive set of historical anthropogenic emissions
and land use and land cover change data have been assembled for
the CMIP5 experiments in order to produce a relatively homogeneous
ensemble of historical simulations with common time series of forcing
agents. Emissions of natural aerosols including soil dust, sea salt and
volcanic species are still left to the discretion of the individual model-
ling groups.
For AOGCMs without chemical and biogeochemical cycles, the forcing
agents are prescribed as a set of concentrations. The concentrations
for GHGs and related compounds include CO
2
, CH
4
, N
2
O, all fluori-
nated gases controlled under the Kyoto Protocol (hydrofluorocarbons
(HFCs), perfluorocarbons (PFCs), and sulphur hexafluoride (SF
6
)), and
ozone-depleting substances controlled under the Montreal Proto-
col (chlorofluorocarbons (CFCs), hydrochlorofluorocarbons (HCFCs),
Halons, carbon tetrachloride (CCl
4
), methyl bromide (CH
3
Br), methyl
chloride (CH
3
Cl)). The concentrations for aerosol species include sul-
phate (SO
4
), ammonium nitrate (NH
4
NO
3
), hydrophobic and hydrophilic
black carbon, hydrophobic and hydrophilic organic carbon, secondary
organic aerosols (SOAs) and four size categories of dust and sea salt.
For ESMs that include chemical and biogeochemical cycles, the forc-
ing agents are prescribed both as a set of concentrations and as a
set of emissions with provisions to separate the forcing by natural
and anthropogenic CO
2
(Hibbard et al., 2007). The emissions include
time-dependent spatially resolved fluxes of CH
4
, NO
X
, CO, NH
3
, black
and organic carbon, and volatile organic compounds (VOCs). For
models that treat the chemical processes associated with biomass
burning, emissions of additional species such as C
2
H
4
O (acetaldehyde),
C
2
H
5
OH (ethanol), C
2
H
6
S (dimethylsulphide) and C
3
H
6
O (acetone) are
also prescribed. Historical land use and land cover change is described
in terms of the time-evolving partitioning of land surface area among
cropland, pasture, primary land and secondary (recovering) land,
including the effects of wood harvest and shifting cultivation, as well
as land use changes and transitions from/to urban land (Hurtt et al.,
2009). These emissions data are aggregated from empirical recon-
structions of grassland and forest fires (Schultz et al., 2008; Mieville
et al., 2010); international shipping (Eyring et al., 2010); aviation (Lee
et al., 2009), sulphur (Smith et al., 2011b), black and organic carbon
(Bond et al., 2007); and NO
X
, CO, CH
4
and non methane volatile organic
compounds (NMVOCs) (Lamarque et al., 2010) contributed by all other
sectors.
For the natural forcings a recommended monthly averaged total solar
irradiance time series was given, but there was no recommended treat-
ment of volcanic forcing. Both integrated solar irradiance and its spec-
trum were available, but not all CMIP5 models used the spectral data.
The data employed an 1850-2008 reconstruction of the solar cycle and
its secular trend using observations of sunspots and faculae, the 10.7
cm solar irradiance measurements and satellite observations (Frohlich
and Lean, 2004).For volcanic forcing CMIP5 models typically employed
one of two prescribed volcanic aerosol data sets (Sato et al., 1993)
or (Ammann et al., 2003) but at least one ESM employed interactive
aerosol injection (Driscoll et al., 2012). The prescribed data sets did not
incorporate injection from explosive volcanoes after 2000.
9.3.2.3 Relationship of Decadal and Longer-Term Simulations
The CMIP5 archive also includes a new class of decadal-prediction
experiments (Meehl et al., 2009, 2013b) (Figure 9.1). The goal is to
understand the relative roles of forced changes and internal variability
in historical and near-term climate variables, and to assess the predict-
ability that might be realized on decadal time scales. These experiments
comprise two sets of hindcast and prediction ensembles with initial
conditions spanning 1960 through 2005. The set of 10-year ensembles
are initialized starting at 1960 in 1-year increments through the year
2005 while the 30-year ensembles are initialized at 1960, 1980 and
2005. The same physical models are often used for both the short-term
and long-term experiments (Figure 9.1) despite the different initiali-
zation of these two sets of simulations. Results from the short-term
experiments are described in detail in Chapter 11.
9.4 Simulation of Recent and Longer-Term
Records in Global Models
9.4.1 Atmosphere
Many aspects of the atmosphere have been more extensively evaluat-
ed than other climate model components. One reason is the availability
of near-global observationally based data for energy fluxes at the TOA,
cloud cover and cloud condensate, temperature, winds, moisture, ozone
and other important properties. As discussed in Box 2.3, atmospheric
reanalyses have also enabled integrating independent observations in
a physically consistent manner. In this section we use this diversity of
data (see Table 9.3) to evaluate the large-scale atmospheric behaviour.
9.4.1.1 Temperature and Precipitation Spatial Patterns of
the Mean State
Surface temperature is perhaps the most routinely examined quantity
in atmospheric models. Many processes must be adequately represent-
ed in order for a model to realistically capture the observed temper-
ature distribution. The dominant external influence is incoming solar
radiation, but many aspects of the simulated climate play an important
role in modulating regional temperature such as the presence of clouds
and the complex interactions between the atmosphere and the under-
lying land, ocean, snow, ice and biosphere.
The annual mean surface air temperature (at 2 m) is shown in Figure
9.2(a) for the mean of all available CMIP5 models, and the error, rela-
tive to an observationally constrained reanalysis (ECMWF reanalysis of
the global atmosphere and surface conditions (ERA)-Interim; Dee et al.,
2011) is shown in Figure 9.2(b). In most areas the multi-model mean
agrees with the reanalysis to within 2°C, but there are several loca-
tions where the biases are much larger, particularly at high elevations
over the Himalayas and parts of both Greenland and Antarctica, near
the ice edge in the North Atlantic, and over ocean upwelling regions
761
Evaluation of Climate Models Chapter 9
9
( )
( ) ( )
( )
off the west coasts of South America and Africa. Averaging the abso-
lute error of the individual CMIP5 models (Figure 9.2c) yields similar
magnitude as the multi-model mean bias (Figure 9.2b), implying that
compensating errors across models is limited. The inconsistency across
the three available global reanalyses (Figure 9.2d) that have assimilat-
ed temperature data at two metres (Onogi et al., 2007; Simmons et al.,
2010) provides an indication of observational uncertainty. Although
the reanalysis inconsistency is smaller than the mean absolute bias in
almost all regions, areas where inconsistency is largest (typically where
observations are sparse) tend to be the same regions where the CMIP5
models show largest mean absolute error.
Seasonal performance of models can be evaluated by examining the
difference between means for December–January–February (DJF) and
June–July–August (JJA). Figures 9.3(a) and (b) show the CMIP5 mean
model seasonal cycle amplitude in surface air temperature (as meas-
ured by the difference between the DJF and JJA and the absolute value
of this difference). The seasonal cycle amplitude is much larger over
land where the thermal inertia is much smaller than over the oceans,
Figure 9.2 | Annual-mean surface (2 m) air temperature (°C) for the period 1980–2005. (a) Multi-model (ensemble) mean constructed with one realization of all available models
used in the CMIP5 historical experiment. (b) Multi-model-mean bias as the difference between the CMIP5 multi-model mean and the climatology from ECMWF reanalysis of the
global atmosphere and surface conditions (ERA)-Interim (Dee et al., 2011); see Table 9.3. (c) Mean absolute model error with respect to the climatology from ERA-Interim. (d) Mean
inconsistency between ERA-Interim, ERA 40-year reanalysis (ERA40) and Japanese 25-year ReAnalysis (JRA-25) products as the mean of the absolute pairwise differences between
those fields for their common period (1979–2001).
and it is generally larger at higher latitudes as a result of the larger
seasonal amplitude in insolation. Figures 9.3(c) and (d) show the mean
model bias of the seasonal cycle relative to the ERA-Interim reanaly-
sis (Dee et al., 2011). The largest biases correspond to areas of large
seasonal amplitude, notably high latitudes over land, but relatively
large biases are also evident in some lower latitude regions such as
over northern India. Over most land areas the amplitude of the mod-
elled seasonal cycle is larger than observed, whereas over much of the
extratropical oceans the modelled amplitude is too small.
The simulation of precipitation is a more stringent test for models as it
depends heavily on processes that must be parameterized. Challenges
are compounded by the link to surface fields (topography, coastline,
vegetation) that lead to much greater spatial heterogeneity at regional
scales. Figure 9.4 shows the mean precipitation rate simulated by the
CMIP5 multi-model ensemble, along with measures of error relative to
precipitation analyses from the Global Precipitation Climatology Pro-
ject (Adler et al., 2003). The magnitude of observational uncertainty for
precipitation varies with region, which is why many studies make use
762
Chapter 9 Evaluation of Climate Models
9
Figure 9.3 | Seasonality (December–January–February minus June–July–August ) of surface (2 m) air temperature (°C) for the period 1980–2005. (a) Multi-model mean, calcu-
lated from one realization of all available CMIP5 models for the historical experiment. (b) Multi-model mean of absolute seasonality. (c) Difference between the multi-model mean
and the ECMWF reanalysis of the global atmosphere and surface conditions (ERA)-Interim seasonality. (d) Difference between the multi-model mean and the ERA-Interim absolute
seasonality.
( )
( ) ( )
( )
of several estimates of precipitation. Known large-scale features are
reproduced by the multi-model mean, such as a maximum precipita-
tion just north of the equator in the central and eastern tropical Pacific,
dry areas over the eastern subtropical ocean basins, and the minimum
rainfall in Northern Africa (Dai, 2006).
While many large-scale fea-
tures of the tropical circulation are reasonably well simulated, there
are persistent biases. These include too low precipitation along the
equator in the Western Pacific associated with ocean–atmosphere
feedbacks maintaining the equatorial cold tongue (Collins et al.,
2010) and excessive precipitation in tropical convergence zones
south of the equator in the Atlantic and the Eastern Pacific (Lin, 2007;
Pincus et al., 2008). Other errors occurring in several models include
an overly zonal orientation of the South-Pacific Convergence Zone
(Brown et al., 2013) as well as an overestimate of the frequency of
occurrence of light rain events (Stephens et al., 2010). Regional-scale
precipitation simulation has strong parameter dependence (Rougi-
er et al., 2009; Chen et al., 2010; Neelin et al., 2010), and in some
models substantial improvements have been shown through increas-
es in resolution (Delworth et al., 2012) and improved representa-
tions of sub-gridscale processes, particularly convection (Neale et al.,
2008)
. Judged by similarity with the spatial pattern of observations,
the overall quality of the simulation of the mean state of precipitation
in the CMIP5 ensemble is slightly better than in the CMIP3 ensemble
(see FAQ 9.1 and Figure 9.6).
In summary, there is high confidence that large-scale patterns of sur-
face temperature are well simulated by the CMIP5 models. In certain
regions this agreement with observations is limited, particularly at
elevations over the Himalayas and parts of both Greenland and Ant-
arctica. The broad-scale features of precipitation as simulated by the
CMIP5 models are in modest agreement with observations, but there
are systematic errors in the Tropics.
9.4.1.2 Atmospheric Moisture, Clouds and Radiation
The global annual mean precipitable water is a measure of the total
moisture content of the atmosphere. For the CMIP3 ensemble, the
values of precipitable water agreed with one another and with multi-
ple estimates from the National Centers for Environmental Prediction/
National Center for Atmospheric Research (NCEP/NCAR) and ECMWF
763
Evaluation of Climate Models Chapter 9
9
( )
( )
( )
( )
Figure 9.4 | Annual-mean precipitation rate (mm day
–1
) for the period 1980–2005. (a) Multi-model-mean constructed with one realization of all available AOGCMs used in the
CMIP5 historical experiment. (b) Difference between multi-model mean and precipitation analyses from the Global Precipitation Climatology Project (Adler et al., 2003). (c) Multi-
model-mean absolute error with respect to observations. (d) Multi-model-mean error relative to the multi-model-mean precipitation itself.
ERA40 meteorological reanalyses to within approximately 10% (Walis-
er et al., 2007). Initial analysis of the CMIP5 ensemble shows the model
results are within the uncertainties of the observations (Jiang et al.,
2012a).
Modelling the vertical structure of water vapour is subject to great-
er uncertainty since the humidity profile is governed by a variety of
processes. The CMIP3 models exhibited a significant dry bias of up to
25% in the boundary layer and a significant moist bias in the free
troposphere of up to 100% (John and Soden, 2007). Upper tropospher-
ic water vapour varied by a factor of three across the multi-model
ensemble (Su et al., 2006). Many models have large biases in lower
stratospheric water vapour (Gettelman et al., 2010), which could have
implications for surface temperature change (Solomon et al., 2010).
The limited number of studies available for the CMIP5 model ensem-
ble broadly confirms the results from the earlier model generation. In
tropical regions, the models are too dry in the lower troposphere and
too moist in the upper troposphere, whereas in the extratropics they
are too moist throughout the troposphere (Tian et al., 2013). However,
many of the model values lie within the observational uncertainties.
Jiang et al. (2012a) show that the largest biases occur in the upper
troposphere, with model values up to twice that observed, while in the
middle and lower troposphere models simulate water vapour to within
10% of the observations.
The spatial patterns and seasonal cycle of the radiative fluxes at the
TOA are fundamental energy balance quantities. Both the CMIP3 and
CMIP5 model ensembles reproduce these patterns with considerable
fidelity relative to the National Aeronautics and Space Adminsitration
(NASA) Clouds and the Earth’s Radiant Energy System (CERES) data
sets (Pincus et al., 2008; Wang and Su, 2013). Globally averaged TOA
shortwave and longwave components of the radiative fluxes in 12
atmosphere-only versions of the CMIP5 models were within 2.5 W m
–2
of the observed values (Wang and Su, 2013).
Comparisons against surface components of radiative fluxes show
that, on average, the CMIP5 models overestimate the global mean
downward all-sky shortwave flux at the surface by 2 ± 6 W m
–2
(1 ±
3%) and underestimate the global downward longwave flux by 6 ± 9
W m
–2
(2 ± 2%) (Stephens et al., 2012). Although in tropical regions
764
Chapter 9 Evaluation of Climate Models
9
( )
Figure 9.5 | Annual-mean cloud radiative effects of the CMIP5 models compared against the Clouds and the Earth’s Radiant Energy System Energy Balanced and Filled 2.6 (CERES
EBAF 2.6) data set (in W m
–2
; top row: shortwave effect; middle row: longwave effect; bottom row: net effect). On the left are the global distributions of the multi-model-mean
biases, and on the right are the zonal averages of the cloud radiative effects from observations (solid black: CERES EBAF 2.6; dashed black: CERES ES-4), individual models (thin
grey lines), and the multi-model mean (thick red line). Model results are for the period 1985–2005, while the available CERES data are for 2001–2011. For a definition and maps
of cloud radiative effect, see Section 7.2.1.2 and Figure 7.7.
between 1 and 3 W m
–2
of the bias may be due to systematic omission
of precipitating and/or convective core ice hydrometeors (Waliser et al.,
2011), the correlation between the biases in the all-sky and clear-sky
downwelling fluxes suggests that systematic errors in clear-sky radia-
tive transfer calculations may be a primary cause for these biases. This
is consistent with an analysis of the global annual mean estimates
of clear-sky atmospheric absorption from the CMIP3 ensemble and
the systematic underestimation of clear-sky solar absorption by radi-
ative transfer codes (Oreopoulos et al., 2012). The underestimation of
absorption can be attributed to the omission or underestimation of
765
Evaluation of Climate Models Chapter 9
9
Figure 9.6 | Centred pattern correlations between models and observations for the annual mean climatology over the period 1980–1999. Results are shown for individual CMIP3
(black) and CMIP5 (blue) models as thin dashes, along with the corresponding ensemble average (thick dash) and median (open circle). The four variables shown are surface air
temperature (TAS), top of the atmosphere (TOA) outgoing longwave radiation (RLUT), precipitation (PR) and TOA shortwave cloud radiative effect (SW CRE). The observations used
for each variable are the default products and climatological periods identified in Table 9.3. The correlations between the default and alternate (Table 9.3) observations are also
shown (solid green circles). To ensure a fair comparison across a range of model resolutions, the pattern correlations are computed at a resolution of 4º in longitude and 5º in
latitude. Only one realization is used from each model from the CMIP3 20C3M and CMIP5 historical simulations.
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TAS
RLUT PR
SW CRE
0.5
0.6
0.7
0.8
0.9
1
Correlation
CMIP3
OBS
CMIP5
absorbing aerosols, in particular carbonaceous species (Kim and Ram-
anathan, 2008), or to the omission of weak-line (Collins et al., 2006b)
or continuum (Ptashnik et al., 2011) absorption by water vapour (Wild
et al., 2006).
One of the major influences on radiative fluxes in the atmosphere is
the presence of clouds and their radiative properties. To measure the
influence of clouds on model deficiencies in the TOA radiation budget,
Figure 9.5 shows maps of deviations from observations in annual mean
shortwave (top left), longwave (middle left) and net (bottom left) cloud
radiative effect (CRE) for the CMIP5 multi-model mean. The figure (right
panels) also shows zonal averages of the same quantities from two
sets of observations, the individual CMIP5 models, and the multi-model
average. The definition of CRE and observed mean fields for these quan-
tities can be found in Chapter 7 (Section 7.2.1.2, Figure 7.7).
Models show large regional biases in CRE in the shortwave component,
and these are particularly pronounced in the subtropics with too weak
an effect (positive error) of model clouds on shortwave radiation in
the stratocumulus regions and too strong an effect (negative error) in
the trade cumulus regions. This error has been shown to largely result
from an overestimation of cloud reflectance, rather than cloud cover
(Nam et al., 2012). A too weak cloud influence on shortwave radia-
tion is evident over the subpolar oceans of both hemispheres and the
Northern Hemisphere (NH) land areas. It is evident in the zonal mean
graphs that there is a wide range in both longwave and shortwave CRE
between individual models. As is also evident, a significant reduction
in the difference between models and observations has resulted from
changes in the observational estimates of CRE, in particular at polar
and subpolar as well as subtropical latitudes (Loeb et al., 2009).
Understanding the biases in CRE in models requires a more in-depth
analysis of the biases in cloud properties, including the fractional cov-
erage of clouds, their vertical distribution as well as their liquid water
and ice content. Major progress in this area has resulted from both the
availability of new observational data sets and improved diagnostic
techniques, including the increased use of instrument simulators (e.g.,
Cesana and Chepfer, 2012; Jiang et al., 2012a). Many models have
particular difficulties simulating upper tropospheric clouds (Jiang et al.,
2012a), and low and mid-level cloud occurrence are frequently under-
estimated (Cesana and Chepfer, 2012; Nam et al., 2012; Tsushima et
al., 2013). Global mean values of both simulated ice and liquid water
path vary by factors of 2 to 10 between models (Jiang et al., 2012a; Li
et al., 2012a). The global mean fraction of clouds that can be detected
with confidence from satellites (optical thickness >1.3, Pincus et al.
(2012)) is underestimated by 5 to 10 % (Klein et al., 2013). Some of the
above errors in clouds compensate to provide the global mean balance
in radiation required by model tuning (Tsushima et al., 2013; Wang and
Su, 2013; Box 9.1).
In-depth analysis of several global and regional models (Karlsson et al.,
2008; Teixeira et al., 2011) has shown that the interaction of boundary
layer and cloud processes with the larger scale circulation systems that
ultimately drive the observed subtropical cloud distribution remains
poorly simulated. Large errors in subtropical clouds have been shown
to negatively affect SST patterns in coupled model simulations (Hu
766
Chapter 9 Evaluation of Climate Models
9
et al., 2011; Wahl et al., 2011). Several studies have highlighted the
potential importance and poor simulation of subpolar clouds in the
Arctic and Southern Oceans (Karlsson and Svensson, 2010; Trenberth
and Fasullo, 2010b; Haynes et al., 2011; Bodas-Salcedo et al., 2012). A
particular challenge for models is the simulation of the correct phase
of the cloud condensate, although very few observations are available
to evaluate models particularly with respect to their representation
of cloud ice (Waliser et al., 2009b; Li et al., 2012a). Regime-oriented
approaches to the evaluation of model clouds (see Section 9.2.1) have
identified that compensating errors in the CRE are largely a result of
misrepresentations of the frequency of occurrence of key observed
cloud regimes, while the radiative properties of the individual regimes
contribute less to the overall model deficiencies (Tsushima et al., 2013).
Several studies have identified progress in the simulation of clouds in
the CMIP5 models compared to their CMIP3 counterparts. Particular
examples include the improved simulation of vertically integrated ice
water path (Jiang et al., 2012a; Li et al., 2012a) as well as a reduction
of overabundant optically thick clouds in the mid-latitudes (Klein et al.,
2013; Tsushima et al., 2013).
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Figure 9.7 | Relative error measures of CMIP5 model performance, based on the global seasonal-cycle climatology (1980–2005) computed from the historical experiments. Rows
and columns represent individual variables and models, respectively. The error measure is a space–time root-mean-square error (RMSE), which, treating each variable separately, is
portrayed as a relative error by normalizing the result by the median error of all model results (Gleckler et al., 2008). For example, a value of 0.20 indicates that a model’s RMSE is
20% larger than the median CMIP5 error for that variable, whereas a value of –0.20 means the error is 20% smaller than the median error. No colour (white) indicates that model
results are currently unavailable. A diagonal split of a grid square shows the relative error withrespect to both the default reference data set (upper left triangle) and the alternate
(lower right triangle). The relative errors are calculated independently for the default and alternate data sets. All reference data used in the diagram are summarized in Table 9.3.
In summary, despite modest improvements there remain significant
errors in the model simulation of clouds. There is very high confidence
that these errors contribute significantly to the uncertainties in esti-
mates of cloud feedbacks (see Section 9.7.2.3; Section 7.2.5, Figure
7.10) and hence the spread in climate change projections reported in
Chapter 12.
9.4.1.3 Quantifying Model Performance with Metrics
Performance metrics were used to some extent in the Third Assessment
Report (TAR) and the Fourth Assessment Report (AR4), and are expand-
ed upon here because of their increased appearance in the recent lit-
erature. As a simple example, Figure 9.6 illustrates how the pattern
correlation between the observed and simulated climatological annual
mean spatial patterns depends very much on the quantity examined.
All CMIP3 and CMIP5 models capture the mean surface temperature
distribution quite well, with correlations above 0.95, which are large-
ly determined by the meridional temperature gradient. Correlations
for outgoing longwave radiation are somewhat lower. For precipita-
tion and the TOA shortwave cloud radiative effect, the correlations
767
Evaluation of Climate Models Chapter 9
9
between models and observations are below 0.90, and there is con-
siderable scatter among model results. This example quantifies how
some aspects of the simulated large-scale climate agree with observa-
tions better than others. Some of these differences are attributable to
smoothly varying fields (e.g., temperature, water vapour) often agree-
ing better with observations than fields that exhibit fine structure (e.g.,
precipitation) (see also Section 9.6.1.1). Incremental improvement in
each field is also evident in Figure 9.6, as gauged by the mean and
median results in the CMIP5 ensemble having higher correlations than
CMIP3. This multi-variate quantification of model improvement across
development cycles is evident in several studies (e.g., Reichler and Kim,
2008; Knutti et al., 2013)
Figure 9.7 (following Gleckler et al., 2008) depicts the space–time root-
mean-square error (RMSE) for the 1980–2005 climatological season-
al cycle of the historically forced CMIP5 simulations. For each of the
fields examined, this ‘portrait plot’ depicts relative performance, with
blue shading indicating performance being better, and red shading
worse, than the median of all model results. In each case, two obser-
vations-based estimates are used to demonstrate the impact of the
selection of reference data on the results. Some models consistently
compare better with observations than others, some exhibit mixed
performance and some stand out with relatively poor agreement with
observations. For most fields, the choice of the observational data set
does not substantially change the result for global error measures (e.g.,
between a state-of-the-art and an older-generation reanalysis), indi-
cating that inter-model differences are substantially larger than the
differences between the two reference data sets or the impact of two
different climatological periods (e.g., for radiation fields: Earth Radia-
tion Budget Experiment (ERBE) 1984–1988; CERES EBAF, 2001–2011).
Nevertheless, it is important to recognize that different data sets often
rely on the same source of measurements, and that the results in this
figure can have some sensitivity to a variety of factors such as instru-
ment uncertainty, sampling errors (e.g., limited record length of obser-
vations), the spatial scale of comparison, the domain considered and
the choice of metric.
Another notable feature of Figure 9.7 is that in most cases the mul-
ti-model mean agrees more favourably with observations than any
individual model. This has been long recognized to hold for surface
temperature and precipitation (e.g., Lambert and Boer, 2001). However,
since the AR4, it has become clear that this holds for a broad range of
climatological fields (Gleckler et al., 2008; Pincus et al., 2008; Knutti
et al., 2010a) and is theoretically better understood (Annan and Harg-
reaves, 2011). It is worth noting that when most models suffer from a
common error, such as the cold bias at high latitudes in the upper trop-
osphere (see TA 200 hPa of Figure 9.7), individual models can agree
better with observations than the multi-model mean.
Correlations between the relative errors for different quantities in
Figure 9.7 are known to exist, reflecting physical relationships in the
model formulations and in the real world. Cluster analysis methods
have recently been used in an attempt to reduce this redundancy (e.g.,
Yokoi et al., 2011; Nishii et al., 2012), thereby providing more succinct
summaries of model performance. Some studies have attempted an
overall skill score by averaging together the results from multiple met-
rics (e.g., Reichler and Kim, 2008). Although this averaging process is
largely arbitrary, combining the results of multiple metrics can reduce
the chance that a poorer performing model will score well for the
wrong reasons. Recent work (Nishii et al., 2012) has demonstrated that
different methods used to produce a multi-variate skill measure for the
CMIP3 models did not substantially alter the conclusions about the
better and lesser performing models.
Large scale performance metrics are a typical first-step toward quan-
tifying model agreement with observations, and summarizing broad
characteristics of model performance that are not focussed on a par-
ticular application. More specialized performance tests target aspects
of a simulation believed to be especially important for constraining
model projections, although to date the connections between particu-
lar performance metrics and reliability of future projections are not
well established. This important topic is addressed in Section 9.8.3,
which highlights several identified relationships between model per-
formance and projection responses.
9.4.1.4 Long-Term Global-Scale Changes
The comparison of observed and simulated climate change is compli-
cated by the fact that the simulation results depend on both model
formulation and the time-varying external forcings imposed on the
models (Allen et al., 2000; Santer et al., 2007). De-convolving the
importance of model and forcing differences in the historical simula-
tions is an important topic that is addressed in Chapter 10; however,
in this section a direct comparison is made to illustrate the ability of
models to reproduce past changes.
9.4.1.4.1 Global surface temperature
Figure 9.8 compares the observational record of 20th century changes
in global surface temperature to that simulated by each CMIP5 and
EMIC model and the respective multi-model means. The inset on the
right of the figure shows the climatological mean temperature for each
model, averaged over the 1961–1990 reference period. Although biases
in mean temperature are apparent, there is less confidence in observa-
tional estimates of climatological temperature than in variations about
this mean (Jones et al. (1999). For the CMIP5 models, interannual varia-
bility in most of the simulations is qualitatively similar to that observed
although there are several exceptions. The magnitude of interannual
variations in the observations is noticeably larger than the multi-mod-
el mean because the averaging of multiple model results acts to filter
much of the simulated variability. On the other hand, the episodic
volcanic forcing that is applied to most models (see Section 9.3.2.2)
is evident in the multi-model agreement with the observed cooling
particularly noticeable after the 1991 Pinatubo eruption. The gradual
warming evident in the observational record, particularly in the more
recent decades, is also evident in the simulations, with the multi-model
mean tracking the observed value closely over most of the century, and
individual model results departing by less than about 0.5
o
C. Because
the interpretation of differences in model behaviour can be confounded
by internal variability and forcing, some studies have attempted to iden-
tify and remove dominant factors such as El Niño-Southern Oscillation
(ENSO) and the impacts of volcanic eruptions (e.g., Fyfe et al., 2010).
Figure 9.8 shows the similar capability for EMICs to simulate the glob-
al-scale response to the 20th century forcings (Eby et al. 2013). These
768
Chapter 9 Evaluation of Climate Models
9
Figure 9.8 | Observed and simulated time series of the anomalies in annual and global mean surface temperature. All anomalies are differences from the 1961–1990 time-mean
of each individual time series. The reference period 1961–1990 is indicated by yellow shading; vertical dashed grey lines represent times of major volcanic eruptions. (a) Single
simulations for CMIP5 models (thin lines); multi-model mean (thick red line); different observations (thick black lines). Observational data (see Chapter 2) are Hadley Centre/Climatic
Research Unit gridded surface temperature data set 4 (HadCRUT4; Morice et al., 2012), Goddard Institute for Space Studies Surface Temperature Analysis (GISTEMP; Hansen et
al., 2010) and Merged Land–Ocean Surface Temperature Analysis (MLOST; Vose et al., 2012) and are merged surface temperature (2 m height over land and surface temperature
over the ocean). All model results have been sub-sampled using the HadCRUT4 observational data mask (see Chapter 10). Following the CMIP5 protocol (Taylor et al., 2012b), all
simulations use specified historical forcings up to and including 2005 and use RCP4.5 after 2005 (see Figure 10.1 and note different reference period used there; results will differ
slightly when using alternative RCP scenarios for the post-2005 period). (a) Inset: the global mean surface temperature for the reference period 1961–1990, for each individual
model (colours), the CMIP5 multi-model mean (thick red), and the observations (thick black: Jones et al., 1999). (Bottom) Single simulations from available EMIC simulations (thin
lines), from Eby et al. (2013). Observational data are the same as in (a). All EMIC simulations ended in 2005 and use the CMIP5 historical forcing scenario. (b) Inset: Same as in (a)
but for the EMICs.
results demonstrate a level of consistency between the EMICs with both
the observations and the CMIP5 ensemble.
In summary, there is very high confidence that models reproduce the
general features of the global-scale annual mean surface temperature
increase over the historical period, including the more rapid warming
in the second half of the 20th century, and the cooling immediately
following large volcanic eruptions. The disagreement apparent over the
most recent 10 to 15 years is discussed in detail in Box 9.2.
769
Evaluation of Climate Models Chapter 9
9
Box 9.2 | Climate Models and the Hiatus in Global Mean Surface Warming of the Past 15 Years
The observed global mean surface temperature (GMST) has shown a much smaller increasing linear trend over the past 15 years than
over the past 30 to 60 years (Section 2.4.3, Figure 2.20, Table 2.7; Figure 9.8; Box 9.2 Figure 1a, c). Depending on the observational
data set, the GMST trend over 1998–2012 is estimated to be around one-third to one-half of the trend over 1951–2012 (Section 2.4.3,
Table 2.7; Box 9.2 Figure 1a, c). For example, in HadCRUT4 the trend is 0.04ºC per decade over 1998–2012, compared to 0.11ºC per
decade over 1951–2012. The reduction in observed GMST trend is most marked in Northern Hemisphere winter (Section 2.4.3; Cohen
et al., 2012). Even with this “hiatus” in GMST trend, the decade of the 2000s has been the warmest in the instrumental record of GMST
(Section 2.4.3, Figure 2.19). Nevertheless, the occurrence of the hiatus in GMST trend during the past 15 years raises the two related
questions of what has caused it and whether climate models are able to reproduce it.
Figure 9.8 demonstrates that 15-year-long hiatus periods are common in both the observed and CMIP5 historical GMST time series
(see also Section 2.4.3, Figure 2.20; Easterling and Wehner, 2009; Liebmann et al., 2010). However, an analysis of the full suite of
CMIP5 historical simulations (augmented for the period 2006–2012 by RCP4.5 simulations, Section 9.3.2) reveals that 111 out of
114 realizations show a GMST trend over 1998–2012 that is higher than the entire HadCRUT4 trend ensemble (Box 9.2 Figure 1a;
CMIP5 ensemble mean trend is 0.21ºC per decade). This difference between simulated and observed trends could be caused by some
combination of (a) internal climate variability, (b) missing or incorrect radiative forcing and (c) model response error. These potential
sources of the difference, which are not mutually exclusive, are assessed below, as is the cause of the observed GMST trend hiatus.
Internal Climate Variability
Hiatus periods of 10 to 15 years can arise as a manifestation of internal decadal climate variability, which sometimes enhances and
sometimes counteracts the long-term externally forced trend. Internal variability thus diminishes the relevance of trends over periods
as short as 10 to 15 years for long-term climate change (Box 2.2, Section 2.4.3). Furthermore, the timing of internal decadal climate
variability is not expected to be matched by the CMIP5 historical simulations, owing to the predictability horizon of at most 10 to 20
years (Section 11.2.2; CMIP5 historical simulations are typically started around nominally 1850 from a control run). However, climate
models exhibit individual decades of GMST trend hiatus even during a prolonged phase of energy uptake of the climate system (e.g.,
Figure 9.8; Easterling and Wehner, 2009; Knight et al., 2009), in which case the energy budget would be balanced by increasing
subsurface–ocean heat uptake (Meehl et al., 2011, 2013a; Guemas et al., 2013).
Owing to sampling limitations, it is uncertain whether an increase in the rate of subsurface–ocean heat uptake occurred during the
past 15 years (Section 3.2.4). However, it is very likely
2
that the climate system, including the ocean below 700 m depth, has continued
to accumulate energy over the period 1998–2010 (Section 3.2.4, Box 3.1). Consistent with this energy accumulation, global mean sea
level has continued to rise during 1998–2012, at a rate only slightly and insignificantly lower than during 1993–2012 (Section 3.7). The
consistency between observed heat-content and sea level changes yields high confidence in the assessment of continued ocean energy
accumulation, which is in turn consistent with the positive radiative imbalance of the climate system (Section 8.5.1; Section 13.3, Box
13.1). By contrast, there is limited evidence that the hiatus in GMST trend has been accompanied by a slower rate of increase in ocean
heat content over the depth range 0 to 700 m, when comparing the period 2003–2010 against 1971–2010. There is low agreement on
this slowdown, since three of five analyses show a slowdown in the rate of increase while the other two show the increase continuing
unabated (Section 3.2.3, Figure 3.2).
During the 15-year period beginning in 1998, the ensemble of HadCRUT4 GMST trends lies below almost all model-simulated trends
(Box 9.2 Figure 1a), whereas during the 15-year period ending in 1998, it lies above 93 out of 114 modelled trends (Box 9.2 Figure
1b; HadCRUT4 ensemble-mean trend 0.26°C per decade, CMIP5 ensemble-mean trend 0.16°C per decade). Over the 62-year period
1951–2012, observed and CMIP5 ensemble-mean trends agree to within 0.02ºC per decade (Box 9.2 Figure 1c; CMIP5 ensemble-mean
trend 0.13°C per decade). There is hence very high confidence that the CMIP5 models show long-term GMST trends consistent with
observations, despite the disagreement over the most recent 15-year period. Due to internal climate variability, in any given 15-year
period the observed GMST trend sometimes lies near one end of a model ensemble (Box 9.2, Figure 1a, b; Easterling and Wehner, 2009),
an effect that is pronounced in Box 9.2, Figure 1a, b because GMST was influenced by a very strong El Niño event in 1998.
(continued on next page)
2
In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: Virtually certain 99–100% probability, Very likely
90–100%, Likely 66–100%, About as likely as not 33–66%, Unlikely 0–33%, Very unlikely 0–10%, Exceptionally unlikely 0–1%. Additional terms (Extremely likely:
95–100%, More likely than not >50–100%, and Extremely unlikely 0–5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very
likely (see Section 1.4 and Box TS.1 for more details).
770
Chapter 9 Evaluation of Climate Models
9
Box 9.2 (continued)
Unlike the CMIP5 historical simulations referred to above, some CMIP5 predictions were initialized from the observed climate state
during the late 1990s and the early 21st century (Section 11.1, Box 11.1; Section 11.2). There is medium evidence that these initialized
predictions show a GMST lower by about 0.05ºC to 0.1ºC compared to the historical (uninitialized) simulations and maintain this lower
GMST during the first few years of the simulation (Section 11.2.3.4, Figure 11.3 top left; Doblas-Reyes et al., 2013; Guemas et al.,
2013). In some initialized models this lower GMST occurs in part because they correctly simulate a shift, around 2000, from a positive
to a negative phase of the Interdecadal Pacific Oscillation (IPO, Box 2.5; e.g., Meehl and Teng, 2012; Meehl et al., 2013a). However,
the improvement of this phasing of the IPO through initialization is not universal across the CMIP5 predictions (cf. Section 11.2.3.4).
Moreover, while part of the GMST reduction through initialization indeed results from initializing at the correct phase of internal
variability, another part may result from correcting a model bias that was caused by incorrect past forcing or incorrect model response
to past forcing, especially in the ocean. The relative magnitudes of these effects are at present unknown (Meehl and Teng, 2012);
moreover, the quality of a forecasting system cannot be evaluated from a single prediction (here, a 10-year prediction within the period
1998–2012; Section 11.2.3). Overall, there is medium confidence that initialization leads to simulations of GMST during 1998–2012
that are more consistent with the observed trend hiatus than are the uninitialized CMIP5 historical simulations, and that the hiatus is
in part a consequence of internal variability that is predictable on the multi-year time scale.
Radiative Forcing
On decadal to interdecadal time scales and under continually increasing effective radiative forcing (ERF), the forced component of
the GMST trend responds to the ERF trend relatively rapidly and almost linearly (medium confidence, e.g., Gregory and Forster, 2008;
Held et al., 2010; Forster et al., 2013). The expected forced-response GMST trend is related to the ERF trend by a factor that has been
estimated for the 1% per year CO
2
increases in the CMIP5 ensemble as 2.0 [1.3 to 2.7] W m
–2
°C
–1
(90% uncertainty range; Forster et
al., 2013). Hence, an ERF trend can be approximately converted to a forced-response GMST trend, permitting an assessment of how
much of the change in the GMST trends shown in Box 9.2 Figure 1 is due to a change in ERF trend.
The AR5 best-estimate ERF trend over 1998–2011 is 0.22 [0.10 to 0.34] W m
–2
per decade (90% uncertainty range), which is substantially
lower than the trend over 1984–1998 (0.32 [0.22 to 0.42] W m
–2
per decade; note that there was a strong volcanic eruption in 1982)
and the trend over 1951–2011 (0.31 [0.19 to 0.40] W m
–2
per decade; Box 9.2, Figure 1d–f; numbers based on Section 8.5.2, Figure
8.18; the end year 2011 is chosen because data availability is more limited than for GMST). The resulting forced-response GMST trend
would approximately be 0.12 [0.05 to 0.29] °C per decade, 0.19 [0.09 to 0.39] °C per decade, and 0.18 [0.08 to 0.37] °C per decade
for the periods 1998–2011, 1984–1998 and 1951–2011, respectively (the uncertainty ranges assume that the range of the conversion
factor to GMST trend and the range of ERF trend itself are independent). The AR5 best-estimate ERF forcing trend difference between
1998–2011 and 1951–2011 thus might explain about one-half (0.05°C per decade) of the observed GMST trend difference between
these periods (0.06 to 0.08°C per decade, depending on observational data set).
The reduction in AR5 best-estimate ERF trend over 1998–2011 compared to both 1984–1998 and 1951–2011 is mostly due to
decreasing trends in the natural forcings,–0.16 [–0.27 to –0.06] W m
–2
per decade over 1998–2011 compared to 0.01 [–0.00 to 0.01]
W m
–2
per decade over 1951–2011 (Section 8.5.2, Figure 8.19). Solar forcing went from a relative maximum in 2000 to a relative
minimum in 2009, with a peak-to-peak difference of around 0.15 W m
–2
and a linear trend over 1998–2011 of around –0.10 W m
–2
per decade (cf. Section 10.3.1, Box 10.2). Furthermore, a series of small volcanic eruptions has increased the observed stratospheric
aerosol loading after 2000, leading to an additional negative ERF linear-trend contribution of around –0.06 W m
–2
per decade over
1998–2011 (cf. Section 8.4.2.2, Section 8.5.2, Figure 8.19; Box 9.2 Figure 1d, f). By contrast, satellite-derived estimates of tropospheric
aerosol optical depth (AOD) suggests little overall trend in global mean AOD over the last 10 years, implying little change in ERF due
to aerosol-radiative interaction (low confidence because of low confidence in AOD trend itself, Section 2.2.3; Section 8.5.1; Murphy,
2013). Moreover, because there is only low confidence in estimates of ERF due to aerosol–cloud interaction (Section 8.5.1, Table 8.5),
there is likewise low confidence in its trend over the last 15 years.
For the periods 1984–1998 and 1951–2011, the CMIP5 ensemble-mean ERF trend deviates from the AR5 best-estimate ERF trend
by only 0.01 W m
–2
per decade (Box 9.2 Figure 1e, f). After 1998, however, some contributions to a decreasing ERF trend are missing
in the CMIP5 models, such as the increasing stratospheric aerosol loading after 2000 and the unusually low solar minimum in 2009.
Nonetheless, over 1998–2011 the CMIP5 ensemble-mean ERF trend is lower than the AR5 best-estimate ERF trend by 0.03 W m
–2
per
decade (Box 9.2 Figure 1d). Furthermore, global mean AOD in the CMIP5 models shows little trend over 1998–2012, similar to the
observations (Figure 9.29). Although the forcing uncertainties are substantial, there are no apparent incorrect or missing global mean
forcings in the CMIP5 models over the last 15 years that could explain the model–observations difference during the warming hiatus.
(continued on next page)
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Evaluation of Climate Models Chapter 9
9
Box 9.2 (continued)
Model Response Error
The discrepancy between simulated and observed GMST trends during 1998–2012 could be explained in part by a tendency for some
CMIP5 models to simulate stronger warming in response to increases in greenhouse gas (GHG) concentration than is consistent
with observations (Section 10.3.1.1.3, Figure 10.4). Averaged over the ensembles of models assessed in Section 10.3.1.1.3, the best-
estimate GHG and other anthropogenic (OA) scaling factors are less than one (though not significantly so, Figure 10.4), indicating
that the model-mean GHG and OA responses should be scaled down to best match observations. This finding provides evidence that
some CMIP5 models show a larger response to GHGs and other anthropogenic factors (dominated by the effects of aerosols) than
the real world (medium confidence). As a consequence, it is argued in Chapter 11 that near-term model projections of GMST increase
should be scaled down by about 10% (Section 11.3.6.3). This downward scaling is, however, not sufficient to explain the model-mean
overestimate of GMST trend over the hiatus period.
Another possible source of model error is the poor representation of water vapour in the upper atmosphere (Section 9.4.1.2). It has
been suggested that a reduction in stratospheric water vapour after 2000 caused a reduction in downward longwave radiation and
hence a surface-cooling contribution (Solomon et al., 2010), possibly missed by the models, However, this effect is assessed here to be
small, because there was a recovery in stratospheric water vapour after 2005 (Section 2.2.2.1, Figure 2.5). (continued on next page)
1998-2012
0.0 0.2 0.4 0.6
(°C per decade) (°C per decade)(°C per decade)
0
2
4
6
8
Normalized density
CMIP5
HadCRUT4
(a)
1984-1998
0.0 0.2 0.4 0.6
(b)
1951-2012
0.0 0.2 0.4 0.6
(c)
1998-2011
-0.3 0.0 0.3 0.6 0.9
(W m
-2
per decade) (W m
-2
per decade) (W m
-2
per decade)
0
1
2
3
4
5
Normalized density
(d)
1984-1998
-0.3 0.0 0.3 0.6 0.9
(e)
1951-2011
-0.3 0.0 0.3 0.6 0.9
(f)
Box 9.2, Figure 1 | (Top) Observed and simulated global mean surface temperature (GMST) trends in degrees Celsius per decade, over the periods 1998–2012
(a), 1984–1998 (b), and 1951–2012 (c). For the observations, 100 realizations of the Hadley Centre/Climatic Research Unit gridded surface temperature data set 4
(HadCRUT4) ensemble are shown (red, hatched: Morice et al., 2012). The uncertainty displayed by the ensemble width is that of the statistical construction of the
global average only, in contrast to the trend uncertainties quoted in Section 2.4.3, which include an estimate of internal climate variability. Here, by contrast, internal
variability is characterized through the width of the model ensemble. For the models, all 114 available CMIP5 historical realizations are shown, extended after 2005 with
the RCP4.5 scenario and through 2012 (grey, shaded: after Fyfe et al., 2010). (Bottom) Trends in effective radiative forcing (ERF, in W m
–2
per decade) over the periods
1998–2011 (d), 1984–1998 (e), and 1951–2011 (f). The figure shows AR5 best-estimate ERF trends (red, hatched; Section 8.5.2, Figure 8.18) and CMIP5 ERF (grey,
shaded: from Forster et al., 2013). Black lines are smoothed versions of the histograms. Each histogram is normalized so that its area sums up to one.
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Chapter 9 Evaluation of Climate Models
9
Box 9.2 (continued)
In summary, the observed recent warming hiatus, defined as the reduction in GMST trend during 1998–2012 as compared to the trend
during 1951–2012, is attributable in roughly equal measure to a cooling contribution from internal variability and a reduced trend in
external forcing (expert judgment, medium confidence). The forcing trend reduction is primarily due to a negative forcing trend from
both volcanic eruptions and the downward phase of the solar cycle. However, there is low confidence in quantifying the role of forcing
trend in causing the hiatus, because of uncertainty in the magnitude of the volcanic forcing trend and low confidence in the aerosol
forcing trend.
Almost all CMIP5 historical simulations do not reproduce the observed recent warming hiatus. There is medium confidence that the
GMST trend difference between models and observations during 1998–2012 is to a substantial degree caused by internal variability,
with possible contributions from forcing error and some CMIP5 models overestimating the response to increasing GHG and other
anthropogenic forcing. The CMIP5 model trend in ERF shows no apparent bias against the AR5 best estimate over 1998–2012. However,
confidence in this assessment of CMIP5 ERF trend is low, primarily because of the uncertainties in model aerosol forcing and processes,
which through spatial heterogeneity might well cause an undetected global mean ERF trend error even in the absence of a trend in the
global mean aerosol loading.
The causes of both the observed GMST trend hiatus and of the model–observation GMST trend difference during 1998–2012 imply
that, barring a major volcanic eruption, most 15-year GMST trends in the near-term future will be larger than during 1998–2012
(high confidence; see 11.3.6.3. for a full assessment of near-term projections of GMST). The reasons for this implication are fourfold:
first, anthropogenic greenhouse-gas concentrations are expected to rise further in all RCP scenarios; second, anthropogenic aerosol
concentration is expected to decline in all RCP scenarios, and so is the resulting cooling effect; third, the trend in solar forcing is
expected to be larger over most near-term 15-year periods than over 1998–2012 (medium confidence), because 1998–2012 contained
the full downward phase of the solar cycle; and fourth, it is more likely than not that internal climate variability in the near-term will
enhance and not counteract the surface warming expected to arise from the increasing anthropogenic forcing.
9.4.1.4.2 Tropical tropospheric temperature trends
Most climate model simulations show a larger warming in the tropical
troposphere than is found in observational data sets (e.g., McKitrick et
al., 2010; Santer et al., 2013). There has been an extensive and some-
times controversial debate in the published literature as to whether
this difference is statistically significant, once observational uncertain-
ties and natural variability are taken into account (e.g., Douglass et
al., 2008; Santer et al., 2008, 2013; Christy et al., 2010; McKitrick et
al., 2010; Bengtsson and Hodges, 2011; Fu et al., 2011; McKitrick et
al., 2011; Thorne et al., 2011). For the period 1979–2012, the various
observational data sets find, in the tropical lower troposphere (LT),
a linear warming trend ranging from 0.06°C to 0.13°C per decade
(Section 2.4.4, Figure 2.27). In the tropical middle troposphere (MT),
the linear warming trend ranges from 0.02°C to 0.12°C per decade
(Section 2.4.4, Figure 2.27). Uncertainty in these trend values arises
from different methodological choices made by the groups deriving
satellite products (Mears et al., 2011) and radiosonde compilations
(Thorne et al., 2011), and from fitting a linear trend to a time series
containing substantial interannual and decadal variability (Box 2.2;
Section 2.4.4; (Santer et al., 2008; McKitrick et al., 2010)). Although
there have been substantial methodological debates about the calcu-
lation of trends and their uncertainty, a 95% confidence interval of
around ±0.1°C per decade has been obtained consistently for both LT
and MT (e.g., Section 2.4.4; McKitrick et al., 2010). In summary, despite
unanimous agreement on the sign of the observed trends, there exists
substantial disagreement between available estimates as to the rate
of temperature changes in the tropical troposphere, and there is only
low confidence in the rate of change and its vertical structure (Section
2.4.4).
For the 30-year period 1979–2009 (sometimes updated through 2010
or 2011), the CMIP3 models simulate a tropical warming trend ranging
from 0.1°C to somewhat above 0.4°C per decade for both LT and MT
(McKitrick et al., 2010), while the CMIP5 models simulate a tropical
warming trend ranging from slightly below 0.15°C to somewhat above
0.4°C per decade for both LT and MT (Santer et al., 2013; see also Po-
Chedley and Fu, 2012, who considered the period 1979–2005). Both
model ensembles show trends that on average are higher than in the
observational estimates, although both model ensembles overlap the
observational ensemble. Because the differences between the various
observational estimates are largely systematic and structural (Section
2.4.4; Mears et al., 2011), the uncertainty in the observed trends cannot
be reduced by averaging the observations as if the differences between
the data sets were purely random. Likewise, to properly represent inter-
nal climate variability, the full model ensemble spread must be used in
a comparison against the observations (e.g., Box 9.2; Section 11.2.3.2;
Raftery et al., 2005; Wilks, 2006; Jolliffe and Stephenson, 2011). The
very high significance levels of model–observation discrepancies in LT
and MT trends that were obtained in some studies (e.g., Douglass et
al., 2008; McKitrick et al., 2010) thus arose to a substantial degree from
using the standard error of the model ensemble mean as a measure
of uncertainty, instead of the ensemble standard deviation or some
other appropriate measure for uncertainty arising from internal climate
773
Evaluation of Climate Models Chapter 9
9
variability (e.g., Box 9.2; Section 11.2.3.2; Raftery et al., 2005; Wilks,
2006; Jolliffe and Stephenson, 2011). Nevertheless, almost all model
ensemble members show a warming trend in both LT and MT larger
than observational estimates (McKitrick et al., 2010; Po-Chedley and
Fu, 2012; Santer et al., 2013).
The CMIP3 models show a 1979–2010 tropical SST trend of 0.19°C
per decade in the multi-model mean, significantly larger than the
various observational trend estimates ranging from 0.10°C to 0.14°C
per decade (including the 95% confidence interval; Fu et al., 2011).
As a consequence, simulated tropospheric temperature trends are
also too large because models attempt to maintain static stability. By
contrast, atmospheric models that are forced with the observed SST
are in better agreement with observations, as was found in the CMIP3
model ECHAM5 (Bengtsson and Hodges, 2011) and the CMIP5 atmos-
phere-only runs. In the latter, the LT trend range for the period 1981–
2008 is 0.13 to 0.19ºC per decade—less than in the CMIP5 coupled
models, but still an overestimate (Po-Chedley and Fu, 2012). The influ-
ence of SST trend errors on the analysis can be reduced by considering
trends in tropospheric static stability, measured by the amplification
of MT trends against LT trends; another approach is to consider the
amplification of tropospheric trends against SST trends. The results of
such analyses strongly depend on the time scale considered. Month-
to-month variations are consistent between observations and models
concerning amplification aloft against SST variations (Santer et al.,
2005) and concerning amplification of MT against LT variations (Po-
Chedley and Fu, 2012). By contrast, the 30-year trend in tropical static
stability has been found to be larger than in the satellite observations
for almost all ensemble members in both CMIP3 (Fu et al., 2011) and
CMIP5 (Po-Chedley and Fu, 2012). However, if the radiosonde compi-
lations are used for the comparison, the trends in static stability in the
CMIP3 models agree much better with the observations, and inconsist-
ency cannot be diagnosed unambiguously (Seidel et al., 2012) . What
caused the remaining trend overestimate in static stability is not clear
but has been argued recently to result from an upward propagation of
bias in the model climatology (O’Gorman and Singh, 2013).
In summary, most, though not all, CMIP3 and CMIP5 models overesti-
mate the observed warming trend in the tropical troposphere during
the satellite period 1979–2012. Roughly one-half to two-thirds of this
difference from the observed trend is due to an overestimate of the
SST trend, which is propagated upward because models attempt to
maintain static stability. There is low confidence in these assessments,
however, due to the low confidence in observed tropical tropospheric
trend rates and vertical structure (Section 2.4.4).
9.4.1.4.3 Extratropical circulation
The AR4 concluded that models, when forced with observed SSTs,
are capable of producing the spatial distribution of storm tracks, but
generally show deficiencies in the numbers and depth of cyclones and
the exact locations of the storm tracks. The ability to represent extra-
tropical cyclones in climate models has been improving, partly due to
increases in horizontal resolution.
Storm track biases over the North Atlantic have decreased in CMIP5
models compared to CMIP3 (Zappa et al., 2013) although models
still produce too zonal a storm track in this region and most models
underestimate cyclone intensity (Colle et al., 2013; Zappa et al., 2013).
Chang et al. (2012) also find the storm tracks in the CMIP5 models
to be too weak and too equatorwards in their position, similar to the
CMIP3 models. The performance of the CMIP5 models in representing
North Atlantic cyclones was found to be strongly dependent on model
resolution (Colle et al., 2013). Studies based on individual models typi-
cally find that models capture the general characteristics of storm tracks
and extratropical cyclones (Ulbrich et al., 2008; Catto et al., 2010) and
their associated fronts (Catto et al., 2013) and show improvements over
earlier model versions (Loptien et al., 2008). However, some models
have deficiencies in capturing the location of storm tracks (Greeves et
al., 2007; Catto et al., 2011), in part owing to problems related to the
location of warm waters such as the Gulf Stream and Kuroshio Current
(Greeves et al., 2007; Keeley et al., 2012). This is an important issue
because future projections of storm tracks are sensitive to changes in
SSTs (Catto et al., 2011; Laine et al., 2011; McDonald, 2011; Woollings
et al., 2012). Some studies find that storm track and cyclone biases are
strongly related to atmospheric processes and parameterizations (Bauer
et al., 2008a; Boer and Lambert, 2008; Zappa et al., 2013). Representa-
tion of the Mediterranean storm track has been shown to be particularly
dependent on model resolution (Pinto et al., 2006; Raible et al., 2007;
Bengtsson et al., 2009; Ulbrich et al., 2009), as is the representation
of storm intensity and associated extremes in this area (Champion et
al., 2011). Most studies have focussed on NH storm tracks. However,
recently two CMIP3 models were found to differ significantly in their
simulation of extratropical cyclones affecting Australia (Dowdy et al.,
2013) and only about a third of the CMIP3 models were able to capture
the observed changes and trends in Southern Hemisphere (SH) baro-
clinicity responsible for a reduction in the growth rate of the leading
winter storm track modes (Frederiksen et al., 2011). There is still a lack
of information on SH storm track evaluation for the CMIP5 models.
9.4.1.4.4 Tropical circulation
Earlier assessments of a weakening Walker circulation (Vecchi et al.,
2006; Vecchi and Soden, 2007; DiNezio et al., 2009) from models and
reanalyses (Yu and Zwiers, 2010) have been tempered by subsequent
evidence that tropical Pacific Trade winds may have strengthened
since the early 1990s
(e.g., Merrifield and Maltrud, 2011)
. Models
suggest that the width of the Hadley cell should increase
(Frierson
et al., 2007; Lu et al., 2007)
, and there are indications that this has
been observed over the past 25 years
(Seidel et al., 2008)
but at an
apparent rate (2 to 5 degrees of latitude since 1979) that is faster
than in the CMIP3 models
(Johanson and Fu, 2009)
.
The tendency in a warming climate for wet areas to receive more
precipitation and subtropical dry areas to receive less, often termed
the ‘rich-get richer’ mechanism
(Chou et al., 2006; Held and Soden,
2006)
is simulated in CMIP3 models
(Chou and Tu, 2008),
and obser-
vational support for this is found from ocean salinity observations
(Durack et al., 2012) and
precipitation gauge data over land
(Zhang
et al., 2007)
.
There is medium confidence that models are correct in
simulating precipitation increases in wet areas and decreases in dry
areas on broad spatial scales in a warming climate based on agree-
ment among models and some evidence that this has been detected in
observed trends (see Section 2.5.1).
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Chapter 9 Evaluation of Climate Models
9
Several recent studies have examined the co-variability of tropical cli-
mate variables as a further means of evaluating climate models. Spe-
cifically, there are observed relationships between lower tropospheric
temperature and total column precipitable water (Mears et al., 2007),
and between surface temperature and relative humidity (Willett et al.,
2010). Figure 9.9 (updated from Mears et al., 2007) shows the relation-
ship between 25-year (1988–2012) linear trends in tropical precipita-
ble water and lower tropospheric temperature for individual historical
simulations (extended by appending RCP8.5 simulations after 2005,
see Santer et al., 2013). As described by Mears et al. (2007), the ratio
between changes in these two quantities is fairly tightly constrained
in the model simulations and similar across a range of time scales,
indicating that relative humidity is close to invariant in each model. In
the updated figure, the Remote Sensing System (RSS) observations are
in fairly good agreement with model expectations, and the University
of Alabama in Huntsville (UAH) observations less so. The points asso-
ciated with two of the reanalyses are also relatively far from the line,
consistent with long-term changes in relative humidity. It is not known
whether thesediscrepancies are due to remaining inhomogeneity in
the observational data and/orreanalysis results, or due to problems
with the climate simulations. All of the observational and reanalysis
points lie at the lower end of the model distribution, consistent with
the findings of (Santer et al., 2013).
9.4.1.4.5 Ozone and lower stratospheric temperature trends
Stratospheric ozone has been subject to a major perturbation since
the late 1970s due to anthropogenic emissions of ozone-depleting
Trend in precitable water (% per decade)
Trend in TLT (°C per decade)
Slope = 5.7 (% °C
-1
)
Figure 9.9 | Scatter plot of decadal trends in tropical (20ºS to 20ºN) precipitable water as a function of trends in lower tropospheric temperature (TLT) over the world’s oceans.
Coloured symbols are from CMIP5 models; black symbols are from satellite observations or from reanalysis output. Trends are calculated over the 1988–2012 period, so CMIP5
historical runs, which typically end in December 2005, were extended using RCP8.5 simulations initialized using these historical runs. Figure updated from Mears et al. (2007).
substances (see also Section 2.2.2.2 and Figure 2.6). Since the AR4,
there is increasing evidence that the ozone hole has led to a poleward
shift and strengthening of the SH mid-latitude tropospheric jet during
summer (Perlwitz et al., 2008; Son et al., 2008, 2010; SPARC-CCMVal,
2010; McLandress et al., 2011; Polvani et al., 2011; WMO, 2011; Swart
and Fyfe, 2012b). These trends are well captured in both chemistry–cli-
mate models (CCMs) with interactive stratospheric chemistry and in
CMIP3 models with prescribed time-varying ozone (Son et al., 2010;
SPARC-CCMVal, 2010). However, around half of the CMIP3 models pre-
scribe ozone as a fixed climatological value, and so these models are
not able to simulate trends in surface climate attributable to changing
stratospheric ozone amount (Karpechko et al., 2008; Son et al., 2008,
2010; Fogt et al., 2009). For CMIP5, a new time-varying ozone data
set (Cionni et al., 2011) was developed and prescribed in the majority
of models without interactive chemistry. This zonal mean data set is
based on observations by Randel and Wu (2007) and CCM projections
in the future (SPARC-CCMVal, 2010). Further, nine of the CMIP5 models
include interactive chemistry and so compute their own ozone evolu-
tion. As a result, all CMIP5 models consider stratospheric ozone deple-
tion and capture associated effects on SH surface climate, a significant
advance over CMIP3. Figure 9.10 shows the global annual mean and
Antarctic October mean of total column ozone in the CMIP5 models.
The simulated trends in total column ozone are in medium agreement
with observations, noting that some models that calculate ozone inter-
actively show significant deviations from observation (Eyring et al.,
2013). The multi-model mean agrees well with observations, and there
is robust evidence that this constitutes a significant improvement over
CMIP3, where around half of the models did not include stratospheric
775
Evaluation of Climate Models Chapter 9
9
ozone trends. Correspondingly, there is high confidence that the rep-
resentation of associated effects on high-latitude surface climate and
lower stratospheric cooling trends has improved compared to CMIP3.
Lower stratospheric temperature change is affected by ozone, and
since 1958 the change is characterized by a long-term global cooling
trend interrupted by three 2-year warming episodes following large
volcanic eruptions (Figure 2.24). During the satellite era (since 1979)
the cooling occurred mainly in two step-like transitions in the after-
math of the El Chichón eruption in 1982 and the Mt Pinatubo eruption
in 1991, with each cooling transition followed by a period of relatively
steady temperatures (Randel et al., 2009; Seidel et al., 2011). This spe-
cific evolution of global lower stratosphere temperatures since 1979 is
well captured in the CMIP5 models when forced with both natural and
anthropogenic climate forcings, although the models tend to underes-
Figure 9.10 | Time series of area-weighted total column ozone from 1960 to 2005 for (a) annual and global mean (90°S to 90°N) and (b) Antarctic October mean (60°S to 90°S).
Individual CMIP5 models with interactive or semi-interactive chemistry are shown in thin coloured lines, their multi-model mean (CMIP5Chem) in thick red and their standard
deviation as the blue shaded area. Further shown are the multi-model mean of the CMIP5 models that prescribe ozone (CMIP5noChem, thick green), the International Global
Atmospheric Chemistry/Stratospheric Processes and their Role in Climate (IGAC/SPARC) ozone database (thick pink), the Chemistry Climate Model Validation-2 (CCMVal-2) multi-
model mean (thick orange), and observations from five different sources (black symbols). These sources include ground-based measurements (updated from Fioletov et al., 2002),
National Aeronautics and Space Administration (NASA) Total Ozone Mapping Spectrometer/Ozone Monitoring Instrument/Solar Backscatter Ultraviolet(/2) (TOMS/OMI/SBUV(/2))
merged satellite data (Stolarski and Frith, 2006), the National Institute of Water and Atmospheric Research (NIWA) combined total column ozone database (Bodeker et al., 2005),
Solar Backscatter Ultraviolet (SBUV, SBUV/2) retrievals (updated from Miller et al. 2002), and Deutsches Zentrum für Luft- und Raumfahrt/ Global Ozone Monitoring Experiment/
SCanning Imaging Absorption spectrometer for atmospheric chartography /GOME-2 (DLR GOME/SCIA/GOME-2; Loyola et al., 2009; Loyola and Coldewey-Egbers, 2012). Note that
the IGAC/SPARC database over Antarctica (and thus the majority of the CMIP5noChem models) is based on ozonesonde measurements at the vortex edge (69°S) and as a result
underestimates Antarctic ozone depletion compared to the observations shown. Ozone depletion was more pronounced after 1960 as equivalent stratospheric chlorine values
steadily increased throughout the stratosphere. (Adapted from Figure 2 of Eyring et al., 2013.)
timate the long-term cooling trend (Charlton-Perez et al., 2012; Eyring
et al., 2013; Santer et al., 2013) (see Chapter 10).
Tropospheric ozone is an important GHG and as such needs to be
well represented in climate simulations. In the historical period it has
increased due to increases in ozone precursor emissions from anthro-
pogenic activities (see Chapters 2 and 8). Since the AR4, a new emis-
sion data set has been developed (Lamarque et al., 2010), which has
led to some differences in tropospheric ozone burden compared to pre-
vious studies, mainly due to biomass burning emissions (Lamarque et
al., 2010; Cionni et al., 2011; Young et al., 2013). Climatological mean
tropospheric ozone in the CMIP5 simulations generally agrees well
with satellite observations and ozonesondes, although as in the strato-
sphere, biases exist for individual models (Eyring et al., 2013; Young et
al., 2013) (see also Chapter 8).
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Chapter 9 Evaluation of Climate Models
9
9.4.1.5 Model Simulations of the Last Glacial Maximum and
the Mid-Holocene
Simulations of past climate can be used to test a model’s response
to forcings larger than those of the 20th century (see Chapter 5), and
the CMIP5 protocol includes palaeoclimate simulations referred to as
PMIP3 (Paleoclimate Model Intercomparison Project, version 3) (Taylor
et al., 2012b). Specifically, the Last Glacial Maximum (LGM, 21000
years BP) allows testing of the modelled climate response to the pres-
ence of a large ice sheet in the NH and to lower concentrations of
radiatively active trace gases, whereas the mid-Holocene (MH, 6000
years BP) tests the response to changes in seasonality of insolation in
the NH (see Chapter 5). For these periods, palaeoclimate reconstruc-
tions allow quantitative model assessment (Braconnot et al., 2012).
( )
(mm year
-1
)
ba
dc
(mm year
-1
)
In addition the CMIP5/PMIP3 simulations can compared to previous
palaeoclimate intercomparisons (Joussaume and Taylor, 1995; Bracon-
not et al., 2007c).
Figure 9.11 compares model results to palaeoclimate reconstructions
for both LGM (left) and MH (right). For most models the simulated LGM
cooling is within the range of the climate reconstructions (Braconnot
et al., 2007c; Izumi et al., 2013), however Hargreaves et al. (2011) find
a global mean model warm bias over the ocean of about 1°C for this
period (Hargreaves et al., 2011). LGM simulations tend to overestimate
tropical cooling and underestimate mid-latitude cooling (Kageyama et
al., 2006; Otto-Bliesner et al., 2009). They thus underestimate polar
amplification which is a feature also found for the mid-Holocene (Mas-
son-Delmotte et al., 2006; Zhang et al., 2010a) and other climatic con-
Figure 9.11 | Reconstructed and simulated conditions for the Last Glacial Maximum (LGM, 21,000 years BP, left) and the mid-Holocene (MH, 6000 years BP, right). (a) LGM change
in annual mean surface temperature (°C) over land as shown by palaeo-environmental climate reconstructions from pollen, macrofossils, and ice cores (Bartlein et al., 2010; Bracon-
not et al., 2012), and in annual mean sea surface temperature (°C) over the ocean from different type of marine records (Waelbroeck et al., 2009). (b) MH change in annual mean
precipitation (mm yr
–1
) over land (Bartlein et al., 2010). In (a) and (b), the size of the dots is proportional to the uncertainties at the different sites as provided in the reconstructions.
(c) Annual mean temperature changes over land against changes over the ocean, in the tropics (downward triangles) and over the North Atlantic and Europe (upward triangles).
The mean and range of the reconstructions are shown in black, the Paleoclimate Modelling Intercomparison Project version 2 (PMIP2) simulations as grey triangles, and the CMIP5/
PMIP3 simulations as coloured triangles. The 5 to 95% model ranges are in red for the tropics and in blue for the North Atlantic/Europe. (d) Changes in annual mean precipitation in
different data-rich regions. Box plots for reconstructions provide the range of reconstructed values for the region. For models, the individual model average over the region is plotted
for PMIP2 (small grey circle) and CMIP5/PMIP3 simulations (coloured circles). Note that in PMIP2, ‘ESM’ indicates that vegetation is computed using a dynamical vegetation model,
whereas in CMIP5/PMIP3 it indicates that models have an interactive carbon cycle with different complexity in dynamical vegetation (see Table 9.A.1). The limits of the boxes are
as follows: Western Europe (40°N to 50°N, 10°W to 30°E); northeast America (35°N to 60°N, 95°W to 60°W); North Africa (10°N to 25°N, 20°W to 30°W), and East Asia (25°N
to 40°N, 75°E to 105°E). (Adapted from Braconnot et al., 2012.)
777
Evaluation of Climate Models Chapter 9
9
Figure 9.12 | Relative model performance for the Last Glacial Maximum (LGM, about 21,000 yr BP) and the mid-Holocene (MH, about 6000 yr BP) for seven bioclimatic variables:
annual mean sea surface temperature, mean annual temperature (over land), mean temperature of the coldest month, mean temperature of the warmest month, growing degree
days above a threshold of 5°C, and ratio of actual to equilibrium evapotranspiration. Model output is compared to the Bartlein et al. (2010) data set over land, including ice core data
over Greenland and Antarctica (Braconnot et al., 2012) and the Margo data set (Waelbroeck et al., 2009) over the ocean. The CMIP5/Paleoclimate Modelling Intercomparison Project
version 3 (PMIP3) ensemble of Ocean–Atmosphere (OA) and Earth System Model (ESM) simulations are compared to the respective PMIP2 ensembles in the first four columns of
each panel. A diagonal divides each cell in two parts to show in the upper triangle a measure of the distance between model and data, taking into account the uncertainties in the
palaeoclimate reconstructions (Guiot et al., 1999), and in the lower triangle the normalized mean-square error (NMSE) that indicates how well the spatial pattern is represented.
In this graph all the values have been normalized following (Gleckler et al., 2008) using the median of the CMIP5/PMIP3 ensemble. The colour scale is such that blue colours mean
that the result is better than the median CMIP5 model and red means that it is worse.
ba
texts (Masson-Delmotte et al., 2010). Part of this can be attributed to
uncertainties in the representation of sea ice and vegetation feedbacks
that have been shown to amplify the response at the LGM and the MH
in these latitudes (Braconnot et al., 2007b; Otto et al., 2009; O’ishi and
Abe-Ouchi, 2011). Biases in the representation of the coupling between
vegetation and soil moisture are also responsible for excessive conti-
nental drying at the LGM (Wohlfahrt et al., 2008) and uncertainties in
vegetation feedback in monsoon regions (Wang et al., 2008; Dallmeyer
et al., 2010). Nevertheless, the ratio between the simulated change
in temperature over land and over the ocean (Figure 9.11c) is rather
similar in different models, resulting mainly from simulation of the
hydrological cycle over land and ocean (Sutton et al., 2007; Laine et al.,
2009). At a regional scale, models tend to underestimate the changes
in the north-south temperature gradient over Europe both at the LGM
(Ramstein et al., 2007) and at the mid-Holocene (Brewer et al., 2007;
Davis and Brewer, 2009).
The large-scale pattern of precipitation change during the MH (Figure
9.11d) is reproduced, but models tend to underestimate the magnitude
of precipitation change in most regions. In the SH (not shown in the
figure), the simulated change in atmospheric circulation is consistent
with precipitation records in Patagonia and New Zealand, even though
the differences between model results are large and the reconstruc-
tions have large uncertainties (Rojas et al., 2009; Rojas and Moreno,
2011).
A wider range of model performance metrics is provided in Figure 9.12
(Guiot et al., 1999; Brewer et al., 2007; Annan and Hargreaves, 2011;
Izumi et al., 2013). Results for the MH are less reliable than for the
LGM, because the forcing is weaker and involves smaller scale respons-
es over the continent (Hargreaves et al., 2013). As is the case for the
simulations of present day climate, there is only modest improvement
between the results of the more recent models (CMIP5/PMIP3) and
those of earlier model versions (PMIP2) despite higher resolution and
sophistication.
9.4.2 Ocean
Accurate simulation of the ocean in climate models is essential for the
correct estimation of transient ocean heat uptake and transient climate
response, ocean CO
2
uptake, sea level rise, and coupled climate modes
such as ENSO. In this section model performance is assessed for the
mean state of ocean properties, surface fluxes and their impact on the
simulation of ocean heat content and sea level, and aspects of impor-
tance for climate variability. Simulations of both the recent and more
distant past are evaluated against available data. Following Chapter 3,
ocean reanalyses are not used for model evaluation as many of their
properties depend on the model used to build the reanalysis.
9.4.2.1 Simulation of Mean Temperature and Salinity Structure
Potential temperature and salinity are the main ocean state variables
and their zonal distribution offers an evaluation of climate models in
different parts of the ocean (upper ocean, thermocline, deep ocean).
Over most latitudes, at depths ranging from 200 m to 2000 m, the
CMIP5 multi-model mean zonally averaged ocean temperature is too
warm (Figure 9.13a), albeit with a cooler deep ocean. Similar biases
were evident in the CMIP3 multi-model mean. Above 200 m, however,
778
Chapter 9 Evaluation of Climate Models
9
the CMIP5 (and CMIP3) multi-model mean is too cold, with maximum
cold bias (more than 1°C) near the surface at mid-latitudes of the NH
and near 200 m at 15°S. Zonal salinity errors (Figure 9.13b) exhibit a
different pattern from those of the potential temperature indicating
that most do not occur via density compensation. Some near surface
structures in the tropics and in the northern mid-latitude are indicative
of density compensation and are presumably due to surface fluxes
errors. At intermediate depths, errors in water mass formation translate
into errors in both salinity and potential temperature.
In the AR4 it was noted that the largest errors in SST in CMIP3 were
found in mid and high latitudes. While this is still the case in CMIP5,
there is marginal improvement with fewer individual models exhibiting
serious bias—the inter-model zonal mean SST error standard deviation
is significantly reduced at all latitudes north of 40
o
S—even though the
multi-model mean is only slightly improved (Figure 9.14a, c). Near the
equator, the cold tongue error in the Pacific (see Section 9.4.2.5.1) is
reduced by 30% in CMIP5; the Atlantic still exhibits serious errors and
the Indian is still well simulated (Figure 9.14b,d). In the Tropics, Li and
Xie (2012) have shown that SST errors could be classified into those
exhibiting broad meridional structures that are due to cloud errors, and
those associated with Pacific and Atlantic cold tongue errors that are
due to thermocline depth errors.
Sea surface salinity (SSS) is more challenging to observe, even though
the last decade has seen substantial improvements in the development
of global salinity observations, such as those from the Array for Real-
time Geostrophic Oceanography (ARGO) network (see Chapter 3).
Whereas SST is strongly constrained by air–sea interactions, the sources
of SSS variations (surface forcing via evaporation minus precipitation,
Figure 9.13 | (a) Potential temperature (oC) and (b) salinity (PSS-78); shown in colour are the time-mean differences between the CMIP5 ensemble mean and observations, zonally
averaged for the global ocean (excluding marginal and regional seas). The observed climatological values are sourced from the World Ocean Atlas 2009 (WOA09; Prepared by the
Ocean Climate Laboratory, National Oceanographic Data Center, Silver Spring, MD, USA), and are shown as labelled black contours. White contours show regions in (a) where poten-
tial temperature differences exceed positive or negative 1, 2 or 3°C, and in (b) where salinity differences exceed positive or negative 0.25, 0.5, 0.75 or 1 (PSS-78). The simulated
annual mean climatologies are obtained for 1975 to 2005 from available historical simulations, whereas WOA09 synthesizes observed data from 1874 to 2008 in calculations of the
annual mean; however, the median time for gridded observations most closely resembles the 1980–2010 period (Durack and Wijffels, 2012). Multiple realizations from individual
models are first averaged to form a single-model climatology, before the construction of the multi-model ensemble mean. A total of 43 available CMIP5 models have contributed
to the temperature panel (a) and 41 models to the salinity panel (b).
0
0
0
5
5
5
5
5
10
10
15
20
25
Depth (m)
0
200
400
600
800
0
0
5
Latitude
Temperature
A
90S 60S 30S EQU 30N 60N 90N
1000
2000
3000
4000
5000
−3 −2 −1 0 1 2 3
32
33
34
34
34
34.
5
34.5
34.5
3
4
.5
35
35
35
35
35
.5
35.5
34.5
Latitude
Salinity
B
90S 60S 30S EQU 30N 60N 90N
−1 −0.75 −0.5 −0.25 0 0.25 0.5 0.75 1
sea ice formation/melt and river runoff) are only loosely related to the
SSS itself, allowing errors to develop unchecked in coupled models. An
analysis of CMIP3 models showed that, whereas the historical trend in
global mean SSS is well captured by the models, regional SSS biases
are as high as ±2.5 psu (Terray et al., 2012). Comparisons of modelled
versus observed estimates of evaporation minus precipitation suggest
that model biases in surface freshwater flux play a role in some regions
(e.g., double Intertropical Convergence Zone (ITCZ) in the East Pacific;
Lin, 2007) or over the Indian ocean (Pokhrel et al., 2012).
The performance of coupled climate models in simulating hydrograph-
ic structure and variability were assessed in two important regions,
the Labrador and Irminger Seas and the Southern Ocean (de Jong et
al., 2009) and (Sloyan and Kamenkovich, 2007). Eight CMIP3 models
produce simulations of the intermediate and deep layers in the Lab-
rador and Irminger Seas that are generally too warm and saline, with
biases up to 0.7 psu and 2.9°C. The biases arise because the convective
regime is restricted to the upper 500 m; thus, intermediate water that
in reality is formed by convection is, in the models, partly replaced
by warmer water from the south. In the Southern Ocean, Subantarctic
Mode Water (SAMW) and Antarctic Intermediate Water (AAIW), two
water masses indicating very efficient ocean ventilation, are found to
be well simulated in some CMIP3 and CMIP5 models but not in others,
some having a significant fresh bias (Sloyan and Kamenkovich, 2007;
Salle et al., 2013). McClean and Carman (2011) found biases in the
properties of the North Atlantic mode waters and their formation rates
in the CMIP3 models. Errors in Subtropical Mode Water (STMW) forma-
tion rate and volume produce a turnover time of 1 to 2 years, approx-
imately half of that observed. Bottom water properties assessment in
CMIP5 shows that about half of the models create dense water on
779
Evaluation of Climate Models Chapter 9
9
Figure 9.14 | (a) Zonally averaged sea surface temperature (SST) error in CMIP5 models. (b) Equatorial SST error in CMIP5 models. (c) Zonally averaged multi-model mean SST
error for CMIP5 (red curve) and CMIP3 (blue curve), together with inter-model standard deviation (shading). (d) Equatorial multi-model mean SST in CMIP5 (red curve), CMIP3 (blue
curve) together with inter-model standard deviation (shading) and observations (black). Model climatologies are derived from the 1979–1999 mean of the historical simulations.
The Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) (Rayner et al., 2003) observational climatology for 1979–1999 is used as reference for the error calculation (a),
(b), and (c); and for observations in (d).
the Antarctic shelf, but it mixes with lighter water and is not export-
ed as bottom water. Instead most models create deep water by open
ocean deep convection, a process occurring rarely in reality (Heuzé et
al., 2013) which leads to errors in deep water formation and properties
in the Southern Ocean as shown in Figure 9.15.
Few studies have assessed the performance of models in simulating
Mixed Layer Depth (MLD). In the North East Pacific region, Jang et al.
(2011) found that the CMIP3 models exhibit the observed deep MLD
in the Kuroshio Extension, though with a deep bias and only one large
deep MLD region, rather than the observed two localized maxima.
Other studies have noted MLD biases near sea ice edges (Capotondi
et al., 2012).
9.4.2.2 Simulation of Sea Level and Ocean Heat Content
Steric and dynamic components of the mean dynamic topography (MDT)
and sea surface height (SSH) patterns can be compared to observations
(Maximenko et al., 2009). Pattern correlations between simulated and
observed MDT are above 0.95 for all of the CMIP5 models (Figure
9.16), an improvement compared to CMIP3. MDT biases over tropical
ocean regions are consistent with surface wind stress biases (Lee et al.,
2013). Over the Antarctic Circumpolar Current, the parameterization of
eddy-induced transports is essential for the models’ density structure
and thus MDT (Kuhlbrodt et al., 2012). High-resolution eddy resolving
ocean models show improved SSH simulations over coarser resolution
versions (McClean et al., 2006). Chapter 13 provides a more extensive
780
Chapter 9 Evaluation of Climate Models
9
Figure 9.15 | Time-mean bottom potential temperature in the Southern Ocean, observed (a) and the differences between individual CMIP5 models and observations (b–p); left
colour bar corresponds to the observations, right colour bar to the differences between model and observations (same unit). Thick dashed black line is the mean August sea ice
extent (concentration >15%); thick continuous black line is the mean February sea ice extent (concentration >15%). Numbers indicate the area-weighted root-mean-square (RMS)
error for all depths between the model and the climatology (unit °C); mean RMS error = 0.97 °C. (After Heuzé et al., 2013.)
a) b) c) d)
e) f) g) h)
i) j) k) l)
m)
-2 -1 0123-2 -1 012
(°C)
n) o) p)
Clim.
Obs.
CNRM-CM5
1.90
HiGEM
0.67
GISS-E2-R
1.24
NorESM1-M
0.73
INMCM4
1.30
MIROC4h
1.14
HadGM2-ES
0.62
CanESM2
0.54
CSIRO
Mk3-6-0
0.72
GFDL
ESM2M
1.52
IPSL
CM5A-LR
0.66
GFDL
ESM2G
0.72
MIROC
ESM-CHEM
0.52
MPI
ESM-LR
0.94
MRI
CGCM3
1.34
assessment of sea level changes in CMIP5 simulations, including com-
parisons with century-scale historical records.
Ocean heat content (OHC) depends only on ocean temperature, where-
as absolute changes in sea level are also influenced by processes that
are only now being incorporated into global models (e.g., mass loss
from large ice sheets discussed in Section 9.1.3.2.7). However, glob-
al-scale changes in OHC are highly correlated with the thermosteric
contribution to global SSH changes (Domingues et al., 2008). Approx-
imately half of the historical CMIP3 simulations did not include the
effects of volcanic eruptions, resulting in substantially greater than
observed ocean heat uptake during the late 20th century (Gleckler et
781
Evaluation of Climate Models Chapter 9
9
0.1
0.2
0
.3
0
.4
0.5
0.6
0
0
0.4
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0
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0.57
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0
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0.7
0
0
0.8
0.8
0
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0.99
Standard deviation
C
o
r
r
e
l
a
t
i
o
n
Observations
CMIP3 mean
CMIP5 mean
R
MS
D
Observations
CMIP5 mean
CMIP3 mean
ACCESS1.0
ACCESS1.3
BCC−CSM1.1
CCSM4
CESM1(BGC)
CESM1(WACCM)
CMCC−CM
CNRM−CM5
CSIRO−Mk3.6.0
CanCM4
CanESM2
EC−EARTH
GFDL−CM2p1
GFDL−CM3
GFDL−ESM2G
GFDL−ESM2M
GISS−E2−R
HadGEM2−CC
HadGEM2−ES
INM−CM4
IPSL−CM5A−LR
IPSL−CM5A−MR
IPSL−CM5B−LR
MIROC−ESM−CHEM
MIROC−ESM
MIROC4h
MIROC5
MPI−ESM−LR
MPI−ESM−MR
MPI−ESM−P
MRI−CGCM3
NorESM1−ME
NorESM1−M
Figure 9.16 | Taylor diagram for the dynamic sea surface height climatology (1987–2000). The radial coordinate shows the standard deviation of the spatial pattern, normalized
by the observed standard deviation. The azimuthal variable shows the correlation of the modelled spatial pattern with the observed spatial pattern. The root-mean square error with
bias removed is indicated by the dashed grey circles about the observational point. Analysis is for the global ocean, 50°S to 50°N. The reference data set is Archiving, Validation
and Interpretation of Satellite Oceanographic data (AVISO), a merged satellite product (Ducet et al., 2000), which is described in Chapter 3. One realization per model is shown for
each CMIP5 and CMIP3 model result. Grey filled circles are for individual CMIP3 models; other symbols as in legend.
al., 2006; Domingues et al., 2008). Figure 9.17 shows observed and
simulated global 0 to 700 m and total OHC changes during the overlap
period of the observational record and the CMIP5 historical experiment
(1961–2005). Three upper-ocean observational estimates, assessed in
Chapter 3, are also shown to indicate observational uncertainty. The
CMIP5 multi-model mean falls within the range of observations for
most of the period, and the intermodel spread is reduced relative to
CMIP3 (Gleckler et al., 2006; Domingues et al., 2008). This may result
from most CMIP5 models including volcanic forcings. When the deep
ocean is included, the CMIP5 multi-model mean also agrees well with
the observations, although the deeper ocean estimates are much more
uncertain (Chapter 3). There is high confidence that many CMIP5
models reproduce the observed increase in ocean heat content since
1960.
EMIC results for changes in total OHC are also compared with obser-
vations in Figure 9.17. (Note: results in this figure are based on Eby
et al. (2013) who show OHC changes for 0 to 2000 m, whereas here
the time-integrated net heat flux into the ocean surface is shown to
compare with CMIP5 results (Figure 9.17b)). There is a tendency for
the EMICs to overestimate total OHC changes and this could alter the
temperature related feedbacks on the oceanic carbon cycle, and affect
the long-term millennium projections in Chapter 12. However, it should
be noted that high OHC changes can compensate for biases in climate
sensitivity or RF so as to reproduce surface temperature changes over
the 20th century. This will result in biased thermosteric sea level rise
for millennial projections. Calibrated EMICs (Meinshausen et al., 2009;
Sokolov et al., 2010) would remove such biases.
In idealized CMIP5 experiments (CO
2
increasing 1% yr
–1
), the heat
uptake efficiency of the CMIP5 models varies by a factor of two,
explaining about 50% of the model spread (Kuhlbrodt and Gregory,
2012). Despite observational uncertainties, this recent work also pro-
vides limited evidence that in the upper 2000 m, most CMIP5 models
are less stratified (in the global mean) than is observed, which sug-
gests that these models transport heat downwards more efficiently
than the real ocean. These results are consistent with earlier studies
(Forest et al., 2006, 2008; Boe et al., 2009a; Sokolov et al., 2010) that
conclude the CMIP3 models may overestimate oceanic mixing efficien-
cy and therefore underestimate the Transient Climate Response (TCR)
and its impact on future surface warming. However, Kuhlbrodt and
Gregory (2012) also find that this apparent bias explains very little of
the model spread in TCR. Although some progress has been made in
understanding mixing deficiencies in ocean models (Griffies and Great-
batch, 2012; Ilicak et al., 2012), this remains a key challenge in improv-
ing the representation of physical processes that impact the evolution
of ocean heat content and thermal expansion.
782
Chapter 9 Evaluation of Climate Models
9
Figure 9.17 | Time series of simulated and observed global ocean heat content anomalies (with respect to 1971). CMIP5 historical simulations and observations for both the
upper 700 meters of the ocean (a) as well as for the total ocean heat content (b). Total ocean heat content results are also shown for EMICs and observations (c). EMIC estimates
are based on time-integrated surface heat flux into the ocean. The 0 to 700 m and total heat content observational estimates (thick lines) are respectively described in Figure 3.2
and Box 3.1, Figure 1. Simulation drift has been removed from all CMIP5 runs with a contemporaneous portion of a quadratic fit to each corresponding pre-industrial control run
(Gleckler et al., 2012). Units are 10
22
Joules.
9.4.2.3 Simulation of Circulation Features Important for
Climate Response
9.4.2.3.1 Simulation of recent ocean circulation
Atlantic Meridional Overturning Circulation
The Atlantic Meridional Overturning Circulation (AMOC) consists of
northward transport of shallow warm water overlying a southward
transport of deep cold water and is responsible for a considerable part
of the northward oceanic heat transport. Long-term AMOC estimates
have had to be inferred from hydrographic measurements sporadically
available over the last decades (e.g., Bryden et al., 2005; Lumpkin et
al., 2008, Chapter 3.6.3). Continuous AMOC monitoring at 26.5°N was
started in 2004 (Cunningham et al., 2007) and now provides a 5-year
mean value of 18.5 Sv with annual means having a standard devia-
tion of 1 Sv (McCarthy et al., 2012). The ability of models to simulate
this important circulation feature is tied to the credibility of simulated
AMOC weakening during the 21st century because the magnitude of
783
Evaluation of Climate Models Chapter 9
9
the weakening is correlated with the initial AMOC strength (Gregory
et al., 2005). The mean AMOC strength in CMIP5 models ranges from
15 to 30 Sv for the historical period which is comparable to the CMIP3
models (Weaver et al., 2012; see Figure 12.35). The variability of the
AMOC is assessed in Section 9.5.3.3.1.
Southern Ocean circulation
The Southern Ocean is an important driver for the meridional over-
turning circulation and is closely linked to the zonally continuous
Antarctic Circumpolar Current (ACC). Gupta et al. (2009) noted that
relatively small deficiencies in the position of the ACC lead to more
obvious biases in the SST in the models. The ability of CMIP3 models
to adequately represent Southern Ocean circulation and water masses
seems to be affected by several factors (Russell et al., 2006). The most
important are the strength of the westerlies at the latitude of the Drake
Passage, the heat flux gradient over this region, and the change in
salinity with depth across the ACC. Kuhlbrodt et al. (2012) found that
the strongest influence on ACC transport in the CMIP3 models was the
Gent-McWilliams thickness diffusivity. The ACC has a typical transport
through the Drake Passage of about 135 Sv (e.g., Cunningham et al.,
2003). A comparison of CMIP5 models (Meijers et al., 2012) shows
that, firstly, the ACC transport through Drake Passage is improved as
compared to the CMIP3 models, and secondly, that the inter-model
44.8
45.2
45.6
46
46.4 46.8 47.2
47.6
48
48.4
48.
8
Salinity(psu)
34.5 35.534.035.036.
05
.735.630.73
2
06
42-
Present LGM
N. Atl.
SO
N. Atl.
SO
N. Atl. = Site 981, 55.5N 14.5W 2184m
SO = Site 1093, 50S, 6E 3626m
Observed
Black circles
CMIP5/PMIP3
Big colored triangles
CCSM4
CNRM-CM5
FGOALS-g2
GISS-E2-R_p150
GISS-E2-R_p151
IPSL-CM5A-LR
MIROC-ESM
MPI-ESM-P_p1
MPI-ESM-P_p2
MRI-CGCM3
PMIP2
Small grey triangles
CCSM
ECBILT
ECHAM_oav
FGOALS
HadCM
HadCM_oav
IPSL
MIROC
Potential temperature (°C)
range in the zonal mean ACC position is smaller than in the CMIP3
ensemble (in CMIP5, the mean transport is 148 Sv and the standard
deviation is 50 Sv across an ensemble of 21 models).
Simulation of glacial ocean conditions
Reconstructions of the last glacial maximum from sediment cores dis-
cussed in Chapter 5 indicate that the regions of deep water forma-
tion in the North Atlantic were shifted southward, that the boundary
between North Atlantic Deep Water (NADW) and Antarctic Bottom
Water (AABW) was substantially shallower than today, and that
NADW formation was less intense (Duplessy et al., 1988; Dokken
and Jansen, 1999; McManus et al., 2004; Curry and Oppo, 2005). This
signal, although estimated from a limited number of sites, is robust
(see Chapter 5). The AR4 reported that model simulations showed a
wide range of AMOC response to LGM forcing (Weber et al., 2007),
with some models exhibiting reduced strength of the AMOC and its
extension at depth and other showing no change or an increase. Figure
9.18 provides an update of the diagnosis proposed by Otto-Bliesner
et al. (2007) to compare model results with the deep ocean data from
Adkins et al. (2002) using PMIP2 and CMIP5/PMIP3 pre-industrial and
LGM simulations (Braconnot et al., 2012). These models reproduce the
modern deep ocean temperature–salinity (T–S) structure in the Atlan-
tic basin, but most of them do not capture the cold and salty bottom
Figure 9.18 | Temperature and salinity for the modern period (open symbols) and the Last Glacial Maximum (LGM, filled symbols) as estimated from proxy data at Ocean Drilling
Program (ODP) sites (black symbols, from Adkins et al., 2002) and simulated by the Paleoclimate Modelling Intercomparison Project version 2 (PMIP2, small triangles) and PMIP3/
CMIP5 (big triangles) models. The isolines represent lines of equal density. Site 981 (triangles) is located in the North Atlantic (Feni Drift, 55ºN, 15ºW, 2184 m). Site 1093 (upside-
down triangles) is located in the South Atlantic (Shona Rise, 50ºS, 6ºE, 3626 m). In PMIP2, only Community Climate System Model (CCSM) included a 1 psu adjustment of ocean
salinity at initialization to account for freshwater frozen into LGM ice sheets; the other PMIP2 model-simulated salinities have been adjusted to allow a comparison. In PMIP3, all
simulations include the 1 psu adjustment as required in the PMIP2/CMIP5 protocol (Braconnot et al., 2012). The dotted lines allow a comparison of the values at the NH and SH
sites for a same model. This figure is adapted from Otto-Bliesner et al. (2007).
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Chapter 9 Evaluation of Climate Models
9
water suggested by the LGM reconstructions, providing evidence that
processes responsible for such palaeoclimate changes may not be well
reproduced in contemporary climate models. This is expected to also
affect projected changes in deep ocean properties.
9.4.2.4 Simulation of Surface Fluxes and Meridional Transports
Surface fluxes play a large part in determining the fidelity of ocean
simulations. As noted in the AR4, large uncertainties in surface heat
and fresh water flux observations (usually obtained indirectly) do not
allow useful evaluation of models. This is still the case and so the focus
here is on an integrated quantity, meridional heat transport, which is
less prone to errors. Surface wind stress is better observed and models
are evaluated against observed products below.
The zonal component of wind stress is particularly important in driv-
ing ocean surface currents; modelled and observed values are shown
in Figure 9.19. At middle to high latitudes, the model-simulated wind
stress maximum lies 5 to 10 degree equatorward of that in the obser-
vationally based estimates, and so mid-latitude westerly winds are
Figure 9.19 | Zonal-mean zonal wind stress over the oceans in (a) CMIP5 models and (b) multi-model mean comparison with CMIP3. Shown is the time-mean of the period
1970–1999 from the historical simulations. The black solid, dashed, and dotted curves represent ECMWF reanalysis of the global atmosphere and surface conditions (ERA)-Interim
(Dee et al., 2011), National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis I (Kalnay et al., 1996), and QuikSCAT satellite
measurements (Risien and Chelton, 2008), respectively. In (b) the shading indicates the inter-model standard deviation.
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Evaluation of Climate Models Chapter 9
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Figure 9.20 | Equatorial (2°S to 2°N averaged) zonal wind stress for the Indian, Pacific, and Atlantic oceans in (a) CMIP5 models and (b) multi-model mean comparison with
CMIP3. Shown is the time-mean of the period 1970–1999 from the historical simulations. The black solid, dashed, and dotted curves represent ERA-Interim (Dee et al., 2011),
National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis I (Kalnay et al., 1996) and QuikSCAT satellite measurements
(Risien and Chelton, 2008), respectively. In (b) the shading indicates the inter-model standard deviation.
too strong in models. This equatorward shift in the southern ocean
is slightly reduced in CMIP5 relative to CMIP3. At these latitudes, the
largest near surface wind speed biases in CMIP5 are located over the
Pacific sector and the smallest are in the Atlantic sector (Bracegirdle et
al., 2013). Such wind stress errors may adversely affect oceanic heat
and carbon uptake (Swart and Fyfe, 2012a). At middle to low latitudes,
the CMIP3 and CMIP5 model spreads are smaller than at high lati-
tudes, although near the equator this can occur through compensating
errors (Figure 9.20). The simulated multi-model mean equatorial zonal
wind stress is too weak in the Atlantic and Indian Oceans and too
strong in the western Pacific, with no major improvement from CMIP3
to CMIP5.
The CMIP5 model simulations qualitatively agree with the various
observational estimates on the most important features of ocean heat
transport (Figure 9.21) and, in a multi-model sense, no major change
from CMIP3 can be seen. All CMIP5 models are able to the represent the
strong north-south asymmetry, with the largest values in the NH, con-
sistent with the observational estimates. At most latitudes the majority
of CMIP5 model results fall within the range of observational estimates,
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Chapter 9 Evaluation of Climate Models
9
Figure 9.21 | Annual- and zonal-mean oceanic heat transport implied by net heat flux imbalances at the sea surface for CMIP5 simulations, under an assumption of negligible
changes in oceanic heat content. Observational estimates include: the data set from Trenberth and Caron (2001) for the period February 1985 to April 1989, derived from reanalysis
products from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR; Kalnay et al., 1996; dash-dotted black) and European
Centre for Medium Range Weather Forecasts 40-year reanalysis (ERA40; Uppala et al., 2005; short-dashed black), an updated version by Trenberth and Fasullo (2008) with
improved top of the atmosphere (TOA) radiation data from the Clouds and Earth’s Radiant Energy System (CERES) for March 2000 to May 2004, and updated NCEP reanalysis
(Kistler et al., 2001) up to 2006 (solid black), the Large and Yeager (2009) analysis based on the range of annual mean transport estimated over the years 1984–2006, computed
from air–sea surface fluxes adjusted to agree in the mean with a variety of satellite and in situ measurements (long-dashed black), and direct estimates by Ganachaud and Wunsch
(2003) obtained from hydrographic sections during the World Ocean Circulation Experiment combined with inverse models (black diamonds). The model climatologies are derived
from the years 1986 to 2005 in the historical simulations in CMIP5. The multi-model mean is shown as a thick red line. The CMIP3 multi-model mean is added as a thick blue line.
although there is some suggestion of modest underestimate between
15°N and 25°N and south of about 60°S. Some models show an equa-
torward transport at Southern-Hemisphere mid-latitudes that is also
featured in the observation estimate of Large and Yeager (2009). This
highlights the difficulties in representing large-scale energy processes
in the Southern ocean as discussed by Trenberth and Fasullo (2010b).
Note that climate models should exhibit a vanishing net energy balance
when long time averages are considered but unphysical sources and
sinks lead to energy biases (Trenberth and Fasullo, 2009, 2010a; Luca-
rini and Ragone, 2011) that are also found in reanalysis constrained by
observations (Trenberth et al., 2009). When correcting for the imperfect
closure of the energy cycle, as done here, comparison between models
and observational estimates become possible.
9.4.2.5 Simulation of Tropical Mean State
9.4.2.5.1 Tropical Pacific Ocean
Although the basic east–west structure of the tropical Pacific is well
captured, models have shown persistent biases in important proper-
ties of the mean state (AchutaRao and Sperber, 2002; Randall et al.,
2007; Guilyardi et al., 2009b) with severe local impacts (Brown et al.,
2012). Among these biases are the mean thermocline depth and slope
along the equator, the structure of the equatorial current system, and
the excessive equatorial cold tongue (Reichler and Kim, 2008; Brown
et al., 2010a; Zheng et al., 2012). Many reasons for these biases have
been proposed, such as: too strong trade winds; a too diffusive ther-
mocline; deficient horizontally isotropic mixing coefficients; insufficient
penetration of solar radiation; and too weak tropical instability waves
(Meehl et al., 2001; Wittenberg et al., 2006; Lin, 2007). It is noteworthy
that CMIP5 models exhibit some improvements in the western equato-
rial Pacific when compared to CMIP3, with reduced SST and trade wind
errors (Figures 9.14 and 9.20). Because of strong interactions between
the processes involved, it is difficult to identify the ultimate source of
these errors, although new approaches using the rapid adjustment of
initialized simulations hold promise (Vannière et al., 2011).
A particular problem in simulating the seasonal cycle in the tropical
Pacific arises from the ‘double ITCZ’, defined as the appearance of a
spurious ITCZ in the SH associated with excessive tropical precipita-
tion. Further problems are too strong a seasonal cycle in simulated
SST and winds in the eastern Pacific and the appearance of a spurious
semi-annual cycle. The latter has been attributed to meridional asym-
metry in the background state that is too weak, possibly in conjunc-
tion with incorrect regional water vapour feedbacks (Li and Philander,
1996; Guilyardi, 2006; Timmermann et al., 2007; De Szoeke and Xie,
2008; Wu et al., 2008a; Hirota et al., 2011).
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Evaluation of Climate Models Chapter 9
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A further persistent problem is insufficient marine stratocumulus cloud
in the eastern tropical Pacific, caused presumably by weak coastal
upwelling off South America leading to a warm SST bias (Lin, 2007).
Although the problem persists, improvements are being made (Achuta-
Rao and Sperber, 2006).
9.4.2.5.2 Tropical Atlantic Ocean
CMIP3 and CMIP5 models exhibit severe biases in the tropical Atlantic
Ocean, so severe that some of the most fundamental features—the
east–west SST gradient and the eastward shoaling thermocline along
the equator—cannot be reproduced (Figure 9.14; (Chang et al., 2007;
Chang et al., 2008; Richter and Xie, 2008; Richter et al., 2013). In many
models, the warm SST bias along the Benguela coast is in excess of
5°C and the Atlantic warm pool in the western basin is grossly under-
estimated (Liu et al., 2013a). As in the Pacific, CMIP3 models suffer the
double ITCZ error in the Atlantic. Hypotheses for the complex Atlantic
bias problem tend to draw on the fact that the Atlantic Ocean has a far
smaller basin, and thus encourages a tighter and more complex land–
atmosphere–ocean interaction. A recent study using a high-resolution
coupled model suggests that the warm eastern equatorial Atlantic SST
bias is more sensitive to the local rather than basin-wide trade wind
bias and to a wet Congo basin instead of a dry Amazon—a finding
that differs from previous studies (Patricola et al., 2012). Recent ocean
model studies show that a warm subsurface temperature bias in the
eastern equatorial Atlantic is common to virtually all ocean models
forced with ‘best estimated’ surface momentum and heat fluxes, owing
to problems in parameterization of vertical mixing (Hazeleger and
Haarsma, 2005). Toniazzo and Woolnough (2013) show that among a
variety of causes for the initial bias development, ocean–atmosphere
coupling is key for their maintenance.
9.4.2.5.3 Tropical Indian Ocean
CMIP3 and CMIP5 models simulate equatorial Indian Ocean climate
reasonably well (e.g., Figure 9.14), though most models produce weak
westerly winds and a flat thermocline on the equator. The models show
a large spread in the modelled depth of the 20°C isotherm in the east-
ern equatorial Indian Ocean (Saji et al., 2006). The reasons are unclear
but may be related to differences in the various parameterizations of
vertical mixing as well as the wind structure (Schott et al., 2009).
CMIP3 models generally simulate the Seychelles Chagos thermocline
ridge in the Southwest Indian Ocean, a feature important for the Indian
monsoon and tropical cyclone activity in this basin (Xie et al., 2002). The
models, however, have significant problems in accurately representing
its seasonal cycle because of the difficulty in capturing the asymmetric
nature of the monsoonal winds over the basin, resulting in too weak
a semi-annual harmonic in the local Ekman pumping over the ridge
region compared to observations (Yokoi et al., 2009b). In about half
of the models, the thermocline ridge is displaced eastward associated
with the easterly wind biases on the equator (Nagura et al., 2013).
9.4.2.6 Summary
There is high confidence that the CMIP3 and CMIP5 models simulate
the main physical and dynamical processes at play during transient
ocean heat uptake, sea level rise, and coupled modes of variability.
There is little evidence that CMIP5 models differ significantly from
CMIP3, although there is some evidence of modest improvement. Many
improvements are seen in individual CMIP5 ocean components (some
now including interactive ocean biogeochemistry) and the number of
relatively poor-performing models has been reduced (thereby reducing
inter-model spread). New since the AR4, process-based model evalua-
tion is now helping identify the cause of some specific biases, helping
to overcome the limits set by the short observational records available.
9.4.3 Sea Ice
Evaluation of sea ice performance requires accurate information on ice
concentration, thickness, velocity, salinity, snow cover and other fac-
tors. The most reliably measured characteristic of sea ice remains sea
ice extent (usually understood as the area covered by ice with a con-
centration above 15%). Caveats, however, exist related to the uneven
reliability of different sources of sea ice extent estimates (e.g., satellite
vs. pre-satellite observations; see Chapter 4), as well as to limitations of
this characteristic as a metric of model performance (Notz et al., 2013).
Sea ice extent (10
6
km
2
)Sea ice extent (10
6
km
2
)
Figure 9.22 | Mean (1980–1999) seasonal cycle of sea ice extent (the ocean area with
a sea ice concentration of at least 15%) in the Northern Hemisphere (upper) and the
Southern Hemisphere (lower) as simulated by 42 CMIP5 and 17 CMIP3 models. Each
model is represented with a single simulation. The observed seasonal cycles (1980–
1999) are based on the Hadley Centre Sea Ice and Sea Surface Temperature (HadISST;
Rayner et al., 2003), National Aeronautics and Space Administration (NASA; Comiso
and Nishio, 2008) and the National Snow and Ice Data Center (NSIDC; Fetterer et al.,
2002) data sets. The shaded areas show the inter-model standard deviation for each
ensemble. (Adapted from Pavlova et al., 2011.)
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Chapter 9 Evaluation of Climate Models
9
Figure 9.23 | Sea ice distribution (1986–2005) in the Northern Hemisphere (upper
panels) and the Southern Hemisphere (lower panels) for February (left) and Septem-
ber (right). AR5 baseline climate (1986–2005) simulated by 42 CMIP5 AOGCMs. Each
model is represented with a single simulation. For each 1° × 1° longitude-latitude grid
cell, the figure indicates the number of models that simulate at least 15% of the area
covered by sea ice. The observed 15% concentration boundaries (red line) are based on
the Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) data set (Rayner et
al., 2003). (Adapted from Pavlova et al., 2011.)
The CMIP5 multi-model ensemble exhibits improvements over CMIP3
in simulation of sea ice extent in the both hemispheres (Figure 9.22).
In the Arctic, the multi-model mean error do not exceed 10% of the
observationally based estimates for any given month. In the Antarc-
tic, the corresponding multi-model mean error exceeds 10% (but is
less than 20%) near the annual minimum of sea ice extent; around
the annual maximum, the CMIP5 multi-model mean shows a clear
improvement over CMIP3.
In many models the regional distribution of sea ice concentration is
poorly simulated, even if the hemispheric extent is approximately cor-
rect. In Figure 9.23, however, one can see that the median ice edge
position (indicated by the colour at which half of the models have ice
of 15% concentration) agrees reasonably well with observations in
both hemispheres (except austral summer in Antarctica), as was the
case for the CMIP3 models.
A widely discussed feature of the CMIP3 models as a group is a pro-
nounced underestimation of the trend in the September (annual min-
imum) sea ice extent in the Arctic over the past several decades (e.g.,
Stroeve et al., 2007; Zhang, 2010; Rampal et al., 2011; Winton, 2011).
Possible reasons for the discrepancy include variability inherent to high
latitudes, model shortcomings, and observational uncertainties (e.g.,
Kattsov et al., 2010; Kay et al., 2011; Day et al., 2012). Compared to
CMIP3, the CMIP5 models better simulate the observed trend of Sep-
tember Arctic ice extent (Figure 9.24). It has been suggested (Stroeve
et al., 2012) that in some cases model improvements, such as new sea
ice albedo parameterization schemes (e.g., Pedersen et al., 2009; Hol-
land et al., 2012), have been responsible. (Holland et al., 2010) show
that models with initially thicker ice generally retain more extensive ice
throughout the 21st century, and indeed several of the CMIP5 models
start the 20th century with rather thin winter ice cover promoting more
rapid melt (Stroeve et al., 2012). Notz et al. (2013) caution, however,
against direct comparison of modelled trends with observations unless
the models’ internal variability is carefully taken into account. Their
analysis of the MPI-ESM ensemble shows that internal variability in
the Arctic can result in individual model realizations exhibiting a range
of trends (negative, or even positive) for the 29-year-long period start-
ing in 1979, even if the background climate is warming. According to
the distribution of sea ice extent trends over the period 1979–2010
obtained in an ensemble of simulations with individual CMIP5 models
(Figure 9.24) about one quarter of the simulations shows a September
trend in the Arctic as strong as, or stronger, than in observations.
The majority of CMIP5 (and CMIP3) models exhibit a decreasing trend
in SH austral summer sea ice extent over the satellite era, in contrast
to the observed weak but significant increase (see Chapter 4). A large
spread in the modelled trends is present, and a comparison of multi-
ple ensemble members from the same model suggests large internal
variability during the late 20th century and the first decade of the 21st
century (e.g., Landrum et al., 2012; Zunz et al., 2013). Compared to
observations, CMIP5 models strongly overestimate the variability of
sea ice extent, at least in austral winter (Zunz et al., 2013).Therefore,
using the models to assess the potential role of the internal variability
in the trend of sea ice extent in the Southern Ocean over the last three
decades presents a significant challenge.
Sea ice is a product of atmosphere–ocean interaction. There are a
number of ways in which sea ice is influenced by and interacts with the
atmosphere and ocean, and some of these feedbacks are still poorly
quantified. As noted in the AR4, among the primary causes of biases
in simulated sea ice extent, especially its geographical distribution, are
problems with simulating high-latitude winds, ocean heat advection
and mixing. For example, Koldunov et al. (2010) have shown, for a par-
ticular CMIP3 model, that significant ice thickness errors originate from
biases in the atmospheric component. Similarly, Melsom et al. (2009)
note sea ice improvements associated with improved description of
heat transport by ocean currents. Biases imparted on modelled sea ice,
common to many models, may also be related to representation of
high-latitude processes (e.g., polar clouds) or processes not yet com-
monly included in models (e.g., deposition of carbonaceous aerosols
on snow and ice). Some CMIP5 models show improvements in simula-
tion of sea ice that are connected to improvements in simulation of the
atmosphere (e.g., Notz et al., 2013).
9.4.3.1 Summary
CMIP5 models reproduce the seasonal cycle of sea ice extent in both
hemispheres. There is robust evidence that the downward trend in
Arctic summer sea ice extent is better simulated than at the time of
the AR4, with about one quarter of the simulations showing a trend
as strong as, or stronger than, that observed over the satellite era. The
performance improvements are not only a result of improvements in
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Evaluation of Climate Models Chapter 9
9
(c) CMIP5 Arctic ice extent September trends (1979 - 2010)
(d) CMIP5 Antarctic ice extent February trends (1979 - 2010)
(10
6
km
2
per decade) (10
6
km
2
per decade)
Sea ice extent (10
6
km
2
)Sea ice extent (10
6
km
2
)
Figure 9.24 | (Top and middle rows) Time series of sea ice extent from 1900 to 2012 for (a) the Arctic in September and (b) the Antarctic in February, as modelled in CMIP5
(coloured lines) and observations-based (NASA; Comiso and Nishio, 2008) and NSIDC; (Fetterer et al., 2002), solid and dashed thick black lines, respectively). The CMIP5 multi-
model ensemble mean (thick red line) is based on 37 CMIP5 models (historical simulations extended after 2005 with RCP4.5 projections). Each model is represented with a single
simulation. The dotted black line for the Arctic in (a) relates to the pre-satellite period of observation-based time series (Stroeve et al., 2012). In (a) and (b) the panels on the right
are based on the corresponding 37-member ensemble means from CMIP5 (thick red lines) and 12-model ensemble means from CMIP3 (thick blue lines). The CMIP3 12-model
means are based on CMIP3 historical simulations extended after 1999 with Special Report on Emission Scenarios (SRES) A2 projections. The pink and light blue shadings denote
the 5 to 95 percentile range for the corresponding ensembles. Note that these are monthly means, not yearly minima. (Adapted from Pavlova et al., 2011.) (Bottom row) CMIP5 sea
ice extent trend distributions over the period 1979–2010 for (c) the Arctic in September and (d) the Antarctic in February. Altogether 66 realizations are shown from 26 different
models (historical simulations extended after 2005 with RCP4.5 projections). They are compared against the observations-based estimates of the trends (green vertical lines in
(c) and (d) from Comiso and Nishio (2008); blue vertical line in (d) from Parkinson and Cavalieri (2012)). In (c), the observations-based estimates (Cavalieri and Parkinson, 2012;
Comiso and Nishio, 2008) coincide.
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Chapter 9 Evaluation of Climate Models
9
sea ice components themselves but also in atmospheric circulation.
Most CMIP5 models simulate a decrease in Antarctic sea ice extent
over the past few decades compared to the small but significant
increase observed.
9.4.4 Land Surface, Fluxes and Hydrology
The land surface determines the partitioning of precipitation into evap-
otranspiration and runoff, and the partitioning of surface net radiation
into sensible and latent heat fluxes. Land surface processes therefore
impact strongly on both the climate and hydrological resources. This
subsection summarizes recent studies on the evaluation of land sur-
face models, wherever possible emphasizing their performance in
CMIP3 and CMIP5 climate models.
9.4.4.1 Snow Cover and Near-Surface Permafrost
The modelling of snow and near-surface permafrost (NSP) processes
has received increased attention since the AR4, in part because of the
recognition that these processes can provide significant feedbacks on
climate change (e.g., Koven et al., 2011; Lawrence et al., 2011). The
SnowMIP2 project compared results from 33 snowpack models of vary-
ing complexity, including some snow models that are used in AOGCMs,
using driving data from five NH locations (Rutter et al., 2009). Most
snow models were found to be consistent with observations at open
sites, but there was much greater discrepancy at forested sites due
to the complex interactions between plant canopy and snow cover.
Despite these difficulties, the CMIP5 multi-model ensemble reproduces
key features of the large-scale snow cover (Figure 9.25). In the NH,
models are able to simulate the seasonal cycle of snow cover over
the northern parts of continents, with more disagreement in south-
erly regions where snow cover is sparse, particularly over China and
Mongolia (Brutel-Vuilmet et al., 2013). The latter weaknesses are asso-
ciated with incorrect timing of the snow onset and melt, and possibly
with the choice of thresholds for diagnosing snow cover in the model
output. In spite of the good performance of the multi-model mean,
there is a significant inter-model scatter of spring snow cover extent
in some regions. There is a strong linear correlation between North-
ern-Hemisphere spring snow cover extent and annual mean surface
air temperature in the models, consistent with available observations.
The recent negative trend in spring snow cover is underestimated by
the CMIP5 (and CMIP3) models (Derksen and Brown, 2012), which is
associated with an underestimate of the boreal land surface warming
(Brutel-Vuilmet et al., 2013).
Some CMIP5 models now represent NSP and frozen soil process-
es (Koven et al., 2013), but this is not generally the case. Therefore
it is difficult to make a direct quantitative evaluation of most CMIP5
models against permafrost observations. A less direct but more inclu-
sive approach is to diagnose NSP extent using snow depths and skin
temperatures generated by climate models to drive a stand-alone mul-
ti-layer permafrost model (Pavlova et al., 2007). Figure 9.25 shows the
result of using this approach on the CMIP5 ensemble. The multi-model
mean is able to simulate the approximate location of the NSP bound-
ary (as indicated by the 0°C soil temperature isotherm). However, the
range of present-day (1986–2005) NSP area inferred from individu-
al models spans a factor of more than six (~4 to 25 × 10
6
km
2
) due
to differences in simulated surface climate and to varying abilities of
the underlying land surface models. Even though many CMIP5 models
include some representation of soil freezing in mineral soils, very few
include key processes necessary to accurately model NSP changes,
such as the distinct properties of organic soils, the existence of local
water tables and the heat released by microbial respiration (Nicolsky et
al., 2007; Wania et al., 2009; Koven et al., 2011, 2013).
Despite large differences in the absolute NSP area, the relationship
between the decrease in NSP area and the warming air temperature
over the present-day NSP region is similar, and approximately linear, in
many models (Slater and Lawrence, 2013).
9.4.4.2 Soil Moisture and Surface Hydrology
The partitioning of precipitation into evapotranspiration and runoff is
highly dependent on the moisture status of the land surface, especially
the amount of soil moisture available for evapotranspiration, which in
turn depends on properties of the land cover such as the rooting depth
of plants.
There has been a long history of off-line evaluation of land surface
schemes, aided more recently by the increasing availability of site-spe-
cific data (Friend et al., 2007; Blyth et al., 2010). Throughout this time,
Figure 9.25 | Terrestrial snow cover distribution (1986–2005) in the Northern Hemi-
sphere (NH) as simulated by 30 CMIP5 models for February, updated for CMIP5 from
Pavlova et al. (2007). For each 1° × 1° longitude-latitude grid cell, the figure indicates
the number of models that simulate at least 5 kg m
–2
of snow-water equivalent. The
observations-based boundaries (red line) mark the territory with at least 20% of the
days per month with snow cover (Robinson and Frei, 2000) over the period 1986–2005.
The annual mean 0°C isotherm at 3.3 m depth averaged across 24 CMIP5 models
(yellow line) is a proxy for the near-surface permafrost boundary. Observed permafrost
extent in the NH (magenta line) is based on Brown et al. (1997, 1998).
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Evaluation of Climate Models Chapter 9
9
representations of the land surface have significantly increased in
complexity, allowing the representation of key processes such as links
between stomatal conductance and photosynthesis, but at the cost of
increasing the number of poorly known internal model parameters.
These more sophisticated land surface models are based on physical
principles that should make them more appropriate for projections of
future climate and increased CO
2
.
However for specific data-rich sites,
current land surface models still struggle to perform as well as statis-
tical models in predicting year-to-year variations in latent and sensible
heat fluxes (Abramowitz et al., 2008) and runoff (Materia et al., 2010).
There are few evaluations of the performance of land surface schemes
in coupled climate models, but those that have been undertaken find
major limitations associated with the atmospheric forcing rather than
the land surface schemes themselves. For example, an evaluation of
the soil moisture simulations of CMIP3 models found that long-term
soil moisture trends could only be reproduced in models that simu-
lated the reduction in solar radiation at the surface associated with
‘global dimming’ (Li et al., 2007). A comparison of simulated evapo-
transpiration fluxes from CMIP3 against large-scale observation-based
estimates, showed underestimates in India and parts of eastern South
America, and overestimates in the western USA, Australia and China
(Mueller et al., 2011).
Land–atmosphere coupling determines the ability of climate models
to simulate the influence of soil moisture anomalies on rainfall,
droughts and high-temperature extremes (Fischer et al., 2007; Lorenz
et al., 2012). The coupling strength depends both on the sensitivity of
evapotranspiration to soil moisture, which is determined by the land
surface scheme, and the sensitivity of precipitation to evapotranspi-
ration, which is determined by the atmospheric model (Koster et al.,
2004; Seneviratne et al., 2010). Comparison of climate model simu-
lations to observations suggests that the models correctly represent
the soil-moisture impacts on temperature extremes in southeastern
Europe, but overestimate them in central Europe (Hirschi et al., 2011).
The influence of soil moisture on rainfall varies significantly with region,
and with the lead-time between a soil moisture anomaly and a rainfall
event (Seneviratne et al., 2010). In some regions, such as the Sahel,
enhanced precipitation can even be induced by dry anomalies (Taylor
et al., 2011). Recent analyses of CMIP5 models reveals considerable
spread in the ability of the models to reproduce observed correlations
between precipitation and soil moisture in the tropics (Williams et al.,
2012), and a systematic failure to simulate the positive impact of dry
soil moisture anomalies on rainfall in the Sahel (Taylor et al., 2012a).
9.4.4.3 Dynamic Global Vegetation and Nitrogen Cycling
At the time of the AR4 very few climate models included dynamic veg-
etation, with vegetation being prescribed and fixed in all but a handful
of coupled climate–carbon cycle models (Friedlingstein et al., 2006).
Dynamic Global Vegetation Models (DGVMs) certainly existed at the
time of the AR4 (Cramer et al., 2001) but these were not typically
incorporated in climate models. Since the AR4 there has been continual
development of offline DGVMs, and some climate models incorporate
dynamic vegetation in at least a subset of the runs submitted to CMIP5
(also see Section 9.1.3.2.4), with likely consequences for climate model
biases and regional climate projection (Martin and Levine, 2012).
DGVMs are designed to simulate the large-scale geographical distri-
bution of plant functional types and how these patterns will change
in response to climate change, CO
2
increases, and other forcing factors
(Cramer et al., 2001). These models typically include rather detailed
representations of plant photosynthesis but less sophisticated treat-
ments of soil carbon, with a varying number of soil carbon pools. In
the absence of nitrogen limitations on CO
2
fertilization, offline DGVMs
agree qualitatively that CO
2
increase alone will tend to enhance
carbon uptake on the land while the associated climate change will
tend to reduce it. There is also good agreement on the degree of CO
2
fertilization in the case of no nutrient limitation (Sitch et al., 2008).
However, under more extreme emissions scenarios the responses of
the DGVMs diverge markedly. Large uncertainties are associated with
the responses of tropical and boreal ecosystems to elevated tempera-
tures and changing soil moisture status. Particular areas of uncertainty
are the high-temperature response of photosynthesis (Galbraith et al.,
2010), and the extent of CO
2
fertilization (Rammig et al., 2010) in the
Amazonian rainforest.
Most of the land surface models and DGVMs used in the CMIP5
models continue to neglect nutrient limitations on plant growth (see
Section 6.4.6.2), even though these may significantly moderate the
response of photosynthesis to CO
2
(Wang and Houlton, 2009). Recent
extensions of two land surface models to include nitrogen limitations
improve the fit to ‘Free-Air CO
2
Enrichment Experiments’, and suggest
that models without these limitations are expected to overestimate
the land carbon sink in the nitrogen-limited mid and high latitudes
(Thornton et al., 2007; Zaehle et al., 2010).
9.4.4.4 Land Use Change
A major innovation in the land component of ESMs since the AR4 is the
inclusion of the effects of land use change associated with the spread
of agriculture, urbanization and deforestation. These affect climate
by altering the biophysical properties of the land surface, such as its
albedo, aerodynamic roughness and water-holding capacity (Bondeau
et al., 2007; Bonan, 2008; Bathiany et al., 2010; Levis, 2010). Land
use change has also contributed almost 30% of total anthropogen-
ic CO
2
emissions since 1850 (see Table 6.1), and affects emissions of
trace gases, and VOCs such as isoprene. The latest ESMs used in CMIP5
attempt to model the CO
2
emissions implied by prescribed land use
change and many also simulate the associated changes in the biophys-
ical properties of the land surface. This represents a major advance on
the CMIP3 models which typically neglected land use change, aside
from its assumed contribution to anthropogenic CO
2
emissions.
However, the increasing sophistication of the modelling of the impacts
of land use change has introduced additional spread in climate model
projections. The first systematic model intercomparison demonstrated
that large-scale land cover change can significantly affect regional cli-
mate (Pitman et al., 2009) and showed a large spread in the response
of different models to the same imposed land cover change (de Nob-
let-Ducoudre et al., 2012). This uncertainty arises from the often coun-
teracting effects of evapotranspiration and albedo changes (Boisier et
al., 2012) and has consequences for the simulation of temperature and
rainfall extremes (Pitman et al., 2012b).
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Chapter 9 Evaluation of Climate Models
9
9.4.5 Carbon Cycle
An important development since the AR4 is the more widespread
implementation of ESMs that include an interactive carbon cycle.
Coupled climate-carbon cycle models are used extensively for the pro-
jections presented in Chapter 12. The evaluation of the carbon cycle
within coupled models is discussed here, while the performance of the
individual land and ocean carbon models, together with the detailed
analysis of climate–carbon cycle feedbacks, is presented in Chapter 6
(Section 6.4 and Box 6.4).
The transition from AOGCMs to ESMs was motivated in part by the
results from the first generation coupled climate–carbon cycle models,
which suggested that feedbacks between the climate and the carbon
cycle were uncertain but potentially very important in the context of
21st century climate change (Cox et al., 2000; Friedlingstein et al.,
2001). The first-generation models used in the Coupled Climate Carbon
Cycle Model Intercomparison Project (C
4
MIP) included both extended
AOGCMS and EMICs. The C
4
MIP experimental design involved running
each model under a common emission scenario (SRES A2) and cal-
culating the evolution of the global atmospheric CO
2
concentration
interactively within the model. The impacts of climate–carbon cycle
feedbacks were diagnosed by carrying out parallel “uncoupled” sim-
ulations in which increases in atmospheric CO
2
did not influence cli-
mate. Analysis of the C
4
MIP runs highlighted a greater than 200 ppmv
range in the CO
2
concentration by 2100 due to uncertainties in cli-
mate–carbon cycle feedbacks, and that the largest uncertainties were
associated with the response of land ecosystems to climate and CO
2
(Friedlingstein et al., 2006).
For CMIP5 a different experimental design was proposed in which the
core simulations use prescribed RCPs of atmospheric CO
2
and other
GHGs (Moss et al., 2010). Under a prescribed CO
2
scenario, ESMs calcu-
late land and ocean carbon fluxes interactively, but these fluxes do not
affect the evolution of atmospheric CO
2
. Instead the modelled land and
ocean fluxes, along with the prescribed increase in atmospheric CO
2
,
can be used to diagnose the ‘compatible’ emissions of CO
2
consistent
with the simulation (see Section 6.3; Miyama and Kawamiya, 2009;
Arora et al., 2011). The compatible emissions for each model can then
be evaluated against the best estimates of the actual historical CO
2
emissions. Parallel model experiments in which the carbon cycle does
not respond to the simulated climate change (which are equivalent to
the ‘uncoupled’ simulations in C
4
MIP) provide a means to diagnose
climate–carbon cycle feedbacks in terms of their impact on the com-
patible emissions of CO
2
(Hibbard et al., 2007).
Carbon cycle model evaluation is limited by the availability of direct
observations at appropriately large spatial scales. Field studies and
eddy-covariance flux measurements provide detailed information on
the land carbon cycle over short-time scales and for specific locations,
and ocean inventories are able to constrain the long-term uptake of
anthropogenic CO
2
by the ocean (Sabine et al., 2004; Takahashi et al.,
2009). However the stores of carbon on the land are less well-known,
even though these are important determinants of the CO
2
fluxes from
land use change. ESM simulations vary by a factor of at least six in
global soil carbon (Anav et al., 2013; Todd-Brown et al., 2013) and by
a factor of four in global vegetation carbon, although about two thirds
of models are within 50% of the uncertain observational estimates
(Anav et al., 2013).
Large-scale land–atmosphere and global atmosphere fluxes are not
directly measured, but global estimates can be made from the carbon
balance, and large-scale regional fluxes can be estimated from the
inversion of atmospheric CO
2
measurements (see Section 6.3.2). Figure
9.26 shows modelled annual mean ocean–atmosphere and net land–
atmosphere CO
2
fluxes from the historical simulations in the CMIP5
archive (Anav et al., 2013). Also shown are estimates derived from
offline ocean carbon cycle models, measurements of atmospheric CO
2
,
and best estimates of the CO
2
fluxes from fossil fuels and land use
change (Le Quere et al., 2009). Uncertainties in these latter annual
estimates are approximately ±0.5 PgC yr
–1
, arising predominantly from
the uncertainty in the model-derived ocean CO
2
uptake. The confidence
limits for the ensemble mean are derived by assuming that the CMIP5
models form a t-distribution centred on the ensemble mean (Anav et
al., 2013).
The evolution of the global ocean carbon sink is shown in the top
panel of Figure 9.26. The CMIP5 ensemble mean global ocean uptake
(± standard deviation of the multi-model ensemble), computed using
all the 23 models that reported ocean CO
2
fluxes, increases from 0.47
± 0.32 PgC yr
–1
over the period 1901–1930 to 1.53 ± 0.36 PgC yr
–1
for the period 1960–2005. For comparison, the Global Carbon Project
(GCP) estimates a stronger ocean carbon sink of 1.92 ± 0.3 PgC yr
–1
for
1960–2005 (Anav et al., 2013). The bottom panel of Figure 9.26 shows
the variability in global land carbon uptake evident in the GCP esti-
mates, with the global land carbon sink being strongest during La Niña
years and after volcanoes, and turning into a source during El Niño
years. The CMIP5 models cannot be expected to precisely reproduce
this year-to-year variability as these models will naturally simulate cha-
otic ENSO variability that is out of phase with the historical variability.
However, the ensemble mean does successfully simulate a strengthen-
ing global land carbon sink during the 1990s, especially after the Mt
Pinatubo eruption in 1991. The CMIP5 ensemble mean land–atmos-
phere flux (± standard deviation of the multi-model ensemble) evolves
from a small source of –0.34 ± 0.49 PgC yr
–1
over the period 1901–
1930, predominantly due to land use change, to a sink of 0.47 ± 0.72
PgC yr
–1
in the period 1960–2005. The GCP estimates give a weaker
sink of 0.36 ± 1 PgC yr
–1
for the 1960–2005 period.
Figure 9.27 shows the ocean–atmosphere fluxes (top panel) and mean
land–atmosphere fluxes (bottom panel) simulated by ESMs and EMICs
(Eby et al., 2013) for the period 1986–2005, and compares these
to observation-based estimates from GCP and Atmospheric Tracer
Transport Model Intercomparison Project (TRANSCOM3) atmospheric
inversions (Gurney et al., 2003). Unlike Figure 9.26, only models that
reported both land and ocean carbon fluxes are included in this figure.
The atmospheric inversions results are taken from the Japanese Meteo-
rological Agency (JMA) as this was the only TRANSCOM3 model that
reported results for all years of the 1986–2005 reference period. The
error bars on the observational estimates (red triangles) and the ESM
simulations (black diamonds) represent the interannual variation in
the form of the standard deviation of the annual fluxes. EMICs do
not typically simulate interannual variability, so only mean values are
shown for these models (green boxes). Here, as in Figure 9.26, the net
793
Evaluation of Climate Models Chapter 9
9
Year
Atmosphere−Ocean CO
2
Flux (Pg C yr
-1
)
1900 1915 1930 1945 1960 1975 1990 2005
−1
0
1
2
3
4
CMIP5
GCP
Confidence (%)
10 20 30 40 50 60 70 80 90
Sink
Source
Year
Atmosphere−Land CO
2
Flux (Pg C yr
-1
)
1900 1915 1930 1945 1960 1975 1990 2005
−9
−7
−5
−3
−1
1
3
5
7
9
CMIP5
GCP
Confidence (%)
10 20 30 40 50 60 70 80 90
Source
Sink
Figure 9.26 | Ensemble-mean global ocean carbon uptake (top) and global land carbon uptake (bottom) in the CMIP5 ESMs for the historical period 1900–2005. For comparison,
the observation-based estimates provided by the Global Carbon Project (Le Quere et al., 2009) are also shown (thick black line). The confidence limits on the ensemble mean are
derived by assuming that the CMIP5 models come from a t-distribution. The grey areas show the range of annual mean fluxes simulated across the model ensemble. This figure
includes results from all CMIP5 models that reported land CO
2
fluxes, ocean CO
2
fluxes, or both (Anav et al., 2013).
land–atmosphere flux is ‘Net Biome Productivity (NBP)’ which includes
the net CO
2
emissions from land use change as well as the changing
carbon balance of undisturbed ecosystems.
For the period 1986–2005 the observation-based estimates of the
global ocean carbon sink are 1.71 PgC yr
–1
(JMA), 2.19 PgC yr
–1
(GCP)
and 2.33 PgC yr
–1
(Takahashi et al., 2009). Taking into account the
uncertainties in the mean values of these fluxes associated with inter-
annual variability, the observationally constrained range is approxi-
mately 1.4 to 2.4 PgC yr
–1
. All of the ESMs, and all but one of the
EMICs, simulate ocean sinks within this range. The observation-based
estimates of GCP and JMA agree well on the mean global land carbon
sink over the period 1986–2005, and most ESMs fit within the uncer-
tainty bounds of these estimates (i.e., 1.17 ± 1.06 PgC yr
–1
for JMA).
The exceptions are two ESMs sharing common atmosphere and land
components (CESM1-BGC and NorESM1-ME) which model a net land
carbon source rather than a sink over this period. The EMICs tend to
systematically underestimate the contemporary land carbon sink (Eby
et al., 2013). Some ESMs (notably GFDL-ESM2M and GFDL-ESM2G)
significantly overestimate the interannual variation in the global land–
atmosphere CO
2
flux, with a possible consequence being an overes-
timate of the vulnerability of tropical ecosystems to future climate
change (Cox et al., 2013), and see Figure 9.45). All ESMs qualitatively
simulate the expected pattern of ocean CO
2
fluxes, with outgassing in
the tropics and uptake in the mid and high latitudes (Anav et al., 2013).
However, there are systematic differences between the ESMs and the
JMA inversion estimates for the zonal land CO
2
fluxes, with the ESMs
tending to produce weaker uptake in the NH, and simulating a net land
carbon sink rather than a source in the tropics.
In summary, there is high confidence that CMIP5 ESMs can simu-
late the global mean land and ocean carbon sinks within the range
of observation-based estimates. Overall, EMICs reproduce the recent
global ocean CO
2
fluxes uptake as well as ESMs, but estimate a lower
794
Chapter 9 Evaluation of Climate Models
9
1.3
1.5
1.7
1.9
2.1
2.3
2.5
a) Global Atmosphere-Ocean CO
2
Flux
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
GCP
JMA
BNU-ESM
CanESM2
CESM1-BGC
GFDL-ESM2G
GFDL-ESM2M
HadGEM2-CC
HadGEM2-ES
IPSL-CM5A-LR
IPSL-CM5A-MR
IPSL-CM5B-LR
MIROC-ESM-CHEM
MIROC-ESM
MPI-ESM-LR
MPI-ESM-MR
MRI-ESM1
NorESM1-ME
Bern3D
DCESS
GENIE
IGSM
MESMO
UMD
UVic
b) Global Atmosphere-Land CO
2
Flux
(Pg C yr
-1
)(Pg C yr
-1
)
land carbon sink compared with most ESMs while remaining consist-
ent with the observations (Eby et al., 2013). With few exceptions, the
CMIP5 ESMs also reproduce the large-scale pattern of ocean–atmos-
phere CO
2
fluxes, with uptake in the Southern Ocean and northern
mid-latitudes, and outgassing in the tropics. However, the geographical
pattern of simulated land–atmosphere fluxes agrees much less well
with inversion estimates, which suggest a larger sink in the northern
mid-latitudes, and a net source rather than a sink in the tropics. While
there are also inherent uncertainties in atmospheric inversions, dis-
crepancies like this might be expected from known deficiencies in the
CMIP5 generation of ESMs—namely the failure to correctly simulate
nitrogen fertilization in the mid-latitudes, and a rudimentary treat-
ment of the net CO
2
emissions arising from land use change and forest
regrowth.
9.4.6 Aerosol Burdens and Effects on Insolation
9.4.6.1 Recent Trends in Global Aerosol Burdens and Effects
on Insolation
The ability of CMIP5 models to simulate the current burden of tropo-
spheric aerosol and the decadal trends in this burden can be assessed
using observations of aerosol optical depth (AOD, see Section 7.3.1.2).
The historical data used to drive the CMIP5 20th century simulations
reflect recent trends in anthropogenic SO
2
emissions, and hence these
trends should be manifested in the modelled and observed AOD. During
the last three decades, anthropogenic emissions of SO
2
from North
America and Europe have declined due to the imposition of emission
controls, while the emissions from Asia have increased. The combina-
tion of the European, North American, and Asians trends has yielded a
global reduction in SO
2
emissions of 20 Gg(SO
2
), or 15% between 1970
and 2000 although emissions subsequently increased by 9 Gg(SO
2
)
Figure 9.27 | Simulation of global mean (a) atmosphere–ocean CO
2
fluxes (‘fgCO2’)
and (b) net atmosphere–land CO
2
fluxes (‘NBP’), by ESMs (black diamonds) and EMICs
(green boxes), for the period 1986–2005. For comparison, the observation-based esti-
mates provided by Global Carbon Project (GCP; Le Quere et al., 2009), and the Japa-
nese Meteorological Agency (JMA) atmospheric inversion (Gurney et al., 2003) are also
shown as the red triangles. The error bars for the ESMs and observations represent
interannual variability in the fluxes, calculated as the standard deviation of the annual
means over the period 1986–2005.
between 2000 and 2005 (Smith et al., 2011b). For the period 2001 to
2005, CMIP5 models underestimate the mean AOD at 550nm relative
to satellite-retrieved AOD by at least 20% over virtually all land surfac-
es (Figure 9.28). The differences between the modelled and measured
AODs exceed the errors in the Multi-angle Imaging Spectro-Radiome-
ter (MISR) retrievals over land of ±0.05 or 0.2×AOD (Kahn et al., 2005)
and the RMS errors in the corrected Moderate Resolution Imaging
Spectrometer (MODIS) retrievals over ocean of 0.061(Shi et al., 2011).
The effects of sulphate and other aerosol species on surface insolation
through direct and indirect forcing appear to be one of the principal
causes of the ‘global dimming’ between the 1960s and 1980s and
subsequent ‘global brightening’ in the last two decades (see Section
2.3.3.1). This inference is supported by trends in aerosol optical depth
and trends in surface insolation under cloud-free conditions. Thirteen
out of 14 CMIP3 models examined by Ruckstuhl and Norris (2009) pro-
duce a transition from “dimming” to ‘brightening’ that is consistent
with the timing of the transition from increasing to decreasing global
anthropogenic aerosol emissions. The transition from ‘dimming’ to
‘brightening’ in both Europe and North America is well simulated with
the HadGEM2 model (Haywood et al., 2011).
Figure 9.28 | (a): Annual mean visible aerosol optical depth (AOD) for 2001 through
2005 using the Moderate Resolution Imaging Spectrometer (MODIS) version5 satellite
retrievals for ocean regions (Remer et al., 2008) with corrections (Zhang et al., 2008a;
Shi et al., 2011) and version31 of MISR retrievals over land (Zhang and Reid, 2010;
Stevens and Schwartz, 2012). (b): The absolute error in visible AOD from the median of a
subset of CMIP5 models’ historical simulations relative to the satellite retrievals of AOD
shown in (a). The model outputs for 2001 through 2005 are from 21 CMIP5 models with
interactive or semi-interactive aerosol representation.
795
Evaluation of Climate Models Chapter 9
9
Figure 9.29 | Time series of global oceanic mean aerosol optical depth (AOD) from individual CMIP5 models’ historical (1850–2005) and RCP4.5 (2006–2010) simulations, cor-
rected Moderate Resolution Imaging Spectrometer (MODIS) satellite observations by Shi et al. (2011) and Zhang et al. (2008a), and the Atmospheric Chemistry and Climate Model
Intercomparison Project (ACCMIP) simulations for the 1850s by Shindell et al. (2013b).
These recent trends are superimposed on a general upward trend in
aerosol loading since 1850 reflected by an increase in global mean
oceanic AODs from the CMIP5 historical and RCP 4.5 simulations from
1850 to 2010 (Figure 9.29). Despite the use of common anthropogenic
aerosol emissions for the historical simulations (Lamarque et al., 2010),
the simulated oceanic AODs for 2010 range from 0.08 to 0.215, with
nearly equal numbers of models over and underestimating the satellite
retrieved AOD of 0.12 (Figure 9.29). This range in AODs results from
differing estimates of the trends and of the initial global mean oceanic
AOD at 1850 across the CMIP5 ensemble (Figure 9.29).
9.4.6.2 Principal Sources of Uncertainty in Projections of
Sulphate Burdens
Natural sources of sulphate from oxidation of dimethylsulphide (DMS)
emissions from the ocean surface are not specified under the RCP pro-
tocol and therefore represent a source of uncertainty in the sulphur
cycle simulated by the CMIP5 ensemble. In simulations of present-day
conditions, DMS emissions span a 5 to 95% confidence interval of
10.7 to 28.1 TgS yr
–1
(Faloona, 2009). After chemical processing, DMS
contributes between 18 and 42% of the global atmospheric sulphate
burden and up to 80% of the sulphate burden over most the SH (Car-
slaw et al., 2010). Several CMIP5 models include prognostic calculation
of the biogenic DMS source; however, the effects from differences in
DMS emissions on modelled sulphate burdens remain to be quantified.
In contrast to CMIP3, the models in the CMIP5 ensemble are provid-
ed with a single internally consistent set of future anthropogenic SO
2
emissions. The use of a single set of emissions removes an important,
but not dominant, source of uncertainty in the AR5 simulations of the
sulphur cycle. In experiments based on a single chemistry–climate
model with perturbations to both emissions and sulphur-cycle process-
es, uncertainties in emissions accounted for 53.3% of the ensemble
variance (Ackerley et al., 2009). The next largest source of uncertain-
ty was associated with the wet scavenging of sulphate (see Section
7.3.2), which accounted for 29.5% of the intra-ensemble variance and
represents the source/sink term with the largest relative range in the
aerosol models evaluated by Faloona (2009). Similarly, simulations run
with heterogeneous or harmonized emissions data sets yielded approx-
imately the same intermodel standard deviation in sulphate burden of
25 Tg. These results show that a dominant source of the spread among
the sulphate burdens is associated with differences in the treatment
of chemical production, transport, and removal from the atmosphere
(Liu et al., 2007; Textor et al., 2007). Errors in modelled aerosol burden
systematically affect anthropogenic RF (Shindell et al., 2013b).
9.5 Simulation of Variability and Extremes
9.5.1 Importance of Simulating Climate Variability
The ability of a model to simulate the mean climate, and the slow,
externally forced change in that mean state, was evaluated in the pre-
vious section. However, the ability to simulate climate variability, both
unforced internal variability and forced variability (e.g., diurnal and
seasonal cycles) is also important. This has implications for the signal-
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Chapter 9 Evaluation of Climate Models
9
to-noise estimates inherent in climate change detection and attribu-
tion studies where low-frequency climate variability must be estimat-
ed, at least in part, from long control integrations of climate models
(Section 10.2). It also has implications for the ability of models to
make quantitative projections of changes in climate variability and the
statistics of extreme events under a warming climate. In many cases,
the impacts of climate change will be experienced more profoundly in
terms of the frequency, intensity or duration of extreme events (e.g.,
heat waves, droughts, extreme rainfall events; see Section 12.4). The
ability to simulate climate variability is also central to achieving skill
in climate prediction by initializing models from the observed climate
state (Sections 11.1 and 11.2).
Evaluating model simulations of climate variability also provides a
means to explore the representation of certain processes, such as the
coupled processes underlying the ENSO and other important modes of
variability. A model’s representation of the diurnal or seasonal cycle—
both of which represent responses to external (rotational or orbital)
forcing – may also provide some insight into a model’s ‘sensitivity’ and
by extension, the ability to respond correctly to GHG, aerosol, volcanic
and solar forcing.
9.5.2 Diurnal-to-Seasonal Variability
9.5.2.1 Diurnal Cycles of Temperature and Precipitation
The diurnally varying solar radiation received at a given location
drives, through complex interactions with the atmosphere, land sur-
face and upper ocean, easily observable diurnal variations in surface
and near-surface temperature, precipitation, level stability and winds.
The AR4 noted that climate models simulated the global pattern of
the diurnal temperature range, zonally and annually averaged over the
continent, but tended to underestimate its magnitude in many regions
(Randall et al., 2007). New analyses over land indicate that model
deficiencies in surface–atmosphere interactions and the planetary
boundary layer are also expected to contribute to some of the diurnal
cycle errors and that model agreement with observations depends on
region, vegetation type and season (Lindvall et al., 2012). Analyses of
CMIP3 simulations show that the diurnal amplitude of precipitation is
realistic, but most models tend to start moist convection prematurely
over land (Dai, 2006; Wang et al., 2011a). Many CMIP5 models also
have peak precipitation several hours too early compared to surface
observations and TRMM satellite observations (Figure 9.30). This
and the so-called ‘drizzling bias’ (Dai, 2006) can have large adverse
impacts on surface evaporation and runoff (Qian et al., 2006). Over
the ocean, models often rain too frequently and underestimated the
diurnal amplitude (Stephens et al., 2010). It has also been suggested
that a weak diurnal cycle of surface air temperature is produced over
the ocean because of a lack of diurnal variations in SST (Bernie et al.,
2008), and most models have difficulty with this due to coarse vertical
resolution and coupling frequency (Dai and Trenberth, 2004; Danaba-
soglu et al., 2006).
Improved representation of the diurnal cycle has been found with
increased atmospheric resolution (Sato et al., 2009; Ploshay and Lau,
2010) or with improved representation of cloud physics (Khairoutdinov
et al., 2005), but the reasons for these improvements remain poorly
understood. Other changes such as the representation of entrainment
in deep convection (Stratton and Stirling, 2012), improved coupling
between shallow and deep convection, and inclusion of density cur-
rents (Peterson et al., 2009) have been shown to greatly improve the
diurnal cycle of convection over tropical land and provide a good rep-
resentation of the timing of convection over land in coupled ocean–
atmosphere simulations (Hourdin et al., 2013). Thanks to improve-
ments like this, the best performing models in Figure 9.30 appear now
to be able to capture the land and ocean diurnal phase and amplitude
quite well.
9.5.2.2 Blocking
In the mid latitudes, climate is often characterized by weather regimes
(see Chapter 2), amongst which blocking regimes play a role in the
occurrence of extreme weather events (Buehler et al., 2011; Sillmann
et al., 2011; Pfahl and Wernli, 2012). During blocking, the prevailing
mid-latitude westerly winds and storm systems are interrupted by a
local reversal of the zonal flow. Climate models in the past have uni-
versally underestimated the occurrence of blocking, in particular in the
Euro-Atlantic sector (Scaife et al., 2010).
There are important differences in methods used to identify blocking
(Barriopedro et al., 2010a), and the diagnosed blocking frequency can
be very sensitive to details such as the choice of latitude (Barnes et al.,
2012). Blocking indices can be sensitive to biases in the representa-
tion of mean state (Scaife et al., 2010) or in variability (Barriopedro
et al., 2010b; Vial and Osborn, 2012). When blocking is measured via
anomaly fields, rather than reversed absolute fields, model skill can
be high even in relatively low-resolution models (e.g., Sillmann and
Croci-Maspoli, 2009).
Recent work has shown that models with high horizontal (Matsueda,
2009; Matsueda et al., 2009, 2010) or vertical resolution (Anstey et al.,
2013) are better able to simulate blocking. These improvement arise
from increased representation of orography and atmospheric dynamics
(Woollings et al., 2010b; Jung et al., 2012; Berckmans et al., 2013), as
well as reduced ocean surface temperature errors in the extra tropics
(Scaife et al., 2011). Improved physical parameterizations have also
been shown to improve simulations of blocking (Jung et al., 2010).
However, as in CMIP3 (Scaife et al., 2010; Barnes et al., 2012), most of
the CMIP5 models still significantly underestimate winter Euro-Atlantic
blocking (Anstey et al., 2013; Masato et al., 2012; Dunn-Sigouin and
Son, 2013). These new results show that the representation of blocking
events is improving in models, even though the overall quality of CMIP5
ensemble is medium. There is high confidence that model representa-
tion of blocking is improved through increases in model resolution.
9.5.2.3 Madden–Julian Oscillation
During the boreal winter the eastward propagating feature known
as the Madden–Julian Oscillation (MJO; (Madden and Julian, 1972,
1994) predominantly affects the deep tropics, while during the boreal
summer there is also northward propagation over much of southern
Asia (Annamalai and Sperber, 2005). The MJO has received much
attention given the prominent role it plays in tropical climate variabil-
ity (e.g., monsoons, ENSO, and mid-latitudes; Lau and Waliser, 2011)
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Evaluation of Climate Models Chapter 9
9
Figure 9.30 | Composite diurnal cycle of precipitation averaged over land (left) and ocean (right) for three different latitude bands at each local time and season (June–July–August
(JJA), December–January–February (DJF), or their sum). For most of the CMIP5 models, data from 1980–2005 from the historical runs were averaged to derived the composite cycle;
however, a few models had the required 3-hourly data only for 1990–2005 or 1996–2005. For comparison with the model results, a similar diagnosis from observations are shown
(black solid line: surface-observed precipitation frequency; black dashed line: TRMM 3B42 data set, 1998–2003 mean). (Update of Figure 17 of Dai, 2006.)
Phenomenological diagnostics (Waliser et al., 2009a) and process-ori-
ented diagnostics (e.g., Xavier, 2012) have been used to evaluate MJO
in climate models. An important reason for model errors in repre-
senting the MJO is that convection parameterizations do not provide
sufficient build-up of moisture in the atmosphere for the large scale
organized convection to occur (Kim et al., 2012; Mizuta et al., 2012).
Biases in the model mean state also contribute to poor MJO simulation
(Inness et al., 2003). High-frequency coupling with the ocean is also
an important factor (Bernie et al., 2008). While new parameterizations
of convection may improve the MJO (Hourdin et al., 2013), this some-
times occurs at the expense of a good simulation of the mean tropical
climate (Kim et al., 2012). In addition, high resolution models with an
improved diurnal cycle do not necessarily produce an improved MJO
(Mizuta et al., 2012).
Most models underestimate the strength and the coherence of convec-
tion and wind variability (Lin et al., 2006; Lin and Li, 2008). The sim-
plified metric shown in Figure 9.31 provides a synthesis of CMIP3 and
CMIP5 model results (Sperber and Kim, 2012). It shows that simulation
of the MJO is still a challenge for climate models (Lin et al., 2006; Kim
et al., 2009; Xavier et al., 2010). Most models have weak coherence in
their MJO propagation (smaller maximum positive correlation). Even
so, relative to CMIP3 there has been improvement in CMIP5 in simu-
lating the eastward propagation of boreal winter MJO convection from
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Chapter 9 Evaluation of Climate Models
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the Indian Ocean into the western Pacific (Hung et al., 2013) and north-
ward propagation during boreal summer (Sperber et al., 2012). In addi-
tion there is evidence that models reproduce MJO characteristics in the
east Pacific (Jiang et al., 2012b), and that, overall, there is improvement
compared to previous generations of climate models (Waliser et al.,
2003; Lin et al., 2006; Sperber and Annamalai, 2008).
9.5.2.4 Large-Scale Monsoon Rainfall and Circulation
Monsoons are the dominant modes of annual variation in the tropics
(Trenberth et al., 2000; Wang and Ding, 2008), and affect weather and
climate in numerous regions (Chapter 14). High-fidelity simulation of
the mean monsoon and its variability is of great importance for simulat-
ing future climate impacts (Wang, 2006; Sperber et al., 2010; Colman et
al., 2011; Turner and Annamalai, 2012). The monsoon is characterized by
an annual reversal of the low level winds and well defined dry and wet
seasons (Wang and Ding, 2008), and its variability is primarily connect-
ed to the MJO and ENSO (Section 9.5.3). The AR4 reported that most
CMIP3 models poorly represent the characteristics of the monsoon and
monsoon teleconnections (Randall et al., 2007), though improvement in
CMIP5 has been noted for the mean climate, seasonal cycle, intrasea-
sonal and interannual variability (Sperber et al., 2012).
Figure 9.31 | (a, b) The two leading Empirical Orthogonal Functions (EOFs) of outgoing longwave radiation (OLR) from years of strong Madden–Julian Oscillation (MJO) variability
computed following Sperber (2003). The 20- to 100-day filtered OLR from observations and each of the CMIP5 historical simulations and the CMIP3 simulations of 20th century
climate is projected on these two leading EOFs to obtain MJO Principal Component time series. The scatterplot (c) shows the maximum positive correlation between the resulting
MJO Principal Components and the time lag at which it occurred for all winters (November to March). The maximum positive correlation is an indication of the coherence with
which the MJO convection propagates from the Indian Ocean to the Maritime Continent/western Pacific, and the time lag is approximately one fourth of the period of the MJO.
(Constructed following Sperber and Kim, 2012.)
The different monsoon systems are connected through the large-scale
tropical circulation, offering the possibility to evaluate a models’ rep-
resentation of monsoon domain and intensity through the global mon-
soon concept (Wang and Ding, 2008; Wang et al., 2011a). The CMIP5
multi-model ensemble generally reproduces the observed spatial pat-
terns but somewhat underestimates the extent and intensity, especial-
ly over Asia and North America (Figure 9.32). The best model has simi-
lar performance to the multi-model mean, whereas the poorest models
fail to capture the monsoon precipitation domain and intensity over
Asia and the western Pacific, Central America, and Australia. Fan et al.
(2010) also show that CMIP3 simulations capture the observed trend
of weakening of the South Asian summer circulation over the past half
century, but are unable to reproduce the magnitude of the observed
trend in precipitation. On longer time scales, mid-Holocene simulations
show that even though models capture the sign of the monsoon pre-
cipitation changes, they tend to underestimate its magnitude (Bracon-
not et al., 2007b; Zhao and Harrison, 2012)
Poor simulation of the monsoon has been attributed to cold SST biases
over the Arabian Sea (Levine and Turner, 2012), a weak meridional
temperature gradient (Joseph et al., 2012), unrealistic development
of the Indian Ocean dipole (Achuthavarier et al., 2012; Boschat et al.,
799
Evaluation of Climate Models Chapter 9
9
2012) and changes to the circulation through excessive precipitation
over the southwest equatorial Indian Ocean (Bollasina and Ming,
2013). These biases lead to too weak inland moisture transport and an
underestimate of monsoon precipitation over India. Similar SST biases
contribute to model-data mismatch in the simulation of the mid-Hol-
ocene Asian monsoon (Ohgaito and Abe-Ouchi, 2009), even though
the representation of atmospheric processes such as convection seems
to dominate the model spread in this region (Ohgaito and Abe-Ouchi,
2009) or over Africa (Zheng and Braconnot, 2013). Factors that have
contributed to improved representation of the monsoon in some
CMIP5 models include better simulation of topography-related mon-
soon precipitation due to higher horizontal resolution (Mizuta et al.,
2012), a more realistic ENSO–monsoon teleconnection (Meehl et al.,
2012) and improved propagation of intraseasonal variations (Sperber
and Kim, 2012). The impact of aerosols on monsoon precipitation and
its variability is the subject of ongoing investigation (Lau et al., 2008).
These results provide robust evidence that CMIP5 models simulate
more realistic monsoon climatology and variability than their CMIP3
predecessors, but they still suffer from biases in the representation of
the monsoon domain and intensity leading to medium model quality at
the global scale and declining quality at the regional scale.
9.5.3 Interannual-to-Centennial Variability
In addition to the annual, intra-seasonal and diurnal cycles described
above, a number of other modes of variability arise on multi-annual to
multi-decadal time scales (see also Box 2.5). Most of these modes have
a particular regional manifestation whose amplitude can be larger
Figure 9.32 | Monsoon precipitation intensity (shading, dimensionless) and monsoon precipitation domain (lines) are shown for (a) observation-based estimates from Global Pre-
cipitation Climatology Project (GPCP), (b) the CMIP5 multi-model mean, (c) the best model and (d) the worst model in terms of the threat score for this diagnostic. These measures
are based on the seasonal range of precipitation using hemispheric summer (May through September in the Northern Hemisphere (NH)) minus winter (November through March in
the NH) values. The monsoon precipitation domain is defined where the annual range is >2.5 mm day
–1
, and the monsoon precipitation intensity is the seasonal range divided by
the annual mean. The threat scores (Wilks, 1995) indicate how well the models represent the monsoon precipitation domain compared to the GPCP data. The threat score in panel
(a) is between GPCP and CMAP rainfall to indicate observational uncertainty, whereas in the other panel it is between the simulations and the GPCP observational data set. A threat
score of 1.0 would indicate perfect agreement between the two data sets. See Wang and Ding (2008),Wang et al. (2011a), and Kim et al. (2011) for details of the calculations.
than that of human-induced climate change. The observational record
is usually too short to fully evaluate the representation of variability
in models and this motivates the use of reanalysis or proxies, even
though these have their own limitations.
9.5.3.1 Global Surface Temperature Multi-Decadal Variability
The AR4 concluded that modelled global temperature variance at dec-
adal to inter-decadal time scales was consistent with 20th century
observations. In addition, results from the last millennium suggest that
simulated variability is consistent with indirect estimates (Hegerl et
al., 2007).
Figure 9.33a shows simulated internal variability of mean surface tem-
perature from CMIP5 pre-industrial control simulations. Model spread
is largest in the tropics and mid to high latitudes (Jones et al., 2012),
where variability is also large; however, compared to CMIP3, the spread
is smaller in the tropics owing to improved representation of ENSO var-
iability (Jones et al., 2012). The power spectral density of global mean
temperature variance in the historical simulations is shown in Figure
9.33b and is generally consistent with the observational estimates.
At longer time scale of the spectra estimated from last millennium
simulations, performed with a subset of the CMIP5 models, can be
assessed by comparison with different NH temperature proxy records
(Figure 9.33c; see Chapter 5 for details). The CMIP5 millennium sim-
ulations include natural and anthropogenic forcings (solar, volcanic,
GHGs, land use) (Schmidt et al., 2012). Significant differences between
unforced and forced simulations are seen for time scale larger than
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Chapter 9 Evaluation of Climate Models
9
Figure 9.33 | Global climate variability as represented by: (a) Standard deviation of zonal-mean surface temperature of the CMIP5 pre-industrial control simulations (after Jones
et al., 2012). (b) Power spectral density for 1901–2010 global mean surface temperature for both historical CMIP5 simulations and the observations (after Jones et al., 2012). The
grey shading provides the 5 to 95% range of the simulations. (c) Power spectral density for Northern Hemisphere surface temperature from the CMIP5/ Paleoclimate Modelling
Intercomparison Project version 3 (PMIP3) last-millennium simulations (colour, solid) using common external forcing configurations (Schmidt et al., 2012), together with the cor-
responding pre-industrial simulations (colour, dashed), previous last-millennium AOGCM simulations (black: Fernandez-Donado et al., 2013), and temperature reconstructions from
different proxy records (see Section 5.3.5). For comparison between model results and proxy records, the spectra in (c) have been computed from normalized Northern Hemisphere
time series. The small panel included in the bottom panel shows for the different models and reconstructions the percentage of spectral density cumulated for periods longer than
50 years, to highlight the differences between unforced (pre-industrial control) and forced (PMIP3 and pre-PMIP3) simulations, compared to temperature reconstruction for the
longer time scales. In (b) and (c) the spectra have been computed using a Tukey–Hanning filter of width 97 and 100 years, respectively. The model outputs were not detrended,
except for the MIROC-ESM millennium simulation. The 5 to 95% intervals (vertical lines) provide the accuracy of the power spectra estimated given a typical length of 110 years
for (b) and 1150 years for (c).
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Evaluation of Climate Models Chapter 9
9
50 years, indicating the importance of forced variability at these time
scales (Fernandez-Donado et al., 2013). It should be noted that a few
models exhibit slow background climate drift which increases the
spread in variance estimates at multi-century time scales. Nevertheless,
the lines of evidence above suggest with high confidence that models
reproduce global and NH temperature variability on a wide range of
time scales.
9.5.3.2 Extratropical Circulation, North Atlantic Oscillation
and Other Related Dipolar and Annular Modes
Based on CMIP3 models, Gerber et al. (2008) confirmed the AR4 assess-
ment that climate models are able to capture the broad spatial and
temporal features of the North Atlantic Oscillation (NAO), but there
are substantial differences in the spatial patterns amongst individual
models (Casado and Pastor, 2012; Handorf and Dethloff, 2012). Climate
models tend to have patterns of variability that are more annular in
character than observed (Xin et al., 2008). Models substantially overes-
timate persistence on sub-seasonal and seasonal time scales, and have
difficulty simulating the seasonal cycle of annular mode time scales
found in reanalyses (Gerber et al., 2008). The unrealistically long time
scale of variability is worse in models with particularly strong equator-
ward biases in the mean jet location, a result which has been found to
hold in the North Atlantic and in the SH (Barnes and Hartmann, 2010;
Kidston and Gerber, 2010).
As described in the AR4, climate models have generally been unable
to simulate changes as strong as the observed NAO trend over the
period 1965–1995, either in coupled mode (Gillett, 2005; Stephenson
et al., 2006; Stoner et al., 2009) or forced with observed boundary con-
ditions (Scaife et al. (2009). However, there are a few exceptions to
this (e.g., Selten et al., 2004; Semenov et al., 2008), so it is unclear to
what extent the underestimation of late 20th century trends reflects
model shortcomings versus internal variability. Further evidence has
emerged of the coupling of NAO variability between the troposphere
and the stratosphere, and even models with improved stratospheric
resolution appear to underestimate the vertical coupling (Morgenstern
et al., 2010) with consequences for the NAO response to anthropogen-
ic forcing (Sigmond and Scinocca, 2010; Karpechko and Manzini, 2012;
Scaife et al., 2012).
The Pacific basin analogue of the NAO, the North Pacific Oscillation
(NPO) is a prominent pattern of wintertime atmospheric circulation
variability characterized by a north–south dipole in sea level pressure
(Linkin and Nigam, 2008). Although climate models simulate the main
spatial features of the NPO, many of them are unable to capture the
observed linkages with tropical variability and the ocean (Furtado et
al., 2011).
Raphael and Holland (2006) showed that coupled models produce a
clear Southern Annular Mode (SAM) but that there are relatively large
differences between models in terms of the exact shape and orien-
tation of this pattern. Karpechko et al. (2009) found that the CMIP3
models have problems representing linkages between the SAM and
SST, surface air temperature, precipitation and particularly sea ice in
the Antarctic region.
9.5.3.3 Atlantic Modes
9.5.3.3.1 Atlantic Meridional Overturning Circulation variability
Previous comparisons of the observed and simulated AMOC were
restricted to its mean strength, as it had only been sporadically
observed (see Chapter 3 and Section 9.4.2.3.1). Continuous AMOC
time series now exist for latitudes 41°N (reconstructions since 1993)
and 26.5°N (estimate based on direct observations since 2004) (Cun-
ningham et al., 2010; Willis, 2010). At 26.5°N, CMIP3 and CMIP5 model
simulations show total AMOC variability that is within the observa-
tional uncertainty (Baehr et al., 2009; Marsh et al., 2009; Balan Saro-
jini et al., 2011; Msadek et al., 2013). However, the total AMOC is the
sum of a wind-driven component and a mid-ocean geostrophic com-
ponent. While both CMIP3 and CMIP5 models tend to overestimate
the wind-driven variability, they tend to underestimate the mid-ocean
geostrophic variability (Baehr et al., 2009; Balan Sarojini et al., 2011;
Msadek et al., 2013). The latter is suggested to result from deficien-
cies in the simulation of the hydrographic characteristics (Baehr et al.,
2009), specifically the Nordic Seas overflows (Yeager and Danaba-
soglu, 2012; Msadek et al., 2013).
9.5.3.3.2 Atlantic multi-decadal variability/Atlantic
Multi-decadal Oscillation
The Atlantic Multi-decadal Variability (AMV), also known as Atlantic
Multi-decadal Oscillation (AMO), is a mode of climate variability with
an apparent period of about 70 years, and a pattern centred in the
North Atlantic Ocean (see Section 14.7.6). In the AR4, it was shown
that a number of climate models produced AMO-like multidecadal var-
iability in the North Atlantic linked to variability in the strength of the
AMOC. Subsequent analyses has confirmed this, however simulated
time scales range from 40 to 60 years (Frankcombe et al., 2010; Park
and Latif, 2010; Kavvada et al., 2013), to a century or more (Msadek
and Frankignoul, 2009; Menary et al., 2012). In addition, the spatial
patterns of variability related to the AMOC differ in many respects from
one model to another as shown in Figure 9.34.
The presence of AMO-like variability in unforced simulations, and the
fact that forced 20th century simulations in the CMIP3 multi-mod-
el ensemble produce AMO variability that is not in phase with that
observed, implies the AMO is not predominantly a result of the forc-
ings imposed on the models (Kravtsov and Spannagle, 2008; Knight,
2009; Ting et al., 2009). Results from the CMIP5 models also show a
key role for internal variability, alongside a contribution from external
forcings in recent decades (Terray, 2012). Historical AMO fluctuations
have been better reproduced in a model with a more sophisticated aer-
osol treatment than was typically used in CMIP3 (Booth et al., 2012a),
albeit at the expense of introducing other observational inconsisten-
cies (Zhang et al., 2013). This could suggest that at least part of the
AMO may in fact be forced, and that aerosols play a role. In addition
to tropospheric aerosols, Otterå et al. (2010) showed the potential for
simulated volcanic forcing to have influenced AMO fluctuations over
the last 600 years.
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Chapter 9 Evaluation of Climate Models
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( )
(
)
(
)
9.5.3.3.3 Tropical zonal and meridional modes
The Atlantic Meridional Mode (AMM) is the dominant mode of inter-
annual variability in the tropical Atlantic, is characterized by an anom-
alous meridional shift in the ITCZ (Chiang and Vimont, 2004), and has
impacts on hurricane tracks over the North Atlantic (Xie et al., 2005;
Smirnov and Vimont, 2011). Virtually all CMIP models simulate AMM-
like SST variability in their 20th century climate simulations. However,
Figure 9.34 | Sequence of physical links postulated to connect Atlantic Meridional Overturning Circulation (AMOC) and Atlantic Multi-decadal Variability (AMV), and how they are
represented in three climate models. Shown are regression patters for the following quantities (from top to bottom): sea surface temperature (SST) composites using AMOC time
series; precipitation composites using cross-equatorial SST difference time series; equatorial salinity composites using Intertropical Convergence Zone (ITCZ)-strength time series;
subpolar-gyre depth-averaged salinity (top 800 to 1000 m) using equatorial salinity time series; subpolar gyre depth averaged density using subpolar gyre depth averaged salinity
time series. From left to right: the two CMIP3 models HadCM3 and ECHAM/MPI-OM (MPI), and the non-CMIP model KCM. Black outlining signifies areas statistically significant at
the 5% level for a two-tailed t test using the moving-blocks bootstrapping technique (Wilks, 1995). (Figure 3 from Menary et al., 2012.)
most models underestimate the SST variance associated with the
AMM, and position the north tropical Atlantic SST anomaly too far
equatorward. More problematic is the fact that the development of
the AMM in many models is led by a zonal mode during boreal win-
ter—a feature that is not observed in nature (Breugem et al., 2006).
This spurious AMM behaviour in the models is expected to be associ-
ated with the severe model biases in simulating the ITCZ (see Section
9.4.2.5.2).
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Evaluation of Climate Models Chapter 9
9
Atlantic Niño
CMIP3 models have considerable difficulty simulating Atlantic Niño
in their 20th century climate simulations. For many models the so-
called Atl-3’ SST index (20°W to 0°W, 3°S to 3°N) displays the wrong
seasonality, with the maximum value in either DJF or SON instead of
JJA as is observed (Breugem et al., 2006). Despite large biases in the
simulated climatology (Section 9.4.2.5.2), about one third of CMIP5
models capture some aspects of Atlantic Niño variability, including
amplitude, spatial pattern and seasonality (Richter et al., 2013). This
represents an improvement over CMIP3.
9.5.3.4 Indo-Pacific Modes
9.5.3.4.1 El Niño-Southern Oscillation
The ENSO phenomenon is the dominant mode of climate variability on
seasonal to interannual time scales (see Wang and Picaut (2004) and
Chapter 14). The representation of ENSO in climate models has stead-
ily improved and now bears considerable similarity to observed ENSO
properties (AchutaRao and Sperber, 2002; Randall et al., 2007; Guil-
yardi et al., 2009b). However, as was the case in the AR4, simulations
Figure 9.35 | Maximum entropy power spectra of surface air temperature averaged over the NINO3 region (5°N to 5°S, 150°W to 90°W) for (a) the CMIP5 models and (b) the
CMIP3 models. ECMWF reanalysis in (a) refers to the European Centre for Medium Range Weather Forecasts (ECMWF) 15-year reanalysis (ERA15). The vertical lines correspond
to periods of two and seven years. The power spectra from the reanalyses and for SST from the Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) version 1.1, Hadley
Centre/Climatic Research Unit gridded surface temperature data set 4 (HadCRU 4), ECMWF 40-year reanalysis (ERA40) and National Centers for Environmental Prediction/National
Center for Atmospheric Research (NCEP/NCAR) data set are given by the series of black curves. (Adapted from AchutaRao and Sperber, 2006.)
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Chapter 9 Evaluation of Climate Models
9
Figure 9.36 | ENSO metrics for pre-industrial control simulations in CMIP3 and CMIP5. (a) and (b): SST anomaly standard deviation (°C) in Niño 3 and Niño 4, respectively, (c)
precipitation response (standard deviation, mm/day) in Niño4. Reference data sets, shown as dashed lines: Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) version
1.1 for (a) and (b), CPC Merged Analysis of Precipitation (CMAP) for (c). The CMIP5 and CMIP3 multi-model means are shown as squares on the left of each panel with the whiskers
representing the model standard deviation. Individual CMIP3 models shown as filled grey circles, and individual CMIP5 models are identified in the legend.
CMIP5
CMIP
3
BCC
BCCR
BNU
CCCma
CMCC
CNRM
CSIRO
FIO
GFDL
GISS
IAP
IPSL
MIROC
INM
MIUB
MOHC
MPI
MRI
NCAR
NCC
NSF-DO
E
INGV
Obs.
Obs.
Obs.
per
(
)
(°C) (°C)
of both background climate (time mean and seasonal cycle, see Section
9.4.2.5.1) and internal variability exhibit serious systematic errors (van
Oldenborgh et al., 2005; Capotondi et al., 2006; Guilyardi, 2006; Wit-
tenberg et al., 2006; Watanabe et al., 2011; Stevenson et al., 2012; Yeh
et al., 2012), many of which can be traced to the representation of
deep convection, trade wind strength and cloud feedbacks, with little
improvement from CMIP3 to CMIP5 (Braconnot et al., 2007a; L’Ecuyer
and Stephens, 2007; Guilyardi et al., 2009a; Lloyd et al., 2009, 2010;
Sun et al., 2009; Zhang and Jin, 2012).
While a number of CMIP3 models do not exhibit an ENSO variability
maximum at the observed 2- to 7- year time scale, most CMIP5 models
do have a maximum near the observed range and fewer models have
the tendency for biennial oscillations (Figure 9.35; see also Stevenson,
2012). In CMIP3 the amplitude of El Niño ranged from less than half
to more than double the observed amplitude (van Oldenborgh et al.,
2005; AchutaRao and Sperber, 2006; Guilyardi, 2006; Guilyardi et al.,
2009b). By contrast, the CMIP5 models show less inter-model spread
(Figure 9.36; Kim and Yu, 2012). The CMIP5 models still exhibit errors
in ENSO amplitude, period, irregularity, skewness, spatial patterns (Lin,
2007; Leloup et al., 2008; Guilyardi et al., 2009b; Ohba et al., 2010; Yu
and Kim, 2011; Su and Jiang, 2012) or teleconnections (Watanabe et
al., 2012; Weller and Cai, 2013a).
Since AR4, new analysis methods have emerged and are now being
applied. For example, Jin et al. (2006) and Kim and Jin (2011a) iden-
tified five different feedbacks affecting the Bjerknes (or BJ) index,
which in turn characterizes ENSO stability. Kim and Jin (2011b) applied
this process-based analysis to the CMIP3 multi-model ensemble and
demonstrated a significant positive correlation between ENSO ampli-
tude and the BJ index. When respective components of the BJ index
obtained from the coupled models were compared with those from
observations, it was shown that most coupled models underestimated
the negative thermal damping feedback (Lloyd et al., 2012; Chen et
al., 2013) and the positive zonal advective and thermocline feedbacks.
Detailed quantitative evaluation of ENSO performance is hampered by
the short observational record of key processes (Wittenberg, 2009; Li
et al., 2011b; Deser et al., 2012) and the complexity and diversity of
the processes involved (Wang and Picaut, 2004). While shortcomings
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Evaluation of Climate Models Chapter 9
9
remain (Guilyardi et al., 2009b), the CMIP5 model ensemble shows
some improvement compared to CMIP3, but there has been no major
breakthrough and the multi-model improvement is mostly due to a
reduced number of poor-performing models.
9.5.3.4.2 Indian Ocean basin and dipole modes
Indian Ocean SST displays a basin-wide warming following El Niño
(Klein et al., 1999). This Indian Ocean basin (IOB) mode peaks in boreal
spring and persists through the following summer. Most CMIP5 models
capture this IOB mode, an improvement over CMIP3 (Du et al., 2013).
However, only about half the CMIP5 models capture its long tempo-
ral persistence, and these models tend to simulate ENSO-forced ocean
Rossby waves in the tropical south Indian Ocean (Zheng et al., 2011).
The Indian Ocean zonal dipole mode (IOD) (Saji et al., 1999; Webster
et al., 1999) appears to be part of a hemispheric response to tropi-
cal atmospheric forcing (Fauchereau et al., 2003; Hermes and Reason,
2005). Most CMIP3 models are able to reproduce the general features
of the IOD, including its phase lock onto the July to November season
(Saji et al., 2006). The modelled SST anomalies, however, tend to show
too strong a westward extension along the equator in the eastern
Indian Ocean. CMIP3 models exhibit considerable spread in IOD ampli-
tude, some of which can be explained by differences in the strength
of the simulated Bjerknes feedback (Liu et al., 2011; Cai and Cowan,
2013). No substantial change is seen in CMIP5 (Weller and Cai, 2013a).
Many models simulate the observed correlation between IOD and
ENSO. The magnitude of this correlation varies substantially between
models, but is apparently not tied to the amplitude of ENSO (Saji et
al., 2006). A subset of CMIP3 models show a spurious correlation with
ENSO following the decay of ENSO events, instead of during the ENSO
developing phase, possibly due to erroneous representation of oceanic
pathways connecting the equatorial Pacific and Indian Oceans (Cai et
al., 2011).
9.5.3.4.3 Tropospheric biennial oscillation
The tropospheric biennial oscillation (TBO, Section 14.7.4) is a bien-
nial tendency of many phenomena in the Indo-Pacific region that
affects droughts and floods over large areas of south Asia and Aus-
tralia (e.g., Chang and Li, 2000; Li et al., 2001; Meehl et al., 2003).
The IOD involves regional patterns of SST anomalies in the TBO in the
Indian Ocean during the northern fall season following the south Asian
monsoon (Loschnigg et al., 2003). The TBO has been simulated in a
number of global coupled climate model simulations (e.g., Ogasawara
et al., 1999; Loschnigg et al., 2003; Nanjundiah et al., 2005; Turner et
al., 2007; Meehl and Arblaster, 2011).
9.5.3.5 Indo-Pacific Teleconnections
Tropical SST variability provides a significant forcing of atmospheric
teleconnections and drives a large portion of the climate variability
over land (Goddard and Mason, 2002; Shin et al., 2010). Although
local forcings and feedbacks can play an important role (Pitman et
al., 2012a), the simulation of land surface temperatures and precip-
itation requires accurate predictions of SST patterns (Compo and
Sardeshmukh, 2009; Shin et al., 2010) as well as zonal wind variability
patterns (Handorf and Dethloff, 2012). Teleconnections hence play a
central role in regional climate change (see Chapter 14).
9.5.3.5.1 Teleconnections affecting North America
The Pacific North American (PNA) pattern is a wavetrain-like pattern in
mid-level geopotential heights. The majority of CMIP3 models simulate
the spatial structure of the PNA pattern in wintertime (Stoner et al.,
2009). The PNA pattern has contributions from both internal atmos-
pheric variability (Johnson and Feldstein, 2010) and ENSO and PDO
teleconnections (Deser et al., 2004). The power spectrum of this tempo-
ral behaviour is generally captured by the CMIP3 models, although the
level of year-to-year autocorrelation varies according to the strength of
the simulated ENSO and PDO (Stoner et al., 2009).
9.5.3.5.2 Tropical ENSO teleconnections
These moist teleconnection pathways involve mechanisms related to
those at play in the precipitation response to global warming (Chiang
and Sobel, 2002; Neelin et al., 2003) and provide challenging test sta-
tistics for model precipitation response. Compared to earlier genera-
tion climate models, CMIP3 and CMIP5 models tend to do somewhat
better (Neelin, 2007; Cai et al., 2009; Coelho and Goddard, 2009; Lan-
genbrunner and Neelin, 2013) at precipitation reductions associated
with El Niño over equatorial South America and the Western Pacific,
although CMIP5 offers little further improvement over CMIP3 (see for
instance the standard deviation of precipitation in the western Pacific
in Figure 9.36). CMIP5 models simulate the sign of the precipitation
change over broad regions, and do well at predicting the amplitude of
the change (for a given SST forcing) (Langenbrunner and Neelin, 2013).
A regression of the West African monsoon precipitation index with
global SSTs reveals two major teleconnections (Fontaine and Janicot,
1996). The first highlights the strong influence of ENSO, while the
second reveals a relationship between the SST in the Gulf of Guinea
and the northward migration of the monsoon rain belt over West
Africa. Most CMIP3 models show a single dominant Pacific telecon-
nection, which is, however, of the wrong sign for half of the models
(Joly et al., 2007). Only one model shows a significant second mode,
emphasizing the difficulty in simulating the response of the African
rain belt to Atlantic SST anomalies that are not synchronous with Pacif-
ic anomalies.
Both CMIP3 and CMIP5 models have been evaluated and found to vary
in their abilities to represent both the seasonal cycle of correlations
between the Niño 3.4 and North Australian SSTs (Catto et al., 2012a,
2012b) with little change in quality from CMIP3 to CMIP5. Generally
the models do not capture the strength of the negative correlations
during the second half of the year. The models also still struggle to
capture the SST evolution in the North Australian region during El Niño
and La Niña. Teleconnection patterns from both ENSO and the Indian
Ocean Dipole to precipitation over Australia are reasonably well simu-
lated in the key September-November season (Cai et al., 2009; Weller
and Cai, 2013b) in the CMIP3 and CMIP5 multi-model mean.
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Chapter 9 Evaluation of Climate Models
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9.5.3.6 Pacific Decadal Oscillation and Interdecadal
Pacific Oscillation
The Pacific Decadal Oscillation (PDO) refers to a mode of variabil-
ity involving SST anomalies over the North Pacific (north of 20°N)
(Mantua et al., 1997). Although the PDO time series exhibits consider-
able decadal variability, it is difficult to ascertain whether there are any
robust spectral peaks given the relatively short observational record
(Minobe, 1997, 1999; Pierce, 2001; Deser et al., 2004). The ability of
climate models to represent the PDO has been assessed by Stoner et
al. (2009) and Furtado et al. (2011). Their results indicate that approxi-
mately half of the CMIP3 models simulate the observed spatial pattern
and temporal behaviour (e.g., enhanced variance at low frequencies);
however, spectral peaks are consistently higher in frequency than
those suggested by the short observational record. The modelled PDO
correlation with SST anomalies in the tropical Indo-Pacific are strongly
underestimated by the CMIP3 models (Wang et al., 2010; Deser et al.,
2011; Furtado et al., 2011; Lienert et al., 2011). Climate models have
been shown to simulate features of the closely related Interdecadal
Pacific Oscillation (IPO, based on SSTs over the entire Pacific basin; see
Section 14.7.3; Power and Colman, 2006; Power et al., 2006; Meehl et
al., 2009), although deficiencies remain in the strength of the tropical–
extratropical connections.
9.5.3.7 The Quasi-Biennial Oscillation
Significant progress has been made in recent years to model and
understand the impacts of the Quasi-Biennial Oscillation (QBO; Bald-
win et al., 2001). Many climate models have now increased their
vertical domain and/or improved their physical parameterizations
(see Tables 9.1 and 9.A.1), and some of these reproduce a QBO (e.g.,
Short
Name
Level of
Confidence
Level of
Evidence for
Evaluation
Degree of
Agreement
Model
Quality
Difference with AR4
(including CMIP5
vs. CMIP3)
Section
Global sea surface tem-
perature (SST) variability
SST-var High Robust Medium Medium Slight improvement in the tropics 9.5.3.1
North Atlantic Oscillation
and Northern Annular Mode
NAO Medium Medium Medium High No assessment 9.5.3.2
Southern Annular Mode SAM Low Limited Medium Medium No assessment 9.5.3.2
Atlantic Meridional Overturn-
ing Circulation Variability
AMOC-var Low Limited Medium Medium No improvement 9.5.3.3
Atlantic Multi-decadal
Variability
AMO Low Limited Medium Medium No improvement 9.5.3.3
Atlantic Meridional Mode AMM High Medium High Low No assessment 9.5.3.3
Atlantic Niño AN Low Limited Medium Low Slight improvement 9.5.3.3
El Niño Southern Oscillation ENSO High Medium High Medium Slight improvement 9.5.3.4
Indian Ocean Basin mode IOB Medium Medium Medium High Slight improvement 9.5.3.4
Indian Ocean Dipole IOD Medium Medium Medium Medium No improvement 9.5.3.4
Pacific North American PNA High Medium High Medium Slight improvement 9.5.3.5
Tropical ENSO tele-
connections
ENSOtele High Robust Medium Medium No improvement 9.5.3.5
Pacific Decadal Oscillation PDO Low Limited Medium Medium No assessment 9.5.3.6
Interdecadal Pacific
Oscillation
IPO Low Limited Medium High No assessment 9.5.3.6
Quasi-Biennial Oscillation QBO Medium Medium Medium High No assessment 9.5.3.7
HadGEM2, MPI-ESM-LR, MIROC). Many features of the QBO such as
its width and phase asymmetry also appear spontaneously in these
simulations due to internal dynamics (Dunkerton, 1991; Scaife et al.,
2002; Haynes, 2006). Some of the QBO effects on the extratropical cli-
mate (Holton and Tan, 1980; Hamilton, 1998; Naoe and Shibata, 2010)
as well as ozone (Butchart et al., 2003; Shibata and Deushi, 2005) are
also reproduced in models.
9.5.3.8 Summary
In summary, most modes of interannual to interdecadal variability are
now present in climate models. As in AR4, their assessment presents
a varied picture and CMIP5 models only show a modest improvement
over CMIP3, mostly due to fewer poor-performing models. New since
the AR4, process-based model evaluation is now helping identify
sources of specific biases, although the observational record is often
too short or inaccurate to offer strong constraints. The assessment of
modes and patterns is summarized in Table 9.4.
9.5.4 Extreme Events
Extreme events are realizations of the tail of the probability distribu-
tion of weather and climate variability. They are higher-order statistics
and thus generally more difficult to realistically represent in climate
models. Shorter time scale extreme events are often associated with
smaller scale spatial structure, which may be better represented as
model resolution increases. In the AR4, it was concluded that models
could simulate the statistics of extreme events better than expected
from the generally coarse resolution of the models at that time, espe-
cially for temperature extremes (Randall et al., 2007).
Table 9.4 | Summary of assessment of interannual to interdecadal variability in climate models. See also Figure 9.44.
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Evaluation of Climate Models Chapter 9
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The IPCC has conducted an assessment of extreme events in the con-
text of climate change—the Special Report on Managing the Risks
of Extreme Events and Disasters to Advance Climate Change Adap-
tation (SREX) (IPCC, 2012). Although there is no comprehensive cli-
mate model evaluation with respect to extreme events in SREX, there
is some consideration of model performance taken into account in
assessing uncertainties in projections.
9.5.4.1 Extreme Temperature
Since the AR4, evaluation of CMIP3 and CMIP5 models has been
undertaken with respect to temperature extremes. Both model ensem-
bles simulate present-day warm extremes, in terms of 20-year return
values, reasonably well, with errors typically within a few degrees Cel-
sius over most of the globe (Kharin et al., 2007; Kharin et al., 2012). The
CMIP5 and CMIP3 models perform comparably for various tempera-
ture extreme indices, but with smaller inter-model spread in CMIP5.The
inter-model range of simulated indices is similar to the spread amongst
observationally based estimates in most regions (Sillmann et al., 2013).
Figure 9.37 shows relative error estimates of available CMIP5 models
for various extreme indices based on Sillmann et al. (2013). Although
the relative performance of an individual model may depend on the
choice of the reference data set (four different reanalyses are used),
the mean and median models tend to outperform individual models.
According to the standardized multi-model median errors (RMSE
std
) for
CMIP3 and CMIP5 shown on the right side of Figure 9.37, the perfor-
mance of the two ensembles is similar.
In terms of historical trends, CMIP3 and CMIP5 models generally cap-
ture observed trends in temperature extremes in the second half of
the 20th century (Sillmann et al., 2013), as illustrated in Figure 9.37.
The modelled trends are consistent with both reanalyses and sta-
tion-based estimates. It is also clear in the figure that model-based
indices respond coherently to major volcanic eruptions. Detection and
attribution studies based on CMIP3 models suggest that models tend
to overestimate the observed warming of warm temperature extremes
and underestimate the warming of cold extremes in the second half
of 20th century (Christidis et al., 2011; Zwiers et al., 2011) as noted
in SREX (Seneviratne et al., 2012). See also Chapter 10. This is not as
obvious in the CMIP5 model evaluation (Figure 9.37 and Sillmann et al.
(2013)) and needs further investigation.
9.5.4.2 Extreme Precipitation
For extreme precipitation, observational uncertainty is much larger
than for temperature, making model evaluation more challenging. Dis-
crepancies between different reanalyses for extreme precipitation are
substantial, whereas station-based observations have limited spatial
coverage (Kharin et al., 2007, 2012; Sillmann et al., 2013). Moreover,
a station-based observational data set, which is interpolated from sta-
tion measurements, has a potential mismatch of spatial scale when
compared to model results or reanalyses (Chen and Knutson, 2008).
Uncertainties are especially large in the tropics. In the extratropics,
precipitation extremes in terms of 20-year return values simulated by
CMIP3 and CMIP5 models compared relatively well with the observa-
tional data sets, with typical discrepancies in the 20% range (Kharin
et al., 2007, 2012). Figure 9.37 shows relative errors of CMIP5 models
for five precipitation-related indices. Darker grey shadings in the RMSE
columns for precipitation indicate larger discrepancies between models
and reanalyses for precipitation extremes in general. Sillmann et al.
(2013) found that the CMIP5 models tend to simulate more intense
precipitation and fewer consecutive wet days than the CMIP3, and
thus are closer to the observationally based indices.
It is known from sensitivity studies that simulated extreme precipita-
tion is strongly dependent on model resolution. Growing evidence has
shown that high-resolution models (50 km or finer in the atmosphere)
can reproduce the observed intensity of extreme precipitation (Wehner
et al., 2010; Endo et al., 2012; Sakamoto et al., 2012), though some of
these results are based on models with observationally constrained
surface or lateral boundary conditions (i.e., Atmospheric General Circu-
lation Models (AGCMs) or Regional Climate Models (RCMs)).
In terms of historical trends, a detection and attribution study by Min et
al. (2011) found consistency in sign between the observed increase in
heavy precipitation over NH land areas in the second half of the 20th
century and that simulated by CMIP3 models, but they found that the
models tend to underestimate the magnitude of the trend (see also
Chapter 10). Related to this, it has been pointed out from comparisons
to satellite-based data sets that the majority of models underestimate
the sensitivity of extreme precipitation intensity to temperature in the
tropics (Allan and Soden, 2008; Allan et al., 2010; O’Gorman, 2012)
and globally (Liu et al., 2009; Shiu et al., 2012). O’Gorman (2012)
showed that this implies possible underestimation of the projected
future increase in extreme precipitation in the tropics.
9.5.4.3 Tropical Cyclones
It was concluded in the AR4 that high-resolution AGCMs generally
reproduced the frequency and distribution, but underestimated inten-
sity of tropical cyclones (Randall et al., 2007). Since then, Mizuta et
al. (2012) have shown that a newer version of the MRI-AGCM with
improved parameterizations (at 20 km horizontal resolution) simulates
tropical cyclones as intense as those observed with improved distri-
bution as well. Another remarkable finding since the AR4 is that the
observed year-to-year count variability of Atlantic hurricanes can be
well simulated by modestly high resolution (100 km or finer) AGCMs
forced by observed SST, though with less skill in other basins (Larow et
al., 2008; Zhao et al., 2009; Strachan et al., 2013). Vortices that have
some characteristics of tropical cyclones can also be detected and
tracked in AOGCMs in CMIP3 and 5, but their intensities are generally
too weak (Yokoi et al., 2009a; Yokoi et al., 2012; Tory et al., 2013; Walsh
et al., 2013).
9.5.4.4 Droughts
Drought is caused by long time scale (months or longer) variability of
both precipitation and evaporation. Sheffield and Wood (2008) found
that models in the CMIP3 ensemble simulated large-scale droughts in
the 20th century reasonably well, in the sense that multi-model spread
includes the observational estimate in each of several regions. Howev-
er, it should be noted that there are various definitions of drought (see
Chapter 2 and Seneviratne et al., 2012) and the performance of simu-
lated drought can depend on the definition. Moreover, different models
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Chapter 9 Evaluation of Climate Models
9
Figure 9.37 | (a) Portrait plot of relative error metrics for the CMIP5 temperature and precipitation extreme indices based on Sillmann et al. (2013). (b)–(e) Time series of global
mean temperature extreme indices over land from 1948 to 2010 for CMIP3 (blue) and CMIP5 (red) models, ECMWF 40-year reanalysis (ERA40, green dashed) and National Centers
for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR, green dotted) reanalyses and HadEX2 station-based observational data set (black) based on
Sillmann et al. (2013). In (a), reddish and bluish colours indicate, respectively, larger and smaller root-mean-square (RMS) errors for an individual model relative to the median model.
The relative error is calculated for each observational data set separately. The grey-shaded columns on the right side indicate the RMS error for the multi-model median standardized
by the spatial standard deviation of the index climatology in the reanalysis, representing absolute errors for CMIP3 and CMIP5 ensembles. Results for four different reference data
sets, ERA-interim (top), ERA40 (left), NCEP/NCAR (right) and NCEP- Department of Energy (DOE) (bottom) reanalyses, are shown in each box. The analysis period is 1981–2000, and
only land areas are considered. The indices shown are simple daily precipitation intensity index (SDII), very wet days (R95p), annual maximum 5-day/1-day precipitation (RX5day/
RX1day), consecutive dry days (CDD), tropical nights (TR), frost days (FD), annual minimum/maximum daily maximum surface air temperature (TXn/TXx), and annual minimum/
maximum daily minimum surface air temperature (TNn/TNx). See Box 2.4 for the definitions of indices. Note that only a small selection of the indices analysed in Sillmann et al.
(2013) is shown, preferentially those that appear in Chapters 2, 10, 11, 12, 14. Also note that the NCEP/NCAR reanalysis has a known defect for TXx (Sillmann et al., 2013), but its
impact on this figure is small. In (b)–(e), shading for model results indicates the 25th to 75th quantile range of inter-model spread. Grey shading along the horizontal axis indicates
the evolution of globally averaged volcanic forcing according to Sato et al. (1993). The indices shown are the frequency of daily minimum/maximum surface air temperature below
the 10th percentile (b: Cold nights/c: Cold days) and that above 90th percentile (d: Warm nights/e: Warm days) of the 1961–1990 base period. Note that, as these indices essentially
represent changes relative to the base period, they are particularly suitable for being shown in time series and not straightforward for being shown in (a).
1950 1960 1970 1980 1990 2000 2010
5
10
15
20
Year
Exceedance rate (%)
5
10
15
20
Cold nights
CMIP3
CMIP5
ERA−40
NCEP/NCAR
HadEX2
1950 1960 1970 1980 1990 20002010
5
10
15
20
Year
Exceedance rate (%)
5
10
15
20
Cold days
CMIP3
CMIP5
ERA−40
NCEP/NCAR
HadEX2
1950 1960 1970 1980 1990 2000 2010
5
10
15
20
Year
Exceedance rate (%)
5
10
15
20
Warm nights
CMIP3
CMIP5
ERA−40
NCEP/NCAR
HadEX2
1950 1960 1970 1980 1990 2000 2010
5
10
15
20
Year
Exceedance rate (%)
5
10
15
20
Warm days
CMIP3
CMIP5
ERA−40
NCEP/NCAR
HadEX2
)c()b(
)e()d(
(a)
ENSMEAN
ENSMEDIAN
ACCESS1−0
BCC−CSM1−1
BCC−CSM1−1−M
BNU−ESM
CanCM4
CanESM2
CCSM4
CESM1−BGC
CMCC−CM
CNRM−CM5
CSIRO−Mk3−6−0
EC−EARTH
GFDL−CM3
GFDL−ESM2G
GFDL−ESM2M
GISS−E2−R
HadCM3
HadGEM2−CC
HadGEM2−ES
INMCM4
IPSL−CM5A−LR
IPSL−CM5B−LR
IPSL−CM5A−MR
MIROC4h
MIROC5
MIROC−ESM
M
IROC−ESM−CHEM
MPI−ESM−P
MPI−ESM−LR
MPI−ESM−MR
MRI−CGCM3
NorESM1−M
TNx
TNn
TXx
TXn
FD
TR
CDD
RX1day
RX5day
R95p
SDII
CMIP5 global land 1981−2000
CMIP5 RMSE
std
CMIP3 RMSE
std
−0.5
−0.4
−0.3
−0.2
−0.1
0
0.1
0.2
0.3
0.4
0.5
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1.1
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Evaluation of Climate Models Chapter 9
9
can simulate drought with different mechanisms (McCrary and Ran-
dall, 2010; Taylor et al., 2012a). A comprehensive evaluation of CMIP5
models for drought is currently not available, although Sillmann et al.
(2013) found that consecutive dry days simulated by CMIP5 models are
comparable to observations in magnitude and distribution.
9.5.4.5 Summary
There is medium evidence (i.e., a few multi-model studies) and high
agreement that the global distribution of temperature extremes are
represented well by CMIP3 and CMIP5 models. The observed global
warming trend of temperature extremes in the second half of the 20th
century is reproduced in models, but there is medium evidence (a few
CMIP3 studies) and medium agreement (not evident in a preliminary
look at CMIP5) that models tend to overestimate the warming of warm
temperature extremes and underestimate the warming of cold temper-
ature extremes.
There is medium evidence (single multi-model study) and medium
agreement (as inter-model difference is large) that CMIP5 models
Box 9.3 | Understanding Model Performance
This Box provides a synthesis of findings on understanding model performance based on the model evaluations discussed in this chapter.
Uncertainty in Process Representation
Some model errors can be traced to uncertainty in representation of processes (parameterizations). Some of these are long-standing
issues in climate modelling, reflecting our limited, though gradually increasing, understanding of very complex processes and the
inherent challenges in mathematically representing them. For the atmosphere, cloud processes, including convection and its interaction
with boundary layer and larger-scale circulation, remain major sources of uncertainty (Section 9.4.1). These in turn cause errors or
uncertainties in radiation which propagate through the coupled climate system. Distribution of aerosols is also a source of uncertainty
arising from modelled microphysical processes and transport (Sections 9.4.1 and 9.4.6). Ocean models are subject to uncertainty in
parameterizations of vertical and horizontal mixing and convection (Sections 9.4.2, 9.5.2 and 9.5.3), and ocean errors in turn affect the
atmosphere through resulting SST biases. Simulation of sea ice is also affected by errors in both the atmosphere and the ocean as well
as the parameterization of sea ice itself (Section 9.4.3). With respect to biogeochemical components in Earth System Models (ESMs),
parameterizations of nitrogen limitation and forest fires are thought to be important for simulating the carbon cycle, but very few ESMs
incorporate these so far (Sections 9.4.4 and 9.4.5).
Error Propagation
Causes of one model bias can sometimes be associated with another. Although the root cause of those biases is often unclear, knowl-
edge of the causal chain or a set of interrelated biases can provide a key to further understanding and improvement of model per-
formance. For example, biases in storm track position are partly due to a SST biases in the Gulf Stream and Kuroshio Current (Section
9.4.1). Some biases in variability or trend can be partly traced back to biases in mean states. The decreasing trend in September Arctic
ice extent tends to be underestimated when sea ice thickness is overestimated (Section 9.4.3). In such cases, improvement of the mean
state may improve simulated variability or trend.
Sensitivity to Resolution
Some phenomena or aspects of climate are found to be better simulated with models run at higher horizontal and/or vertical resolution.
In particular, increased resolution in the atmosphere has improved, at least in some models, storm track and extratropical cyclones
(Section 9.4.1), diurnal variation of precipitation over land (Section 9.5.2), extreme precipitation, and tropical cyclone intensity and
structure (Section 9.5.4). Similarly, increased horizontal resolution in the ocean is shown to improve sea surface height variability,
western boundary currents, tropical instability waves and coastal upwelling (Section 9.4.2), and variability of Atlantic meridional over-
turning circulation (Section 9.5.3). High vertical resolution and a high model top, as well as high horizontal resolution, are important for
simulating lower stratospheric climate variability (Section 9.4.1), blocking (Section 9.5.2), the Quasi-Biennial Oscillation and the North
Atlantic Oscillation (Section 9.5.3). (continued on next page)
tend to simulate more intense and thus more realistic precipitation
extremes than CMIP3, which could be partly due to generally higher
horizontal resolution. There is medium evidence and high agreement
that CMIP3 models tend to underestimate the sensitivity of extreme
precipitation intensity to temperature. There is medium evidence and
high agreement that high resolution (50 km or finer) AGCMs tend to
simulate the intensity of extreme precipitation comparable to observa-
tional estimates.
There is medium evidence and high agreement that year-to-year count
variability of Atlantic hurricanes can be well simulated by modestly
high resolution (100 km or finer) AGCMs forced by observed SSTs.
There is medium evidence and medium agreement (as inter-model
difference is large) that the intensity of tropical cyclones is too weak
in CMIP3 and CMIP5 models. Finally, there is medium evidence (a
few multi-model studies) and medium agreement (as it might depend
on definitions of drought) that models can simulate aspects of large-
scale drought.
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Chapter 9 Evaluation of Climate Models
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9.6 Downscaling and Simulation of
Regional-Scale Climate
Regional-scale climate information can be obtained directly from
global models; however, their horizontal resolution is often too low to
resolve features that are important at regional scales. High-resolution
AGCMs, variable-resolution global models, and statistical and dynam-
ical downscaling (i.e., regional climate modelling) are used to comple-
ment AOGCMs, and to generate region-specific climate information.
These approaches are evaluated in the following.
9.6.1 Global Models
9.6.1.1 Regional-Scale Simulation by Atmosphere–Ocean
General Circulation Models
A comparison of CMIP3 and CMIP5 seasonal cycles of temperature
and precipitation for different regions (Figure 9.38) shows that tem-
perature is generally better simulated than precipitation in terms of
the amplitude and phase of the seasonal cycle. The multi-model mean
is closer to observations than most of the individual models. The sys-
tematic difference between the CMIP5 and CMIP3 ensembles is small
in most regions, although there is evident improvement in South Asia
(SAS) and Tropical South America (TSA) in the rainy seasons. In some
cases the spread amongst observational estimates can be of compa-
rable magnitude to the model spread, e.g., winter in the Europe and
Mediterranean (EUM) region.
There are as yet rather few published studies in which regional behav-
iour of the CMIP5 models is evaluated in great detail. Cattiaux et al.
(2013) obtained results for Europe similar to those discussed above.
Joetzjer et al. (2013) considered 13 models that participated in both
CMIP3 and in CMIP5 and found that the seasonal cycle of precipitation
over the Amazon improved in the latter.
Based on the CMIP archives, regional biases in seasonal and annual
mean temperature and precipitation are shown for several land
Box 9.3 (continued)
Uncertainty in Observational Data
In some cases, insufficient length or quality of observational data makes model evaluation challenging, and is a frequent problem in the
evaluation of simulated variability or trends. This is evident for evaluation of upper tropical tropospheric temperature, tropical atmos-
pheric circulation (Section 9.4.1), the Atlantic meridional overturning circulation, the North Atlantic Oscillation and the Pacific Decadal
Oscillation (Section 9.5.3). Data quality has been pointed out as an issue for arctic cloud properties (Section 9.4.1), ocean heat content,
heat and fresh water fluxes over the ocean (Section 9.4.2) and extreme precipitation (Section 9.5.4). Palaeoclimate reconstructions
also have large inherent uncertainties (Section 9.5.2). It is clear therefore that updated or newly available data affect model evaluation
conclusions.
Other Factors
Model evaluation can be affected by how models are forced. Uncertainties in specified greenhouse gases, aerosols emissions, land use
change, etc. will all affect model results and hence evaluation of model quality. Different statistical methods used in model evaluation
can also lead to subtle or substantive differences in the assessment of model quality.
regions in Figure 9.39, and for polar and oceanic regions in Figure
9.40. The CMIP5 median temperature biases range from about –3°C
to 1.5°C. Substantial cold biases over NH regions are more prevalent
in winter (December to February) than summer (June to August). The
median biases appear in most cases slightly less negative for CMIP5
than CMIP3. The spread amongst models, as characterized by the 25
to 75% and 5 to 95% ranges, is slightly reduced from CMIP3 to CMIP5
in a majority of the regions and is roughly ±3ºC. The RMS error of
individual CMIP5 models is smaller than that for CMIP3 in 24 of the 26
regions in Figure 9.39 in DJF, JJA and the annual mean. The absolute
value of the ensemble mean bias has also been reduced in most cases.
The inter-model spread remains large, particularly in high-latitude
regions in winter and in regions with steep orography (such as CAS,
SAS, TIB and WSA). The inter-model temperature spread has decreased
from CMIP3 to CMIP5 over most of the oceans and over the Arctic and
Antarctic land regions. The cold winter bias over the Arctic has been
reduced. There is little systematic inter-ensemble difference in temper-
ature over lower latitude oceans.
Biases in seasonal total precipitation are shown in the right column
of Figures 9.39 and 9.40 for the NH winter (October to March) and
summer (April to September) half years as well as the annual mean.
The largest systematic biases over land regions occur in ALA, WSA and
TIB, where the annual precipitation exceeds that observed in all CMIP5
models, with a median bias on the order of 100%. All these regions are
characterized by high orography and / or a large fraction of solid pre-
cipitation, both of which are expected to introduce a negative bias in
gauge-based precipitation (Yang and Ohata, 2001; Adam et al., 2006)
that may amplify the model-observation discrepancy. A large negative
relative bias in SAH occurs in October to March, but it is of negligible
magnitude in absolute terms. In nearly all other seasonal and region-
al cases over land, the observational estimate falls within the range
of the CMIP5 simulations. Compared with CMIP3, the CMIP5 median
precipitation is slightly higher in most regions. In contrast with tem-
perature, the seasonal and annual mean ensemble mean and the root-
mean square precipitation biases are larger for CMIP5 than for CMIP3
in a slight majority of land regions (Figure 9.39) and in most of the
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Evaluation of Climate Models Chapter 9
9
Figure 9.38 | Mean seasonal cycle of (a) temperature (ºC) and (b) precipitation (mm day
–1
). The average is taken over land areas within the indicated regions, and over the period
1980–1999. The red line is the average over 45 CMIP5 models; the blue line is the average over 22 CMIP3 models. The standard deviation of the respective data set is indicated
with shading. The different line styles in black refer to observational and reanalysis data: Climatic Research Unit (CRU) TS3.10, ECMWF 40-year reanalysis (ERA40) and ERA-Interim
for temperature; CRU TS3.10.1, Global Precipitation Climatology Project (GPCP), and CPC Merged Analysis of Precipitation (CMAP) for precipitation. Note the different axis-ranges
for some of the sub-plots. The 15 regions shown are: Western North America (WNA), Eastern North America (ENA), Central America (CAM), Tropical South America (TSA), Southern
South America (SSA), Europe and Mediterranean (EUM), North Africa (NAF), Central Africa (CAF), South Africa (SAF), North Asia (NAS), Central Asia (CAS), East Asia (EAS), South
Asia (SAS), Southeast Asia (SEA) and Australia (AUS).
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Chapter 9 Evaluation of Climate Models
9
14 other regions (Figure 9.40). However, considering the observational
uncertainty, the performance of the CMIP3 and CMIP5 ensembles is
assessed to be broadly similar. The inter-model spreads are similar and
typically largest in arid areas when expressed in relative terms.
Figure 9.39 | Seasonal- and annual mean biases of (left) temperature (°C) and (right) precipitation (%) in the IPCC Special Report on Managing the Risks of Extreme Events and
Disasters to Advance Climate Change Adaptation (SREX) land regions (cf. Seneviratne et al., 2012, p. 12. The region’s coordinates can be found from their online Appendix 3.A). The
5th, 25th, 50th, 75th and 95th percentiles of the biases in 42 CMIP5 models are shown in box-and-whisker format, and corresponding values for 23 CMIP3 models with crosses.
The CMIP3 20C3M simulations are complemented with the corresponding A1B runs for the 2001–2005 period. The biases are calculated over 1986–2005, using Climatic Research
Unit (CRU) T3.10 as the reference for temperature and CRU TS 3.10.01 for precipitation. The regions are labelled with red when the root-mean-square error for the individual CMIP5
models is larger than that for CMIP3 and blue when it is smaller. The regions are: Alaska/NW Canada (ALA), Eastern Canada/Greenland/Iceland (CGI), Western North America
(WNA), Central North America (CNA), Eastern North America (ENA), Central America/Mexico (CAM), Amazon (AMZ), NE Brazil (NEB), West Coast South America (WSA), South-
Eastern South America (SSA), Northern Europe (NEU), Central Europe (CEU), Southern Europe/the Mediterranean (MED), Sahara (SAH), Western Africa (WAF), Eastern Africa (EAF),
Southern Africa (SAF), Northern Asia (NAS), Western Asia (WAS), Central Asia (CAS), Tibetan Plateau (TIB), Eastern Asia (EAS), Southern Asia (SAS), Southeast Asia (SEA), Northern
Australia (NAS) and Southern Australia/New Zealand (SAU). Note that the region WSA is poorly resolved in the models.
Especially over the oceans and polar regions (Figure 9.40), the scarci-
ty of observations and their uncertainty complicates the evaluation of
simulated precipitation. Of two commonly used data sets, CMAP indi-
cates systematically more precipitation than GPCP over low-latitude
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Evaluation of Climate Models Chapter 9
9
Figure 9.40 | As Figure 9.39, but for polar and ocean regions, with ECMWF reanalysis
of the global atmosphere and surface conditions (ERA)-Interim reanalysis as the refer-
ence for temperature and Global Precipitation Climatology Project (GPCP) for precipi-
tation. Global land, ocean and overall means are also shown. The regions are: Arctic:
67.5 to 90°N, Caribbean (area defined by the following coordinates): 68.8°W, 11.4°N;
85.8°W, 25°N; 60°W, 25°N, 60°W, 11.44°N; Western Indian Ocean: 25°S to 5°N, 52°E
to 75°E; Northern Indian Ocean: 5°N to 30°N, 60°E to 95°E; Northern Tropical Pacific:
5°N to 25°N, 155°E to 150°W; Equatorial Tropical Pacific: 5°S to 5°N, 155°E to 130°W;
Southern Tropical Pacific: 5°S to 25°S, 155°E to 130°W; Antarctic: 50°S to 90°S. The
normalized difference between CPC Merged Analysis of Precipitation (CMAP) and GPCP
precipitation is shown with dotted lines.
oceans and less over many high-latitude regions (Yin et al., 2004; Shin
et al., 2011). Over most low-latitude ocean regions, annual precipita-
tion in most CMIP3 and CMIP5 models exceeds GPCP. The difference
relative to CMAP is smaller although mostly of the same sign. In Arctic
and Antarctic Ocean areas, simulated precipitation is much above
CMAP, but more similar to GPCP. Over Antarctic land, precipitation in
most models is below CMAP, but close to or above GPCP.
Continental to sub-continental mean values may not be representative
for smaller-scale biases, as biases generally increase with decreasing
spatial averaging (Masson and Knutti, 2011b; Raisanen and Ylhaisi,
2011). A typical order of magnitude for grid-box-scale annual mean
biases in individual CMIP3 models was 2°C for temperature and 1 mm
day
–1
for precipitation (Raisanen, 2007; Masson and Knutti, 2011b),
with some geographical variation. This has been noted also in studies
on how much spatial averaging would be needed in order to filter out
the most unreliable small-scale features (e.g., Räisänen and Ylhäisi,
2011). In order to reduce such errors while still retaining information
on small scales, Masson and Knutti (2011b) found, depending on the
variable and the region, that smoothing needed to vary from the grid-
point scale to around 2000 km.
On the whole, based on analysis of both ensemble means and
inter-model spread, there is high confidence that the CMIP5 models
simulate regional-scale temperature distributions somewhat better
than the CMIP3 models did. This improvement is evident for most
regions. For precipitation, there is medium confidence that there is no
systematic change in model performance. In many regions, precipita-
tion biases relative to CRU TS 3.10.01 and GPCP (and CMAP) are larger
for CMIP5 than for CMIP3, but given observational uncertainty, the
two ensembles are broadly similar.
9.6.1.2 Regional-Scale Simulation by Atmospheric General
Circulation Models
Stand-alone global atmospheric models (AGCMs) run at higher res-
olution than AOGCMs provide complementary regional-scale climate
information, sometimes referred to as ‘global downscaling’. One
important example of this is the simulation of tropical cyclones (e.g.,
Zhao et al., 2009, 2012; Murakami and Sugi, 2010; Murakami et al.,
2012). A number of advantages of high-resolution AGCMs have been
identified, including improved regional precipitation (Zhao et al., 2009;
Kusunoki et al., 2011) and blocking (Matsueda et al., 2009, 2010). As
AGCMs do not simulate interactions with the ocean, their ability to
capture some high-resolution phenomena, such as the cold wake in the
surface ocean after a tropical cyclone, is limited (e.g., Hasegawa and
Emori, 2007). As in lower-resolution models, performance is affected by
the quality of physical parameterizations (Lin et al., 2012; Mizuta et al.,
2012; Zhao et al., 2012).
9.6.1.3 Regional-Scale Simulation by Variable-Resolution
Global Climate Models
An alternative to global high resolution is the use of variable reso-
lution (so-called ‘stretched grid’) models with higher resolution over
the region of interest. Some examples are Abiodun et al. (2011) who
showed that such simulations improve the simulation of West African
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Chapter 9 Evaluation of Climate Models
9
monsoon systems and African easterly jets, and White et al. (2013)
who demonstrated improvements in temperature and precipitation
related extreme indices. Fox-Rabinovitz et al. (2008) showed that
regional biases in the high-resolution portion of a stretched grid model
were similar to that of a global model with the same high resolution
everywhere. Markovic et al. (2010) and Déqué (2010) reported similar
results. Although not widely used, such methods can complement more
conventional climate models.
9.6.2 Regional Climate Downscaling
Regional Climate Models (RCMs) are applied over a limited-area
domain with boundary conditions either from global reanalyses or
global climate model output. The use of RCMs for ‘dynamical down-
scaling’ has grown since the AR4, their resolution has increased,
process-descriptions have developed further, new components have
been added, and coordinated experimentation has become more
widespread (Laprise, 2008; Rummukainen, 2010). Statistical downscal-
ing (SD) involves deriving empirical relationships linking large-scale
atmospheric variables (predictors) and local/regional climate variables
(predictands). These relationships may then be applied to equivalent
predictors from global models. SD methods have also been applied
to RCM output (e.g., Boe et al., 2007; Déqué, 2007; Segui et al., 2010;
Paeth, 2011; van Vliet et al., 2011). A significant constraint in a com-
prehensive evaluation of regional downscaling is that available studies
often involve different methods, regions, periods and observational
data for evaluation. Thus, evaluation results are difficult to generalize.
9.6.2.1 Recent Developments of Statistical Methods
The development of SD since the AR4 has been quite vigorous (e.g.,
Fowler et al., 2007; Maraun et al., 2010b), and many state-of-the-art
approaches combine different methods (e.g., Vrac and Naveau, 2008;
van Vliet et al., 2011). There is an increasing number of studies on
extremes (e.g., Vrac and Naveau, 2008; Wang and Zhang, 2008), and
on features such as hurricanes (Emanuel et al., 2008), river flow and
discharge, sediment, soil erosion and crop yields (e.g., Zhang, 2007;
Prudhomme and Davies, 2009; Lewis and Lamoureux, 2010). Tech-
niques have also been developed to consider multiple climatic vari-
ables simultaneously in order to preserve some physical consistency
(e.g., Zhang and Georgakakos, 2011). The methods used to evaluate
SD approaches vary with the downscaled variable and include metrics
related to intensities (e.g., Ning et al., 2011; Tryhorn and DeGaetano,
2011), temporal behaviour (e.g., May, 2007; Timbal and Jones, 2008;
Maraun et al., 2010a; Brands et al., 2011), and physical processes (Len-
derink and Van Meijgaard, 2008; Maraun et al., 2010a). SD capabilities
are also examined through secondary variables like runoff, river dis-
charge and stream flow (e.g., Boe et al., 2007; Teutschbein et al., 2011).
9.6.2.2 Recent Developments of Dynamical Methods
Since the AR4, typical RCM resolution has increased from around 50
km to around 25 km (e.g., Christensen et al., 2010). Long RCM runs
at very high resolution are still, however, rather few (e.g., Yasutaka et
al., 2008; Chan et al., 2012; Kendon et al., 2012). Coupled RCMs, with
interactive ocean and, when appropriate, also sea ice have also been
developed (Somot et al., 2008; Dorn et al., 2009; Artale et al., 2010;
Doscher et al., 2010). Smith et al. (2011a) added vegetation dynamics–
ecosystem biogeochemistry in an RCM.
At the time of the AR4, RCMs were typically used for time-slice experi-
ments. Since then, multi-decadal and centennial RCM simulations have
emerged in larger numbers (e.g., Diffenbaugh et al., 2011; Kjellstrom
et al., 2011; de Elia et al., 2013). Coordinated RCM experiments and
ensembles have also become much more common and today, with
domains covering Europe (e.g., Christensen et al., 2010; Vautard et
al., 2013), North America (e.g., Gutowski et al., 2010; Lucas-Picher et
al., 2012a; Mearns et al., 2012), South America (e.g., Menendez et al.,
2010; Chou et al., 2012; Krüger et al., 2012), Africa (e.g., Druyan et al.,
2010; Ruti et al., 2011; Nikulin et al., 2012; Paeth et al., 2012; Hernán-
dez-Díaz et al., 2013), the Arctic (e.g., Inoue et al., 2006) and Asian
regions (e.g., Feng and Fu, 2006; Shkolnik et al., 2007; Feng et al., 2011;
Ozturk et al., 2012; Suh et al., 2012).
9.6.3 Skill of Downscaling Methods
Downscaling skill varies with location, season, parameter and bounda-
ry conditions (see Section 9.6.5) (e.g., Schmidli et al., 2007; Maurer and
Hidalgo, 2008). Although there are indications that model skill increas-
es with higher resolution, it does not do so linearly. Rojas (2006) found
more improvement when increasing resolution from 135 km to 45 km
than from 45 km to 15 km. Walther et al. (2013) found that the diurnal
precipitation cycle and light precipitation improved more when going
from 12 km to 6 km resolution than when going from 50 km to 25 km
or from 25 km to 12 km. Higher resolution does enable better sim-
ulation of extremes (Seneviratne et al., 2012). For example, Pryor et
al. (2012) noted that an increase in RCM resolution from 50 km to 6
km increased extreme wind speeds more than the mean wind speed.
Kawazoe and Gutowski (2013) compared six RCMs and the two GCMs
to high resolution observations, concluding that precipitation extremes
were more representative in the RCMs than in the GCMs. Vautard et al.
(2013) found that warm extremes in Europe were generally better sim-
ulated in RCMs with 12 km resolution compared to 50 km. Kendon et
al. (2012) and Chan et al. (2012) found mixed results in daily precipita-
tion simulated at 12 km and 1.5 km resolution, although the latter had
improved sub-daily features, perhaps as convection could be explicitly
resolved.
Coupled RCMs, with an interactive ocean, offer further improvements.
Döscher et al. (2010) reproduced empirical relationships between
Arctic sea ice extent and sea ice thickness and NAO in a coupled RCM.
Zou and Zhou (2013) found that a regional ocean–atmosphere model
improved the simulation of precipitation over the western North
Pacific compared to an uncoupled model. Samuelsson et al. (2010)
showed that coupling a lake model with an RCM captured the effect
of lakes on the air temperature over adjacent land. Lenaerts et al.
(2012) added drifting snow in an RCM run for the Antarctica, which
increased the area of ablation and improved the fit to observations.
Smith et al. (2011a) added vegetation dynamics–ecosystem biogeo-
chemistry into an RCM, and found some evidence of local feedback
to air temperature.
Applying an RCM developed for a specific region to other regions
exposes it to a wider range of conditions and therefore provides an
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Evaluation of Climate Models Chapter 9
9
opportunity for more rigorous evaluation. Transferability experiments
target this by running RCMs for different regions while holding their
process-descriptions constant (cf. Takle et al., 2007; Gbobaniyi et al.,
2011; Jacob et al., 2012). Suh et al. (2012) noted that 10 RCMs run
for Africa did well overall for average and maximum temperature, but
systematically overestimated the daily minimum temperature. Precip-
itation was generally simulated betted for wet regions than for dry
regions. Similarly, Nikulin et al. (2012) reported on 10 RCMs over
Africa, run with boundary conditions from ERA-Interim, and evaluated
against different observational data sets. Many of the RCMs simulated
precipitation better than the ERA-Interim reanalysis itself.
Christensen et al. (2010) examined a range metrics related to simula-
tion of extremes, mesoscale features, trends, aspects of variability and
consistency with the driving boundary conditions. Only one of these
metrics led to clear differentiation among RCMs (Lenderink, 2010). This
may imply a general skilfulness of models, but may also simply indicate
that the metrics were not very informative. Nevertheless, using some of
these metrics, Coppola et al. (2010) and Kjellström et al. (2010) found
that weighted sets of RCMs outperformed sets without weighting for
both temperature and precipitation. Sobolowski and Pavelsky (2012)
demonstrated a similar impact.
9.6.4 Value Added through RCMs
RCMs are regularly tested to evaluate whether they show improve-
ments over global models (Laprise et al., 2008), that is, whether they
do indeed ‘add value’. In essence, added value is a measure of the
extent to which the downscaled climate is closer to observations than
the model from which the boundary conditions were obtained. Differ-
ences between RCM and GCM simulations are not always very obvious
for time-averaged quantities on larger scales or in fairly homogeneous
regions. RCM fields are, however, richer in spatial and temporal detail.
Indeed, the added value of RCMs is mainly expected in the simulation
of topography-influenced phenomena and extremes with relatively
small spatial or short temporal character (e.g., Feser et al., 2011; Feser
and Barcikowska, 2012; Shkol’nik et al., 2012). As an example, RCM
downscaling led to better large-scale monsoon precipitation patterns
(Gao et al., 2012) for East Asia than in the global models used for
boundary conditions. In the few instances where RCMs have been
interactively coupled to global models (i.e., ‘two-way’ coupling), the
effects of improved small scales propagate to larger scales and this
has been found to improve the simulation of larger scale phenomena
(Lorenz and Jacob, 2005; Inatsu and Kimoto, 2009; Inatsu et al., 2012).
Other examples include improved simulation of convective precipita-
tion (Rauscher et al., 2010), near-surface temperature (Feser, 2006),
near-surface temperature and wind (Kanamaru and Kanamitsu, 2007),
temperature and precipitation (Lucas-Picher et al., 2012b), extreme
precipitation (Kanada et al., 2008), coastal climate features (Winter-
feldt and Weisse, 2009; Winterfeldt et al., 2011; Kawazoe and Gutows-
ki, 2013; Vautard et al., 2013), Atlantic hurricanes (Bender et al.,
2010), European storm damage (Donat et al., 2010), strong mesoscale
cyclones (Cavicchia and Storch, 2011), cutoff lows (Grose et al., 2012),
polar lows (Zahn and von Storch, 2008) and higher statistical moments
of the water budget (e.g., Bresson and Laprise, 2011).
In summary, there is high confidence that downscaling adds value to
the simulation of spatial climate detail in regions with highly varia-
ble topography (e.g., distinct orography, coastlines) and for mesoscale
phenomena and extremes. Regional downscaling is therefore comple-
mentary to results obtained directly from global climate models. These
results are from a variety of distinct studies with different RCMs.
9.6.5 Sources of Model Errors and Uncertainties
In addition to issues related to resolution and model complexity (see
Section 9.6.3), errors and uncertainties arise from observational uncer-
tainty in evaluation data and parameterizations (see Box 9.3), choice of
model domain and application of boundary conditions (driving data).
In the case of SD, sources of model errors and uncertainties depend on
the choice of method, including the choice of the predictors, the esti-
mation of empirical relationships between predictors and predictands
from limited data sets, and also the data used to estimate the predic-
tors (Frost et al., 2011). There are numerous different SD methods, and
the findings are difficult to generalize.
Small domains allow less freedom for RCMs to generate the small-scale
features that give rise to added value (e.g., Leduc and Laprise, 2009).
Therefore large domains –covering entire continents– have become
more common. Køltzow et al. (2008) found improvements with the
use of a larger domain, but the RCM solution can become increasingly
‘decoupled’ from the driving data (e.g., Rockel et al., 2008), which can
introduce inconsistencies. Large domains also introduce large internal
variability, which can significantly contaminate interannual variability
of seasonal means (Kanamitsu et al., 2010). Techniques such as spec-
tral nudging (Misra, 2007; Separovic et al., 2012) can be used to con-
strain such inconsistencies (Feser et al., 2011). Winterfeldt and Weisse
(2009) concluded that nudging improved the simulation of marine
wind climate, while Otte et al. (2012) demonstrated improvements in
temperature and precipitation. Nudging may, however, also lead to
deterioration of features such as precipitation extremes (Alexandru et
al., 2009; Kawazoe and Gutowski, 2013). Veljovic et al. (2010) showed
that an RCM can in fact improve the large scales with respect to those
inherent in the boundary conditions, and argued that nudging may be
undesirable.
The quality of RCM results may vary according to the synoptic situation,
season, and the geographic location of the lateral boundaries (Alexan-
dru et al., 2007; Xue et al., 2007; Laprise et al., 2008; Separovic et al.,
2008; Leduc and Laprise, 2009; Nikiema and Laprise, 2010; Rapaić et
al., 2010). In addition to lateral boundary conditions, RCMs also need
sea surface information. Few studies have explored the dependency
of RCM results on the treatment of the SSTs and sea ice, although
Koltzow et al. (2011) found that the specification of SSTs was less influ-
ential than was the domain or the lateral boundaries. Woollings et al.
(2010a) investigated the effect of specified SST on the simulation of
the Atlantic storm track and found that it was better simulated with
high-resolution SSTs, whereas increasing temporal resolution gave
mixed results.
As is the case in global models, RCM errors are directly related to
shortcomings in process parameterizations. Examples include the
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Chapter 9 Evaluation of Climate Models
9
representation of clouds, convection and land surface–atmosphere
interactions, the planetary boundary layer, horizontal diffusion, and
microphysics (Tjernstrom et al., 2008; Wyser et al., 2008; Lynn et al.,
2009; Pfeiffer and Zängl, 2010; Axelsson et al., 2011; Crétat et al., 2012;
Evans et al., 2012; Roy et al., 2012; Solman and Pessacg, 2012). The
representation of land surface and atmosphere coupling is also impor-
tant, particularly for simulating monsoon regions (Cha et al., 2008;
Yhang and Hong, 2008; Boone et al., 2010; Druyan et al., 2010; van
den Hurk and van Meijgaard, 2010).
9.6.6 Relating Downscaling Performance to Credibility
of Regional Climate Information
A fundamental issue is how the performance of a downscaling method
relates to its ability to provide credible future projections (Raisanen,
2007). This subject is discussed further in Section 9.8. The credibility of
downscaled information of course depends on the quality of the down-
scaling method itself (e.g., Dawson et al., 2012; Déqué et al., 2012;
Eum et al., 2012), and on the quality of the global climate models pro-
viding the large-scale boundary conditions (e.g., van Oldenborgh et al.,
2009; Diaconescu and Laprise, 2013).
Specific to SD is the statistical stationarity hypothesis, that is, that the
relationships inferred from historical data remain valid under a chang-
ing climate (Maraun, 2012). Vecchi et al. (2008) note that a statistical
method that captures interannual hurricane variability gives very dif-
ferent results for projections compared to RCMs. Such results suggest
that good performance of statistical downscaling as assessed against
observations does not guarantee credible regional climate information.
Some recent studies have proposed ways to evaluate SD approaches
using RCM outputs (e.g., Vrac and Naveau, 2008; Driouech et al., 2010)
or long series of observations (e.g. Schmith, 2008).
Giorgi and Coppola (2010) argued that regional-scale climate pro-
jections over land in the CMIP3 models were not sensitive to their
temperature biases. For precipitation, the same was found for about
two thirds of the global land area. However, there is some recent evi-
dence that regional biases may be nonlinear for temperature extremes
(Christensen et al., 2008; Boberg and Christensen, 2012; Christensen
and Boberg, 2013) in both global and regional models. A mechanism
at play may be that models tend to dry out the soil too effectively at
high temperatures, which can lead to systematic biases in projected
warm summertime conditions (Christensen et al., 2008; Kostopoulou
et al., 2009). This is illustrated in Figure 9.41 for the Mediterranean
region, which suggests a tendency in RCMs, CMIP3 and CMIP5 models
towards an enhanced warm bias in the warmer months. The implica-
tion is that the typically large warming signal in these regions could
be biased (Boberg and Christensen, 2012; Mearns et al., 2012). Find-
ings such as these stress the importance of a thorough assessment of
models’ biases when they are applied for projections (e.g., de Elia and
Cote, 2010; Boberg and Christensen, 2012; Christensen and Boberg,
2013).
Di Luca et al. (2012) analysed downscaled climate change projections
from six RCMs run over North America. The climate change signals for
seasonal precipitation and temperature were similar to those in the
driving AOGCMs, and the spatial detail gained by downscaling was
comparable in both present and future climate. Déqué et al. (2012)
studied projections with several combinations of AOGCM and RCM
for Europe. A larger part of the spread in winter temperature and
Figure 9.41 | Ranked modelled versus observed monthly mean temperature for the Mediterranean region for the 1961–2000 period. The Regional Climate Model (RCM) data (a)
are from Christensen et al. (2008) and are adjusted to get a zero mean in model temperature with respect to the diagonal. The smaller insert shows uncentred data. The General
Circulation Model (GCM) data (b) are from CMIP5 and CMIP3 and adjusted in the same way. (After Boberg and Christensen, 2012.)
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Evaluation of Climate Models Chapter 9
9
precipitation projections was explained by the differences in global
model boundary conditions, although much of the spread in project-
ed summer precipitation was explained by RCM. This underlines the
importance of both the quality of the boundary conditions and the
downscaling method.
9.7 Climate Sensitivity and Climate Feedbacks
An overall assessment of climate sensitivity and transient climate
response is given in Box 12.2. Observational constraints based on
observed warming over the last century are discussed in Section 10.8.2
and shown in Box 12.2, Figure 2.
9.7.1 Equilibrium Climate Sensitivity, Idealized Radiative
Forcing, and Transient Climate Response in the
Coupled Model Intercomparison Project Phase 5
Ensemble
Equilibrium climate sensitivity (ECS) is the equilibrium change in global
and annual mean surface air temperature after doubling the atmos-
pheric concentration of CO
2
relative to pre-industrial levels. In the AR4,
the range in equilibrium climate sensitivity of the CMIP3 models was
2.1°C to 4.4°C, and the single largest contributor to this spread was
differences among modelled cloud feedbacks. These assessments carry
over to the CMIP5 ensemble without any substantial change (Table
9.5).
The method of diagnosing climate sensitivity in CMIP5 differs funda-
mentally from the method employed in CMIP3 and assessed in the
AR4 (Randall et al., 2007). In CMIP3, an AGCM was coupled to a
non-dynamic mixed-layer (slab) ocean model with prescribed ocean
heat transport convergence. CO
2
concentration was then instantane-
ously doubled, and the model was integrated to a new equilibrium
with unchanged implied ocean heat transport. While computationally
efficient, this method had the disadvantage of employing a different
model from that used for the historical simulations and climate projec-
tions. However, in the few comparisons that were made, the resulting
disagreement in ECS was less than about 10% (Boer and Yu, 2003;
Williams et al., 2008; Danabasoglu and Gent, 2009; Li et al., 2013a).
In CMIP5, climate sensitivity is diagnosed directly from the AOGCMs
following the approach of Gregory etal. (2004). In this case the CO
2
concentration is instantaneously quadrupled and kept constant for 150
years of simulation, and both equilibrium climate sensitivity and RF
are diagnosed from a linear fit of perturbations in global mean surface
temperature to the instantaneous radiative imbalance at the TOA.
The transient climate response (TCR) is the change in global and annual
mean surface temperature from an experiment in which the CO
2
con-
centration is increased by 1% yr
–1
, and calculated using the difference
between the start of the experiment and a 20-year period centred on
the time of CO
2
doubling. TCR is smaller than ECS because ocean heat
uptake delays surface warming. TCR is linearly correlated with ECS in
the CMIP5 ensemble (Figure 9.42), although the relationship may be
nonlinear outside the range spanned in Table 9.5 (Knutti et al., 2005).
Based on the methods outlined above and explained in Section
9.7.2 below, Table 9.5 shows effective ERF, ECS, TCR and feedback
strengths for the CMIP5 ensemble. The two estimates of ERF agree
with each other to within 5% for six models (CanESM2, INM-CM4,
IPSL-CM5A-LR, MIROC5, MPI-ESM-LR and MPI-ESM-P), although the
deviation exceeds 10% for four models (CCSM4, CSIRO-Mk3-6-0,
HadGEM2-ES, and MRI-CGCM3) and is indicative of deviations from
the basic assumptions underlying one or both ERF estimation methods.
However, the mean difference of 0.3 W m
–2
between the two meth-
ods for diagnosing ERF is only about half of the ensemble standard
deviation of 0.5 W m
–2
, or 15% of the mean value for ERF by CO
2
using fixed SSTs. ECS and TCR vary across the ensemble by a factor
of approximately 2. The multi-model ensemble mean in ECS is 3.2°C,
a value nearly identical to that for CMIP3, while the CMIP5 ensemble
range is 2.1°C to 4.7°C, a spread which is also nearly indistinguishable
from that for CMIP3. While every CMIP5 model whose heritage can
Figure 9.42 | (a) Equilibrium climate sensitivity (ECS) against the global mean surface temperature of CMIP5 models, both for the period 1961–1990 (larger symbols, cf. Figure
9.8, Table9.5) and for the pre-industrial control runs (smaller symbols). (b) Equilibrium climate sensitivity against transient climate response (TCR). The ECS and TCR information are
based on Andrews et al. (2012) and Forster et al. (2013) and updated from the CMIP5 archive.
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Chapter 9 Evaluation of Climate Models
9
Table 9.5 | Effective radiative forcing (ERF), climate sensitivity and climate feedbacks estimated for the CMIP5 AOGCMs (see Table 9.1 for model details). ERF, equilibrium climate sensitivity (ECS) and transient climate response (TCR) are based
on Andrews et al. ( 2012) and Forster et al. (2013) and updated from the CMIP5 archive. The ERF entries are calculated according to Hansen etal. (2005) using fixed sea surface temperatures (SSTs) and Gregory etal. (2004) using regression. ECS
is calculated using regressions following Gregory etal. (2004). TCR is calculated from the CMIP5 simulations with 1% CO
2
increase per year (Taylor et al., 2012b), using the 20-year mean centred on the year of CO
2
doubling. The climate sensitivity
parameter and its inverse, the climate feedback parameter, are calculated from the regression-based ERF and the ECS. Strengths of the individual feedbacks are taken from Vial et al. (2013), following Soden etal. (2008) and using radiative kernel
methods with two different kernels. The sign convention is such that a positive entry for an individual feedback marks a positive feedback; the sum of individual feedback strengths must hence be multiplied by –1 to make it comparable to the
climate feedback parameter. The entries for radiative forcing and equilibrium climate sensitivity were obtained by dividing by two the original results, which were obtained for CO
2
quadrupling. ERF and ECS for BNU-ESM are from Vial et al. (2013).
Model
Effective Radiative Forcing
2 × CO
2
(W m
–2
)
Equilibrium
Climate
Sensitivity
(°C)
Transient
Climate
Response
(°C)
Climate
Sensitivity
Parameter
(°C(W m
–2
)
–1
)
Climate Feed-
back Parameter
(W m
–2
°C
–1
)
Planck Feedback
(W m
–2
°C
–1
)
Water Vapour
Feedback
(W m
–2
°C
–1
)
Lapse Rate
Feedback
(W m
–2
°C
–1
)
Surface Albedo
Feedback
(W m
–2
°C
–1
)
Cloud Feedback
(W m
–2
°C
–1
)
Fixed SST Regression
ACCESS1.0 n.a. 3.0 3.8 2.0 1.3 0.8 n.a. n.a. n.a. n.a. n.a.
ACCESS1.3 n.a. n.a. n.a. 1.7 n.a. n.a. n.a. n.a. n.a. n.a. n.a.
BCC–CSM1.1 n.a. 3.2 2.8 1.7 0.9 1.1 n.a. n.a. n.a. n.a. n.a.
BCC–CSM1.1(m) n.a. 3.6 2.9 2.1 0.8 1.2 n.a. n.a. n.a. n.a. n.a.
BNU–ESM n.a. 3.9 4.1 2.6 1.1 1.0 –3.1 1.4 –0.2 0.4 0.1
CanESM2 3.7 3.8 3.7 2.4 1.0 1.0 –3.2 1.7 –0.6 0.3 0.5
CCSM4 4.4 3.6 2.9 1.8 0.8 1.2 –3.2 1.5 –0.4 0.4 –0.4
CESM1(BGC) n.a. n.a. n.a. 1.7 n.a. n.a. n.a. n.a. n.a. n.a. n.a.
CESM1(CAM5) n.a. n.a. n.a. 2.3 n.a. n.a. n.a. n.a. n.a. n.a. n.a.
CNRM–CM5 n.a. 3.7 3.3 2.1 0.9 1.1 n.a. n.a. n.a. n.a. n.a.
CSIRO–Mk3.6.0 3.1 2.6 4.1 1.8 1.6 0.6 n.a. n.a. n.a. n.a. n.a.
FGOALS–g2 n.a. n.a. n.a. 1.4 n.a. n.a. n.a. n.a. n.a. n.a. n.a.
GFDL–CM3 n.a. 3.0 4.0 2.0 1.3 0.8 n.a. n.a. n.a. n.a. n.a.
GFDL–ESM2G n.a. 3.1 2.4 1.1 0.8 1.3 n.a. n.a. n.a. n.a. n.a.
GFDL–ESM2M n.a. 3.4 2.4 1.3 0.7 1.4 n.a. n.a. n.a. n.a. n.a.
GISS–E2–H n.a. 3.8 2.3 1.7 0.6 1.7 n.a. n.a. n.a. n.a. n.a.
GISS–E2–R n.a. 3.8 2.1 1.5 0.6 1.8 n.a. n.a. n.a. n.a. n.a.
HadGEM2–ES 3.5 2.9 4.6 2.5 1.6 0.6 –3.2 1.4 –0.5 0.3 0.4
INM–CM4 3.1 3.0 2.1 1.3 0.7 1.4 –3.2 1.7 –0.7 0.3 0
IPSL–CM5A–LR 3.2 3.1 4.1 2.0 1.3 0.8 –3.3 1.9 –1 0.2 1.2
IPSL–CM5A–MR n.a. n.a. n.a. 2.0 n.a. n.a. n.a. n.a. n.a. n.a. n.a.
IPSL–CM5B–LR n.a. 2.7 2.6 1.5 1.0 1.0 n.a. n.a. n.a. n.a. n.a.
MIROC5 4.0 4.1 2.7 1.5 0.7 1.5 –3.2 1.7 –0.6 0.3 0.1
MIROC–ESM n.a. 4.3 4.7 2.2 1.1 0.9 n.a. n.a. n.a. n.a. n.a.
MPI–ESM–LR 4.3 4.1 3.6 2.0 0.9 1.1 –3.3 1.8 –0.9 0.3 0.5
MPI–ESM–MR n.a. n.a. n.a. 2.0 n.a. n.a. n.a. n.a. n.a. n.a. n.a.
MPI–ESM–P 4.3 4.3 3.5 2.0 0.8 1.2 n.a. n.a. n.a. n.a. n.a.
MRI–CGCM3 3.6 3.2 2.6 1.6 0.8 1.2 –3.2 1.6 –0.6 0.3 0.2
NorESM1–M n.a. 3.1 2.8 1.4 0.9 1.1 –3.2 1.6 –0.5 0.3 0.2
NorESM1–ME n.a. n.a. n.a. 1.6 n.a. n.a. n.a. n.a. n.a. n.a. n.a.
Model mean 3.7 3.4 3.2 1.8 1.0 1.1 –3.2 1.6 –0.6 0.3 0.3
90% uncertainty ±0.8 ±0.8 ±1.3 ±0.6 ±0.5 ±0.5 ±0.1 ±0.3 ±0.4 ±0.1 ±0.7
819
Evaluation of Climate Models Chapter 9
9
Figure 9.43 | (a) Strengths of individual feedbacks for CMIP3 and CMIP5 models (left and right columns of symbols) for Planck (P), water vapour (WV), clouds (C), albedo (A), lapse
rate (LR), combination of water vapour and lapse rate (WV+LR) and sum of all feedbacks except Planck (ALL), from Soden and Held (2006) and Vial et al. (2013), following Soden
etal. (2008). CMIP5 feedbacks are derived from CMIP5 simulations for abrupt fourfold increases in CO
2
concentrations (4 × CO
2
). (b) ECS obtained using regression techniques by
Andrews et al. (2012) against ECS estimated from the ratio of CO
2
ERF to the sum of all feedbacks. The CO
2
ERF is one-half the 4 × CO
2
forcings from Andrews et al. (2012), and
the total feedback (ALL + Planck) is from Vial et al. (2013).
be traced to CMIP3 shows some change in ECS, there is no discernible
systematic tendency. This broad similarity between CMIP3 and CMIP5
and the good agreement between different methods where they were
applied to the same atmospheric GCM indicate that the uncertainty
in methodology is minor compared to the overall spread in ECS. The
change in TCR from CMIP3 to CMIP5 is generally of the same sign but
of smaller magnitude compared to the change in ECS. The relationship
between ECS and an estimates derived from total feedbacks are dis-
cussed in Section 9.7.2.
Although ECS can vary with global mean surface temperature owing to
the temperature dependencies of the various feedbacks (Colman and
McAvaney, 2009; cf. Section 9.7.2), Figure 9.42 shows no discernible
correlation for the CMIP5 historical temperature ranges, a fact that
suggests that ECS is less sensitive to errors in the current climate than
to other sources of uncertainty.
9.7.2 Understanding the Range in Model Climate
Sensitivity: Climate Feedbacks
The strengths of individual feedbacks for the CMIP3 and CMIP5
models are compared in Figure 9.43. The feedbacks are generally
similar between CMIP3 and CMIP5, and the water vapour, lapse rate,
and cloud feedbacks are assessed in detail in Chapter 7. The surface
albedo feedback is assessed here to be likely positive. There is high
confidence that the sum of all feedbacks (excluding the Planck feed-
back) is positive. Advances in estimating and understanding each of
the feedback parameters in Table 9.5 are described in detail below (see
also Chapters 7 and 8).
9.7.2.1 Role of Humidity and Lapse Rate Feedbacks in
Climate Sensitivity
The compensation between the water vapour and lapse-rate feed-
backs noted in the CMIP3 models is still present in the CMIP5 models,
and possible explanations of the compensation have been developed
(Ingram, 2010; Ingram, 2013). New formulations of the feedbacks,
replacing specific with relative humidity, eliminate most of the cancel-
lation between the water vapour and lapse rate feedbacks and reduce
the inter-model scatter in the individual feedback terms (Held and
Shell, 2012).
9.7.2.2 Role of Surface Albedo in Climate Sensitivity
Analysis of observed declines in sea ice and snow coverage from 1979
to 2008 suggests that the NH albedo feedback is between 0.3 and 1.1
W m
–2
°C
–1
(Flanner et al., 2011). This range is substantially above the
global feedback of 0.3 ± 0.1 W m
–2
°C
–1
of the CMIP5 models ana-
lysed in Table9.5. One possible explanation is that the CMIP5 models
underestimate the strength of the feedback as did the CMIP3 models
based upon the systematic errors in simulated sea ice coverage decline
relative to observed rates (Boe et al., 2009b).
9.7.2.3 Role of Cloud Feedbacks in Climate Sensitivity
Cloud feedbacks represent the main cause for the range in modelled
climate sensitivity (Chapter7). The spread due to inter-model differenc-
es in cloud feedbacks is approximately 3 times larger than the spread
contributed by feedbacks due to variations in water vapour and lapse
820
Chapter 9 Evaluation of Climate Models
9
rate combined (Dufresne and Bony, 2008), and is a primary factor
governing the range of climate sensitivity across the CMIP3 ensem-
ble (Volodin, 2008a). Differences in equilibrium and effective climate
sensitivity are due primarily to differences in the shortwave cloud feed-
back (Yokohata et al., 2008).
In perturbed ensembles of three different models, the primary cloud-re-
lated factor contributing to the spread in equilibrium climate sensitivity
is the low-level shortwave cloud feedback (Yokohata et al., 2010; Klocke
et al., 2011). Changes in the high-altitude clouds also induce climate
feedbacks due to the large areal extent and significant longwave cloud
radiative effects of tropical convective cloud systems. In experiments
with perturbed physics ensembles of AOGCMs, the parameterization
of ice fall speed also emerges as one of the most important determi-
nants of climate sensitivity (Sanderson et al., 2008a, 2010; Sexton et
al., 2012). Other non-microphysical feedback mechanisms are detailed
in Chapter 7.
Cloud feedbacks in AOGCMs are generally positive or near neutral
(Shell et al., 2008; Soden et al., 2008), as evidenced by the net positive
or neutral cloud feedbacks in all of the models examined in a mul-
ti-thousand member ensemble of AOGCMs constructed by parameter
perturbations (Sanderson et al., 2010). The sign of cloud feedbacks in
the current climate deduced from observed relationships between SSTs
and TOA radiative fluxes are discussed further in Section 7.2.5.7.
9.7.2.4 Relationship of Feedbacks to Modelled Climate
Sensitivity
The ECS can be estimated from the ratio of forcing to the total cli-
mate feedback parameter. This approach is applicable to simulations
in which the net radiative balance is much smaller than the forcing
and hence the modelled climate system is essentially in equilibrium.
This approach can also serve to check the internal consistency of esti-
mates of the ECS, forcing, and feedback parameters obtained using
independent methods. The relationship between ECS from Andrews et
al. (2012) and estimates of ECS obtained from the ratio of forcings to
feedbacks is shown in Figure 9.43b. The forcings are estimated using
both regression and fixed SST techniques (Gregory et al., 2004; Hansen
et al., 2005) by Andrews et al. (2012) and the feedbacks are calculated
using radiative kernels (Soden et al., 2008). On average, the ECS from
forcing to feedback ratios underestimate the ECS from Andrews et al.
(2012) by 25% and 35%, or up to 50% for individual models, using
fixed-SST and regression forcings, respectively.
9.7.2.5 Relationship of Feedbacks to Uncertainty in
Modelled Climate Sensitivity
Objective methods for perturbing uncertain model parameters to
optimize performance relative to a set of observational metrics have
shown a tendency toward an increase in the mean and a narrowing
of the spread of estimated climate sensitivity (Jackson et al., 2008a).
This tendency is opposed by the effects of structural biases related
to incomplete process representations in GCMs. If common structur-
al biases are replicated across models in a MME (cf. Section 9.2.2.7),
the most likely sensitivity for the MME tends to shift towards lower
sensitivities while the possibility of larger sensitivities increases at the
same time (Lemoine, 2010). Following Schlesinger and Mitchell (1987),
Roe and Baker (2007) suggest that symmetrically distributed uncer-
tainties in feedbacks lead to inherently asymmetrical uncertainties in
climate sensitivity with increased probability in extreme positive values
of the sensitivity. Roe and Baker (2007) conclude that this relationship
makes it extremely difficult to reduce uncertainties in climate sensitiv-
ity through incremental improvements in the specification of feedback
parameters. While subsequent analysis has suggested that this finding
could be an artifact of the statistical formulation (Hannart et al., 2009)
and linearization (Zaliapin and Ghil, 2010) of the relationship between
feedback and sensitivity adopted by (Roe and Baker, 2007), these
issues remain unsettled (Roe and Armour, 2011; Roe and Baker, 2011).
9.7.3 Climate Sensitivity and Model Performance
Despite the range in equilibrium sensitivity of 2.1°C to 4.4°C for CMIP3
models, they reproduce the global surface air temperature anomaly
of 0.76°C over 1850–2005 to within 25% relative error. The relative-
ly small range of historical climate response suggests that there is
another mechanism, for example a compensating non-GHG forcing,
present in the historical simulations that counteracts the relatively
large range in sensitivity obtained from idealized experiments forced
only by increasing CO
2
. One possible mechanism is a systematic neg-
ative correlation across the multi-model ensemble between ECS and
anthropogenic aerosol forcing (Kiehl, 2007; Knutti, 2008; Anderson et
al., 2010). A second possible mechanism is a systematic overestimate
of the mixing between the oceanic mixed layer and the full depth
ocean underneath (Hansen et al., 2011). However, despite the same
range of ECS in the CMIP5 models as in the CMIP3 models, there is
no significant relationship across the CMIP5 ensemble between ECS
and the 20th-century ERF applied to each individual model (Forster et
al., 2013). This indicates a lesser role of compensating ERF trends from
GHGs and aerosols in CMIP5 historical simulations than in CMIP3. Dif-
ferences in ocean heat uptake also do not appreciably affect the spread
in projected changes in global mean temperature by 2095 (Forster et
al., 2013).
9.7.3.1 Constraints on Climate Sensitivity from Earth System
Models of Intermediate Complexity
An EMIC intercomparison (Eby et al., 2013; Zickfeld et al., 2013) allows
an assessment of model response characteristics, including ECS, TCR,
and heat uptake efficiency (Table 9.6). In addition, Bayesian methods
applied to PPE experiments using EMICs have estimated uncertainty
in model response characteristics (see Box 12.2) based on simulated
climate change in 20th century, past millennia, and LGM scenarios.
Here, the range of response metrics (Table 9.6) described for default
model configurations (Eby et al., 2013) indicates consistency with the
CMIP5 ensemble.
9.7.3.2 Climate Sensitivity During the Last Glacial Maximum
Climate sensitivity can also be explored in another climatic context.
The AR4 assessed attempts to relate simulated LGM changes in trop-
ical SST to global climate sensitivity (Hegerl et al., 2007; Knutti and
Hegerl, 2008). LGM temperature changes in the tropics (Hargreaves
et al., 2007), but not in Antarctica (Hargreaves et al., 2012), have been
821
Evaluation of Climate Models Chapter 9
9
shown to scale well with climate sensitivity because the signal is mostly
dominated by CO
2
forcing in these regions (Braconnot et al., 2007b;
Jansen et al., 2007). The analogy between the LGM climate sensitivity
and future climate sensitivity is, however, not perfect (Crucifix, 2006).
In a single-model ensemble of simulations, the magnitudes of the LGM
cooling and the warming induced by a doubling of CO
2
are nonline-
ar in the forcings applied (Hargreaves et al., 2007). Differences in the
cloud radiative feedback are at the origin of this asymmetric response
to equivalent positive and negative forcings (Yoshimori et al., 2009).
There is thus still low confidence that the regional LGM model-data
comparisons can be used to evaluate model climate sensitivity. How-
ever, even if the results do not scale perfectly with equilibrium or tran-
sient climate sensitivity, the LGM simulations allow the identification
of the different feedback factors that contributed to the LGM global
cooling (Yoshimori et al., 2011) and model spread in these feedbacks.
The largest spread in LGM model feedbacks is found for the shortwave
cloud feedback, just as for the modern climate. This correspondence
between LGM and modern climates adds to the high confidence that
the shortwave cloud feedback is the dominant source of model spread
in climate sensitivity (cf. Section 5.3.3).
9.7.3.3 Constraints on Equilibrium Climate Sensitivity from
Climate-Model Ensembles and Observations
The large scale climatological information available has so far been
insufficient to constrain model behaviour to a range tighter than
CMIP3, at least on a global scale. Sanderson and Knutti (2012) sug-
gest that much of the available and commonly used large scale obser-
vations have already been used to develop and evaluate models and
Table 9.6 | Model response metrics for EMICs in Table 9.2. TCR
2X
, TCR
4X
and ECS
4X
are the changes in global average model surface air temperature from the decades centred
at years 70, 140 and 995 respectively, from the idealized 1% increase to 4 × CO
2
experiment. The ocean heat uptake efficiency, κ
4X
, is calculated from the global average heat
flux divided by TCR
4X
for the decade centred at year 140, from the same idealized experiment. ECS
2x
was calculated from the decade centred about year 995 from a 2 × CO
2
pulse
experiment. (Data from Eby et al., 2013.)
Model TCR
2X
(°C) ECS
2x
(°C) TCR
4X
(°C) ECS
4X
(°C) κ
4X
(W m
–2
°C
–1
)
Bern3D 2.0 3.3 4.6 6.8 0.58
CLIMBER2 2.1 3.0 4.7 5.8 0.84
CLIMBER3 1.9 3.2 4.5 5.9 0.93
DCESS 2.1 2.8 3.9 4.8 0.72
FAMOUS 2.3 3.5 5.2 8.0 0.55
GENIE 2.5 4.0 5.4 7.0 0.51
IAP RAS CM 1.6 3.7 4.3
IGSM2 1.5 1.9 3.7 4.5
LOVECLIM1.2 1.2 2.0 2.1 3.5 1.17
MESMO 2.4 3.7 5.3 6.9 0.55
MIROC-lite 1.6 2.4 3.6 4.6 0.66
MIROC-lite-LCM 1.6 2.8 3.7 5.5 1.00
SPEEDO 0.8 3.6 2.9 5.2 0.84
UMD 1.6 2.2 3.2 4.3
Uvic 1.9 3.5 4.3 6.6 0.92
EMIC mean 1.8 3.0 4.0 5.6 0.8
EMIC range 0.8–2.5 1.9–4.0 2.1–5.4 3.5–8.0 0.5–1.2
are therefore of limited value to further constrain climate sensitivity
or TCR. The assessed literature suggests that the range of climate
sensitivities and transient responses covered by CMIP3/5 cannot be
narrowed significantly by constraining the models with observations
of the mean climate and variability, consistent with the difficulty of
constraining the cloud feedbacks from observations (see Chapter 7).
Studies based on PPE and CMIP3 support the conclusion that a credi-
ble representation of the mean climate and variability is very difficult
to achieve with equilibrium climate sensitivities below 2°C (Piani et al.,
2005; Stainforth et al., 2005; Sanderson et al., 2008a, 2008b; Huber et
al., 2011; Klocke et al., 2011; Fasullo and Trenberth, 2012). High climate
sensitivity values above 5°C (in some cases above 10°C) are found in
the PPE based on HadAM/HadCM3. Several recent studies find that
such high values cannot be excluded based on climatological con-
straints, but comparison with observations shows the smallest errors
for many fields if ECS is between 3 and 4°C (Piani et al., 2005; Knutti
et al., 2006; Rodwell and Palmer, 2007; Sanderson et al., 2008a, 2008b,
2010; Sanderson, 2011, 2013).
9.8 Relating Model Performance to
Credibility of Model Applications
9.8.1 Synthesis Assessment of Model Performance
This chapter has assessed the performance of individual climate
models as well as the multi-model mean. In addition, changes between
models available now and those that were available at the time of the
AR4 have been documented. The models display a range of abilities to
822
Chapter 9 Evaluation of Climate Models
9
simulate climate characteristics, underlying processes, and phenome-
na. No model scores high or low in all performance metrics, but some
models perform substantially better than others for specific climate
variables or phenomena. For a few climate characteristics, the assess-
ment has shown that some classes of models, for example, those with
higher horizontal resolution, higher model top or a more complete
representation of the carbon cycle, aerosols or chemistry, agree better
with observations, although this is not universally true.
Figure 9.44 provides a synthesis of key model evaluation results for
AOGCMs and ESMs. The figure makes use of the calibrated language
as defined in Mastrandrea et al. (2011). The x-axis refers to the level
of confidence which increases towards the right as suggested by the
increasing strength of shading. The level of confidence is a combina-
tion of the level of evidence and the degree of agreement. The level of
Very low Low Medium High Very high
Model Performance
Confidence in Assessment
Low Medium High
(a) Mean State
TAS
PR
TropO3
TAS-diur
PR-diur
Blocking
Monsoon
NBP
fgCO2
fgCO2-sp
NBP-sp
SAF
ArcSIE
AntSIE
SST
SSS
SNC
MHT
TrPacific
TrInOcean
TrAtlantic CRE
ZTaux
TAS-RS
PR-RS
AMOC EqTaux
SMO
VAR-diur TrSST
SSS-RS
AOD
-t
Low Medium High
(b) Trends
OHC-t
TotalO3-t
TTT-t
LST-t
ArcSIE-t
AntSIE-t
TAS-t
Model Performance
Confidence in Assessment
NBP-t
fgCO2-t
Very low Low Medium High Very high
Low Medium High
(c) Variability
NAO
SAM
AMO
CO2-iav
ENSO
IOD
QBO
PDO
PNA
ENSOtele
MJO
AMM
AMOC-var
SST-var
IPO
AN
Model Performance
Confidence in Assessment
dCO2-iav
Very low Low Medium High Very high
IOB
Low Medium High
(d) Extremes
TAS-ext
Droughts
TAS-ext-t PR-ext-t
PR-ext PR-ext-hr
TC-hr
Hurric-hr
Model Performance
Confidence in Assessment
TC
Very low Low Medium High Very high
Degradation since CMIP3
No changes since CMIP3
Improvements since CMIP3
No relative assessment CMIP3 vs. CMIP5
evidence includes the number of studies and quality of observation-
al data. Generally, evidence is most robust when there are multiple,
independent studies that evaluate multiple models using high-quality
observations. The degree of agreement measures whether different
studies come to the same conclusions or not. The figure shows that
several important aspects of the climate are simulated well by con-
temporary models, with varying levels of confidence. The colour coding
provides an indication of how model quality has changed from CMIP3
to CMIP5. For example, there is high confidence that the model perfor-
mance for global mean surface air temperature (TAS) is high, and it is
shown in green because there is robust evidence of improvement since
CMIP3. By contrast, the diurnal cycle of global mean surface air tem-
perature (TAS-diur) is simulated with medium performance, but there
is low confidence in this assessment owing to as yet limited analy-
ses. It should be noted that there are no instances in the figure for
Figure 9.44 | Summary of the findings of Chapter 9 with respect to how well the CMIP5 models simulate important features of the climate of the 20th century. Confidence in the
assessment increases towards the right as suggested by the increasing strength of shading. Model performance improves from bottom to top. The colour coding indicates changes
since CMIP3 (or models of that generation) to CMIP5. The assessment of model performance is expert judgment based on the agreement with observations of the multi-model
mean and distribution of individual models around the mean, taking into account internal climate variability. Note that assessed model performance is simplified for representation
in the figure and it is referred to the text for details of each assessment. The figure highlights the following key features, with the sections that back up the assessment added in
parentheses:
823
Evaluation of Climate Models Chapter 9
9
PANEL a:
AMOC Atlantic Meridional Overturning Circulation mean
(Section 9.4.2.3)
AntSIE Seasonal cycle Antarctic sea ice extent (Section 9.4.3)
AOD Aerosol Optical Depth (Section 9.4.6)
ArctSIE Seasonal cycle Arctic sea ice extent (Section 9.4.3)
Blocking Blocking events (Section 9.5.2.2)
CRE Cloud radiative effects (Section 9.4.1.2)
EqTaux Equatorial zonal wind stress (Section 9.4.2.4)
fgCO2 Global ocean carbon sink (Section 9.4.5)
fgCO2-sp Spatial pattern of ocean–atmosphere CO
2
fluxes (Section 9.4.5)
MHT Meridional heat transport (Section 9.4.2.4)
Monsoon Global monsoon (Section 9.5.2.4)
NBP Global land carbon sink (Section 9.4.5)
NBP-sp Spatial pattern of land–atmosphere CO
2
fluxes (Section 9.4.5)
PR Large scale precipitation (Sections 9.4.1.1, 9.4.1.3)
PR-diur Diurnal cycle precipitation (Section 9.5.2.1)
PR-RS Regional scale precipitation (Section 9.6.1.1)
SAF Snow albedo feedbacks (Section 9.8.3)
SMO Soil moisture (Section 9.4.4)
SNC Snow cover (Section 9.4.4)
SSS Sea surface salinity (Section 9.4.2.1)
SSS-RS Regional Sea surface salinity (Section 9.4.2.1)
SST Sea surface temperature (Section 9.4.2.1)
TAS Large scale surface air temperature (Sections 9.4.1.1, 9.4.1.3)
TAS-diur Diurnal cycle surface air temperature (Section 9.5.2.1)
TAS-RS Regional scale surface air temperature (Section 9.6.1.1)
TrSST Tropical sea surface temperature (Section 9.4.2.1)
TropO3 Tropospheric column ozone climatology (Section 9.4.1.4.5)
TrAtlantic Tropical Atlantic mean state (Section 9.4.2.5)
TrInOcean Tropical Indian Ocean mean state (Section 9.4.2.5)
TrPacific Tropical Pacific mean state (Section 9.4.2.5)
VAR-diur Diurnal cycle other variables (Section 9.5.2.1)
ZTaux Zonal mean zonal wind stress (Section 9.4.2.4)
PANEL b (Trends)
AntSIE-t Trend in Antarctic sea ice extent (Section 9.4.3)
ArctSIE-t Trend in Arctic sea ice extent (Section 9.4.3)
fgCO2-t Global ocean carbon sink trends (Section 9.4.5)
LST-t Lower stratospheric temperature trends (Section 9.4.1.4.5)
NBP-t Global land carbon sink trends (Section 9.4.5)
OHC-t Global ocean heat content trends (Section 9.4.2.2)
TotalO3-t Total column ozone trends (Section 9.4.1.4.5)
TAS-t Surface air temperature trends (Section 9.4.1.4.1)
TTT-t Tropical tropospheric temperature trends (Section 9.4.1.4.2)
PANEL c (Variability):
AMM Atlantic Meridional Mode (Section 9.5.3.3)
AMO Atlantic Multi-decadal Variability (Section 9.5.3.3)
AMOC-var Atlantic Meridional Overturning Circulation (Section 9.5.3.3)
AN Atlantic Niño (Section 9.5.3.3)
CO2-iav Interannual variability of atmospheric CO
2
(Section 9.8.3)
dCO2-iav Sensitivity of CO
2
growth rate to tropical temperature
(Section 9.8.3)
ENSO El Niño Southern Oscillation (Section 9.5.3.4)
ENSOtele Tropical ENSO teleconnections (Section 9.5.3.5)
IOB Indian Ocean basin mode (Section 9.5.3.4)
IOD Indian Ocean dipole (Section 9.5.3.4)
IPO Interdecadal Pacific Oscillation (Section 9.5.3.6)
MJO Madden-Julian Oscillation (Section 9.5.2.3)
NAO North Atlantic Oscillation and Northern annular mode
(Section 9.5.3.2)
PDO Pacific Decadal Oscillation (Section 9.5.3.6)
PNA Pacific North American (Section 9.5.3.5)
QBO Quasi-Biennial Oscillation (Section 9.5.3.7)
SAM Southern Annular Mode (Section 9.5.3.2)
SST-var Global sea surface temperature variability (Section 9.5.3.1)
PANEL d (Extremes):
Hurric-hr Year-to-year counts of Atlantic hurricanes in high-resolution
AGCMs (Section 9.5.4.3)
PR-ext Global distributions of precipitation extremes (Section 9.5.4.2)
PR-ext-hr Global distribution of precipitation extremes in high-resolution
AGCMs (Section 9.5.4.2)
PR-ext-t Global trends in precipitation extremes (Section 9.5.4.2)
TAS-ext Global distributions of surface air temperature extremes
(Section 9.5.4.1)
TAS-ext-t Global trends in surface air temperature extremes
(Section 9.5.4.1)
TC Tropical cyclone tracks and intensity (Section 9.5.4.3)
TC-hr Tropical cyclone tracks and intensity in high-resolution
AGCMs (Section 9.5.4.3)
Droughts Droughts (Section 9.5.4.4)
which CMIP5 models perform worse than CMIP3 models (something
that would have been indicated by the red colour). A description that
explains the expert judgment for each of the results presented in Figure
9.44 can be found in the body of this chapter, with a link to the specific
sections given in the figure caption.
EMICs have also been evaluated to some extent in this chapter as they
are used to provide long-term projections (in Chapter 12) beyond year
2300, and to provide large ensembles emulating the response of more
comprehensive ESMs and allowing probabilistic estimates. Results
from the EMIC intercomparison project (Eby et al., 2013; Zickfeld et al.,
2013) illustrate the ability to reproduce the large-scale climate chang-
es in GMST (Figure 9.8) and OHC (Figure 9.17) during the 20th century.
The models also estimate CO
2
fluxes for land and oceans, which are as
consistent with observations as are fluxes estimated by ESMs (Figure
9.27). This gives confidence that the EMICs, albeit limited in the scope
and resolution of information they can provide, can be used for long-
term projections compatible with those of ESMs (Plattner et al., 2008;
Eby et al., 2013). Overall, these studies imply that EMICs are well suited
for simulations extending beyond the CMIP5 ensemble.
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Chapter 9 Evaluation of Climate Models
9
Frequently Asked Questions
FAQ 9.1 | Are Climate Models Getting Better, and How Would We Know?
Climate models are extremely sophisticated computer programs that encapsulate our understanding of the climate
system and simulate, with as much fidelity as currently feasible, the complex interactions between the atmosphere,
ocean, land surface, snow and ice, the global ecosystem and a variety of chemical and biological processes.
The complexity of climate models—the representation of physical processes like clouds, land surface interactions
and the representation of the global carbon and sulphur cycles in many models—has increased substantially since
the IPCC First Assessment Report in 1990, so in that sense, current Earth System Models are vastly ‘better’ than the
models of that era. This development has continued since the Fourth Assessment, while other factors have also
contributed to model improvement. More powerful supercomputers allow current models to resolve finer spatial
detail. Today’s models also reflect improved understanding of how climate processes work—understanding that has
come from ongoing research and analysis, along with new and improved observations.
Climate models of today are, in principle, better than their predecessors. However, every bit of added complexity,
while intended to improve some aspect of simulated climate, also introduces new sources of possible error (e.g., via
uncertain parameters) and new interactions between model components that may, if only temporarily, degrade a
model’s simulation of other aspects of the climate system. Furthermore, despite the progress that has been made,
scientific uncertainty regarding the details of many processes remains.
An important consideration is that model performance
can be evaluated only relative to past observations,
taking into account natural internal variability. To have
confidence in the future projections of such models, his-
torical climate—and its variability and change—must be
well simulated. The scope of model evaluation, in terms
of the kind and quantity of observations available, the
availability of better coordinated model experiments,
and the expanded use of various performance met-
rics, has provided much more quantitative information
about model performance. But this alone may not be
sufficient. Whereas weather and seasonal climate pre-
dictions can be regularly verified, climate projections
spanning a century or more cannot. This is particularly
the case as anthropogenic forcing is driving the climate
system toward conditions not previously observed in the
instrumental record, and it will always be a limitation.
Quantifying model performance is a topic that has fea-
tured in all previous IPCC Working Group I Reports.
Reading back over these earlier assessments provides
a general sense of the improvements that have been
made. Past reports have typically provided a rather
broad survey of model performance, showing differenc-
es between model-calculated versions of various climate
quantities and corresponding observational estimates.
Inevitably, some models perform better than others for
certain climate variables, but no individual model clear-
ly emerges as ‘the best’ overall. Recently, there has been
progress in computing various performance metrics,
which synthesize model performance relative to a range
of different observations according to a simple numeri-
cal score. Of course, the definition of such a score, how
it is computed, the observations used (which have their
CMIP2 CMIP3
CMIP5
0.5
0.6
0.7
0.8
0.9
1
Pattern correlation
Precipitation
CMIP2 CMIP3
CMIP5
0.95
0.96
0.97
0.98
0.99
1
Pattern correlation
Surface Temperature
FAQ 9.1, Figure 1 | Model capability in simulating annual mean temperature
and precipitation patterns as illustrated by results of three recent phases of
the Coupled Model Intercomparison Project (CMIP2, models from about year
2000; CMIP3, models from about 2005; and CMIP5, the current generation
of models). The figure shows the correlation (a measure of pattern similarity)
between observed and modelled temperature (upper panel) and precipitation
(lower panel). Larger values indicate better correspondence between modelled
and observed spatial patterns. The black symbols indicate correlation coefficient
for individual models, and the large green symbols indicate the median value
(i.e., half of the model results lie above and the other half below this value).
Improvement in model performance is evident by the increase in correlation for
successive model generations.
(continued on next page)
825
Evaluation of Climate Models Chapter 9
9
FAQ 9.1 (continued)
own uncertainties), and the manner in which various scores are combined are all important, and will affect the end
result.
Nevertheless, if the metric is computed consistently, one can compare different generations of models. Results
of such comparisons generally show that, although each generation exhibits a range in performance, the aver-
age model performance index has improved steadily between each generation. An example of changes in model
performance over time is shown in FAQ 9.1, Figure 1, and illustrates the ongoing, albeit modest, improvement. It
is interesting to note that both the poorest and best performing models demonstrate improvement, and that this
improvement comes in parallel with increasing model complexity and an elimination of artificial adjustments to
atmosphere and ocean coupling (so-called ‘flux adjustment’). Some of the reasons for this improvement include
increased understanding of various climate processes and better representation of these processes in climate
models. More comprehensive Earth observations are also driving improvements.
So, yes, climate models are getting better, and we can demonstrate this with quantitative performance metrics
based on historical observations. Although future climate projections cannot be directly evaluated, climate models
are based, to a large extent, on verifiable physical principles and are able to reproduce many important aspects of
past response to external forcing. In this way, they provide a scientifically sound preview of the climate response to
different scenarios of anthropogenic forcing.
9.8.2 Implications of Model Evaluation for Climate
Change Detection and Attribution
The evaluation of model simulations of historical climate is of direct
relevance to detection and attribution (D&A) studies (Chapter 10)
since these rely on model-derived patterns (or ‘fingerprints’) of climate
response to external forcing, and on the ability of models to simulate
decadal and longer-time scale internal variability (Hegerl and Zwiers,
2011). Conversely, D&A research contributes to model evaluation
through estimation of the amplitude of modeled response to vari-
ous forcings (Section 10.3.1.1.3). The estimated fingerprint for some
variables such as water vapor is governed by basic physical process-
es that are well represented in models and are rather insensitive to
model uncertainties (Santer et al., 2009). Figure 9.44 illustrates slight
improvements in the representation of some of the modes of variability
and climate phenomena discussed in Sections 9.5.2 and 9.5.3, sug-
gesting with medium confidence that models now better reproduce
internal variability. On the other hand, biases that affect D&A studies
remain. An example is the warm bias of lower-stratosphere tempera-
ture trends during the satellite period (Section 9.4.1.4.5) that can be
linked to uncertainties in stratospheric ozone forcing (Solomon et al.,
2012; Santer et al., 2013). Recent studies of climate extremes (Sec-
tion 9.5.4) also provide evidence that models have reasonable skill in
these important attributes of a changing climate; however, there is an
indication that models have difficulties in reproducing the right bal-
ance between historical changes in cold and warm extremes. They also
confirm that resolution affects the confidence that can be placed in the
analyses of extreme in precipitation. D&A studies focussed on extreme
events are therefore constrained by current model limitations. Lastly,
some D&A studies have incorporated model quality results by repeat-
ing a multi-model analysis with only the models that agree best with
observations (Santer et al., 2009). This model discrimination or weight-
ing is less problematic for D&A analysis than it is for model projections
of future climate (Section 9.8.3), because D&A research is focussed on
historical and control-run simulations which can be directly evaluated
against observations.
9.8.3 Implications of Model Evaluation for Model
Projections of Future Climate
Confidence in climate model projections is based on physical under-
standing of the climate system and its representation in climate
models, and on a demonstration of how well models represent a wide
range of processes and climate characteristics on various spatial and
temporal scales (Knutti et al., 2010b). A climate model’s credibility is
increased if the model is able to simulate past variations in climate,
such as trends over the 20th century and palaeoclimatic changes. Pro-
jections from previous IPCC assessments can also be directly compared
to observations (see Figures 1.4 and 1.5), with the caveat that these
projections were not intended to be predictions over the short time
scales for which observations are available to date. Unlike shorter lead
forecasts, longer-term climate change projections push models into
conditions outside the range observed in the historical period used for
evaluation.
In some cases, the spread in climate projections can be reduced by
weighting of models according to their ability to reproduce past
observed climate. Several studies have explored the use of unequally
weighted means, with the weights based on the models’ performance
in simulating past variations in climate, typically using some perfor-
mance metric or collection of metrics (Connolley and Bracegirdle,
2007; Murphy et al., 2007; Waugh and Eyring, 2008; Pierce et al., 2009;
Reifen and Toumi, 2009; Christensen et al., 2010; Knutti et al., 2010b;
Raisanen et al., 2010; Abe et al., 2011; Shiogama et al., 2011; Wat-
terson and Whetton, 2011; Tsushima et al., 2013). When applied to
projections of Arctic sea ice, averages in which extra weight is given
826
Chapter 9 Evaluation of Climate Models
9
to models with the most realistic historical sea ice do give different
results than the unweighted mean (Stroeve et al., 2007, 2012; Scher-
rer, 2011; Massonnet et al., 2012; Wang and Overland, 2012; Overland
and Wang, 2013). Another frequently used approach is the re-calibra-
tion of model outputs to a given observed value (Boe et al., 2009b;
Mahlstein and Knutti, 2012; Wang and Overland, 2012), see further
discussion in Section 12.4.6.1. Some studies explicitly formulate a
statistical frameworks that relate future observables to climate model
output (reviewed in Knutti et al. (2010b) and Stephenson et al. (2012)).
Such frameworks not only provide weights for the mean response but
also allow the uncertainty in the predicted response to be quantified
(Bracegirdle and Stephenson, 2012).
There are several encouraging examples of ‘emergent constraints’,
which are relationships across an ensemble of models between some
aspect of Earth System sensitivity and an observable trend or varia-
tion in the contemporary climate (Allen and Ingram, 2002; Hall and
Qu, 2006; Eyring et al., 2007; Boe et al., 2009a, 2009b; Mahlstein
and Knutti, 2010; Son et al., 2010; Huber et al., 2011; Schaller et al.,
2011; Bracegirdle and Stephenson, 2012; Fasullo and Trenberth, 2012;
O’Gorman, 2012). For example, analyzing the CMIP3 ensemble, Hall
and Qu (2006) showed that inter-model variations of snow albedo
feedback in the contemporary seasonal cycle strongly correlate with
comparably large inter-model variations in this feedback under future
climate change. An update of this analysis with CMIP5 models added
is shown in Figure 9.45 (left panel). This relationship presumably arises
from the fact that surface albedo values in areas covered by snow vary
widely across the models, particularly in the heavily vegetated boreal
forest zone. Models with higher surface albedos in these areas have a
larger contrast between snow-covered and snow-free areas, and hence
a stronger snow albedo feedback whether the context is the seasonal
variation in sunshine or anthropogenic forcing. Comparison with an
observational estimate of snow albedo feedback reveals a large spread
with both high and low biases.
The right panel of Figure 9.45 shows another example of an emergent
constraint, where the sensitivity of tropical land carbon to warming
(i.e., without CO
2
fertilization effects) is related to the sensitivity of the
annual CO
2
growth rate to tropical temperature anomalies (Cox et al.,
2013) ). The horizontal axis is the regression of the atmospheric CO
2
growth rate on the tropical temperature anomaly for each model. The
strong statistical relationship between these two variables is consist-
ent with the fact that interannual variability in the CO
2
growth-rate is
known to be dominated by the response of tropical land to climatic
anomalies, associated particularly with ENSO. Thus the relationship has
a physical as well as a statistical basis. The interannual sensitivity of
the CO
2
growth rate to tropical temperature can be estimated from
observational data. Like the snow albedo feedback example, this inter-
model relationship provides a credible means to reduce model spread
in the sensitivity of tropical land carbon to tropical climate change.
On the other hand, many studies have failed to find strong relation-
ships between observables and projections. Whetton et al. (2007) and
Knutti et al. (2010a) found that correlations between local to region-
al climatological values and projected changes are small except for
a few regions. Scherrer (2011) finds no robust relationship between
the ability of the CMIP3 models to represent interannual variability
of near-surface air temperature and the amplitude of future warm-
ing.Raisanen et al. (2010) report only small (10–20%) reductions in
cross-validation error of simulated 21st century temperature changes
when weighting the CMIP3 models based on their simulation of the
present-day climatology. The main difficulties are sparse coverage in
Figure 9.45 | (Left) Scatterplot of simulated springtime snow–albedo feedback (Δα
s
T
s
) values in climate change (y-axis) versus simulated springtime Δα
s
T
s
values in the
seasonal cycle (x-axis) in transient climate change experiments from 17 CMIP3 (blue) and 24 CMIP5 models (α
s
and T
s
are surface albedo and surface air temperature, respectively).
(Adapted from Hall and Qu, 2006.) (Right) Constraint on the climate sensitivity of land carbon in the tropics (30°N to 30°S) from interannual variability in the growth rate of global
atmospheric CO
2
(Cox et al., 2013). This is based on results from Earth System Models (ESMs) with free-running CO
2
; Coupled Climate Carbon Cycle Model Intercomparison Project
General Circulation Models (C4MIP GCMs, black labels; Friedlingstein et al., 2006), and three land carbon ‘physics ensembles’ with HadCM3 (red labels; Booth et al., 2012b) . The
values on the y-axis are calculated over the period 1960–2099 inclusive, and those on the x-axis over the period 1960–2010 inclusive. In both cases the temperature used is the
mean (land+ocean) temperature over 30°N to 30°S. The width of the vertical yellow bands in both (a) and (b) shows the observation-based estimate of the variable on the x-axis.
827
Evaluation of Climate Models Chapter 9
9
many observed variables, short time series for observed trends, lack of
correlation between observed quantities and projected past or future
trends, and systematic errors in the models (Tebaldi and Knutti, 2007;
Jun et al., 2008; Knutti, 2010; Knutti et al., 2010a), the ambiguity of
possible performance metrics and the difficulty of associating them
with predictive skill (Parker et al., 2007; Gleckler et al., 2008; Pincus
et al., 2008; Reichler and Kim, 2008; Pierce et al., 2009; Knutti et al.,
2010a).
Emergent constraints can be difficult to identify if climate models are
structurally similar and share common biases, thereby reducing the
effective ensemble size. Comparison of emergent constraints in MMEs
from different modelling experiments can help reveal which constraints
are robust (Massonnet et al., 2012; Bracegirdle and Stephenson, 2013).
Another issue is that testing of large numbers of predictors will find
statistically significant correlations that do not remain significant in
a different ensemble. This is particularly important if many predictors
are tested using only small ensembles like CMIP3 (DelSole and Shukla,
2009; Raisanen et al., 2010; Huber et al., 2011; Masson and Knutti,
2013). All of these potential pitfalls underscore the need for analysis
of the mechanism underpinning the statistical relationship between
current and future climate parameters for any proposed emergent
constraint.
828
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9
References
Abe, M., H. Shiogama, T. Nozawa, and S. Emori, 2011: Estimation of future surface
temperature changes constrained using the future-present correlated modes
in inter-model variability of CMIP3 multimodel simulations. J. Geophys. Res.
Atmos., 116, D18104.
Abiodun, B., W. Gutowski, A. Abatan, and J. Prusa, 2011: CAM-EULAG: A non-
hydrostatic atmospheric climate model with grid stretching. Acta Geophys., 59,
1158–1167.
Abramowitz, G., R. Leuning, M. Clark, and A. Pitman, 2008: Evaluating the
performance of land surface models. J. Clim., 21, 5468–5481.
AchutaRao, K., and K. Sperber, 2002: Simulation of the El Niño Southern Oscillation:
Results from the coupled model intercomparison project. Clim. Dyn., 19, 191–
209.
AchutaRao, K., and K. Sperber, 2006: ENSO simulations in coupled ocean-atmosphere
models: Are the current models better? Clim. Dyn., 27, 1–16.
Achuthavarier, D., V. Krishnamurthy, B. P. Kirtman, and B. H. Huang, 2012: Role of the
Indian Ocean in the ENSO-Indian Summer Monsoon Teleconnection in the NCEP
Climate Forecast System. J. Clim., 25, 2490–2508.
Ackerley, D., E. J. Highwood, and D. J. Frame, 2009: Quantifying the effects of
perturbing the physics of an interactive sulfur scheme using an ensemble of
GCMs on the climateprediction.net platform. J. Geophys. Res. Atmos., 114,
D01203
Adachi, Y., et al., 2013: Basic performance of a new earth system model of the
Meteorological Research Institute (MRI-ESM1). Papers Meteorol. Geophys.,
doi:10.2467/mripapers.64.1.
Adam, J., E. Clark, D. Lettenmaier, and E. Wood, 2006: Correction of global
precipitation products for orographic effects. J. Clim., 19, 15–38.
Adkins, J. F., K. McIntyre, and D. P. Schrag, 2002: The salinity, temperature, and delta
O-18 of the glacial deep ocean. Science, 298, 1769–1773.
Adler, R. F., et al., 2003: The Version 2 Global Precipitation Climatology Project (GPCP)
Monthly Precipitation Analysis (1979–Present). J. Hydrometeor., 4, 1147–1167.
Alekseev, V. A., E. M. Volodin, V. Y. Galin, V. P. Dymnikov, and V. N. Lykossov, 1998:
Modeling of the present-day climate by the atmospheric model of INM RAS
DNM GCM. Description of the model version A5421 and results of AMIP2
simulations. Institute of Numerical Mathematics, Moscow, Russia, 200 pp.
Alessandri, A., P. G. Fogli, M. Vichi, and N. Zeng, 2012: Strengthening of the
hydrological cycle in future scenarios: Atmospheric energy and water balance
perspective. Earth Syst. Dyn., 3, 199–212.
Alexander, M. J., et al., 2010: Recent developments in gravity-wave effects in climate
models and the global distribution of gravity-wave momentum flux from
observations and models. Q. J. R. Meteorol. Soc., 136, 1103–1124.
Alexandru, A., R. de Elia, and R. Laprise, 2007: Internal variability in regional climate
downscaling at the seasonal scale. Mon. Weather Rev., 135 3221–3238.
Alexandru, A., R. de Elia, R. Laprise, L. Separovic, and S. Biner, 2009: Sensitivity study
of regional climate model simulations to large-scale nudging parameters. Mon.
Weather Rev., 137, 1666–1686.
Allan, R. P., and B. J. Soden, 2008: Atmospheric warming and the amplification of
precipitation extremes. Science, 321, 1481–1484.
Allan, R. P., M. A. Ringer, and A. Slingo, 2003: Evaluation of moisture in the Hadley
Centre climate model using simulations of HIRS water-vapour channel radiances.
Q. J. R. Meteorol. Soc., 129, 3371–3389.
Allan, R. P., A. Slingo, S. F. Milton, and M. E. Brooks, 2007: Evaluation of the Met
Office global forecast model using Geostationary Earth Radiation Budget
(GERB) data. Q. J. R. Meteorol. Soc., 133, 1993–2010.
Allan, R. P., B. J. Soden, V. O. John, W. Ingram, and P. Good, 2010: Current changes in
tropical precipitation. Environ. Res. Lett., 5, 025205.
Allen, M., P. Stott, J. Mitchell, R. Schnur, and T. Delworth, 2000: Quantifying the
uncertainty in forecasts of anthropogenic climate change. Nature, 407, 617–
620.
Allen, M. R., and W. J. Ingram, 2002: Constraints on future changes in climate and
the hydrologic cycle. Nature, 419, 224–232.
Ammann, C. M., G. A. Meehl, W. M. Washington, and C. S. Zender, 2003: A monthly
and latitudinally varying volcanic forcing dataset in simulations of 20th century
climate. Geophys. Res. Lett., 30, 1657.
Anav, A., et al., 2013: Evaluating the land and ocean components of the global
carbon cycle in the CMIP5 Earth System Models. J. Clim., 26, 6801–6843.
Anderson, B. T., J. R. Knight, M. A. Ringer, C. Deser, A. S. Phillips, J. H. Yoon, and
A. Cherchi, 2010: Climate forcings and climate sensitivities diagnosed from
atmospheric global circulation models. Clim. Dyn., 35, 1461–1475.
Andrews, T., J. M. Gregory, M. J. Webb, and K. E. Taylor, 2012: Forcing, feedbacks
and climate sensitivity in CMIP5 coupled atmosphere-ocean climate models.
Geophys. Res. Lett., 39, L09712.
Annamalai, H., and K. R. Sperber, 2005: Regional heat sources and the active and
break phases of boreal summer intraseasonal (30–50 day) variability. J. Atmos.
Sci., 62, 2726–2748.
Annan, J., and J. Hargreaves, 2011: Understanding the CMIP3 Multimodel Ensemble.
J. Clim., 24, 4529–4538.
Annan, J. D., and J. C. Hargreaves, 2010: Reliability of the CMIP3 ensemble. Geophys.
Res. Lett., 37, L02703.
Annan, J. D., D. J. Lunt, J. C. Hargreaves, and P. J. Valdes, 2005: Parameter estimation in
an atmospheric GCM using the Ensemble Kalman Filter. Nonlin. Proc. Geophys.,
12, 363–371.
Anstey, J. A., et al., 2013: Multi-model analysis of Northern Hemisphere winter
blocking and its relation to the stratosphere. J. Geophys. Res. Atmos., 118,
3956–3971.
Antonov, J. I., et al., 2010: World Ocean Atlas 2009, Vol. 2: Salinity. [S. Levitus (eds.)].
NOAA Atlas NESDIS 69, U.S. Gov. Printing Office, Washington, D.C., 184 pp.
Archer, D. E., G. Eshel, A. Winguth, W. Broecker, R. Pierrehumbert, M. Tobis, and R.
Jacob, 2000: Atmospheric pCO(2) sensitivity to the biological pump in the ocean.
Global Biogeochem. Cycles, 14, 1219–1230.
Arneth, A., et al., 2010: From biota to chemistry and climate: Towards a
comprehensive description of trace gas exchange between the biosphere and
atmosphere. Biogeosciences, 7, 121–149.
Arora, V. K., and G. J. Boer, 2005: Fire as an interactive component of dynamic
vegetation models. J. Geophys. Res.-Biogeosciences, 110, G02008.
Arora, V. K., and G. J. Boer, 2010: Uncertainties in the 20th century carbon budget
associated with land use change. Global Change Biol., 16, 3327–3348.
Arora, V. K., et al., 2011: Carbon emission limits required to satisfy future
representative concentration pathways of greenhouse gases. Geophys. Res.
Lett., 38, L05805.
Arora, V. K., et al., 2009: The effect of terrestrial photosynthesis down regulation on
the twentieth-century carbon budget simulated with the CCCma Earth System
Model. J. Clim., 22, 6066–6088.
Artale, V., et al., 2010: An atmosphere–ocean regional climate model for the
Mediterranean area: Assessment of a present climate simulation. Clim. Dyn.,
35, 721–740.
Arzhanov, M. M., P. F. Demchenko, A. V. Eliseev, and I. I. Mokhov, 2008: Simulation of
characteristics of thermal and hydrologic soil regimes in equilibrium numerical
experiments with a Climate Model of Intermediate Complexity. Izvestiya Atmos.
Ocean. Phys., 44, 548–566.
Assmann, K. M., M. Bentsen, J. Segschneider, and C. Heinze, 2010: An isopycnic
ocean carbon cycle model. Geosci. Model Dev., 3, 143–167.
Aumont, O., and L. Bopp, 2006: Globalizing results from ocean in situ iron fertilization
studies. Global Biogeochem. Cycles, 20, Gb2017.
Aumont, O., E. Maier-Reimer, S. Blain, and P. Monfray, 2003: An ecosystem model
of the global ocean including Fe, Si, P colimitations. Global Biogeochem. Cycles,
17, 1060.
Austin, J., and R. J. Wilson, 2006: Ensemble simulations of the decline and recovery
of stratospheric ozone. J. Geophys. Res. Atmos., 111, D16314.
Axelsson, P., M. Tjernström, S. Söderberg, and G. Svensson, 2011: An ensemble of
Arctic simulations of the AOE-2001 field experiment. Atmosphere, 2, 146–170.
Baehr, J., S. Cunnningham, H. Haak, P. Heimbach, T. Kanzow, and J. Marotzke, 2009:
Observed and simulated estimates of the meridional overturning circulation at
26.5 N in the Atlantic. Ocean Sci., 5, 575–589.
Balan Sarojini, B., et al., 2011: High frequency variability of the Atlantic meridional
overturning circulation. Ocean Science, 7, 471–486.
Baldwin, M. P., et al., 2001: The quasi-biennial oscillation. Rev. Geophys., 39, 179–
229.
Balsamo, G., P. Viterbo, A. Beljaars, B. van den Hurk, M. Hirschi, A. K. Betts, and K.
Scipal, 2009: A revised hydrology for the ECMWF Model: Verification from field
site to terrestrial water storage and impact in the Integrated Forecast System. J.
Hydrometeorol., 10, 623–643.
829
Evaluation of Climate Models Chapter 9
9
Bao, Q., G. Wu, Y. Liu, J. Yang, Z. Wang, and T. Zhou, 2010: An introduction to the
coupled model FGOALS1.1-s and its performance in East Asia. Adv. Atmos. Sci.,
27, 1131–1142.
Bao, Q., et al., 2013: The Flexible Global Ocean-Atmosphere-Land System model
Version: FGOALS-s2. Adv. Atmos. Sci., doi:10.1007/s00376-012-2113-9.
Bao, Y., F. L. Qiao, and Z. Y. Song, 2012: Historical simulation and twenty-first century
prediction of oceanic CO
2
sink and pH change. Acta Ocean. Sin., 31, 87–97.
Barkstrom, B. R., 1984: The Earth Radiation Budget Experiment (ERBE). Bull. Am.
Meteorol. Soc., 65, 1170–1185.
Barnes, E. A., and D. L. Hartmann, 2010: Influence of eddy-driven jet latitude on
North Atlantic jet persistence and blocking frequency in CMIP3 integrations.
Geophys. Res. Lett., 37, L23802.
Barnes, E. A., J. Slingo, and T. Woollings, 2012: A methodology for the comparison of
blocking climatologies across indices, models and climate scenarios. Clim. Dyn.,
38, 2467–2481.
Barnier, B., et al., 2006: Impact of partial steps and momentum advection schemes
in a global ocean circulation model at eddy-permitting resolution. Ocean Dyn.,
56, 543–567.
Barriopedro, D., R. Garcia-Herrera, and R. M. Trigo, 2010a: Application of blocking
diagnosis methods to General Circulation Models. Part I: A novel detection
scheme. Clim. Dyn., 35, 1373–1391.
Barriopedro, D., R. Garcia-Herrera, J. F. Gonzalez-Rouco, and R. M. Trigo, 2010b:
Application of blocking diagnosis methods to General Circulation Models. Part
II: Model simulations. Clim. Dyn., 35, 1393–1409.
Bartlein, P. J., et al., 2010: Pollen-based continental climate reconstructions at 6 and
21 ka: A global synthesis. Clim. Dyn., 37, 775–802.
Bathiany, S., M. Claussen, V. Brovkin, T. Raddatz, and V. Gayler, 2010: Combined
biogeophysical and biogeochemical effects of large-scale forest cover changes
in the MPI earth system model. Biogeosciences, 7, 1383–1399.
Bauer, H. S., V. Wulfmeyer, and L. Bengtsson, 2008a: The representation of a synoptic-
scale weather system in a thermodynamically adjusted version of the ECHAM4
general circulation model. Meteorol. Atmos. Phys., 99, 129–153.
Bauer, S. E., D. Koch, N. Unger, S. M. Metzger, D. T. Shindell, and D. G. Streets, 2007:
Nitrate aerosols today and in 2030: A global simulation including aerosols and
tropospheric ozone. Atmos. Chem. Phys., 7, 5043–5059.
Bauer, S. E., et al., 2008b: MATRIX (Multiconfiguration Aerosol TRacker of mIXing
state): An aerosol microphysical module for global atmospheric models. Atmos.
Chem. Phys., 8, 6003–6035.
Beare, R., et al., 2006: An intercomparison of large-eddy simulations of the Stable
Boundary Layer. Boundary-Layer Meteorol., 118, 247–272.
Bellassen, V., G. Le Maire, J. F. Dhote, P. Ciais, and N. Viovy, 2010: Modelling forest
management within a global vegetation model Part 1: Model structure and
general behaviour. Ecol. Model., 221, 2458–2474.
Bellassen, V., G. le Maire, O. Guin, J. F. Dhote, P. Ciais, and N. Viovy, 2011: Modelling
forest management within a global vegetation model-Part 2: Model validation
from a tree to a continental scale. Ecol. Model., 222, 57–75.
Bellouin, N., J. Rae, A. Jones, C. Johnson, J. Haywood, and O. Boucher, 2011: Aerosol
forcing in the Climate Model Intercomparison Project (CMIP5) simulations by
HadGEM2–ES and the role of ammonium nitrate. J. Geophys. Res., 116, 1–25.
Bellucci, A., S. Gualdi, and A. Navarra, 2010: The Double-ITCZ Syndrome in Coupled
General Circulation Models: The role of large-scale vertical circulation regimes.
J. Clim., 23, 1127–1145.
Bender, M. A., T. R. Knutson, R. E. Tuleya, J. J. Sirutis, G. A. Vecchi, S. T. Garner, and I.
M. Held, 2010: Modeled impact of anthropogenic warming on the frequency of
intense Atlantic hurricanes. Science, 327, 454–458.
Bengtsson, L., and K. Hodges, 2011: On the evaluation of temperature trends in the
tropical troposphere. Clim. Dyn., 36, 419–430.
Bengtsson, L., K. I. Hodges, and N. Keenlyside, 2009: Will extratropical storms
intensify in a warmer climate? J. Clim., 22, 2276–2301.
Berckmans, J., T. Woollings, M.-E. Demory, P.-L. Vidal, and M. Roberts, 2013:
Atmospheric blocking in a high resolution climate model: Influences of mean
state, orography and eddy forcing. Atmos. Sci. Lett., 14, 34–40.
Bergengren, J., D. Waliser, and Y. Yung, 2011: Ecological sensitivity: A biospheric view
of climate change. Clim. Change, 107, 433–457.
Bergengren, J., S. Thompson, D. Pollard, and R. DeConto, 2001: Modeling global
climate-vegetation interactions in a doubled CO
2
world. Clim. Change, 50,
31–75.
Bernie, D. J., E. Guilyardi, G. Madec, J. M. Slingo, S. Woolnough, and J. Cole, 2008:
Impact of resolving the diurnal cycle in an ocean-atmosphere GCM. Part 2: A
diurnally coupled CGCM. Clim. Dyn., 31, 909–925.
Bi, D., et al., 2013a: ACCESS-OM: The Ocean and Sea ice Core of the ACCESS Coupled
Model. Aust. Meteorol. Oceanogr. J., 63, 213–232.
Bi, D., et al., 2013b: The ACCESS Coupled Model: Description, control climate and
evaluation. Aust. Meteorol. Oceanogr. J., 63, 41–64.
Bitz, C. M., and W. H. Lipscomb, 1999: An energy-conserving thermodynamic sea ice
model for climate study. J. Geophys. Res.. Oceans, 104, 15669–15677.
Blyth, E., J. Gash, A. Lloyd, M. Pryor, G. P. Weedon, and J. Shuttleworth, 2010:
Evaluating the JULES Land Surface Model Energy Fluxes Using FLUXNET Data. J.
Hydrometeorol., 11, 509–519.
Boberg, F., and J. H. Christensen, 2012: Overestimation of Mediterranean summer
temperature projections due to model deficiencies. Nature Clim. Change, 2,
433–436.
Boccaletti, G., R. Ferrari, and B. Fox-Kemper, 2007: Mixed layer instabilities and
restratification. J. Phys. Oceanogr., 37, 2228–2250.
Bodas-Salcedo, A., K. D. Williams, P. R. Field, and A. P. Lock, 2012: The surface
downwelling solar radiation surplus over the Southern Ocean in the Met Office
Model: The role of midlatitude cyclone clouds. J. Clim., 25, 7467–7486.
Bodas-Salcedo, A., M. Webb, M. Brooks, M. Ringer, K. Williams, S. Milton, and D.
Wilson, 2008: Evaluating cloud systems in the Met Office global forecast model
using simulated CloudSat radar reflectivities. J. Geophys. Res. Atmos., 113,
D00A13.
Bodas-Salcedo, A., et al., 2011: COSP: Satellite simulation software for model
assessment. Bull. Am. Meteorol. Soc., 92, 1023–1043.
Bodeker, G., H. Shiona, and H. Eskes, 2005: Indicators of Antarctic ozone depletion.
Atmos. Chem. Phys., 5, 2603–2615.
Boe, J., A. Hall, and X. Qu, 2009a: Deep ocean heat uptake as a major source of
spread in transient climate change simulations. Geophys. Res. Lett., 36, L22701.
Boe, J., L. Terray, F. Habets, and E. Martin, 2007: Statistical and dynamical downscaling
of the Seine basin climate for hydro-meteorological studies. Int. J. Climatol. , 27,
1643–1655.
Boe, J. L., A. Hall, and X. Qu, 2009b: September sea-ice cover in the Arctic Ocean
projected to vanish by 2100. Nature Geosci., 2, 341–343.
Boer, G., and S. Lambert, 2008: The energy cycle in atmospheric models. Clim. Dyn.,
30, 371–390.
Boer, G. J., and B. Yu, 2003: Climate sensitivity and climate state. Clim. Dyn., 21,
167–176.
Boisier, J.-P., et al., 2012: Attributing the biogeophysical impacts of land-use induced
Land-Cover Changes on surface climate to specific causes. Results from the first
LUCID set of simulations. J. Geophys. Res., 117, D12116.
Bollasina, M. A., and Y. Ming, 2013: The general circulation model precipitation
bias over the southwestern equatorial Indian Ocean and its implications for
simulating the South Asian monsoon. Clim. Dyn., 40, 823–838.
Bonan, G. B., 2008: Forests and climate change: Forcings, feedbacks, and the climate
benefits of forests. Science, 320, 1444–1449.
Bond, T. C., et al., 2007: Historical emissions of black and organic carbon aerosol
from energy-related combustion, 1850–2000. Global Biogeochem. Cycles, 21,
GB2018.
Bondeau, A., P. C. Smith, S. Zaehle, S. Schaphoff, W. Lucht, W. Cramer, and D. Gerten,
2007: Modelling the role of agriculture for the 20th century global terrestrial
carbon balance. Global Change Biol., 13, 679–706.
Boning, C. W., A. Dispert, M. Visbeck, S. R. Rintoul, and F. U. Schwarzkopf, 2008: The
response of the Antarctic Circumpolar Current to recent climate change. Nature
Geosci., 1, 864–869.
Boone, A., et al., 2009: THE AMMA Land Surface Model Intercomparison Project
(ALMIP). Bull. Am. Meteorol. Soc., 90, 1865–1880.
Boone, A. A., I. Poccard-Leclercq, Y. K. Xue, J. M. Feng, and P. de Rosnay, 2010:
Evaluation of the WAMME model surface fluxes using results from the AMMA
land-surface model intercomparison project. Clim. Dyn., 35, 127–142.
Booth, B. B. B., N. J. Dunstone, P. R. Halloran, T. Andrews, and N. Bellouin, 2012a:
Aerosols implicated as a prime driver of twentieth-century North Atlantic
climate variability. Nature, 484, 228–232.
Booth, B. B. B., et al., 2012b: High sensitivity of future global warming to land carbon
cycle processes. Environ. Res. Lett., 7, 024002.
Boschat, G., P. Terray, and S. Masson, 2012: Robustness of SST teleconnections and
precursory patterns associated with the Indian summer monsoon. Clim. Dyn.,
38, 2143–2165.
830
Chapter 9 Evaluation of Climate Models
9
Boyle, J., and S. A. Klein, 2010: Impact of horizontal resolution on climate model
forecasts of tropical precipitation and diabatic heating for the TWP-ICE period.
J. Geophys. Res., 115, D23113.
Boyle, J., S. Klein, G. Zhang, S. Xie, and X. Wei, 2008: Climate Model Forecast
Experiments for TOGA COARE. Mon. Weather Rev., 136, 808–832.
Bracegirdle, T., et al., 2013: Assessment of surface winds over the Atlantic, Indian
and Pacific Ocean sectors of the Southern Hemisphere in CMIP5 models:
Historical bias, forcing response, and state dependence. J. Geophys. Res. Atmos.,
doi:10.1002/jgrd.50153.
Bracegirdle, T. J., and D. B. Stephenson, 2012: Higher precision estimates of regional
polar warming by ensemble regression of climate model projections. Clim. Dyn.,
39, 2805–2821.
Bracegirdle, T. J., and D. B. Stephenson, 2013: On the robustness of emergent
constraints used in multi-model climate change projections of Arctic warming.
J. Clim., 26, 669–678.
Braconnot, P., F. Hourdin, S. Bony, J. Dufresne, J. Grandpeix, and O. Marti, 2007a:
Impact of different convective cloud schemes on the simulation of the tropical
seasonal cycle in a coupled ocean-atmosphere model. Clim. Dyn., 29, 501–520.
Braconnot, P., et al., 2012: Evaluation of climate models using palaeoclimatic data.
Nature Clim. Change, 2, 417–424.
Braconnot, P., et al., 2007b: Results of PMIP2 coupled simulations of the Mid-
Holocene and Last Glacial Maximum - Part 2: Feedbacks with emphasis on the
location of the ITCZ and mid- and high latitudes heat budget. Clim. Past, 3,
279–296.
Braconnot, P., et al., 2007c: Results of PMIP2 coupled simulations of the Mid-
Holocene and Last Glacial Maximum - Part 1: Experiments and large-scale
features. Clim. Past, 3, 261–277.
Brands, S., J. Taboada, A. Cofino, T. Sauter, and C. Schneider, 2011: Statistical
downscaling of daily temperatures in the NW Iberian Peninsula from global
climate models: Validation and future scenarios. Clim. Res., 48, 163–176.
Bresson, R., and R. Laprise, 2011: Scale-decomposed atmospheric water budget over
North America as simulated by the Canadian Regional Climate Model for current
and future climates. Clim. Dyn., 36, 365–384.
Breugem, W. P., W. Hazeleger, and R. J. Haarsma, 2006: Multimodel study of tropical
Atlantic variability and change. Geophys. Res. Lett., 33, L23706.
Brewer, S., J. Guiot, and F. Torre, 2007: Mid-Holocene climate change in Europe: A
data-model comparison. Clim. Past, 3, 499–512.
Briegleb, B. P., and B. Light, 2007: A Delta-Eddington multiple scattering
parameterization for solar radiation in the sea ice component of the Community
Climate System Model. NCAR Technical Note, National Center for Atmospheric
Research, 100 pp.
Briegleb, B. P., C. M. Blitz, E. C. Hunke, W. H. Lipscomb, M. M. Holland, J. L. Schramm,
and R. E. Moritz, 2004: Scientific description of the sea ice component in the
Community Climate System Model, Version 3. NCAR Technical Note, National
Center for Atmospheric Research, 70 pp.
Brient, F., and S. Bony, 2012: Interpretation of the positive low-cloud feedback
predicted by a climate model under global warming. Clim. Dyn., doi:10.1007/
s00382–011–1279–7.
Brierley, C. M., M. Collins, and A. J. Thorpe, 2010: The impact of perturbations to
ocean-model parameters on climate and climate change in a coupled model.
Clim. Dyn., 34, 325–343.
Brogniez, H., and R. T. Pierrehumbert, 2007: Intercomparison of tropical tropospheric
humidity in GCMs with AMSU-B water vapor data. Geophys. Res. Lett., 34,
L17812
Brogniez, H., R. Roca, and L. Picon, 2005: Evaluation of the distribution of subtropical
free tropospheric humidity in AMIP-2 simulations using METEOSAT water vapor
channel data. Geophys. Res. Lett., 32, L19708.
Brovkin, V., J. Bendtsen, M. Claussen, A. Ganopolski, C. Kubatzki, V. Petoukhov, and A.
Andreev, 2002: Carbon cycle, vegetation, and climate dynamics in the Holocene:
Experiments with the CLIMBER-2 model. Global Biogeochem. Cycles, 16, 1139.
Brown, A., S. Milton, M. Cullen, B. Golding, J. Mitchell, and A. Shelly, 2012: Unified
modeling and prediction of weather and climate: A 25-year journey. Bull. Am.
Meteorol. Soc., 93, 1865–1877.
Brown, J., A. Fedorov, and E. Guilyardi, 2010a: How well do coupled models replicate
ocean energetics relevant to ENSO? Clim. Dyn., 36, 2147–2158.
Brown, J., O. J. Ferrians, J. A. Heginbottom, and E. S. E.S. Melnikov, 1997: International
Permafrost Association Circum-Arctic Map of Permafrost and Ground Ice
Conditions. Geological Survey (U.S.), Denver, CO, USA.
Brown, J., O. J. Ferrians, J. A. Heginbottom, and E. S. E.S. Melnikov, 1998: Digital
circum-arctic map of permafrost and ground ice conditions. In: Circumpolar
Active-Layer Permafrost System (CAPS). CD-ROM. 1.0 ed., University of Colorado
at Boulder National Snow and Ice Data Center. Boulder, CO, USA.
Brown, J. R., C. Jakob, and J. M. Haynes, 2010b: An evaluation of rainfall frequency
and intensity over the Australian region in a Global Climate Model. J. Clim., 23,
6504–6525.
Brown, J. R., A. F. Moise, and R. A. Colman, 2013: The South Pacific Convergence Zone
in CMIP5 simulations of historical and future climate. Clim. Dyn., doi:10.1007/
s00382-012-1591–x.
Brutel-Vuilmet, C., M. Menegoz, and G. Krinner, 2013: An analysis of present and
future seasonal Northern Hemisphere land snow cover simulated by CMIP5
coupled climate models. Cryosphere, 7, 67–80.
Bryan, F. O., M. W. Hecht, and R. D. Smith, 2007: Resolution convergence and
sensitivity studies with North Atlantic circulation models. Part I: The western
boundary current system. Ocean Model., 16, 141–159.
Bryan, F. O., R. Tomas, J. M. Dennis, D. B. Chelton, N. G. Loeb, and J. L. McClean, 2010:
Frontal scale air-sea interaction in high-resolution coupled climate models. J.
Clim., 23, 6277–6291.
Bryan, K., and L. J. Lewis, 1979: Water mass model of the world ocean. J. Geophys.
Res. Oceans Atmos., 84, 2503–2517.
Bryden, H. L., H. R. Longworth, and S. A. Cunningham, 2005: Slowing of the Atlantic
meridional overturning circulation at 25° N. Nature, 438, 655–657.
Buehler, T., C. C. Raible, and T. F. Stocker, 2011: The relationship of winter season
North Atlantic blocking frequencies to extreme cold or dry spells in the ERA-40.
Tellus A, 63, 212–222.
Butchart, N., A. A. Scaife, J. Austin, S. H. E. Hare, and J. R. Knight, 2003: Quasi-biennial
oscillation in ozone in a coupled chemistry-climate model. J. Geophys. Res., 108,
4486.
Cadule, P., et al., 2010: Benchmarking coupled climate-carbon models against long-
term atmospheric CO
2
measurements. Global Biogeochem. Cycles, 24, Gb2016.
Cai, W., and T. Cowan, 2013: Why is the amplitude of the Indian Ocean Dipole overly
large in CMIP3 and CMIP5 climate models? . Geophys. Res. Lett., doi:10.1002/
grl.50208.
Cai, W., A. Sullivan, and T. Cowan, 2011: Interactions of ENSO, the IOD, and the SAM
in CMIP3 Models. J. Clim., 24, 1688–1704.
Cai, W. J., A. Sullivan, and T. Cowan, 2009: Rainfall teleconnections with Indo-Pacific
variability in the WCRP CMIP3 models. J. Clim., 22, 5046–5071.
Calov, R., A. Ganopolski, V. Petoukhov, M. Claussen, and R. Greve, 2002: Large-scale
instabilities of the Laurentide ice sheet simulated in a fully coupled climate-
system model. Geophys. Res. Lett., 29, 2216.
Cameron-Smith, P., J. F. Lamarque, P. Connell, C. Chuang, and F. Vitt, 2006: Toward an
Earth system model: Atmospheric chemistry, coupling, and petascale computing.
Scidac 2006: Scientific Discovery through Advanced Computing [W. M. Tang
(ed.)]. Journal of Physics: Conference Series, Vol. 46, Denver, Colorado, USA.
Capotondi, A., A. Wittenberg, and S. Masina, 2006: Spatial and temporal structure
of Tropical Pacific interannual variability in 20th century coupled simulations.
Ocean Model., 15, 274–298.
Capotondi, A., M. A. Alexander, N. A. Bond, E. N. Curchitser, and J. D. Scott, 2012:
Enhanced upper ocean stratification with climate change in the CMIP3 models.
J. Geophys. Res. Oceans 117, C04031.
Cariolle, D., and H. Teyssedre, 2007: A revised linear ozone photochemistry
parameterization for use in transport and general circulation models: Multi-
annual simulations. Atmos. Chem. Phys., 7, 2183–2196.
Carslaw, K. S., O. Boucher, D. V. Spracklen, G. W. Mann, J. G. L. Rae, S. Woodward, and
M. Kulmala, 2010: A review of natural aerosol interactions and feedbacks within
the Earth system. Atmos. Chem. Phys., 10, 1701–1737.
Casado, M. J., and M. A. Pastor, 2012: Use of variability modes to evaluate AR4
climate models over the Euro-Atlantic region. Clim. Dyn., 38, 225–237.
Cattiaux, J., H. Douville, and Y. Peings, 2013: European temperatures in CMIP5:
Origins of present-day biases and future uncertainties. Clim. Dyn., doi:10.1007/
s00382-013-1731-y.
Catto, J., N. Nicholls, and C. Jakob, 2012a: North Australian sea surface temperatures
and the El Niño Southern Oscillation in observations and models. J. Clim., 25,
5011–5029.
Catto, J., N. Nicholls, and C. Jakob, 2012b: North Australian sea surface temperatures
and the El Niño Southern Oscillation in the CMIP5 models. J. Clim., 25, 6375–
6382.
831
Evaluation of Climate Models Chapter 9
9
Catto, J. L., L. C. Shaffrey, and K. I. Hodges, 2010: Can climate models capture the
structure of extratropical cyclones? J. Clim., 23, 1621–1635.
Catto, J. L., L. C. Shaffrey, and K. I. Hodges, 2011: Northern Hemisphere Extratropical
cyclones in a warming climate in the HiGEM high-resolution climate Model. J.
Clim., 24, 5336–5352.
Catto, J. L., C. Jakob, and N. Nicholls, 2013: A global evaluation of fronts and
precipitation in the ACCESS model. Aust. Meteorol. Oceanogr. J., 63,191-203.
Cavalieri, D. J., and C. L. Parkinson, 2012: Arctic sea ice variability and trends, 1979–
2010. Cryosphere, 6, 881–889.
Cavicchia, L., and H. von Storch, 2011: The simulation of medicanes in a high-
resolution regional climate model. Clim. Dyn., 39 2273–2290.
Cesana, G., and H. Chepfer, 2012: How well do climate models simulate cloud vertical
structure? A comparison between CALIPSO-GOCCP satellite observations and
CMIP5 models. Geophys. Res. Lett., 39, L20803.
Cha, D., D. Lee, and S. Hong, 2008: Impact of boundary layer processes on seasonal
simulation of the East Asian summer monsoon using a Regional Climate Model.
Meteorol. Atmos. Phys., 100, 53–72.
Champion, A. J., K. I. Hodges, L. O. Bengtsson, N. S. Keenlyside, and M. Esch, 2011:
Impact of increasing resolution and a warmer climate on extreme weather from
Northern Hemisphere extratropical cyclones. Tellus A, 63, 893–890.
Chan, S. C., E. J. Kendon, H. J. Fowler, S. Blenkinsop, C. A. T. Ferro, and D. B. Stephenson,
2012: Does increasing resolution improve the simulation of United Kingdom
daily precipitation in a regional climate model? Clim. Dyn., doi:10.1007/s00382-
012-1568-9.
Chang, C. P., and T. Li, 2000: A theory for the tropical tropospheric biennial oscillation.
J. Atmos. Sci., 57, 2209–2224.
Chang, C. Y., S. Nigam, and J. A. Carton, 2008: Origin of the springtime westerly
bias in equatorial Atlantic surface winds in the Community Atmosphere Model
version 3 (CAM3) simulation. J. Clim., 21, 4766–4778.
Chang, C. Y., J. A. Carton, S. A. Grodsky, and S. Nigam, 2007: Seasonal climate of the
tropical Atlantic sector in the NCAR community climate system model 3: Error
structure and probable causes of errors. J. Clim., 20, 1053–1070.
Chang, E. K. M., Y. Guo, and X. Xia, 2012: CMIP5 multi-model ensemble projection
of storm track change under global warming. J. Geophys. Res., 117, D23118.
Charbit, S., D. Paillard, and G. Ramstein, 2008: Amount of CO
2
emissions irreversibly
leading to the total melting of Greenland. Geophys. Res. Lett., 35, L12503.
Charlton-Perez, A. J., et al., 2012: Mean climate and variability of the stratosphere in
CMIP5 models. J. Geophys. Res., doi:10.1002/jgrd.50125.
Chen, C. T., and T. Knutson, 2008: On the verification and comparison of extreme
rainfall indices from climate models. J. Clim., 21, 1605–1621.
Chen, H. M., T. J. Zhou, R. B. Neale, X. Q. Wu, and G. J. Zhang, 2010: Performance of
the New NCAR CAM3.5 in East Asian summer monsoon simulations: Sensitivity
to modifications of the Convection Scheme. J. Clim., 23, 3657–3675.
Chen, L., Y. Yu, and D. Sun, 2013: Cloud and water vapor feedbacks to the El Niño
warming: Are they still biased in CMIP5 models? J. Clim., doi:10.1175/JCLI-D-
12-00575.1.
Chen, Y. H., and A. D. Del Genio, 2009: Evaluation of tropical cloud regimes in
observations and a general circulation model. Clim. Dyn., 32, 355–369.
Chiang, J. C. H., and A. H. Sobel, 2002: Tropical tropospheric temperature variations
caused by ENSO and their influence on the remote tropical climate. J. Clim., 15,
2616–2631.
Chiang, J. C. H., and D. J. Vimont, 2004: Analogous Pacific and Atlantic meridional
modes of tropical atmosphere-ocean variability. J. Clim., 17, 4143–4158.
Chou, C., and J. Y. Tu, 2008: Hemispherical asymmetry of tropical precipitation in
ECHAM5/MPI-OM during El Niño and under global warming. J. Clim., 21, 1309–
1332.
Chou, C., J. D. Neelin, J. Y. Tu, and C. T. Chen, 2006: Regional tropical precipitation
change mechanisms in ECHAM4/OPYC3 under global warming. J. Clim., 19,
4207–4223.
Chou, S., et al., 2012: Downscaling of South America present climate driven by
4-member HadCM3 runs. Clim. Dyn., 38, 635–653.
Christensen, J., F. Boberg, O. Christensen, and P. Lucas-Picher, 2008: On the need
for bias correction of regional climate change projections of temperature and
precipitation. Geophys. Res. Lett., 35, L20709.
Christensen, J., E. Kjellstrom, F. Giorgi, G. Lenderink, and M. Rummukainen, 2010:
Weight assignment in regional climate models. Clim. Res., 44, 179–194.
Christensen, J. H., and F. Boberg, 2013: Temperature dependent climate projection
deficiencies in CMIP5 models. Geophys. Res. Lett., 39, L24705.
Christian, J. R., et al., 2010: The global carbon cycle in the Canadian Earth system
model (CanESM1): Preindustrial control simulation. J. Geophys. Res. Biogeosci.,
115, G03014
Christidis, N., P. A. Stott, and S. J. Brown, 2011: The role of human activity in the recent
warming of extremely warm daytime temperatures. J. Clim., 24, 1922–1930.
Christy, J. R., W. B. Norris, R. W. Spencer, and J. J. Hnilo, 2007: Tropospheric temperature
change since 1979 from tropical radiosonde and satellite measurements. J.
Geophys. Res. Atmos., 112, D06102.
Christy, J. R., et al., 2010: What do observational datasets say about modeled
tropospheric temperature trends since 1979? Remote Sens., 2, 2148–2169.
Cimatoribus, A. A., S. S. Drijfhout, and H. A. Dijkstra, 2012: A global hybrid coupled
model based on atmosphere-SST feedbacks. Clim. Dyn., 38, 745–760.
Cionni, I., et al., 2011: Ozone database in support of CMIP5 simulations: Results and
corresponding radiative forcing. Atmos. Chem. Phys., 11, 11267–11292.
Clark, D. B., et al., 2011: The Joint UK Land Environment Simulator (JULES), model
description - Part 2: Carbon fluxes and vegetation dynamics. Geosci. Model Dev.,
4, 701–722.
Claussen, M., et al., 2002: Earth system models of intermediate complexity: Closing
the gap in the spectrum of climate system models. Clim. Dyn., 18, 579–586.
Coelho, C. A. S., and L. Goddard, 2009: El Niño-induced tropical droughts in climate
change projections. J. Clim., 22, 6456–6476.
Cohen, J. L., J. C. Furtado, M. Barlow, V. A. Alexeev, and J. E. Cherry, 2012: Asymmetric
seasonal temperature trends. Geophys. Res. Lett., 39, L04705.
Collatz, G. J., M. Ribas-Carbo, and J. A. Berry, 1992: Coupled photosynthesis-stomatal
conductance model for leaves of C4 Plants. Aust. J. Plant Physiol., 19, 519–538.
Collatz, G. J., J. T. Ball, C. Grivet, and J. A. Berry, 1991: Physiological and environmental
regulation of stomatal conductance, photosynthesis and transpiration: A model
that includes a laminar boundary layer. Agr. Forest Meteorol., 54, 107–136.
Colle, B. A., Z. Zhang, K. A. Lombardo, E. Chang, P. Liu, and M. Zhang, 2013: Historical
evaluation and future prediction of eastern North America and western Atlantic
extratropical cyclones in the CMIP5 models during the cool season. J. Clim.,
doi:10.1175/JCLI-D-12–00498.1.
Collins, M., S. Tett, and C. Cooper, 2001: The internal climate variability of HadCM3,
a version of the Hadley Centre coupled model without flux adjustments. Clim.
Dyn., 17, 61–81.
Collins, M., C. M. Brierley, M. MacVean, B. B. B. Booth, and G. R. Harris, 2007: The
sensitivity of the rate of transient climate change to ocean physics perturbations.
J. Clim., 20, 2315–2320.
Collins, M., B. B. B. Booth, G. R. Harris, J. M. Murphy, D. M. H. Sexton, and M. J. Webb,
2006a: Towards quantifying uncertainty in transient climate change. Clim. Dyn.,
27, 127–147.
Collins, M., R. Chandler, P. Cox, J. Huthnance, J. Rougier, and D. Stephenson, 2012:
Quantifying future climate change. Nature Clim. Change, 2, 403–409.
Collins, M., B. Booth, B. Bhaskaran, G. Harris, J. Murphy, D. Sexton, and M. Webb,
2010: Climate model errors, feedbacks and forcings: A comparison of perturbed
physics and multi-model ensembles. Clim. Dyn., 36, 1737–1766.
Collins, W. D., J. M. Lee-Taylor, D. P. Edwards, and G. L. Francis, 2006b: Effects of
increased near-infrared absorption by water vapor on the climate system. J.
Geophys. Res. Atmos., 111, D18109.
Collins, W. D., et al., 2006c: The formulation and atmospheric simulation of the
Community Atmosphere Model version 3 (CAM3). J. Clim., 19, 2144–2161.
Collins, W. D., et al., 2006d: The Community Climate System Model version 3
(CCSM3). J. Clim., 19, 2122–2143.
Collins, W. J., et al., 2011: Development and evaluation of an Earth-System model-
HadGEM2. Geosci. Model Dev., 4, 1051–1075.
Colman, R., and B. McAvaney, 2009: Climate feedbacks under a very broad range of
forcing. Geophys. Res. Lett., 36, L01702.
Colman, R. A., A. F. Moise, and L. I. Hanson, 2011: Tropical Australian climate and the
Australian monsoon as simulated by 23 CMIP3 models. J. Geophys. Res. Atmos.,
116, D10116.
Comiso, J. C., and F. Nishio, 2008: Trends in the sea ice cover using enhanced and
compatible AMSR-E, SSM/I, and SMMR data. J. Geophys. Res. Oceans, 113,
C02s07.
Compo, G. P., and P. D. Sardeshmukh, 2009: Oceanic influences on recent continental
warming. Clim. Dyn., 32, 333–342.
Connolley, W., and T. Bracegirdle, 2007: An Antarctic assessment of IPCC AR4
coupled models. Geophys. Res. Lett., 34 L22505.
832
Chapter 9 Evaluation of Climate Models
9
Coon, M., R. Kwok, G. Levy, M. Pruis, H. Schreyer, and D. Sulsky, 2007: Arctic
Ice Dynamics Joint Experiment (AIDJEX) assumptions revisited and found
inadequate. J. Geophys. Res., 112, C11S90.
Coppola, E., F. Giorgi, S. Rauscher, and C. Piani, 2010: Model weighting based
on mesoscale structures in precipitation and temperature in an ensemble of
regional climate models. Clim. Res., 44 121–134.
Cox, P., 2001: Description of the “TRIFFID” Dynamic GlobalVegetation ModelHadley
Centre, Met Office Hadley Centre, Berks, United Kingdom, 16 pp.
Cox, P. M., R. A. Betts, C. D. Jones, S. A. Spall, and I. J. Totterdell, 2000: Acceleration
of global warming due to carbon-cycle feedbacks in a coupled climate model.
Nature, 408, 184–187.
Cox, P. M., R. A. Betts, C. B. Bunton, R. L. H. Essery, P. R. Rowntree, and J. Smith, 1999:
The impact of new land surface physics on the GCM simulation of climate and
climate sensitivity. Clim. Dyn., 15, 183–203.
Cox, P. M., D. Pearson, B. B. B. Booth, P. Friedlingstein, C. Huntingford, C. D. Jones, and
C. M. Luke, 2013: Sensitivity of tropical carbon to climate change constrained by
carbon dioxide variability. Nature, 494, 341–344.
Cramer, W., et al., 2001: Global response of terrestrial ecosystem structure and
function to CO
2
and climate change: Results from six dynamic global vegetation
models. Global Change Biol., 7, 357–373.
Crétat, J., B. Pohl, Y. Richard, and P. Drobinski, 2012: Uncertainties in simulating
regional climate of Southern Africa: Sensitivity to physical parameterizations
using WRF. Clim. Dyn., 38, 613–634.
Croft, B., U. Lohmann, and K. von Salzen, 2005: Black carbon ageing in the Canadian
Centre for Climate modelling and analysis atmospheric general circulation
model. Atmos. Chem. Phys., 5, 1931–1949.
Crucifix, M., 2006: Does the Last Glacial Maximum constrain climate sensitivity?
Geophys. Res. Lett., 33, L18701.
Cunningham, S., et al., 2010: The present and future system for measuring the
Atlantic meridional overturning circulation and heat transport. In: Proceedings
of OceanObs’09: Sustained Ocean Observations and Information for Society (Vol.
2), Venice, Italy, 21–25 September 2009, ESA Publication.
Cunningham, S. A., S. G. Alderson, B. A. King, and M. A. Brandon, 2003: Transport and
variability of the Antarctic Circumpolar Current in Drake Passage. J. Geophys.
Res.-Oceans, 108, 8084.
Cunningham, S. A., et al., 2007: Temporal variability of the Atlantic meridional
overturning circulation at 26.5°N. Science, 317, 935–938.
Curry, W. B., and D. W. Oppo, 2005: Glacial water mass geometry and the distribution
of delta C-13 of sigma CO
2
in the western Atlantic Ocean. Paleoceanography,
20, Pa1017.
Cuxart, J., et al., 2006: Single-column model intercomparison for a stably stratified
atmospheric boundary layer. Boundary-Layer Meteorol., 118, 273–303.
Dai, A., 2001: Global precipitation and thunderstorm frequencies. Part II: Diurnal
variations. J. Clim., 14, 1112–1128.
Dai, A., 2006: Precipitation characteristics in eighteen coupled climate models. J.
Clim., 19, 4605–4630.
Dai, A., and C. Deser, 1999: Diurnal and semidiurnal variations in global surface wind
and divergence fields. J. Geophys. Res. Atmos., 104, 31109–31125.
Dai, A., and K. E. Trenberth, 2004: The diurnal cycle and its depiction in the Community
Climate System Model. J. Clim., 17, 930–951.
Dai, Y. J., R. E. Dickinson, and Y. P. Wang, 2004: A two-big-leaf model for canopy
temperature, photosynthesis, and stomatal conductance. J. Clim., 17, 2281–
2299.
Dai, Y. J., et al., 2003: The Common Land Model. Bull. Am. Meteorol. Soc., 84, 1013–
1023.
Dallmeyer, A., M. Claussen, and J. Otto, 2010: Contribution of oceanic and vegetation
feedbacks to Holocene climate change in monsoonal Asia. Clim. Past, 6, 195–
218.
Danabasoglu, G., and P. R. Gent, 2009: Equilibrium climate sensitivity: Is it accurate
to use a Slab Ocean Model? J. Clim., 22, 2494–2499.
Danabasoglu, G., R. Ferrari, and J. C. McWilliams, 2008: Sensitivity of an ocean
general circulation model to a parameterization of near-surface eddy fluxes. J.
Clim., 21, 1192–1208.
Danabasoglu, G., W. G. Large, and B. P. Briegleb, 2010: Climate impacts of
parameterized Nordic Sea overflows. J. Geophys. Res. Oceans, 115, C11005.
Danabasoglu, G., W. G. Large, J. J. Tribbia, P. R. Gent, B. P. Briegleb, and J. C.
McWilliams, 2006: Diurnal coupling in the tropical oceans of CCSM3. J. Clim.,
19, 2347–2365.
Danabasoglu, G., et al., 2012: The CCSM4 Ocean Component. J. Clim., 25, 1361–
1389.
Davies, T., M. J. P. Cullen, A. J. Malcolm, M. H. Mawson, A. Staniforth, A. A. White, and
N. Wood, 2005: A new dynamical core for the Met Office’s global and regional
modelling of the atmosphere. Q. J. R. Meteorol. Soc., 131, 1759–1782.
Davis, B. A. S., and S. Brewer, 2009: Orbital forcing and role of the latitudinal
insolation/temperature gradient. Clim. Dyn., 32, 143–165.
Dawson, A., T. N. Palmer, and S. Corti, 2012: Simulating regime structures in weather
and climate prediction models. Geophys. Res. Lett., 39, L21805.
Day, J. J., J. C. Hargreaves, J. D. Annan, and A. Abe-Ouchi, 2012: Sources of multi-
decadal variability in Arctic sea ice extent. Environ. Res. Lett., 7, 034011.
de Elia, R., and H. Cote, 2010: Climate and climate change sensitivity to model
configuration in the Canadian RCM over North America. Meteorol. Z., 19, 325–
339.
de Elia, R., S. Biner, and A. Frigon, 2013: Interannual variability and expected regional
climate change over North America. Clim. Dyn., doi:10.1007/s00382-013-1717-
9.
de Jong, M. F., S. S. Drijfhout, W. Hazeleger, H. M. van Aken, and C. A. Severijns,
2009: Simulations of hydrographic properties in the Northwestern North Atlantic
Ocean in Coupled Climate Models. J. Clim., 22, 1767–1786.
de Noblet-Ducoudre, N., et al., 2012: Determining robust impacts of land-use
induced land-cover changes on surface climate over North America and Eurasia;
Results from the first set of LUCID experiments. J. Clim., 25, 3261–3281.
De Szoeke, S. P., and S. P. Xie, 2008: The tropical eastern Pacific seasonal cycle:
Assessment of errors and mechanisms in IPCC AR4 coupled ocean - atmosphere
general circulation models. J. Clim., 21, 2573–2590.
Dee, D. P., et al., 2011: The ERA-Interim reanalysis: Configuration and performance of
the data assimilation system. Q. J. R. Meteorol. Soc., 137, 553–597.
DelSole, T., and J. Shukla, 2009: Artificial skill due to predictor screening. J. Clim.,
22, 331–345.
Delworth, T. L., et al., 2012: Simulated climate and climate change in the GFDL
CM2.5 High-Resolution Coupled Climate Model. J. Clim., 25, 2755–2781.
Delworth, T. L., et al., 2006: GFDL’s CM2 global coupled climate models. Part I:
Formulation and simulation characteristics. J. Clim., 19, 643–674.
Déqué, M., 2007: Frequency of precipitation and temperature extremes over France
in an anthropogenic scenario: Model results and statistical correction according
to observed values. Global Planet. Change, 57, 16–26.
Déqué, M., 2010: Regional climate simulation with a mosaic of RCMs. Meteorol. Z.,
19, 259–266.
Déqué, M., C. Dreveton, A. Braun, and D. Cariolle, 1994: The ARPEGE/IFS atmosphere
model: A contribution to the French community climate modelling. Clim. Dyn.,
10, 249–266.
Déqué, M., S. Somot, E. Sanchez-Gomez, C. Goodess, D. Jacob, G. Lenderink, and
O. Christensen, 2012: The spread amongst ENSEMBLES regional scenarios:
Regional climate models, driving general circulation models and interannual
variability. Clim. Dyn., 38, 951–964.
Derksen, C., and R. Brown, 2012: Spring snow cover extent reductions in the
2008–2012 period exceeding climate model projections. Geophys. Res. Lett., 39,
L19504.
Deser, C., A. S. Phillips, and J. W. Hurrell, 2004: Pacific interdecadal climate variability:
Linkages between the tropics and the North Pacific during boreal winter since
1900. J. Clim., 17, 3109–3124.
Deser, C., A. S. Phillips, V. Bourdette, and H. Teng, 2011: Uncertainty in climate change
projections: The role of internal variability. Clim. Dyn., 38, 527–546.
Deser, C., et al., 2012: ENSO and Pacific decadal variability in Community Climate
System Model Version 4. J. Clim., 25, 2622–2651.
Deushi, M., and K. Shibata, 2011: Development of a Meteorological Research
Institute Chemistry-Climate Model version 2 for the Study of Tropospheric and
Stratospheric Chemistry. Papers Meteorol. Geophys., 62, 1–46.
Di Luca, A., R. Elía, and R. Laprise, 2012: Potential for small scale added value of
RCM’s downscaled climate change signal. Clim. Dyn., 40, 601–618.
Diaconescu, E. P., and R. Laprise, 2013: Can added value be expected in RCM-
simulated large scales? Clim. Dyn., doi:10.1007/s00382-012-1649-9.
Diffenbaugh, N., M. Ashfaq, and M. Scherer, 2011: Transient regional climate
change: Analysis of the summer climate response in a high-resolution, century-
scale ensemble experiment over the continental United States. J. Geophys. Res.
Atmos., 116, D24111.
DiNezio, P. N., A. C. Clement, G. A. Vecchi, B. J. Soden, and B. P. Kirtman, 2009: Climate
response of the equatorial Pacific to global warming. J. Clim., 22, 4873–4892.
833
Evaluation of Climate Models Chapter 9
9
Dix, M., et al., 2013: The ACCESS Coupled Model: Documentation of core CMIP5
simulations and initial results. Aust. Meteorol. Oceanogr. J., 63, 83–99.
Doblas-Reyes, F. J., et al., 2013: Initialized near-term regional climate change
prediction. Nature Commun., 4, 1715.
Dokken, T. M., and E. Jansen, 1999: Rapid changes in the mechanism of ocean
convection during the last glacial period. Nature, 401, 458–461.
Domingues, C., J. Church, N. White, P. Gleckler, S. Wijffels, P. Barker, and J. Dunn, 2008:
Improved estimates of upper-ocean warming and multi-decadal sea-level rise.
Nature, 453, 1090–1093.
Donat, M., G. Leckebusch, S. Wild, and U. Ulbrich, 2010: Benefits and limitations
of regional multi-model ensembles for storm loss estimations. Clim. Res., 44,
211–225.
Donat, M. G., et al., 2013: Updated analyses of temperature and precipitation
extreme indices since the beginning of the twentieth century: The HadEX2
dataset. J. Geophys. Res., doi:10.1002/2012JD018606.
Donner, L. J., et al., 2011: The dynamical core, physical parameterizations, and basic
simulation characteristics of the atmospheric component AM3 of the GFDL
Global Coupled Model CM3. J. Clim., 24, 3484–3519.
Dorn, W., K. Dethloff, and A. Rinke, 2009: Improved simulation of feedbacks between
atmosphere and sea ice over the Arctic Ocean in a coupled regional climate
model. Ocean Model., 29, 103–114.
Doscher, R., K. Wyser, H. E. M. Meier, M. W. Qian, and R. Redler, 2010: Quantifying
Arctic contributions to climate predictability in a regional coupled ocean-ice-
atmosphere model. Clim. Dyn., 34, 1157–1176.
Douglass, D., J. Christy, B. Pearson, and S. Singer, 2008: A comparison of tropical
temperature trends with model predictions. Int. J. Climatol. , 28, 1693–1701.
Dowdy, A. J., G. A. Mills, B. Timbal, and Y. Wang, 2013: Changes in the risk of
extratropical cyclones in Eastern Australia. J. Clim., 26, 1403–1417.
Driesschaert, E., et al., 2007: Modeling the influence of Greenland ice sheet melting
on the Atlantic meridional overturning circulation during the next millennia.
Geophys. Res. Lett., 34, L10707.
Driouech, F., M. Deque, and E. Sanchez-Gomez, 2010: Weather regimes-Moroccan
precipitation link in a regional climate change simulation. Global Planet.
Change, 72, 1–10.
Driscoll, S., A. Bozzo, L. J. Gray, A. Robock, and G. Stenchikov, 2012: Coupled Model
Intercomparison Project 5 (CMIP5) simulations of climate following volcanic
eruptions. J. Geophys. Res. Atmos., 117, D17105.
Druyan, L. M., et al., 2010: The WAMME regional model intercomparison study. Clim.
Dyn., 35, 175–192.
Du, Y., S.-P. Xie, Y.-L. Yang, X.-T. Zheng, L. Liu, and G. Huang, 2013: Indian Ocean
variability in the CMIP5 multi-model ensemble: The basin mode. J. Clim., 26,
7240–7266.
Ducet, N., P. Y. Le Traon, and G. Reverdin, 2000: Global high-resolution mapping
of ocean circulation from TOPEX/Poseidon and ERS-1 and-2. J. Geophys. Res.
Oceans, 105, 19477–19498.
Dufresne, J.-L., et al., 2012: Climate change projections using the IPSL-CM5 Earth
System Model: From CMIP3 to CMIP5. Clim. Dyn., doi:10.1007/s00382-012-
1636-1.
Dufresne, J. L., and S. Bony, 2008: An assessment of the primary sources of spread
of global warming estimates from coupled atmosphere-ocean models. J. Clim.,
21, 5135–5144.
Dunkerton, T. J., 1991: Nonlinear propagation of zonal winds in an atmosphere with
Newtonian cooling and equatorial wavedriving. J. Atmos. Sci., 48, 236–263.
Dunn-Sigouin, E., and S.-W. Son, 2013: Northern Hemisphere blocking frequency and
duration in the CMIP5 models. J. Geophys. Res., 118, 1179–1188.
Dunne, J. P., et al., 2013: GFDLs ESM2 global coupled climate-carbon Earth
System Models Part II: Carbon system formulation and baseline simulation
characteristics. J. Clim., doi:10.1175/JCLI-D-12-00150.1.
Dunne, J. P., et al., 2012: GFDLs ESM2 Global coupled climate-carbon Earth System
models. Part I: Physical formulation and baseline simulation characteristics. J.
Clim., 25, 6646–6665.
Duplessy, J. C., N. J. Shackleton, R. Fairbanks, L. Labeyrie, D. Oppo, and N. Kallel, 1988:
Deep water source variation during the last climatic cycle and their impact on th
global deep water circulation. Paleoceanography, 3, 343–360.
Durack, P. J., and S. E. Wijffels, 2010: Fifty-year trends in global ocean salinities and
their relationship to broad-scale warming. J. Clim., 23, 4342-4362.
Durack, P. J., S. E. Wijffels, and R. J. Matear, 2012: Ocean salinities reveal strong
global water cycle intensification during 1950 to 2000. Science, 336, 455–458.
Easterling, D. R., and M. F. Wehner, 2009: Is the climate warming or cooling?
Geophys. Res. Lett., 36, L08706.
Eby, M., K. Zickfeld, A. Montenegro, D. Archer, K. J. Meissner, and A. J. Weaver, 2009:
Lifetime of anthropogenic climate change: Millennial time scales of potential
CO
2
and surface temperature perturbations. J. Clim., 22, 2501–2511.
Eby, M., et al., 2013: Historical and idealized climate model experiments:An EMIC
intercomparison. Clim. Past, 9, 1111–1140.
Edwards, N., and R. Marsh, 2005: Uncertainties due to transport-parameter
sensitivity in an efficient 3-D ocean-climate model. Clim. Dyn., 24, 415–433.
Edwards, N. R., D. Cameron, and J. Rougier, 2011: Precalibrating an intermediate
complexity climate model. Clim. Dyn., 37, 1469–1482.
Ek, M. B., et al., 2003: Implementation of Noah land surface model advances in the
National Centers for Environmental Prediction operational mesoscale Eta model.
J. Geophys. Res. Atmos., 108, 8851.
Eliseev, A. V., and I. I. Mokhov, 2011: Uncertainty of climate response to natural and
anthropogenic forcings due to different land use scenarios. Adv. Atmos. Sci., 28,
1215–1232.
Emanuel, K., R. Sundararajan, and J. Williams, 2008: Hurricanes and global
warming—Results from downscaling IPCC AR4 simulations. Bull. Am. Meteorol.
Soc., 89, 347–367.
Endo, H., A. Kitoh, T. Ose, R. Mizuta, and S. Kusunoki, 2012: Future changes and
uncertainties in Asian precipitation simulated by multiphysics and multi-sea
surface temperature ensemble experiments with high-resolution Meteorological
Research Institute atmospheric general circulation models (MRI-AGCMs). J.
Geophys. Res. Atmos., 117, D16118.
Essery, R. L. H., M. J. Best, R. A. Betts, P. M. Cox, and C. M. Taylor, 2003: Explicit
representation of subgrid heterogeneity in a GCM land surface scheme. J.
Hydrometeorol., 4, 530–543.
Eum, H., P. Gachon, R. Laprise, and T. Ouarda, 2012: Evaluation of regional climate
model simulations versus gridded observed and regional reanalysis products
using a combined weighting scheme. Clim. Dyn., 38, 1433–1457.
Evans, J. P., M. Ekstroem, and F. Ji, 2012: Evaluating the performance of a WRF
physics ensemble over South-East Australia. Clim. Dyn., 39, 1241–1258.
Eyring, V., et al., 2010: Transport impacts on atmosphere and climate: Shipping.
Atmos. Environ., 44, 4735–4771.
Eyring, V., et al., 2013: Long-term ozone changes and associated climate impacts in
CMIP5 simulations. J. Geophys. Res., doi:10.1002/jgrd.50316.
Eyring, V., et al., 2007: Multimodel projections of stratospheric ozone in the 21st
century. J. Geophys. Res. Atmos., 112, D16303.
Faloona, I., 2009: Sulfur processing in the marine atmospheric boundary layer: A
review and critical assessment of modeling uncertainties. Atmos. Environ., 43,
2841–2854.
Fan, F. X., M. E. Mann, S. Lee, and J. L. Evans, 2010: Observed and modeled changes
in the South Asian summer monsoon over the Historical Period. J. Clim., 23,
5193–5205.
Fanning, A. F., and A. J. Weaver, 1996: An atmospheric energy-moisture balance
model: Climatology, interpentadal climate change, and coupling to an ocean
general circulation model. J. Geophys. Res. Atmos., 101, 15111–15128.
Farneti, R., and P. R. Gent, 2011: The effects of the eddy-inducedadvection coefficient
in a coarse-resolution coupled climate model. Ocean Model., 39, 135–145.
Farneti, R., T. L. Delworth, A. J. Rosati, S. M. Griffies, and F. R. Zeng, 2010: The role of
mesoscale eddies in the rectification of the Southern Ocean response to climate
change. J. Phys. Oceanogr., 40, 1539–1557.
Fasullo, J., and K. E. Trenberth, 2012: A less cloudy future: The role of subtropical
subsidence in climate sensitivity. Science, 338, 792–794.
Fauchereau, N., S. Trzaska, Y. Richard, P. Roucou, and P. Camberlin, 2003: Sea-surface
temperature co-variability in the southern Atlantic and Indian Oceans and its
connections with the atmospheric circulation in the Southern Hemisphere. Int. J.
Climatol. , 23, 663–677.
Felzer, B., D. Kicklighter, J. Melillo, C. Wang, Q. Zhuang, and R. Prinn, 2004: Effects of
ozone on net primary production and carbon sequestration in the conterminous
United States using a biogeochemistry model. Tellus B, 56, 230–248.
Feng, J., and C. Fu, 2006: Inter-comparison of 10–year precipitation simulated by
several RCMs for Asia. Adv. Atmos. Sci., 23 531–542.
Feng, J., et al., 2011: Comparison of four ensemble methods combining regional
climate simulations over Asia. Meteorol. Atmos. Phys., 111, 41–53.
Fernandes, R., H. X. Zhao, X. J. Wang, J. Key, X. Qu, and A. Hall, 2009: Controls on
Northern Hemisphere snow albedo feedback quantified using satellite Earth
observations. Geophys. Res. Lett., 36, L21702.
834
Chapter 9 Evaluation of Climate Models
9
Fernandez-Donado, L., et al., 2013: Large-scale temperature response to external
forcing in simulations and reconstructions of the last millennium. Clim. Past, 9,
393–421.
Ferrari, R., J. C. McWilliams, V. M. Canuto, and M. Dubovikov, 2008: Parameterization
of eddy fluxes near oceanic boundaries. J. Clim., 21, 2770–2789.
Ferrari, R., S. M. Griffies, A. J. G. Nurser, and G. K. Vallis, 2010: A boundary-value
problem for the parameterized mesoscale eddy transport. Ocean Model., 32,
143–156.
Feser, F., 2006: Enhanced detectability of added value in limited-area model results
separated into different spatial scales. Mon. Weather Rev., 134, 2180–2190.
Feser, F., and M. Barcikowska, 2012: The influence of spectral nudging on typhoon
formation in regional climate models. Environ. Res. Lett., 7, 014024.
Feser, F., B. Rockel, H. von Storch, J. Winterfeldt, and M. Zahn, 2011: Regional climate
models add value to global model data: A review and selected examples. Bull.
Am. Meteorol. Soc., 92, 1181–1192.
Fetterer, F., K. Knowles, W. Meier, and M. Savoie, 2002: Sea Ice Index. National Snow
and Ice Data Center. Boulder, CO, USA.
Fichefet, T., and M. A. M. Maqueda, 1997: Sensitivity of a global sea ice model to
the treatment of ice thermodynamics and dynamics. J. Geophys. Res., 102,
12609–12646.
Fichefet, T., and M. A. M. Maqueda, 1999: Modelling the influence of snow
accumulation and snow-ice formation on the seasonal cycle of the Antarctic
sea-ice cover. Clim. Dyn., 15, 251–268.
Field, P. R., A. Gettelman, R. B. Neale, R. Wood, P. J. Rasch, and H. Morrison, 2008:
Midlatitude cyclone compositing to constrain climate model behavior using
satellite observations. J. Clim., 21, 5887–5903.
Fioletov, V., G. Bodeker, A. Miller, R. McPeters, and R. Stolarski, 2002: Global and
zonal total ozone variations estimated from ground-based and satellite
measurements: 1964–2000. J. Geophys. Res. Atmos., 107, 4647.
Fischer, E. M., S. I. Seneviratne, D. Lüthi, and C. Schär, 2007: Contribution of land-
atmosphere coupling to recent European summer heat waves. Geophys. Res.
Lett., 34, L06707.
Flanner, M. G., K. M. Shell, M. Barlage, D. K. Perovich, and M. A. Tschudi, 2011:
Radiative forcing and albedo feedback from the Northern Hemisphere
cryosphere between 1979 and 2008. Nature Geosci., 4, 151–155.
Flato, G., 2011: Earth system models: an overview. Wiley Interdisciplinary Reviews,
Climate Change, 2, 783–800.
Flocco, D., D. Schroeder, D. L. Feltham, and E. C. Hunke, 2012: Impact of melt ponds
on Arctic sea ice simulations from 1990 to 2007. J. Geophys. Res.Oceans, 117,
C09032.
Fogli, P. G., et al., 2009: INGV-CMCC Carbon (ICC): A Carbon Cycle Earth System
Model. CMCC Res. Papers. Euro-Mediterranean Center on Climate Change,
Bologna, Italy, 31 pp.
Fogt, R. L., J. Perlwitz, A. J. Monaghan, D. H. Bromwich, J. M. Jones, and G. J. Marshall,
2009: Historical SAM variability. Part II: Twentieth-century variability and trends
from reconstructions, observations, and the IPCC AR4 models. J. Clim., 22,
5346–5365.
Fontaine, B., and S. Janicot, 1996: Sea surface temperature fields associated with
West African rainfall anomaly types. J. Clim., 9, 2935–2940.
Forest, C. E., P. H. Stone, and A. P. Sokolov, 2006: Estimated PDFs of climate system
properties including natural and anthropogenic forcings. Geophys. Res. Lett., 33,
L01705.
Forest, C. E., P. H. Stone, and A. P. Sokolov, 2008: Constraining climate model
parameters from observed 20th century changes. Tellus A, 60, 911–920.
Forest, C. E., P. H. Stone, A. P. Sokolov, M. R. Allen, and M. D. Webster, 2002:
Quantifying uncertainties in climate system properties with the use of recent
climate observations. Science, 295, 113–117.
Forster, P. M., T. Andrews, P. Good, J. M. Gregory, L. S. Jackson, and M. Zelinka, 2013:
Evaluating adjusted forcing and model spread for historical and future scenarios
in the CMIP5 generation of climate models. J. Geophys. Res. Atmos., 118, 1139–
1150.
Fowler, H., S. Blenkinsop, and C. Tebaldi, 2007: Linking climate change modelling to
impacts studies: Recent advances in downscaling techniques for hydrological
modelling. Int. J. Climatol., 27, 1547–1578.
Fox-Kemper, B., R. Ferrari, and R. Hallberg, 2008: Parameterization of mixed layer
eddies. Part I: Theory and diagnosis. J. Phys. Oceanogr., 38, 1145–1165.
Fox-Kemper, B., et al., 2011: Parameterization of mixed layer eddies. III:
Implementation and impact in global ocean climate simulations. Ocean Model.,
39, 61–78.
Fox-Rabinovitz, M., J. Cote, B. Dugas, M. Deque, J. McGregor, and A. Belochitski,
2008: Stretched-grid Model Intercomparison Project: Decadal regional climate
simulations with enhanced variable and uniform-resolution GCMs. Meteorol.
Atmos. Phys., 100, 159–177.
Frame, D., B. Booth, J. Kettleborough, D. Stainforth, J. Gregory, M. Collins, and M.
Allen, 2005: Constraining climate forecasts: The role of prior assumptions.
Geophys. Res. Lett., 32, L09702.
Frankcombe, L. M., A. von der Heydt, and H. A. Dijkstra, 2010: North Atlantic
multidecadal climate variability: An investigation of dominant time scales and
processes. J. Clim., 23, 3626–3638.
Frederiksen, C. S., J. S. Frederiksen, J. M. Sisson, and S. L. Osbrough, 2011: Australian
winter circulation and rainfall changes and projections. Int. J. Clim. Change Strat.
Manage., 3, 170–188.
Friedlingstein, P., et al., 2001: Positive feedback between future climate change and
the carbon cycle. Geophys. Res. Lett., 28, 1543–1546.
Friedlingstein, P., et al., 2006: Climate-carbon cycle feedback analysis: Results from
the (CMIP)-M-4 model intercomparison. J. Clim., 19, 3337–3353.
Friend, A. D., et al., 2007: FLUXNET and modelling the global carbon cycle. Global
Change Biol., 13, 610–633.
Frierson, D. M. W., J. Lu, and G. Chen, 2007: Width of the Hadley cell in simple and
comprehensive general circulation models. Geophys. Res. Lett., 34, L18804.
Frohlich, C., and J. Lean, 2004: Solar radiative output and its variability: Evidence and
mechanisms. Astron. Astrophys. Rev., 12, 273–320.
Frost, A. J., et al., 2011: A comparison of multi-site daily rainfall downscaling
techniques under Australian conditions. J. Hydrol., 408, 1–18.
Fu, Q., S. Manabe, and C. M. Johanson, 2011: On the warming in the tropical upper
troposphere: Models versus observations. Geophys. Res. Lett., 38, L15704.
Furrer, R., R. Knutti, S. Sain, D. Nychka, and G. Meehl, 2007: Spatial patterns of
probabilistic temperature change projections from a multivariate Bayesian
analysis. Geophys. Res. Lett., 34, L06711.
Furtado, J., E. Di Lorenzo, N. Schneider, and N. A. Bond, 2011: North Pacific decadal
variability and climate change in the IPCC AR4 models. J. Clim., 24, 3049–3067
Fyfe, J. C., N. P. Gillett, and D. W. J. Thompson, 2010: Comparing variability and trends
in observed and modelled global-mean surface temperature. Geophys. Res. Lett.,
37, L16802.
Fyke, J. G., A. J. Weaver, D. Pollard, M. Eby, L. Carter, and A. Mackintosh, 2011: A new
coupled ice sheet/climate model: Description and sensitivity to model physics
under Eemian, Last Glacial Maximum, late Holocene and modern climate
conditions. Geosci. Model Dev., 4, 117–136.
Galbraith, D., P. E. Levy, S. Sitch, C. Huntingford, P. Cox, M. Williams, and P. Meir,
2010: Multiple mechanisms of Amazonian forest biomass losses in three
dynamic global vegetation models under climate change. New Phytologist, 187,
647–665.
Ganachaud, A., and C. Wunsch, 2003: Large-scale ocean heat and freshwater
transports during the World Ocean Circulation Experiment. J. Clim., 16, 696–705.
Gangsto, R., F. Joos, and M. Gehlen, 2011: Sensitivity of pelagic calcification to ocean
acidification. Biogeosciences, 8, 433–458.
Gao, X., Y. Shi, D. Zhang, J. Wu, F. Giorgi, Z. Ji, and Y. Wang, 2012: Uncertainties in
monsoon precipitation projections over China: Results from two high-resolution
RCM simulations. Clim. Res., 52, 213–226.
Gates, W. L., et al., 1999: An overview of the results of the Atmospheric Model
Intercomparison Project (AMIP I). Bull. Am. Meteorol. Soc., 80, 29–55.
Gbobaniyi, E. O., B. J. Abiodun, M. A. Tadross, B. C. Hewitson, and W. J. Gutowski,
2011: The coupling of cloud base height and surface fluxes: A transferability
intercomparison. Theor. Appl. Climatol., 106, 189–210.
Gehlen, M., R. Gangsto, B. Schneider, L. Bopp, O. Aumont, and C. Ethe, 2007: The fate
of pelagic CaCO
3
production in a high CO
2
ocean: A model study. Biogeosciences,
4, 505–519.
Geller, M. A., et al., 2011: New gravity wave treatments for GISS climate models. J.
Clim., 24, 3989–4002.
Gent, P. R., and J. C. McWilliams, 1990: Isopycnal mixing in ocean circulation models.
J. Phys. Oceanogr., 20, 150–155.
Gent, P. R., and G. Danabasoglu, 2011: Response toincreasing Southern Hemisphere
winds in CCSM4. J. Clim., 24, 4992–4998.
Gent, P. R., J. Willebrand, T. J. McDougall, and J. C. McWilliams, 1995: Parameterizing
eddy-induced tracer transports in ocean circulation models. J. Phys. Oceanogr.,
25, 463–474.
Gent, P. R., et al., 2011: The Community Climate System Model Version 4. J. Clim.,
24, 4973–4991.
835
Evaluation of Climate Models Chapter 9
9
Gerber, E. P., L. M. Polvani, and D. Ancukiewicz, 2008: Annular mode time scales
in the Intergovernmental Panel on Climate Change Fourth Assessment Report
models. Geophys. Res. Lett., 35, L22707.
Gerber, S., L. O. Hedin, M. Oppenheimer, S. W. Pacala, and E. Shevliakova, 2010:
Nitrogen cycling and feedbacks in a global dynamic land model. Global
Biogeochem. Cycles, 24, Gb1001.
Gettelman, A., et al., 2010: Multimodel assessment of the upper troposphere and
lower stratosphere: Tropics and global trends. J. Geophys. Res. Atmos., 115,
D00m08.
Ghan, S., X. Liu, R. Easter, P. Rasch, J. Yoon, and B. Eaton, 2012: Toward a minimal
representation of aerosols in climate models: Comparative decomposition of
aerosol direct, semi-direct and indirect radiative forcing. J. Clim., doi:10.1175/
JCLI-D-11-00650.1.
Gillett, N. P., 2005: Climate modelling —Northern Hemisphere circulation. Nature,
437, 496–496.
Giorgi, F., and E. Coppola, 2010: Does the model regional bias affect the projected
regional climate change? An analysis of global model projections. Clim. Change,
100, 787–795.
Girard, L., J. Weiss, J. M. Molines, B. Barnier, and S. Bouillon, 2009: Evaluation of
high-resolution sea ice models on the basis of statistical and scaling properties
of Arctic sea ice drift and deformation. J. Geophys. Res., 114, C08015.
Gleckler, P., K. Taylor, and C. Doutriaux, 2008: Performance metrics for climate
models. J. Geophys. Res. Atmos., 113, D06104.
Gleckler, P., K. AchutaRao, J. Gregory, B. Santer, K. Taylor, and T. Wigley, 2006:
Krakatoa lives: The effect of volcanic eruptions on ocean heat content and
thermal expansion. Geophys. Res. Lett., 33, L17702.
Gleckler, P. J., et al., 2012: Human-induced global ocean warming on multidecadal
timescales, Nature Climate Change, 2, 524–529.
Gnanadesikan, A., S. M. Griffies, and B. L. Samuels, 2007: Effects in a climate model
of slope tapering in neutral physics schemes. Ocean Model., 16, 1–16.
Goddard, L., and S. J. Mason, 2002: Sensitivity of seasonal climate forecasts to
persisted SST anomalies. Clim. Dyn., 19, 619–631.
Golaz, J.-C., M. Salzmann, L. J. Donner, L. W. Horowitz, Y. Ming, and M. Zhao, 2011:
Sensitivity of the aerosol indirect effect to subgrid variability in the cloud
parameterization of the GFDL Atmosphere General Circulation Model AM3. J.
Clim., 24, 3145–3160.
Goosse, H., and T. Fichefet, 1999: Importance of ice-ocean interactions for the global
ocean circulation: A model study. J. Geophys. Res. Oceans, 104, 23337–23355.
Goosse, H., et al., 2010: Description of the Earth system model of intermediate
complexity LOVECLIM version 1.2. Geosci. Model Dev., 3, 603–633.
Gordon, C., et al., 2000: The simulation of SST, sea ice extents and ocean heat
transports in a version of the Hadley Centre coupled model without flux
adjustments. Clim. Dyn., 16, 147–168.
Gordon, H., et al., 2010: The CSIRO Mk3.5 Climate Model. CAWCR Technical
Report,21, 1–74.
Gordon, H. B., et al., 2002: The CSIRO Mk3 Climate System Model. Technical Paper
No. 60. CSIRO Atmospheric Research, Aspendale, Vic., Australia.
Greeves, C. Z., V. D. Pope, R. A. Stratton, and G. M. Martin, 2007: Representation
of Northern Hemisphere winter storm tracks in climate models. Clim. Dyn., 28,
683–702.
Gregory, J. M., and P. M. Forster, 2008: Transient climate response estimated from
radiative forcing and observed temperature change. J. Geophys. Res. Atmos.,
113, D23105.
Gregory, J. M., et al., 2004: A new method for diagnosing radiative forcing and
climate sensitivity. Geophys. Res. Lett., 31, L03205.
Gregory, J. M., et al., 2005: A model intercomparison of changes in the Atlantic
thermohaline circulation in response to increasing atmospheric CO
2
concentration. Geophys. Res. Lett., 32, L12703.
Griffies, S. M., 2009: Elements of MOM4p1. GFDL Ocean Group Technical Report No.
6. NOAA/GFDL. Princeton, USA, 371 pp.
Griffies, S. M., and R. J. Greatbatch, 2012: Physical processes that impact the
evolution of global mean sea level in ocean climate models. Ocean Model., 51,
37–72.
Griffies, S. M., M. J. Harrison, R. C. Pacanowski, and A. Rosati, 2004: A Technical Guide
to MOM4. GFDL Ocean Group Technical Report No. 5, Princeton, USA,337 pp.
Griffies, S. M., et al., 2005: Formulation of an ocean model for global climate
simulations. Ocean Sci., 1, 45–79.
Griffies, S. M., et al., 2009: Coordinated Ocean-ice Reference Experiments (COREs).
Ocean Model., 26, 1–46.
Grose, M., M. Pook, P. McIntosh, J. Risbey, and N. Bindoff, 2012: The simulation of
cutoff lows in a regional climate model: Reliability and future trends. Clim. Dyn.,
39, 445–459.
Guemas, V., F. J. Doblas-Reyes, I. Andreu-Burillo, and M. Asif, 2013: Retrospective
prediction of the global warming slowdown in the last decade. Nature Clim.
Change, doi:10.1038/nclimate1863.
Guilyardi, E., 2006: El Niño - mean state - seasonal cycle interactions in a multi-
model ensemble. Clim. Dyn., 26, 229–348.
Guilyardi, E., P. Braconnot, F. F. Jin, S. T. Kim, M. Kolasinski, T. Li, and I. Musat,
2009a: Atmosphere feedbacks during ENSO in a coupled GCM with a modified
atmospheric convection scheme. J. Clim., 22, 5698–5718.
Guilyardi, E., et al., 2009b: Understanding El Niño in ocean–atmosphere general
circulation models: Progress and challenges. Bull. Am. Meteorol. Soc., 90, 325–
340.
Guiot, J., J. J. Boreux, P. Braconnot, F. Torre, and P. Participants, 1999: Data-model
comparison using fuzzy logic in paleoclimatology. Clim. Dyn., 15, 569–581.
Gupta, A. S., A. Santoso, A. S. Taschetto, C. C. Ummenhofer, J. Trevena, and M. H.
England, 2009: Projected changes to the Southern Hemisphere ocean and sea
ice in the IPCC AR4 climate models. J. Clim., 22, 3047–3078.
Gurney, K. R., et al., 2003: TransCom 3 CO
2
inversion intercomparison: 1. Annual
mean control results and sensitivity to transport and prior flux information.
Tellus B, 55, 555–579.
Gutowski, W., et al., 2010: Regional extreme monthly precipitation simulated by
NARCCAP RCMs. J. Hydrometeorol., 11, 1373–1379.
Hall, A., and X. Qu, 2006: Using the current seasonal cycle to constrain snow albedo
feedback in future climate change. Geophys. Res. Lett., 33, L03502.
Hallberg, R., and A. Gnanadesikan, 2006: The role of eddies in determining the
structure and response of the wind-driven southern hemisphere overturning:
Results from the Modeling Eddies in the Southern Ocean (MESO) project. J. Phys.
Oceanogr., 36, 2232–2252.
Hallberg, R., and A. Adcroft, 2009: Reconciling estimates of the free surface height
in Lagrangian vertical coordinate ocean models with mode-split time stepping.
Ocean Model., 29, 15–26.
Halloran, P. R., 2012: Does atmospheric CO
2
seasonality play an important role in
governing the air-sea flux of CO
2
? Biogeosciences, 9, 2311–2323.
Ham, Y.-G., J. S. Kug, I. S. Kang, F. F. Jin, and A. Timmermann, 2010: Impact of diurnal
atmospher-ocean coupling on tropical climate simulations using a coupled
GCM. Clim. Dyn., 34, 905–917.
Hamilton, K., 1998: Effects of an imposed Quasi-Biennial Oscillation in
a comprehensive troposphere-stratosphere-mesosphere general
circulationmodel. J. Atmos. Sci., 55, 2393– 2418.
Handorf, D., and K. Dethloff, 2012: How well do state-of-the-art atmosphere-ocean
general circulation models reproduce atmospheric teleconnection patterns?
Tellus A, 64, 19777.
Hannart, A., J. L. Dufresne, and P. Naveau, 2009: Why climate sensitivity may not be
so unpredictable. Geophys. Res. Lett., 36, L16707.
Hannay, C., et al., 2009: Evaluation of Forecasted Southeast Pacific Stratocumulus in
the NCAR, GFDL, and ECMWF Models. J. Clim., 22, 2871–2889.
Hansen, J., R. Ruedy, M. Sato, and K. Lo, 2010: Global surface temperature change.
Rev. Geophys., 48, Rg4004.
Hansen, J., M. Sato, P. Kharecha, and K. von Schuckmann, 2011: Earth’s energy
imbalance and implications. Atmos. Chem. Phys., 11, 13421–13449.
Hansen, J., et al., 1983: Efficient Three-Dimensional Global Models for Climate
Studies: Models I and II. Mon. Weath. Rev., 111, 609–662.
Hansen, J., et al., 1984: Climate Sensitivity: Analysis of Feedback Mechanisms. Clim.
Proc. Clim. Sens. Geophys. Monogr., 29, 130–163.
Hansen, J., et al., 2005: Efficacy of climate forcings. J. Geophys. Res. Atmos., 110,
D18104.
Hardiman, S. C., N. Butchart, T. J. Hinton, S. M. Osprey, and L. J. Gray, 2012: The effect
of a well-resolved stratosphere on surface climate: Differences between CMIP5
simulations with high and low top versions of the Met Office Climate Model. J.
Clim., 25, 7083–7099.
Hargreaves, J. C., A. Abe-Ouchi, and J. D. Annan, 2007: Linking glacial and future
climates through an ensemble of GCM simulations. Clim. Past, 3, 77–87.
Hargreaves, J. C., J. D. Annan, M. Yoshimori, and A. Abe-Ouchi, 2012: Can the Last
Glacial Maximum constrain climate sensitivity? Geophys. Res. Lett., 39, L24702.
Hargreaves, J. C., A. Paul, R. Ohgaito, A. Abe-Ouchi, and J. D. Annan, 2011: Are
paleoclimate model ensembles consistent with the MARGO data synthesis?
Clim. Past, 7, 917–933.
836
Chapter 9 Evaluation of Climate Models
9
Hargreaves, J. C., J. D. Annan, R. Ohgaito, A. Paul, and A. Abe-Ouchi, 2013: Skill and
reliability of climate model ensembles at the Last Glacial Maximum and mid
Holocene. Clim. Past, 9, 811–823.
Hasegawa, A., and S. Emori, 2007: Effect of air-sea coupling in the assessment of
CO
2
-induced intensification of tropical cyclone activity. Geophys. Res. Lett., 34,
L05701.
Hasumi, H., 2006: CCSR Ocean Component Model (COCO) Version 4.0. CCSR Report.
Centre for Climate System Research, University of Tokyo, Tokyo, Japan, 68 pp.
Hasumi, H., and S. Emori, 2004: K-1 Coupled GCM (MIROC) Description. Center for
Climate System Research, University of Tokyo, Tokyo, Japan, 34 pp.
Hawkins, E., and R. Sutton, 2009: The potential to narrow uncertainty in regional
climate predictions. Bull. Am. Meteorol. Soc., 90, 1095–1107.
Haynes, J. M., C. Jakob, W. B. Rossow, G. Tselioudis, and J. Brown, 2011: Major
characteristics of Southern Ocean cloud regimes and their effects on the energy
budget. J. Clim., 24, 5061–5080.
Haynes, P. H., 2006: The latitudinal structure of the QBO. Q. J. R. Meteorol. Soc., 124,
2645–2670.
Haywood, J. M., N. Bellouin, A. Jones, O. Boucher, M. Wild, and K. P. Shine, 2011: The
roles of aerosol, water vapor and cloud in future global dimming/brightening. J.
Geophys. Res. Atmos., 116, D20203.
Hazeleger, W., and R. J. Haarsma, 2005: Sensitivity of tropical Atlantic climate to
mixing in a coupled ocean-atmosphere model. Clim. Dyn., 25, 387–399.
Hazeleger, W., et al., 2012: EC-Earth V2.2: Description and validation of a new
seamless earth system prediction model. Clim. Dyn., 39, 2611–2629.
Hegerl, G., and F. Zwiers, 2011: Use of models in detection and attribution of climate
change. Clim. Change, 2, 570–591.
Hegerl, G. C., et al., 2007: Understanding and attributing climate change. In: Climate
Change 2007: The Physical Science Basis. Contribution of Working Group I to the
Fourth Assessment Report of the Intergovernmental Panel on Climate Change
[Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor
and H. L. Miller (eds.)] Cambridge University Press, Cambridge, United Kingdom
and New York, NY, USA, pp. 665–775.
Heinze, C., 2004: Simulating oceanic CaCO
3
export production in the greenhouse.
Geophys. Res. Lett., 31, L16308.
Heinze, C., I. Kriest, and E. Maier-Reimer, 2009: Age offsets among different biogenic
and lithogenic components of sediment cores revealed by numerical modeling.
Paleoceanography, 24, PA4214.
Held, I. M., 2005: The gap between simulation and understanding in climate
modeling. Bull. Am. Meteorol. Soc., 86, 1609–1614.
Held, I. M., and B. J. Soden, 2006: Robust responses of the hydrological cycle to
global warming. J. Clim., 19, 5686–5699.
Held, I. M., and K. M. Shell, 2012: Using Relative Humidity as a State Variable in
Climate Feedback Analysis. J. Clim., 25, 2578–2582.
Held, I. M., M. Winton, K. Takahashi, T. Delworth, F. R. Zeng, and G. K. Vallis, 2010:
Probing the fast and slow components of global warming by returning abruptly
to preindustrial forcing. J. Clim., 23, 2418–2427.
Henson, S. A., D. Raitsos, J. P. Dunne, and A. McQuatters-Gollop, 2009: Decadal
variability in biogeochemical models: Comparison with a 50-year ocean colour
dataset. Geophys. Res. Lett., 36, L21601.
Hermes, J. C., and C. J. C. Reason, 2005: Ocean model diagnosis of interannual
coevolving SST variability in the South Indian and South Atlantic Oceans. J. Clim.,
18, 2864–2882.
Hernández-Díaz, L., R. Laprise, L. Sushama, A. Martynov, K. Winger, and B. Dugas,
2013: Climate simulation over CORDEX Africa domain using the fifth-generation
Canadian Regional Climate Model (CRCM5). Clim. Dyn., 40, 1415–1433.
Heuzé, C., K. J. Heywood, D. P. Stevens, and J. K. Ridley, 2013: Southern Ocean
bottom water characteristics in CMIP5 models. Geophys. Res. Lett., doi:10.1002/
grl.50287.
Hewitt, H. T., et al., 2011: Design and implementation of the infrastructure of
HadGEM3: The next-generation Met Office climate modelling system. Geosci.
Model Dev., 4, 223–253.
Hibbard, K. A., G. A. Meehl, P. M. Cox, and P. Friedlingstein, 2007: A strategy for
climate change stabilization experiments. Eos Trans. Am. Geophys. Union, 88,
217–221.
Hirai, M., T. Sakashita, H. Kitagawa, T. Tsuyuki, M. Hosaka, and M. Oh’Izumi, 2007:
Development and validation of a new land surface model for JMA’s operational
global model using the CEOP observation dataset. J. Meteorol. Soc. Jpn., 85A,
1–24.
Hirota, N., Y. N. Takayabu, M. Watanabe, and M. Kimoto, 2011: Precipitation
reproducibility over tropical oceans and its relationship to the double ITCZ
problem in CMIP3 and MIROC5 climate models. J. Clim., 24, 4859–4873.
Hirschi, M., et al., 2011: Observational evidence for soil-moisture impact on hot
extremes in southeastern Europe. Nature Geosci., 4, 17–21.
Hofmann, M., and M. A. Morales Maqueda, 2011: The response of Southern Ocean
eddies to increased midlatitude westerlies: A non-eddy resolving model study.
Geophys. Res. Lett., 38, L03605.
Holden, P. B., N. R. Edwards, D. Gerten, and S. Schaphoff, 2013: A model based
constraint of CO
2
fertilisation. Biogeosciences, 10, 339–355.
Holian, G. L., A. P. Sokolov, and R. G. Prinn, 2001: Uncertainty in atmospheric CO
2
predictions from a parametric uncertainty analysis of a Global Ocean Carbon
Cycle Model. Joint Program Report Series. MIT Joint Program on the Science and
Policy of Global Change, Cambridge, MA, USA, 25 pp.
Holland, M., D. Bailey, B. Briegleb, B. Light, and E. Hunke, 2012: Improved sea ice
shortwave radiation physics in CCSM4: The impact of melt ponds and aerosols
on arctic aea ice. J. Clim., 25, 1413–1430.
Holland, M. M., M. C. Serreze, and J. Stroeve, 2010: The sea ice mass budget of the
Arctic and its future change as simulated by coupled climate models. Clim. Dyn.,
doi:10.1007/s00382-008-0493-4.
Holton, J. R., and H. C. Tan, 1980: The influence of the equatorial Quasi-Biennial
Oscillation on the global circulation at 50 mb. J. Atmos. Sci., 37, 2200–2208.
Horowitz, L. W., et al., 2003: A global simulation of tropospheric ozone and related
tracers: Description and evaluation of MOZART, version 2. J. Geophys. Res.
Atmos., 108, 4784.
Hourdin, F., et al., 2012: Impact of the LMDZ atmospheric grid configuration on the
climate and sensitivity of the IPSL-CM5A coupled model. Clim. Dyn., doi:10.1007/
s00382–012–1411–3.
Hourdin, F., et al., 2013: LMDZ5B: The atmospheric component of the IPSL climate
model with revisited parameterizations for clouds and convection. Clim. Dyn.,
40, 2193–2222.
Hourdin, F., et al., 2010: AMMA-Model Intercomparison Project. Bull. Am. Meteorol.
Soc., 91, 95–104.
Hu, Z.-Z., B. Huang, Y.-T. Hou, W. Wang, F. Yang, C. Stan, and E. Schneider, 2011:
Sensitivity of tropical climate to low-level clouds in the NCEP climate forecast
system. Clim. Dyn., 36, 1795–1811.
Huang, C. J., F. Qiao, Q. Shu, and Z. Song, 2012: Evaluating austral summer mixed-
layer response to surface wave-induced mixing in the Southern Ocean. J.
Geophys. Res.-Oceans, 117, C00j18.
Huber, M., I. Mahlstein, M. Wild, J. Fasullo, and R. Knutti, 2011: Constraints on climate
sensitivity from radiation patterns in climate models. J. Clim., 24, 1034–1052.
Hung, M., J. Lin, W. Wang, D. Kim, T. Shinoda, and S. Weaver, 2013: MJO and
convectively coupled equatorial waves simulated by CMIP5 climate models. J.
Clim., doi:10.1175/JCLI-D-12-00541.1.
Hunke, E. C., and J. K. Dukowicz, 1997: An elastic-viscous-plastic model for sea ice
dynamics. J. Phys. Oceanogr., 27, 1849–1867.
Hunke, E. C., and W. H. Lipscomb, 2008: CICE: The Los Alamos Sea Ice
ModelDocumentation and Software User’s ManualVersion 4.1. Los Alamos
National Laboratory, Los Alamos, NM, USA, 76 pp.
Hunke, E. C., W. H. Lipscomb, and A. K. Turner, 2010: Sea ice models for climate study:
Retorspective and new directions. J. Glaciol., 56, 1162–1172.
Hunke, E. C., D. A. Hebert, and O. Lecomte, 2013: Level-ice melt ponds in the Los
Alamos sea ice model, CICE. Ocean Model., doi:10.1016/j.ocemod.2012.11.008.
Hunke, E. C., D. Notz, A. K. Turner, and M. Vancoppenolle, 2011: The multiphase
physics of sea ice : A review for model developers. Cryosphere, 5, 989–1009.
Hurrell, J., G. A. Meehl, D. Bader, T. L. Delworth, B. Kirtman, and B. Wielicki, 2009:
A unified modeling approach to climate system prediction. Bull. Am. Meteorol.
Soc., 90, 1819–1832.
Hurrell, J., et al., 2013: The Community Earth System Model: A framework for
collaborative research. Bull. Am. Meteorol. Soc., doi:10.1175/BAMS-D-12–
00121.
Hurtt, G. C., et al., 2009: Harmonization of global land-use scenarios for the period
1500–2100 for IPCC-AR5. iLEAPS Newsl., 7, 6–8.
Hutchings, J. K., A. Roberts, C. A. Geiger, and J. Richter-Menge, 2011: Spatial and
temporal characterization of sea-ice deformation. Ann. Glaciol., 52, 360–368.
Huybrechts, P., 2002: Sea-level changes at the LGM from ice-dynamic reconstructions
of the Greenland and Antarctic ice sheets during the glacial cycles. Quat. Sci.
Rev., 21, 203–231.
837
Evaluation of Climate Models Chapter 9
9
Huybrechts, P., H. Goelzer, I. Janssens, E. Driesschaert, T. Fichefet, H. Goosse, and M.
F. Loutre, 2011: Response of the Greenland and Antarctic ice sheets to multi-
millennial greenhouse warming in the Earth System Model of Intermediate
Complexity LOVECLIM. Surv. Geophys., 32, 397–416.
Iacono, M. J., J. S. Delamere, E. J. Mlawer, and S. A. Clough, 2003: Evaluation of upper
tropospheric water vapor in the NCAR Community Climate Model (CCM3) using
modeled and observed HIRS radiances. J. Geophys. Res., 108, 4037.
Ichikawa, H., H. Masunaga, Y. Tsushima, and H. Kanzawa, 2012: Reproducibility by
climate models of cloud radiative forcing associated with tropical convection. J.
Clim., 25, 1247–1262.
Ilicak, M., A. J. Adcroft, S. M. Griffies, and R. W. Hallberg, 2012: Spurious dianeutral
mixing and the role of momentum closure. Ocean Model., 45–46, 37–58.
Illingworth, A. J., et al., 2007: Cloudnet. Bull. Am. Meteor. Soc., 88, 883–898.
Ilyina, T., R. E. Zeebe, E. Maier-Reimer, and C. Heinze, 2009: Early detection of ocean
acidification effects on marine calcification. Global Biogeochem. Cycles, 23,
Gb1008.
Ilyina, T., K. Six, J. Segschneider, J. Maier-Reimer, H. Li, and I. Nunez-Riboni, 2013:
The global ocean biogeochemistry model HAMOCC: Model architecture
and performance as component of the MPI-Earth System Model in different
CMIP5experimental realizations. J. Adv. Model. Earth Syst., 5, 287–315.
Inatsu, M., and M. Kimoto, 2009: A scale interaction study on East Asian cyclogenesis
using a General Circulation Model coupled with an Interactively Nested Regional
Model. Mon. Weather Rev., 137, 2851–2868.
Inatsu, M., Y. Satake, M. Kimoto, and N. Yasutomi, 2012: GCM bias of the western
Pacific summer monsoon and its correction by two-way nesting system. J.
Meteorol. Soc. Jpn., 90B, 1–10.
Ingram, W., 2010: A very simple model for the water vapour feedback on climate
change. Q. J. R. Meteorol. Soc., 136, 30–40.
Ingram, W., 2013: Some implications of a new approach to the water vapour
feedback. Clim. Dyn., 40, 925–933.
Inness, P. M., J. M. Slingo, E. Guilyardi, and J. Cole, 2003: Simulation of the Madden-
Julian oscillation in a coupled general circulation model. Part II: The role of the
basic state. J. Clim., 16, 365–382.
Inoue, J., J. P. Liu, J. O. Pinto, and J. A. Curry, 2006: Intercomparison of Arctic Regional
Climate Models: Modeling clouds and radiation for SHEBA in May 1998. J. Clim.,
19, 4167–4178.
IPCC, 2007: Climate Change 2007: The Physical Science Basis. Contribution of
Working Group I to the Fourth Assessment Report of the Intergovernmental
Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis,
K. B. Averyt, M. Tignor and H. L. Miller (eds.)] Cambridge University Press,
Cambridge, United Kingdom and New York, NY, USA, 996 pp.
IPCC, 2012: IPCC WGI/WGII Special Report on Managing the Risks of Extreme
Events and Disasters to Advance Climate Change Adaptation (SREX). [Field,
C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J.
Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (Eds.)]. Cambridge
University Press, The Edinburgh Building, Shaftesbury Road, Cambridge CB2 8RU
ENGLAND, 582 pp.
Ishii, M., and M. Kimoto, 2009: Reevaluation of historical ocean heat content
variations with time-varying XBT and MBT depth bias corrections. J. Oceanogr.,
65, 287–299.
Ito, A., and T. Oikawa, 2002: A simulation model of the carbon cycle in land
ecosystems (Sim-CYCLE): A description based on dry-matter production theory
and plot-scale validation. Ecol. Model., 151, 143–176.
Iversen, T., et al., 2013: The Norwegian Earth System Model, NorESM1–M. Part
2:Climate response and scenario projections.Geosci. Model Dev., 6, 1–27.
Izumi, K., P. J. Bartlein, and S. P. Harrison, 2013: Consistent large-scale temperature
responses in warm and cold climates. Geophys. Res. Lett., doi:2013GL055097.
Jackson, C. S., M. K. Sen, G. Huerta, Y. Deng, and K. P. Bowman, 2008a: Error reduction
and convergence in climate prediction. J. Clim., 21, 6698–6709.
Jackson, L., R. Hallberg, and S. Legg, 2008b: A parameterization of shear-driven
turbulence for ocean climate models. J. Phys. Oceanogr., 38, 1033–1053.
Jacob, D., et al., 2012: Assessing the transferability of the Regional Climate Model
REMO to Different COordinated Regional Climate Downscaling EXperiment
(CORDEX) regions. Atmosphere, 3, 181–199.
Jakob, C., 2010: Accelerating progress in Global Atmospheric Model development
through improved parameterizations: Challenges, opportunities, and strategies.
Bull. Am. Meteorol. Soc., 91, 869–875.
Jang, C. J., J. Park, T. Park, and S. Yoo, 2011: Response of the ocean mixed layer depth
to global warming and its impact on primary production: A case for the North
Pacific Ocean. Ices J. Mar. Sci., 68, 996–1007.
Jansen, E., et al., 2007: Paleoclimate. In: Climate Change 2007: The Physical Science
Basis. Contribution of Working Group I to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning,
Z. Chen, M. Marquis, K. B. Averyt, M. Tignor and H. L. Miller (eds.)] Cambridge
University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 433–
498.
Jayne, S. R., 2009: The impact of abyssal mixing parameterizations in an ocean
General Circulation Model. J. Phys. Oceanogr., 39, 1756–1775.
Ji, J., M. Huang, and K. Li, 2008: Prediction of carbon exchanges between China
terrestrial ecosystem and atmosphere in 21st century. Sci. China D, 51, 885–898.
Ji, J. J., 1995: A climate-vegetation interaction model: Simulating physical and
biological processes at the surface. J. Biogeogr., 22, 445–451.
Jiang, J. H., et al., 2012a: Evaluation of cloud and water vapor simulations in CMIP5
climate models using NASA A-Train” satellite observations. J. Geophys. Res.,
117, D14105.
Jiang, X., et al., 2012b: Simulation of the intraseasonal variability over the Eastern
Pacific ITCZ in climate models. Clim. Dyn., 39, 617–636.
Jin, F. F., S. T. Kim, and L. Bejarano, 2006: A coupled-stability index for ENSO. Geophys.
Res. Let., 33, L23708.
Joetzjer, E., H. Douville, C. Delire, and P. Ciais, 2013: Present-day and future
Amazonian precipitation in global climate models: CMIP5 versus CMIP3. Clim.
Dyn., doi: 10.1007/s00382–012-1644-1.
Johanson, C. M., and Q. Fu, 2009: Hadley Cell Widening: Model Simulations versus
Observations. J. Clim., 22, 2713–2725.
John, V., and B. Soden, 2007: Temperature and humidity biases in global climate
models and their impact on climate feedbacks. Geophys. Res. Lett., 34, L18704.
Johns, T. C., et al., 2003: Anthropogenic climate change for 1860 to 2100 simulated
with the HadCM3 model under updated emissions scenarios. Clim. Dyn., 20,
583–612.
Johns, T. C., et al., 2006: The new Hadley Centre Climate Model (HadGEM1):
Evaluation of coupled simulations. J. Clim., 19, 1327–1353
Johnson, N. C., and S. B. Feldstein, 2010: The continuum of north Pacific sea level
pressure patterns: Intraseasonal, interannual, and interdecadal variability. J.
Clim., 23, 851–867.
Jolliffe, I. T., and D. B. Stephenson, 2011: Forecast Verification: A Practitioner’s Guide
in Atmospheric Science. 2nd ed. John Wiley & Sons, Hoboken, NJ, 292 pp.
Joly, M., A. Voldoire, H. Douville, P. Terray, and J. F. Royer, 2007: African monsoon
teleconnections with tropical SSTs: Validation and evolution in a set of IPCC4
simulations. Clim. Dyn., 29, 1–20.
Jones, A., D. L. Roberts, M. J. Woodage, and C. E. Johnson, 2001: Indirect sulphate
aerosol forcing in a climate model with an interactive sulphur cycle. J. Geophys.
Res. Atmos., 106, 20293–20310.
Jones, G. S., P. A. Stott, and N. Christidis, 2012: Attribution of observed historical
near surface temperature variations to anthropogenic and natural causes using
CMIP5 simulations. J. Geophys. Res., doi:10.1002/jgrd.50239.
Jones, P. D., M. New, D. E. Parker, S. Martin, and I. G. Rigor, 1999: Surface air
temperature and its changes over the past 150 years. Rev. Geophys., 37, 173–
199.
Joseph, S., A. K. Sahai, B. N. Goswami, P. Terray, S. Masson, and J. J. Luo, 2012: Possible
role of warm SST bias in the simulation of boreal summer monsoon in SINTEX-F2
coupled model. Clim. Dyn., 38, 1561–1576.
Joussaume, S., and K. E. Taylor, 1995: Status of the Paleoclimate Modeling
Intercomparison Project. In: Proceedings of the first international AMIP scientific
conference, WCRP-92, Monterey, USA, 425–430.
Jun, M., R. Knutti, and D. Nychka, 2008: Spatial analysis to quantify numerical model
bias and dependence: How many climate models are there? J. Am. Stat. Assoc.,
103, 934–947.
Jung, T., et al., 2010: The ECMWF model climate: Recent progress through improved
physical parametrizations. Q. J. R. Meteorol. Soc., 136, 1145–1160.
Jung, T., et al., 2012: High-resolution global climate simulations with the ECMWF
Model in Project Athena: Experimental design, model climate, and seasonal
forecast skill. J. Clim., 25, 3155–3172.
Jungclaus, J. H., et al., 2006: Ocean circulation and tropical variability in the coupled
model ECHAM5/MPI-OM. J. Clim., 19, 3952–3972.
838
Chapter 9 Evaluation of Climate Models
9
Jungclaus, J. H., et al., 2013: Characteristics of the ocean simulations in MPIOM,
the ocean componentof the MPI-Earth System Model. J. Adv. Model. Earth Syst.,
doi:10.1002/jame.20023.
Jungclaus, J. H., et al., 2010: Climate and carbon-cycle variability over the last
millennium. Clim. Past, 6, 723–737.
Kageyama, M., et al., 2006: Last Glacial Maximum temperatures over the North
Atlantic, Europe and western Siberia: A comparison between PMIP models,
MARGO sea-surface temperatures and pollen-based reconstructions. Quat. Sci.
Rev., 25, 2082–2102.
Kahn, R. A., B. J. Gaitley, J. V. Martonchik, D. J. Diner, K. A. Crean, and B. Holben, 2005:
Multiangle Imaging Spectroradiometer (MISR) global aerosol optical depth
validation based on 2 years of coincident Aerosol Robotic Network (AERONET)
observations. J. Geophys. Res. Atmos., 110, D10s04.
Kalnay, E., et al., 1996: The NCEP/NCAR 40-year reanalysis project. Bull. Am.
Meteorol. Soc., 77, 437–471.
Kanada, S., M. Nakano, S. Hayashi, T. Kato, M. Nakamura, K. Kurihara, and A. Kitoh,
2008: Reproducibility of Maximum Daily Precipitation Amount over Japan by a
High-resolution Non-hydrostatic Model. Sola, 4, 105–108.
Kanamaru, H., and M. Kanamitsu, 2007: Fifty-seven-year California reanalysis
downscaling at 10 km (CaRD10). Part II: Comparison with North American
regional reanalysis. J. Clim., 20, 5572–5592.
Kanamitsu, M., K. Yoshimura, Y. B. Yhang, and S. Y. Hong, 2010: Errors of interannual
variability and trend in dynamical downscaling of reanalysis. J. Geophys. Res.
Atmos., 115, D17115.
Kanamitsu, M., W. Ebisuzaki, J. Woollen, S. K. Yang, J. J. Hnilo, M. Fiorino, and G. L.
Potter, 2002: NCEP-DOE AMIP-II reanalysis (R-2). Bull. Am. Meteorol. Soc., 83,
1631–1643.
Karlsson, J., and G. Svensson, 2010: The simulation of Arctic clouds and their
influence on the winter surface temperature in present-day climate in the CMIP3
multi-model dataset. Clim. Dyn., 36, 623–635.
Karlsson, J., G. Svensson, and H. Rodhe, 2008: Cloud radiative forcing of subtropical
low level clouds in global models. Clim. Dyn., 30, 779–788.
Karpechko, A., N. Gillett, G. Marshall, and A. Scaife, 2008: Stratospheric influence on
circulation changes in the Southern Hemisphere troposphere in coupled climate
models. Geophys. Res. Lett., 35, L20806.
Karpechko, A. Y., and E. Manzini, 2012: Stratospheric influence on tropospheric
climate change in the Northern Hemisphere. J. Geophys. Res. Atmos., 117,
D05133.
Karpechko, A. Y., N. P. Gillett, G. J. Marshall, and J. A. Screen, 2009: Climate impacts
of the southern annular mode simulated by the CMIP3 models. J. Clim., 22,
6149–6150.
Kattsov, V. M., et al., 2010: Arctic sea-ice change: A grand challenge of climate
science. J. Glaciol., 56, 1115–1121.
Kavvada, A., A. Ruiz-Barradas, and S. Nigam, 2013: AMO’s structure and climate
footprint in observations and IPCC AR5 climate simulations. Clim. Dyn.,
doi:10.1007/s00382–013–1712–1.
Kawazoe, S., and W. Gutowski, 2013: Regional, very heavy daily precipitation in
NARCCAP simulations. J. Hydrometeorol., doi:10.1175/JHM-D-12-068.1.
Kay, J. E., M. M. Holland, and A. Jahn, 2011: Inter-annual to multi-decadal Arctic sea
ice extent trends in a warming world. Geophys. Res. Lett., 38, L15708.
Keeley, S. P. E., R. T. Sutton, and L. C. Shaffrey, 2012: The impact of North Atlantic sea
surface temperature errors on the simulation of North Atlantic European region
climate. Q. J. R. Meteorol. Soc., doi:10.1002/qj.1912.
Kendon, E. J., N. M. Roberts, C. A. Senior, and M. J. Roberts, 2012: Realism of rainfall
in a very high resolution regional climate model. J. Clim., 25, 5791–5806.
Khairoutdinov, M. F., D. A. Randall, and C. DeMott, 2005: Simulations of the
Atmospheric general circulation using a cloud-resolving model as a
superparameterization of physical processes. J. Atmos. Sci., 62, 2136–2154.
Kharin, V. V., F. W. Zwiers, X. B. Zhang, and G. C. Hegerl, 2007: Changes in temperature
and precipitation extremes in the IPCC ensemble of global coupled model
simulations. J. Clim., 20, 1419–1444.
Kharin, V. V., F. W. Zwiers, X. Zhang, and M. Wehner, 2012: Changes in temperature
and precipitation extremes in the CMIP5 ensemble. Clim. Change, doi:10.1007/
s10584-013-0705-8.
Khvorostyanov, D. V., G. Krinner, P. Ciais, M. Heimann, and S. A. Zimov, 2008a:
Vulnerability of permafrost carbon to global warming. Part I: Model description
and role of heat generated by organic matter decomposition. Tellus B, 60, 250–
264.
Khvorostyanov, D. V., P. Ciais, G. Krinner, S. A. Zimov, C. Corradi, and G. Guggenberger,
2008b: Vulnerability of permafrost carbon to global warming. Part II: Sensitivity
of permafrost carbon stock to global warming. Tellus B, 60, 265–275.
Kidston, J., and E. P. Gerber, 2010: Intermodel variability of the poleward shift of the
austral jet stream in the CMIP3 integrations linked to biases in 20th century
climatology. Geophys. Res. Lett., 37, L09708.
Kiehl, J. T., 2007: Twentieth century climate model response and climate sensitivity.
Geophys. Res. Lett., 34.
Kim, D., and V. Ramanathan, 2008: Solar radiation budget and radiative forcing due
to aerosols and clouds. J. Geophys. Res. Atmos., 113, D02203.
Kim, D., et al., 2012: The tropical subseasonal variability simulated in the NASA GISS
general circulation model. J. Clim., 25, 4641–4659.
Kim, D., et al., 2009: Application of MJO simulation diagnostics to climate models. J.
Clim., 22, 6413–6436.
Kim, H.-J., K. Takata, B. Wang, M. Watanabe, M. Kimoto, T. Yokohata, and T. Yasunari,
2011: Global monsoon, El Niño, and their interannual linkage simulated by
MIROC5 and the CMIP3 CGCMs. J. Clim., 24, 5604–5618.
Kim, S., and F.-F. Jin, 2011a: An ENSO stability analysis. Part I: Results from a hybrid
coupled model. Clim. Dyn., 36, 1593–1607.
Kim, S., and F.-F. Jin, 2011b: An ENSO stability analysis. Part II: Results from the
twentieth and twenty-first century simulations of the CMIP3 models. Clim. Dyn.,
36, 1609–1627.
Kim, S. T., and J.-Y. Yu, 2012: The two types of ENSO in CMIP5 models. Geophys. Res.
Lett., 39, L11704.
Kirkevåg, K., et al., 2013: Aerosol-climate interactions in the Norwegian Earth
System Model – NorESM1–M. Geophys. Model Dev., 6, 207–244.
Kistler, R., et al., 2001: The NCEP-NCAR 50–year reanalysis: Monthly means CD-ROM
and documentation. Bull. Am. Meteorol. Soc., 82, 247–267.
Kjellstrom, E., G. Nikulin, U. Hansson, G. Strandberg, and A. Ullerstig, 2011: 21st
century changes in the European climate: Uncertainties derived from an
ensemble of regional climate model simulations. Tellus A, 63, 24–40.
Kjellstrom, E., F. Boberg, M. Castro, J. Christensen, G. Nikulin, and E. Sanchez, 2010:
Daily and monthly temperature and precipitation statistics as performance
indicators for regional climate models. Clim. Res., 44 135–150.
Klein, P., and G. Lapeyre, 2009: The oceanic vertical pump induced by mesoscale and
submesoscale turbulence. Annu. Rev. Mar. Sci., 1, 351–375.
Klein, S. A., and C. Jakob, 1999: Validation and sensitivities of frontal clouds
simulated by the ECMWF model. Mon. Weather Rev., 127, 2514–2531.
Klein, S. A., B. J. Soden, and N. C. Lau, 1999: Remote sea surface temperature
variations during ENSO: Evidence for a tropical atmospheric bridge. J. Clim., 12,
917–932.
Klein, S. A., X. Jiang, J. Boyle, S. Malyshev, and S. Xie, 2006: Diagnosis of the
summertime warm and dry bias over the U.S. Southern Great Plains in the GFDL
climate model using a weather forecasting approach. Geophys. Res. Lett., 33,
L18805.
Klein, S. A., Y. Zhang, M. D. Zelinka, R. Pincus, J. S. Boyle, and P. J. Glecker, 2013: Are
climate model simulations of clouds improving? An evaluation using the ISCCP
simulator. J. Geophys. Res., doi:10.1002/jgrd.50141.
Klocke, D., R. Pincus, and J. Quaas, 2011: On constraining estimates of climate
sensitivity with present-day observations through model weighting. J. Clim., 24,
6092–6099.
Kloster, S., N. M. Mahowald, J. T. Randerson, and P. J. Lawrence, 2012: The impacts of
climate, land use, and demography on fires during the 21st century simulated by
CLM-CN. Biogeosciences, 9, 509–525.
Knight, J., et al., 2009: Do global temperature trends over the last decade falsify
climate predictions? [In: State of the Climate in 2008]. Bull. Am. Meteorol. Soc.,
90, S22–S23.
Knight, J. R., 2009: The Atlantic Multidecadal Oscillation inferred from the forced
climate response in Coupled General Circulation Models. J. Clim., 22, 1610–
1625.
Knutti, R., 2008: Why are climate models reproducing the observed global surface
warming so well? Geophys. Res. Lett., 35, L18704
Knutti, R., 2010: The end of model democracy? Clim. Change, 102, 395–404.
Knutti, R., and G. C. Hegerl, 2008: The equilibrium sensitivity of the Earth’s
temperature to radiation changes. Nature Geosci., 1, 735–743.
Knutti, R., and L. Tomassini, 2008: Constraints on the transient climate response
from observed global temperature and ocean heat uptake. Geophys. Res. Lett.,
35, L09701.
839
Evaluation of Climate Models Chapter 9
9
Knutti, R., and J. Sedlácek, 2013: Robustness and uncertainties in the new CMIP5
climate model projections. Nature Clim. Change, 3, 369–373.
Knutti, R., D. Masson, and A. Gettelman, 2013: Climate model genealogy: Generation
CMIP5 and how we got there. Geophys. Res. Lett., 40, 1194–1199.
Knutti, R., G. A. Meehl, M. R. Allen, and D. A. Stainforth, 2006: Constraining climate
sensitivity from the seasonal cycle in surface temperature. J. Clim., 19, 4224–
4233.
Knutti, R., F. Joos, S. A. Muller, G. K. Plattner, and T. F. Stocker, 2005: Probabilistic
climate change projections for CO
2
stabilization profiles. Geophys. Res. Lett.,
32, L20707.
Knutti, R., R. Furrer, C. Tebaldi, J. Cermak, and G. A. Meehl, 2010a: Challenges in
combining projections from multiple climate models. J. Clim., 23, 2739–2758.
Knutti, R., G. Abramowitz, M. Collins, V. Eyring, P. J. Gleckler, B. Hewitson, and L.
Mearns, 2010b: Good practice guidance paper on assessing and combining
multi model climate projections. In: Meeting Report of the Intergovernmental
Panel on Climate Change Expert Meeting on Assessing and Combining Multi
Model Climate Projections [T. F. Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, and
P.M. Midgley (eds.)]. IPCC Working Group I Technical Support Unit, University of
Bern, Bern, Switzerland.
Koch, D., et al., 2011: Coupled Aerosol-Chemistry-Climate Twentieth-Century
Transient Model investigation: Trends in short-lived species and climate
responses. J. Clim., 24, 2693–2714.
Koldunov, N. V., D. Stammer, and J. Marotzke, 2010: Present-day Arctic sea ice
variability in the coupled ECHAM5/MPI-OM model. J. Clim., 23, 2520–2543.
Koltzow, M., T. Iversen, and J. Haugen, 2008: Extended Big-Brother experiments: The
role of lateral boundary data quality and size of integration domain in regional
climate modelling. Tellus A, 60, 398–410.
Koltzow, M. A. O., T. Iversen, and J. E. Haugen, 2011: The importance of lateral
boundaries, surface forcing and choice of domain size for dynamical downscaling
of global climate simulations. Atmosphere, 2, 67–95.
Komuro, Y., et al., 2012: Sea-ice in twentieth-century simulations by new MIROC
Coupled Models: A comparison between models with high resolution and with
ice thickness distribution. J. Meteorol. Soc. Jpn,, 90A, 213–232.
Konsta, D., H. Chepfer, and J.-L. Dufresne, 2012: A process oriented characterization
of tropical oceanic clouds for climate model evaluation, based on a statistical
analysis of daytime A-train observations. Clim. Dyn., 39, 2091–2108.
Koster, R., et al., 2004: Regions of strong coupling between soil moisture and
precipitation. Science, 305, 1138–1140.
Kostopoulou, E., K. Tolika, I. Tegoulias, C. Giannakopoulos, S. Somot, C.
Anagnostopoulou, and P. Maheras, 2009: Evaluation of a regional climate model
using in situ temperature observations over the Balkan Peninsula. Tellus A, 61,
357–370.
Koven, C. D., W. J. Riley, and A. Stern, 2013: Analysis of permafrost thermal dynamics
and response to climate change in the CMIP5 Earth System Models. J. Clim., 26,
1877–1900.
Koven, C. J., et al., 2011: Permafrost carbon-climate feedbacks accelerate global
warming. Proc. Natl. Acad. Sci. U.S.A., 108, 14769–14774.
Kowalczyk, E. A., Y. P. Wang, R. M. Law, H. L. Davies, J. L. McGregor, and G. Abramowitz
2006: The CSIRO Atmosphere Biosphere LandExchange (CABLE) model for use
in climate models and as an offline model. CSIRO Marine and Atmospheric
Research paper 013,Victoria, Australia, 37 pp.
Kowalczyk, E. A., et al., 2013: The land surface model component of ACCESS:
Description and impact on the simulated surface climatology. Aust. Meteorol.
Oceanogr. J., 63, 65–82.
Kravtsov, S., and C. Spannagle, 2008: Multidecadal climate variability in observed
and modeled surface temperatures. J. Clim., 21, 1104–1121.
Krinner, G., et al., 2005: A dynamic global vegetation model for studies of the coupled
atmosphere-biosphere system. Global Biogeochem. Cycles, 19, GB1015.
Krüger, L., R. da Rocha, M. Reboita, and T. Ambrizzi, 2012: RegCM3 nested in
HadAM3 scenarios A2 and B2: Projected changes in extratropical cyclogenesis,
temperature and precipitation over the South Atlantic Ocean. Clim. Change,
113, 599–621.
Kuhlbrodt, T., and J. Gregory, 2012: Ocean heat uptake and its consequences
for the magnitude of sea level rise and climate change. Geophys. Res. Lett.,
doi:10.1029/2012GL052952.
Kuhlbrodt, T., R. S. Smith, Z. Wang, and J. M. Gregory, 2012: The influence of eddy
parameterizations on the transport of the Antarctic Circumpolar Current in
coupled climate models. Ocean Model., 52–53, 1–8.
Kusaka, H., T. Takata, and Y. Takane, 2010: Reproducibility of regional climate in
central Japan using the 4-km Resolution WRF Model. Sola, 6, 113–116.
Kusunoki, S., R. Mizuta, and M. Matsueda, 2011: Future changes in the East Asian
rain band projected by global atmospheric models with 20-km and 60-km grid
size. Clim. Dyn., 37, 2481–2493.
L’Ecuyer, T., and G. Stephens, 2007: The tropical atmospheric energy budget from the
TRMM perspective. Part II: Evaluating GCM representations of the sensitivity
of regional energy and water cycles to the 1998–99 ENSO Cycle. J. Clim., 20,
4548–4571.
Laine, A., G. Lapeyre, and G. Riviere, 2011: A quasigeostrophic model for moist storm
tracks. J. Atmos. Sci., 68, 1306–1322.
Laine, A., M. Kageyama, P. Braconnot, and R. Alkama, 2009: Impact of greenhouse
gas concentration changes on surface energetics in IPSL-CM4: Regional
warming patterns, land-sea warming ratios, and glacial-interglacial differences.
J. Clim., 22, 4621–4635.
Lamarque, J. F., et al., 2012: CAM-chem: Description and evaluation of interactive
atmospheric chemistry in the Community Earth System Model. Geosci. Model
Dev., 5, 369–411.
Lamarque, J. F., et al., 2010: Historical (1850–2000) gridded anthropogenic and
biomass burning emissions of reactive gases and aerosols: Methodology and
application. Atmos. Chem. Phys., 10, 7017–7039.
Lambert, F. H., G. R. Harris, M. Collins, J. M. Murphy, D. M. H. Sexton, and B. B.
B. Booth, 2012: Interactions between perturbations to different Earth system
componentssimulated by a fully-coupled climate model. Clim. Dyn., doi:10.1007/
s00382-012-1618-3.
Lambert, S., and G. Boer, 2001: CMIP1 evaluation and intercomparison of coupled
climate models. Clim. Dyn., 17, 83–106.
Landrum, L., M. M. Holland, D. P. Schneider, and E. Hunke, 2012: Antarctic sea ice
climatology, variability and late 20th century change in CCSM4. J. Clim., 25,
4817–4838.
Langenbrunner, B., and J. D. Neelin, 2013: Analyzing ENSO teleconnections in CMIP
models as a measure of model fidelity in simulating precipitation. J. Clim.,
doi:10.1175/JCLI-D-12-00542.1.
Laprise, R., 2008: Regional climate modelling. J. Comput. Phys., 227, 3641–3666.
Laprise, R., et al., 2008: Challenging some tenets of regional climate modelling.
Meteorol. Atmos. Phys., 100, 3–22.
Large, W., and S. Yeager, 2009: The global climatology of an interannually varying
air-sea flux data set. Clim. Dyn., 33, 341–364.
Larow, T. E., Y. K. Lim, D. W. Shin, E. P. Chassignet, and S. Cocke, 2008: Atlantic basin
seasonal hurricane simulations. J. Clim., 21, 3191–3206.
Lau, K. M., et al., 2008: The Joint Aerosol-Monsoon Experiment —A new challenge
for monsoon climate research. Bull. Am. Meteorol. Soc., 89, 369–383.
Lau, W. K. M., and D. E. Waliser, 2011: Intraseasonal Variability of the Atmosphere-
Ocean Climate System. Springer Science+Business Media, New York, NY, USA,
and Heidelberg, Germany.
Lawrence, D. M., et al., 2012: The CCSM4 Land Simulation, 1850–2005: Assessment
of surface climate and new capabilities. J. Clim., 25, 2240–2260.
Lawrence, D. M., et al., 2011: Parameterization improvements and functional and
structural advances in version 4 of the Community Land Model. J. Adv. Model.
Earth Syst., 3, 2011MS000045.
Le Quere, C., et al., 2005: Ecosystem dynamics based on plankton functional types
for global ocean biogeochemistry models. Global Change Biol., 11, 2016–2040.
Le Quere, C., et al., 2009: Trends in the sources and sinks of carbon dioxide. Nature
Geosci., 2, 831–836.
Lecomte, O., T. Fichefet, M. Vancoppenolle, and M. Nicolaus, 2011: A new snow
thermodynamic scheme for large-scale sea-ice models. Ann. Glaciol., 52, 337–
346.
Leduc, M., and R. Laprise, 2009: Regional climate model sensitivity to domain size.
Clim. Dyn., 32, 833–854.
Lee, D. S., et al., 2009: Aviation and global climate change in the 21st century. Atmos.
Environ., 43, 3520–3537.
Lee, T., D. E. Waliser, J.-L. F. Li, F. W. Landerer, and M. M. Gierach, 2013: Evaluation of
CMIP3 and CMIP5 wind stress climatology using satellite measurements and
atmospheric reanalysis products. J. Clim., doi:10.1175/JCLI-D-12-00591.1.
Legg, S., L. Jackson, and R. W. Hallberg, 2008: Eddy-resolving modelingof overflows.
In: Eddy Resolving Ocean Models, 177 ed. [M. Hecht, and H. Hasumi (eds.)].
American Geophysical Union, Washington, DC, pp. 63–82.
840
Chapter 9 Evaluation of Climate Models
9
Legg, S., et al., 2009: Improving oceanic overflow representation in climate models:
The Gravity Current Entrainment Climate Process Team. Bull. Am. Meteorol. Soc.,
90, 657–670.
Leloup, J., M. Lengaigne, and J.-P. Boulanger, 2008: Twentieth century ENSO
characteristics in the IPCC database. Clim. Dyn., 30, 277–291.
Lemoine, D. M., 2010: Climate sensitivity distributions dependence on the possibility
that models share biases. J. Clim., 23, 4395–4415.
Lenaerts, J., M. van den Broeke, S. Dery, E. van Meijgaard, W. van de Berg, S. Palm, and
J. Rodrigo, 2012: Modeling drifting snow in Antarctica with a regional climate
model: 1. Methods and model evaluation. J. Geophys. Res. Atmos., 117, D05108.
Lenderink, G., 2010: Exploring metrics of extreme daily precipitation in a large
ensemble of regional climate model simulations. Clim. Res., 44 151–166.
Lenderink, G., and E. Van Meijgaard, 2008: Increase in hourly precipitation extremes
beyond expectations from temperature changes. Nature Geosci., 1, 511–514.
Levine, R. C., and A. G. Turner, 2012: Dependence of Indian monsoon rainfall on
moisture fluxes across the Arabian Sea and the impact of coupled model sea
surface temperature biases. Clim. Dyn., 38, 2167–2190.
Levis, S., 2010: Modeling vegetation and land use in models of the Earth System.
Clim. Change, 1, 840–856.
Levitus, S., J. I. Antonov, T. P. Boyer, R. A. Locarnini, H. E. Garcia, and A. V. Mishonov,
2009: Global ocean heat content 1955–2008 in light of recently revealed
instrumentation problems. Geophys. Res. Lett., 36, L07608
Levy, H., L. W. Horowitz, M. D. Schwarzkopf, Y. Ming, J.-C. Golaz, V. Naik, and V.
Ramaswamy, 2013: The roles of aerosol direct and indirect effects in pastand
future climate change. J. Geophys. Res., doi:10.1002/jgrd.50192.
Lewis, T., and S. Lamoureux, 2010: Twenty-first century discharge and sediment yield
predictions in a small high Arctic watershed. Global Planet. Change, 71, 27–41.
Li, C., J.-S. von Storch, and J. Marotzke, 2013a: Deep-ocean heat uptake and
equilibrium climate response. Clim. Dyn., 40, 1071–1086.
Li, G., and S.-P. Xie, 2012: Origins of tropical-wide SST biases in CMIP multi-model
ensembles. Geophys. Res. Lett., 39, L22703.
Li, H. B., A. Robock, and M. Wild, 2007: Evaluation of Intergovernmental Panel on
Climate Change Fourth Assessment soil moisture simulations for the second half
of the twentieth century. J. Geophys. Res. Atmos., 112, D06106
Li, J.-L. F., D. E. Waliser, and J. H. Jiang, 2011a: Correction to “Comparisons of satellites
liquid water estimates to ECMWF and GMAO analyses, 20th century IPCC AR4
climate simulations, and GCM simulations”. Geophys. Res. Lett., 38, L24807.
Li, J.-L. F., et al., 2008: Comparisons of satellites liquid water estimates to ECMWF
and GMAO analyses, 20th century IPCC AR4 climate simulations, and GCM
simulations. Geophys. Res. Lett., 35, L19710.
Li, J., S.-P. X. and A. Mestas-Nunez, E. R. C. and Gang Huang, R. D’Arrigo, F. Liu, J. Ma,
and X. Zheng, 2011b: Interdecadal modulation of ENSO amplitude during the
last millennium. Nature Clim. Change, 1, 114–118.
Li, J. L. F., et al., 2012a: An observationally-based evaluation of cloud ice water in
CMIP3 and CMIP5 GCMs and contemporary reanalyses using contemporary
satellite data. J. Geophys. Res., 117, D16105.
Li, L., et al., 2013b: Development and Evaluation of Grid-point Atmospheric Model of
IAP LASG, Version 2.0 (GAMIL 2.0). Adv. Atmos. Sci., 30, 855–867.
Li, L., et al., 2012b: The Flexible Global Ocean-Atmosphere-Land System Model: Grid-
point Version 2: FGOALS-g2. Adv. Atmos. Sci., doi:10.1007/s00376–012–2140–6.
Li, T., and G. H. Philander, 1996: On the annual cycle in the eastern equatorial Pacific.
J. Clim., 9, 2986–2998.
Li, T., C. W. Tham, and C. P. Chang, 2001: A coupled air-sea-monsoon oscillator for the
tropospheric biennial oscillation. J. Clim., 14, 752–764.
Liebmann, B., R. M. Dole, C. Jones, I. Blade, and D. Allured, 2010: Influence of
choice of time period on global surface temperature trend wstimates. Bull. Am.
Meteorol. Soc., 91, 1485–1491.
Lienert, f., J. C. Fyfe, and W. J. Merryfield, 2011: Do climate models capture the
tropical influences on North Pacific sea surface temperature variability? J. Clim.,
24, 6203–6209.
Lin, A. L., and T. Li, 2008: Energy spectrum characteristics of Boreal Summer
Intraseasonal Oscillations: Climatology and variations during the ENSO
developing and decaying phases. J. Clim., 21, 6304–6320.
Lin, J.-L., 2007: The double-ITCZ problem in IPCC AR4 Coupled GCMs: Ocean-
atmosphere feedback analysis. J. Clim., 20, 4497–4525.
Lin, J. L., et al., 2006: Tropical intraseasonal variability in 14 IPCC AR4 climate models.
Part I: Convective signals. J. Clim., 19, 2665–2690.
Lin, P., Y. Yongqiang, and H. Liu, 2013: Long-term stability and oceanic mean state
simulated by the coupled model FGOALS-s2. Adv. Atmos. Sci., 30, 175–192.
Lin, Y., et al., 2012: TWP-ICE global atmospheric model intercomparison: Convection
responsiveness and resolution impact. J. Geophys. Res., 117, D09111.
Lindvall, J., G. Svensson, and C. Hannay, 2012: Evaluation of near-surface parameters
in the two versions of the atmospheric model in CESM1 using flux station
observations. J. Clim., 26 26–44.
Linkin, M., and S. Nigam, 2008: The north pacific oscillation-west Pacific
teleconnection pattern: Mature-phase structure and winter impacts. J. Clim., 21,
1979–1997.
Liu, H., C. Wang, S. K. Lee, and D. Enfield, 2013a: Atlantic Warm Pool Variability in the
CMIP5 Simulations. J. Clim., doi:10.1175/JCLI-D-12–00556.1.
Liu, H. L., P. F. Lin, Y. Q. Yu, and X. H. Zhang, 2012a: The baseline evaluation of LASG/
IAP Climate system Ocean Model (LICOM) version 2.0. Acta Meteorol. Sin., 26,
318–329.
Liu, J., 2010: Sensitivity of sea ice and ocean simulations to sea ice salinity in a
coupled global climate model. Science China Earth Sci., 53, 911–918.
Liu, L., W. Yu, and T. Li, 2011: Dynamic and thermodynamic air–sea coupling
associated with the Indian Ocean dipole diagnosed from 23 WCRP CMIP3
Models. J. Clim., 24, 4941–4958.
Liu, S. C., C. B. Fu, C. J. Shiu, J. P. Chen, and F. T. Wu, 2009: Temperature dependence
of global precipitation extremes. Geophys. Res. Lett., 36, L17702.
Liu, X., et al., 2012b: Toward a minimal representation of aerosols in climate models:
Description and evaluation in the Community Atmosphere Model CAM5.
Geophys. Model Dev., 5, 709–739.
Liu, X. H., et al., 2007: Uncertainties in global aerosol simulations: Assessment using
three meteorological data sets. J. Geophys. Res. Atmos., 112, D11212
Liu, Y., 1996: Modeling the emissions of nitrous oxide and methane from the
terrestrial biosphere to the atmosphere. In: Joint Program Report Series. MIT
Joint Program on the Science and Policy of Global Change, Cambridge, MA,
USA, 219 pp.
Liu, Y., J. Hu, B. He, Q. Bao, A. Duan, and G. X. Wu, 2013b: Seasonal evolution of
subtropical anticyclones in the Climate System Model FGOALS-s2. Adv. Atmos.
Sci., 30, 593–606.
Lloyd, J., E. Guilyardi, and H. Weller, 2010: The role of atmosphere feedbacks during
ENSO in the CMIP3 models. Part II: Using AMIP runs to understand the heat flux
feedback mechanisms. Clim. Dyn., 37, 1271–1292.
Lloyd, J., E. Guilyardi, and H. Weller, 2012: The role of atmosphere feedbacks during
ENSO in the CMIP3 Models. Part III: The Shortwave Flux Feedback. J. Clim., 25,
4275–4293.
Lloyd, J., E. Guilyardi, H. Weller, and J. Slingo, 2009: The role of atmosphere feedbacks
during ENSO in the CMIP3 models. Atmos. Sci. Lett., 10, 170–176.
Loeb, N. G., et al., 2009: Toward optimal closure of the Earth’s top-of-atmosphere
radiation budget. J. Clim., 22, 748–766.
Lohmann, U., K. von Salzen, N. McFarlane, H. G. Leighton, and J. Feichter, 1999:
Tropospheric sulfur cycle in the Canadian general circulation model. J. Geophys.
Res. Atmos., 104, 26833–26858.
Long, M. C., K. Lindsay, S. Peacock, J. K. Moore, and S. C. Doney, 2012: Twentieth-
century oceanic carbon uptake and storage in CESM1(BGC). J. Clim., doi:10.1175/
JCLI-D-12-00184.1.
Loptien, U., O. Zolina, S. Gulev, M. Latif, and V. Soloviov, 2008: Cyclone life cycle
characteristics over the Northern Hemisphere in coupled GCMs. Clim. Dyn., 31,
507–532.
Lorenz, P., and D. Jacob, 2005: Influence of regional scale information on the global
circulation: A two-way nesting climate simulation. Geophys. Res. Lett., 32,
L18706.
Lorenz, R., E. L. Davin, and S. I. Seneviratne, 2012: Modeling land-climate coupling
in Europe: Impact of land surface representation on climate variability and
extremes. J. Geophys. Res., 117, doi:10.1029/2012JD017755.
Losch, M., D. Menemenlis, J.-M. Campin, P. Heimbach, and C. Hill, 2010: On the
formulation of sea-ice models. Part 1: Effects of different solver implementations
and parameterizations. Ocean Model., 33, 129–144.
Loschnigg, J., G. A. Meehl, P. J. Webster, J. M. Arblaster, and G. P. Compo, 2003: The
Asian monsoon, the tropospheric biennial oscillation, and the Indian Ocean
zonal mode in the NCAR CSM. J. Clim., 16, 1617–1642.
Loutre, M. F., A. Mouchet, T. Fichefet, H. Goosse, H. Goelzer, and P. Huybrechts,
2011: Evaluating climate model performance with various parameter sets using
observations over the recent past. Clim. Past, 7, 511–526.
Loyola, D., and M. Coldewey-Egbers, 2012: Multi-sensor data merging with stacked
neural networks for the creation of satellite long-term climate data records.
Eurasip J. Adv. Signal Proc., doi:10.1186/1687–6180–2012–91.
841
Evaluation of Climate Models Chapter 9
9
Loyola, D., et al., 2009: Global long-term monitoring of the ozone layer—a
prerequisite for predictions. Int. J. Remote Sens., 30, 4295–4318.
Lu, J., G. A. Vecchi, and T. Reichler, 2007: Expansion of the Hadley cell under global
warming. Geophys. Res. Lett., 34, L06805.
Lu, J. H., and J. J. Ji, 2006: A simulation and mechanism analysis of long-term
variations at land surface over arid/semi-arid area in north China. J. Geophys.
Res. Atmos., 111, D09306.
Lucarini, V., and F. Ragone, 2011: Energetics of climate models: Net energy balance
and meridional enthalpy transport. Rev. Geophys., 49, RG1001.
Lucas-Picher, P., S. Somot, M. Déqué, B. Decharme, and A. Alias, 2012a: Evaluation of
the regional climate model ALADIN to simulate the climate over North America
in the CORDEX framework. Clim. Dyn., doi:10.1007/s00382-012-1613-8.
Lucas-Picher, P., M. Wulff-Nielsen, J. Christensen, G. Adalgeirsdottir, R. Mottram, and
S. Simonsen, 2012b: Very high resolution regional climate model simulations
over Greenland: Identifying added value. J. Geophys. Res. Atmos., 117, D02108.
Lumpkin, R., K. G. Speer, and K. P. Koltermann, 2008: Transport across 48°N in the
Atlantic Ocean. J. Phys. Oceanogr., 38, 733–752.
Luo, J. J., S. Masson, E. Roeckner, G. Madec, and T. Yamagata, 2005: Reducing
climatology bias in an ocean-atmosphere CGCM with improved coupling
physics. J. Clim., 18, 2344–2360.
Lynn, B., R. Healy, and L. Druyan, 2009: Quantifying the sensitivity of simulated
climate change to model configuration. Clim. Change, 92, 275–298.
MacKinnon, J., et al., 2009: Using global arrays to investigate internal-wavesand
mixing. In: OceanObs09: Sustained Ocean Observations and Information for
Society, Venice, Italy, ESA.
Madden, R. A., and P. R. Julian, 1972: Description of global-scale circulation ells in
tropics with a 40–50 day period. J. Atmos. Sci., 29, 1109–1123.
Madden, R. A., and P. R. Julian, 1994: Observations of the 40–50-Day Tropical
Oscillation—a Review. Mon. Weather Rev., 122, 814–837.
Madec, G., 2008: NEMO ocean engine. Technical Note. Institut Pierre-Simon Laplace
(IPSL), France, 300pp.
Madec, G., P. Delecluse, M. Imbard, and C. Levy, 1998: OPA 8.1 ocean general
circulation model reference manual. IPSL Note du Pole de Modelisation, Institut
Pierre-Simon Laplace (IPSL), France, 91 pp.
Mahlstein, I., and R. Knutti, 2010: Regional climate change patterns identified by
cluster analysis. Clim. Dyn., 35, 587–600.
Mahlstein, I., and R. Knutti, 2012: September Arctic sea ice predicted to disappear
near 2C global warming above present. J. Geophys. Res., 117, D06104.
Maier-Reimer, E., I. Kriest, J. Segschneider, and P. Wetze, 2005: The HAMburg Ocean
Carbon Cycle Model HAMOCC 5.1-Technical Description Release 1.1. Tech.
Rep. 14, Rep. Earth Syst. Sci., Max Planck Institute for Meteorology, Hamburg,
Germany, 50 pp.
Mantua, N. J., S. R. Hare, Y. Zhang, J. M. Wallace, and R. C. Francis, 1997: A Pacific
interdecadal climate oscillation with impacts on salmon production. Bull. Am.
Meteorol. Soc., 78, 1069–1079.
Manzini, E., C. Cagnazzo, P. G. Fogli, A. Bellucci, and W. A. Muller, 2012: Stratosphere-
troposphere coupling at inter-decadal time scales: Implications for the North
Atlantic Ocean. Geophys. Res. Lett., 39, L05801.
Maraun, D., 2012: Nonstationarities of regional climate model biases in European
seasonal mean temperature and precipitation sums. Geophys. Res. Lett., 39,
L06706.
Maraun, D., H. Rust, and T. Osborn, 2010a: Synoptic airflow and UK daily precipitation
extremes: Development and validation of a vector generalised linear model.
Extremes, 13, 133–153.
Maraun, D., et al., 2010b: Precipitation downscaling under climate change: Recent
developments to bridge the gap between dynamical models and the end user.
Rev. Geophys., 48, RG3003.
Marchand, R., N. Beagley, and T. P. Ackerman, 2009: Evaluation of hydrometeor
occurrence profiles in the Multiscale Modeling Framework Climate Model using
atmospheric classification. J. Clim., 22, 4557–4573.
Markovic, M., H. Lin, and K. Winger, 2010: Simulating global and North American
climate using the Global Environmental Multiscale Model with a Variable-
Resolution Modeling Approach. Mon. Weather Rev., 138, 3967–3987.
Marsh, R., S. A. Mueller, A. Yool, and N. R. Edwards, 2011: Incorporation of the
C-GOLDSTEIN efficient climate model into the GENIE framework: “eb_go_gs”
configurations of GENIE. Geophys. Model Dev., 4, 957–992.
Marsh, R., et al., 2009: Recent changes in the North Atlantic circulation simulated
with eddy-permitting and eddy-resolving ocean models. Ocean Model., 28,
226–239.
Marsland, S. J., et al., 2013: Evaluation of ACCESS Climate Model ocean diagnostics
in CMIP5 simulations. Aust. Meteorol. and Oceanogr. J., 63,101–119.
Martin, G. M., and R. C. Levine, 2012: The influence of dynamic vegetation on
the present-day simulation and future projectons of the South Asian summer
monsoon in the HadGEM2 family. Earth Syst. Dyn., 2, 245–261.
Martin, G. M., et al., 2011: The HadGEM2 family of Met Office Unified Model climate
configurations. Geophys. Model Dev., 4, 723–757.
Masarie, K. A., and P. P. Tans, 1995: Extension and integration of atmospheric carbon
dioxide data into a globally consistent measurement record. J. Geophys. Res.
Atmos., 100, 11593–11610.
Masato, G., B. Hoskins, and T. Woollings, 2012: Winter and summer Northern
Hemisphere blocking in CMIP5 models. J. Clim., doi:10.1175/JCLI-D-12-00466.1.
Masson-Delmotte, V., et al., 2010: EPICA Dome C record of glacial and interglacial
intensities. Quat. Sci. Rev., 29, 113–128.
Masson-Delmotte, V., et al., 2006: Past and future polar amplification of climate
change: Climate model intercomparisons and ice-core constraints. Clim. Dyn.,
27, 437–440.
Masson, D., and R. Knutti, 2011a: Climate model genealogy. Geophys. Res. Lett.,
38, L08703.
Masson, D., and R. Knutti, 2011b: Spatial-scale dependence of climate model
performance in the CMIP3 ensemble. J. Clim., 24, 2680–2692.
Masson, D., and R. Knutti, 2013: Predictor screening, calibration and observational
constraints in climate model ensembles: An illustration using climate sensitivity.
J. Clim., 26, 887–898.
Massonnet, F., T. Fichefet, H. Goosse, C. M. Bitz, G. Philippon-Berthier, M. M. Holland,
and P.-Y. Barriat, 2012: Constraining projections of summer Arctic sea ice.
Cryosphere, 6, 1383–1394.
Mastrandrea, M. D., et al., 2011: Guidance Note for Lead Authors of the IPCC Fifth
Assessment Report on Consistent Treatment of Uncertainties. Intergovernmental
Panel on Climate Change (IPCC). IPCC guidance note, Jasper Ridge, CA, USA, 7
pp.
Materia, S., P. A. Dirmeyer, Z. C. Guo, A. Alessandri, and A. Navarra, 2010: The
sensitivity of simulated river discharge to land surface representation and
meteorological forcings. J. Hydrometeorol., 11, 334–351.
Matsueda, M., 2009: Blocking predictability in operational medium-range ensemble
forecasts. Sola, 5, 113–116.
Matsueda, M., R. Mizuta, and S. Kusunoki, 2009: Future change in wintertime
atmospheric blocking simulated using a 20-km-mesh atmospheric global
circulation model. J. Geophys. Res. Atmos., 114, D12114.
Matsueda, M., H. Endo, and R. Mizuta, 2010: Future change in Southern Hemisphere
summertime and wintertime atmospheric blockings simulated using a
20-km-mesh AGCM. Geophys. Res. Lett., 37, L02803.
Matsumoto, K., K. S. Tokos, A. R. Price, and S. J. Cox, 2008: First description of the
Minnesota Earth System Model for Ocean biogeochemistry (MESMO 1.0).
Geophys. Model Dev., 1, 1–15.
Maurer, E., and H. Hidalgo, 2008: Utility of daily vs. monthly large-scale climate data:
An intercomparison of two statistical downscaling methods. Hydrol. Earth Syst.
Sci., 12, 551–563.
Mauritsen, T., et al., 2012: Tuning the climate of a global model. J. Adv. Model. Earth
Syst., 4, M00A01.
Maximenko, N., et al., 2009: Mean dynamic topography of the ocean derived from
satellite and drifting buoy data using three different techniques. J. Atmos. Ocean.
Technol., 26, 1910–1919.
May, P. T., J. H. Mather, G. Vaughan, K. N. Bower, C. Jakob, G. M. McFarquhar, and G.
G. Mace, 2008: The Tropical Warm Pool International Cloud Experiment. Bull. Am.
Meteorol. Soc., 89, 629–645.
May, W., 2007: The simulation of the variability and extremes of daily precipitation
over Europe by the HIRHAM regional climate model. Global Planet. Change,
57, 59–82.
McCarthy, G., et al., 2012: Observed interannual variability of the Atlantic meridional
overturning circulation at 26.5 degrees N. Geophys. Res. Lett., 39, L19609.
McClean, J. L., and J. C. Carman, 2011: Investigation of IPCC AR4 coupled climate
model North Atlantic modewater formation. Ocean Model., 40, 14–34.
McClean, J. L., M. E. Maltrud, and F. O. Bryan, 2006: Measures of the fidelity of
eddying ocean models. Oceanography, 19, 104–117.
McClean, J. L., et al., 2011: A prototype two-decade fully-coupled fine-resolution
CCSM simulation. Ocean Model. 39, 10–30.
842
Chapter 9 Evaluation of Climate Models
9
McCormack, J. P., S. D. Eckermann, D. E. Siskind, and T. J. McGee, 2006: CHEM2D-OPP:
A new linearized gas-phase ozone photochemistry parameterization for high-
altitude NWP and climate models. Atmos. Chem. Phys., 6, 4943–4972.
McCrary, R. R., and D. A. Randall, 2010: Great plains drought in simulations of the
twentieth century. J. Clim., 23, 2178–2196.
McDonald, R. E., 2011: Understanding the impact of climate change on Northern
Hemisphere extra-tropical cyclones. Clim. Dyn., 37, 1399–1425.
McDougall, T. J., and P. C. McIntosh, 2001: The temporal-residual-mean velocity. Part
II: Isopycnal interpretation and the tracer and momentum equations. J. Phys.
Oceanogr., 31, 1222–1246.
McKitrick, R., S. McIntyre, and C. Herman, 2010: Panel and multivariate methods for
tests of trend equivalence in climate data series. Atmos. Sci. Lett., 11, 270–277.
McKitrick, R., S. McIntyre, and C. Herman, 2011: Panel and multivariate methods for
tests of trend equivalence in climate data series. Atmos. Sci. Lett., 12, 386–388.
McLandress, C., T. Shepherd, J. Scinocca, D. Plummer, M. Sigmond, A. Jonsson, and
M. Reader, 2011: Separating the dynamical effects of climate change and ozone
depletion. Part II Southern Hemisphere troposphere. J. Clim., 24, 1850–1868.
McLaren, A. J., et al., 2006: Evaluation of the sea ice simulation in a new coupled
atmosphere-ocean climate model (HadGEM1). J. Geophys. Res. Oceans, 111,
C12014.
McManus, J. F., R. Francois, J. M. Gherardi, L. D. Keigwin, and S. Brown-Leger, 2004:
Collapse and rapid resumption of Atlantic meridional circulation linked to
deglacial climate changes. Nature, 428, 834–837.
McWilliams, J. C., 2008: The nature and consequences of oceanic eddies. In: Ocean
Modeling in an Eddying Regime [M. Hecht and H. Hasumi (eds.)]. American
Geophysical Union, Washington, DC, pp. 5–15.
Mearns, L. O., et al., 2012: The North American Regional Climate Change Assessment
Program: Overview of Phase I Results. Bull. Am. Meteorol. Soc., 93, 1337–1362.
Mears, C. A., F. J. Wentz, P. Thorne, and D. Bernie, 2011: Assessing uncertainty in
estimates of atmospheric temperature changes from MSU and AMSU using a
Monte-Carlo estimation technique. J. Geophys. Res., 116, D08112.
Mears, C. A., B. D. Santer, F. J. Wentz, K. E. Taylor, and M. F. Wehner, 2007: Relationship
between temperature and precipitable water changes over tropical oceans.
Geophys. Res. Lett., 34, L24709.
Meehl, G. A., and J. M. Arblaster, 2011: Decadal variability of Asian-Australian
Monsoon-ENSO-TBO relationships. J. Clim., 24, 4925–4940.
Meehl, G. A., and H. Teng, 2012: Case studies for initialized decadal hindcasts and
predictions for the Pacific region. Geophys. Res. Lett., 39, L22705.
Meehl, G. A., J. M. Arblaster, and J. Loschnigg, 2003: Coupled ocean-atmosphere
dynamical processes in the tropical Indian and Pacific Oceans and the TBO. J.
Clim., 16, 2138–2158.
Meehl, G. A., J. M. Arblaster, J. T. Fasullo, A. X. Hu, and K. E. Trenberth, 2011: Model-
based evidence of deep-ocean heat uptake during surface-temperature hiatus
periods. Nature Clim. Change, 1, 360–364.
Meehl, G. A., A. Hu, J. Arblaster, J. Fasullo, and K. E. Trenberth, 2013a: Externally
forced and internally generated decadal climate variability associated with the
Interdecadal Pacific Oscillation. J. Clim., doi:10.1175/JCLI-D-12-00548.1.
Meehl, G. A., P. R. Gent, J. M. Arblaster, B. L. Otto-Bliesner, E. C. Brady, and A. Craig,
2001: Factors that affect the amplitude of El Niño in global coupled climate
models. Clim. Dyn., 17, 515–526.
Meehl, G. A., J. M. Arblaster, J. M. Caron, H. Annamalai, M. Jochum, A. Chakraborty,
and R. Murtugudde, 2012: Monsoon regimes and processes in CCSM4. Part I: The
Asian-Australian Monsoon. J. Clim., 25, 2583–2608.
Meehl, G. A., et al., 2007: The WCRP CMIP3 multimodel dataset —A new era in
climate change research. Bull. Am. Meteorol. Soc., 88, 1383–1394.
Meehl, G. A., et al., 2009: Decadal prediction: Can it be skillful? Bull. Am. Meteorol.
Soc., 90, 1467–1485.
Meehl, G. A., et al., 2013b: Decadal climate prediction: An update from the trenches.
Bull. Am. Meteorol. Soc., doi:10.1175/BAMS-D-12-00241.1.
Meijers, A., E. Shuckburgh, N. Bruneau, J.-B. Sallee, T. Bracegirdle, and Z. Wang,
2012: Representation of the Antarctic Circumpolar Current in the CMIP5 climate
models and future changes under warming scenarios. J. Geophys. Res. Oceans,
117, C12008.
Meinshausen, M., et al., 2009: Greenhouse-gas emission targets for limiting global
warming to 2 degrees C. Nature, 458, 1158–1162.
Meinshausen, M., et al., 2011: The RCP greenhouse gas concentrations and their
extensions from 1765 to 2300. Clim. Change, 109, 213–241.
Meissner, K. J., A. J. Weaver, H. D. Matthews, and P. M. Cox, 2003: The role of land
surface dynamics in glacial inception: a study with the UVic Earth System Model.
Clim. Dyn., 21, 515–537.
Melillo, J. M., A. D. McGuire, D. W. Kicklighter, B. Moore, C. J. Vorosmarty, and A.
L. Schloss, 1993: Global climate-change and terrestrial net primary production.
Nature, 363, 234–240.
Melsom, A., V. Lien, and W. P. Budgell, 2009: Using the Regional Ocean Modeling
System (ROMS) to improve the ocean circulation from a GCM 20th century
simulation. Ocean Dyn., 59, 969–981.
Menary, M., W. Park, K. Lohmann, M. Vellinga, D. Palmer, M. Latif, and J. H. Jungclaus,
2012: A multimodel comparison of centennial Atlantic meridional overturning
circulation variability. Clim. Dyn., 38, 2377–2388.
Menendez, C., M. de Castro, A. Sorensson, J. Boulanger, and C. M. Grp, 2010: CLARIS
Project: Towards climate downscaling in South America. Meteorol. Z., 19, 357–
362.
Menon, S., D. Koch, G. Beig, S. Sahu, J. Fasullo, and D. Orlikowski, 2010: Black carbon
aerosols and the third polar ice cap. Atmos. Chem. Phys., 10, 4559–4571.
Mercado, L. M., C. Huntingford, J. H. C. Gash, P. M. Cox, and V. Jogireddy, 2007:
Improving the representation of radiation interception and photosynthesis for
climate model applications. Tellus B, 59, 553–565.
Merrifield, M. A., and M. E. Maltrud, 2011: Regional sea level trends due to a Pacific
trade wind intensification. Geophys. Res. Lett., 38, L21605.
Merryfield, W. J., et al., 2013: The Canadian Seasonal to Interannual Prediction
System. Part I: Models and Initialization. Mon. Weather Rev., doi:10.1175/MWR-
D-12-00216.1.
Mieville, A., et al., 2010: Emissions of gases and particles from biomass burning
during the 20th century using satellite data and an historical reconstruction.
Atmos. Environ., 44, 1469–1477.
Miller, A., et al., 2002: A cohesive total ozone data set from the SBUV(/2) satellite
system. J. Geophys. Res. Atmos., 107, 4701.
Milliff, R., A. Bonazzi, C. Wikle, N. Pinardi, and L. Berliner, 2011: Ocean ensemble
forecasting. Part I: Ensemble Mediterranean winds from a Bayesian hierarchical
model. Q. J. R. Meteorol. Soc., 137, 858–878.
Milly, P. C. D., and A. B. Shmakin, 2002: Global modeling of land water and energy
balances. Part I: the land dynamics (LaD) model. J. Hydrometeorol., 3, 283–299.
Min, S. K., X. B. Zhang, F. W. Zwiers, and G. C. Hegerl, 2011: Human contribution to
more-intense precipitation extremes. Nature, 470, 376–379.
Minobe, S., 1997: A 50–70 year climatic oscillation over the North Pacific and North
America. Geophys. Res. Lett., 24, 683–686.
Minobe, S., 1999: Resonance in bidecadal and pentadecadal climate oscillations
over the North Pacific: Role in climatic regime shifts. Geophys. Res. Lett., 26,
855–858.
Misra, V., 2007: Addressing the issue of systematic errors in a regional climate
model. J. Clim., 20, 801–818.
Mitchell, T. D., and P. D. Jones, 2005: An improved method of constructing a database
of monthly climate observations and associated high-resolution grids. Int. J.
Climatol. , 25, 693–712.
Miyama, T., and M. Kawamiya, 2009: Estimating allowable carbon emission for
CO(2) concentration stabilization using a GCM-based Earth system model.
Geophys. Res. Lett., 36, L19709.
Mizuta, R., et al., 2012: Climate simulations using MRI-AGCM3.2 with 20-km grid. J.
Meteorol. Soc. Jpn., 90A, 233–258.
Molteni, F., 2003: Atmospheric simulations using a GCM with simplified physical
parameterizations. I: Model climatology and variability in multi-decadal
experiments. Clim. Dyn., 20, 175–191.
Montoya, M., A. Griesel, A. Levermann, J. Mignot, M. Hofmann, A. Ganopolski, and S.
Rahmstorf, 2005: The earth system model of intermediate complexity CLIMBER-3
alpha. Part 1: description and performance for present-day conditions. Clim.
Dyn., 25, 237–263.
Morgenstern, O., et al., 2010: Anthropogenic forcing of the Northern Annular Mode
in CCMVal-2 models. J. Geophys. Res., 115, D00M03.
Morice, C. P., J. J. Kennedy, N. A. Rayner, and P. D. Jones, 2012: Quantifying
uncertainties in global and regional temperature change using an ensemble of
observational estimates: The HadCRUT4 data set. J. Geophys. Res. Atmos., 117,
D08101.
Moss, R. H., et al., 2010: The next generation of scenarios for climate change research
and assessment. Nature, 463, 747–756.
843
Evaluation of Climate Models Chapter 9
9
Mouchet, A., and L. M. François, 1996: Sensitivity of a global oceanic carbon cycle
model to the circulation and the fate of organic matter: Preliminary results. Phys.
Chem. Earth, 21, 511–516.
Msadek, R., and C. Frankignoul, 2009: Atlantic multidecadal oceanic variability and
its influence on the atmosphere in a climate model. Clim. Dyn., 33, 45–62.
Msadek, R., W. E. Johns, S. G. Yeager, G. Danabasoglu, T. Delworth, and T. Rosati,
2013: The Atlantic meridional heat transport at 26.5ºN and its relationship with
the MOC in the RAPID-array and GFDL and NCAR coupled models. J. Clim.,
doi:10.1175/JCLI-D-12–00081.1.
Mueller, B., et al., 2011: Evaluation of global observations-based evapotranspiraion
datasets and IPCC AR4 simulations. Geophys. Res. Lett., 38, L06402.
Muller, S. A., F. Joos, N. R. Edwards, and T. F. Stocker, 2006: Water mass distribution
and ventilation time scales in a cost-efficient, three-dimensional ocean model.
J. Clim., 19, 5479–5499.
Murakami, H., and M. Sugi, 2010: Effect of model resolution on tropical cyclone
climate projections. Sola, 6, 73–76.
Murakami, H., et al., 2012: Future changes in tropical cyclone activity projected by
the new high-resolution MRI-AGCM. J. Clim., 25, 3237–3260.
Murphy, D. M., 2013: Little net clear-sky radiative forcing from recent regional
redistribution of aerosols. Nature Geosci., 6, 258–262.
Murphy, J., B. Booth, M. Collins, G. Harris, D. Sexton, and M. Webb, 2007: A
methodology for probabilistic predictions of regional climate change from
perturbed physics ensembles. Philos.Trans. R. Soc. London A, 365 1993–2028.
Murphy, J. M., D. M. H. Sexton, D. N. Barnett, G. S. Jones, M. J. Webb, M. Collins,
and D. A. Stainforth, 2004: Quantification of modelling uncertainties in a large
ensemble of climate change simulations. Nature, 430, 768–772.
Murtugudde, R., J. Beauchamp, C. R. McClain, M. Lewis, and A. J. Busalacchi, 2002:
Effects of penetrative radiation on the upper tropical ocean circulation. J. Clim.,
15, 470–486.
Muryshev, K. E., A. V. Eliseev, I. I. Mokhov, and N. A. Diansky, 2009: Validating and
assessing the sensitivity of the climate model with an ocean general circulation
model developed at the Institute of Atmospheric Physics, Russian Academy of
Sciences. Izvestiya Atmos. Ocean. Phys., 45, 416–433.
Nagura, M., W. Sasaki, T. Tozuka, J.-J. Luo, S. K. Behera, and T. Yamagata, 2013:
Longitudinal biases in the Seychelles Dome simulated by 35 ocean-atmosphere
coupled general circulation models. J. Geophys. Res., doi:10.1029/2012JC008352.
Nakano, H., H. Tsujino, M. Hirabara, T. Yasuda, T. Motoi, M. Ishii, and G. Yamanaka,
2011: Uptake mechanism of anthropogenic CO
2
in the Kuroshio Extension
region in an ocean general circulation model. J. Oceanogr., 67, 765–783.
Nam, C., S. Bony, J. L. Dufresne, and H. Chepfer, 2012: The ‘too few, too bright’
tropical low-cloud problem in CMIP5 models. Geophys. Res. Lett., 39, L21801.
Nanjundiah, R. S., V. Vidyunmala, and J. Srinivasan, 2005: The impact of increase
in CO
2
on the simulation of tropical biennial oscillations (TBO) in 12 coupled
general circulation models. Atmos. Sci. Lett., 6, 183–191.
Naoe, H., and K. Shibata, 2010: Equatorial quasi-biennial oscillation influence on
northern winter extratropical circulation. J. Geophys. Res. Atmos., 115, D19102.
Neale, R. B., J. H. Richter, and M. Jochum, 2008: The Impact of Convection on ENSO:
From a delayed oscillator to a series of events. J. Clim., 21, 5904–5924.
Neale, R. B., J. Richter, S. Park, P. H. Lauritzen, S. J. Vavrus, P. J. Rasch, and M. Zhang,
2013: The Mean Climate of the Community Atmosphere Model (CAM4) in forced
SST and fully coupled experiments. J. Clim., doi:10.1175/JCLI-D-12-00236.1.
Neale, R. B., et al., 2010: Description of the NCAR Community Atmosphere Model
(CAM 4.0). NCAR Technical Note NCAR/TN-486+STR, National Center for
Atmopsheric Research, Boulder, CO, 268 pp.
Neelin, J. D., 2007: Moist dynamics of tropical convection zones in monsoons,
teleconnections and global warming. In: The Global Circulation of the
Atmosphere [T. Schneider and A. Sobel (eds.)]. Princeton University Press,
Princeton, NJ. 385 pp.
Neelin, J. D., and N. Zeng, 2000: A quasi-equilibrium tropical circulation model—
Formulation. J. Atmos. Sci., 57, 1741–1766.
Neelin, J. D., C. Chou, and H. Su, 2003: Tropical drought regions in global warming
and El Niño teleconnections. Geophys. Res. Lett., 30, 2275.
Neelin, J. D., A. Bracco, H. Luo, J. C. McWilliams, and J. E. Meyerson, 2010:
Considerations for parameter optimization and sensitivity in climate models.
Proc. Nat. Acad. Sci. U.S.A., 107, 21349–21354.
Neggers, R. A. J., 2009: A dual mass flux framework for boundary layer convection.
Part II: Clouds. J. Atmos. Sci., 66, 1489–1506.
Neggers, R. A. J., M. Kohler, and A. C. M. Beljaars, 2009: A dual mass flux framework
for boundary layer convection. Part I: Transport. J. Atmos. Sci., 66, 1465–1487.
Nicolsky, D. J., V. E. Romanovsky, V. A. Alexeev, and D. M. Lawrence, 2007: Improved
modeling of permafrost dynamics in a GCM land-surface scheme. J. Geophys.
Res., 34, L08501.
Nikiema, O., and R. Laprise, 2010: Diagnostic budget study of the internal variability
in ensemble simulations of the Canadian RCM. Clim. Dyn., 36 2313–2337.
Nikulin, G., et al., 2012: Precipitation climatology in an ensemble of CORDEX-Africa
regional climate simulations. J. Clim., doi:10.1175/jcli-d-11–00375.1.
Ning, L., M. E. Mann, R. Crane, and T. Wagener, 2011: Probabilistic projections of
climate change for the Mid-Atlantic region of the United States—Validation of
precipitation downscaling during the Historical Era. J. Clim., 25, 509–526.
Nishii, K., et al., 2012: Relationship of the reproducibility of multiple variables among
Global Climate Models. J. Meteorol. Soc. Jpn., 90A, 87–100.
Notz, D., F. A. Haumann, H. Haak, J. H. Jungclaus, and J. Marotzke, 2013: Sea-ice
evolution in the Arctic as modeled by MPI-ESM. J. Adv. Model. Earth Syst.,
doi:10.1002/jame.20016.
O’Connor, F. M., C. E. Johnson, O. Morgenstern, and W. J. Collins, 2009: Interactions
between tropospheric chemistry and climate model temperature and humidity
biases. Geophys. Res. Lett., 36, L16801.
O’Farrell, S. P., 1998: Investigation of the dynamic sea ice component of a coupled
atmosphere sea ice general circulation model. J. Geophys. Res.-Oceans, 103,
15751–15782.
O’Gorman, P. A., 2012: Sensitivity of tropical precipitation extremes to climate
change. Nature Geosci., 5, 697–700.
O’Gorman, P. A., and M. S. Singh, 2013: Vertical structure of warming consistent
with an upward shift in the middle and upper troposphere. Geophys. Res. Lett.,
doi:10.1002/grl.50328.
O’ishi, R., and A. Abe-Ouchi, 2011: Polar amplification in the mid-Holocene derived
from dynamical vegetation change with a GCM. Geophys. Res. Lett., 38, L14702.
Ogasawara, N., A. Kitoh, T. Yasunari, and A. Noda, 1999: Tropospheric biennial
oscillation of ENSO-monsoon system in the MRI coupled GCM. J. Meteorol. Soc.
Jpn., 77, 1247–1270.
Ohba, M., D. Nohara, and H. Ueda, 2010: Simulation of asymmetric ENSO transition
in WCRP CMIP3 Multimodel Experiments. J. Clim., 23, 6051–6067.
Ohgaito, R., and A. Abe-Ouchi, 2009: The effect of sea surface temperature bias in
the PMIP2 AOGCMs on mid-Holocene Asian monsoon enhancement. Clim. Dyn.,
33, 975–983.
Oka, A., E. Tajika, A. Abe-Ouchi, and K. Kubota, 2011: Role of the ocean in controlling
atmospheric CO
2
concentration in the course of global glaciations. Clim. Dyn.,
37, 1755–1770.
Oleson, K. W., 2004: Technical description of the Community Land Model (CLM).
NCAR Technical Note NCAR/TN-461+STR, National Center for Atmospheric
Research, Boulder, CO, 174 pp.
Oleson, K. W., G. B. Bonan, J. Feddema, M. Vertenstein, and C. S. B. Grimmond, 2008a:
An urban parameterization for a global climate model. Part I: Formulation and
evaluation for two cities. J. Appl. Meteorol. Climatol., 47, 1038–1060.
Oleson, K. W., et al., 2010: Technical Description of version 4.0 ofthe Community
Land Model (CLM)NCAR Technical Note NCAR/TN-478+STR, National Center
for Atmospheric Research, Boulder, CO, 257 pp.
Oleson, K. W., et al., 2008b: Improvements to the Community Land Model and their
impact on the hydrological cycle. J. Geophys. Res. Biogeosci., 113, G01021
Onogi, K., et al., 2007: The JRA-25 reanalysis. J. Meteorol. Soc. Jpn., 85, 369–432.
Opsteegh, J. D., R. J. Haarsma, F. M. Selten, and A. Kattenberg, 1998: ECBILT: A
dynamic alternative to mixed boundary conditions in ocean models. Tellus A,
50, 348–367.
Oreopoulos, L., et al., 2012: The continual intercomparison of radiation codes:
Results from Phase I. J. Geophys. Res. Atmos., 117, D06118.
Ostle, N. J., et al., 2009: Integrating plant-soil interactions into global carbon cycle
models. J. Ecol., 97, 851–863.
Otte, T. L., C. G. Nolte, M. J. Otte, and J. H. Bowden, 2012: Does nudging squelch the
extremes in Regional Climate Modeling? J. Clim., 25, 7046–7066.
Ottera, O. H., M. Bentsen, H. Drange, and L. Suo, 2010: External forcing as a
metronome for Atlantic multidecadal variability. Nature Geosci., 3, 688–694.
Otto-Bliesner, B. L., et al., 2007: Last Glacial Maximum ocean thermohaline
circulation: PMIP2 model intercomparisons and data constraints. Geophys. Res.
Lett., 34, L12706.
Otto-Bliesner, B. L., et al., 2009: A comparison of PMIP2 model simulations and
the MARGO proxy reconstruction for tropical sea surface temperatures at last
glacial maximum. Clim. Dyn., 32, 799–815.
844
Chapter 9 Evaluation of Climate Models
9
Otto, J., T. Raddatz, M. Claussen, V. Brovkin, and V. Gayler, 2009: Separation of
atmosphere-ocean-vegetation feedbacks and synergies for mid-Holocene
climate. Global Biogeochem. Cycles, 23, L09701.
Overland, J. E., and M. Wang, 2013: When will the summer Arctic be nearly sea ice
free? Geophys. Res. Lett., doi:10.1002/grl.50316, doi:10.1002/grl.50316.
Ozturk, T., H. Altinsoy, M. Turkes, and M. Kurnaz, 2012: Simulation of temperature
and precipitation climatology for the central Asia CORDEX domain using RegCM
4.0. Clim. Res., 52, 63–76.
Paeth, H., 2011: Postprocessing of simulated precipitation for impact research in
West Africa. Part I: Model output statistics for monthly data. Clim. Dyn., 36,
1321–1336.
Paeth, H., et al., 2012: Progress in regional downscaling of west African precipitation.
Atmos. Sci. Lett., 12, 75–82.
Palmer, J. R., and I. J. Totterdell, 2001: Production and export in a global ocean
ecosystem model. Deep-Sea R. Pt. I, 48, 1169–1198.
Parekh, P., F. Joos, and S. A. Muller, 2008: A modeling assessment of the interplay
between aeolian iron fluxes and iron-binding ligands in controlling carbon
dioxide fluctuations during Antarctic warm events. Paleoceanography, 23,
Pa4202.
Park, S., and C. S. Bretherton, 2009: The University of Washington Shallow Convection
and Moist Turbulence schemes and their impact on climate simulations with the
Community Atmosphere Model. J. Clim., 22, 3449–3469.
Park, W., and M. Latif, 2010: Pacific and Atlantic multidecadal variability in the Kiel
Climate Model. Geophys. Res. Lett., 37, L24702.
Parker, D., C. Folland, A. Scaife, J. Knight, A. Colman, P. Baines, and B. W. Dong, 2007:
Decadal to multidecadal variability and the climate change background. J.
Geophys. Res. Atmos., 112, D18115.
Parkinson, C. L., and D. J. Cavalieri, 2012: Antarctic sea ice variability and trends,
1979–2010. Cryosphere, 6, 871–880.
Patricola, C. M., M. Li, Z. Xu, P. Chang, R. Saravanan, and J.-S. Hsieh, 2012: An
investigation of tropical Atlantic bias in a high-resolution Coupled Regional
Climate Model. Clim. Dyn., doi:10.1007/s00382-012-1320-5.
Pavlova, T. V., V. M. Kattsov, and V. A. Govorkova, 2011: Sea ice in CMIP5 models:
Closer to reality? Trudy GGO (MGO Proc., in Russian), 564, 7–18.
Pavlova, T. V., V. M. Kattsov, Y. D. Nadyozhina, P. V. Sporyshev, and V. A. Govorkova,
2007: Terrestrial cryosphere evolution through the 20th and 21st centuries as
simulated with the new generation of global climate models. Earth Cryosphere
(in Russian), 11, 3–13.
Pechony, O., and D. T. Shindell, 2009: Fire parameterization on a global scale. J.
Geophys. Res. Atmos., 114, D16115
Pedersen, C. A., E. Roeckner, M. Lüthje, and J. Winther, 2009: A new sea ice albedo
scheme including melt ponds for ECHAM5 general circulation model. J. Geophys.
Res., 114, D08101.
Pennell, C., and T. Reichler, 2011: On the effective number of climate models. J. Clim.,
24 2358–2367
Perlwitz, J., S. Pawson, R. Fogt, J. Nielsen, and W. Neff, 2008: Impact of stratospheric
ozone hole recovery on Antarctic climate. Geophys. Res. Lett., 35, L08714.
Peterson, T. C., et al., 2009: State of the Climate in 2008. Bull. Am. Meteorol. Soc.,
90, S1–S196.
Petoukhov, V., I. I. Mokhov, A. V. Eliseev, and V. A. Semenov, 1998: The IAP RAS global
climate model. Dialogue-MSU, Moscow, Russia.
Petoukhov, V., A. Ganopolski, V. Brovkin, M. Claussen, A. Eliseev, C. Kubatzki, and
S. Rahmstorf, 2000: CLIMBER-2: a climate system model of intermediate
complexity. Part I: Model description and performance for present climate. Clim.
Dyn., 16, 1–17.
Petoukhov, V., et al., 2005: EMIC Intercomparison Project (EMIP-CO2): Comparative
analysis of EMIC simulations of climate, and of equilibrium and transient
responses to atmospheric CO
2
doubling. Clim. Dyn., 25, 363–385.
Pfahl, S., and H. Wernli, 2012: Quantifying the relevance of atmospheric blocking
for co-located temperature extremes in the Northern Hemisphere on (sub-)daily
time scales. Geophys. Res. Lett., 39, L12807.
Pfeiffer, A., and G. Zängl, 2010: Validation of climate-mode MM5–simulations for the
European Alpine Region. Theor. Appl. Climatol., 101, 93–108.
Phillips, T. J., et al., 2004: Evaluating parameterizations in General Circulation
Models: Climate simulation meets weather prediction. Bull. Am. Meteorol. Soc.,
85, 1903–1915.
Piani, C., D. J. Frame, D. A. Stainforth, and M. R. Allen, 2005: Constraints on climate
change from a multi-thousand member ensemble of simulations. Geophys. Res.
Lett., 32, L23825.
Pierce, D. W., 2001: Distinguishing coupled ocean-atmosphere interactions from
background noise in the North Pacific. Prog. Oceanogr., 49, 331–352.
Pierce, D. W., T. P. Barnett, B. D. Santer, and P. J. Gleckler, 2009: Selecting global
climate models for regional climate change studies. Proc. Natl. Acad. Sci. U.S.A.,
106, 8441–8446.
Pincus, R., C. P. Batstone, R. J. P. Hofmann, K. E. Taylor, and P. J. Glecker, 2008:
Evaluating the present-day simulation of clouds, precipitation, and radiation in
climate models. J. Geophys. Res. Atmos., 113, D14209
Pincus, R., S. Platnick, S. A. Ackerman, R. S. Hemler, and R. J. P. Hofmann, 2012:
Reconciling simulated and observed views of clouds: MODIS, ISCCP, and the
limits of instrument simulators. J. Clim., 25, 4699–4720.
Pinto, J. G., T. Spangehl, U. Ulbrich, and P. Speth, 2006: Assessment of winter cyclone
activity in a transient ECHAM4–OPYC3 GHG experiment. Meteorol. Z., 15,
279–291.
Piot, M., and R. von Glasow, 2008: The potential importance of frost flowers,
recycling on snow, and open leads for ozone depletion events. Atmos. Chem.
Phys., 8, 2437–2467.
Pitman, A., A. Arneth, and L. Ganzeveld, 2012a: Regionalizing global climate models.
Int. J. Climatol. , 32, 321–337.
Pitman, A. J., 2003: The evolution of, and revolution in, land surface schemes
designed for climate models. Int. J. Climatol. , 23, 479–510.
Pitman, A. J., et al., 2012b: Effects of land cover change on temperature and rainfall
extremes in multi-model ensemble simulations. Earth Syst. Dyn., 13, 213–231.
Pitman, A. J., et al., 2009: Uncertainties in climate responses to past land cover
change: First results from the LUCID intercomparison study. Geophys. Res. Lett.,
36, L14814.
Plattner, G. K., et al., 2008: Long-term climate commitments projected with climate-
carbon cycle models. J. Clim., 21, 2721–2751.
Ploshay, J. J., and N.-C. Lau, 2010: Simulation of the diurnal cycle in tropical rainfall
and circulation during Boreal Summer with a high-resolution GCM. Mon.
Weather Rev., 138, 3434–3453.
Po-Chedley, S., and Q. Fu, 2012: Discrepancies in tropical upper tropospheric
warming between atmospheric circulation models and satellites. Environ. Res.
Lett., 7, 044018.
Pokhrel, S., H. Rahaman, A. Parekh, S. K. Saha, A. Dhakate, H. S. Chaudhari, and R.
M. Gairola, 2012: Evaporation-precipitation variability over Indian Ocean and
its assessment in NCEP Climate Forecast System (CFSv2). Clim. Dyn., 39, 2585–
2608.
Polvani, L., D. Waugh, G. Correa, and S. Son, 2011: Stratospheric ozone depletion:
The main driver of twentieth-century atmospheric circulation changes in the
Southern Hemisphere. J. Clim., 24, 795–812.
Pope, V. D., M. L. Gallani, P. R. Rowntree, and R. A. Stratton, 2000: The impact of new
physical parametrizations in the Hadley Centre climate model: HadAM3. Clim.
Dyn., 16, 123–146.
Power, S., and R. Colman, 2006: Multi-year predictability in a coupled general
circulation model. Clim. Dyn., 26, 247–272
Power, S., M. Haylock, R. Colman, and X. D. Wang, 2006: The predictability of
interdecadal changes in ENSO activity and ENSO teleconnections. J. Clim., 19,
4755–4771.
Prudhomme, C., and H. Davies, 2009: Assessing uncertainties in climate change
impact analyses on the river flow regimes in the UK. Part 1: Baseline climate.
Clim. Change, 93, 177–195.
Pryor, S., G. Nikulin, and C. Jones, 2012: Influence of spatial resolution on regional
climate model derived wind climates. J. Geophys. Res. Atmos., 117, D03117.
Ptashnik, I. V., R. A. McPheat, K. P. Shine, K. M. Smith, and R. G. Williams, 2011:
Water vapor self-continuum absorption in near-infrared windows derived from
laboratory measurements. J. Geophys. Res. Atmos., 116, D16305.
Qian, T. T., A. Dai, K. E. Trenberth, and K. W. Oleson, 2006: Simulation of global land
surface conditions from 1948 to 2004. Part I: Forcing data and evaluations. J.
Hydrometeorol., 7, 953–975.
Qiao, F., Y. Yuan, Y. Yang, Q. Zheng, C. Xia, and J. Ma, 2004: Wave-induced mixing
in the upper ocean: Distribution and application to a global ocean circulation
model. Geophys. Res. Lett., 31, L11303.
Quaas, J., 2012: Evaluating the “critical relative humidity” as a measure of
subgrid-scale variability of humidity in general circulation model cloud cover
parameterizations using satellite data. J. Geophys. Res. Atmos., 117, D09208.
Raftery, A. E., T. Gneiting, F. Balabdaoui, and M. Polakowski, 2005: Using Bayesian
model averaging to calibrate forecast ensembles. Mon. Weather Rev., 133,
1155–1174.
845
Evaluation of Climate Models Chapter 9
9
Raible, C. C., M. Yoshimori, T. F. Stocker, and C. Casty, 2007: Extreme midlatitude
cyclones and their implications for precipitation and wind speed extremes in
simulations of the Maunder Minimum versus present day conditions. Clim. Dyn.,
28, 409–423.
Raisanen, J., 2007: How reliable are climate models? Tellus A, 59, 2–29.
Raisanen, J., and J. S. Ylhaisi, 2011: How much should climate model output be
smoothed in space? J. Clim., 24, 867–880.
Raisanen, J., L. Ruokolainen, and J. Ylhaisi, 2010: Weighting of model results for
improving best estimates of climate change. Clim. Dyn., 35, 407–422.
Rammig, A., et al., 2010: Estimating the risk of Amazonian forest dieback. New
Phytologist, 187, 694–706.
Rampal, P., J. Weiss, C. Dubois, and J. M. Campin, 2011: IPCC climate models do not
capture Arctic sea ice drift acceleration: Consequences in terms of projected sea
ice thinning and decline. J. Geophys. Res. Oceans, 116, C00d07.
Ramstein, G., M. Kageyama, J. Guiot, H. Wu, C. Hely, G. Krinner, and S. Brewer, 2007:
How cold was Europe at the Last Glacial Maximum? A synthesis of the progress
achieved since the first PMIP model-data comparison. Clim. Past, 3, 331–339.
Randall, D. A., M. F. Khairoutdinov, A. Arakawa, and W. W. Grabowski, 2003: Breaking
the cloud parameterization deadlock. Bull. Am. Meteorol. Soc., 84, 1547–1564.
Randall, D. A., et al., 2007: Climate models and their evaluation. In: Climate Change
2007: The Physical Science Basis. Contribution of Working Group I to the
Fourth Assessment Report of the Intergovernmental Panel on Climate Change
[Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor
and H. L. Miller (eds.)] Cambridge University Press, Cambridge, United Kingdom
and New York, NY, USA, pp. 589–662.
Randel, W., and F. Wu, 2007: A stratospheric ozone profile data set for 1979–2005:
Variability, trends, and comparisons with column ozone data. J. Geophys. Res.
Atmos., 12, D06313.
Randel, W. J., et al., 2009: An update of observed stratospheric temperature trends. J.
Geophys. Res. Atmos., 114, D02107.
Rapaić, M., M. Leduc, and R. Laprise, 2010: Evaluation of the internal variability and
estimation of the downscaling ability of the Canadian Regional Climate Model
for different domain sizes over the north Atlantic region using the Big-Brother
experimental approach. Clim. Dyn., 36 1979–2001.
Raphael, M. N., and M. M. Holland, 2006: Twentieth century simulation of the
Southern Hemisphere climate in coupled models. Part 1: Large scale circulation
variability. Clim. Dyn., 26, 217–228.
Rashid, H. A., A. C. Hirst, and M. Dix, 2013: Atmospheric circulation features in
the ACCESS model simulations for CMIP5: Historical simulation and future
projections Aust. Meteorol. Oceanogr. J., 63, 145–160.
Rauscher, S. A., E. Coppola, C. Piani, and F. Giorgi, 2010: Resolution effects on
regional climate model simulations of seasonal precipitation over Europe. Clim.
Dyn., 35, 685–711.
Rayner, N. A., et al., 2003: Global analysis of sea surface temperature, sea ice, and
night marine air temperature since the late ninteeth century. J. Geophys. Res.,
108, 4407.
Redelsperger, J.-L., C. D. Thorncroft, A. Diedhiou, T. Lebel, D. J. Parker, and J. Polcher,
2006: African Monsoon Multidisciplinary Analysis: An international research
project and field campaign. Bull. Am. Meteorol. Soc., 87, 1739–1746.
Redi, M. H., 1982: Oceanic isopycnal mixing by coordinate rotation. J. Phys.
Oceanogr., 12, 1154–1158.
Reichler, T., and J. Kim, 2008: How well do coupled models simulate today’s climate?
Bull. Am. Meteorol. Soc., 89, 303–311.
Reick, C. H., T. Raddatz, V. Brovkin, and V. Gayler, 2013: The representation of natural
and anthropogenic land coverchange in MPI-ESM.J. Adv. Model. Earth Syst.,
doi:10.1002/jame.20022.
Reifen, C., and R. Toumi, 2009: Climate projections: Past performance no guarantee
of future skill? Geophys. Res. Lett., 36, L13704.
Remer, L. A., et al., 2008: Global aerosol climatology from the MODIS satellite
sensors. J. Geophys. Res. Atmos., 113, D14s07.
Richter, I., and S.-P. Xie, 2008: On the origin of equatorial Atlantic biases in coupled
general circulation models. Clim. Dyn., 31, 587–598.
Richter, I., S.-P. Xie, S. K. Behera, T. Doi, and Y. Masumoto, 2013: Equatorial
Atlantic variability and its relation to mean state biases in CMIP5. Clim. Dyn.,
doi:10.1007/s00382-012-1624-5.
Richter, J. H., F. Sassi, and R. R. Garcia, 2010: Toward a physically based gravity
wave source parameterization in a General Circulation Model. J. Atmos. Sci., 67,
136–156.
Ridgwell, A., and J. C. Hargreaves, 2007: Regulation of atmospheric CO(2) by deep-
sea sediments in an Earth system model. Global Biogeochem. Cycles, 21,
Gb2008.
Ridgwell, A., I. Zondervan, J. C. Hargreaves, J. Bijma, and T. M. Lenton, 2007a:
Assessing the potential long-term increase of oceanic fossil fuel CO
2
uptake due
to CO
2
–calcification feedback. Biogeosciences, 4, 481–492.
Ridgwell, A., et al., 2007b: Marine geochemical data assimilation in an efficient Earth
System Model of global biogeochemical cycling. Biogeosciences, 4, 87–104.
Rienecker, M. M., et al., 2011: MERRA: NASA’s modern-era retrospective analysis for
research and applications. J. Clim., 24, 3624–3648.
Ringer, M. A., J. M. Edwards, and A. Slingo, 2003: Simulation of satellite channel
radiances in the Met Office Unified Model. Q. J. R. Meteorol. Soc., 129, 1169–
1190.
Rio, C., and F. Hourdin, 2008: A thermal plume model for the convective boundary
layer: Representation of cumulus clouds. J. Atmos. Sci., 65, 407–425.
Rio, C., F. Hourdin, F. Couvreux, and A. Jam, 2010: Resolved versus parametrized
boundary-layer plumes. Part II: Continuous formulations of mixing rates for
mass-flux schemes. Boundary-Layer Meteorol., 135, 469–483.
Risi, C., et al., 2012a: Process-evaluation of tropospheric humidity simulated by
general circulation models using water vapor isotopic observations: 2. Using
isotopic diagnostics to understand the mid and upper tropospheric moist bias in
the tropics and subtropics. J. Geophys. Res. Atmos., 117, D05304.
Risi, C., et al., 2012b: Process-evaluation of tropospheric humidity simulated by
general circulation models using water vapor isotopologues: 1. Comparison
between models and observations. J. Geophys. Res. Atmos., 117, D05303.
Risien, C. M., and D. B. Chelton, 2008: A global climatology of surface wind and
wind stress fields from eight years of QuikSCAT Scatterometer data. J. Phys.
Oceanogr., 38, 2379–2413.
Ritz, S. P., T. F. Stocker, and F. Joos, 2011: A coupled dynamical ocean-energy balance
atmosphere model for paleoclimate studies. J. Clim., 24, 349–375.
Roberts, M. J., et al., 2004: Impact of an eddy-permitting ocean resolution on control
and climate change simulations with a global coupled GCM. J. Clim., 17, 3–20.
Robinson, A., R. Calov, and A. Ganopolski, 2012: Multistability and critical thresholds
of the Greenland ice sheet. Nature Clim. Change, 2, 429–432.
Robinson, D. A., and A. Frei, 2000: Seasonal variability of northern hemisphere snow
extent using visible satellite data. Prof. Geograph., 51, 307–314.
Rockel, B., C. L. Castro, R. A. Pielke, H. von Storch, and G. Leoncini, 2008: Dynamical
downscaling: Assessment of model system dependent retained and added
variability for two different regional climate models. J. Geophys. Res. Atmos.,
113, D21107.
Rodwell, M., and T. Palmer, 2007: Using numerical weather prediction to assess
climate models. Q. J. R. Meteorol. Soc., 133, 129–146.
Roe, G. H., and M. B. Baker, 2007: Why is climate sensitivity so unpredictable?
Science, 318, 629–632.
Roe, G. H., and K. C. Armour, 2011: How sensitive is climate sensitivity? Geophys.
Res. Lett., 38, L14708.
Roe, G. H., and M. B. Baker, 2011: Comment on Another look at climate sensitivity”
by Zaliapin and Ghil (2010). Nonlin.Proc. Geophys., 18, 125–127.
Roeckner, E., et al., 2006: Sensitivity of simulated climate to horizontal and vertical
resolution in the ECHAM5 atmosphere model. J. Clim., 19, 3771–3791.
Rojas, M., 2006: Multiply nested regional climate simulation for southern South
America: Sensitivity to model resolution. Mon. Weather Rev., 134, 2208–2223.
Rojas, M., and P. I. Moreno, 2011: Atmospheric circulation changes and neoglacial
conditions in the Southern Hemisphere mid-latitudes: Insights from PMIP2
simulations at 6 kyr. Clim. Dyn., 37, 357–375.
Rojas, M., et al., 2009: The Southern Westerlies during the last glacial maximum in
PMIP2 simulations. Clim. Dyn., 32, 525–548.
Romanou, A., et al., 2013: Natural air–sea flux of CO
2
in simulations of the NASA-
GISS climate model: Sensitivity to the physical ocean model formulation. Ocean
Model., doi:10.1016/j.ocemod.2013.01.008.
Rotstayn, L. D., and U. Lohmann, 2002: Simulation of the tropospheric sulfur cycle
in a global model with a physically based cloud scheme. J. Geophys. Res., 107,
4592.
Rotstayn, L. D., M. A. Collier, R. M. Mitchell, Y. Qin, S. K. Campbell, and S. M. Dravitzki,
2011: Simulated enhancement of ENSO-related rainfall variability due to
Australian dust. Atmos. Chem. Phys., 11, 6575–6592.
846
Chapter 9 Evaluation of Climate Models
9
Rotstayn, L. D., S. J. Jeffrey, M. A. Collier, S. M. Dravitzki, A. C. Hirst, J. I. Syktus, and
K. K. Wong, 2012: Aerosol- and greenhouse gas-induced changes in summer
rainfall and circulation in the Australasian region: A study using single-forcing
climate simulations. Atmos. Chem. Phys., 12, 6377–6404.
Rotstayn, L. D., et al., 2010: Improved simulation of Australian climate and ENSO-
related rainfall variability in a global climate model with an interactive aerosol
treatment. Int. J. Climatol. , 30, 1067–1088.
Rougier, J., D. M. H. Sexton, J. M. Murphy, and D. Stainforth, 2009: Analyzing the
climate sensitivity of the HadSM3 climate model using ensembles from different
but related experiments. J. Clim., 22, 3540–3557.
Roy, P., P. Gachon, and R. Laprise, 2012: Assessment of summer extremes and climate
variability over the north-east of North America as simulated by the Canadian
Regional Climate Model. Int. J. Climatol. , 32 1615–1627.
Ruckstuhl, C., and J. R. Norris, 2009: How do aerosol histories affect solar “dimming”
and “brightening” over Europe?: IPCC-AR4 models versus observations. J.
Geophys. Res. Atmos., 114, D00d04.
Rummukainen, M., 2010: State-of-the-art with regional climate models. Clim.
Change, 1, 82–96.
Russell, J. L., R. J. Stouffer, and K. W. Dixon, 2006: Intercomparison of the Southern
Ocean circulations in IPCC coupled model control simulations. J. Clim., 19,
4560–4575.
Ruti, P. M., et al., 2011: The West African climate system: A review of the AMMA
model inter-comparison initiatives. Atmos. Sci. Lett., 12 116–122
Rutter, N., et al., 2009: Evaluation of forest snow processes models (SnowMIP2). J.
Geophys. Res. Atmos., 114, D06111.
Sabine, C. L., et al., 2004: The oceanic sink for anthropogenic CO
2
. Science, 305,
367–371.
Saha, S., et al., 2010: The NCEP Climate Forecast System Reanalysis. Bull. Am.
Meteorol. Soc., 91, 1015–105.
Sahany, S., J. D. Neelin, K. Hales, and R. B. Neale, 2012: Temperature–moisture
dependence of the Deep Convective Transition as a constraint on entrainment in
climate models. J. Atmos. Sci., 69, 1340–1358.
Saji, N. H., S. P. Xie, and T. Yamagata, 2006: Tropical Indian Ocean variability in the
IPCC twentieth-century climate simulations. J. Clim., 19, 4397–4417.
Saji, N. H., B. N. Goswami, P. N. Vinayachandran, and T. Yamagata, 1999: A dipole
mode in the tropical Indian Ocean. Nature, 401, 360–363.
Sakaguchi, K., X. B. Zeng, and M. A. Brunke, 2012: The hindcast skill of the CMIP
ensembles for the surface air temperature trend. J. Geophys. Res. Atmos., 117,
D16113.
Sakamoto, T. T., et al., 2012: MIROC4h – a new high-resolution atmosphere-ocean
coupled general circulation model. J. Meteorol. Soc. Jpn., 90, 325–359.
Salas-Melia, D., 2002: A global coupled sea ice-ocean model. Ocean Model., 4,
137–172
Salle, J. B., E. Shuckburgh, N. Bruneau, A. J. S. Meijers, T. J. Bracegirdle, Z. Wang,
and T. Roy, 2013: Assessment of Southern Ocean water mass circulation and
characteristics in CMIP5 models: Historical bias and forcing response. J. Geophys.
Res. Oceans, doi:10.1002/jgrc.20135.
Samuelsson, P., E. Kourzeneva, and D. Mironov, 2010: The impact of lakes on the
European climate as simulated by a regional climate model. Boreal Environ. Res.,
15, 113–129.
Sander, S. P., 2006: Chemical Kinetics and Photochemical Data for Use in Atmospheric
Studies.Evaluation 15. JPL Publications, Pasadena, CA, USA, 523 pp.
Sanderson, B. M., 2011: A multimodel study of parametric uncertainty in predictions
of climate response to rising greenhouse gas concentrations. J. Clim., 25, 1362–
1377.
Sanderson, B. M., 2013: On the estimation of systematic error in regression-
basedpredictions of climate sensitivity. Clim. Change, doi:10.1007/s10584-012-
0671-6.
Sanderson, B. M., and R. Knutti, 2012: On the interpretation of constrained climate
model ensembles. Geophys. Res. Lett., 39, L16708.
Sanderson, B. M., K. M. Shell, and W. Ingram, 2010: Climate feedbacks determined
using radiative kernels in a multi-thousand member ensemble of AOGCMs. Clim.
Dyn., 35, 1219–1236.
Sanderson, B. M., C. Piani, W. J. Ingram, D. A. Stone, and M. R. Allen, 2008a: Towards
constraining climate sensitivity by linear analysis of feedback patterns in
thousands of perturbed-physics GCM simulations. Clim. Dyn., 30, 175–190.
Sanderson, B. M., et al., 2008b: Constraints on model response to greenhouse gas
forcing and the role of subgrid-scale processes. J. Clim., 21, 2384–2400.
Sansom, P. G., D. B. Stephenson, C. A. T. Ferro, G. Zappa, and L. Shaffrey, 2013: Simple
uncertainty frameworks for selecting weighting schemes and interpreting multi-
model ensemble climate change experiments doi:10.1175/JCLI-D-12-00462.1.
Santer, B., et al., 2009: Incorporating model quality information in climate change
detection and attribution studies. Proc. Natl. Acad. Sci. U.S.A., 106, 14778–
14783.
Santer, B., et al., 2007: Identification of human-induced changes in atmospheric
moisture content. Proc. Natl. Acad. Sci. U.S.A., 104, 15248–15253.
Santer, B., et al., 2008: Consistency of modelled and observed temperature trends in
the tropical troposphere. Int. J. Climatol. , 28, 1703–1722.
Santer, B., et al., 2005: Amplification of surface temperature trends and variability in
the tropical atmosphere. Science, 309, 1551–1556.
Santer, B. D., et al., 2013: Identifying human influences on atmospheric temperature.
Proc. Natl. Acad. Sci. U.S.A, 110, 26–33.
Sato, M., J. E. Hansen, M. P. McCormick, and J. B. Pollack, 1993: Stratospheric aerosol
optical depth, 1850– 1990. J. Geophys. Res. Atmos., 98, 22987–22994
Sato, T., H. Miura, M. Satoh, Y. N. Takayabu, and Y. Q. Wang, 2009: Diurnal cycle of
precipitation in the Tropics simulated in a global cloud-resolving model. J. Clim.,
22, 4809–4826.
Scaife, A. A., N. Butchart, C. D. Warner, and R. Swinbank, 2002: Impact of a spectral
gravity wave parameterization on the stratosphere in the met office unified
model. J. Atmos. Sci., 59, 1473–1489.
Scaife, A. A., T. Woollings, J. Knight, G. Martin, and T. Hinton, 2010: Atmospheric
blocking and mean biases in climate models. J. Clim., 23, 6143–6152.
Scaife, A. A., et al., 2011: Improved Atlantic winter blocking in a climate model.
Geophys. Res. Lett., 38, L23703.
Scaife, A. A., et al., 2012: Climate change and stratosphere-troposphere interaction.
Clim. Dyn., 38, 2089–2097.
Scaife, A. A., et al., 2009: The CLIVAR C20C project: Selected twentieth century
climate events. Clim. Dyn., 33, 603–614.
Schaller, N., I. Mahlstein, J. Cermak, and R. Knutti, 2011: Analyzing precipitation
projections: A comparison of different approaches to climate model evaluation.
J. Geophys. Res. Atmos., 116, D10118.
Scherrer, S., 2011: Present-day interannual variability of surface climate in CMIP3
models and its relation to future warming. Int. J. Climatol. , 31, 1518–1529.
Schlesinger, M. E., and J. F. B. Mitchell, 1987: Climate model simulations of the
equilibrium climatic response to increased carbon-dioxide. Rev. Geophys., 25,
760–798.
Schmidli, J., C. Goodess, C. Frei, M. Haylock, Y. Hundecha, J. Ribalaygua, and T.
Schmith, 2007: Statistical and dynamical downscaling of precipitation: An
evaluation and comparison of scenarios for the European Alps. J. Geophys. Res.
Atmos., 112, D04105.
Schmidt, G. A., et al., 2012: Climate forcing reconstructions for use in PMIP
simulations of the Last Millennium (v1.1). Geophys. Model Dev., 5, 185–191.
Schmidt, G. A., et al., 2006: Present day atmospheric simulations using GISS ModelE:
Comparison to in-situ, satellite and reanalysis data. J. Clim., 19, 153–192.
Schmith, T., 2008: Stationarity of regression relationships: Application to empirical
downscaling. J. Clim., 21, 4529–4537.
Schmittner, A., A. Oschlies, X. Giraud, M. Eby, and H. L. Simmons, 2005: A global
model of the marine ecosystem for long-term simulations: Sensitivity to ocean
mixing, buoyancy forcing, particle sinking, and dissolved organic matter cycling.
Global Biogeochem. Cycles, 19, Gb3004.
Schott, F. A., S.-P. Xie, and J. P. McCreary, Jr., 2009: Indian Ocean circulation and
climate variability. Rev. Geophys., 47, RG1002.
Schramm, J. L., M. M. Holland, J. A. Curry, and E. E. Ebert, 1997: Modeling the
thermodynamics of a sea ice thickness 1. Sensitivity to ice thickness resolution.
J. Geophys. Res., 102, 23079–23091.
Schultz, M. G., et al., 2008: Global wildland fire emissions from 1960 to 2000. Global
Biogeochem. Cycles, 22, GB2002.
Schurgers, G., U. Mikolajewicz, M. Groger, E. Maier-Reimer, M. Vizcaino, and A.
Winguth, 2008: Long-term effects of biogeophysical and biogeochemical
interactions between terrestrial biosphere and climate under anthropogenic
climate change. Global Planet. Change, 64, 26–37.
Scoccimarro, E., et al., 2011: Effects of tropical cyclones on ocean heat transport in
a high resolution Coupled General Circulation Model. J. Clim., 24, 4368–4384.
Séférian, R., et al., 2013: Skill assessment of three earth system models with common
marine biogeochemistry. Clim. Dyn., 40, 2549–2573.
847
Evaluation of Climate Models Chapter 9
9
Segui, P., A. Ribes, E. Martin, F. Habets, and J. Boe, 2010: Comparison of three
downscaling methods in simulating the impact of climate change on the
hydrology of Mediterranean basins. J. Hydrol., 383, 111–124.
Seidel, D. J., M. Free, and J. S. Wang, 2012: Reexamining the warming in the tropical
upper troposphere: Models versus radiosonde observations. Geophys. Res. Lett.,
39, L22701.
Seidel, D. J., Q. Fu, W. J. Randel, and T. J. Reichler, 2008: Widening of the tropical belt
in a changing climate. Nature Geosci., 1, 21–24.
Seidel, D. J., N. P. Gillett, J. R. Lanzante, K. P. Shine, and P. W. Thorne, 2011: Stratospheric
temperature trends: Our evolving understanding. Clim. Change, 2, 592–616.
Selten, F. M., G. W. Branstator, H. A. Dijkstra, and M. Kliphuis, 2004: Tropical origins
for recent and future Northern Hemisphere climate change. Geophys. Res. Lett.,
31, L21205.
Semenov, V. A., M. Latif, J. H. Jungclaus, and W. Park, 2008: Is the observed NAO
variability during the instrumental record unusual? Geophys. Res. Lett., 35,
L11701.
Seneviratne, S., et al., 2012: Changes in climate extremes and their impacts
on the natural physical environment. In: IPCC WGI/WGII Special Report on
Managing the Risks of Extreme Events and Disasters to Advance Climate
Change Adaptation (SREX), [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken,
K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and
P.M. Midgley (Eds.)]. Cambridge University Press, The Edinburgh Building,
Shaftesbury Road, Cambridge CB2 8RU ENGLAND, pp. 109–230.
Seneviratne, S. I., D. Luethi, M. Litschi, and C. Schaer, 2006: Land-atmosphere
coupling and climate change in Europe. Nature, 443, 205–209.
Seneviratne, S. I., et al., 2010: Investigating soil moisture-climate interactions in a
changing climate: A review. Earth Sci. Rev., 99, 125–161.
Separovic, L., R. De Elia, and R. Laprise, 2008: Reproducible and irreproducible
components in ensemble simulations with a Regional Climate Model. Mon.
Weather Rev., 136, 4942–4961.
Separovic, L., R. Elía, and R. Laprise, 2012: Impact of spectral nudging and domain
size in studies of RCM response to parameter modification. Clim. Dyn., 38,
1325–1343.
Severijns, C. A., and W. Hazeleger, 2010: The efficient global primitive equation
climate model SPEEDO V2.0. Geophys. Model Dev., 3, 105–122.
Sexton, D. M. H., and J. M. Murphy, 2012: Multivariate probabilistic projections using
imperfect climate models. Part II: robustness of methodological choices and
consequences for climate sensitivity. Clim. Dyn., 38, 2543–2558.
Sexton, D. M. H., J. M. Murphy, M. Collins, and M. J. Webb, 2012: Multivariate
probabilistic projections using imperfect climate models part I: Outline of
methodology. Clim. Dyn., 38, 2513–2542.
Shaffer, G., and J. L. Sarmiento, 1995: Biogeochemical cycling in the global ocean.
1. A new, analytical model with continuous vertical resolution and high-latitude
dynamics. J. Geophys. Res. Oceans, 100, 2659–2672.
Shaffer, G., S. M. Olsen, and J. O. P. Pedersen, 2008: Presentation, calibration and
validation of the low-order, DCESS Earth System Model (Version 1). Geophys.
Model Dev., 1, 17–51.
Shaffrey, L. C., et al., 2009: UK HiGEM: The new UK High-Resolution Global
Environment Model—Model description and basic evaluation. J. Clim., 22,
1861–1896.
Sheffield, J., and E. F. Wood, 2008: Projected changes in drought occurrence under
future global warming from multi-model, multi-scenario, IPCC AR4 simulations.
Clim. Dyn., 31, 79–105.
Shell, K. M., J. T. Kiehl, and C. A. Shields, 2008: Using the radiative kernel technique to
calculate climate feedbacks in NCAR’s Community Atmospheric Model. J. Clim.,
21, 2269–2282.
Shevliakova, E., et al., 2009: Carbon cycling under 300 years of land use change:
Importance of the secondary vegetation sink. Global Biogeochem. Cycles, 23,
GB2022
Shi, Y., J. Zhang, J. S. Reid, B. Holben, E. J. Hyer, and C. Curtis, 2011: An analysis of the
collection 5 MODIS over-ocean aerosol optical depth product for its implication
in aerosol assimilation. Atmos. Chem. Phys., 11, 557–565.
Shibata, K., and M. Deushi, 2005: Radiative effect of ozone on the quasi-biennial
oscillation in the equatorial stratosphere. Geophys. Res. Lett., 32, L24802.
Shin, D., J. Kim, and H. Park, 2011: Agreement between monthly precipitation
estimates from TRMM satellite, NCEP reanalysis, and merged gauge-satellite
analysis. J. Geophys. Res. Atmos., 116, D16105.
Shin, S. I., D. Sardeshmukh, and K. Pegion, 2010: Realism of local and remote
feedbacks on tropical sea surface temperatures in climate models. J. Geophys.
Res. Atmos., 115, D21110.
Shindell, D. T., et al., 2013a: Interactive ozone and methane chemistry in GISS-
E2historical and future climate simulationsAtmos. Chem. Phys., 13, 2653–2689.
Shindell, D. T., et al., 2013b: Radiative forcing in the ACCMIP historical and future
climate simulations. Atmos. Chem. Phys., 13, 2939–2974.
Shiogama, H., S. Emori, N. Hanasaki, M. Abe, Y. Masutomi, K. Takahashi, and T.
Nozawa, 2011: Observational constraints indicate risk of drying in the Amazon
basin. Nature Commun., 2, 253.
Shiogama, H., et al., 2012: Perturbed physics ensemble using the MIROC5 coupled
atmosphere–ocean GCM without flux corrections: Experimental design and
results. Clim. Dyn., 39, 3041–3056.
Shiu, C.-J., S. C. Liu, C. Fu, A. Dai, and Y. Sun, 2012: How much do precipitation
extremes change in a warming climate? Geophys. Res. Lett., 39, L17707.
Shkol’nik, I., V. Meleshko, S. Efimov, and E. Stafeeva, 2012: Changes in climate
extremes on the territory of Siberia by the middle of the 21st century: An
ensemble forecast based on the MGO regional climate model. Russ. Meteorol.
Hydrol., 37, 71–84.
Shkolnik, I., V. Meleshko, and V. Kattsov, 2007: The MGO climate model for Siberia.
Russ. Meteorol. Hydrol., 32, 351–359.
Siebesma, A. P., P. M. M. Soares, and J. Teixeira, 2007: A combined eddy-diffusivity
mass-flux approach for the convective boundary layer. J. Atmos. Sci., 64, 1230–
1248.
Sigmond, M., and J. F. Scinocca, 2010: The influence of the basic state on the Northern
Hemisphere circulation response to climate change. J. Clim., 23, 1434–1446.
Sillmann, J., and M. Croci-Maspoli, 2009: Present and future atmospheric blocking
and its impact on European mean and extreme climate. Geophys. Res. Lett., 36,
L10702.
Sillmann, J., M. Croci-Maspoli, M. Kallache, and R. W. Katz, 2011: Extreme cold winter
temperatures in Europe under the influence of North Atlantic atmospheric
blocking. J. Clim., 24, 5899–5913.
Sillmann, J., V. V. Kharin, X. Zhang, and F. W. Zwiers, 2013: Climate extreme indices
in the CMIP5 multi-model ensemble. Part 1: Model evaluation in the present
climate. J. Geophys. Res., doi:10.1029/2012JD018390.
Simmons, A. J., K. M. Willett, P. D. Jones, P. W. Thorne, and D. P. Dee, 2010: Low-
frequency variations in surface atmospheric humidity, temperature, and
precipitation: Inferences from reanalyses and monthly gridded observational
data sets. J. Geophys. Res. Atmos., 115, D01110.
Simmons, H. L., S. R. Jayne, L. C. St Laurent, and A. J. Weaver, 2004: Tidally driven
mixing in a numerical model of the ocean general circulation. Ocean Model.,
6, 245–263.
Sitch, S., et al., 2003: Evaluation of ecosystem dynamics, plant geography and
terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global
Change Biol., 9, 161–185.
Sitch, S., et al., 2008: Evaluation of the terrestrial carbon cycle, future plant
geography and climate-carbon cycle feedbacks using five Dynamic Global
Vegetation Models (DGVMs). Global Change Biol., 14, 2015–2039.
Six, K. D., and E. Maier-Reimer, 1996: Effects of plankton dynamics on seasonal
carbon fluxes in an Ocean General Circulation Model. Global Biogeochem.
Cycles, 10, 559–583.
Slater, A. G., and D. M. Lawrence, 2013: Diagnosing present and future permafrost
from climate models. J. Clim., doi:10.1175/JCLI-D-12-00341.1.
Sloyan, B. M., and I. V. Kamenkovich, 2007: Simulation of Subantarctic Mode and
Antarctic Intermediate Waters in climate models. J. Clim., 20, 5061–5080.
Smirnov, D., and D. J. Vimont, 2011: Variability of the Atlantic Meridional Mode
during the Atlantic Hurricane Season. J. Clim., 24, 1409–1424.
Smith, B., P. Samuelsson, A. Wramneby, and M. Rummukainen, 2011a: A model
of the coupled dynamics of climate, vegetation and terrestrial ecosystem
biogeochemistry for regional applications. Tellus A, 63, 87–106.
Smith, P. C., N. De Noblet-Ducoudre, P. Ciais, P. Peylin, N. Viovy, Y. Meurdesoif, and
A. Bondeau, 2010a: European-wide simulations of croplands using an improved
terrestrial biosphere model: Phenology and productivity. J. Geophys. Res.
Biogeosci., 115, G01014.
Smith, P. C., P. Ciais, P. Peylin, N. De Noblet-Ducoudre, N. Viovy, Y. Meurdesoif, and
A. Bondeau, 2010b: European-wide simulations of croplands using an improved
terrestrial biosphere model: 2. Interannual yields and anomalous CO
2
fluxes in
2003. J. Geophys. Res. Biogeosci., 115, G04028
848
Chapter 9 Evaluation of Climate Models
9
Smith, R. D., M. E. Maltrud, F. O. Bryan, and M. W. Hecht, 2000: Numerical simulation
of the North Atlantic Ocean at 1/10 degrees. J. Phys. Oceanogr., 30, 1532–1561.
Smith, R. S., J. M. Gregory, and A. Osprey, 2008: A description of the FAMOUS
(version XDBUA) climate model and control run. Geophys. Model Dev., 1, 53–68.
Smith, S. J., J. van Aardenne, Z. Klimont, R. J. Andres, A. Volke, and S. D. Arias, 2011b:
Anthropogenic sulfur dioxide emissions: 1850–2005. Atmos. Chem. Phys., 11,
1101–1116.
Sobolowski, S., and T. Pavelsky, 2012: Evaluation of present and future North
American Regional Climate Change Assessment Program (NARCCAP) regional
climate simulations over the southeast United States. J. Geophys. Res. Atmos.,
117, D01101.
Soden, B. J., and I. M. Held, 2006: An assessment of climate feedbacks in coupled
ocean-atmosphere models. J. Clim., 19, 3354–3360.
Soden, B. J., I. M. Held, R. Colman, K. M. Shell, J. T. Kiehl, and C. A. Shields, 2008:
Quantifying climate feedbacks using radiative kernels. J. Clim., 21, 3504–3520.
Sokolov, A. P., and P. H. Stone, 1998: A flexible climate model for use in integrated
assessments. Clim. Dyn., 14, 291–303.
Sokolov, A. P., C. E. Forest, and P. H. Stone, 2010: Sensitivity of climate change
projections to uncertainties in the estimates of observed changes in deep-ocean
heat content. Clim. Dyn., 34, 735–745.
Sokolov, A. P., et al., 2009: Probabilistic forecast for twenty-first-century climate
based on uncertainties in emissions (without policy) and climate parameters. J.
Clim., 22, 5175–5204.
Sokolov, A. P., et al., 2005: The MIT Integrated Global System Model (IGSM) Version 2:
Model description and baseline evaluation. MIT JP Report 124. MIT, Cambridge,
MA.
Solman, S., and N. Pessacg, 2012: Regional climate simulations over South America:
Sensitivity to model physics and to the treatment of lateral boundary conditions
using the MM5 model. Clim. Dyn., 38, 281–300.
Solomon, S., P. J. Young, and B. Hassler, 2012: Uncertainties in the evolution of
stratospheric ozone and implications for recent temperature changes in the
tropical lower stratosphere. Geophys. Res. Lett., 39 L17706.
Solomon, S., K. H. Rosenlof, R. W. Portmann, J. S. Daniel, S. M. Davis, T. J. Sanford,
and G. K. Plattner, 2010: Contributions of stratospheric water vapor to decadal
changes in the rate of global warming. Science, 327, 1219–1223.
Somot, S., F. Sevault, M. Deque, and M. Crepon, 2008: 21st century climate change
scenario for the Mediterranean using a coupled atmosphere-ocean regional
climate model. Global Planet. Change, 63 112–126.
Son, S., et al., 2008: The impact of stratospheric ozone recovery on the Southern
Hemisphere westerly jet. Science, 320, 1486–1489.
Son, S., et al., 2010: Impact of stratospheric ozone on Southern Hemisphere
circulation change: A multimodel assessment. J. Geophys. Res. Atmos., 115,
D00M07.
Song, Z., F. Qiao, and Y. Song, 2012: Response of the equatorial basin-wide SST to
non-breakingsurface wave-induced mixing in a climate model: An amendment
to tropical bias. J. Geophys. Res., doi:10.1029/2012JC007931.
SPARC-CCMVal, 2010: SPARC Report on the Evaluation of Chemistry-Climate Models
[V. Eyring, T.G. Shepherd, D.W. Waugh (eds.)], SPARC Report No. 5, WCRP-132,
WMO/TD-No. 1526.
Sperber, K., and D. Kim, 2012: Simplified metrics for the identification of the Madden-
Julian oscillation in models. Atmos. Sci. Let., doi:10.1002/asl.378.
Sperber, K., et al., 2012: The Asian summer monsoon: An intercomparison of CMIP-5
vs. CMIP-3 simulations of the late 20th century. Clim. Dyn., doi:10.1007/s00382-
012-1607-6.
Sperber, K. R., 2003: Propagation and the vertical structure of the Madden-Julian
oscillation. Mon. Weather Rev., 131, 3018–3037.
Sperber, K. R., and H. Annamalai, 2008: Coupled model simulations of boreal summer
intraseasonal (30–50 day) variability, Part 1: Systematic errors and caution on
use of metrics. Clim. Dyn., 31, 345–372.
Sperber, K. R., et al., 2010: Monsoon Fact Sheet: CLIVAR Asian-Australian Monsoon
Panel.
Stainforth, D. A., et al., 2005: Uncertainty in predictions of the climate response to
rising levels of greenhouse gases. Nature, 433, 403–406.
Stephens, G. L., and C. D. Kummerow, 2007: The remote sensing of clouds and
precipitation from space: A review. J. Atmos. Sci., 64, 3742–3765.
Stephens, G. L., et al., 2010: Dreary state of precipitation in global models. J. Geophys.
Res., 115, D24211.
Stephens, G. L., et al., 2012: An Update on the Earth’s energy balance in light of new
global observations. Nature Geosci., 5, 691–696.
Stephenson, D. B., M. Collins, J. C. Rougier, and R. E. Chandler, 2012: Statistical
problems in the probabilistic prediction of climate change. Environmetrics, 23,
364–372.
Stephenson, D. B., V. Pavan, M. Collins, M. M. Junge, R. Quadrelli, and C. M. G.
Participating, 2006: North Atlantic Oscillation response to transient greenhouse
gas forcing and the impact on European winter climate: A CMIP2 multi-model
assessment. Clim. Dyn., 27, 401–420.
Stevens, B., and S. E. Schwartz, 2012: Observing and modeling Earth’s energy flows.
Surv. Geophys., 33, 779–816.
Stevens, B., et al., 2012: The atmospheric component of the MPI-M EarthSystem
Model: ECHAM6.J. Adv. Model. Earth Syst., doi:10.1002/jame.20015.
Stevenson, S., 2012: Significant changes to ENSO strength and impacts
in the twenty-first century: Results from CMIP5. Geophys. Res. Lett.,
doi:10.1029/2012GL052759.
Stevenson, S., B. Fox-Kemper, M. Jochum, R. Neale, C. Deser, and G. Meehl, 2012:
Will there be a significant change to El Niño in the twenty-first century? J. Clim.,
25, 2129–2145.
Stocker, B. D., K. Strassmann, and F. Joos, 2011: Sensitivity of Holocene atmospheric
CO
2
and the modern carbon budget to early human land use: Analyses with a
process-based model. Biogeosciences, 8, 69–88.
Stocker, B. D., et al., 2012: Multiple greenhouse gas feedbacks from the land
biosphere under future climate change scenarios. Nature Clim. Change,
doi:10.1038/nclimate1864.
Stolarski, R., and S. Frith, 2006: Search for evidence of trend slow-down in the long-
term TOMS/SBUV total ozone data record: The importance of instrument drift
uncertainty. Atmos. Chem. Phys., 6, 4057–4065.
Stoner, A. M. K., K. Hayhoe, and D. J. Wuebbles, 2009: Assessing General Circulation
Model simulations of atmospheric teleconnection patterns. J. Clim., 22, 4348–
4372.
Stott, P. A., and C. E. Forest, 2007: Ensemble climate predictions using climate models
and observational constraints. Philos. R. Soc. London A, 365, 2029–2052.
Strachan, J., P. L. Vidale, K. Hodges, M. Roberts, and M.-E. Demory, 2013: Investigating
global tropical cyclone activity with a hierarchy of AGCMs: The role of model
resolution. J. Clim., 26, 133–152.
Strassmann, K. M., F. Joos, and G. Fischer, 2008: Simulating effects of land use
changes on carbon fluxes: Past contributions to atmospheric CO
2
increases
and future commitments due to losses of terrestrial sink capacity. Tellus B, 60,
583–603.
Stratton, R. A., and A. J. Stirling, 2012: Improving the diurnal cycle of convection in
GCMs. Q. J. R. Meteorol. Soc., 138, 1121–1134.
Stroeve, J., M. Holland, W. Meier, T. Scambos, and M. Serreze, 2007: Arctic sea ice
decline: Faster than forecast. Geophys. Res. Lett., 34, L09501.
Stroeve, J. C., V. Kattsov, A. Barrett, M. Serreze, T. Pavlova, M. Holland, and W. N. Meier,
2012: Trends  in Arctic sea ice extent from CMIP5, CMIP3 and observations.
Geophys. Res. Lett., 39, L16502.
Su, H., and J. H. Jiang, 2012: Tropical clouds and circulation changes during the
2006–07 and 2009–10 El Niños. J. Clim., doi:10.1175/JCLI-D-1200152.1.
Su, H., D. E. Waliser, J. H. Jiang, J. L. Li, W. G. Read, J. W. Waters, and A. M. Tompkins,
2006: Relationships of upper tropospheric water vapor, clouds and SST: MLS
observations, ECMWF analyses and GCM simulations. Geophys. Res. Lett., 33,
L22802
Su, H., et al. , 2012: Diagnosis of regime-dependent cloud simulation errors in CMIP5
models using A-Train” satellite observations and reanalysis data. J. Geophys.
Res., doi:10.1029/2012JD018575.
Sudo, K., M. Takahashi, J. Kurokawa, and H. Akimoto, 2002: CHASER: A global
chemical model of the troposphere - 1. Model description. J. Geophys. Res.
Atmos., 107, 4339.
Suh, M., S. Oh, D. Lee, D. Cha, S. Choi, C. Jin, and S. Hong, 2012: Development of new
ensemble methods based on the performance skills of regional climate models
over South Korea. J. Clim., 25, 7067–7082.
Sun, D.-Z., Y. Yu, and T. Zhang, 2009: Tropical water vapor and cloud feedbacks in
climate models: A further assessment using coupled simulations. J. Clim., 22,
1287–1304.
Sutton, R. T., B. W. Dong, and J. M. Gregory, 2007: Land/sea warming ratio in response
to climate change: IPCC AR4 model results and comparison with observations.
Geophys. Res. Lett., 34, L02701
Svensson, G., and A. Holtslag, 2009: Analysis of model results for the turning of the
wind and related momentum fluxes in the stable boundary layer. Boundary-
Layer Meteorol., 132, 261–277.
849
Evaluation of Climate Models Chapter 9
9
Svensson, G., et al., 2011: Evaluation of the diurnal cycle in the atmospheric
boundary layer over land as represented by a variety of single-column models:
The Second GABLS Experiment. Boundary-Layer Meteorol., 140, 177–206.
Swart, N. C., and J. C. Fyfe, 2012a: Ocean carbon uptake and storage influenced by
wind bias in global climate models. Nature Clim. Change, 2, 47–52.
Swart, N. C., and J. C. Fyfe, 2012b: Observed and simulated changes in the
Southern Hemisphere surface westerly wind-stress. Geophys. Res. Lett.,
doi:10.1029/2012GL052810.
Tachiiri, K., J. C. Hargreaves, J. D. Annan, A. Oka, A. Abe-Ouchi, and M. Kawamiya,
2010: Development of a system emulating the global carbon cycle in Earth
system models. Geophys. Model Dev., 3, 365–376.
Takahashi, T., et al., 2009: Climatological mean and decadal change in surface ocean
pCO(2), and net sea-air CO
2
flux over the global oceans. Deep-Sea Res. Pt., 56,
554–577.
Takata, K., S. Emori, and T. Watanabe, 2003: Development of the minimal advanced
treatments of surface interaction and runoff. Global Planet. Change, 38, 209–
222.
Takemura, T., T. Nakajima, O. Dubovik, B. N. Holben, and S. Kinne, 2002: Single-
scattering albedo and radiative forcing of various aerosol species with a global
three-dimensional model. J. Clim., 15, 333–352.
Takemura, T., T. Nozawa, S. Emori, T. Y. Nakajima, and T. Nakajima, 2005: Simulation
of climate response to aerosol direct and indirect effects with aerosol transport-
radiation model. J. Geophys. Res. Atmos., 110, D02202.
Takemura, T., H. Okamoto, Y. Maruyama, A. Numaguti, A. Higurashi, and T. Nakajima,
2000: Global three-dimensional simulation of aerosol optical thickness
distribution of various origins. J. Geophys. Res. Atmos., 105, 17853–17873.
Takemura, T., M. Egashira, K. Matsuzawa, H. Ichijo, R. O’Ishi, and A. Abe-Ouchi, 2009:
A simulation of the global distribution and radiative forcing of soil dust aerosols
at the Last Glacial Maximum. Atmos. Chem. Phys., 9, 3061–3073.
Takle, E. S., et al., 2007: Transferability intercomparison—An opportunity for new
insight on the global water cycle and energy budget. Bull. Am. Meteorol. Soc.,
88, 375–384.
Taylor, C. M., R. A. M. de Jeu, F. Guichard, P. P. Harris, and W. A. Dorigo, 2012a:
Afternoon rain more likely over drier soils. Nature, 489, 423–426.
Taylor, C. M., A. Gounou, F. Guichard, P. P. Harris, R. J. Ellis, F. Couvreux, and M. De
Kauwe, 2011: Frequency of Sahelian storm initiation enhanced over mesoscale
soil-moisture patterns. Nature Geosci., 4, 430–433.
Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012b: An overview of CMIP5 and the
experiment design. Bull. Am. Meteorol. Soc., 93, 485–498.
Tebaldi, C., and R. Knutti, 2007: The use of the multi-model ensemble in probabilistic
climate projections. Philos. Trans. R. Soc. London A, 365 2053–2075.
Teixeira, J., et al., 2008: Parameterization of the atmospheric boundary layer. Bull.
Am. Meteorol. Soc., 89, 453–458.
Teixeira, J., et al., 2011: Tropical and subtropical cloud transitions in weather
and climate prediction models: The GCSS/WGNE Pacific Cross-Section
Intercomparison (GPCI). J. Clim., 24, 5223–5256.
Terray, L., 2012: Evidence for multiple drivers of North Atlantic multi-decadal climate
variability. Geophys. Res. Lett., 39, L19712.
Terray, L., L. Corre, S. Cravatte, T. Delcroix, G. Reverdin, and A. Ribes, 2012: Near-
surface salinity as nature’s rain gauge to detect human influence on the tropical
water cycle. J. Clim., 25, 958–977.
Teutschbein, C., F. Wetterhall, and J. Seibert, 2011: Evaluation of different
downscaling techniques for hydrological climate-change impact studies at the
catchment scale. Clim. Dyn., 37, 2087–2105.
Textor, C., et al., 2007: The effect of harmonized emissions on aerosol properties in
global models—an AeroCom experiment. Atmos. Chem. Phys., 7, 4489–4501.
Thorndike, A. S., D. A. Rothrock, G. A. Maykut, and R. Colony, 1975: Thickness
distribution of sea ice J. Geophys. Res. Oceans Atmos., 80, 4501–4513.
Thorne, P. W., et al., 2011: A quantification of uncertainties in historical tropical
tropospheric temperature trends from radiosondes. J. Geophys. Res., 116,
D12116.
Thornton, P. E., J. F. Lamarque, N. A. Rosenbloom, and N. M. Mahowald, 2007:
Influence of carbon-nitrogen cycle coupling on land model response to CO
2
fertilization and climate variability. Global Biogeochem. Cycles, 21, GB4018.
Tian, B., E. J. Fetzer, B. H. Kahn, J. Teixeira, E. Manning, and T. Hearty, 2013: Evaluating
CMIP5 models using AIRS tropospheric air temperature and specific humidity
climatology. J. Geophys. Res. Atmos., 118, 114–134.
Timbal, B., and D. Jones, 2008: Future projections of winter rainfall in southeast
Australia using a statistical downscaling technique. Clim. Change, 86, 165–187.
Timmermann, A., S. Lorenz, S.-I. An, A. Clement, and S.-P. Xie, 2007: The effect of
orbital forcing on the mean climate and variability of the tropical Pacific. J. Clim.,
20, 4147–4159.
Timmermann, R., H. Goosse, G. Madec, T. Fichefet, C. Ethe, and V. Duliere, 2005: On
the representation of high latitude processes in the ORCA-LIM global coupled
sea ice-ocean model. Ocean Model., 8, 175–201.
Ting, M., Y. Kushnir, R. Seager, and C. Li, 2009: Forced and internal twentieth-century
SST trends in the north Atlantic. J. Clim., 22, 1469–1481.
Tjernstrom, M., J. Sedlar, and M. Shupe, 2008: How well do regional climate
models reproduce radiation and clouds in the Arctic? An evaluation of ARCMIP
simulations. J. Appl. Meteorol. Climatol., 47, 2405–2422.
Tjiputra, J. F., K. Assmann, M. Bentsen, I. Bethke, O. H. Ottera, C. Sturm, and C. Heinze,
2010: Bergen Earth system model (BCM-C): Model description and regional
climate-carbon cycle feedbacks assessment. Geophys. Model Dev., 3, 123–141.
Tjiputra, J. F., et al., 2013: Evaluation of the carbon cycle components inthe
Norwegian Earth System Model (NorESM). Geophys. Model Dev., 6, 301–325.
Todd-Brown, K. E. O., J. T. Randerson, W. M. Post, F. M. Hoffman, C. Tarnocai, E. A. G.
Schuur, and S. D. Allison, 2013: Causes of variation in soil carbon simulations from
CMIP5 Earth system models and comparison with observations. Biogeosciences,
10, 1717–1736.
Toniazzo, T., and S. Woolnough, 2013: Development of warm SST errors in the
southern tropical Atlantic in CMIP5 decadal hindcasts. Clim. Dyn., doi:10.1007/
s00382-013-1691-2.
Tory, K., S. Chand, R. Dare, and J. McBride, 2013: An assessment of a model-, grid-
and basin-independent tropical cyclone detection scheme in selected CMIP3
global climate models. J. Clim., doi:10.1175/JCLI-D-12-00511.1.
Trenberth, K. E., and J. M. Caron, 2001: Estimates of meridional atmosphere and
ocean heat transports. J. Clim., 14, 3433–3443.
Trenberth, K. E., and J. T. Fasullo, 2008: An observational estimate of inferred ocean
energy divergence. J. Phys. Oceanogr., 38, 984–999.
Trenberth, K. E., and J. T. Fasullo, 2009: Global warming due to increasing absorbed
solar radiation. Geophys. Res. Lett., 36, L07706.
Trenberth, K. E., and J. T. Fasullo, 2010a: Climate change: Tracking Earth’s energy.
Science, 328, 316–317.
Trenberth, K. E., and J. T. Fasullo, 2010b: Simulation of present-day and twenty-first-
century energy budgets of the Southern Oceans. J. Clim., 23, 440–454.
Trenberth, K. E., D. P. Stepaniak, and J. M. Caron, 2000: The global monsoon as seen
through the divergent atmospheric circulation. J. Clim., 13, 3969–3993.
Trenberth, K. E., J. T. Fasullo, and J. Kiehl, 2009: Earth’s global energy budget. Bull.
Am. Meteorol. Soc., 90, 311–323.
Tryhorn, L., and A. DeGaetano, 2011: A comparison of techniques for downscaling
extreme precipitation over the northeastern United States. Int. J. Climatol. , 31,
1975–1989.
Tschumi, T., F. Joos, and P. Parekh, 2008: How important are Southern Hemisphere
wind changes for low glacial carbon dioxide? A model study. Paleoceanography,
23, PA4208.
Tschumi, T., F. Joos, M. Gehlen, and C. Heinze, 2011: Deep ocean ventilation, carbon
isotopes, marine sedimentation and the deglacial CO(2) rise. Clim. Past, 7,
771–800.
Tsigaridis, K., and M. Kanakidou, 2007: Secondary organic aerosol importance in the
future atmosphere. Atmos. Environ., 41, 4682–4692.
Tsujino, H., M. Hirabara, H. Nakano, T. Yasuda, T. Motoi, and G. Yamanaka,
2011: Simulating present climate of the global ocean–ice system using the
Meteorological Research Institute Community Ocean Model (MRI. COM):
Simulation characteristics and variability in the Pacific sector. J. Oceanogr., 67,
449–479.
Tsushima, Y., M. Ringer, M. Webb, and K. Williams, 2013: Quantitative evaluation of
the seasonal variations in climate model cloud regimes. Clim. Dyn., doi:10.1007/
s00382-012-1609-4.
Turner, A. G., and H. Annamalai, 2012: Climate change and the south Asian summer
monsoon. Nature Clim. Change, 2, 1–9.
Turner, A. G., P. M. Inness, and J. M. Slingo, 2007: The effect of doubled CO
2
and
model basic state biases on the monsoon-ENSO system. II: Changing ENSO
regimes. Q. J. R. Meteorol. Soc., 133, 1159–1173.
Ulbrich, U., G. C. Leckebusch, and J. G. Pinto, 2009: Extra-tropical cyclones in the
present and future climate: a review. Theor. Appl. Climatol., 96, 117–131.
Ulbrich, U., J. G. Pinto, H. Kupfer, G. C. Leckebusch, T. Spangehl, and M. Reyers, 2008:
Changing northern hemisphere storm tracks in an ensemble of IPCC climate
change simulations. J. Clim., 21, 1669–1679.
850
Chapter 9 Evaluation of Climate Models
9
UNESCO, 1981: Tenth report of the joint panel on oceanographic tables and
standardsUNESCO.
Uotila, P., S. O’Farrell, S. J. Marsland, and D. Bi, 2012: A sea-ice sensitivity study with
a global ocean-ice model. Ocean Model., 51, 1–18.
Uotila, P., S. O’Farrell, S. J. Marsland, and D. Bi, 2013: The sea-ice performance of the
Australian climatemodels participating in the CMIP5.Aust. Meteorol. Oceanogr.
J., 63, 121–143.
Uppala, S. M., et al., 2005: The ERA-40 re-analysis. Q. J. R. Meteorol. Soc., 131,
2961–3012.
van den Hurk, B., and E. van Meijgaard, 2010: Diagnosing land-atmosphere
interaction from a regional climate model simulation over West Africa. J.
Hydrometeorol., 11, 467–481.
van Oldenborgh, G., et al., 2009: Western Europe is warming much faster than
expected. Clim. Past, 5, 1–12.
van Oldenborgh, G. J., S. Y. Philip, and M. Collins, 2005: El Niño in a changing climate:
A multi-model study. Ocean Sci., 1, 81–95.
van Roosmalen, L., J. H. Christensen, M. B. Butts, K. H. Jensen, and J. C. Refsgaard,
2010: An intercomparison of regional climate model data for hydrological
impact studies in Denmark. J. Hydrol., 380, 406–419.
van Vliet, M., S. Blenkinsop, A. Burton, C. Harpham, H. Broers, and H. Fowler, 2011: A
multi-model ensemble of downscaled spatial climate change scenarios for the
Dommel catchment, Western Europe. Clim. Change, 111, 249–277.
van Vuuren, D., et al., 2011: The representative concentration pathways: An overview.
Clim. Change, 109, 5–31.
Vancoppenolle, M., T. Fichefet, H. Goosse, S. Bouillon, G. Madec, and M. A. M.
Maqueda, 2009: Simulating the mass balance and salinity of Arctic and Antarctic
sea ice. 1. Model description and validation. Ocean Model., 27, 33–53.
Vancoppenolle, M., H. Goosse, A. de Montety, T. Fichefet, B. Tremblay, and J. L. Tison,
2010: Interactions between brine motion, nutrients and primary production in
sea ice. J. Geophys. Res., 115, C02005.
Vannière, B., E. Guilyardi, G. Madec, F. J. Doblas-Reyes, and S. Woolnough, 2011:
Using seasonal hindcasts to understand the origin of the equatorial cold tongue
bias in CGCMs and its impact on ENSO. Clim. Dyn., 40, 963–981.
Vautard, R., et al., 2013: The simulation of European heat waves from an ensemble
of regional climate models within the EURO-CORDEX project. Clim. Dyn.,
doi:10.1007/s00382-013-1714-z.
Vecchi, G. A., and B. J. Soden, 2007: Global warming and the weakening of the
tropical circulation. J. Clim., 20, 4316–4340.
Vecchi, G. A., K. L. Swanson, and B. J. Soden, 2008: Climate Change: Whither hurricane
activity? Science, 322, 687–689.
Vecchi, G. A., B. J. Soden, A. T. Wittenberg, I. M. Held, A. Leetmaa, and M. J. Harrison,
2006: Weakening of tropical Pacific atmospheric circulation due to anthropogenic
forcing. Nature, 327, 216–-219.
Veljovic, K., B. Rajkovic, M. J. Fennessy, E. L. Altshuler, and F. Mesinger, 2010: Regional
climate modeling: Should one attempt improving on the large scales? Lateral
boundary condition scheme: Any impact? Meteorol. Z., 19, 237–246.
Verlinde, J., et al., 2007: The Mixed-Phase Arctic Cloud Experiment. Bull. Am.
Meteorol. Soc., 88, 205–221.
Verseghy, D. L., 2000: The Canadian Land Surface Scheme (CLASS): Its history and
future. Atmos. Ocean, 38, 1–13.
Vial, J., and T. J. Osborn, 2012: Assessment of atmosphere-ocean general circulation
model simulations of winter northern hemisphere atmospheric blocking. Clim.
Dyn., 39, 95–112.
Vial, J., J.-L. Dufresne, and S. Bony, 2013: On the interpretation of inter-model spread
in CMIP5 climate sensitivity estimates. Clim. Dyn., doi:10.1007/s00382-013-
1725-9.
Vichi, M., S. Masina, and A. Navarra, 2007: A generalized model of pelagic
biogeochemistry for the global ocean ecosystem. Part II: Numerical simulations.
J. Mar. Syst., 64, 110–134.
Vichi, M., et al., 2011: Global and regional ocean carbon uptake and climate change:
Sensitivity to a substantial mitigation scenario. Clim. Dyn., 37, 1929–1947.
Vizcaino, M., U. Mikolajewicz, M. Groger, E. Maier-Reimer, G. Schurgers, and A. M. E.
Winguth, 2008: Long-term ice sheet-climate interactions under anthropogenic
greenhouse forcing simulated with a complex Earth System Model. Clim. Dyn.,
31, 665–690.
Voldoire, A., et al., 2013: The CNRM-CM5.1 global climate model : Description and
basic evaluation. Clim. Dyn., 40, 2091–2121.
Volodin, E. M., 2007: Atmosphere-ocean general circulation model with the carbon
cycle. Izvestiya Atmos. Ocean. Phys., 43, 298–313.
Volodin, E. M., 2008a: Relation between temperature sensitivity to doubled carbon
dioxide and the distribution of clouds in current climate models. Izvestiya Atmos.
Ocean. Phys., 44, 288–299.
Volodin, E. M, 2008b: Methane cycle in the INM RAS climate model. Izvestiya Atmos.
Ocean. Phys., 44, 153–159.
Volodin, E. M., and V. N. Lykosov, 1998: Parametrization of heat and moisture transfer
in the soil-vegetation system for use in atmospheric general circulation models:
1. Formulation and simulations based on local observational data. Izvestiya
Akad. Nauk Fizik. Atmosf. Okean., 34, 453–465.
Volodin, E. M., N. A. Dianskii, and A. V. Gusev, 2010: Simulating present-day climate
with the INMCM4.0 coupled model of the atmospheric and oceanic general
circulations. Izvestiya Atmos. Ocean. Phys., 46, 414–431.
von Salzen, K., et al., 2013: The Canadian Fourth Generation Atmospheric Global
Climate Model (CanAM4). Part I: Representation of physical processes. Atmos.
Ocean, 51, 104–125.
Vose, R. S., et al., 2012: NOAA’S merged land-ocean surface temperature analysis.
Bull. Am. Meteorol. Soc., 93, 1677–1685.
Vrac, M., and P. Naveau, 2008: Stochastic downscaling of precipitation: From dry
events to heavy rainfall Water Resour. Res., 43, W07402.
Waelbroeck, C., et al., 2009: Constraints on the magnitude and patterns of ocean
cooling at the Last Glacial Maximum. Nature Geosci., 2, 127–132.
Wahl, S., M. Latif, W. Park, and N. Keenlyside, 2011: On the Tropical Atlantic SST warm
bias in the Kiel Climate Model. Clim. Dyn., 36, 891–906.
Waliser, D., K. W. Seo, S. Schubert, and E. Njoku, 2007: Global water cycle agreement
in the climate models assessed in the IPCC AR4. Geophys. Res. Lett., 34, L16705
Waliser, D., et al., 2009a: MJO simulation diagnostics. J. Clim., 22, 3006–3030.
Waliser, D. E., J. L. F. Li, T. S. L’Ecuyer, and W. T. Chen, 2011: The impact of precipitating
ice and snow on the radiation balance in global climate models. Geophys. Res.
Lett., 38, L06802.
Waliser, D. E., et al., 2003: AGCM simulations of intraseasonal variability associated
with the Asian summer monsoon. Clim. Dyn., 21, 423–446.
Waliser, D. E., et al., 2009b: Cloud ice: A climate model challenge with signs and
expectations of progress. J. Geophys. Res., 114, D00A21.
Walsh, K., S. Lavender, E. Scoccimarro, and H. Murakami, 2013: Resolution
dependence of tropical cyclone formation in CMIP3 and finer resolution models.
Clim. Dyn., 40, 585–599.
Walther, A., J.-H. Jeong, G. Nikulin, C. Jones, and D. Chen, 2013: Evaluation of the
warm season diurnal cycle of precipitation over Sweden simulated by the Rossby
Centre regional climate model RCA3. Atmos. Res., 119, 131–139.
Wang, B., 2006: The Asian Monsoon. Springer Science+Business Media, Praxis, New
York, NY, USA, 787 pp.
Wang, B., and Q. H. Ding, 2008: Global monsoon: Dominant mode of annual variation
in the tropics. Dyn. Atmos. Oceans, 44, 165–183.
Wang, B., H. J. Kim, K. Kikuchi, and A. Kitoh, 2011a: Diagnostic metrics for evaluation
of annual and diurnal cycles. Clim. Dyn., 37, 941–955.
Wang, B., H. Wan, Z. Z. Ji, X. Zhang, R. C. Yu, Y. Q. Yu, and H. T. Liu, 2004: Design of
a new dynamical core for global atmospheric models based on some efficient
numerical methods. Sci. China A, 47, 4–21.
Wang, C., and J. Picaut, 2004: Understanding ENSO physics —A review. In: Earth’s
Climate: The Ocean-Atmosphere Interaction [C. Wang, S.-P. Xie and J.A. Carton
(eds.)]. American Geophysical Union, Washington, DC, pp. 21–48.
Wang, H., and W. Su, 2013: Evaluating and understanding top of the atmosphere
cloud radiative effects. In Intergovernmental Panel on Climate Change (IPCC)
Fifth Assessment Report (AR5) Coupled Model Intercomparison Project Phase
5 (CMIP5) models using satellite observations. J. Geophys. Res. Atmos., 118,
683–699.
Wang, J., Q. Bao, N. Zeng, Y. Liu, G. Wu, and D. Ji, 2013: The Earth System Model
FGOALS-s2: Coupling a dynamic global vegetation and terrestrial carbon model
with the physical climate. Adv. Atmos. Sci., doi:10.1007/s00376–013-2169-1.
Wang, J. F., and X. B. Zhang, 2008: Downscaling and projection of winter extreme
daily precipitation over North America. J. Clim., 21, 923–937.
Wang, M., and J. E. Overland, 2012: A sea ice free summer Arctic within 30 years: An
update from CMIP5 models. Geophys. Res. Lett., 39, L18501.
Wang, M., J. E. Overland, and N. A. Bond, 2010: Climate projections for selected large
marine ecosystems. J. Mar. Syst., 79, 258–266.
851
Evaluation of Climate Models Chapter 9
9
Wang, Y., M. Notaro, Z. Liu, R. Gallimore, S. Levis, and J. E. Kutzbach, 2008: Detecting
vegetation-precipitation feedbacks in mid-Holocene North Africa from two
climate models. Clim. Past, 4, 59–67.
Wang, Y. P., and B. Z. Houlton, 2009: Nitrogen constraints on terrestrial carbon
uptake: Implications for the global carbon-climate feedback. Geophys. Res. Lett.,
36, L24403.
Wang, Y. P., et al., 2011b: Diagnosing errors in a land surface model (CABLE) in the
time and frequency domains. J. Geophys. Res. Biogeosci., 116, G01034.
Wania, R., I. Ross, and I. C. Prentice, 2009: Integrating peatlands and permafrost into
a dynamic global vegetation model: 1. Evaluation and sensitivity of physical land
surface processes. Global Biogeochem. Cycles, 23, Gb3014.
Watanabe, M., 2008: Two regimes of the equatorial warm pool. Part I: A simple
tropical climate model. J. Clim., 21, 3533–3544.
Watanabe, M., M. Chikira, Y. Imada, and M. Kimoto, 2011: Convective control of
ENSO simulated in MIROC. J. Clim., 24, 543–562.
Watanabe, M., J. S. Kug, F. F. Jin, M. Collins, M. Ohba, and A. T. Wittenberg, 2012:
Uncertainty in the ENSO amplitude change from the past to the future. Geophys.
Res. Lett., 39, L20703.
Watanabe, M., et al., 2010: Improved climate simulation by MIROC5: Mean states,
variability, and climate sensitivity. J. Clim., 23, 6312–6335.
Watanabe, S., Y. Kawatani, Y. Tomikawa, K. Miyazaki, M. Takahashi, and K. Sato, 2008:
General aspects of a T213L256 middle atmosphere general circulation model. J.
Geophys. Res. Atmos., 113, D12110.
Watterson, I., and P. Whetton, 2011: Distributions of decadal means of temperature
and precipitation change under global warming. J. Geophys. Res. Atmos., 116,
D07101.
Waugh, D., and V. Eyring, 2008: Quantitative performance metrics for stratospheric-
resolving chemistry-climate models. Atmos. Chem. Phys., 8, 5699–5713.
Weaver, A. J., et al., 2001: The UVic Earth System Climate Model: Model description,
climatology, and applications to past, present and future climates. Atmos.
Ocean, 39, 361–428.
Weaver, A. J., et al., 2012: Stability of the Atlantic meridional overturning circulation:
A model intercomparison. Geophys. Res. Lett., 39, L20709.
Webb, M., C. Senior, S. Bony, and J. J. Morcrette, 2001: Combining ERBE and ISCCP
data to assess clouds in the Hadley Centre, ECMWF and LMD atmospheric
climate models. Clim. Dyn., 17, 905–922.
Weber, S. L., et al., 2007: The modern and glacial overturning circulation in the
Atlantic Ocean in PMIP coupled model simulations. Clim. Past, 3, 51–64.
Webster, P. J., A. M. Moore, J. P. Loschnigg, and R. R. Leben, 1999: Coupled ocean-
atmosphere dynamics in the Indian Ocean during 1997–98. Nature, 401, 356–
360.
Wehner, M. F., R. L. Smith, G. Bala, and P. Duffy, 2010: The effect of horizontal
resolution on simulation of very extreme US precipitation events in a global
atmosphere model. Clim. Dyn., 34, 241–247.
Weller, E., and W. Cai, 2013a: Realism of the Indian Ocean Dipole in CMIP5
models: The implication for 1 climate projections. J. Clim., doi:10.1175/JCLI-D-
12-00807.1.
Weller, E., and W. Cai, 2013b: Asymmetry in the IOD and ENSO teleconnection
in a CMIP5 model ensemble and its relevance to regional rainfall. J. Clim.,
doi:10.1175/JCLI-D-12-00789.1.
Wentz, F. J., L. Ricciardulli, K. Hilburn, and C. Mears, 2007: How much more rain will
global warming bring? Science, 317, 233–235.
Westerling, A. L., H. G. Hidalgo, D. R. Cayan, and T. W. Swetnam, 2006: Warming
and earlier spring increase western US forest wildfire activity. Science, 313,
940–943.
Wetzel, P., E. Maier-Reimer, M. Botzet, J. H. Jungclaus, N. Keenlyside, and M. Latif,
2006: Effects of ocean biology on the penetrative radiation in a Coupled Climate
Model. J. Clim., 19, 3973–3987.
Whetton, P., I. Macadam, J. Bathols, and J. O’Grady, 2007: Assessment of the use of
current climate patterns to evaluate regional enhanced greenhouse response
patterns of climate models. Geophys. Res. Lett., 34, L14701.
White, C. J., et al., 2013: On regional dynamical downscaling for the assessment
and projection of temperature and precipitation extremes across Tasmania,
Australia. Clim. Dyn., doi:10.1007/s00382-013-1718-8.
Wilcox, L. J., A. J. Charlton-Perez, and L. J. Gray, 2012: Trends in Austral jet position in
ensembles of high- and low-top CMIP5 models. J. Geophys. Res., 117, D13115.
Wild, M., C. N. Long, and A. Ohmura, 2006: Evaluation of clear-sky solar fluxes
in GCMs participating in AMIP and IPCC-AR4 from a surface perspective. J.
Geophys. Res. Atmos., 111, D01104
Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences. Vol. 59, Academic
Press, San Diego, CA, USA, 467 pp.
Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. Vol. 91, Academic
Press, Elsevier, San Diego, CA, USA, 627 pp.
Willett, K., P. Jones, P. Thorne, and N. Gillett, 2010: A comparison of large scale
changes in surface humidity over land in observations and CMIP3 general
circulation models. Environ. Res. Lett., 5, 025210.
Williams, C. J. R., R. P. Allan, and D. R. Kniveton, 2012: Diagnosing atmosphere-land
feedbacks in CMIP5 climate models. Environ. Res. Lett., 7, 044003.
Williams, K., and M. Webb, 2009: A quantitative performance assessment of cloud
regimes in climate models. Clim. Dyn., 33 141–157.
Williams, K. D., and M. E. Brooks, 2008: Initial tendencies of cloud regimes in the Met
Office unified model. J. Clim., 21, 833–840.
Williams, K. D., W. J. Ingram, and J. M. Gregory, 2008: Time variation of effective
climate sensitivity in GCMs. J. Clim., 21, 5076–5090.
Williamson, D. L., and J. G. Olson, 2007: A comparison of forecast errors in CAM2 and
CAM3 at the ARM Southern Great Plains site. J. Clim., 20, 4572–4585.
Williamson, M. S., T. M. Lenton, J. G. Shepherd, and N. R. Edwards, 2006: An efficient
numerical terrestrial scheme (ENTS) for Earth system modelling. Ecol. Model.,
198, 362–374.
Willis, J. K., 2010: Can in situ floats and satellite altimeters detect long-term changes
in Atlantic Ocean overturning? Geophys. Res. Lett., 37, L06602.
Winterfeldt, J., and R. Weisse, 2009: Assessment of value added for surface marine
wind speed obtained from two regional climate models. Mon. Weather Rev.,
137, 2955–2965.
Winterfeldt, J., B. Geyer, and R. Weisse, 2011: Using QuikSCAT in the added value
assessment of dynamically downscaled wind speed. Int. J. Climatol. , 31, 1028–
1039.
Winton, M., 2000: A reformulated three-layer sea ice model. J. Atmos. Ocean.
Technol., 17, 525–531.
Winton, M., 2011: Do climate models underestimate the sensitivity of Northern
Hemisphere sea ice cover? J. Clim., 24, 3924–3934.
Wittenberg, A. T., 2009: Are historical records sufficient to constrain ENSO
simulations? Geophys. Res. Lett., 36, L12702.
Wittenberg, A. T., A. Rosati, N. C. Lau, and J. J. Ploshay, 2006: GFDL’s CM2 Global
Coupled Climate Models. Part III: Tropical Pacific climate and ENSO. J. Clim., 19,
698–722.
WMO, 2011: Scientific Assessment of Ozone Depletion: 2010. Global Ozone
Research and Monitoring Project–Report. World Meteorological Organisation,
Geneva, Switzerland.
Wohlfahrt, J., et al., 2008: Evaluation of coupled ocean-atmosphere simulations of
the mid-Holocene using palaeovegetation data from the northern hemisphere
extratropics. Clim. Dyn., 31, 871–890.
Wood, R., et al., 2011: The VAMOS Ocean-Cloud-Atmosphere-Land Study Regional
Experiment (VOCALS-REx): Goals, platforms, and field operations. Atmos. Chem.
Phys., 11, 627–654.
Woollings, T., B. Hoskins, M. Blackburn, D. Hassell, and K. Hodges, 2010a: Storm
track sensitivity to sea surface temperature resolution in a regional atmosphere
model. Clim. Dyn., 35, 341–353.
Woollings, T., A. Charlton-Perez, S. Ineson, A. G. Marshall, and G. Masato, 2010b:
Associations between stratospheric variability and tropospheric blocking. J.
Geophys. Res. Atmos., 115, D06108.
Woollings, T., J. M. Gregory, J. G. Pinto, M. Reyers, and D. J. Brayshaw, 2012: Response
of the North Atlantic storm track to climate change shaped by ocean-atmosphere
coupling. Nature Geosci., 5, 313–317.
Wright, D. G., and T. F. Stocker, 1992: Sensitivities of a zonally averaged Global Ocean
Circulation Model.. J. Geophys. Res. Oceans, 97, 12707–12730.
Wu, Q. G., D. J. Karoly, and G. R. North, 2008a: Role of water vapor feedback on the
amplitude of season cycle in the global mean surface air temperature. Geophys.
Res. Lett., 35, L08711.
Wu, T., 2012: A mass-flux cumulus parameterization scheme for large-scale models:
Description and test with observations. Clim. Dyn., 38, 725–744.
Wu, T., R. Yu, and F. Zhang, 2008b: A modified dynamic framework for the atmospheric
spectral model and its application. J. Atmos. Sci., 65, 2235–2253.
Wu, T., et al., 2010a: Erratum—The Beijing Climate Center atmospheric general
circulation model: Description and its performance for the present-day climate.
Clim. Dyn., 34, 149–150.
852
Chapter 9 Evaluation of Climate Models
9
Wu, T., et al., 2010b: The Beijing Climate Center atmospheric general circulation
model: Description and its performance for the present-day climate. Clim. Dyn.,
34, 123–147.
Wyant, M. C., C. S. Bretherton, and P. N. Blossey, 2009: Subtropical low cloud
response to a warmer climate in a superparameterized climate model. Part I:
Regime sorting and physical mechanisms. J. Adv. Model. Earth Syst., 1, 7.
Wyser, K., et al., 2008: An evaluation of Arctic cloud and radiation processes during
the SHEBA year: Simulation results from eight Arctic regional climate models.
Clim. Dyn., 30, 203–223.
Xavier, P. K., 2012: Intraseasonal convective moistening in CMIP3 models. J. Clim.,
25, 2569–2577.
Xavier, P. K., J. P. Duvel, P. Braconnot, and F. J. Doblas-Reyes, 2010: An evaluation
metric for intraseasonal variability and its application to CMIP3 twentieth-
century simulations. J. Clim., 23, 3497–3508.
Xiao, X., et al., 1998: Transient climate change and net ecosystem production of the
terrestrial biosphere. Global Biogeochem. Cycles, 12, 345–360.
Xie, L., T. Z. Yan, L. J. Pietrafesa, J. M. Morrison, and T. Karl, 2005: Climatology and
interannual variability of North Atlantic hurricane tracks. J. Clim., 18, 5370–5381.
Xie, P., and P. A. Arkin, 1997: Global Precipitation: A 17-year monthly analysis based
on gauge observations, satellite estimates, and numerical model outputs. Bull.
Am. Meteorol. Soc., 78, 2539–2558.
Xie, S., J. Boyle, S. A. Klein, X. Liu, and S. Ghan, 2008: Simulations of Arctic mixed-
phase clouds in forecasts with CAM3 and AM2 for M-PACE. J. Geophys. Res.,
113, D04211.
Xie, S., H.-Y. Ma, J. S. Boyle, S. A. Klein, and Y. Zhang, 2012: On the correspondence
between short- and long- timescale systematic errors in CAM4/CAM5 for the
years of tropical convection. J. Clim., 25, 7937–7955.
Xie, S. P., H. Annamalai, F. A. Schott, and J. P. McCreary, 2002: Structure and
mechanisms of South Indian Ocean climate variability. J. Clim., 15, 864–878.
Xin, X.-G., T.-J. Zhou, and R.-C. Yu, 2008: The Arctic Oscillation in coupled climate
models. Chin. J. Geophys. Chinese Edition, 51, 337–351.
Xin, X., L. Zhang, J. Zhang, T. Wu, and Y. Fang, 2013: Climate change projections over
East Asia with BCC_CSM1.1 climate model under RCP scenarios. J. Meteorol.
Soc. Jpn., 91, 413–429.
Xin, X., T. Wu, J. Li, Z. Wang, W. Li, and F. Wu, 2012: How well does BCC_CSM1.1
reproduce the 20th century climate change over China? . Atmos. Ocean. Sci.
Lett., 6, 21–26.
Xu, Y. F., Y. Huang, and Y. C. Li, 2012: Summary of recent climate change studies on
the carbon and nitrogen cycles in the terrestrial ecosystem and ocean in China.
Adv. Atmos. Sci., 29, 1027–1047.
Xue, Y. K., R. Vasic, Z. Janjic, F. Mesinger, and K. E. Mitchell, 2007: Assessment of
dynamic downscaling of the continental US regional climate using the Eta/SSiB
regional climate model. J. Clim., 20, 4172–4193.
Yakovlev, N. G., 2009: Reproduction of the large-scale state of water and sea ice in
the Arctic Ocean in 1948–2002: Part I. Numerical model. Izvestiya Atmos. Ocean.
Phys., 45, 628–641.
Yang, D., and T. Ohata, 2001: A bias-corrected Siberian regional precipitation
climatology. J. Hydrometeorol., 2, 122–139.
Yasutaka, W., N. Masaomi, K. Sachie, and M. Chiashi, 2008: Climatological
reproducibility evaluation and future climate projection of extreme precipitation
events in the Baiu season using a high-resolution non-hydrostatic RCM in
comparison with an AGCM. J. Meteorol. Soc. Jpn., 86, 951–967.
Yeager, S., and G. Danabasoglu, 2012: Sensitivity of Atlantic Meridional Overturning
Circulation variability to parameterized Nordic Sea overflows in CCSM4. J. Clim.,
25, 2077–2103.
Yeh, S. W., Y. G. Ham, and J. Y. Lee, 2012: Changes in the tropical Pacific SST trend
from CMIP3 to CMIP5 and its implication of ENSO. J. Clim., 25, 7764–7771.
Yhang, Y. B., and S. Y. Hong, 2008: Improved physical processes in a regional climate
model and their impact on the simulated summer monsoon circulations over
East Asia. J. Clim., 21, 963–979.
Yin, X., A. Gruber, and P. Arkin, 2004: Comparison of the GPCP and CMAP merged
gauge-satellite monthly precipitation products for the period 1979–2001. J.
Hydrometeorol., 5, 1207–1222.
Yokohata, T., M. J. Webb, M. Collins, K. D. Williams, M. Yoshimori, J. C. Hargreaves, and
J. D. Annan, 2010: Structural similarities and differences in climate responses
to CO
2
increase between two perturbed physics ensembles. J. Clim., 23, 1392–
1410.
Yokohata, T., J. Annan, M. Collins, C. Jackson, M. Tobis, M. Webb, and J. Hargreaves,
2012: Reliability of multi-model and structurally different single-model
ensembles. Clim. Dyn., 39, 599–616.
Yokohata, T., et al., 2013: Reliability and importance of structural diversityof climate
model ensembles. Clim. Dyn., doi:10.1007/s00382-013-1733–9.
Yokohata, T., et al., 2008: Comparison of equilibrium and transient responses to CO
2
increase in eight state-of-the-art climate models. Tellus A, 60, 946–961.
Yokoi, S., Y. N. Takayabu, and J. C. L. Chan, 2009a: Tropical cyclone genesis frequency
over the western North Pacific simulated in medium-resolution coupled general
circulation models. Clim. Dyn., 33, 665–683.
Yokoi, S., C. Takahashi, K. Yasunaga, and R. Shirooka, 2012: Multi-model projection of
tropical cyclone genesis frequency over the western North Pacific: CMIP5 results.
Sola, 8, 137–140.
Yokoi, S., et al., 2011: Application of cluster analysis to climate model performance
metrics. J. Appl. Meteorol. Climatol., 50, 1666–1675.
Yokoi, T., T. Tozuka, and T. Yamagata, 2009b: Seasonal variations of the Seychelles
Dome simulated in the CMIP3 models. J. Phys. Oceanogr., 39, 449–457.
Yoshimori, M., T. Yokohata, and A. Abe-Ouchi, 2009: A comparison of climate
feedback strength between CO
2
doubling and LGM experiments. J. Clim., 22,
3374–3395.
Yoshimori, M., J. C. Hargreaves, J. D. Annan, T. Yokohata, and A. Abe-Ouchi, 2011:
Dependency of feedbacks on forcing and climate state in physics parameter
ensembles. J. Clim., 24, 6440–6455.
Young, P. J., et al., 2013: Pre-industrial to end 21st century projections of tropospheric
ozone from the Atmospheric Chemistry and Climate Model Intercomparison
Project (ACCMIP). Atmos. Chem. Phys., 13, 2063–2090.
Yu, B., and F. W. Zwiers, 2010: Changes in equatorial atmospheric zonal circulations
in recent decades. Geophys. Res. Lett., 37, L05701.
Yu, J.-Y., and S. T. Kim, 2011: Reversed spatial asymmetries between El Niño and La
Niña and their linkage to decadal ENSO modulation in CMIP3 models. J. Clim.,
24, 5423–5434.
Yu, W., M. Doutriaux, G. Seze, H. LeTreut, and M. Desbois, 1996: A methodology
study of the validation of clouds in GCMs using ISCCP satellite observations.
Clim. Dyn., 12, 389–401.
Yukimoto, S., et al., 2011: Meteorological Research Institute-Earth System Model v1
(MRI-ESM1)—Model Description. Technical Report of MRI. Ibaraki, Japan, 88 pp.
Yukimoto, S., et al., 2012: A new global climate model of the Meteorological
Research Institute: MRI-CGCM3–Model description and basic performance. J.
Meteorol. Soc. Jpn., 90A, 23–64.
Zaehle, S., and D. Dalmonech, 2011: Carbon-nitrogen interactions on land at global
scales: Current understanding in modelling climate biosphere feedbacks. Curr.
Opin. Environ. Sustain., 3, 311–320.
Zaehle, S., P. Friedlingstein, and A. D. Friend, 2010: Terrestrial nitrogen feedbacks may
accelerate future climate change. Geophys. Res. Lett., 37, L01401.
Zahn, M., and H. von Storch, 2008: A long-term climatology of North Atlantic polar
lows. Geophys. Res. Lett., 35, L22702.
Zalesny, V. B., et al., 2010: Numerical simulation of large-scale ocean circulation
based on the multicomponent splitting method. Russ. J. Numer. Anal. Math.
Model., 25, 581–609.
Zaliapin, I., and M. Ghil, 2010: Another look at climate sensitivity. Nonlin. Proc.
Geophys., 17, 113–122.
Zappa, G., L. C. Shaffrey, and K. I. Hodges, 2013: The ability of CMIP5 models to
simulate North Atlantic extratropical cyclones. J. Clim., doi:10.1175/JCLI-D-12-
00501.1.
Zeng, N., 2003: Glacial-interglacial atmospheric CO
2
change—The glacial burial
hypothesis. Adv. Atmos. Sci., 20, 677–693.
Zeng, N., 2006: Quasi-100ky glacial-interglacial cycles triggered by subglacial burial
carbon release. Clim. Past, 2, 371–397.
Zeng, N., J. D. Neelin, and C. Chou, 2000: A quasi-equilibrium tropical circulation
model—Implementation and simulation. J. Atmos. Sci., 57, 1767–1796.
Zeng, N., A. Mariotti, and P. Wetzel, 2005: Terrestrial mechanisms of interannual CO
2
variability. Global Biogeochem. Cycles, 19, GB1016.
Zeng, N., H. F. Qian, E. Munoz, and R. Iacono, 2004: How strong is carbon cycle-
climate feedback under global warming? Geophys. Res. Lett., 31, L20203.
Zhang, F., and A. Georgakakos, 2011: Joint variable spatial downscaling. Clim.
Change, 111, 945–972
Zhang, J., and J. S. Reid, 2010: A decadal regional and global trend analysis of the
aerosol optical depth using a data-assimilation grade over-water MODIS and
Level 2 MISR aerosol products. Atmos. Chem. Phys., 10, 10949–10963.
853
Evaluation of Climate Models Chapter 9
9
Zhang, J., J. S. Reid, D. L. Westphal, N. L. Baker, and E. J. Hyer, 2008a: A system
for operational aerosol optical depth data assimilation over global oceans. J.
Geophys. Res. Atmos., 113, D10208.
Zhang, Q., H. S. Sundqvist, A. Moberg, H. Kornich, J. Nilsson, and K. Holmgren, 2010a:
Climate change between the mid and late Holocene in northern high latitudes—
Part 2: Model-data comparisons. Clim. Past, 6, 609–626.
Zhang, R., et al., 2013: Have aerosols caused the observed Atlantic multidecadal
variability? J. Atmos. Sci., doi:10.1175/JAS-D-12-0331.1.
Zhang, W., and F.-F. Jin, 2012: Improvements in the CMIP5 simulations of ENSO-SSTA
meridional width. Geophys. Res. Lett., 39, L23704.
Zhang, X., 2007: A comparison of explicit and implicit spatial downscaling of GCM
output for soil erosion and crop production assessments. Clim. Change, 84,
337–363.
Zhang, X., 2010: Sensitivity of arctic summer sea ice coverage to global warming
forcing: towards reducing uncertainty in arctic climate change projections. Tellus
A, 62, 220–227.
Zhang, X., et al., 2007: Detection of human influence on twentieth-century
precipitation trends. Nature, 448, 461–465.
Zhang, Y., S. A. Klein, J. Boyle, and G. G. Mace, 2010b: Evaluation of tropical cloud
and precipitation statistics of Community Atmosphere Model version 3 using
CloudSat and CALIPSO data. J. Geophys. Res., 115, D12205.
Zhang, Y., et al., 2008b: On the diurnal cycle of deep convection, high-level cloud,
and upper troposphere water vapor in the Multiscale Modeling Framework. J.
Geophys. Res., 113, D16105.
Zhao, M., I. M. Held, and S.-J. Lin, 2012: Some counterintuitive dependencies of
tropical cyclone frequency on parameters in a GCM. J. Atmos. Sci., 69, 2272–
2283.
Zhao, M., I. M. Held, S. J. Lin, and G. A. Vecchi, 2009: Simulations of global hurricane
climatology, interannual variability, and response to global warming using a
50-km resolution GCM. J. Clim., 22, 6653-6678.
Zhao, T. L., et al., 2008: A three-dimensional model study on the production of BrO
and Arctic boundary layer ozone depletion. J. Geophys. Res., 113, D24304.
Zhao, Y., and S. P. Harrison, 2012: Mid-Holocene monsoons: A multi-model analysis
of the inter-hemispheric differences in the responses to orbital forcing and
ocean feedbacks. Clim. Dyn., 39, 1457-1487.
Zheng, W. P., and P. Braconnot, 2013: Characterization of model spread in PMIP2
Mid-Holocene simulations of the African monsoon. J. Clim., 26, 1192-1210.
Zheng, X.-T., S.-P. Xie, and Q. Liu, 2011: Response of the Indian Ocean basin mode
and its capacitor effect to global warming. J. Clim., 24, 6146-6164.
Zheng, Y., J.-L. Lin, and T. Shinoda, 2012: The equatorial Pacific cold tongue simulated
by IPCC AR4 coupled GCMs: Upper ocean heat budget and feedback analysis. J.
Geophys. Res. Oceans, 117, C05024.
Zickfeld, K., et al., 2013: Long-term climate change commitment and reversibility: An
EMIC intercomparison. J. Clim., doi:10.1175/JCLI-D-12-00584.1.
Zou, L., and T. Zhou, 2013: Can a regional ocean-atmosphere coupled model improve
the simulation of the interannual variability of the western North Pacific summer
monsoon? J. Clim., 26, 2353–2367.
Zunz, V., H. Goosse, and F. Massonnet, 2013: How does internal variability influence
the ability of CMIP5 models to reproduce the recent trend in Southern Ocean sea
ice extent? Cryosphere, 7, 451-468.
Zwiers, F. W., X. Zhang, and Y. Feng, 2011: Anthropogenic influence on long return
period daily temperature extremes at regional scales. J. Clim., 24, 881-892.
854
Chapter 9 Evaluation of Climate Models
9
Appendix 9.A: Climate Models Assessed in Chapter 9
Table 9.A.1 | Salient features of the Atmosphere–Ocean General Circulation Models (AOGCMs) and Earth System Models (ESMs) participating in CMIP5 (see also Table 9.1). Column 1: Official CMIP5 model name along with the calendar year
(‘vintage’) of the first publication for each model; Column 2: sponsoring institution(s), main reference(s); subsequent columns for each of the model components, with names and main component reference(s). In addition, there are standard
entries for the atmosphere component: horizontal grid resolution, number of vertical levels, grid top (low or high top); and for the ocean component: horizontal grid resolution, number of vertical levels, top level, vertical coordinate type, ocean
free surface type (‘Top BC’). This table information was initially extracted from the CMIP5 online questionnaire (http://q.cmip5.ceda.ac.uk/) as of January 2013. A blank entry indicates that information was not available.
(1) Model Name
(2) Vintage
(1) Institution
(2) Main Reference(s)
Atmosphere
(1) Component Name
(2) Horizontal Grid
(3) Number of Vert
Levels
(4) Grid Top
(5) References
Aerosol
(1) Component
Name or type
(2) References
Atmos Chemistry
(1) Component Name
(2) References
Land Surface
(1) Component Name
(2) References
Ocean
(1) Component Name
(2) Horizontal Resolution
(3) Number of Vertical Levels
(4) Top Level
(5) z Co-ord
(6) Top BC
(7) References
Ocean Biogeo-
chemistry
(1) Component Name
(2) References
Sea Ice
(1) Component
Name
(2) References
(1) ACCESS1.0
(2) 2011
(1) Commonwealth
Scientific and Industrial
Research Organization
(CSIRO) and Bureau of
Meteorology (BOM),
Australia
(2) (Bi et al., 2013b;
Dix et al., 2013)
(1) Included (as in
HadGEM2 (r1.1))
(2) 192 × 145 N96
(3) 38
(4) 39,255 m
(5) (Martin et al.,
2011; Bi et al., 2013b;
Rashid et al., 2013)
(1) CLASSIC
(2) (Bellouin et al.,
2011; Dix et al., 2013)
Not implemented (1) MOSES2.2
(2) (Cox et al., 1999;
Essery et al., 2003;
Kowalczyk et al., 2013)
(1) ACCESS-OM (MOM4p1)
(2) primarily 1° latitude/longitude
tripolar with enhanced resolution
near equator and at high latitudes
(3) 50
(4) 0–10 m
(5) z*
(6) nonlinear split-explicit
(7) (Bi et al., 2013a; Marsland et
al., 2013)
Not implemented (1) CICE4.1
(2) (Uotila et al.,
2012; Bi et al.,
2013a; Uotila
et al., 2013)
(1) ACCESS1.3
(2) 2011
(1) Commonwealth
Scientific and Industrial
Research Organization
(CSIRO) and Bureau of
Meteorology (BOM),
Australia
(2) (Bi et al., 2013b;
Dix et al., 2013)
(1) Included (similar to
UK Met Office Global
Atmosphere 1.0)
(2) 192 × 145 N96
(3) 38
(4) 39,255 m
(5)
(Hewitt et al., 2011; Bi
et al., 2013b; Rashid et
al., 2013)
(1) CLASSIC
(2) (Bellouin et al.,
2011; Dix et al., 2013)
Not implemented (1) CABLE
(2) (Kowalczyk et al.,
2006; Wang et al., 2011b;
Kowalczyk et al., 2013)
(1) ACCESS-OM (MOM4p1)
(2) primarily 1° latitude/longitude
tripolar with enhanced resolution
near equator and at high latitudes
(3) 50
(4) 0–10 m
(5) z*
(6) nonlinear split-explicit
(7) (Bi et al., 2013a; Marsland et
al., 2013)
Not implemented (1) CICE4.1
(2) (Uotila et al.,
2012; Bi et al.,
2013a; Uotila
et al., 2013)
(1) BCC-CSM1.1
(2) 2011
(1) Beijing Climate Center,
China Meteorological
Administration
(2) (Wu, 2012; Xin et al.,
2012; Xin et al., 2013)
(1) BCC_AGCM2.1
(2) T42 T42L26
(3) 26
(4) 2.917 hPa
(5) (Wu et al., 2008b;
Wu et al., 2010b,
2010a; Wu, 2012)
Prescribed Not implemented (1) BCC-AVIM1.0
(2) (Ji, 1995; Lu and
Ji, 2006; Ji et al.,
2008; Wu, 2012)
(1) MOM4-L40
(2) 1° with enhanced resolution
in the meridional direction in the
tropics (1/3° meridional resolution
at the equator) tripolar
(3) 40
(4) 25 m
(5) z
(6) linear split-explicit
(7) (Griffies et al., 2005)
(1) Included
(2) Based on the
protocols from the Ocean
Carbon Cycle Model
Intercomparison Proj-
ect–Phase 2 (OCMIP2,
http://www.ipsl.jussieu.
fr/OCMIP/ phase2/)
(1) GFDL Sea Ice
Simulator (SIS)
(2) (Winton, 2000)
(1) BCC-CSM1.1(m)
(2) 2011
(1) Beijing Climate Center,
China Meteorological
Administration
(2) (Wu, 2012; Xin et al.,
2012; Xin et al., 2013)
(1) BCC_AGCM2.1
(2) T106
(3) 26
(4) 2.917 hPa
(5) (Wu et al., 2008b;
Wu et al., 2010b,
2010a; Wu, 2012)
Prescribed Not implemented (1) BCC-AVIM1.0
(2) (Ji, 1995; Lu and
Ji, 2006; Ji et al.,
2008; Wu, 2012)
(1) MOM4-L40
(2) Tri-polar: 1° with enhanced
resolution in the meridional direc-
tion in the tropics (1/3° meridional
resolution at the equator)
(3) 40
(4) 25 m
(5) z
(6) implicit
(7) (Griffies et al., 2005)
(1) Included
(2) Based on the
protocols from the Ocean
Carbon Cycle Model
Intercomparison Proj-
ect–Phase 2 (OCMIP2,
http://www.ipsl.jussieu.
fr/OCMIP/ phase2/)
(1) GFDL Sea Ice
Simulator (SIS)
(2) (Winton, 2000)
(continued on next page)
855
Evaluation of Climate Models Chapter 9
9
Table 9.A.1 (continued)
(continued on next page)
(1) Model Name
(2) Vintage
(1) Institution
(2) Main Reference(s)
Atmosphere
(1) Component Name
(2) Horizontal Grid
(3) Number of Vert
Levels
(4) Grid Top
(5) References
Aerosol
(1) Component
Name or type
(2) References
Atmos Chemistry
(1) Component Name
(2) References
Land Surface
(1) Component Name
(2) References
Ocean
(1) Component Name
(2) Horizontal Resolution
(3) Number of Vertical Levels
(4) Top Level
(5) z Co-ord
(6) Top BC
(7) References
Ocean Biogeo-
chemistry
(1) Component Name
(2) References
Sea Ice
(1) Component
Name
(2) References
(1) BNU-ESM
(2) 2011
(1) Beijing Normal
University
(2)
(1) CAM3.5
(2) T42
(3) 26
(4) 2.194 hPa
Semi-interactive Not implemented (1)CoLM+B-
NUDGVM(C/N)
(2) (Dai et al., 2003;
Dai et al., 2004)
(1) MOM4p1
(2) 200(lat) × 360(lon)
(3) 50
IBGC CICE4.1
(1) CanCM4
(2) 2010
(1) Canadian Center for
Climate Modelling and
Analysis
(2) (von Salzen et al., 2013)
(1) Included
(2) Spectral T63
(3) 35 levels
(4) 0.5 hPa
(5) (von Salzen
et al., 2013)
(1) Interactive
(2) (Lohmann et al.,
1999; Croft et al., 2005;
von Salzen et al., 2013)
(1) Included
(2) (von Salzen
et al., 2013)
(1) CLASS 2.7 (2)
(Verseghy, 2000; von
Salzen et al., 2013)
(1) Included
(2) 256 × 192
(3) 40
(4) 0 m
(5) depth
(6) rigid lid
(7) (Merryfield et al., 2013)
Not implemented (1) Included
(2) (Merryfield
et al., 2013)
(1) CanESM2
(2) 2010
(1) Canadian Center for
Climate Modelling and
Analysis
(2) (Arora et al., 2011;
von Salzen et al., 2013)
(1) Included
(2) Spectral T63
(3) 35 levels
(4) 0.5 hPa
(5) (von Salzen
et al., 2013)
(1) Interactive
(2) (Lohmann et al.,
1999; Croft et al., 2005;
von Salzen et al., 2013)
(1) Included
(2) (von Salzen
et al., 2013)
(1) CLASS 2.7; CTEM
(2) (Verseghy, 2000)
(Arora et al., 2009; von
Salzen et al., 2013)
(1) Included
(2) 256 × 192
(3) 40
(4) 0 m
(5) depth
(6) rigid lid
(7) (Merryfield et al., 2013)
(1) CMOC
(2) (Arora et al., 2009;
Christian et al., 2010)
(1) Included
(2) (Merryfield
et al., 2013)
(1) CCSM4
(2) 2010
(1) US National Centre for
Atmospheric Research
(2) (Gent et al., 2011)
(1) CAM4
(2) 0.9º ×1.25º
(3) 27
(4) 2.194067 hPa
(5) (Neale et al., 2010;
Neale et al., 2013)
(1) Interactive
(2) (Neale et al., 2010;
Oleson et al., 2010;
Holland et al., 2012)
Not implemented (1) Community Land
Model 4 (CLM4)
(2) (Oleson et al., 2010;
Lawrence et al., 2011;
Lawrence et al., 2012)
(1) POP2 with modifications
(2) Nominal 1° (1.125° in longitude,
0.27–0.64° variable in latitude)
(3) 60
(4) 10 m thick with sur-
face variables at 5 m
(5) depth (level)
(6) linearized, implicit free surface
with constant-volume ocean
(7) (Danabasoglu et al., 2012)
Not implemented (1) CICE4 with
modifications
(2) (Hunke and
Lipscomb, 2008;
Holland et al., 2012)
(1) CESM1(BGC)
(2) 2010
(1) NSF-DOE-NCAR
(2) (Long et al., 2012;
Hurrell et al., 2013)
(1) CAM4
(2) 0.9º ×1.25º
(3) 27
(4) 2.194067 hPa
(5) (Neale et al., 2010;
Neale et al., 2013)
(1) Semi-interactive
(2) (Neale et al., 2010;
Oleson et al., 2010;
Holland et al., 2012)
Not implemented (1) CLM4
(2) (Oleson et al., 2010;
Lawrence et al., 2011;
Lawrence et al., 2012)
(1) POP2 with modifications
(2) Nominal 1° (1.125° in longitude,
0.27–0.64° variable in latitude)
(3) 60
(4) 10 m with surface
variables at 5 m
(5) depth (level)
(6) linearized, implicit free surface
with constant-volume ocean
(7) (Danabasoglu et al., 2012)
(1) Biogeochemical
Elemental Cycling (BEC)
(1) CICE4 with
modifications
(2) (Hunke and
Lipscomb, 2008;
Holland et al., 2012)
856
Chapter 9 Evaluation of Climate Models
9
(1) Model Name
(2) Vintage
(1) Institution
(2) Main Reference(s)
Atmosphere
(1) Component Name
(2) Horizontal Grid
(3) Number of Vert
Levels
(4) Grid Top
(5) References
Aerosol
(1) Component
Name or type
(2) References
Atmos Chemistry
(1) Component Name
(2) References
Land Surface
(1) Component Name
(2) References
Ocean
(1) Component Name
(2) Horizontal Resolution
(3) Number of Vertical Levels
(4) Top Level
(5) z Co-ord
(6) Top BC
(7) References
Ocean Biogeo-
chemistry
(1) Component Name
(2) References
Sea Ice
(1) Component
Name
(2) References
(1) CESM1(CAM5)
(2) 2010
(1) NSF-DOE-NCAR
(2) (Hurrell et al., 2013)
(1) Community Atmo-
sphere Model 5 (CAM5)
(2) 0.9º × 1.25º
(3) 27
(4) 2.194067 hPa
(5) (Neale et al., 2010;
Neale et al., 2013)
(1) Semi-interactive
(2) (Neale et al., 2010;
Oleson et al., 2010;
Holland et al., 2012)
Not implemented (1) CLM4
(2) (Oleson et al., 2010)
(Lawrence et al., 2011;
Lawrence et al., 2012)
Same as CESM1 (BGC) Not implemented (1) CICE4 with
modifications
(2) (Hunke and
Lipscomb, 2008;
Holland et al., 2012)
(1)
CESM1(CAM5.1.FV2)
(2) 2012
(1) NSF-DOE-NCAR
(2) (Hurrell et al., 2013)
(1) Community Atmo-
sphere Model (CAM5.1)
(2) 1.9º × 2.0º
(3) 30
(4) 10 hPa
(5) (Neale et al., 2013)
(1) Modal Aerosol
Module (MAM3)
(2) (Ghan et al., 2012;
Liu et al., 2012b)
Not implemented (1) Community Land
Model (CLM4)
(2) (Oleson et al., 2010;
Lawrence et al., 2011)
Same as CESM1 (BGC) Not implemented (1) CICE4 with
modifications
(2) (Hunke and
Lipscomb, 2008;
Holland et al., 2012)
(1) CESM1(WACCM)
(2) 2010
(1) NSF-DOE-NCAR
(2) (Hurrell et al., 2013)
(1) WACCM4
(2) 1.9
o
× 2.5
o
(3) 66
(4) 5.1 × 10
–6
hPa
Semi-interactive Included (1) CLM4
(2) (Oleson et al., 2010;
Lawrence et al., 2011;
Lawrence et al., 2012)
Same as CESM1 (BGC) Not implemented (1) CICE4 with
modifications
(2) (Hunke and
Lipscomb, 2008;
Holland et al., 2012)
(1) CESM1(FASTCHEM)
(2) 2010
(1) NSF-DOE-NCAR
(2) (Cameron-Smith et al.,
2006; Eyring et al., 2013;
Hurrell et al., 2013)
(1) Included,
CAM4-CHEM
(2) 0.9º × 1.25º
(3) 27
(4) 2.194067 hPa
(5) (Neale et al., 2010;
Lamarque et al., 2012;
Neale et al., 2013)
(1) Interactive
(2) (Neale et al., 2010;
Oleson et al., 2010;
Holland et al., 2012;
Lamarque et al., 2012)
(1) Included, CAM-CHEM
(2) (Lamarque
et al., 2012)
(1) Community Land
Model 4 (CLM4)
(2) (Oleson et al., 2010;
Lawrence et al., 2011;
Lawrence et al., 2012)
Same as CESM1 (BGC) Not implemented (1) CICE4 with
modifications
(2) (Hunke and
Lipscomb, 2008;
Holland et al., 2012)
(1) CMCC-CESM
(2) 2009
(1) Centro Euro-Mediter-
raneo per I Cambiamenti
Climatici
(2) (Fogli et al., 2009;
Vichi et al., 2011)
(1) ECHAM5
(2) 3.75° × 3.75° (T31)
(3) 39
(4) 0.01 hPa
(5) (Roeckner et al., 2006;
Manzini et al., 2012)
Semi-interactive Not implemented (1) SILVA
(2) (Alessandri
et al., 2012)
Same as CMCC-CM (1) PELAGOS
(2) (Vichi et al., 2007)
(1) LIM2
(2) (Timmermann
et al., 2005)
(1) CMCC-CM
(2) 2009
(1) Centro Euro-Mediter-
raneo per I Cambiamenti
Climatici
(2) (Fogli et al., 2009;
Scoccimarro et al., 2011)
(1) ECHAM5
(2) 0.75° × 0.75° (T159)
(3) 31
(4) 10 hPa
(5) (Roeckner et al., 2006)
Semi-interactive Not implemented Not implemented (1) OPA8.2
(2) 2° average, 0.5° at the equator
(ORCA2)
(3) 31
(4) 5 m
(5) depth (z-level)
(6) linear implicit
(7) (Madec et al., 1998)
Not implemented (1) LIM2
(2) (Timmermann
et al., 2005)
(1) CMCC-CMS
(2) 2009
(1) Centro Euro-Mediter-
raneo per I Cambiamenti
Climatici
(2) (Fogli et al., 2009)
(1) ECHAM5
(2) 1.875° × 1.875° (T63)
(3) 95
(4) 0.01 hPa
(5) (Roeckner et al., 2006;
Manzini et al., 2012)
Semi-interactive Not implemented Not implemented Same as CMCC-CM Not implemented (1) LIM2
(2) (Timmermann
et al., 2005)
Table 9.A.1 (continued)
(continued on next page)
857
Evaluation of Climate Models Chapter 9
9
Table 9.A.1 (continued)
(continued on next page)
(1) Model Name
(2) Vintage
(1) Institution
(2) Main Reference(s)
Atmosphere
(1) Component Name
(2) Horizontal Grid
(3) Number of Vert
Levels
(4) Grid Top
(5) References
Aerosol
(1) Component
Name or type
(2) References
Atmos Chemistry
(1) Component Name
(2) References
Land Surface
(1) Component Name
(2) References
Ocean
(1) Component Name
(2) Horizontal Resolution
(3) Number of Vertical Levels
(4) Top Level
(5) z Co-ord
(6) Top BC
(7) References
Ocean Biogeo-
chemistry
(1) Component Name
(2) References
Sea Ice
(1) Component
Name
(2) References
(1) CNRM-CM5
1
(2) 2010
(1) Centre National de
Recherches Meteoro-
logiques and Centre
Europeen de Recherche
et Formation Avancees en
Calcul Scientifique.
(2) (Voldoire et al., 2013)
(1) ARPEGE-Climat
(2) TL127
(3) 31
(4) 10 hPa
(5) (Déqué et al., 1994;
Voldoire et al., 2013)
Prescribed (1) (3-D linear ozone
chemistry model)
(2) (Cariolle and
Teyssedre, 2007)
(1) SURFEX (Land and
Ocean Surface)
(2) (Voldoire et al., 2013)
(1) NEMO
(2) 0.7° on average ORCA1
(3) 42
(4) 5 m
(5) z-coordinate
(6) linear filtered
(7) (Madec, 2008)
(1) PISCES
(2) (Aumont and
Bopp, 2006; Séfé-
rian et al., 2013)
(1) Gelato5 (Sea
Ice)
(2) (Salas-Melia,
2002; Voldoire
et al., 2013)
(1) CSIRO-Mk3.6.0
(2) 2009
(1) Queensland
Climate Change
Centre of
Excellence and
Commonwealth
Scientific and
Industrial
Research
Organisation
(2) (Rotstayn et al., 2012)
(1) Included
(2) ~1.875º × 1.875º
(spectral T63)
(3) 18
(4) ~4.5 hPa
(5) (Gordon et al., 2002;
Gordon et al., 2010;
Rotstayn et al., 2012)
(1) Interactive
(2) (Rotstayn and
Lohmann, 2002;
Rotstayn et al., 2011;
Rotstayn et al., 2012)
Not implemented (1) Included
(2) (Gordon et al., 2002;
Gordon et al., 2010)
(1) Modified MOM2.2
(2) ~0.9 × 1.875
(3) 31
(4) 5 m
(5) depth
(6) rigid lid
(7) (Gordon et al., 2002;
Gordon et al., 2010)
Not implemented (1) Included
(2) (O’Farrell, 1998;
Gordon et al., 2010)
(1) EC-EARTH
(2) 2010
(1) Europe
(2) (Hazeleger et al., 2012)
(1) IFS c31r1
(2) 1.125º longitudinal
spacing, Gaussian grid
T159L62
(3) 62
(4) 1 hPa
(5) (Hazeleger
et al., 2012)
Prescribed Not implemented (1) HTESSEL
(2) (Balsamo et al., 2009)
(1) NEMO_ecmwf
(2) The grid is a tripolar curvilinear
grid with a 1° resolution. ORCA1
(3) 31
(4) 1 m
(5) z
(6) free surface linear filtered
(7) (Hazeleger et al., 2012)
Not implemented (1) LIM2
(2) (Fichefet and
Maqueda, 1999)
(1) FGOALS-g2
(2) 2011
(1) LASG (Institute of
Atmospheric Physics)-
CESS(Tsinghua University)
(2) (Li et al., 2012b)
(1) GAMIL2
(2) 2.8125° × 2.8125°
(3) 26 layers
(4) 2.194 hPa
(5) (Wang et al., 2004;
Li et al., 2013b)
Semi-interactive Not implemented (1) CLM3
(2) (Oleson et al., 2010)
(1) LICOM2
(2) 1 × 1° with 0.5 meridional
degree in the tropical region
(3) 30
(4) 10 m
(5) eta co-ordinate
(6)
(7) (Liu et al., 2012a)
Not implemented (1) CICE4-LASG
(2) (Wang and
Houlton, 2009;
Liu, 2010)
(1) FGOALS-s2
(2) 2011
(1) The State Key Labora-
tory of Numerical Modeling
for Atmospheric Sciences
and Geophysical Fluid
Dynamics, The Institute
of Atmospheric Physics
(2) (Bao et al., 2010; Bao et
al., 2013)
(1) SAMIL2.4.7
(2)R42 (2.81° × 1.66°)
(3) 26
(4) 2.19hPa
(5) (Bao et al., 2010;
Liu et al., 2013b)
Semi-interactive Not implemented (1) CLM3.0
(2) (Oleson, 2004;
Zeng et al., 2005;
Wang et al., 2013)
(1) LICOM
(2) The zonal resolution is 1°.
The meridional resolution is 0.5°
between 10°S and 10°N and
increases from 0.5° to 1° from 10°
(3) 30 layers
(4) 10 m (for vertical velocity
and pressure) and 5 meter (for
Temperature and salinity, zonal and
meridional velocity)
(5) depth
(6) linear split-explicit
(7) (Lin et al., 2013)
(1) IAP-OBM
(2) (Xu et al., 2012)
(1) CSIM5
(2) (Briegleb
et al., 2004)
1
A CNRM-CM5-2 version exists that only differs from CNRM-CM5 in the treatment of volcanoes
858
Chapter 9 Evaluation of Climate Models
9
(1) Model Name
(2) Vintage
(1) Institution
(2) Main Reference(s)
Atmosphere
(1) Component Name
(2) Horizontal Grid
(3) Number of Vert
Levels
(4) Grid Top
(5) References
Aerosol
(1) Component
Name or type
(2) References
Atmos Chemistry
(1) Component Name
(2) References
Land Surface
(1) Component Name
(2) References
Ocean
(1) Component Name
(2) Horizontal Resolution
(3) Number of Vertical Levels
(4) Top Level
(5) z Co-ord
(6) Top BC
(7) References
Ocean Biogeo-
chemistry
(1) Component Name
(2) References
Sea Ice
(1) Component
Name
(2) References
(1) FIO-ESM v1.0
(2) 2011
(1) The First Institute of
Oceanography, State Ocea-
nic Administration, China
(1) CAM3.0
(2) T42
(3) 26
(4) 3.545 hPa
(5) (Collins et al., 2006c)
Prescribed Not implemented (1) CLM3.5
(2) (Oleson et al., 2008b)
(1) Modified POP2.0 through
incorporating the non-breaking
surface wave-induced mixing
(2) 1.125° in longitude,
0.27–0.64° variable in latitude
(3) 40
(4) 10 m with surface
variables at 5 m
(5) depth
(6) linear implicit
(7) (Huang et al., 2012)
(1) Improved OCMIP-2
biogeochemical model
(2) (Bao et al., 2012)
(1) CICE4.0
(2) (Hunke and
Lipscomb, 2008)
(1) GFDL-CM2.1
(2) 2006
(2) (Qiao et al., 2004;
Song et al., 2012)
(1) Included
(2) 2.5° longitude, 2°
latitude M45L24
(3) 24
(4) midpoint of top
box is 3.65 hPa
(5) (Delworth et al., 2006)
Semi-interactive Not implemented Included (1) Included
(2) 1° tripolar360 × 200L50
(3) 50
(4) 0 m
(5) depth
(6) nonlinear split-explicit
(7)
Not implemented (1) SIS
(2) (Winton,
2000; Delworth
et al., 2006)
(1) GFDL-CM3
(2) 2011
(1) NOAA Geophysical
Fluid Dynamics Laboratory
(2) (Delworth et al., 2006;
Donner et al., 2011)
(1) Included
(2) ~200 km C48L48
(3) 48
(4) 0.01 hPa
(5) (Donner et al., 2011)
(1) Interactive
(2) (Levy et al., 2013)
(1) Atmospheric
Chemistry
(2) (Horowitz et al.,
2003; Austin and Wilson,
2006; Sander, 2006)
(1) Included
(2) (Milly and Shmakin,
2002; Shevliakova
et al., 2009)
(1) MOM4.1
(2) 1° tripolar360 × 200L50
(3) 50
(4) 0 m
(5) z*
(6) non-linear split-explicit
(7) (Griffies and Greatbatch, 2012)
Not implemented (1) SIS
(2) (Griffies and
Greatbatch, 2012)
(1) GFDL-ESM2G
(2) 2012
(1) NOAA Geophysical
Fluid Dynamics Laboratory
(2) (Dunne et al., 2012;
Dunne et al., 2013)
(1) Included
(2) 2.5° longitude, 2°
latitude M45L24
(3) 24
(4) midpoint of top box is
3.65 hPa
(5) (Delworth et al., 2006)
Semi-interactive Not implemented (1) Included
(2) (Milly and Shmakin,
2002; Shevliakova
et al., 2009; Donner
et al., 2011)
(1) GOLD
(2) 1° tripolar 360 × 2 10L63
(3) 63
(4) 0 m
(5) Isopycnic
(6) nonlinear split-explicit
(7) (Hallberg and Adcroft,
2009; Dunne et al., 2012)
(1) TOPAZ
(2) (Henson et al., 2009;
Dunne et al., 2013)
(1) SIS
(2) (Winton,
2000; Delworth
et al., 2006)
(1) GFDL-ESM2M
(2) 2011
(1) NOAA Geophysical
Fluid Dynamics Laboratory
(2) (Dunne et al., 2012;
Dunne et al., 2013)
(1) Included
(2) 2.5° longitude, 2°
latitude M45L24
(3) 24
(4) midpoint of top
box is 3.65 hPa
(5) (Delworth et al., 2006)
Semi-interactive Not implemented (1) Included
(2) (Milly and Shmakin,
2002; Shevliakova
et al., 2009; Donner
et al., 2011)
(1) MOM4.1
(2) 1° tripolar 360 × 200L50
(3) 50
(4) 0 m
(5) z*
(6) nonlinear split-explicit
(7) (Griffies, 2009;
Dunne et al., 2012)
(1) TOPAZ
(2) (Henson et al., 2009;
Dunne et al., 2013)
(1) SIS
(2) (Winton,
2000; Delworth
et al., 2006)
(1) GFDL-HIRAM-C180
(2) 2011
(1) NOAA Geophysical
Fluid Dynamics Laboratory
(2) (Delworth et al., 2006;
Donner et al., 2011)
(1) Included
(2) Averaged cell size:
approximately 50 × 50
km. C180L32
(3) 32
(4) 2.164 hPa
(5) (Donner et al., 2011)
Prescribed Not implemented (1) Included
(2) (Milly and Shmakin,
2002; Shevliakova
et al., 2009)
Not implemented Not implemented Not implemented
Table 9.A.1 (continued)
(continued on next page)
859
Evaluation of Climate Models Chapter 9
9
Table 9.A.1 (continued)
(continued on next page)
(1) Model Name
(2) Vintage
(1) Institution
(2) Main Reference(s)
Atmosphere
(1) Component Name
(2) Horizontal Grid
(3) Number of Vert
Levels
(4) Grid Top
(5) References
Aerosol
(1) Component
Name or type
(2) References
Atmos Chemistry
(1) Component Name
(2) References
Land Surface
(1) Component Name
(2) References
Ocean
(1) Component Name
(2) Horizontal Resolution
(3) Number of Vertical Levels
(4) Top Level
(5) z Co-ord
(6) Top BC
(7) References
Ocean Biogeo-
chemistry
(1) Component Name
(2) References
Sea Ice
(1) Component
Name
(2) References
(1) GFDL-HIRAM-C360
(2)
(1) NOAA Geophysical
Fluid Dynamics Laboratory
(2) (Delworth et al., 2006;
Donner et al., 2011)
(1) Included
(2) Averaged cell size:
approximately 25 × 25
km. C360L32
(3) 32
(4) 2.164 hPa
(5) (Donner et al., 2011)
Prescribed Not implemented (1) Included
(2) (Milly and Shmakin,
2002; Shevliakova
et al., 2009)
Not implemented Not implemented Not implemented
(1) GISS-E2-H
(2) 2004
(1) NASA Goddard Institute
for Space Studies USA
(2) (Schmidt et al., 2006)
Note: all GISS models come
in three flavours: p1 = non-
interactive composition,
p2= interactive composi-
tion, p3 = interactive com-
position + interactive AIE
(1) Included
(2) 2° latitude × 2.5°lon-
gitude F
(3) 40
(4) 0.1 hPa
(1) Interactive
(2) (Bauer et al.,
2007; Tsigaridis and
Kanakidou, 2007;
Menon et al., 2010;
Koch et al., 2011)
Note: Aerosol is
“fully interactive”
for p2 and p3, “semi
interactive” for p1
(1) G-PUCCINI
(2) (Shindell et al., 2013a)
Note: Atmos Chem
is “fully interactive”
for p2 and p3, “semi
interactive” for p1
Included (1) HYCOM Ocean
(2) 0.2 to 1° latitude × 1° longitude
HYCOM
(3) 26
(4) 0 m
(5) hybrid z isopycnic
(6) nonlinear split-explicit
(7)
Not implemented Included
(1) GISS-E2-H-CC
(2) 2011
(1) NASA Goddard Institute
for Space Studies USA
(2) (Schmidt et al., 2006)
Note: p1 only
(1) Included
(2) Nominally 1°
(3) 40
(4) 0.1 hPa
(1) Interactive (p1 only)
(2) (Bauer et al.,
2007; Tsigaridis and
Kanakidou, 2007;
Menon et al., 2010;
Koch et al., 2011)
(1) G-PUCCINI
(2) (Shindell et al., 2013a)
Included (1) HYCOM Ocean
(2) 0.2 to 1° latitude × 1° longitude
HYCOM
(3) 26
(4) 0 m
(5) hybrid z isopycnic
(6) nonlinear split-explicit
(7)
(1) Included
(2) (Romanou
et al., 2013)
Included
(1) GISS-E2-R
(2) 2011
(1) NASA Goddard Institute
for Space Studies USA
(2) (Schmidt et al., 2006)
See note for GISS-E2-H
(1) Included
(2) 2° latitude × 2.5°
longitude F
(3) 40
(4) 0.1 hPa
(1) Interactive
(2) (Bauer et al.,
2007; Tsigaridis and
Kanakidou, 2007;
Menon et al., 2010;
Koch et al., 2011)
Note: Aerosol is
“fully interactive”
for p2 and p3, “semi
interactive” for p1
(1) G-PUCCINI
(2) (Shindell et al., 2013a)
Note: Atmos Chem
is “fully interactive”
for p2 and p3, “semi
interactive” for p1
Included (1) Russell Ocean
(2) 1° latitude × 1.25° longitude
Russell 1 × 1Q
(3) 32
(4) 0 m
(5) z*-coordinate
(6) other
(7)
Not implemented Included
(1) GISS-E2-R-CC
(2) 2011
(1) NASA Goddard Institute
for Space Studies USA
(2) (Schmidt et al., 2006)
Note: p1 only
(1) Included
(2) Nominally 1°
(3) 40
(4) 0.1 hPa
(1) Interactive (p1 only)
(2) (Bauer et al.,
2007; Tsigaridis and
Kanakidou, 2007;
Menon et al., 2010;
Koch et al., 2011)
(1) G-PUCCINI
(2) (Shindell et al., 2013a)
Included (1) Russell Ocean
(2) 1° latitude × 1.25° longitude
Russell 1×1Q
(3) 32
(4) 0 m
(5) z*-coordinate
(6) other
(7)
(1) Included
(2) (Romanou
et al., 2013)
Included
860
Chapter 9 Evaluation of Climate Models
9
(1) Model Name
(2) Vintage
(1) Institution
(2) Main Reference(s)
Atmosphere
(1) Component Name
(2) Horizontal Grid
(3) Number of Vert
Levels
(4) Grid Top
(5) References
Aerosol
(1) Component
Name or type
(2) References
Atmos Chemistry
(1) Component Name
(2) References
Land Surface
(1) Component Name
(2) References
Ocean
(1) Component Name
(2) Horizontal Resolution
(3) Number of Vertical Levels
(4) Top Level
(5) z Co-ord
(6) Top BC
(7) References
Ocean Biogeo-
chemistry
(1) Component Name
(2) References
Sea Ice
(1) Component
Name
(2) References
(1) HadCM3
(2) 1998
(1) UK Met Office Hadley
Centre
(2) (Gordon et al.,
2000; Pope et al., 2000;
Collins et al., 2001;
Johns et al., 2003)
(1) HadAM3
(2) N48L19
3.75 × 2.5°
(3) 19
(4) 0.005 hPa
(5) (Pope et al., 2000)
(1) Interactive
(2) (Jones et al., 2001)
Not implemented (1) Included
(2) (Collatz et al., 1991;
Collatz et al., 1992; Cox
et al., 1999; Cox, 2001;
Mercado et al., 2007)
(1) HadOM (lat: 1.25 lon: 1.25 L20)
(2) 1.25° in longitude by 1.25° in
latitude N144
(3) 20
(4) 5.0 m
(5) depth
(6) linear implicit
(7) (UNESCO, 1981)
Not implemented Included
(1) HadGEM2-AO
(2) 2009
(1) National Institute of
Meteorological Research/
Korea Meteorological
Administration
(2) ( Collins et al., 2011;
Martin et al., 2011)
(1) HadGAM2
(2) 1.875° in longitude by
1.25° in latitude N96
(3) 60
(4) 84132.439 m
(5) (Davies et al., 2005)
(1) Interactive
(2) (Bellouin et
al., 2011)
Not implemented (1) Included
(2) (Cox et al., 1999;
Essery et al., 2003)
(1) Included
(2) 1.875° in longitude by 1.25° in
latitude N96
(3)
(4)
(5) z
(6) linear implicit
(7) (Bryan and Lewis, 1979;
Johns et al., 2006);
Not implemented (1) Included
(2) (Thorndike et
al., 1975; McLaren
et al., 2006)
(1) HadGEM2-CC
(2) 2010
(1) UK Met Office Hadley
Centre
(2) ( Collins et al., 2011;
Martin et al., 2011)
(1) HadGAM2
(2) 1.875° in longitude by
1.25°in latitude N96
(3) 60
(4) 84132.439 m
(5) (Davies et al., 2005)
(1) Interactive
(2) (Bellouin et
al., 2011)
(1) Atmospheric
Chemistry
(2) (Jones et al., 2001;
Martin et al., 2011)
(1) Included
(2) (Cox et al., 1999;
Essery et al., 2003)
(1) Included
(2) 1.875° in longitude by 1.25° in
latitude N96
(3)
(4)
(5) z
(6) linear implicit
(7) (Bryan and Lewis, 1979;
Johns et al., 2006)
(1) Included
(2) (Palmer and Totterdell,
2001; Halloran, 2012)
(1) Included
(2) (Thorndike et
al., 1975; McLaren
et al., 2006)
(1) HadGEM2-ES
(2) 2009
(1) UK Met Office Hadley
Centre
(2) ( Collins et al., 2011;
Martin et al., 2011)
(1) HadGAM2
(2) 1.875° in longitude by
1.25° in latitude N96
(3) 38
(4) 39254.8 m
(5) (Davies et al., 2005)
(1) Interactive
(2) (Bellouin et
al., 2011)
(1) Atmospheric
Chemistry
(2) (O’Connor
et al., 2009)
(1) Included
(2) (Cox et al., 1999;
Essery et al., 2003)
(1) Included
(2) 1° by 1° between 30 N/S and
the poles; meridional resolution
increases to 1/3° at the equator
N180
(3) 40
(4) 5.0 m
(5) z
(6) linear implicit
(7) (Bryan and Lewis, 1979;
Johns et al., 2006)
(1) Included
(2) (Palmer and Totterdell,
2001; Halloran, 2012)
(1) Included
(2) (Thorndike et
al., 1975; McLaren
et al., 2006)
(1) INM-CM4
(2) 2009
(1) Russian Institute for
Numerical Mathematics
(2) (Volodin et al., 2010)
(1) Included
(2) 2 ×1.5° in longitude
and latitude latitude-
longitude
(3) 21
(4) sigma = 0.01
Prescribed Not implemented (1) Included
(2) (Alekseev et al.,
1998; Volodin and
Lykosov, 1998)
(1) Included
(2) 1 × 0.5° in longitude and
latitude generalized spherical
coordinates with poles displaced
outside ocean
(3) 40
(4) sigma = 0.0010426
(5) sigma
(6) linear implicit
(7) (Volodin et al., 2010;
Zalesny et al., 2010)
(1) Included
(2) (Volodin, 2007)
(1) Included
(2) (Yakovlev, 2009)
Table 9.A.1 (continued)
(continued on next page)
861
Evaluation of Climate Models Chapter 9
9
(1) Model Name
(2) Vintage
(1) Institution
(2) Main Reference(s)
Atmosphere
(1) Component Name
(2) Horizontal Grid
(3) Number of Vert
Levels
(4) Grid Top
(5) References
Aerosol
(1) Component
Name or type
(2) References
Atmos Chemistry
(1) Component Name
(2) References
Land Surface
(1) Component Name
(2) References
Ocean
(1) Component Name
(2) Horizontal Resolution
(3) Number of Vertical Levels
(4) Top Level
(5) z Co-ord
(6) Top BC
(7) References
Ocean Biogeo-
chemistry
(1) Component Name
(2) References
Sea Ice
(1) Component
Name
(2) References
(1) IPSL-CM5A-LR
(2) 2010
(1) Institut Pierre Simon
Laplace
(2) (Dufresne et al., 2012)
(1) LMDZ5
(2) 96 × 95 equivalent
to 1.9° × 3.75° LMDZ96
× 95
(3) 39
(4) 0.04 hPa
(5)(Hourdin et al., 2012)
Semi-interactive Not implemented (1) Included
(2) (Krinner et al., 2005)
(1) Included
(2) 2 × 2-0.5° ORCA2
(3) 31
(4) 0m
(5) depth
(6) linear filtered
(7) (Madec, 2008)
(1) PISCES
(2) (Aumont et al., 2003;
Aumont and Bopp, 2006)
(1) LIM2
(2) (Fichefet and
Maqueda, 1999)
(1) IPSL-CM5A-MR
(2) 2009
(1) Institut Pierre Simon
Laplace
(2) (Dufresne et al., 2012)
(1) LMDZ5
(2) 144 × 143 equivalent
to 1,25° × 2.5° LMDZ144
× 143
(3) 39
(4) 0.04 hPa
(5) (Hourdin et al., 2012)
Semi-interactive Not implemented (1) Included
(2) (Krinner et al., 2005)
(1) Included
(2) 2 × 2-0.5° ORCA2
(3) 31
(4) 0 m
(5) depth
(6) linear filtered
(7) (Madec, 2008)
(1) PISCES
(2) (Aumont et al., 2003;
Aumont and Bopp, 2006)
(1) Included
(2) (Fichefet and
Maqueda, 1999)
(1) IPSL-CM5B-LR
(2) 2010
(1) Institut Pierre Simon
Laplace
(2) (Dufresne et al., 2012)
(1) LMDZ5
(2) 96 × 95 equivalent
to 1.9° × 3.75° LMDZ96
× 95
(3) 39
(4) 0.04 hPa
(5)(Hourdin et al., 2013)
Semi-interactive Not implemented (1) Included
(2) (Krinner et al., 2005)
(1) Included
(2) 2 × 2-0.5° ORCA2
(3) 31
(4) 0 m
(5) depth
(6) linear filtered
(7) (Madec, 2008)
(1) PISCES
(2) (Aumont et al., 2003;
Aumont and Bopp, 2006)
(1) Included
(2) (Fichefet and
Maqueda, 1999)
(1) MIROC4h
(2) 2009
(1) University of Tokyo,
National Institute for
Environmental Studies,
and Japan Agency for
Marine-Earth Science and
Technology
(2) (Sakamoto et al., 2012)
(1) CCSR / NIES / FRCGC
AGCM5.7
(2) 0.5625 × 0.5625°
T213
(3) 56
(4) about 0.9 hPa
(1) SPRINTARS
(2) (Takemura et
al., 2000; Takemura
et al., 2002)
Not implemented (1) MATSIRO
(2) (Takata et al., 2003)
(1) COCO3.4
(2) 1/4° by 1/6° (average grid spac-
ing is 0.28° and 0.19° for zonal and
meridional directions)
(3) 48
(4) 1.25 m
(5) hybrid z-s
(6) nonlinear split-explicit
(7) (Hasumi and Emori, 2004)
Not implemented Included
(1) MIROC5
(2) 2010
(1) University of Tokyo,
National Institute for
Environmental Studies,
and Japan Agency for
Marine-Earth Science and
Technology
(2) (Watanabe et al., 2010)
(1) CCSR/NIES/ FRCGC
AGCM6
(2) 1.40625 × 1.40625°
T85
(3) 40
(4) about 2.9 hPa
(1) SPRINTARS
(2) (Takemura et
al., 2005; Takemura
et al., 2009)
Not implemented (1) MATSIRO
(2) (Takata et al., 2003)
(1) COCO4.5
(2) 1.4° (zonally) × 0.5–1.4°
(meridionally)
(3) 50
(4) 1.25 m
(5) hybrid z-s
(6) linear split-explicit
(7) (Hasumi and Emori, 2004)
Not implemented (1) Included
(2) (Komuro et
al., 2012)
(1) MIROC-ESM
(2) 2010
(1) University of Tokyo,
National Institute for
Environmental Studies,
and Japan Agency for
Marine-Earth Science and
Technology
(2) (Watanabe et al., 2011)
(1) MIROC-AGCM
(2) 2.8125 × 2.8125° T42
(3) 80
(4) 0.003 hPa
(5) (Watanabe, 2008)
(1) SPRINTARS
(2) (Takemura et
al., 2005; Takemura
et al., 2009)
Not implemented (1) MATSIRO
(2) (Takata et al., 2003)
(1) COCO3.4
(2) 1.4° (zonally) × 0.5–1.4°
(meridionally)
(3) 44
(4) 1.25 m
(5) hybrid z-s
(6) linear split-explicit
(7) (Hasumi and Emori, 2004)
(1) NPZD-type
(2) (Schmittner
et al., 2005)
Included
Table 9.A.1 (continued)
(continued on next page)
862
Chapter 9 Evaluation of Climate Models
9
(1) Model Name
(2) Vintage
(1) Institution
(2) Main Reference(s)
Atmosphere
(1) Component Name
(2) Horizontal Grid
(3) Number of Vert
Levels
(4) Grid Top
(5) References
Aerosol
(1) Component
Name or type
(2) References
Atmos Chemistry
(1) Component Name
(2) References
Land Surface
(1) Component Name
(2) References
Ocean
(1) Component Name
(2) Horizontal Resolution
(3) Number of Vertical Levels
(4) Top Level
(5) z Co-ord
(6) Top BC
(7) References
Ocean Biogeo-
chemistry
(1) Component Name
(2) References
Sea Ice
(1) Component
Name
(2) References
(1) MIROC-ESM-CHEM
(2) 2010
(1) University of Tokyo,
National Institute for
Environmental Studies,
and Japan Agency for
Marine-Earth Science and
Technology
(2) (Watanabe et al., 2011)
(1) MIROC-AGCM
(2) 2.8125 × 2.8125° T42
(3) 80
(4) 0.003 hPa
(5) (Watanabe, 2008)
(1) SPRINTARS
(2) (Takemura et
al., 2005; Takemura
et al., 2009)
(1) CHASER
(2) (Sudo et al., 2002)
(1) MATSIRO
(2) (Takata et al., 2003)
(1) COCO3.4
(2) 1.4° (zonally) × 0.5–1.4°
(meridionally)
(3) 44
(4) 1.25 m
(5) hybrid z-s
(6) linear split-explicit
(7) (Hasumi and Emori, 2004)
(1) NPZD-type
(2) (Schmittner et al.,
2005)
Included
(1) MPI-ESM-LR
(2) 2009
(1) Max Planck Institute for
Meteorology
(2)
(1) ECHAM6
(2) approx. 1.8° T63
(3) 47
(4) 0.01 hPa
(5) (Stevens et al., 2012)
Prescribed Not implemented (1) JSBACH
(2) (Reick et al., 2013)
(1) MPIOM
(2) average 1.5° GR15
(3) 40
(4) 6 m
(5) depth
(6) linear implicit
(7) (Jungclaus et al., 2013)
(1) HAMOCC
(2) (Maier-Reimer et al.,
2005; Ilyina et al., 2013)
(1) Included
(2) (Notz et al.,
2013)
(1) MPI-ESM-MR
(2) 2009
(1) Max Planck Institute for
Meteorology
(2)
(1) ECHAM6
(2) approx. 1.8° T63
(3) 95
(4) 0.01 hPa
(5) (Stevens et al., 2012)
Prescribed Not implemented (1) JSBACH
(2) (Reick et al., 2013)
(1) MPIOM
(2) approx. 0.4° TP04
(3) 40
(4) 6 m
(5) depth
(6) linear implicit
(7) (Jungclaus et al., 2013)
(1) HAMOCC
(2) (Maier-Reimer et al.,
2005; Ilyina et al., 2013)
(1) Included
(2) (Notz et al.,
2013)
(1) MPI-ESM-P
(2) 2009
(1) Max Planck Institute for
Meteorology
(2)
(1) ECHAM6
(2) approx. 1.8° T63
(3) 47
(4) 0.01 hPa
(5) (Stevens et al., 2012)
Prescribed Not implemented (1) JSBACH
(2) (Reick et al., 2013)
(1) MPIOM
(2) average 1.5° GR15
(3) 40
(4) 6 m
(5) depth
(6) linear implicit
(7) (Jungclaus et al., 2013)
(1) HAMOCC
(2) (Maier-Reimer et al.,
2005; Ilyina et al., 2013)
(1) Included
(2) (Notz et al.,
2013)
(1) MRI-AGCM3.2H
(2) 2009
(1) Meteorological
Research Institute
(2) (Mizuta et al., 2012)
(1) Included
(2) 640 × 320 TL319
(3) 64
(4) 0.01 hPa
Prescribed Not implemented (1) SiB0109
(2) (Hirai et al., 2007;
Yukimoto et al., 2011;
Yukimoto et al., 2012)
Not implemented Not implemented Not implemented
(1) MRI-AGCM3.2S
(2) 2009
(1) Meteorological
Research Institute (2)
(Mizuta et al., 2012)
(1) Included
(2) 1920 × 960 TL959
(3) 64
(4) 0.01 hPa
(5) (Mizuta et al., 2012)
Prescribed Not implemented (1) SiB0109
(2) (Hirai et al., 2007;
Yukimoto et al., 2011;
Yukimoto et al., 2012)
Not implemented Not implemented Not implemented
(1) MRI-CGCM3
(2) 2011
(1) Meteorological
Research Institute
(2) (Yukimoto et al., 2011;
Yukimoto et al., 2012)
(1) MRI-AGCM3.3
(2) 320 × 160 TL159
(3) 48
(4) 0.01 hPa
(5) (Yukimoto et al., 2011;
Yukimoto et al., 2012)
(1) MASINGAR mk-2
(2) (Yukimoto et al.,
2011; Yukimoto et
al., 2012; Adachi
et al., 2013)
Not implemented (1) HAL
(2) (Yukimoto et al., 2011;
Yukimoto et al., 2012)
(1) MRI.COM3
(2) 1 × 0.5
(3) 50 + 1 Bottom Boundary Layer
(4) 0 m
(5) hybrid sigma-z
(6) nonlinear split-explicit
(7) (Tsujino et al., 2011; Yukimoto
et al., 2011; Yukimoto et al., 2012)
Not implemented (1) Included (MRI.
COM3)
(2) (Tsujino et al.,
2011; Yukimoto et
al., 2011; Yukimoto
et al., 2012)
Table 9.A.1 (continued)
(continued on next page)
863
Evaluation of Climate Models Chapter 9
9
Table 9.A.1 (continued)
(1) Model Name
(2) Vintage
(1) Institution
(2) Main Reference(s)
Atmosphere
(1) Component Name
(2) Horizontal Grid
(3) Number of Vert
Levels
(4) Grid Top
(5) References
Aerosol
(1) Component
Name or type
(2) References
Atmos Chemistry
(1) Component Name
(2) References
Land Surface
(1) Component Name
(2) References
Ocean
(1) Component Name
(2) Horizontal Resolution
(3) Number of Vertical Levels
(4) Top Level
(5) z Co-ord
(6) Top BC
(7) References
Ocean Biogeo-
chemistry
(1) Component Name
(2) References
Sea Ice
(1) Component
Name
(2) References
(1) MRI-ESM1
(2) 2011
(1) Meteorological
Research Institute
(2) (Yukimoto et al., 2011;
Yukimoto et al., 2012;
Adachi et al., 2013)
(1) MRI-AGCM3.3
(2) TL159(320 × 160)
(3) 48
(4) 0.01 hPa
(5) (Yukimoto et al., 2011;
Yukimoto et al., 2012;
Adachi et al., 2013)
(1) MASINGAR mk-2
(2) (Yukimoto et al.,
2011; Yukimoto et
al., 2012; Adachi
et al., 2013)
(1) MRI-CCM2
(2) (Deushi and Shibata,
2011; Yukimoto et al.,
2011; Adachi et al., 2013)
(1) HAL
(2) (Yukimoto et al., 2011;
Yukimoto et al., 2012)
(1) MRI.COM3
(2) 1x0.5
(3) 50 + 1 Bottom Boundary Layer
(4) 0m
(5) hybrid sigma-z
(6) non-linear split-explicit
(7)(Tsujino et al., 2011; Yukimoto
et al., 2011; Yukimoto et al., 2012)
(1) Included (MRI.COM3)
(2) (Nakano et al., 2011;
Adachi et al., 2013)
(1) Included (MRI.
COM3)
(2) (Tsujino et al.,
2011; Yukimoto et
al., 2011; Yukimoto
et al., 2012)
(1) NCEP-CFSv2
(2) 2011
(1) National Centers for
Environmental Prediction
(1) Global Forecast Model
(2) 0.9375 T126
(3) 64
(4) 0.03 hPa
(5) (Saha et al., 2010)
Semi-interactive (1) Ozone chemistry
(2) (McCormack
et al., 2006)
(1) Noah Land Surface
Model
(2) (Ek et al., 2003)
(1) MOM4
(2) 0.5° zonal resolution, meridional
resolution varying from 0.25° at
the equator to 0.5° north/south of
10N/10S. Tripolar.
(3) 40
(4) 5.0 m
(5) depth
(6) nonlinear split explicit
(7) (Griffies et al., 2004)
Not implemented (1) SIS
(2) (Hunke and
Dukowicz, 1997;
Winton, 2000)
(1) NorESM1-M
(2) 2011
(1) Norwegian Climate
Centre
(2) (Iversen et al., 2013)
(1) CAM4-Oslo
(2) Finite Volume 1.9°
latitude, 2.5° longitude
(3) 26
(4) 2.194067 hPa
(5) (Neale et al., 2010;
Kirkevåg et al., 2013)
(1) CAM4-Oslo
(2) (Kirkevåg et
al., 2013)
(1) CAM4-Oslo
(2) (Kirkevåg et al., 2013)
(1) CLM4
(2) (Oleson et al., 2010;
Lawrence et al., 2011)
(1) NorESM-Ocean
(2) 1.125° along the equator
(3) 53
(4) 1 m
(5) hybrid z isopycnic
(6) nonlinear split-explicit
(7)
Not implemented (1)CICE4
(2)(Hunke and
Lipscomb, 2008;
Holland et al., 2012)
(1) NorESM1-ME
(2) 2012
(1) Norwegian Climate
Centre
(2) (Tjiputra et al., 2013)
(1) CAM4-Oslo
(2) Finite Volume 1.9°
latitude, 2.5° longitude
(3) 26
(4) 2.194067 hPa
(5) (Neale et al., 2010;
Kirkevåg et al., 2013)
(1) CAM4-Oslo
(2) (Kirkevåg et
al., 2013)
(1) CAM4-Oslo
(2) (Kirkevåg et al., 2013)
(1) CLM4
(2) (Oleson et al., 2010;
Lawrence et al., 2011)
(1) NorESM-Ocean
(2) 1.125° along the equator
(3) 53
(4) 1 m
(5) hybrid z isopycnic
(6) nonlinear split-explicit
(7)
(1) HAMOCC5
(2) (Maier-Reimer et
al., 2005; Assmann
et al., 2010; Tjipu-
tra et al., 2013)
(1) CICE4
(2) (Hunke and
Lipscomb, 2008;
Holland et al., 2012)
864
Chapter 9 Evaluation of Climate Models
9
Table 9.A.2 | Salient features of the Earth system Models of Intermediate Complexity (EMICs) assessed in the AR5 (see also Table 9.2). Column 1: Model name used in WG1 and the official model version along with the first publication for
each model; subsequent columns for each of the eight component models with specific information and the related references are provided. This information was initially gathered for the EMIC intercomparison project in Eby et al. (2013).
(1) Model name
(2) Model version
(3) Main reference
Atmosphere
a
(1) Model type
(2) Dimensions
(3) Resolution
(4) Radiation
and cloudiness
(5) References
Ocean
b
(1) Model type
(2) Dimensions
(3) Resolution
(4) Parametrizations
(5) References
Sea Ice
c
(1) Schemes
(2) References
Coupling
d
(1) Flux adjustment
(2) References
Land Surface
e
(1) Soil schemes
(2) References
Biosphere
f
(1) Ocean and references
(2) Land and references
(3) Vegetation and references
Ice Sheets
g
(1) Model type
(2) Dimensions
(3) Resolution
(4) References
Sediment and
Weathering
h
(1) Model type
(2) References
(1) Bern3D
(2) Bern3D-LPJ
(3) (Ritz et al., 2011)
(1) EMBM
(2) 2-D(φ, λ)
(3) 10° × (3–19)°
(4) NCL
(5)
(1) FG with parameterized
zonal pressure gradient
(2) 3-D
(3) 10° × (3–19)°, L32
(4) RL, ISO, MESO
(5) (Muller et al., 2006)
(1) 0-LT, DOC, 2-LIT (1) PM, NH, RW (1) Bern3D: 1-LST,
NSM, RIV
LPJ: 8-LST, CSM with
uncoupled hydrology
(2) (Wania et al., 2009)
(1) BO (Parekh et al., 2008; Tschumi
et al., 2008; Gangsto et al., 2011)
(2) BT (Sitch et al., 2003; Strassmann
et al., 2008; Stocker et al., 2011)
(3) BV (Sitch et al., 2003)
N/A (1) CS, SW
(2) (Tschumi et
al., 2011)
(1) CLIMBER2
(2) CLIMBER-2.4
(3) (Petoukhov et al.,
2000)
(1) SD
(2) 3-D
(3) 10° × 51°, L10
(4) CRAD, ICL
(5)
(1) FG,
(2) 2-D(φ,z)
(3) 2.5°, L21
(4) RL
(5) (Wright and Stocker,
1992)
(1) 1-LT, PD, 2-LIT
(2) (Petoukhov
et al., 2000)
(1) NM, NH, NW
(2) (Petoukhov
et al., 2000)
(1) 1-LST, CSM, RIV
(2) (Petoukhov et al.,
2000)
(1,2,3) BO, BT, BV (Brovkin et al., 2002) (1) TM
(2) 3-D
(3) 0.75° × 1.5°, L20
(4) (Calov et al.,
2002)
N/A
(1) CLIMBER3
(2) CLIMBER-3α
(3) (Montoya et al.,
2005)
(1) SD
(2) 3-D
(3) 7.5° × 22.5°, L10
(4) CRAD, ICL
(5) (Petoukhov et al.,
2000)
(1) PE
(2) 3-D
(3) 3.75° × 3.75°, L24
(4) FS, ISO, MESO, TCS, DC
(1) 2-LT, R, 2-LIT
(2) (Fichefet and
Morales Maqueda,
1997)
(1) AM, NH, RW (1) 1-LST, CSM, RIV
(2) (Petoukhov et al.,
2000)
(1) BO (Six and Maier-Reimer, 1996)
(2,3) BT, BV (Brovkin et al., 2002)
N/A N/A
(1) DCESS
(2) DCESS
(3) (Shaffer et al., 2008)
(1) EMBM
(2) 2-box in φ,
(3)
(4) LRAD, CHEM
(5) (Shaffer et al., 2008)
(1) 2-box in φ
(2)
(3) L55
(4) parameterized circulation
and exchange, MESO
(5) (Shaffer and
Sarmiento, 1995)
(1) Parameter-
ized from surface
temperature
(2) (Shaffer et
al., 2008)
(1) NH, NW
(2) (Shaffer et
al., 2008)
(1) NST, NSM
(2) (Shaffer et al., 2008)
(1,2) BO, BT (Shaffer et al., 2008) N/A (1) CS, SW
(2) (Shaffer et
al., 2008)
(1) FAMOUS
(2) FAMOUS XDBUA
(3) (Smith et al., 2008)
(1) PE
(2) 3-D
(3) 5° × 7.5°, L11
(4) CRAD, ICL
(5) (Pope et al., 2000)
(1) PE
(2) 3-D
(3) 2.5° × 3.75°, L20
(4) RL, ISO, MESO
(5) (Gordon et al., 2000)
(1) 0-LT, DOC, 2-LIT (1) NM, NH, NW (1) 4-LST, CSM, RIV
(2) (Cox et al., 1999)
(1) BO (Palmer and Totterdell, 2001) N/A N/A
(1) GENIE
(2) GENIE
(3) (Holden et al., 2013)
(1) EMBM
(2) 2-D(φ, λ)
(3) 10° × (3–19)°
(4) NCL
(5) (Marsh et al., 2011)
(1) FG
(2) 3-D
(3) 10° × (3–19) °, L16
(4) RL, ISO, MESO
(5) (Marsh et al., 2011)
(1) 1-LT, DOC, 2-LIT
(2) (Marsh et al.,
2011)
(1) PM, NH, RW
(2) (Marsh et al.,
2011)
(1) 1-LST, BSM, RIV
(2) (Williamson et al.,
2006)
(1,2) BO, BT (Williamson et al., 2006;
Ridgwell et al., 2007b; Holden et al., 2013)
N/A (1) CS, SW
(2) (Ridgwell and
Hargreaves, 2007)
(1) IAP RAS CM
(2) IAP RAS CM
(3) (Eliseev and
Mokhov, 2011)
(1) SD
(2) 3-D
(3) 4.5° × 6°, L8
(4) CRAD, ICL
(5) (Petoukhov et al.,
1998)
(1) PE
(2) 3-D
(3) 3.5° × 3.5°, L32
(4) RL, ISO, TCS
(5) (Muryshev et al., 2009)
(1) 0-LT, 2-LIT
(2) (Muryshev et al.,
2009)
(1) NM, NH, NW
(2) (Muryshev et al.,
2009)
(1) 240-LST, CSM
(2) (Arzhanov et al., 2008)
(2) BT (Eliseev and Mokhov, 2011) N/A N/A
(continued on next page)
865
Evaluation of Climate Models Chapter 9
9
(continued on next page)
(1) Model name
(2) Model version
(3) Main reference
Atmosphere
a
(1) Model type
(2) Dimensions
(3) Resolution
(4) Radiation
and cloudiness
(5) References
Ocean
b
(1) Model type
(2) Dimensions
(3) Resolution
(4) Parametrizations
(5) References
Sea Ice
c
(1) Schemes
(2) References
Coupling
d
(1) Flux adjustment
(2) References
Land Surface
e
(1) Soil schemes
(2) References
Biosphere
f
(1) Ocean and references
(2) Land and references
(3) Vegetation and references
Ice Sheets
g
(1) Model type
(2) Dimensions
(3) Resolution
(4) References
Sediment and
Weathering
h
(1) Model type
(2) References
(1) IGSM2
(2) IGSM 2.2
(3) (Sokolov et
al., 2005)
(1) SD
(2) 2-D(φ, Z)
(3) 4° × 360° , L11
(4) ICL, CHEM
(5) (Sokolov and
Stone, 1998)
(1) Q-flux mixed-layer,
anomaly diffusing,
(2) 3-D
(3) 4° × 5°, L11
(4)
(5) (Hansen et al., 1984)
(1) 2-LT
(2) (Hansen et
al., 1984)
(1) Q-flux
(2) (Sokolov et
al., 2005)
(1) CSM
(2) (Oleson et al., 2008b)
(1) BO (Holian et al., 2001)
(2) BT (Melillo et al., 1993; Liu, 1996;
Felzer et al., 2004)
N/A N/A
(1) LOVECLIM1.2
(2) LOVECLIM1.2
(3) (Goosse et al.,
2010)
(1) QG
(2) 3-D
(3) 5.6° × 5.6°, L3
(4) LRAD, NCL
(5) (Opsteegh et al.,
1998)
(1) PE
(2) 3-D
(3) 3° × 3°, L30
(4) FS, ISO, MESO, TCS, DC
(5) (Goosse and
Fichefet, 1999)
(1) 3-LT, R, 2-LIT
(2) (Fichefet and
Morales Maqueda,
1997)
(1) NM, NH, RW
(2) (Goosse et al.,
2010)
(1) 1-LST, BSM, RIV
(2) (Goosse et al., 2010)
(1) BO (Mouchet and François, 1996)
(2,3) BT, BV (Brovkin et al., 2002)
(1) TM
(2) 3-D
(3) 10 km × 10 km,
L30
(4) (Huybrechts,
2002)
N/A
(1) MESMO
(2) MESMO 1.0
(3) (Matsumoto
et al., 2008)
(1) EMBM
(2) 2-D(φ, λ)
(3) 10° × (3–19)°
(4) NCL,
(5) (Fanning and
Weaver, 1996)
(1) FG
(2) 3-D
(3) 10° × (3–19)°, L16
(4) RL, ISO, MESO
(5) (Edwards and
Marsh, 2005)
(1) 0-LT, DOC, 2-LIT
(2) (Edwards and
Marsh, 2005)
(1) PM, NH, RW (1) NST, NSM, RIV
(2) (Edwards and
Marsh, 2005)
(1) BO (Matsumoto et al., 2008) N/A N/A
(1) MIROC-lite
(2) MIROC-lite
(3) (Oka et al., 2011)
(1) EMBM
(2) 2-D(φ, λ)
(3) 4° × 4°
(4) NCL
(5) (Oka et al., 2011)
(1) PE
(2) 3-D
(3) 4° × 4°
(4) FS, ISO, MESO, TCS
(5) (Hasumi, 2006)
(1) 0-LT, R, 2-LIT
(2) (Hasumi, 2006)
(1) PM, NH, NW
(2) (Oka et al., 2011)
(1) 1-LST, BSM
(2) (Oka et al., 2011)
N/A N/A N/A
(1) MIROC-lite-LCM
(2) MIROC-lite-LCM
(3) (Tachiiri et al., 2010)
(1) EMBM, tuned
for 3 K equilibrium
climate sensitivity
(2) 2-D(φ, λ)
(3) 6° × 6°
(4) NCL
(5) (Oka et al., 2011)
(1) PE
(2) 3-D
(3) 6° × 6°, L15
(4) FS, ISO, MESO, TCS
(5) (Hasumi, 2006)
(1) 0-LT, R, 2-LIT
(2) (Hasumi, 2006)
(1) NM, NH RW
(2) (Oka et al., 2011)
(Tachiiri et al., 2010)
(1) 1-LST, BSM
(2) (Oka et al., 2011)
(1) BO (Palmer and Totterdell, 2001)
(2) loosely coupled BT (Ito and Oikawa,
2002)
N/A N/A
(1) SPEEDO
(2) SPEEDO V2.0
(3) (Severijns and
Hazeleger, 2010)
(1) PE
(2) 3-D
(3) T30, L8
(4) LRAD, IDL,
(5) (Molteni, 2003)
(1) PE
(2) 3-D
(3) 3° × 3°, L20
(4) FS, ISO, MESO, TCS, DC
(5) (Goosse and
Fichefet, 1999)
(1) 3-LT, R, 2-LIT
(2) (Fichefet and
Morales Maqueda,
1997)
(1)NM, NH, NW
(2) (Cimatoribus
et al., 2012)
(1) 1-LST, BSM, RIV
(2) (Opsteegh et al., 1998)
N/A N/A N/A
(1) UMD
(2) UMD 2.0
(3) (Zeng et al., 2004)
(1) QG
(2) 3-D
(3) 3.75° × 5.625°, L2
(4) LRAD, ICL
(5) (Neelin and Zeng,
2000; Zeng et al., 2000)
(1) Q-flux mixed-layer
(2) 2-D surface, deep
ocean box model
(3) 3.75° × 5.625°
(5) (Hansen et al., 1983),
N/A (1) Energy and water
exchange only
(2) (Zeng et al., 2004)
(1) 2-LST with 2-layer
soil moisture
(2) (Zeng et al., 2000)
(1) BO (Archer et al., 2000)
(2,3) BT, BV (Zeng, 2003; Zeng et al.,
2005; Zeng, 2006)
N/A N/A
Table 9.A.2 (continued)
866
Chapter 9 Evaluation of Climate Models
9
(1) Model name
(2) Model version
(3) Main reference
Atmosphere
a
(1) Model type
(2) Dimensions
(3) Resolution
(4) Radiation
and cloudiness
(5) References
Ocean
b
(1) Model type
(2) Dimensions
(3) Resolution
(4) Parametrizations
(5) References
Sea Ice
c
(1) Schemes
(2) References
Coupling
d
(1) Flux adjustment
(2) References
Land Surface
e
(1) Soil schemes
(2) References
Biosphere
f
(1) Ocean and references
(2) Land and references
(3) Vegetation and references
Ice Sheets
g
(1) Model type
(2) Dimensions
(3) Resolution
(4) References
Sediment and
Weathering
h
(1) Model type
(2) References
(1) Uvic
(2) UVic 2.9
(3) (Weaver et al.,
2001)
(1) DEMBM
(2) 2-D(φ, λ)
(3) 1.8° × 3.6°
(4) NCL
(5) (Weaver et al., 2001)
(1) PE
(2) 3-D
(3) 1.8° × 3.6°, L19
(4) RL, ISO, MESO
(5) (Weaver et al., 2001)
(1) 0-LT, R, 2-LIT
(2) (Weaver et al.,
2001)
(1) AM, NH, NW
(2) (Weaver et al.,
2001)
(1) 1-LST, CSM, RIV
(2) (Meissner et al., 2003)
(1) BO (Schmittner et al., 2005)
(2,3) BT, BV (Cox, 2001)
(1) TM
(2) 3-D
(3) 20 km × 20 km,
L10
(4) (Fyke et al.,
2011)
(1) CS, SW
(2) (Eby et al., 2009)
Table 9.A.2 (continued)
Notes:
(a) EMBM = energy moisture balance model; DEMBM = energy moisture balance model including some dynamics; SD = statistical-dynamical model; QG = quasi-geostrophic model; 2-D(φ, λ) = vertically averaged; 3-D = three-
dimensional; LRAD = linearized radiation scheme; CRAD = comprehensive radiation scheme; NCL = non-interactive cloudiness; ICL = interactive cloudiness; CHEM = chemistry module; × = n degrees latitude by m degrees
longitude horizontal resolution; Lp = p vertical levels.
(b) FG = frictional geostrophic model; PE = primitive equation model; 2-D(φ, z) = zonally averaged; 3-D = three-dimensional; RL = rigid lid; FS = free surface; ISO = isopycnal diffusion; MESO = parameterization of the effect of
mesoscale eddies on tracer distribution; TCS = complex turbulence closure scheme; DC = parameterization of density-driven downward-sloping currents; × = n degrees latitude by m degrees longitude horizontal resolution;
Lp = p vertical levels.
(c) n-LT = n-layer thermodynamic scheme; PD = prescribed drift; DOC = drift with oceanic currents; R = viscous-plastic or elastic-viscous-plastic rheology; 2-LIT = two-level ice thickness distribution (level ice and leads).
(d) PM = prescribed momentum flux; AM = momentum flux anomalies relative to the control run are computed and added to climatological data; NM = no momentum flux adjustment; NH = no heat flux adjustment; RW = regional
freshwater flux adjustment; NW = no freshwater flux adjustment.
(e) NST = no explicit computation of soil temperature; n-LST = n-layer soil temperature scheme; NSM = no moisture storage in soil; BSM = bucket model for soil moisture; CSM = complex model for soil moisture; RIV = river routing
scheme.
(f) BO = model of oceanic carbon dynamics; BT = model of terrestrial carbon dynamics; BV = dynamical vegetation model.
(g) TM = thermomechanical model; 3-D = three-dimensional; × = n degrees latitude by m degrees longitude horizontal resolution; n km × m km = horizontal resolution in kilometres; Lp = p vertical levels.
(h) CS = complex ocean sediment model; SW = simple, specified or diagnostic weathering model.