953

This chapter should be cited as:

Kirtman, B., S.B. Power, J.A. Adedoyin, G.J. Boer, R. Bojariu, I. Camilloni, F.J. Doblas-Reyes, A.M. Fiore, M. Kimoto,

G.A. Meehl, M. Prather, A. Sarr, C. Schär, R. Sutton, G.J. van Oldenborgh, G. Vecchi and H.J. Wang, 2013: Near-term

Climate Change: Projections and Predictability. In: Climate Change 2013: The Physical Science Basis. Contribution

of Working Group I to the Fifth Assessment 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:

Ben Kirtman (USA), Scott B. Power (Australia)

Lead Authors:

Akintayo John Adedoyin (Botswana), George J. Boer (Canada), Roxana Bojariu (Romania), Ines

Camilloni (Argentina), Francisco Doblas-Reyes (Spain), Arlene M. Fiore (USA), Masahide Kimoto

(Japan), Gerald Meehl (USA), Michael Prather (USA), Abdoulaye Sarr (Senegal), Christoph Schär

(Switzerland), Rowan Sutton (UK), Geert Jan van Oldenborgh (Netherlands), Gabriel Vecchi

(USA), Hui-Jun Wang (China)

Contributing Authors:

Nathaniel L. Bindoff (Australia), Philip Cameron-Smith (USA/New Zealand), Yoshimitsu

Chikamoto (USA/Japan), Olivia Clifton (USA), Susanna Corti (Italy), Paul J. Durack (USA/

Australia), Thierry Fichefet (Belgium), Javier García-Serrano (Spain), Paul Ginoux (USA), Lesley

Gray (UK), Virginie Guemas (Spain/France), Ed Hawkins (UK), Marika Holland (USA), Christopher

Holmes (USA), Johnna Infanti (USA), Masayoshi Ishii (Japan), Daniel Jacob (USA), Jasmin John

(USA), Zbigniew Klimont (Austria/Poland), Thomas Knutson (USA), Gerhard Krinner (France),

David Lawrence (USA), Jian Lu (USA/Canada), Daniel Murphy (USA), Vaishali Naik (USA/India),

Alan Robock (USA), Luis Rodrigues (Spain/Brazil), Jan Sedláček (Switzerland), Andrew Slater

(USA/Australia), Doug Smith (UK), David S. Stevenson (UK), Bart van den Hurk (Netherlands),

Twan van Noije (Netherlands), Steve Vavrus (USA), Apostolos Voulgarakis (UK/Greece), Antje

Weisheimer (UK/Germany), Oliver Wild (UK), Tim Woollings (UK), Paul Young (UK)

Review Editors:

Pascale Delecluse (France), Tim Palmer (UK), Theodore Shepherd (Canada), Francis Zwiers

(Canada)

Near-term Climate Change:

Projections and Predictability

954

Table of Contents

Executive Summary ..................................................................... 955

11.1 Introduction ...................................................................... 958

Box 11.1: Climate Simulation, Projection, Predictability

and Prediction ..................................................................... 959

11.2 Near-term Predictions .................................................... 962

11.2.1 Introduction .............................................................. 962

11.2.2 Climate Prediction on Decadal Time Scales ............... 965

11.2.3 Prediction Quality ..................................................... 966

11.3 Near-term Projections .................................................... 978

11.3.1 Introduction .............................................................. 978

11.3.2 Near-term Projected Changes in the Atmosphere

and Land Surface ...................................................... 980

11.3.3 Near-term Projected Changes in the Ocean .............. 993

11.3.4 Near-term Projected Changes in the Cryosphere ....... 995

11.3.5 Projections for Atmospheric Composition and

Air Quality to 2100 ................................................... 996

11.3.6 Additional Uncertainties in Projections of

Near-term Climate................................................... 1004

Box 11.2: Ability of Climate Models to Simulate

Observed Regional Trends ............................................... 1013

References ................................................................................ 1015

Frequently Asked Questions

FAQ 11.1 If You Cannot Predict the Weather Next Month,

How Can You Predict Climate for the

Coming Decade? .................................................... 964

FAQ 11.2 How Do Volcanic Eruptions Affect Climate and

Our Ability to Predict Climate? .......................... 1008

955

Near-term Climate Change: Projections and Predictability Chapter 11

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).

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 >50–10 0%, 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).

Executive Summary

This chapter assesses the scientiﬁc literature describing expectations

for near-term climate (present through mid-century). Unless otherwise

stated, ‘near-term’ change and the projected changes below are for the

period 2016–2035 relative to the reference period 1986–2005. Atmos-

pheric composition (apart from CO

; see Chapter 12) and air quality

projections through to 2100 are also assessed.

Decadal Prediction

The nonlinear and chaotic nature of the climate system imposes natu-

ral limits on the extent to which skilful predictions of climate statistics

may be made. Model-based ‘predictability’ studies, which probe these

limits and investigate the physical mechanisms involved, support the

potential for the skilful prediction of annual to decadal average tem-

perature and, to a lesser extent precipitation.

Predictions for averages of temperature, over large regions of

the planet and for the global mean, exhibit positive skill when

veriﬁed against observations for forecast periods up to ten

years (high conﬁdence

). Predictions of precipitation over some land

areas also exhibit positive skill. Decadal prediction is a new endeavour

in climate science. The level of quality for climate predictions of annual

to decadal average quantities is assessed from the past performance of

initialized predictions and non-initialized simulations. {11.2.3, Figures

11.3 and 11.4}

In current results, observation-based initialization is the dominant con-

tributor to the skill of predictions of annual mean temperature for the

ﬁrst few years and to the skill of predictions of the global mean surface

temperature and the temperature over the North Atlantic, regions of

the South Paciﬁc and the tropical Indian Ocean for longer periods (high

conﬁdence). Beyond the ﬁrst few years the skill for annual and multi-

annual averages of temperature and precipitation is due mainly to the

speciﬁed radiative forcing (high conﬁdence). {Section 11.2.3, Figures

11.3 to 11.5}

Projected Changes in Radiative Forcing of Climate

For greenhouse gas (GHG) forcing, the new Representative Con-

centration Pathway (RCP) scenarios are similar in magnitude and

range to the AR4 Special Report on Emission Scenarios (SRES)

scenarios in the near term, but for aerosol and ozone precursor

emissions the RCPs are much lower than SRES by factors of 1.2

to 3. For these emissions the spread across RCPs by 2030 is much nar-

rower than between scenarios that considered current legislation and

maximum technically feasible emission reductions (factors of 2). In the

near term, the SRES Coupled Model Intercomparison Project Phase 3

(CMIP3) results, which did not incorporate current legislation on air

pollutants, include up to three times more anthropogenic aerosols

than RCP CMIP5 results (high conﬁdence), and thus the CMIP5 global

mean temperatures may be up to 0.2°C warmer than if forced with

SRES aerosol scenarios (medium conﬁdence). {10.3.1.1.3, Figure 10.4,

11.3.1.1, 11.3.5.1, 11.3.6.1, Figure 11.25, Tables AII.2.16 to AII.2.22

and AII.6.8}

Including uncertainties for the chemically reactive GHG meth-

ane gives a range in concentration that is 30% wider than the

spread in RCP concentrations used in CMIP5 models (likely

). By

2100 this range extends 520 ppb above RCP8.5 and 230 ppb below

RCP2.6 (likely), reﬂecting uncertainties in emissions from agricultural,

forestry and land use sources, in atmospheric lifetimes, and in chemical

feedbacks, but not in natural emissions. {11.3.5}

Emission reductions aimed at decreasing local air pollution

could have a near-term impact on climate (high conﬁdence).

Short-lived air pollutants have opposing effects: cooling from sulphate

and nitrate; warming from black carbon (BC) aerosol, carbon monox-

ide (CO) and methane (CH

). Anthropogenic CH

emission reductions

(25%) phased in by 2030 would decrease surface ozone and reduce

warming averaged over 2036–2045 by about 0.2°C (medium conﬁ-

dence). Combined reductions of BC and co-emitted species (78%)

on top of methane reductions (24%) would further reduce warming

(low conﬁdence), but uncertainties increase. {Section 7.6, Chapter 8,

11.3.6.1, Figure 11.24a, 8.7.2.2.2, Table AII.7.5a}

Projected Changes in Near-term Climate

Projections of near-term climate show modest sensitivity to

alternative RCP scenarios on global scales, but aerosols are an

important source of uncertainty on both global and regional

scales.{11.3.1, 11.3.6.1}

Projected Changes in Near-term Temperature

The projected change in global mean surface air temperature

will likely be in the range 0.3 to 0.7°C (medium conﬁdence). This

projection is valid for the four RCP scenarios and assumes there will be

no major volcanic eruptions or secular changes in total solar irradiance

before 2035. A future volcanic eruption similar to the 1991 eruption

of Mt Pinatubo would cause a rapid drop in global mean surface air

temperature of several tenths °C in the following year, with recovery

over the next few years. Possible future changes in solar irradiance

956

Chapter 11 Near-term Climate Change: Projections and Predictability

could inﬂuence the rate at which global mean surface air temperature

increases, but there is high conﬁdence that this inﬂuence will be small

in comparison to the inﬂuence of increasing concentrations of GHGs in

the atmosphere. {11.3.6, Figure 11.25}

It is more likely than not that the mean global mean surface

air temperature for the period 2016–2035 will be more than

1°C above the mean for 1850–1900, and very unlikely that it

will be more than 1.5°C above the 1850–1900 mean (medium

conﬁdence). {11.3.6.3}

In the near term, differences in global mean surface air temper-

ature change across RCP scenarios for a single climate model

are typically smaller than differences between climate models

under a single RCP scenario.In 2030, the CMIP5 ensemble median

values differ by at most 0.2ºC between RCP scenarios, whereas the

model spread (17 to 83% range) for each RCP is about 0.4ºC. The

inter-scenario spread increases in time: by 2050 it is 0.8ºC, whereas

the model spread for each scenario is only 0.6ºC. Regionally, the

largest differences in surface air temperature between RCP2.6 and

RCP8.5 are found in the Arctic.{11.3.2.1.1, 11.3.6.1, 11.3.6.3, Figure

11.24a,b,Table AII.7.5}

It is very likely that anthropogenic warming of surface air tem-

perature will proceed more rapidly over land areas than over

oceans, and that anthropogenic warming over the Arctic in

winter will be greater than the global mean warming over the

same period, consistent with the AR4. Relative to natural internal

variability, near-term increases in seasonal mean and annual mean

temperatures are expected to be larger in the tropics and subtropics

than in mid-latitudes (high conﬁdence). {11.3.2, Figures 11.10 and

11.11}

Projected Changes in the Water Cycle and Atmospheric

Circulation

Zonal mean precipitation will very likely increase in high and

some of the mid latitudes, and will more likely than not decrease

in the subtropics. At more regional scales precipitation changes may

be inﬂuenced by anthropogenic aerosol emissions and will be strongly

inﬂuenced by natural internal variability. {11.3.2, Figures 11.12 and

11.13}

Increases in near-surface speciﬁc humidity over land are very

likely. Increases in evaporation over land are likely in many

regions. There is low conﬁdence in projected changes in soil moisture

and surface run off. {11.3.2, Figure 11.14}

It is likely that the descending branch of the Hadley Circulation

and the Southern Hemisphere (SH) mid-latitude westerlies will

shift poleward. It is likely that in austral summer the projected recov-

ery of stratospheric ozone and increases in GHG concentrations will

have counteracting impacts on the width of the Hadley Circulation and

the meridional position of the SH storm track. Therefore, it is likely that

in the near term the poleward expansion of the descending southern

branch of the Hadley Circulation and the SH mid- latitude westerlies in

austral summer will be less rapid than in recent decades. {11.3.2}

There is medium conﬁdence in near-term projections of a north-

ward shift of Northern Hemisphere storm tracks and westerlies.

{11.3.2}

Projected Changes in the Ocean and Cryosphere

It is very likely that globally averaged surface and vertically

averaged ocean temperatures will increase in the near term.

It is likely that there will be increases in salinity in the tropical and

(especially) subtropical Atlantic, and decreases in the western tropical

Paciﬁc over the next few decades. The Atlantic Meridional Overturning

Circulation is likely to decline by 2050 (medium conﬁdence). However,

the rate and magnitude of weakening is very uncertain and, due to

large internal variability, there may be decades when increases occur.

{11.3.3}

It is very likely that there will be further shrinking and thinning

of Arctic sea ice cover, and decreases of northern high-latitude

spring time snow cover and near surface permafrost (see glos-

sary) as global mean surface temperature rises. For high GHG

emissions such as those corresponding to RCP8.5, a nearly ice-free

Arctic Ocean (sea ice extent less than 1 × 10

for at least 5 con-

secutive years) in September is likely before mid-century (medium con-

ﬁdence). This assessment is based on a subset of models that most

closely reproduce the climatological mean state and 1979 to 2012

trend of Arctic sea ice cover. There is low conﬁdence in projected near-

term decreases in the Antarctic sea ice extent and volume. {11.3.4}

Projected Changes in Extremes

In most land regions the frequency of warm days and warm

nights will likely increase in the next decades, while that of

cold days and cold nights will decrease. Models project near-term

increases in the duration, intensity and spatial extent of heat waves

and warm spells. These changes may proceed at a different rate than

the mean warming. For example, several studies project that European

high-percentile summer temperatures warm faster than mean temper-

atures. {11.3.2.5.1, Figures 11.17 and 11.18}

The frequency and intensity of heavy precipitation events over

land will likely increase on average in the near term. However,

this trend will not be apparent in all regions because of natural vari-

ability and possible inﬂuences of anthropogenic aerosols. {11.3.2.5.2,

Figures 11.17 and 11.18}

There is low conﬁdence in basin-scale projections of changes

in the intensity and frequency of tropical cyclones (TCs) in all

basins to the mid-21st century. This low conﬁdence reﬂects the

small number of studies exploring near-term TC activity, the differences

across published projections of TC activity, and the large role for nat-

ural variability and non-GHG forcing of TC activity up to the mid-21st

century. There is low conﬁdence in near-term projections for increased

TC intensity in the North Atlantic, which is in part due to projected

reductions in North Atlantic aerosols loading. {11.3.2.5.3}

957

Near-term Climate Change: Projections and Predictability Chapter 11

Projected Changes in Air Quality

The range in projections of air quality (O

and PM

2.5

in near-

surface air) is driven primarily by emissions (including CH

rather than by physical climate change (medium conﬁdence).

The response of air quality to climate-driven changes is more

uncertain than the response to emission-driven changes (high

conﬁdence). Globally, warming decreases background surface O

(high conﬁdence). High CH

levels (RCP8.5, SRES A2) can offset this

decrease, raising 2100 background surface O

on average by about 8

ppb (25% of current levels) relative to scenarios with small CH

chang-

es (RCP4.5, RCP6.0) (high conﬁdence). On a continental scale, pro-

jected air pollution levels are lower under the new RCP scenarios than

under the SRES scenarios because the SRES did not incorporate air

quality legislation (high conﬁdence). {11.3.5, 11.3.5.2; Figures 11.22

and 11.23ab, AII.4.2, AII.7.1–AII.7.4}

Observational and modelling evidence indicates that, all else

being equal, locally higher surface temperatures in polluted

regions will trigger regional feedbacks in chemistry and local

emissions that will increase peak levels of O

and PM

2.5

(medium

conﬁdence). Local emissions combined with background levels and

with meteorological conditions conducive to the formation and accu-

mulation of pollution are known to produce extreme pollution epi-

sodes on local and regional scales. There is low conﬁdence in project-

ing changes in meteorological blocking associated with these extreme

episodes. For PM

2.5

, climate change may alter natural aerosol sources

(wildﬁres, wind-lofted dust, biogenic precursors) as well as precipi-

tation scavenging, but no conﬁdence level is attached to the overall

impact of climate change on PM

2.5

distributions. {11.3.5, 11.3.5.2, Box

14.2}

958

Chapter 11 Near-term Climate Change: Projections and Predictability

11.1 Introduction

This chapter describes current scientiﬁc expectations for ‘near-term’ cli-

mate. Here ‘near term’ refers to the period from the present to mid-cen-

tury, during which the climate response to different emissions scenar-

ios is generally similar. Greatest emphasis in this chapter is given to

the period 2016–2035, though some information on projected changes

before and after this period (up to mid-century) is also assessed. An

assessment of the scientiﬁc literature relating to atmospheric compo-

sition (except carbon dioxide (CO

), which is addressed in Chapter 12)

and air quality for the near-term and beyond to 2100 is also provided.

This emphasis on near-term climate arises from (1) a recognition of

its importance to decision makers in government and industry; (2) an

increase in the international research effort aimed at improving our

understanding of near-term climate; and (3) a recognition that near-

term projections are generally less sensitive to differences between

future emissions scenarios than are long-term projections. Climate

prediction on seasonal to multi-annual time scales require accurate

estimates of the initial climate state with less dependence on chang-

es in external forcing

over the period. On longer time scales climate

projections rely on projections of external forcing with little reliance on

the initial state of internal variability. Estimates of near-term climate

depend partly on the committed change (caused by the inertia of the

oceans as they respond to historical external forcing), the time evo-

lution of internally generated climate variability and the future path

of external forcing. Near-term climate is sensitive to rapid changes in

some short-lived climate forcing agents (Jacobson and Streets, 2009;

Wigley et al., 2009; UNEP and WMO, 2011; Shindell et al., 2012b).

The need for near-term climate information has spawned a new ﬁeld of

climate science: decadal climate prediction (Smith et al., 2007; Meehl

et al., 2009b, 2013d). The Coupled Model Intercomparison Project

Phase 5 (CMIP5) experimental protocol includes a sequence of near-

term predictions (1 to 10 years) where observation-based information

is used to initialize the models used to produce the forecasts. The goal

is to exploit the predictability of internally generated climate variability

as well as that of the externally forced component. The result depends

on the ability of current models to reproduce the observed variability

as well as on the accurate depiction of the initial state (see Box 11.1).

Skilful multi-annual to decadal climate predictions (in the technical

sense of ‘skilful’ as outlined in 11.2.3.2 and FAQ 11.1) are being pro-

duced although technical challenges remain that need to be overcome

in order to improve skill. These challenges are now being addressed by

the scientiﬁc community.

Climate change experiments with models that do not depend on initial

condition but on the history and projection of climate forcings (often

referred to as ‘uninitialized’ or ‘non-initialized’ projections or simply

as ‘projections’) are another component of CMIP5. Such projections

have been the main focus of assessments of future climate in previ-

ous IPCC assessments and are considered in Chapters 12 to 14. The

main focus of attention in past assessments has been on the properties

of projections for the late 21st century and beyond. Projections also

Seasonal-to-interannual predictions typically include the impact of external forcing.

provide valuable information on externally forced changes to near-

term climate, however, and are an important source of information

that complements information from the predictions. Projections are

also assessed in this chapter.

The objectives of this chapter are to assess the state of the science con-

cerning both near-term predictions and near-term projections. CMIP5

results are considered for the near term as are other published near-

term predictions and projections. The chapter consists of four major

assessments:

1. The scientiﬁc basis for near-term prediction as reﬂected in esti-

mates of predictability (see Box 11.1), and the dynamical and

physical mechanisms underpinning predictability, and the process-

es that limit predictability (see Section 11.2).

2. The current state of knowledge in near-term prediction (see Sec-

tion 11.2). Here the emphasis is placed on the results from the

decadal (10-year) multi-model prediction experiments in the

CMIP5 database.

3. The current state of knowledge in near-term projection (see Sec-

tion 11.3). Here the emphasis is on what the climate in next few

decades may look like relative to 1986–2005, based on near-term

projections (i.e., the forced climatic response). The focus is on the

‘core’ near-term period (2016–2035), but some information prior

to this period and out to mid-century is also discussed. A key issue

is when, where and how the signal of externally forced climate

change is expected to emerge from the background of natural cli-

mate variability.

4. Projected changes in atmospheric composition and air quality, and

their interactions with climate change during the near term and

beyond, including new ﬁndings from the Atmospheric Chemistry

and Climate Model Intercomparison (ACCMIP) initiative.

959

Near-term Climate Change: Projections and Predictability Chapter 11

Box 11.1 | Climate Simulation, Projection, Predictability and Prediction

This section outlines some of the ideas and the terminology used in this chapter.

Internally generated and externally forced climate variability

It is useful for purposes of analysis and description to consider the pre-industrial climate system as being in a state of climatic equilib-

rium with a ﬁxed atmospheric composition and an unchanging Sun. In this idealized state, naturally occurring processes and interac-

tions within the climate system give rise to ‘internally generated’ climate variability on many time scales (as discussed in Chapter 1).

Variations in climate may also result due to features ‘external’ to this idealized system. Forcing factors, such as volcanic eruptions, solar

variations, anthropogenic changes in the composition of the atmosphere, land use change etc., give rise to ‘externally forced’ climate

variations. In this sense climate system variables such as annual mean temperatures (as in Box 11.1, Figure 1 for instance) may be

characterized as a combination of externally forced and internally generated components with T(t) = T

(t) + T

(t). This separation of T,

and other climate variables, into components is useful when analysing climate behaviour but does not, of course, mean that the climate

system is linear or that externally forced and internally generated components do not interact.

Climate simulation

A climate simulation is a model-based representation of the temporal behaviour of the climate system under speciﬁed external forcing

and boundary conditions. The result is the modelled response to the imposed external forcing combined with internally generated var-

iability. The thin yellow lines in Box 11.1, Figure 1 represent an ensemble of climate simulations begun from pre-industrial conditions

with imposed historical external forcing. The imposed external conditions are the same for each ensemble member and differences

among the simulations reﬂect differences in the evolutions of the internally generated component. Simulations are not intended to be

forecasts of the observed evolution of the system (the black line in Box 11.1, Figure 1) but to be possible evolutions that are consistent

with the external forcings.

In practice, and in Box 11.1, Figure 1, the forced component of the temperature variation is estimated by averaging over the different

simulations of T(t) with T

(t) the component that survives ensemble averaging (the red curve) while T

(t) averages to near zero for a

large enough ensemble. The spread among individual ensemble members (from these or pre-industrial simulations) and their behaviour

with time provides some information on the statistics of the internally generated variability. (continued on next page)

Global mean temperature

Individual

forecasts

Forecast

start time

Ensemble mean

simulation

Individual

simulations

Observed

Ensemble mean

forecast

1960 1970 1980 1990 2000 2010

−0.5

0.0

0.5

1.0

Year

Temperatureanomaly(°C)

Box 11.1, Figure 1 | The evolution of observation-based global mean temperature T (the black line) as the difference from the 1986–2005 average together with

an ensemble of externally forced simulations to 2005 and projections based on the RCP4.5 scenario thereafter (the yellow lines). The model-based estimate of the

externally forced component T

(the red line) is the average over the ensemble of simulations. To the extent that the red line correctly estimates the forced component,

the difference between the black and red lines is the internally generated component T

for global mean temperature. An ensemble of forecasts of global annual mean

temperature, initialized in 1998, is plotted as thin purple lines and their average, the ensemble mean forecast, as the thick green line. The grey areas along the axis

indicate the presence of external forcing associated with volcanoes.

960

Chapter 11 Near-term Climate Change: Projections and Predictability

Climate projection

A climate projection is a climate simulation that extends into the future based on a scenario of future external forcing. The simulations

in Box 11.1, Figure 1 become climate projections for the period beyond 2005 where the results are based on the RCP4.5 forcing scenario

(see Chapters 1 and 8 for a discussion of forcing scenarios).

Climate prediction, climate forecast

A climate prediction or climate forecast is a statement about the future evolution of some aspect of the climate system encompassing

both forced and internally generated components. Climate predictions do not attempt to forecast the actual day-to-day progression of

the system but instead the evolution of some climate statistic such as seasonal, annual or decadal averages or extremes, which may

be for a particular location, or a regional or global average. Climate predictions are often made with models that are the same as, or

similar to, those used to produce climate simulations and projections (assessed in Chapter 9). A climate prediction typically proceeds

by integrating the governing equations forward in time from observation-based initial conditions. A decadal climate prediction com-

bines aspects of both a forced and an initial condition problem as illustrated in Box 11.1, Figure 2. At short time scales the evolution is

largely dominated by the initial state while at longer time scales the inﬂuence of the initial conditions decreases and the importance of

the forcing increases as illustrated in Box 11.1, Figure 4. Climate predictions may also be made using statistical methods which relate

current to future conditions using statistical relationships derived from past system behaviour.

Because of the chaotic and nonlinear nature of the climate system small differences, in initial conditions or in the formulation of the

forecast model, result in different evolutions of forecasts with time. This is illustrated in Box 11.1, Figure 1, which displays an ensemble

of forecasts of global annual mean temperature (the thin purple lines) initiated in 1998. The individual forecasts are begun from slightly

different initial conditions, which are observation-based estimates of the state of the climate system. The thick green line is the average

of these forecasts and is an attempt to predict the most probable outcome and to maximize forecast skill. In this schematic example, the

1998 initial conditions for the forecasts are warmer than the average of the simulations. The individual and ensemble mean forecasts

exhibit a decline in global temperature before beginning to rise again. In this case, initialization has resulted in more realistic values for

the forecasts than for the corresponding simulation, at least for short lead times in the forecast. As the individual forecasts evolve they

diverge from one another and begin to resemble the projection results.

A probabilistic view of forecast behaviour is depicted schematically in Box 11.1, Figure 3. The probability distribution associated with

the climate simulation of temperature evolves in response to external forcing. By contrast, the probability distribution associated with

a climate forecast has a sharply peaked initial distribution representing the comparatively small uncertainty in the observation-based

initial state. The forecast probability distribution broadens with time until, ultimately, it becomes indistinguishable from that of an

uninitialized climate projection.

Climate predictability

The term ‘predictability’, as used here, indicates the extent to which even minor imperfections in the knowledge of the current state or

of the representation of the system limits knowledge of subsequent states. The rate of separation or divergence of initially close states

of the climate system with time (as for the light purple lines in Box 11.1, Figure 1), or the rate of displacement and broadening of its

Box 11.1 (continued)

Box 11.1, Figure 2 | A schematic illustrating the progression from an initial-value based prediction at short time scales to the forced boundary-value problem of

climate projection at long time scales. Decadal prediction occupies the middle ground between the two. (Based on Meehl et al., 2009b.)

(continued on next page)

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Near-term Climate Change: Projections and Predictability Chapter 11

probability distribution (as in Box 11.1, Figure 3) are

indications of the system’s predictability. If initially

close states separate rapidly (or the probability dis-

tribution broadens quickly towards the climatological

distribution), the predictability of the system is low

and vice versa. Formally, predictability in climate sci-

ence is a feature of the physical system itself, rather

than of our ‘ability to make skilful predictions in prac-

tice’. The latter depends on the accuracy of models and

initial conditions and on the correctness with which

the external forcing can be treated over the forecast

period.

Forecast quality, forecast skill

Forecast (or prediction) quality measures the success

of a prediction against observation-based informa-

tion. Forecasts made for past cases, termed retrospec-

tive forecasts or hindcasts, may be analysed to give

an indication of the quality that may be expected for

future forecasts for a particular variable at a particular

location.

The relative importance of initial conditions and of

external forcing for climate prediction, as depicted

schematically in Box 11.1, Figure 2, is further illustrat-

ed in the example of Box 11.1, Figure 4 which plots

correlation measures of both forecast skill and predictability for temperature averages over the globe ranging from a month to a

decade. Initialized forecasts exhibit enhanced values compared to uninitialized simulations for shorter time averages but the advantage

declines as averaging time increases and the forced component grows in importance.

Box 11.1 (continued)

p[X|forcing,initialization]

p[X|forcing]

Box 11.1, Figure 3 | A schematic representation of prediction in terms of probability. The

probability distribution corresponding to a forced simulation is in red, with the deeper shades

indicating higher probability. The probabilistic forecast is in blue. The sharply peaked forecast

distribution based on initial conditions broadens with time as the inﬂuence of the initial condi-

tions fades until the probability distribution of the initialized prediction approaches that of an

uninitialized projection. (Based on Branstator and Teng, 2010.)

Time averaging

Globally averaged correlation skill

1m 2m 3m 6m 1y 2y 3y 4y 6y 8y

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Initialized

Unitialized

Actual skill

Potential skill

Box 11.1, Figure 4 | An example of the relative importance of initial conditions and external forcing for climate prediction and predictability. The global average of

the correlation skill score of ensemble mean initialized forecasts are plotted as solid orange lines and the corresponding model-based predictability measure as dashed

orange lines. The green lines are the same quantities but for uninitialized climate simulations. Results are for temperature averaged over periods from a month to a

decade. Values plotted for the monthly average correspond to the ﬁrst month, those for the annual average to the ﬁrst year and so on up to the decadal average. (Based

on Boer et al., 2013.)

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Chapter 11 Near-term Climate Change: Projections and Predictability

11.2 Near-term Predictions

11.2.1 Introduction

11.2.1.1 Predictability Studies

The innate behaviour of the climate system imposes limits on the abil-

ity to predict its evolution. Small differences in initial conditions, exter-

nal forcing and/or in the representation of the behaviour of the system

produce differences in results that limit useful prediction. Predictability

studies estimate predictability limits for different variables and regions.

11.2.1.2 Prognostic Predictability Studies

Prognostic predictability studies analyse the behaviour of models inte-

grated forward in time from perturbed initial conditions. The study of

Grifﬁes and Bryan (1997) is one of the earliest studies of the predict-

ability of internally generated decadal variability in a coupled atmos-

phere–ocean climate model. The study concentrates on the North

Atlantic and the subsurface ocean temperature while the subsequent

studies of Boer (2000) and Collins (2002) deal mainly with surface

temperature. Long time scale temperature variability in the North

Atlantic has received considerable attention together with its possible

connection to the variability of the Atlantic Meridional Overturn-

ing Circulation (AMOC) in predictability studies by Collins and Sinha

(2003), Collins et al. (2006), Dunstone and Smith (2010), Dunstone et

al. (2011), Grotzner et al. (1999), Hawkins and Sutton (2009), Latif et al.

(2006, 2007), )Msadek et al. (2010), Persechino et al. (2012), Pohlmann

et al. (2004, 2013), Swingedouw et al. (2013), and Teng et al. (2011).

The predictability of the AMOC varies among models and, to some

extent, with initial model states, ranging from several to 10 or more

years. The predictability values are model-based and the realism of the

simulated AMOC in the models cannot be easily judged in the absence

of a sufﬁciently long record of observation-based AMOC values. Many

predictability studies are based on perturbations to surface quantities

but Sevellec and A. Fedorov (2012) and Zanna (2012) note that small

perturbations to deep ocean quantities may also affect upper ocean

values. The predictability of the North Atlantic sea surface temperature

(SST) is typically weaker than that of the AMOC and the connection

between the predictability of the AMOC, and the SST is inconsistent

among models.

Prognostic predictability studies of the Paciﬁc are less plentiful

although Paciﬁc Decadal Variability (PDV) mechanisms (including the

Paciﬁc Decadal Oscillation (PDO) and the Inter-decadal Paciﬁc Oscilla-

tion (IPO) have received considerable study (see Chapters 2 and 12).

Power and Colman (2006) ﬁnd predictability on multi-year time scales

in SST and on decadal time-scales in the sub-surface ocean temper-

ature in the off-equatorial South Paciﬁc in their model. Power et al.

(2006) ﬁnd no evidence for the predictability of inter-decadal changes

in the nature of El Niño-Southern Oscillation (ENSO) impacts on Aus-

tralian rainfall. Sun and Wang (2006) suggest that some of the tem-

perature variability linked to PDV can be predicted approximately 7

years in advance. Teng et al. (2011) investigate the predictability of the

ﬁrst two Empirical Orthogonal Functions (EOFs) of annual mean SST

and upper ocean temperature identiﬁed with PDV and ﬁnd predict-

ability of the order of 6 to 10 years. Meehl et al. (2010) consider the

predictability of 19-year ﬁltered Paciﬁc SSTs in terms of low order EOFs

and ﬁnd predictability on these long time scales.

Hermanson and Sutton (2010) report that predictable signals in dif-

ferent regions and for different variables may arise from differing ini-

tial conditions and that ocean heat content is more predictable than

atmospheric and surface variables. Branstator and Teng (2010) ana-

lyse upper ocean temperatures, and some SSTs, for averages over the

North Atlantic, North Paciﬁc and the tropical Atlantic and Paciﬁc in the

National Center for Atmospheric Research (NCAR) model. Predictabil-

ity associated with the initial state of the system decreases whereas

that due to external forcing increases with time. The ‘cross-over’ time,

when the two contributions are equal, is longer in extratropical (7 to 11

years) compared to tropical (2 years) regions and in the North Atlantic

compared to the North Paciﬁc. Boer et al. (2013) estimate surface air

(rather than upper ocean) temperature predictability in the Canadian

Centre for Climate Modelling and Analysis (CCCma) model and ﬁnd

a cross-over time (using a different measure) on the order of 3 years

when averaged over the globe.

11.2.1.3 Diagnostic Predictability Studies

Diagnostic predictability studies are based on analyses of the observed

record or the output of climate models. Because long data records are

needed, diagnostic multi-annual to decadal predictability studies based

on observational data are comparatively few. Newman (2007) and

Alexander et al. (2008) develop multivariate empirical Linear Inverse

Models (LIMs) from observation-based SSTs and ﬁnd predictability for

ENSO and PDV type patterns that are generally limited to the order of

a year although exceeding this in some areas. Zanna (2012) develops

a LIM based on Atlantic SSTs and infers the possibility of decadal scale

predictability. Hoerling et al. (2011) appeal to forced climate change

relative to the 1971–2000 period together with the statistics of natural

variability to infer the potential for the prediction of temperature over

North America for 2011–2020.

Tziperman et al. (2008) apply LIM-based methods to Geophysical Fluid

Dynamics Laboratory (GFDL) model output, as do Hawkins and Sutton

(2009) and Hawkins et al. (2011) to Hadley Centre model output and

ﬁnd predictability up to a decade or more for the AMOC and North

Atlantic SST. Branstator et al. (2012) use analog and multivariate linear

regression methods to quantify the predictability of the internally gen-

erated component of upper ocean temperature in results from six cou-

pled models. Results differ considerably across models but offer some

areas of commonality. Basin-average estimates indicate predictability

for up to a decade in the North Atlantic and somewhat less in the North

Paciﬁc. Branstator and Teng (2012) assess the predictability of both the

internally generated and forced component of upper ocean temperature

in results from 12 coupled models participating in CMIP5. They infer

potential predictability from initializing the internally generated com-

ponent for 5 years in the North Paciﬁc and 9 years in the North Atlantic

while the forced component dominates after 6.5 and 8 years in the two

basins. Results vary among models, although with some agreement for

internal component predictability in subpolar gyre regions.

Studies of ‘potential predictability’ take a number of forms but broad-

ly assume that overall variability may be separated into a long time

963

Near-term Climate Change: Projections and Predictability Chapter 11

scale component of interest and shorter time scale components that

are unpredictable on these long time scales, written symbolically as

= s

+ s

. The fraction p = s

/ s

is a measure of potentially

predictable variance provided that hypothesis that s

is zero may be

rejected. Small p indicates either a lack of long time scale variability

or its smallness as a fraction of the total. Predictability is ‘potential’ in

the sense that the existence of appreciable long time scale variability

is not a direct indication that it may be skilfully predicted. There are

a number of approaches to estimating potential predictability each

with its statistical difﬁculties (e.g., DelSole and Feng, 2013). At mul-

ti-annual time scales the potential predictability of the internally gen-

erated component of temperature is studied in Boer (2000), Collins

(2002), Pohlmann et al. (2004), Power and Colman (2006) and, in a

multi-model context, in Boer (2004) and Boer and Lambert (2008).

Power and Colman (2006) report that potential predictability in the

ocean tends to increase with latitude and depth. Multi-model results

Figure 11.1 | The potential predictability of 5-year means of temperature (lower), the contribution from the forced component (middle) and from the internally generated compo-

nent (upper). These are multi-model results from CMIP5 RCP4.5 scenario simulations from 17 coupled climate models following the methodology of Boer (2011). The results apply

to the early 21st century.

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Chapter 11 Near-term Climate Change: Projections and Predictability

Frequently Asked Questions

FAQ 11.1 | If You Cannot Predict the Weather Next Month, How Can You Predict Climate

for the Coming Decade?

Although weather and climate are intertwined, they are in fact different things. Weather is deﬁned as the state of

the atmosphere at a given time and place, and can change from hour to hour and day to day. Climate, on the other

hand, generally refers to the statistics of weather conditions over a decade or more.

An ability to predict future climate without the need to accurately predict weather is more commonplace that it

might ﬁrst seem. For example, at the end of spring, it can be accurately predicted that the average air temperature

over the coming summer in Melbourne (for example) will very likely be higher than the average temperature during

the most recent spring—even though the day-to-day weather during the coming summer cannot be predicted with

accuracy beyond a week or so. This simple example illustrates that factors exist—in this case the seasonal cycle in

solar radiation reaching the Southern Hemisphere—that can underpin skill in predicting changes in climate over a

coming period that does not depend on accuracy in predicting weather over the same period.

The statistics of weather conditions used to deﬁne climate include long-term averages of air temperature and

rainfall, as well as statistics of their variability, such as the standard deviation of year-to-year rainfall variability

from the long-term average, or the frequency of days below 5°C. Averages of climate variables over long periods

of time are called climatological averages. They can apply to individual months, seasons or the year as a whole. A

climate prediction will address questions like: ‘How likely will it be that the average temperature during the coming

summer will be higher than the long-term average of past summers?’ or: ‘How likely will it be that the next decade

will be warmer than past decades?’ More speciﬁcally, a climate prediction might provide an answer to the question:

‘What is the probability that temperature (in China, for instance) averaged over the next ten years will exceed the

temperature in China averaged over the past 30 years?’ Climate predictions do not provide forecasts of the detailed

day-to-day evolution of future weather. Instead, they provide probabilities of long-term changes to the statistics of

future climatic variables.

Weather forecasts, on the other hand, provide predictions of day-to-day weather for speciﬁc times in the future.

They help to address questions like: ‘Will it rain tomorrow?’ Sometimes, weather forecasts are given in terms of prob-

abilities. For example, the weather forecast might state that: ‘the likelihood of rainfall in Apia tomorrow is 75%’.

To make accurate weather predictions, forecasters need highly detailed information about the current state of the

atmosphere. The chaotic nature of the atmosphere means that even the tiniest error in the depiction of ‘initial con-

ditions’ typically leads to inaccurate forecasts beyond a week or so. This is the so-called ‘butterﬂy effect’.

Climate scientists do not attempt or claim to predict the detailed future evolution of the weather over coming

seasons, years or decades. There is, on the other hand, a sound scientiﬁc basis for supposing that aspects of climate

can be predicted, albeit imprecisely, despite the butterﬂy effect. For example, increases in long-lived atmospheric

greenhouse gas concentrations tend to increase surface temperature in future decades. Thus, information from the

past can and does help predict future climate.

Some types of naturally occurring so-called ‘internal’ variability can—in theory at least—extend the capacity to

predict future climate. Internal climatic variability arises from natural instabilities in the climate system. If such

variability includes or causes extensive, long-lived, upper ocean temperature anomalies, this will drive changes in

the overlying atmosphere, both locally and remotely. The El Niño-Southern Oscillation phenomenon is probably

the most famous example of this kind of internal variability. Variability linked to the El Niño-Southern Oscillation

unfolds in a partially predictable fashion. The butterﬂy effect is present, but it takes longer to strongly inﬂuence

some of the variability linked to the El Nino-Southern Oscillation.

Meteorological services and other agencies have exploited this. They have developed seasonal-to-interannual pre-

diction systems that enable them to routinely predict seasonal climate anomalies with demonstrable predictive skill.

The skill varies markedly from place to place and variable to variable. Skill tends to diminish the further the predic-

tion delves into the future and in some locations there is no skill at all. ‘Skill’ is used here in its technical sense: it is a

measure of how much greater the accuracy of a prediction is, compared with the accuracy of some typically simple

prediction method like assuming that recent anomalies will persist during the period being predicted.

Weather, seasonal-to-interannual and decadal prediction systems are similar in many ways (e.g., they all incorpo-

rate the same mathematical equations for the atmosphere, they all need to specify initial conditions to kick-start

(continued on next page)

965

Near-term Climate Change: Projections and Predictability Chapter 11

for both externally forced and internally generated components of the

potential predictability of decadal means of surface air temperature in

simulations of 21st century climate in CMIP3 model data are analysed

in Boer (2011) and results based on CMIP5 model data are shown

in Figure 11.2. Potential predictability of 5-year means for internally

generated variability is found over extratropical oceans but is generally

weak over land while that associated with the decadal change in the

forced component is found in tropical areas and over some land areas.

Predictability studies of precipitation on long time scales are com-

paratively few. Jai and DelSole (2012) identify ‘optimally predictable’

fractions of internally generated temperature and precipitation vari-

ance over land on multi-year time scales in the control simulations of

10 models participating in CMIP5, with results that vary considerably

from model to model. Boer and Lambert (2008) ﬁnd little potential

predictability for decadal means of precipitation in the internally gen-

erated variability of a collection of CMIP3 model control simulations

other than over parts of the North Atlantic. This is also the case for the

internally generated component of CMIP3 precipitation in 21st century

climate change simulations in Boer (2011) although there is evidence

of potential predictability for the forced component of precipitation

mainly at higher latitudes and for longer time scales.

11.2.1.4 Summary

Predictability studies suggest that initialized climate forecasts should

be able to provide more detailed information on climate evolution, over

a few years to a decade, than is available from uninitialized climate

simulations alone. Predictability results are, however, based mainly on

climate model results and depend on the verisimilitude with which the

models reproduce climate system behaviour (Chapter 9). There is evi-

dence of multi-year predictability for both the internally generated and

externally forced components of temperature over considerable por-

tions of the globe with the ﬁrst dominating at shorter and the second

at longer time scales. Predictability for precipitation is based on fewer

studies, is more modest than for temperature, and appears to be asso-

ciated mainly with the forced component at longer time scales. Predict-

ability can also vary from location to location.

11.2.2 Climate Prediction on Decadal Time Scales

11.2.2.1 Initial Conditions

A dynamical prediction consists of an ensemble of forecasts pro-

duced by integrating a climate model forward in time from a set of

observation-based initial conditions. As the forecast range increases,

processes in the ocean become increasingly important and the sparse-

ness, non-uniformity and secular change in sub-surface ocean obser-

vations is a challenge to analysis and prediction (Meehl et al., 2009b,

2013d; Murphy et al., 2010) and can lead to differences among ocean

analyses, that is, quantiﬁed descriptions of ocean initial conditions

(Stammer, 2006; Keenlyside and Ba, 2010). Approaches to ocean ini-

tialization include (as listed in Table 11.1): assimilation only of SSTs

to initialize the sub-surface ocean indirectly (Keenlyside et al., 2008;

FAQ 11.1 (continued)

predictions, and they are all subject to limits on forecast accuracy imposed by the butterﬂy effect). However, decadal

prediction, unlike weather and seasonal-to-interannual prediction, is still in its infancy. Decadal prediction systems

nevertheless exhibit a degree of skill in hindcasting near-surface temperature over much of the globe out to at least

nine years. A ‘hindcast’ is a prediction of a past event in which only observations prior to the event are fed into

the prediction system used to make the prediction. The bulk of this skill is thought to arise from external forcing.

‘External forcing’ is a term used by climate scientists to refer to a forcing agent outside the climate system causing

a change in the climate system. This includes increases in the concentration of long-lived greenhouse gases.

Theory indicates that skill in predicting decadal precipitation should be less than the skill in predicting decadal sur-

face temperature, and hindcast performance is consistent with this expectation.

Current research is aimed at improving decadal prediction systems, and increasing the understanding of the reasons

for any apparent skill. Ascertaining the degree to which the extra information from internal variability actually

translates to increased skill is a key issue. While prediction systems are expected to improve over coming decades,

the chaotic nature of the climate system and the resulting butterﬂy effect will always impose unavoidable limits

on predictive skill. Other sources of uncertainty exist. For example, as volcanic eruptions can inﬂuence climate but

their timing and magnitude cannot be predicted, future eruptions provide one of a number of other sources of

uncertainty. Additionally, the shortness of the period with enough oceanic data to initialize and assess decadal

predictions presents a major challenge.

Finally, note that decadal prediction systems are designed to exploit both externally forced and internally generat-

ed sources of predictability. Climate scientists distinguish between decadal predictions and decadal projections. Pro-

jections exploit only the predictive capacity arising from external forcing. While previous IPCC Assessment Reports

focussed exclusively on projections, this report also assesses decadal prediction research and its scientiﬁc basis.

966

Chapter 11 Near-term Climate Change: Projections and Predictability

Dunstone, 2010; Swingedouw et al., 2013); the forcing of the ocean

model with atmospheric observations (e.g., Du et al., 2012; Matei et al.,

2012b; Yeager et al., 2012) and more sophisticated alternatives based

on fully coupled data assimilation schemes (e.g., Zhang et al., 2007a;

Sugiura et al., 2009).

Dunstone and Smith (2010) and Zhang et al. (2010a) found an expected

improvement in skill when sub-surface information was used as part of

the initialization. Assimilation of atmospheric data, on the other hand,

is expected to have little impact after the ﬁrst few months (Balmaseda

and Anderson, 2009). The initialization of sea ice, snow cover, frozen

soil and soil moisture can potentially contribute to seasonal and sub-

seasonal skill (e.g., Koster et al., 2010; Toyoda et al., 2011; Chevallier

and Salas-Melia, 2012; Paolino et al., 2012), although an assessment of

their beneﬁt at longer time scales has not yet been determined.

11.2.2.2 Ensemble Generation

An ensemble can be generated in many different ways and a wide range

of methods have been explored in seasonal prediction (e.g., Stockdale

et al., 1998; Stan and Kirtman, 2008) but not yet fully investigated for

decadal prediction (Corti et al., 2012). Methods being investigated

include adding random perturbations to initial conditions, using atmos-

pheric states displaced in time, using parallel assimilation runs (Doblas-

Reyes et al., 2011; Du et al., 2012) and perturbing ocean initial condi-

tions (Zhang et al., 2007a; Mochizuki et al., 2010). Perturbations leading

to rapidly growing modes, common in weather forecasting, have also

been investigated (Kleeman et al., 2003; Vikhliaev et al., 2007; Hawkins

and Sutton, 2009, 2011; Du et al., 2012). The uncertainty associated

with the limitations of a model’s representation of the climate system

may be partially represented by perturbed physics (Stainforth et al.,

2005; Murphy et al., 2007) or stochastic physics (Berner et al., 2008),

and applied to multi-annual and decadal predictions (Doblas-Reyes et

al., 2009; Smith et al., 2010). Weisheimer et al. (2011) compare these

three approaches in a seasonal prediction context.

The multi-model approach, which is used widely and most common-

ly, combines ensembles of predictions from a collection of models,

thereby increasing the sampling of both initial conditions and model

properties. Multi-model approaches are used across time scales rang-

ing from seasonal–interannual (e.g., DEMETER; Palmer et al. (2004),

to seasonal-decadal (e.g., Weisheimer et al., 2011; van Oldenborgh et

al., 2012), in climate change simulation (e.g., IPCC, 2007, Chapter 10;

Meehl et al., 2007b) and in the ENSEMBLES and CMIP5-based decadal

predictions assessed in Section 11.2.3. A problem with the multi-model

approach is tha inter-dependence of the climate models used in current

forecast systems (Power et al. 2012; Knutti et al. 2013) is expected to

lead to co-dependence of forecast error.

11.2.3 Prediction Quality

11.2.3.1 Decadal Prediction Experiments

Decadal predictions for speciﬁc variables can be made by exploiting

empirical relationships based on past observations and expected phys-

ical relationships. Predictions of North Paciﬁc Ocean temperatures

have been achieved using prior wind stress observations (Schneider

and Miller, 2001). Both global and regional predictions of surface

temperature have been made based on projected changes in external

forcing and the observed state of the natural variability at the start

date (Lean and Rind, 2009; Krueger and von Storch, 2011; Ho et al.,

2012a; Newman, 2013). Some of these forecast systems are also used

as benchmarks to compare with the dynamical systems under devel-

opment. Comparisons (Newman (2013) have shown that there is simi-

larity in the temperature skill between a linear inverse method and the

CMIP5 hindcasts, pointing at a similarity in their sources of skill. In the

future, the combination of information from empirical and dynamical

predictions might be explored to provide a uniﬁed and more skilful

source of information.

Evidence for skilful interannual to decadal temperatures using dynam-

ical models forced only by previous and projected changes in anthro-

pogenic greenhouse gases (GHGs) and aerosols and natural varia-

tions in volcanic aerosols and solar irradiance is reported by Lee et al.

(2006b), Räisänen and Ruokolainen (2006) and Laepple et al. (2008).

Some attempts to predict the 10-year climate over regions have been

done using this approach, and include assessments of the role of the

internal decadal variability (Hoerling et al., 2011). To be clear, in the

context of this report these studies are viewed as projections because

no attempt is made to use observational estimates for the initial con-

ditions. Essentially, an uninitialized prediction is synonymous with a

projection. These projections or uninitialized predictions are referred

to synonymously in the literature as ‘NoInit,’ or ‘NoAssim’, referring to

the fact that no assimilated observations are used for the speciﬁcation

of the initial conditions.

Additional skill can be realized by initializing the models with obser-

vations in order to predict the evolution of the internally generated

component and to correct the model’s response to previously imposed

forcing (Smith et al., 2010; Fyfe et al., 2011; Kharin et al., 2012; Smith

et al., 2012). Again, to be clear, the assessment provided here distin-

guishes between predictions in which attempts are made to initialize

the models with observations, and projections. See Box 11.1 and FAQ

11.1 for further details.

The ENSEMBLES project (van Oldenborgh et al., 2012), for example,

has conducted a multi-model decadal retrospective prediction study,

and the Coupled Model Intercomparison Project phase 5 (CMIP5) pro-

posed a coordinated experiment that focuses on decadal, or near-term,

climate prediction (Meehl et al., 2009b; Taylor et al., 2012). Prior to

these initiatives, several pioneering attempts at initialized decadal pre-

diction were made (Pierce et al., 2004; Smith et al., 2007; Troccoli and

Palmer, 2007; Keenlyside et al., 2008; Pohlmann et al., 2009; Mochizuki

et al., 2010). Results from the CMIP5 coordinated experiment (Taylor et

al., 2012) are the basis for the assessment reported here.

Because the practice of decadal prediction is in its infancy, details of

how to initialize the models included in the CMIP5 near-term exper-

iment were left to the discretion of the modelling groups and are

described in Meehl et al. (2013d) and Table 11.1. In CMIP5 experi-

ments, volcanic aerosol and solar cycle variability are prescribed

along the integration using observation-based values up to 2005, and

assuming a climatological 11-year solar cycle and a background vol-

canic aerosol load in the future. These forcings are shared with CMIP5

967

Near-term Climate Change: Projections and Predictability Chapter 11

1960 1970 1980 1990 2000 2010

1918 20 21

(ºC)

SST 60ºS-60ºN CMIP5 Init / rm=12months

MRI-CGCM3

MIROC4h

MIROC5 CMCC-CM

EC-Earth2.3

HadCM3 CNRM-CM5

IPSL-CM5

GFDL-CM2

CanCM4

MPI-M

1960 1970 1980 1990 2000 2010

Anomaly (°C)

MRI-CGCM3

MIROC4h

MIROC5 CMCC-CM

EC-Earth2.3

HadCM3 CNRM-CM5

IPSL-CM5

GFDL-CM2

CanCM4

MPI-M

-0.5 0.50.0

ERSST

HadISST

ERSST

HadISST

Figure 11.2 | Time series of global mean sea surface temperature from the (a) direct model output and (b) anomalies of the CMIP5 multi-model initialized hindcasts. Results for

each forecast system are plotted with a different colour, with each line representing an individual member of the ensemble. Results for the start dates 1961, 1971, 1981, 1991 and

2001 are shown, while the model and observed climatologies to obtain the anomalies in (b) have been estimated using data from start dates every ﬁve years. The reference data

(ERSST) is drawn in black. All time series have been smoothed with a 24-month centred moving average that ﬁlters out the seasonal cycle and removes data for the ﬁrst and last

years of each time series.

historical runs (i.e., unintialized projections) started from pre-industrial

control simulations, enabling an assessment of the impact of initial-

ization. The speciﬁcation of the volcanic aerosol load and the solar

irradiance in the hindcasts gives an optimistic estimate of the forecast

quality with respect to an operational prediction system, where no

such future information can be used. Table 11.1 summarizes forecast

systems contributing to, and the initialization methods used in, the

CMIP5 near-term experiment.

The coordinated nature of the ENSEMBLES and CMIP5 experiments

also offers a good opportunity to study multi-model ensembles (Gar-

cia-Serrano and Doblas-Reyes, 2012; van Oldenborgh et al., 2012) as

a means of sampling model uncertainty while some modelling groups

have also investigated this using perturbed parameter approaches

(Smith et al., 2010). The relative merit of the different approaches for

decadal predictions has yet to be assessed.

When initialized with states close to the observations, models ‘drift’

towards their imperfect climatology (an estimate of the mean climate),

leading to biases in the simulations that depend on the forecast time.

The time scale of the drift in the atmosphere and upper ocean is, in

most cases, a few years (Hazeleger et al., 2013a). Biases can be largely

removed using empirical techniques a posteriori (Garcia-Serrano and

Doblas-Reyes, 2012; Kharin et al., 2012). The bias correction or adjust-

ment linearly corrects for model drift (e.g., Stockdale, 1997; Garcia-Ser-

rano et al., 2012; Gangstø et al., 2013). The approach assumes that the

model bias is stable over the prediction period (from 1960 onward in

the CMIP5 experiment). This might not be the case if, for instance, the

predicted temperature trend differs from the observed trend (Fyfe et

al., 2011; Kharin et al., 2012). Figure 11.2 is an illustration of the time

scale of the global SST drift, while at the same time showing the sys-

tematic error of several of the forecast systems contributing to CMIP5.

It is important to note that the systematic errors illustrated here are

968

Chapter 11 Near-term Climate Change: Projections and Predictability

Table 11.1 | Initialization methods used in models that entered CMIP5 near-term experiments. (Figures 11.3 to 11.7 have been prepared using those contributions with asterisk on top of the modelling centre’s name.).

(continued on next page)

CMIP5 Near-

term Players

CMIP5

ofﬁcial

model id

AGCM OGCM

Initialization Perturbation Aerosol

Reference

Name of modeling

centre (or group)

Atmosphere/Land Ocean Sea Ice

Anomaly

Assimilation?

Atmosphere Ocean

Concentration

Direct(D)/

Indirect

(I1,I2)

(*) Beijing Climate

Center, China Meteoro-

logical Administration

(BCC) China

BCC-CSM

1.1

2.8°L26 1°L40 No SST, T&S (SODA) No No

Perturbed atmosphere/

ocean/land/sea ice

C D

Xin et al.

(2013)

(*) Canadian Centre

for Climate Model-

ling and Analysis

(CCCMA) Canada

CanCM4 2.8°L35

1.4° ×

0.9°L40

ERA40/Interim

SST (ERSST&OISST),

T&S (SODA & GODAS)

HadISST1.1 No Ensemble assimilation E D, I1

Merryﬁeld et

al. (2013)

(*) Centro Euro-

Mediterraneo per I

Cambiamenti Climatici

(CMCC-CM) Italy

CMCC-CM 0.75°L31

0.5

°–2°

L31

SST, T&S (INGV

ocean analysis)

CMCC-CM

climatology

No Ensemble assimilation C D, I1

Bellucci et

al. (2013)

(*) Centre National de

Recherches Metéoro-

logiques, and Centre

Européen de Recherche

et Formation Avancées

en Calcul Scientiﬁque

(CNRM-CERFACS) France

CNRM-CM5 1.4°L31 1°L42 No

T&S (NEMOVAR-

COMBINE)

No No

1st day

atmospheric

conditions

No C D, I1

Meehl et al.

(2013d)

National Centers for

Environmental

Prediction and Center

for Ocean-Land-

Atmosphere Studies

(NCEP and COLA) USA

CFSv2-2011 0.9°L64

0.25–

0.5°L40

NCEP CFSR reanalysis

NCEP CFSR

ocean analysis

(NCEP runs)

NCEP CFSR

reanalysis

No No No C D, I1

Saha et al.

(2010)

NEMOVAR-S4 ocean

analysis (COLA runs)

(*) EC-EARTH consor-

tium (EC-EARTH)

Europe

EC-EARTH 1.1°L62 1°L42 ERA40/Interim

Ocean assimilation

(ORAS4/NEMOVAR S4)

NEMO3.2-

LIM2 forced

with DFS4.3

No (KNMI & IC3)

yes (SMHI)

Start dates and

singular vectors

Ensemble

ocean

assim

(NEM-

OVAR)

C D

Du et al. (2012)

Hazeleger et

al. (2013a)

(*) Institut Pierre-Simon

Laplace (IPSL) France

IPSL-

CM5A-LR

1.9 ×

3.8

L39

2°L31 No

SST anomalies (Reyn-

olds observations)

No Yes No

White

noise

on SST

C D, I1

Swingedouw

et al. (2013)

(*) AORI/NIES/JAMSTEC,

Japan

MIROC4h 0.6°L56 0.3°L48

SST, T&S (Ishii and

Kimoto, 2009)

No Yes

Start dates and ensemble

assimilation

E D,I1,I2

Tatebe et

al. (2012)

MIROC5 1.4°L40 1.4°L50

(*) Met Ofﬁce Hadley

Centre (MOHC) UK

HadCM3 3.8°L19 1.3°L20

ERA40/ECMWF

operational analysis

SST, T&S (Smith and

Murphy, 2007)

HADISST Yes, also full ﬁeld No

SST per-

turbation

E D

Smith et al.

(2013a)

969

Near-term Climate Change: Projections and Predictability Chapter 11

CMIP5 Near-

term Players

CMIP5

Ofﬁcial

Model ID

AGCM OGCM

Initialization Perturbation Aerosol

Reference

Name of Modeling

Centre (or group)

Atmosphere/Land Ocean Sea Ice

Anomaly

Assimilation?

Atmosphere Ocean

Concentration

Direct(D)/

Indirect

(I1,I2)

(*) Max Planck Institute

for Meteorology

(MPI-M) Germany

MPI-ESM

-LR

1.9°L47 1.5°L40

No T&S from forced OGCM No Yes 1-day lagged C D

Matei et al.

(2012b)

MPI-ESM

-MR

1.9°L95 0.4°L40

(*) Meteorological

Research Institute

(MRI) Japan

MRI-CGCM3 1.1°L48 1°L51 No

SST, T&S (Ishii and

Kimoto, 2009)

No Yes

Start dates and ensemble

assimilation

E D,I1,I2

Tatebe et

al. (2012)

Global Modeling

and Assimilation

Ofﬁce, (NASA) USA

GEOS-5

2.5

°×2

L72

1°L50 MERRA

T&S from ocean assimi-

lation (GEOS iODAS)

GEOS iODAS

reanalysis

No Two-sided breeding method E D

(*) National Center

for Atmospheric

Research (NCAR) USA

CCSM4 1.3°L26 1.0°L60 No

Ocean assimila-

tion (POPDART)

Ice state

from forced

ocean-ice

GCM (strong

salinity

restoring for

POPDART)

Single atm from

AMIP run

Ensemble

assimila-

tion

E D

Ocean state from

forced ocean-ice GCM

Staggered atm

start dates from

uninitialized run

Single

member

ocean

Yeager et

al. (2012)

(*) Geophysical Fluid

Dynamics Labora-

tory (GFDL) USA

GFDL-CM

2.1

2.5°L24 1°L50 NCEP reanalysis

Ocean observations

of 3-D T & S & SST

No No Coupled EnKF C D

Yang et al.

(2013)

LASG, Institute of Atmo-

spheric Physics, Chinese

Academy of Sciences;

and CESS, Tsinghua

University China

FGOALS-g2 2.8°L26 1°L30 No

SST, T&S (Ishii

et al., 2006)

No No A simpliﬁed scheme of 3DVar C D, I1

Wang et al.

(2013)

LASG, Institute of Atmo-

spheric Physics, Chinese

Academy of Sciences

China, Tsinghua

University China

FGOALS-s2 2.8°L26 1°L30 No T&S (EN3_v2a) No Yes

Incremental Analysis

Updates (IAU) scheme

C D

Wu and Zhou

(2012)

(Table 11.1 continued)

970

Chapter 11 Near-term Climate Change: Projections and Predictability

common to both decadal prediction systems and climate-change

projections. The bias adjustment itself is another important source of

uncertainty in climate predictions (e.g., Ho et al., 2012b). There may be

nonlinear relationships between the mean state and the anomalies,

that are neglected in linear bias adjustment techniques. There are also

difﬁculties in estimating the drift in the presence of volcanic eruptions.

It has been recognized that including as many initial states as possible

in computing the drift and adjusting the bias is more desirable than a

greater number of ensemble members per initial state (Meehl et al.,

2013d), although increasing both is desirable to obtain robust fore-

cast quality estimates. A procedure for bias adjustment following the

technique outlined above has been recommended for CMIP5 (ICPO,

2011). A suitable adjustment depends also on there being a sufﬁcient

number of hindcasts for statistical robustness (Garcia-Serrano et al.,

2012; Kharin et al., 2012).

To reduce the impact of the drift many of the early attempts at decadal

prediction (Smith et al., 2007; Keenlyside et al., 2008; Pohlmann et al.,

2009; Mochizuki et al., 2010) use an approach called anomaly initial-

ization (Schneider et al., 1999; Pierce et al., 2004; Smith et al., 2007).

The anomaly initialization approach attempts to circumvent model drift

and the need for a time-varying bias correction. The models are initial-

ized by adding observed anomalies to an estimate of the model mean

climate. The mean model climate is subsequently subtracted from the

predictions to obtain forecast anomalies. Sampling error in the estima-

tion of the mean climatology affects the success of this approach. This

is also the case for full-ﬁeld initialization, although as anomaly initial-

isation is affected to a smaller degree by the drift, the sampling error

is assumed to be smaller (Hazeleger et al., 2013a). The relative merits

of anomaly versus full initialization are being quantiﬁed (Hazeleger et

al., 2013a; Magnusson et al., 2013; Smith et al., 2013a), although no

initialization method was found to be deﬁnitely better in terms of fore-

cast quality. Another less widely explored alternative is dynamic bias

correction in which multi-year monthly mean analysis increments are

added during the integration of the ocean model (Wang et al., 2013).

Figure 11.2 includes predictions performed with both full and anomaly

initialization systems.

11.2.3.2 Forecast Quality Assessment

The quality of a forecast system is assessed by estimating, among

others, the accuracy, skill and reliability of a set of hindcasts (Jolliffe

and Stephenson, 2011). These three terms—accuracy, skill and reli-

ability—are used here in a strict technical sense. A suite of meas-

ures needs to be considered, particularly when a forecast system are

compared. The accuracy of a forecast system refers to the average

distance/error between forecasts and observations. The skill score is

a relative measure of the quality of the forecasting system compared

to some benchmark or reference forecast (e.g., climatology or per-

sistence). The reliability, which is a property of the speciﬁc forecast

system, measures the trustworthiness of the predictions. Reliability

measures how well the predicted probability distribution matches the

observed relative frequency of the forecast event. Accuracy and relia-

bility are aspects of forecast quality that can be improved by improv-

ing the individual forecast systems or by combining several of them

into a multi-model prediction. The reliability can be improved by a

posteriori corrections to model spread. Forecast quality can also be

improved by unequal weighting (Weigel et al., 2010; DelSole et al.,

2013), although this option has not been explored in decadal pre-

diction to date, because a long training sample is required to obtain

robust weights.

The assessment of forecast quality depends on the quantities of great-

est interest to those who use the information. World Meteorological

Organization (WMO)’s Standard Veriﬁcation System (SVS) for Long-

Range Forecasts (LRF) (WMO, 2002) outlines speciﬁcations for long-

range (sub-seasonal to seasonal) forecast quality assessment. These

measures are also described in Jolliffe and Stephenson (2011) and

Wilks (2006). A recommendation for a deterministic metric for dec-

adal climate predictions is the mean square skill score (MSSS), and

for a probabilistic metric, the continuous ranked probability skill score

(CRPSS) as described in Goddard et al. (2013) and Meehl et al. (2013d).

For dynamical ensemble systems, a useful measure of the characteris-

tics of an ensemble forecast system is spread. The relative spread can

be described in terms of the ratio between the mean spread around the

ensemble mean and the root mean square error (RMSE) of the ensem-

ble-mean prediction, or spread-to-RMSE ratio. A ratio of 1 is considered

a desirable feature for a Gaussian-distributed variable of a well-cali-

brated (i.e., reliable) prediction system (Palmer et al., 2006). The impor-

tance of using statistical inference in forecast quality assessments has

been recently emphasized (Garcia-Serrano and Doblas-Reyes, 2012;

Goddard et al., 2013). This is even more important when there are only

small samples available (Kumar, 2009) and a small number of degrees

of freedom (Gangstø et al., 2013). Conﬁdence intervals for the scores

are typically computed using either parametric or bootstrap methods

(Lanzante, 2005; Jolliffe, 2007; Hanlon et al., 2013).

The skill of seasonal predictions can vary from generation to genera-

tion (Power et al. 1999) and from one generation of forecast systems

to the next (Balmaseda et al., 1995). This highlights the possibility

that the skill of decadal predictions might also vary from one period

to another. Certain initial conditions might precede more predictable

near-term states than other initial conditions, and this has the poten-

tial to be reﬂected in predictive skill assessments. However, the short

length of the period available to initialize and verify the predictions

makes the analysis of the variations in skill very difﬁcult.

11.2.3.3 Pre-CMIP5 Decadal Prediction Experiments

Early decadal prediction studies found little additional predictive skill

from initialization, over that due to changes in radiative forcing (RF),

on global (Pierce et al., 2004) and regional scales (Troccoli and Palmer,

2007). However, neither of these studies considered more than two

start dates. More comprehensive tests, which considered at least nine

different start dates indicated temperature skill (Smith et al., 2007;

Keenlyside et al., 2008; Pohlmann et al., 2009; Sugiura et al., 2009;

Mochizuki et al., 2010; Smith et al., 2010; Doblas-Reyes et al., 2011;

Garcia-Serrano and Doblas-Reyes, 2012; Garcia-Serrano et al., 2012;

Kroger et al., 2012; Matei et al., 2012b; van Oldenborgh et al., 2012;

Wu and Zhou, 2012; MacLeod et al., 2013). Moreover, this skill was

enhanced by initialization (local increase in correlation of 0.1 to 0.3,

depending on the system) mostly over the ocean, in particular over

the North Atlantic and subtropical Paciﬁc oceans. Regions with skill

971

Near-term Climate Change: Projections and Predictability Chapter 11

improvements from initialization for precipitation are small and rarely

statistically signiﬁcant (Goddard et al., 2013).

11.2.3.4 Coupled Model Intercomparison Project Phase 5

Decadal Prediction Experiments

Indices of global mean temperature, the Atlantic Multi-decadal Varia-

bility (AMV; (Trenberth and Shea, 2006)) and the Inter-decadal Paciﬁc

Oscillation (IPO; Power et al., 1999) or Paciﬁc Decadal Oscillation (PDO)

are used as benchmarks to assess the ability of decadal forecast sys-

tems to predict multi-annual averages of climate variability (Kim et al.,

2012; van Oldenborgh et al., 2012; Doblas-Reyes et al., 2013; Goddard

et al., 2013; see also Figure 11.3). Initialized predictions of global mean

surface air temperature (GMST) for the following year are now being

performed in almost-real time (Folland et al., 2013).

Non-initialized predictions (or projections) of the global mean tem-

perature are statistically signiﬁcantly skilful for most of the forecast

ranges considered (high conﬁdence), due to the almost monotonic

increase in temperature, pointing to the importance of the time-var-

ying RF (Murphy et al., 2010; Kim et al., 2012). This leads to a high

(above 0.9) correlation of the ensemble mean prediction that varies

very as a function of forecast lead time. This holds whether the changes

in the external forcing (i.e., changes in natural and/or anthropogenic

atmospheric composition) are speciﬁed (i.e., CMIP5) or are projected

(ENSEMBLES). The skill of the multi-annual global mean surface tem-

perature improves with initialization, although this is mainly evidenced

when the accuracy is measured in terms of the RMSE (Doblas-Reyes et

al., 2013). An improved prediction of global mean surface temperature

is evidenced by the closer ﬁt of the initialized predictions during the

21st century (Figure 11.3; Meehl and Teng, 2012; Doblas-Reyes et al.,

2013; Guemas et al., 2013; Box 9.2). The impact of initialization is seen

as a better representation of the phase of the internal variability, in

particular in increasing the upper ocean heat content (Meehl et al.,

2011) and in terms of a correction of the model’s forced response.

The AMV (Chapter 14) has important impacts on temperature and pre-

cipitation over land (Li and Bates, 2007; Li et al., 2008; Semenov et al.,

2010). The AMV index shows a large fraction of its variability on dec-

adal time scales and has multi-year predictability (Murphy et al., 2010;

Garcia-Serrano and Doblas-Reyes, 2012). The AMV has been connected

to multi-decadal variability of Atlantic tropical cyclones (Goldenberg et

al., 2001; Zhang and Delworth, 2006; Smith et al., 2010; Dunstone et al.,

2011). Figure 11.3 shows that the CMIP5 multi-model ensemble mean

has skill on multi-annual time scales, the skill being generally larger

than for the single-model forecast systems (Garcia-Serrano and Doblas-

Reyes, 2012; Kim et al., 2012). The skill of the AMV index improves with

initialization (high conﬁdence) for the early forecast ranges. In particu-

lar, the RMSE is substantially reduced (indicating improved skill) with

initialization for the AMV. The positive correlation of the non-initialized

AMV predictions is consistent with the view that part of the recent

variability is due to external forcings (Evan et al., 2009; Ottera et al.,

2010; Chang et al., 2011; Booth et al., 2012; Garcia-Serrano et al., 2012;

Terray, 2012; Villarini and Vecchi, 2012; Doblas-Reyes et al., 2013).

Paciﬁc decadal variability is associated with potentially important

climate impacts, including rainfall over America, Asia, Africa and Aus-

tralia (Power et al., 1999; Deser et al., 2004; Seager et al., 2008; Zhu

et al., 2011; Li et al., 2012). The combination of Paciﬁc and Atlantic

variability and climate change is an important driver of multi-decadal

USA drought (McCabe et al., 2004; Burgman et al., 2010) including key

events like the American dustbowl of the 1930s (Schubert et al., 2004).

van Oldenborgh et al. (2012) reported weak skill in hindcasting the IPO

in the ENSEMBLES multi-model. Doblas-Reyes et al. (2013) show that

the ensemble-mean skill of the ENSEMBLES multi-model IPO is not

statistically signiﬁcant at the 95% level and shows no clear impact of

the initialization, in agreement with the predictability study of Meehl

et al. (2010). On the other hand, case studies suggest that there might

be some initial states that can produce skill in predicting IPO-related

decadal variability for some time periods (e.g., Chikamoto et al., 2012b;

Meehl and Arblaster, 2012; Meehl et al., 2013a).

The higher AMV and global mean temperature skill of the CMIP5 pre-

dictions with respect to the ENSEMBLES hindcasts (van Oldenborgh

et al., 2012; Goddard et al., 2013) might be partly due to the CMIP5

multi-model using speciﬁed instead of projected aerosol loading (espe-

cially the volcanic aerosol) and solar irradiance variations during the

simulations. As these forcings cannot be speciﬁed in a real forecast set-

ting, ENSEMBLES offers an estimate of the skill closer to what could be

expected from a real-time forecast system such as the one described

in (Smith et al., 2013a). The use of correct forcings nevertheless allows

a more powerful test of the effect of initialization on the ability of

models to reproduce past observations.

Near-term prediction systems have signiﬁcant skill for temperature

over large regions (Figure 11.4), especially over the oceans (Smith et al.,

2010; Doblas-Reyes et al., 2011; Kim et al., 2012; Matei et al., 2012b;

van Oldenborgh et al., 2012; Hanlon et al., 2013). It has been shown

that a large part of the skill corresponds to the correct representation

of the long-term trend (high conﬁdence) as the skill decreases substan-

tially after an estimate of the long-term trend is removed from both

the predictions and the observations (e.g., Corti et al., 2012; van Old-

enborgh et al., 2012; MacLeod et al., 2013). Robust skill increase due

to initialization (Figure 11.4) is limited to areas of the North Atlantic,

the Indian Ocean and the southeast Paciﬁc (high conﬁdence) (Doblas-

Reyes et al., 2013), in agreement with previous results (Pohlmann et

al., 2009; Smith et al., 2010; Mochizuki et al., 2012) and predictability

estimates (Branstator and Teng, 2012). Similar results have been found

in several individual forecast systems (e.g., Muller et al., 2012; Bel-

lucci et al., 2013). However, the impact of initialization on the skill in

those regions, though robust (as shown by the agreement between the

different CMIP5 systems) is small and not statistically signiﬁcant with

90% conﬁdence.

The improvement in retrospective North Atlantic variability predictions

from initialization (Smith et al., 2010; Dunstone et al., 2011; Garcia-

Serrano et al., 2012; Hazeleger et al., 2013b) suggests that internal var-

iability was important to North Atlantic variability during the past few

decades. However, the interpretation of the results is complicated by

the fact that the impact on skill varies slightly with the forecast quality

measure used (Figure 11.3; Doblas-Reyes et al., 2013). This has been

attributed to, among other things, the different impact of the predicted

local trends on the scores used (Goddard et al., 2013). Skill in hindcasts

of subpolar Atlantic temperature, which is evident in Figure 11.4, is

972

Chapter 11 Near-term Climate Change: Projections and Predictability

GMST AMV

CMIP5Init CMIP5 NoInit

1960 1970 1980 1990 2000 2010

(ºC)

-0.4-0.4 -0.2-0.2 0.20.20.40.4

1960 1970 1980 1990 2000 2010

-0.2 -0.1 0.10.2-0.2 -0.1 0.10.20.0

Forecast time (yr)

1-42-5 3-64-7 5-86-9

correlation

0.60.9

rmse (ºC)

Forecast time (yr)

0.00

1-42-5 3-64-7 5-8 6-9

0.150.100.05

Forecast time (yr)

0.00.3 0.60.9

1-42-5 3-6 4-7 5-8 6-9

Forecast time (yr)

0.00

1-42-5 3-6 4-7 5-8 6-9

0.150.100.05

Figure 11.3 | Decadal prediction forecast quality of two climate indices. (Top row) Time series of the 2- to 5-year average ensemble-mean initialized hindcast anomalies and the

corresponding non-initialized experiments for two climate indices: global mean surface temperature (GMST, left) and the Atlantic multi-decadal variability (AMV, right). The obser-

vational time series, Goddard Institute of Space Studies (GISS) GMST and Extended Reconstructed Sea Surface Temperature (ERSST) for the AMV, are represented with dark grey

(positive anomalies) and light grey (negative anomalies) vertical bars, where a 4-year running mean has been applied for consistency with the time averaging of the predictions.

Predicted time series are shown for the CMIP5 Init (solid) and NoInit (dotted) simulations with hindcasts started every 5 years over the period 1960–2005. The lower and upper

quartile of the multi-model ensemble are plotted using thin lines. The AMV index was computed as the SST anomalies averaged over the region Equator to 60ºN and 80ºW to 0ºW

minus the SST anomalies averaged over 60ºS to 60ºN. Note that the vertical axes are different for each time series. (Middle row) Correlation of the ensemble mean prediction with

the observational reference along the forecast time for 4-year averages of the three sets of CMIP5 hindcasts for Init (solid) and NoInit (dashed). The one-sided 95% conﬁdence

level with a t distribution is represented in grey. The effective sample size has been computed taking into account the autocorrelation of the observational time series. A two-sided

t test (where the effective sample size has been computed taking into account the autocorrelation of the observational time series) has been used to test the differences between

the correlation of the initialized and non-initialized experiments, but no differences where found statistically signiﬁcant with a conﬁdence equal or higher than 90%. (Bottom row)

Root mean square error (RMSE) of the ensemble mean prediction along the forecast time for 4-year averages of the CMIP5 hindcasts for Init (solid) and NoInit (dashed). A two-

sided F test (where the effective sample size has been computed taking into account the autocorrelation of the observational time series) has been used to test the ratio between

the RMSE of the Init and NoInit, and those forecast times with differences statistically signiﬁcant with a conﬁdence equal or higher than 90% are indicated with an open square.

(Adapted from Doblas-Reyes et al., 2013.)

973

Near-term Climate Change: Projections and Predictability Chapter 11

improved more by initialization than is skill in hindcasting sub-tropical

Atlantic temperature (Garcia-Serrano et al., 2012; Robson et al., 2012;

Hazeleger et al., 2013b). This is relevant because the sub-polar branch

of the AMV is a source of skill for multi-year North Atlantic tropical

storm frequency predictions (Smith et al., 2010). Vecchi et al. (2013)

argued that the nominal improvement in multi-year forecasts of North

Atlantic hurricane frequency was mainly due to persistence.

Sugiura et al. (2009) reported on skill in hindcasting the Paciﬁc Decadal

Oscillation (PDO) in their forecast system. They ascribed the skill to

the interplay between Rossby waves and a clockwise propagation of

ocean heat content anomalies along the Kuroshio–Oyashio extension

and subtropical subduction pathway. However, as Figure 11.4 shows,

the Paciﬁc Ocean has the lowest temperature skill overall, with no con-

sistent impact from initialization. The central North Paciﬁc has zero or

negative skill, which may be due to the relatively large amplitude of

the interannual variability when compared to the long-term trend; the

overall failure to predict the largest warming events (Guémas et al.,

2012) beyond a few months; and differences (compared to AMV) in

how surface temperature and upper ocean heat content interact for

the PDO (Mochizuki et al., 2010; Chikamoto et al., 2012a; Mochizuki

et al., 2012). There is a robust loss of skill due to initialization in the

CMIP5 predictions over the equatorial Paciﬁc (Doblas-Reyes et al.,

2013) that has not been adequately explained.

The AMV is thought to be related to the AMOC (Knight et al., 2005). An

assessment of the impact of observing systems on AMOC predictability

indicates that the recent dense observations of oceanic temperature

and salinity are crucial to constraining the AMOC in one model Zhang

et al. (2007a). The observing system representative of the pre-2000s

was not as effective, indicating that inadequate observations in the

past might also limit the impact of initialization on the predictions.

This has been conﬁrmed by Pohlmann et al. (2013) using decadal pre-

dictions, where they also ﬁnd a positive impact from initialization that

agrees with Hazeleger et al. (2013b). Assessments of the skill of pre-

diction systems to hindcast past variability in the AMOC have been

attempted (Pohlmann et al., 2013; Swingedouw et al., 2013) although

direct measures of the AMOC are far too short to underpin a relia-

ble estimate of skill, and longer histories are poorly known (Matei et

al., 2012a; Vecchi et al., 2012). There is very low conﬁdence in current

estimates of the skill of the AMOC hindcasts. Sustained ocean observa-

tions, such as Argo, a broad global array of temperature/salinity proﬁl-

ing ﬂoats, and Rapid Climate Change-Meridional Overturning Circula-

tion and Heatﬂux Array (RAPID-MOCHA), will be needed to build a

capability to reliably predict the AMOC (Srokosz et al., 2012).

Climate prediction is, by nature, probabilistic. Probabilistic predictions

are expected to be skilful, but also reliable. Decadal predictions should

be evaluated on the basis of whether they give an accurate estimation

of the relative frequency of the predicted outcome. This question can

be addressed using, among other tools, attributes diagrams (Mason,

2004). They measure how closely the forecast probabilities of an event

correspond to the mean probability of observing the event. They are

based on a discrete binning of many forecast probabilities taken over

a given geographical region. Figure 11.5 illustrates the CMIP5 mul-

ti-model Init and NoInit attributes diagrams for predictions of both the

global and North Atlantic SSTs to be in the lower tercile (where the

tercile threshold has been estimated separately for the predictions and

the observations). The diagrams are constructed using predictions for

each grid point over the corresponding area. For perfect reliability the

forecast probability and the frequency of occurrence should be equal,

and the plotted points should lie on the diagonal (solid black line in

the ﬁgure). When the line joining the bullets (the reliability curve) has

positive slope it indicates that as the forecast probability of the event

increases, so does the chance of observing the event. The predictions

therefore can be considered as moderately reliable. However, if the

slope of the curve is less than the slope of the diagonal, then the fore-

cast system is overconﬁdent. If the reliability curve is mainly horizontal,

then the frequency of occurrence of the event does not depend on the

forecast probabilities and the predictions contain no more information

than a random guess. An ideal forecast should have a good resolution

whilst retaining reliability, that is, probability forecasts should be both

sharp and reliable.

In agreement with Corti et al. (2012), CMIP5 multi-model surface tem-

perature predictions are more reliable for the North Atlantic than when

considered over the global oceans, and have a tendency to be over-

conﬁdent particularly for the global oceans (medium conﬁdence). This

means that the multi-model ensemble spread should not be considered

as a robust measure of the actual uncertainty, at least for multi-an-

nual averages. The attributes diagrams already take into account the

systematic error in the simulated variability by estimating separately

the event thresholds for the predictions and the observational refer-

ence. For the North Atlantic, initialization improves the reliability of the

predictions, which translates into an increase of the Brier skill score, the

probabilistic skill measure with respect to a naïve climatological pre-

diction (which is reliable, but not skilful) used to aggregate the infor-

mation in the attributes diagram. However, the uncertainty associated

with these estimates is not negligible. This is due mainly to the small

sample of start dates, which has the consequence that the number of

predictions with a given probability is small to give a robust estimate

of the observed relative frequency (Brocker and Smith, 2007). In addi-

tion to this, there are biases in the reliability diagram itself (Ferro and

Fricker, 2012). These results suggest that the multi-model ensemble

should be used with care when estimating probability forecasts or the

uncertainty of the mean predictions. Given that the models used for the

dynamical predictions are the same as those used for the projections,

this veriﬁcation also provides useful information for the assessment of

the projections (cf. Box 11.2).

The skill in hindcasting precipitation over land (Figure 11.6) is much

lower than the skill in hindcasting temperature over land. This is con-

sistent with predictability studies discussed previously (e.g., Box 11.1)

(high conﬁdence). Several regions, especially in the Northern Hemi-

sphere (NH) and West Africa (Gaetani and Mohino, 2013), have skill

but these regions are not statistically signiﬁcant with a 95% conﬁ-

dence level. The positive skill in hindcasting precipitation can be attrib-

uted mostly to variable RF (high conﬁdence) as initialization improves

the skill very little (Goddard et al., 2013). The areas with positive skill

agree with those where the precipitation trends of multi-annual aver-

ages are the largest (Doblas-Reyes et al., 2013). The skill in areas like

West Africa might be associated with the positive AMV skill, as the

AMV drives interannual variability in precipitation over this region (van

Oldenborgh et al., 2012).

974

Chapter 11 Near-term Climate Change: Projections and Predictability

RMSSS Init for tas at forecast time 2−5yrs

.......

...

−Inf

−40

−30

−20

−10

Inf

RMSE Init/RMSE NoInit for tas at forecast time 2−5yrs

−75

....

0.6

0.7

0.8

0.9

1.1

1.2

1.3

1.4

Figure 11.4 | (a) Root mean square skill score of the near surface air temperature forecast quality for the forecast time 2 to 5 years from the multi-model ensemble mean of the

CMIP5 Init experiment with 5-year interval between start dates over the period 1960–2005. A combination of temperatures from Global Historical Climatology Network/Climate

Anomaly Monitoring System (GHCN/CAMS) air temperature over land, Extended Reconstructed Sea Surface Temperature (ERSST) and Goddard Institute of Space Studies Surface

Temperature Analysis (GISTEMP) 1200 over the polar areas is used as a reference. Black dots correspond to the points where the skill score is statistically signiﬁcant with 95%

conﬁdence using a one-sided F-test taking into account the autocorrelation of the observation minus prediction time series. (b) Ratio between the root mean square error of the

ensemble mean of Init and NoInit. Dots are used for the points where the ratio is signiﬁcantly above or below 1, with 90% conﬁdence using a two-sided F-test taking into account

the autocorrelation of the observation minus prediction time series. Contours are used for areas where the ratio of at least 75% of the single forecast systems is either above or

below one agreeing with the value of the ratio in the multi-model ensemble. Poorly observationally sampled areas are masked in grey. The original model data have been bilinearly

interpolated to the observational grid. The ensemble mean of each forecast system has been estimated before computing the multi-model ensemble mean. (Adapted from Doblas-

Reyes et al., 2013.)

The small amount of statistically signiﬁcant differences found between

the initialized and non-initialized experiments does not necessarily

mean that the impact of the initialization does not have a physical

basis. A comparison of the global mean temperature and AMV fore-

cast quality using 1- and 5-year intervals between start dates (Garcia-

Serrano et al., 2012) suggests that, although a ﬁve-year interval sam-

pling allows an estimate of the level of skill, local maxima as a function

of forecast time might well be due to poor sampling of the start dates

(Garcia-Serrano and Doblas-Reyes, 2012; Kharin et al., 2012; Doblas-

Reyes et al., 2013; Goddard et al., 2013). Several signals, such as the

975

Near-term Climate Change: Projections and Predictability Chapter 11

skill improvement for temperature over the North Atlantic, are robust

in the sense that it is found in more than 75% of forecast system. How-

ever, it is difﬁcult to obtain statistical signiﬁcance with these limited

samples. The low start date sampling frequency is one of the limita-

tions of the core CMIP5 near-term prediction experiment, the other one

being the short length of the period of study, limited by the availability

of observational data. Results estimated with yearly start dates are

more robust than with a 5-year start date frequency. However, even

with 1-year start date frequency, the impact of the initialization is sim-

ilar. The spatial distribution of the skill does not change substantially

with the different start date frequency. The skill and the initialization

impact are both slightly reduced in the results with yearly start dates,

but at the same time the spatial variability is substantially reduced.

The CMIP5 multi-model overestimates the spread of the multi-annual

average temperature (Doblas-Reyes et al., 2013). Figure 11.7 shows

the ratio of the spread around the ensemble mean prediction and the

RMSE of the ensemble mean prediction of Init and NoInit, which in

a well-calibrated system is expected to be close to 1. However, the

ratio is overestimated over the North Atlantic, the Indian Ocean and

the Arctic, and underestimated over the North Paciﬁc and most conti-

nental areas, suggesting that the CMIP5 systems do not discriminate

0 0.2 0.4 0.6 0.8 1

Forecast probability

0.2

0.4

0.6

0.8

bserved frequency

00.2 0.40.6 0.81

Forecast probability

0.2

0.4

0.6

0.8

bserved frequency

0 0.2 0.4 0.6 0.8 1

Forecast probability

0.2

0.4

0.6

0.8

bserved frequency

00.2 0.40.6 0.81

Forecast probability

0.2

0.4

0.6

0.8

bserved frequency

Figure 11.5 | Attributes diagram for the CMIP5 multi-model decadal initialized (a and c) and non-initialized (b and d) hindcasts for the event ‘surface air temperature anomalies

below the lower tercile over (a) and (b) the global oceans (60ºN to 60ºS) and (c) and (d) the North Atlantic (87.5ºN to 30ºN, 80ºW to 10ºW) for the forecast time 2 to 5 years. The red

bullets in the ﬁgure correspond to the number of probability bins (10 in this case) used to estimate forecast probabilities. The size of the bullets represents the number of forecasts

in a speciﬁc probability category and is a measure of the sharpness (or variance of the forecast probabilities) of the predictions. The blue horizontal and vertical lines indicate the

climatological frequency of the event in the observations and the mean forecast probability, respectively. Grey vertical bars indicate the uncertainty in the observed frequency for

each probability category estimated at 95% level of conﬁdence with a bootstrap resampling procedure based on 1000 samples. The longer the bars, the more the vertical position

of the bullets may change as new hindcasts become available. The black dashed line separates skilful from unskilled regions in the diagram in the Brier skill score sense. The Brier

skill score with respect to the climatological forecast is drawn in the top left corner of each panel. (Adapted from Corti et al., 2012.)

976

Chapter 11 Near-term Climate Change: Projections and Predictability

RMSSS Init for prlr at forecast time 2−5yrs

−Inf

−20

−15

−10

−5

Inf

RMSE Init/RMSE NoInit for prlr at forecast time 2−5yrs

−75

0.6

0.8

0.9

0.95

1.05

1.1

1.2

1.4

Figure 11.6 | (a) Root mean square skill score for precipitation hindcasts for the forecast time 2 to 5 years from the multi-model ensemble mean of the CMIP5 Init experiment

with 5-year interval between start dates over the period 1960–2005. Global Precipitation Climatology Centre (GPCC) precipitation is used as a reference. Black dots correspond to

the points where the skill score is statistically signiﬁcant with 95% conﬁdence using a one-sided F-test taking into account the autocorrelation of the observation minus prediction

time series. (b) Ratio between the root mean square error of the ensemble mean of Init and NoInit. Dots are used for the points where the ratio is signiﬁcantly above or below one

with 90% conﬁdence using a two-sided F-test taking into account the autocorrelation of the observation minus prediction time series. Contours are used for areas where the ratio

of at least 75% of the single forecast systems is either above or below 1, agreeing with the value of the ratio in the multi-model ensemble. The model original data have been

bilinearly interpolated to the observational grid. The ensemble mean of each forecast system has been estimated before computing the multi-model ensemble mean. (Adapted from

Doblas-Reyes et al., 2013.)

between the regions where the spread should be reduced according to

the RMSE level in the area. These results are found for both the Init and

NoInit ensembles and agree with the overconﬁdence of the probability

forecasts shown in Figure 11.6 (Corti et al., 2012). The spread overes-

timation also agrees with the results found for the indices illustrate

in Figure 11.3 (Doblas-Reyes et al., 2013). The spread overestimation

points to the need for a careful interpretation of current ensemble and

probabilistic climate information for climate adaptation and services.

The skill of extreme daily temperature and precipitation in multi-annu-

al time scales has also been assessed (Eade et al., 2012; Hanlon et al.,

2013). There is little improvement in skill with the initialization beyond

977

Near-term Climate Change: Projections and Predictability Chapter 11

Spread/RMSE Init for tas at forecast time 2−5yrs

0.3

0.6

0.8

0.9

1.2

1.5

Figure 11.7 | Ratio between the surface temperature spread around the ensemble mean and the root mean square error (RMSE) of the ensemble-mean prediction of Init and

NoInit for the forecast time 2 to 5 years with 5-year interval between start dates over the period 1960–2005. A combination of temperatures from Global Historical Climatology

Network/Climate Anomaly Monitoring System (GHCN/CAMS) air temperature over land, Extended Reconstructed Sea Surface Temperature (ERSST) v3b over sea and Goddard Insti-

tute of Space Studies Surface Temperature Analysis (GISTEMP) 1200 over the polar areas is used as a reference to compute the RMSE. (Adapted from Doblas-Reyes et al., 2013.)

the ﬁrst year, suggesting that skill then arises largely from the varying

external forcing. The skill for extremes is generally similar to, but slight-

ly lower than, that for the mean.

Responding to the increases in decadal skill in certain regions due to

initialization, a coordinated quasi-operational decadal prediction ini-

tiative has been organized (Smith et al., 2013b). The forecast systems

participating in the initiative are based on those of CMIP5 and have

been evaluated for forecast quality. Statistical predictions are also

included in the initiative. The most recent forecast shows (compared

to the projections) substantial warming of the north Atlantic subpo-

lar gyre, cooling of the north Paciﬁc throughout the next decade and

cooling over most land and ocean regions and in the global average

out to several years ahead. However, in the absence of explosive or

frequent volcanic eruptions, global surface temperature is predicted to

continue to rise and, to a certain degree, recover from the reduced rate

of warming (see Box 9.2).

11.2.3.5 Realizing Potential

Although idealized model experiments show considerable promise for

predicting internal variability, realizing this potential is a challenging

task. There are three main hurdles: (1) the limited availability of data

to initialize and verify predictions, (2) limited progress in initialization

techniques for decadal predictions and (3) dynamical model shortcom-

ings that require validating how the simulated variance compares with

the observed variance.

It is expected that the availability of temperature and salinity data in

the top 2 km of the ocean through the enhanced global deployment of

Argo ﬂoats will give a step change in our ability to initialize and pre-

dict ocean heat and density anomalies (Zhang et al., 2007a; Dunstone

and Smith, 2010). Another important advancement is the availabili-

ty of highly accurate altimetry data, made especially useful after the

launching of TOPography EXperiment (TOPEX)/Poseidon in 1992. Argo

and altimeter data became available only in 2000 and 1992 respec-

tively, so an accurate estimate of their impact on real forecasts has to

wait (Dunstone and Smith, 2010). In all cases, both the length of the

observational data sets and the reduced coverage of the data avail-

able, especially before 2000, are serious limitations to obtain robust

estimates of forecast quality.

Improved initialization of other aspects such as sea ice, snow cover,

frozen soil and soil moisture, may also have potential to contribute to

predictive skill beyond the seasonal time scale. This could be investi-

gated, for example by using measurements of soil moisture from the

Soil Moisture and Ocean Salinity (SMOS) satellite launched in 2009, or

by initializing sea ice thickness with observations from the CryoSat-2

satellite launched in 2010. Along the same line, understanding the

links between the initialization and the correct prediction of both the

internal and external variability should help improving forecast quality

(Solomon et al., 2011).

Many of the current decadal prediction systems use relatively simple

initialization schemes and do not adopt fully coupled initialization/

ensemble generation schemes. Assimilation schemes offer opportuni-

ties for fully coupled initialization including assimilation of variables

such as sea ice, snow cover and soil moisture, although they present

technically and scientiﬁcally challenging problems. This approach has

been tested in schemes like four-dimensional variational data assim-

ilation (4DVAR; Sugiura et al., 2008) and the ensemble Kalman ﬁlter

(Keppenne et al., 2005; Zhang et al., 2007a).

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Chapter 11 Near-term Climate Change: Projections and Predictability

Bias correction is used to reduce the effects of model drift, but the

nonlinearity in the climate system (e.g., Power (1995) might limit the

effectiveness of bias correction and thereby reduce forecast quality.

Understanding and reducing both drift and systematic errors is impor-

tant (Palmer and Weisheimer, 2011), as it is also for seasonal-to-inter-

annual climate prediction and for climate change projections. While

improving models is the highest priority, efforts to quantify the degree

of interference between model bias and predictive signals should not

be overlooked.

11.3 Near-term Projections

11.3.1 Introduction

In this section the outlook for global and regional climate up to

mid-century is assessed, based on climate model projections. In con-

trast to the predictions discussed in Section 11.2, these projections are

not initialized using observations; instead, they are initialized from

historical simulations of the evolution of climate from pre-industrial

conditions up to the present. The historical simulations are forced by

estimates of past anthropogenic and natural climate forcing agents,

and the projections are obtained by forcing the models with scenari-

os for future climate forcing agents. Major use is made of the CMIP5

model experiments forced by the Representative Concentration Path-

way (RCP) scenarios discussed in Chapters 1 and 8. Projections of cli-

mate change in this and subsequent chapters are expressed relative

to the reference period: 1986–2005. In this chapter most emphasis is

given to the period 2016–2035, but some information on changes pro-

jected before and after this period (up to mid-century) is also provided.

Longer-term projections are assessed in Chapters 12 and 13.

Key assessment questions addressed in this section are: What is the

externally forced signal of near-term climate change, and how large

is it compared to natural internal variability? From the point of view

of climate impacts, the absolute magnitude of climate change may

in some instances be less important than the magnitude relative to

the local level of natural internal variability. Because many systems

are naturally adapted to a background level of variability, it may be

changes that move outside of this range that are most likely to trigger

impacts that are unprecedented in the recent past (e.g., Lobell and

Burke (2008) for crops).

An important conclusion of the AR4 (Section 10.3.1) was that near-

term climate projections are not very sensitive to plausible alternative

non-mitigation scenarios for GHG concentrations (speciﬁcally the Spe-

cial Report on Emission Scenarios (SRES) scenarios; comparison with

RCP scenarios is discussed in Chapter 1), that is, in the near term, dif-

ferent scenarios give rise to similar magnitudes and patterns of climate

change. (Note, however, that some impacts may be more sensitive.) For

this reason, most of the projections presented in this chapter are based

on one speciﬁc RCP scenario, RCP4.5. RCP4.5 was chosen because of

its intermediate GHG forcing. However, there is greater sensitivity to

other forcing agents, in particular anthropogenic aerosols (e.g., Chalm-

ers et al., 2012). Consequently, a further question addressed in this

section (especially in Section 11.3.6.1) is: To what extent are near-term

climate projections sensitive to alternative scenarios for anthropogenic

forcing? Note ﬁnally that a great deal of additional information on

near-term projections is provided in Annex I.

11.3.1.1 Uncertainty in Near-term Climate Projections

As discussed in Chapters 1 (Section 1.4) and 12 (Section 12.2), climate

projections are subject to several sources of uncertainty. Here three

main sources are distinguished. The ﬁrst arises from natural internal

variability, which is intrinsic to the climate system, and includes phe-

nomena such as variability in the mid-latitude storm tracks and the

ENSO. The existence of internal variability places fundamental limits

on the precision with which future climate variables can be project-

ed. The second is uncertainty concerning the past, present and future

forcing of the climate system by natural and anthropogenic forcing

agents such as GHGs, aerosols, solar forcing and land use change. Forc-

ing agents may be speciﬁed in various ways, for example, as emissions

or as concentrations (see Section 12.2). The third is uncertainty related

to the response of the climate system to the speciﬁed forcing agents.

Quantifying the uncertainty that arises from each of the three sources is

an important challenge. For projections, no attempt is made to predict

the evolution of the internal variability. Instead, the statistics of this

variability are included as a component of the uncertainty associated

with a projection. The magnitude of internal variability can be estimat-

ed from observations (Chapters 2, 3 and 4) or from climate models

(Chapter 9). Challenges arise in estimating the variability on decadal

and longer time scales, and for rare events such as extremes, as obser-

vational records are often too short to provide robust estimates.

Uncertainty concerning the past forcing of the climate system arises

from a lack of direct or proxy observations, and from observational

errors. This uncertainty can inﬂuence future projections of some vari-

ables (particularly large-scale ocean variables) for years or even dec-

ades ahead (e.g., Meehl and Hu, 2006; Stenchikov et al., 2009; Gregory,

2010). Uncertainty about future forcing arises from the inability to pre-

dict future anthropogenic emissions and land use change, and natural

forcings (e.g., volcanoes), and from uncertainties concerning carbon

cycle and other biogeochemical feedbacks (Chapters 6, 12 and Annex

II.4.1). The uncertainties in future anthropogenic forcing are typically

investigated through the development of speciﬁc scenarios (e.g., for

emissions or concentrations), such as the RCP scenarios (Chapters 1

and 8). Different scenarios give rise to different climate projections,

and the spread of such projections is commonly described as scenario

uncertainty. The sensitivity of climate projections to alternative sce-

narios for future anthropogenic emissions is discussed especially in

Section 11.3.6.1

To project the climate response to speciﬁed forcing agents, climate

models are required. The term model uncertainty describes uncertainty

about the extent to which any particular climate model provides an

accurate representation of the real climate system. This uncertainty

arises from approximations required in the development of models.

Such approximations affect the representation of all aspects of the

climate including natural internal variability and the response to exter-

nal forcings.As discussed in Chapter 1 (Section 1.4.2), the term model

uncertainty is sometimes used in a narrower sense to describe the

spread between projections generated using different models or model

979

Near-term Climate Change: Projections and Predictability Chapter 11

(a) (b)

Temperature change relative to 19862005 [K]

Year

Sources of uncertainty in projected global mean temperature

1960 1980 2000 2020 2040 2060 2080 2100

1

0.5

0.5

1.5

2.5

3.5

4.5

Observations (3 datasets)

Internal variability

Model spread

RCP scenario spread

Historical model spread

2020 2040 2060 2080 210

0.5

1.5

Signaltouncertainty ratio

Regional decadal mean temperature

Year

Global

Europe

Australasia

North America

South America

Africa

East Asia

Year

Fraction of total variance [%]

Uncertainty in Global decadal mean ANN temperature

2020 2040 2060 2080 2100

100

Year

Fraction of total variance [%]

Uncertainty in Europe decadal mean DJF temperature

2020 2040 2060 2080 210

100

(e) (f)

Year

Fraction of total variance [%]

Uncertainty in East Asia decadal mean JJA precipitation

2020 2040 2060 2080 2100

100

Year

Fraction of total variance [%]

Uncertainty in Europe decadal mean DJF precipitation

2020 2040 2060 2080 210

100

Figure 11.8 | Sources of uncertainty in climate projections as afunction of lead time based on an analysis of CMIP5 results. (a)Projections of global mean decadal mean surface

air temperature to 2100together with a quantiﬁcation of the uncertainty arising from internalvariability (orange), model spread (blue) and RCP scenario spread (green). (b) Signal-

to-uncertainty ratio forvarious global and regional averages. The signal is deﬁned as thesimulated multi-model mean change in surface air temperature relative tothe simulated

mean surface air temperature in the period 1986–2005, andthe uncertainty is deﬁned as the total uncertainty. (c–f) The fraction of variance explained by each source of uncertainty

for:global mean decadal and annual mean temperature (c), European (30°N to 75°N,10°W to 40°E) decadal mean boreal winter (December to February) temperature (d)and

precipitation (f), and East Asian (5°N to 45°N, 67.5°E to 130°E) decadal meanboreal summer (June to August) precipitation (e). See text and Hawkins and Sutton (2009) and

Hawkins and Sutton (2011) for further details.

980

Chapter 11 Near-term Climate Change: Projections and Predictability

versions; however, such a measure is crude as it takes no account of

factors such as model quality (Chapter 9) or model independence. The

term model response uncertainty is used here to describe the dimen-

sion of model uncertainty that is directly related to the response to

external forcings. To obtain projections of extreme events such as trop-

ical cyclones, or regional phenomena such as orographic rainfall, it is

sometimes necessary to employ a dynamical or statistical downscaling

procedure. Such downscaling introduces an additional dimension of

model uncertainty (e.g., Alexandru et al., 2007).

The relative importance of the different sources of uncertainty depends

on the variable of interest, the space and time scales involved (Sec-

tion 10.5.4.3 of Meehl et al. (2007b)), and the lead-time of the projec-

tion. Figure 11.8 provides an illustration of these dependencies based

on an analysis of CMIP5 projections (following Hawkins and Sutton,

2009, 2011;Yip et al., 2011). In this example, the forcing-related uncer-

tainty is estimated using the spread of projections for different RCP

scenarios (i.e., scenario uncertainty), while the spread among differ-

ent models for individual RCP scenarios is used as a measure of the

model response uncertainty. Internal variability is estimated from the

models as in Hawkins and Sutton (2009). Key points are: (1) the uncer-

tainty in near-term projections is dominated by internal variability and

model spread. This ﬁnding provides some of the rationale for consid-

ering near-term projections separately from long-term projections.

Note, however, that the RCP scenarios do not sample the full range

of uncertainty in future anthropogenic forcing, and that uncertainty in

aerosol forcings in particular may be more important than is suggested

by Figure 11.8 (see Section 11.3.6.1); (2) internal variability becomes

increasingly important on smaller space and time scales; (3) for pro-

jections of precipitation, scenario uncertainty is less important and (on

regional scales) internal variability is generally more important than for

projections of surface air temperature; (4) the full model uncertainty

may well be larger or smaller than the model spread due to common

errors or unrealistic models.

A key quantity for any climate projection is the signal-to-noise (S/N)

ratio (Christensen et al., 2007), where the ‘signal’ is a measure of the

amplitude of the projected climate change, and the noise is a measure

of the uncertainty in the projection. Higher S/N ratios indicate more

robust projections of change and/or changes that are large relative

to background levels of variability. Depending on the purpose, it may

be useful to identify the noise with the total uncertainty, or with a

speciﬁc component such as the internal variability. The evolution of the

S/N ratio with lead time depends on whether the signal grows more

rapidly than the noise, or vice versa. Figure 11.8 (top right) shows that,

when the noise is identiﬁed with the total uncertainty, the S/N ratio

for surface air temperature is typically higher at lower latitudes and

has a maximum at a lead time of a few decades (Cox and Stephenson,

2007; Hawkins and Sutton, 2009). The former feature is primarily a

consequence of the greater amplitude of internal variability in mid-lat-

itudes. The latter feature arises because over the ﬁrst few decades,

when scenario uncertainty is small, the signal grows most rapidly, but

subsequently, the contribution from scenario uncertainty grows more

rapidly than does the signal, so the S/N ratio falls. See Hawkins and

Sutton (2009, 2011) for further details.

11.3.2 Near-term Projected Changes in the Atmosphere

and Land Surface

11.3.2.1 Surface Temperature

11.3.2.1.1 Global mean surface air temperature

Figure 11.9 (a) and (b) show CMIP5 projections of global mean surface

air temperature under RCP4.5. The 5 to 95% range for the projected

anomaly for the period 2016–2035, relative to the reference period

1986–2005, is 0.47°C to 1.00°C (see also Table 12.2). However, as

discussed in Section 11.3.1.1, this range provides only a very crude

measure of uncertainty, and there is no guarantee that the real world

must lie within this range. Obtaining better estimates is an important

challenge. One approach involves initializing climate models using

observations, as discussed in Section 11.2. Figure 11.9 (b) compares

multi-model initialized climate predictions (8 models from Smith et al.,

2013b), initialized in 2011; 14 CMIP5 decadal prediction experiment

models following the methodology of Meehl and Teng (2012), initial-

ized in 2006 with the ‘raw’ uninitialized CMIP5 projections. The 5 to

95% range for both sets of initialized predictions is cooler (by about

15% for the median values) than the corresponding range for the raw

projections, particularly at the upper end. The differences are partly a

consequence of initializing the models in a state that is cool (in com-

parison to the median of the raw projections) as a result of the recent

hiatus in global mean surface temperature rise (see Box 9.2). However,

it is not yet possible to attribute all of the reasons with conﬁdence

because the raw projections are based on a different, and larger, set

of models than the initialized predictions, and because of uncertainties

related to the bias adjustment of the initialized predictions (Goddard

et al., 2013; Meehl et al., 2013d)

Another approach to making projections involves weighting models

according to some measure of their quality (see Chapter 9). A speciﬁc

approach of this type, known as Allen, Stott and Kettleborough (ASK)

(Allen et al., 2000; Stott and Kettleborough, 2002), is based on the use

of results from detection and attribution studies (Chapter 10), in which

the ﬁt between observations and model simulations of the past is used

to scale projections of the future. ASK requires speciﬁc simulations to

be carried out with individual forcings (e.g., anthropogenic GHG forc-

ing alone), and only some of the centres participating in CMIP5 have

carried out the necessary integrations. Biases in ASK-derived projec-

tions may arise from errors in the speciﬁed forcings, or in the simulated

patterns of response, and/or from nonlinearities in the responses to

forcings.

Figure 11.9c shows the projected range of global mean surface air tem-

perature change derived using the ASK approach for RCP4.5 (Stott and

G. Jones, 2012; Stott et al., 2013) applied to six models and compares

this with the range derived from the 42 CMIP5 models. In this case

decadal means are shown. The 5 to 95% conﬁdence interval for the

projected temperature anomaly for the period 2016–2035, based on

the ASK method, is 0.39°C to 0.87°C. As for the initialized predictions

shown in Figure 11.9b, both the lower and upper values are below the

corresponding values obtained from the raw CMIP5 results, although

there is substantial overlap between the two ranges. The relative

cooling of the ASK results is directly related to evidence presented in

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Near-term Climate Change: Projections and Predictability Chapter 11

Figure 11.9 | (a) Projections of global mean, annual mean surface air temperature 1986–2050 (anomalies relative to 1986–2005) under RCP4.5 from CMIP5 models (blue lines,

one ensemble member per model), with four observational estimates: Hadley Centre/Climate Research Unit gridded surface temperature data set 3 (HadCRUT3: Brohan et al., 2006);

European Centre for Medium range Weather Forecast (ECMWF) interim reanalysis of the global atmosphere and surface conditions (ERA-Interim: Simmons et al., 2010); Goddard

Institute of Space Studies Surface Temperature Analysis (GISTEMP: Hansen et al., 2010); National Oceanic and Atmospheric Administration (NOAA: Smith et al. (2008) for the period

1986–2011 (black lines). (b) As in (a) but showing the 5 to 95% range (grey and blue shades, with the multi-model median in white) of annual mean CMIP5 projections using one

ensemble member per model from RCP4.5 scenario, and annual mean observational estimates (solid black line). The maximum and minimum values from CMIP5 are shown by the

grey lines. Red hatching shows 5 to 95% range for predictions initialized in 2006 for 14 CMIP5 models applying the Meehl and Teng (2012) methodology. Black hatching shows the

5 to 95% range for predictions initialized in 2011 for eight models from Smith et al. (2013b). (c) As (a) but showing the 5 to 95% range (grey and blue shades, with the multi-model

median in white) of decadal mean CMIP5 projections using one ensemble member per model from RCP4.5 scenario, and decadal mean observational estimates (solid black line).

The maximum and minimum values from CMIP5 are shown by the grey lines. The dashed black lines show an estimate of the projected 5 to 95% range for decadal mean global

mean surface air temperature for the period 2016–2040 derived using the ASK methodology applied to six CMIP5 GCMs. (From Stott et al., 2013.) The red line shows a statistical

prediction based on the method of Lean and Rind (2009), updated for RCP4.5.

1990 2000 2010 2020 2030 2040 2050

−0.5

0.5

1.5

2.5

Temperature anomaly [

Global mean temperature projections (RCP 4.5), relative to 1986−2005

All ranges are 5−95%

Annual means

RCP 4.5Historical

(a)

Historical (42 models, 1 ensemble member per model)

RCP 4.5 (42 models, 1 ensemble member per model)

Observations (4 datasets)

Observational uncertainty (HadCRUT4)

Meehl & Teng (2012, updated)

Observations

Smith et al. (2012) forecast

RCP 4.5Historical

Temperature anomaly [

(b)

Annual means

1990 2000 2010 2020 2030 2040 2050

−0.5

0.5

1.5

2.5

RCP 4.5 (42 models, 1 ensemble member per model)

RCP 4.5 (min−max, 107 ensemble members)

RCP 4.5Historical

(c)

Year

Temperature anomaly [

Decadal means

1990 2000 2010 2020 2030 2040 2050

−0.5

0.5

1.5

2.5

RCP 4.5 (42 models, 1 ensemble member per model)

RCP 4.5 (min−max, 107 ensemble members)

Stott et al. (2013) constrained projections

Lean & Rind (2009, updated)

Observations

982

Chapter 11 Near-term Climate Change: Projections and Predictability

Seasonal mean air temperature change (RCP4.5: 2016-2035)

Figure 11.10 | CMIP5 multi-model ensemble mean of projected changes in December, January and February and June, July and August surface air temperature for the period

2016–2035 relative to 1986–2005 under RCP4.5 scenario (left panels). The right panels show an estimate of the model-estimated internal variability (standard deviation of 20-year

means). Hatching in left-hand panels indicates areas where projected changes are small compared to the internal variability (i.e., smaller than one standard deviation of estimated

internal variability), and stippling indicates regions where the multi-model mean projections deviate signiﬁcantly from the simulated 1986–2005 period (by at least two standard

deviations of internal variability) and where at least 90% of the models agree on the sign of change. The number of models considered in the analysis is listed in the top-right portion

of the panels; from each model one ensemble member is used. See Box 12.1 in Chapter 12 for further details and discussion. Technical details are in Annex I.

Chapter 10 (Section 10.3.1) that ‘This provides evidence that some

CMIP5 models have a higher transient response to GHGs and a larger

response to other anthropogenic forcings (dominated by the effects of

aerosols) than the real world (medium conﬁdence).’ The ASK results

and the initialised predictions both suggest that those CMIP5 models

that warm most rapidly over the period (1986–2005) to (2016–2035)

may be inconsistent with the observations. This possibility is also sug-

gested by comparing the models with the observed rate of warming

since 1986—see Box 9.2 for a full discussion of this comparison. Lastly,

Figure 11.9 also shows a statistical prediction for global mean surface

air temperature, using the method of Lean and Rind (2009), which uses

multiple linear regression to decompose observed temperature vari-

ations into distinct components. This prediction is very similar to the

CMIP5 multi-model median.

The projections shown in Figure 11.9 assume the RCP4.5 scenario

and use the 1986–2005 reference period. In Section 11.3.6 addition-

al uncertainties associated with future forcing, climate responses and

sensitivity to the choice of reference period, are discussed. An overall

assessment of the likely range for future global mean surface air tem-

perature is provided in Section 11.3.6.3.

For the remaining projections in this chapter the spread among the

CMIP5 models is used as a simple, but crude, measure of uncertainty.

The extent of agreement between the CMIP5 projections provides

rough guidance about the likelihood of a particular outcome. But—as

partly illustrated by the discussion above—it must be kept ﬁrmly in

mind that the real world could fall outside of the range spanned by

these particular models. See Section 11.3.6 for further discussion.

11.3.2.1.2 Regional and seasonal patterns of surface warming

The geographical pattern of near-term surface warming simulated

by the CMIP5 models (Figure 11.10) is consistent with previous IPCC

reports in a number of key aspects, although weaknesses in the ability

of current models to capture observed regional trends (Box 11.2) must

be kept in mind. First, temperatures over land increase more rapidly

than over sea (e.g., Manabe et al., 1991; Sutton et al., 2007). Process-

es that contribute to this land–sea warming contrast include differ-

ent local feedbacks over ocean and land and changes in atmospheric

energy transport from ocean to land regions (e.g., Lambert and Chiang,

2007; Vidale et al., 2007; Shimpo and Kanamitsu, 2009; Fasullo, 2010;

Boer, 2011; Joshi et al., 2011).

Second, the projected warming in wintertime shows a pronounced

polar ampliﬁcation in the NH (see Box 5.1). This feature is found in

virtually all coupled model projections, but the CMIP3 simulations

generally appeared to underestimate this effect in comparison to

983

Near-term Climate Change: Projections and Predictability Chapter 11

observations (Stroeve et al., 2007; Screen and Simmonds, 2010). Sever-

al studies have isolated mechanisms behind this ampliﬁcation, which

include reductions in snow cover and retreat of sea ice (e.g., Serreze

et al., 2007; Comiso et al., 2008); changes in atmospheric and ocean-

ic circulations (Chylek et al., 2009, 2010; Simmonds and Keay, 2009);

presence of anthropogenic soot in the Arctic environment (Flanner et

al., 2007; Quinn et al., 2008; Jacobson, 2010; Ramana et al., 2010); and

increases in cloud cover and water vapour (Francis, 2007; Schweiger et

al., 2008). Most studies argue that changes in sea ice are central to the

polar ampliﬁcation—see Section 11.3.4.1 for further discussion. Fur-

ther information about the regional changes in surface air temperature

projected by the CMIP5 models is presented in Annex I.

As discussed in Sections 11.1 and 11.3.1, the signal of climate change

is emerging against a background of natural internal variability. The

concept of ‘emergence’ describes the magnitude of the climate change

signal relative to this background variability, and may be useful for

some climate impact assessments (e.g., AR4, Chapter 11, Table 11.1;

Mahlstein et al., 2011; Hawkins and Sutton, 2012; see also FAQ 10.2).

However, it is important to recognize that there is no single metric of

emergence. It depends on user-driven choices of variable, space and

time scale, of the baseline relative to which changes are measured

(e.g., pre-industrial versus recent climate) and of the threshold at

which emergence is deﬁned.

Figure 11.11 quantiﬁes the ‘Time of Emergence’ (ToE) of the mean

warming signal relative to the recent past (1986–2005), based on the

CMIP5 RCP4.5 projections, using a spatial resolution of 2.5° latitude

× 2.5° longitude, the standard deviation of interannual variations as

the measure of internal variability, and a signal-to-noise threshold of 1.

Because of the dependence on user-driven choices, the most important

information in Figure 11.11 is the geographical and seasonal variation

in ToE, seen in the maps, and the variation in ToE between models,

shown in the histograms. Consistent with Mahlstein et al. (2011), the

earliest ToE is found in the tropics, with ToE in mid-latitudes typically a

decade or so later. Over North Africa and Asia, earlier ToE is found for

the warm half-year (April to September) than for the cool half-year.

Earlier ToE is generally found for larger space and time scales, because

the variance of natural internal variability decreases with averaging

(Section 11.3.1.1 and AR4, Section 10.5.4.3). This tendency can be seen

in Figure 11.11 by comparing the median value of the histograms for

area averages with the area average of the median ToE inferred from

the maps (e.g., for Region 2). The large range of values for ToE implied

by different CMIP5 models, which can be as much as 30 years, is a con-

sequence of differences in both the magnitude of the warming signal

simulated by the models (i.e., uncertainty in the climate response, see

Section 11.3.1.1) and in the amplitude of simulated natural internal

variability (Hawkins and Sutton, 2012).

Figure 11.11 | Time of Emergence (ToE) of signiﬁcant local warming derived from 37 CMIP5 models under the RCP4.5 scenario. Warming is quantiﬁed as the half-year mean

temperature anomaly relative to 1986–2005, and the noise as the standard deviation of half-year mean temperature derived from a control simulation of the relevant model. Central

panels show the median time at which the signal-to-noise ratio exceeds a threshold value of 1 for (left) the October to March half year and (right) the April to September half year,

using a spatial resolution of 2.5° × 2.5°. Histograms show the distribution of ToE for area averages over the regions indicated obtained from the different CMIP5 models. Full details

of the methodology may be found in Hawkins and Sutton (2012).

ONDJFM

Time of

Emergence

S/N > 1

37 models

2000 2010 2020 2030 2040 2050

Region 1

NUMBER OF MODELS

2000 2010 2020 2030 2040 2050

Region 2ONDJFM

2000 2010 2020 2030 2040 2050

Region 3

AMJJAS

2010

2020

2030

2040

2000 2010 2020 2030 2040 2050

Region 1

NUMBER OF MODELS

2000 2010 2020 2030 2040 2050

Region 2AMJJAS

2000 2010 2020 2030 2040 2050

Region 3

984

Chapter 11 Near-term Climate Change: Projections and Predictability

In summary, it is very likely that anthropogenic warming of surface air

temperature over the next few decades will proceed more rapidly over

land areas than over oceans, and that the warming over the Arctic in

winter will be greater than the global mean warming over the same

period. Relative to background levels of natural internal variability,

near-term increases in seasonal mean and annual mean temperatures

are expected to occur more rapidly in the tropics and subtropics than

in mid-latitudes (high conﬁdence).

11.3.2.2 Free Atmospheric Temperature

Changes in zonal mean temperature for the near-term period (2016–

2035 compared to the base period 1986–2005) for the multi-model

CMIP5 ensemble show a pattern similar to that in the CMIP3, with

warming in the troposphere and cooling in the stratosphere of a couple

of degrees that is signiﬁcant even in the near term period. There is

relatively greater warming in the tropical upper troposphere and

northern high latitudes. A more detailed assessment of observed and

simulated changes in free atmospheric temperatures can be found in

Sections 10.3.1.2.1 and 12.4.3.2.

11.3.2.3 The Water Cycle

As discussed in the AR4 (Section 10.3.6; Meehl et al., 2007b), the IPCC

Technical Paper on Climate Change and Water (Bates et al., 2008)

and the Special Report on Managing the Risks of Extreme Events and

Disasters to Advance Climate Change Adaptation (Seneviratne et al.,

2012), a general intensiﬁcation of the global hydrological cycle, and

of precipitation extremes, are expected for a future warmer climate

(e.g., (Huntington, 2006; Williams et al., 2007; Wild et al., 2008; Chou

et al., 2009; Dery et al., 2009; O’Gorman and Schneider, 2009; Lu and

Fu, 2010; Seager et al., 2010; Wu et al., 2010; Kao and Ganguly, 2011;

Muller et al., 2011; Durack et al., 2012). In this section, projected

changes in the time-mean hydrological cycle are discussed; changes in

extremes, are presented in Section 11.3.2.5 while processes underlying

precipitation changes are treated in Chapter 7.

11.3.2.3.1 Changes in precipitation

AR4 projections of the spatial patterns of precipitation change in

response to GHG forcing (Chapter 10, Section 10.3.2) showed consist-

ency between models on the largest scales (i.e., zonal means) but large

uncertainty on smaller scales. The consistent pattern was characterized

by increases at high latitudes and in wet regions (including the maxima

in mean precipitation found in the tropics), and decreases in dry regions

(including large parts of the subtropics). Large uncertainties in the sign

of projected change were seen especially in regions located on the

borders between regions of increases and regions of decreases. More

recent research has highlighted the fact that if models agree that the

projected change is small in some sense relative to internal variability,

then agreement on the sign of the change is not expected (Tebaldi et

al., 2011; Power et al., 2012). This recognition led to the identiﬁca-

tion of subregions within the border regions, where models agree that

projected changes are either zero or small (Power et al., 2012). This,

and other considerations, also led to the realization that the consensus

among models on precipitation projections is more widespread than

might have been inferred on the basis of the projections described in

the AR4 (Power et al., 2012). Information on the reliability of near-

term projections can also be obtained from veriﬁcation of past regional

trends (Räisänen (2007); Box 11.2)

Since the AR4 there has also been considerable progress in under-

standing the factors that govern the spatial pattern of change in pre-

cipitation (P), precipitation minus evaporation (P – E), and inter-model

differences in these patterns. The general pattern of wet-get-wetter

(also referred to as ‘rich-get-richer’, e.g., Held and Soden, 2006; Chou

et al., 2009; Allan et al., 2010) and dry-get-drier has been conﬁrmed,

although with deviations in some dry regions at present that are pro-

jected to become wetter by some models, e.g., Northeast Brazil in

austral summer and East Africa (see Annex I). It has been demon-

strated that the wet-get-wetter pattern implies an enhanced season-

al precipitation range between wet and dry seasons in the tropics,

and enhanced inter-hemispheric precipitation gradients (Chou et al.,

2007).

It has recently been proposed that analysis of the energy budget, pre-

viously applied only to the global mean, may provide further insights

into the controls on regional changes in precipitation (Levermann et

al., 2009; Muller and O’Gorman, 2011; O’Gorman et al., 2012). Muller

and O’Gorman (2011) argue in particular that changes in radiative and

surface sensible heat ﬂuxes provide a guide to the local precipitation

response over land. Projected and observed patterns of oceanic pre-

cipitation change in the tropics tend to follow patterns of SST change

because of local changes in atmospheric stability, such that regions

warming more than the tropics as a whole tend to exhibit an increase

in local precipitation, while regions warming less tend to exhibit

reduced precipitation (Johnson and Xie, 2010; Xie et al., 2010).

AR4 (Section 10.3.2 and Chapter 11) showed that, especially in

the near term, and on regional or smaller scales, the magnitude of

projected changes in mean precipitation was small compared to the

magnitude of natural internal variability (Christensen et al., 2007).

Recent work has conﬁrmed this result, and provided more quantiﬁ-

cation (e.g., Hawkins and Sutton, 2011; Hoerling et al., 2011; Rowell,

2011; Deser et al., 2012; Power et al., 2012). Hawkins and Sutton

(2011) presented further analysis of CMIP3 results and found that, on

spatial scales of the order of 1000 km, internal variability contributes

50 to 90% of the total uncertainty in all regions for projections of dec-

adal and seasonal mean precipitation change for the next decade, and

is the most important source of uncertainty for many regions for lead

times up to three decades ahead (Figure 11.8). Thereafter, response

uncertainty is generally dominant. Forcing uncertainty (except for that

relating to aerosols, see Section 11.4.7) is generally negligible for near-

term projections. The S/N ratio for projected changes in seasonal mean

precipitation is highest in the subtropics and at high latitudes. Rowell

(2011) found that the contribution of response uncertainty to the total

uncertainty (response plus internal variability) in local precipitation

change is highest in the deep tropics, particularly over South Amer-

ica, Africa, the east and central Paciﬁc, and the Atlantic. Over trop-

ical land and summer mid-latitude continents the representation of

SST changes, atmospheric processes, land surface processes, and the

terrestrial carbon cycle all contribute to the uncertainty in projected

changes in rainfall.

985

Near-term Climate Change: Projections and Predictability Chapter 11

In addition to the response to GHG forcing, forcing from natural and

anthropogenic aerosols may exert signiﬁcant impacts on regional pat-

terns of precipitation change as well as on global mean temperature

(Bollasina et al., 2011; Yue et al., 2011; Fyfe et al., 2012). Precipitation

changes may arise as a consequence of temperature and stratiﬁcation

changes driven by aerosol-induced radiative effects, and/or as indirect

aerosol effects on cloud microphysics (Chapter 7). Future emissions of

aerosols and aerosol precursors are subject to large uncertainty, and

Seasonal mean percentage precipitation change (RCP4.5: 2016-2035)

further large uncertainties arise in assessing the responses to these

emissions. These issues are discussed in Section 11.3.6.

Figures 11.12 and 11.13a present projections of near-term changes

in precipitation from CMIP5. Regional maps and time series are pre-

sented in Annex I. The basic pattern of wet regions tending to get

wetter and dry regions tending to get dryer is apparent, although

with some regional deviations as mentioned previously. However, the

Figure 11.12 | CMIP5 multi-model ensemble mean of projected changes (%) in precipitation for 2016–2035 relative to 1986–2005 under RCP4.5 for the four seasons. The

number of CMIP5 models used is indicated in the upper right corner. Hatching and stippling as in Figure 11.10.

-10

-5

90S 60S 30S EQ 30N 60N 90N

Precipitation change (%)

Latitude

-0.2

-0.1

0.1

0.2

0.3

0.4

90S 60S 30S EQ 30N 60N

90N

Precipitation minus evaporation change (mm day

-1

)

Latitude

Figure 11.13 | CMIP5 multi-model projections of changes in annual and zonal mean (a) precipitation (%) and (b) precipitation minus evaporation (mm day

–1

) for the period

2016–2035 relative to 1986–2005 under RCP4.5. The light blue denotes the 5 to 95% range, the dark blue the 17 to 83% range of model spread. The grey indicates the 1s range

of natural variability derived from the pre-industrial control runs (see Annex I for details).

(a) (b)

986

Chapter 11 Near-term Climate Change: Projections and Predictability

large response uncertainty is evident in the substantial spread in the

magnitude of projected change simulated by different climate models

(Figure 11.13a). In addition, it is important to recognize—as discussed

in previous sections—that models may agree and still be in error (e.g.,

Power et al. 2012). In particular, there is some evidence from com-

paring observations with simulations of the recent past that climate

models might be underestimating the magnitude of changes in precip-

itation in many regions (Pincus et al., 2008; Liepert and Previdi, 2009;

Schaller et al., 2011; Joetzjer et al., 2012) This evidence is discussed

in detail in Chapter 9 (Section 9.4.1) and Box 11.2, and could imply

that projected changes in precipitation are underestimated by current

models. However, the magnitude of any underestimation has yet to be

quantiﬁed, and is subject to considerable uncertainty.

Figures 11.12 and 11.13a also highlight the large amplitude of the nat-

ural internal variability of mean precipitation. On regional scales, mean

projected changes are almost everywhere smaller than the estimated

standard deviation of natural internal variability. The only exceptions

are the northern high latitudes and the equatorial Paciﬁc Ocean (Figure

11.12). For zonal means (Figure 11.13a) and at high latitudes only,

the projected changes relative to the recent past exceed the estimated

standard deviation of internal variability.

Overall, zonal mean precipitation will very likely increase in high and

some of the mid latitudes, and will more likely than not decrease in

the subtropics. At more regional scales precipitation changes may be

inﬂuenced by anthropogenic aerosol emissions and will be strongly

inﬂuenced by natural internal variability.

11.3.2.3.2 Changes in evaporation, evaporation minus precipitation,

runoff, soil moisture, relative humidity and speciﬁc

humidity

Because the variability of the atmospheric moisture storage is negli-

gible, global mean increases in evaporation are required to balance

increases in precipitation in response to anthropogenic forcing (Meehl

et al., 2007a; Trenberth et al., 2007; Bates et al., 2008; Lu and M. Cai,

2009). The global atmospheric water content is constrained by the

Clausius–Clapeyron equation to increase at around 7% K

–1

; howev-

er, both the global precipitation and evaporation in global warming

simulations increase at 1 to 3% K

–1

(Lambert and Webb, 2008; Lu and

M.Cai, 2009).

Changes in evapotranspiration over land are inﬂuenced not only by the

response to RF, but also by the vegetation response to elevated CO

concentrations. Physiological effects of CO

may involve both the sto-

matal response, which acts to restrict transpiration (Field et al., 1995;

Hungate et al., 2002; Cao et al., 2009, 2010; Lammertsma et al., 2011),

and an increase in plant growth and leaf area, which acts to increase

evapotranspiration (El Nadi, 1974; Bounoua et al., 2010). Simulation of

the latter process requires the inclusion of vegetation models that allow

spatial and temporal variability in the amount of active biomass, either

by changes in the phenological cycle or changes in the biome structure.

In response to GHG forcing, dry land areas tend to show a reduction

of evaporation and often precipitation, accompanied by a drying of the

soil and an increase of surface temperature, in response to decreases

in latent heat ﬂuxes from the surface (e.g., Fischer et al., 2007; Sen-

eviratne et al., 2010). Jung et al. (2010) use a mixture of observations

and models to illustrate a recent global mean decline in land surface

evaporation due to soil-moisture limitations. Accompanying precipita-

tion effects are more subtle, as there are signiﬁcant uncertainties and

large geographical variations regarding the soil-moisture precipitation

feedback (Hohenegger et al., 2009; Taylor et al., 2011). AR4 projec-

tions (Meehl et al. (2007b) of annual mean soil moisture changes for

the 21st century showed a tendency for decreases in the subtropics,

southern South America and the Mediterranean region, and increases

in limited areas of east Africa and central Asia. Changes seen in other

regions were mostly not consistent or statistically signiﬁcant.

AR4 projections of 21st century runoff changes (Meehl et al., 2007b)

showed consistency in sign among models indicating annual mean

reductions in southern Europe and increases in Southeast Asia and at

high northern latitudes. Projected changes in global mean runoff asso-

ciated with the physiological effects of doubled CO

concentrations

show increases of 6 to 8% relative to pre-industrial levels, an increase

that is comparable to that simulated in response to RF changes (11%

± 6%) (Betts et al., 2007; Cao et al., 2010). Gosling et al. (2011) assess

the projected impacts of climate change on river runoff from global

and basin-scale hydrological models obtaining increased runoff with

global warming in the Liard (Canada), Rio Grande (Brazil) and Xiangxi

(China) basins and decrease for the Okavango (southwest Africa).

Consideration of hydrological drought conditions employs a range of

different dryness indicators, such as soil moisture or other drought indi-

ces that integrate precipitation and evaporation effects (Seneviratne et

al., 2012). There are large uncertainties in regional drought projections

(Burke and Brown, 2008), and very few studies have addressed the

near-term future (Shefﬁeld and Wood, 2008; Dai, 2011). In order to

provide an indication of future changes of water availability, Figure

11.13b presents zonal mean changes in precipitation minus evapora-

tion (P – E) from CMIP5. As in the case of precipitation (Figure 11.13a),

the uncertainty is dominated by model differences as opposed to

natural variability (compare blue versus grey shading). The results are

consistent with the wet-get-wetter and dry-get-drier pattern (e.g., Held

and Soden 2006): In the high latitudes and the tropics, most of the

models project zonal-mean increases in P – E, which over land would

need to be compensated by increases in runoff (see next paragraph).

In contrast, zonal mean projected changes in the subtropics are nega-

tive, indicating decreases in water availability. Although this pattern

is evident in most or all of the models, and although several studies

project drought increases in the near term future (Shefﬁeld and Wood,

2008; Dai, 2011), the assessment is debated in the literature based on

discrepancies in the recent past and due to natural variability (Senevi-

ratne et al., 2012; Shefﬁeld et al., 2012).

The global distribution of the 2016–2035 changes in annual mean

evaporation, evaporation minus precipitation (E – P), surface runoff, soil

moisture, relative humidity and surface-level speciﬁc humidity from the

CMIP5 multi-model ensemble under RCP4.5 are shown in Figure 11.14.

Changes in evaporation over land (Figure11.14a), are mostly positive

with the largest values at northern high latitudes, in agreement with

projected temperature increases (Figure 11.10). Over the oceans, evap-

oration is also projected to increase in most regions. Projected changes

987

Near-term Climate Change: Projections and Predictability Chapter 11

are larger than the estimated standard deviation of internal variability

only at high latitudes and over the tropical oceans. Decreases in evap-

oration over land (i.e., Australia, southern Africa, northeastern South

America and Mexico) and oceans are smaller than the estimated stand-

ard deviation of internal variability; the only exception is the western

North Atlantic, although the model agreement is low in that region.

Projected changes in (E – P) over land (Figure 11.14b) are generally

consistent with the zonal mean changes shown in Figure 11.13b. In the

high northern latitudes and the tropics, (E – P) changes are mostly neg-

ative as dominated by precipitation increases (Figure 11.12), while in

Figure 11.14 | CMIP5 multi-model annual mean projected changes for the period 2016–2035 relative to 1986–2005 under RCP4.5 for: (a) evaporation (%), (b) evaporation minus

precipitation (E – P, mm day

–1

), (c) total runoff (%), (d) soil moisture in the top 10 cm (%), (e) relative change in speciﬁc humidity (%), and (f) absolute change in relative humidity

(%). The number of CMIP5 models used is indicated in the upper right corner of each panel. Hatching and stippling as in Figure 11.10.

988

Chapter 11 Near-term Climate Change: Projections and Predictability

the subtropics several areas exhibit increases in (E – P), in particular in

Europe, western Australia and central-western USA. However, in most

locations changes are smaller than internal variability.

Annual mean shallow soil moisture (Figure 11.14d) shows decreases in

most subtropical regions (except La Plata basin in South America) and

in central Europe, and increases in northern mid-to-high latitudes. Pro-

jected changes are larger than the estimated internal variability only

in southern Africa, the Amazon region and Europe. Projected changes

in runoff (Figure 11.14c) show decreases in northern Africa, western

Australia, southern Europe and southwestern USA and increases larger

than the internal variability in northwestern Africa, southern Arabia

and southeastern South America associated to the projected changes

in precipitation (Figure 11.12). Owing to the simpliﬁed hydrological

models in many CMIP5 climate models, the projections of soil moisture

and runoff have large model uncertainties.

Changes in near-surface speciﬁc humidity are positive, with the larg-

est values at northern high latitudes when expressed in percentage

terms (Figure 11.14e). This is consistent with the projected increases in

temperature when assuming constant relative humidity. These changes

are larger than the estimated standard deviation of internal variabil-

ity almost everywhere: the only exceptions are oceanic regions such

as the northern North Atlantic and around Antarctica. In comparison,

absolute changes in near-surface relative humidity (Figure 11.14f) are

much smaller, on the order of a few percent, with general decreases

over most land areas, and small increases over the oceans. Signiﬁcant

decreases relative to natural variability are projected in the Amazonia,

southern Africa and Europe, although the model agreement in these

regions is low.

Over the next few decades projected increases in near-surface speciﬁc

humidity are very likely, and projected increases in evaporation are

likely in many land regions. There is low conﬁdence in projected chang-

es in soil moisture and surface runoff.

11.3.2.4 Atmospheric Circulation

11.3.2.4.1 Northern Hemisphere extratropical circulation

In the NH extratropics, some Atmosphere–Ocean General Circulation

Models (AOGCMs) indicate changes to atmospheric circulation from

anthropogenic forcing by the mid-21st century, including a pole-

ward shift of the jet streams and associated zonal mean storm tracks

(Miller et al., 2006; Pinto et al., 2007; Paeth and Pollinger, 2010) and

a strengthening of the Atlantic storm track (Pinto et al., 2007), Figure

11.15. Consistent with this, the CMIP5 AOGCMs exhibit an ensemble

mean increase in the North Atlantic Oscillation (NAO) and Northern

Annular Model (NAM) indices by 2050, especially in autumn and

winter (Gillett et al., 2013).

However, there are reasons to be cautious over these near-term projec-

tions. Although models simulate the broad features of the large-scale

circulation well, there remain quite signiﬁcant biases in many models

(see Sections 9.4.1.4.3 and 9.5.3.2). The response of the NH circulation

can be sensitive to small changes in model formulation (Sigmond et al.,

2007), and to features that are known to be poorly simulated in many

climate models. These features include high- and low-latitude physics

(Rind, 2008; Woollings, 2010), ocean circulation (Woollings and Black-

burn, 2012), tropical circulation (Haarsma and Selten, 2012) and strat-

ospheric dynamics (Huebener et al., 2007; Morgenstern et al., 2010;

Scaife et al., 2012). As a result, there is considerable model uncertainty

in the response of the NH storm track position (Ulbrich et al., 2008),

stationary waves (Brandefelt and Kornich, 2008) and the jet streams

(Miller et al., 2006; Ihara and Kushnir, 2009; Woollings and Blackburn,

2012). Further, CMIP5 models show that the response of NH extratrop-

ical circulation to even strong GHG forcing remains weak compared to

recent multidecadal variability and a recent detection and attribution

study suggests that tropospheric ozone and aerosol changes may have

been a key driver to NH extratropical circulation changes (Gillett et al.,

2013). Some AOGCMs simulate multi-decadal NAO variability as large

as that recently observed with no external forcing (Selten et al., 2004;

Semenov et al., 2008). This suggests that internal variability could dom-

inate the anthropogenically forced response in the near term (Deser et

al., 2012).

Some studies have predicted a shift to the negative phase of the Atlan-

tic Multi-decadal Oscillation (AMO) over the coming few decades, with

potential impacts on atmospheric circulation around the Atlantic sector

(Knight et al., 2005; Sutton and Hodson, 2005; Folland et al., 2009). It

has also been suggested that there may be signiﬁcant changes in solar

forcing over the next few decades, which could have an inﬂuence on

NAO-related atmospheric circulation (Lockwood et al., 2011), although

these predictions are highly uncertain (see Section 11.3.6.2.2).

There is only medium conﬁdence in near-term projections of a north-

ward shift of NH storm track and westerlies, and an increase of the

NAO/NAM because of the large response uncertainty and the poten-

tially large inﬂuence of internal variability.

11.3.2.4.2 Southern Hemisphere extratropical circulation

Increases in GHGs, and related dynamical processes, are projected

to lead to poleward shifts in the annual mean position of Southern

Hemisphere (SH) extratropical storm tracks and winds (Figure 11.17;

Chapters 10 and 12). A key issue in projections of near-term SH extra-

tropical circulation change is the extent to which changes driven by

stratospheric ozone recovery will counteract changes driven by increas-

ing GHGs. Several observational and modeling studies (Gillett and

Thompson, 2003; Shindell and Schmidt, 2004; Arblaster and Meehl,

2006; Roscoe and Haigh, 2007; Fogt et al., 2009; Polvani et al., 2011a;

Gillett et al., 2013) indicate that, over the late 20th and early 21st cen-

turies, the observed summertime poleward shift of the westerly jet (a

positive Southern Annular Mode (SAM)) has been caused primarily by

the depletion of stratospheric ozone, with increasing GHGs contributing

only a smaller fraction to the observed trends. The latest generation

of climate models project substantially smaller poleward trends in SH

atmospheric circulation in austral summer over the coming half century

compared to those over the late 20th century, as the recovery of strat-

ospheric ozone will oppose the effects of continually increasing GHGs

(Arblaster et al., 2011; McLandress et al., 2011; Polvani et al., 2011a;

Eyring et al., 2013). Locally, internal variability may be a dominant con-

tributor to near-term changes in lower-tropospheric zonal winds (Figure

11.17). The average 2016–2035 SH extratropical storm tracks and zonal

989

Near-term Climate Change: Projections and Predictability Chapter 11

winds are likely to shift poleward relative to 1986–2005. However, even

though a full recovery of the ozone hole is not expected until the 2060s

to 2070s (Table 5.4; WMO, 2010; see Chapter 12), it is likely that over

the near term there will be a reduced rate in the austral summertime

poleward shift of the SH circumpolar trough, SH extratropical storm

tracks and winds compared to its movement over the past 30 years,

including the possibility of no detectable shift.

11.3.2.4.3 Tropical circulation

Increases in GHGs are expected to lead to a poleward shift of the

Hadley Circulation (Lu et al., 2007; Chapter 12, Figure 11.18). Relative

to the late 20th century, the tendency towards a poleward expansion

of the Hadley Circulation will start to emerge by the mid-2030s, with

certain intra-model consensus in the SH expansion, despite the coun-

teracting effect of ozone recovery (Figure 11.18). As with near-term

changes in SH extratropical circulation, a key for near-term projections

of the structure of the SH Hadley Circulation is the extent to which

future stratospheric ozone recovery will counteract the impact of

GHGs. The poleward expansion of the Hadley Circulation, particularly

of the SH branch during austral summer, during the later decades of

the 20th century has been largely attributed to the combined impact

of stratospheric ozone depletion (Thompson and Solomon, 2002; Son

et al., 2008, 2009a, 2009b; Polvani et al., 2011a, 2011b; Min and Son,

2013) and the concurrent increase in GHGs (Arblaster and Meehl,

2006; Arblaster et al., 2011) as discussed in the previous section. The

poleward expansion of the Hadley Circulation driven by the response

of the atmosphere to increasing GHGs (Lu et al., 2007; Kang et al.,

2011; Staten et al., 2011; Butler et al., 2012) would be counteract-

ed in the SH by reduced stratospheric ozone depletion but depends

on the rate of ozone recovery (UNEP and WMO, 2011). Increases in

the incoming solar radiation can lead to a widening of the Hadley

Cell (Haigh, 1996; Haigh et al., 2005) and large volcanic eruption to

Figure 11.15 | CMIP5 multi-model ensemble mean of projected changes (m s

–1

) in

zonal (west-to-east) wind at 850 hPa for 2016–2035 relative to 1986–2005 under

RCP4.5. The number of CMIP5 models used is indicated in the upper right corner. Hatch-

ing and stippling as in Figure 11.10.

−0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8

−1

−0.8

−0.6

−0.4

−0.2

0.2

0.4

0.6

0.8

1 CanESM2

Displacement of the Hadely Cell boundaries (°lat)

Displacement of dry zone (°lat)

2 CCSM4

3 CNRM−CM5

4 FGOALS−g2

5 GFDL−CM3

6 GFDL−ESM2G

7 GFDL−ESM2M

8 HadGEM2−ES

9 IPSL−CM5A−LR

10 IPSL−CM5A−MR

11 MIROC−ESM−CHEM

12 MIROC−ESM

13 MPI−ESM−LR

14 MRI−CGCM3

15 NorESM1−M

RCP4.5(2016−2035) − Historical(1986−2005)

northward

southward

northward

southward

Figure 11.16 | Projected changes in the annual averaged poleward edge of the Hadley Circulation (horizontal axis) and sub-tropical dry zones (vertical axis) based on 15 Atmo-

sphere–Ocean General Circulation Models (AOGCMs) from the CMIP5 (Taylor et al., 2012) multi-model ensemble, under 21st century RCP4.5. Orange symbols show the change

in the northern edge of the Hadley Circulation/dry zones, while blue symbols show the change in the southern edge of the Hadley Circulation/dry zones. Open circles indicate the

multi-model average, while horizontal and vertical coloured lines indicate the ±1 standard deviation range for internal climate variability estimated from each model. Values refer-

enced to the 1986–2005 climatology. (Figure based on the methodology of Lu et al., 2007.)

990

Chapter 11 Near-term Climate Change: Projections and Predictability

Figure 11.17 | Global projections of the occurrence of (a) warm days (TX90p), (b) cold days (TX10p) and (c) precipitation amount from very wet days (R95p). Results are shown

from CMIP5 for the RCP2.6, RCP4.5 and RCP8.5 scenarios. Solid lines indicate the ensemble median and shading indicates the interquartile spread between individual projections

(25th and 75th percentiles). The speciﬁc deﬁnitions of the indices shown are (a) percentage of days annually with daily maximum surface air temperature (T

max

) exceeding the 90th

percentile of T

max

for 1961–1990, (b) percentage of days with T

max

below the 10th percentile and (c) percentage change relative to 1986–2005 of the annual precipitation amount

from daily events above the 95th percentile. (From Sillmann et al., 2013.)

contraction of the tropics and the tropical circulation (Lu et al., 2007;

Birner, 2010). So future solar variations and volcanic activities could

also lead to variations in the width of the Hadley Cell. The poleward

extent of the Hadley Circulation and associated dry zones can exhibit

substantial internal variability (e.g., Birner, 2010; Davis and Rosenlof,

2012) that can be as large as its near-term projected changes (Figure

11.16). There is also considerable uncertainty in the amplitude of the

poleward shift of the Hadley Circulation in response to GHGs across

multiple AOGCMs (Lu et al., 2007; Figure 11.16). It is likely that the

poleward extent of the Hadley Circulation will increase through the

mid-21st century. However, because of the counteracting impacts of

future changes in stratospheric ozone and GHG concentrations, it is

unlikely that it will continue to expand poleward in the SH as rapidly

as it did in recent decades.

The Hadley Cell expansion in the NH has been largely attributed to the

low-frequency variability of the SST (Hu et al., 2013), the increase of

black carbon (BC) and tropospheric ozone (Allen and Sherwood, 2011).

Internal variability in the poleward edge of the NH Hadley Circulation

is large relative the radiatively forced signal (Figure 11.16. Given the

complexity in the forcing mechanism of the NH expansion and the

uncertainties in future concentrations of tropospheric pollutants, there

is low conﬁdence in the character of near-term changes to the struc-

ture of the NH Hadley Circulation.

Global climate models and theoretical considerations suggest that

a warming of the tropics should lead to a weakening of the zonally

asymmetric or Walker Circulation (Knutson and Manabe, 1995; Held

and Soden, 2006; Vecchi and Soden, 2007; Gastineau et al., 2009).

Aerosol forcing can modify both Hadley and Walker Circulations,

which—depending on the details of the aerosol forcing—may lead to

temporary reversals or enhancements in any GHG-driven weakening

of the Walker Circulation (Sohn and Park, 2010; Bollasina et al., 2011;

Merriﬁeld, 2011; DiNezio et al., 2013). Meanwhile, the strength and

structure of the Walker Circulation are impacted by internal climate

variations, such as the ENSO (e.g., Battistiand Sarachik, 1995), the PDO

(e.g., Zhang et al. 1997) and the IPO (Power et al., 1999, 2006; Meehl

and Hu, 2006; Meehl and Arblaster, 2011; Power and Kociuba, 2011b;

Meehl and Arblaster, 2012; Meehl et al., 2013a). Even on time scales

of 30 to 100 years, substantial variations in the strength of the Paciﬁc

Walker Circulation in the absence of changes in RF are possible (Power

et al., 2006; Vecchi et al., 2006). Estimated near-term weakening of

the Walker Circulation from CMIP3 models under the A1B scenario

(Vecchi and Soden, 2007; Power and Kociuba, 2011a) are very likely to

be smaller than the impact of internal climate variations over 50-year

time scales (Vecchi et al., 2006). There is also considerable response

uncertainty in the amplitude of the weakening of Walker Circulation in

response to GHG increase across multiple AOGCMs (Vecchi and Soden,

2007; DiNezio et al., 2009; Power and Kociuba, 2011a, 2011b). Thus,

there is low conﬁdence in projected near-term changes to the Walker

Circulation. It is very likely that there will be decades in which the

Walker Circulation strengthens and weakens due to internal variability

through the mid-century as the externally forced change is small com-

pared to internally generated decadal variability.

11.3.2.5 Atmospheric Extremes

Extreme events in a changing climate are the subject of Chapter 3 (Sen-

eviratne et al., 2012) of the IPCC Special Report on Extremes (SREX).

This previous IPCC chapter provides an assessment of more than 1000

studies. Here the focus is on near-term aspects and an assessment of

more recent studies is provided.

11.3.2.5.1 Temperature extremes

In the AR4 (Meehl et al., 2007b), cold episodes were projected to

decrease signiﬁcantly in a future warmer climate and it was considered

very likely that heat waves would be more intense, more frequent and

last longer towards the end of the 21st century. These conclusions

have generally been conﬁrmed in subsequent studies addressing both

global scales (Clark et al., 2010; Diffenbaugh and Scherer, 2011; Caesar

and Lowe, 2012; Orlowsky and Seneviratne, 2012; Sillmann et al.,

2013) and regional scales (e.g., Gutowski et al., 2008; Alexander and

Arblaster, 2009; Fischer and Schar, 2009; Marengo et al., 2009; Meehl

et al., 2009a; Diffenbaugh and Ashfaq, 2010; Fischer and Schar, 2010;

Cattiaux et al., 2012; Wang et al., 2012). In the SREX assessment it is

1960 1980 2000 2020 2040 2060 2080 2100

Year

Exceedance rate (%)

historical

RCP2.5

RCP4.5

RCP8.5

Warm days (TX90p)(a)

1960 1980 2000 2020 2040 2060 2080 2100

Year

Exceedance rate (%)

Cold Days (TX10p)

1960 1980 2000 2020 2040 2060 2080 2100

Year

Relative change (%)

Very Wet Days (R95p)(b) (c)

historical

RCP2.5

RCP4.5

RCP8.5

historical

RCP2.5

RCP4.5

RCP8.5

991

Near-term Climate Change: Projections and Predictability Chapter 11

concluded that increases in the number of warm days and nights and

decreases in the number of cold days and nights are virtually certain

on the global scale.

None of the aforementioned studies speciﬁcally addressed the near

term. However, detection and attribution studies (see also Chapter

10) show that temperature extremes have already increased in many

regions, consistent with climate change projections, and analyses

of CMIP5 global projections show that this trend will continue and

become more notable. The CMIP5 model ensemble exhibits a signiﬁ-

cant decrease in the frequency of cold nights, an increase in the fre-

quency of warm days and nights and an increase in the duration of

warm spells (Sillmann et al., 2013). These changes are particularly evi-

dent in global mean projections (see Figure 11.17). Figure 11.17 shows

that for the next few decades—as discussed in the introduction to

the current chapter—these changes are remarkably insensitive to the

emission scenario considered (Caesar and Lowe, 2012). In most land

regions and in the near-term, the frequency of warm days and warm

nights will thus likely continue to increase, while that of cold days and

cold nights will likely continue to decrease.

Near-term projections from General Circulation Model–Regional Cli-

mate Model (GCM–RCM) model chains (van der Linden and Mitchell,

2009) for Europe are shown in Figure 11.18, displaying near-term

changes in mean and extreme temperature (left-hand panels) and

precipitation (right-hand panels) relative to the reference period 1986–

2005. In terms of mean June, July and August (JJA) temperatures (Figure

11.18a), projections show a warming of 0.6°C to 1.5°C, with highest

changes over the land portion of the Mediterranean. The north–south

gradient in the projections is consistent with the AR4. Daytime extreme

summer temperatures in southern and central Europe are projected to

warm substantially faster than mean temperatures (compare Figure

11.18a and b). This difference between changes in mean and extremes

can be explained by increases in interannual and/or synoptic variability,

or increases in diurnal temperature range (Gregory and Mitchell, 1995;

Schar et al., 2004; Fischer and Schar, 2010; Hansen, 2012; Quesada et

al., 2012; Seneviratne et al., 2012). There is some evidence, however,

that this effect is overestimated in some of the models (Fischer et al.,

2012; Stegehuis et al., 2012), leading to a potential overestimation of

the projected Mediterranean summer mean warming (Buser et al., 2009;

Boberg and Christensen, 2012). With regard to near-term projections of

(°C)

(%)

Figure 11.18 | European-scale projections from the ENSEMBLES regional climate modelling project for 2016–2035 relative to 1986–2005, with top and bottom panels applicable

to June, July and August (JJA) and December, January, February (DJF), respectively. For temperature, projected changes (°C) are displayed in terms of ensemble mean changes of (a,

c) mean seasonal surface temperature, and (b, d) the 90th percentile of daily maximum temperatures. For precipitation, projected changes (%) are displayed in terms of ensemble

mean changes of (e, g) mean seasonal precipitation and (f, h) the 95th percentile of daily precipitation. The stippling in (e–h) highlights regions where 80% of the models agree in

the sign of the change (for temperature all models agree on the sign of the change). The analysis includes the following 10 GCM-RCM simulation chains for the SRES A1B scenario

(naming includes RCM group and GCM simulation): HadRM3Q0-HadCM3Q0, ETHZ-HadCM3Q0, HadRM3Q3-HadCM3Q3, SMHI-HadCM3Q3, HadRM3Q16-HadCM3Q16, SMHI-

BCM, DMI-ARPEGE, KNMI-ECHAM5, MPI-ECHAM5, DMI-ECHAM5. (Rajczak et al., 2013.)

992

Chapter 11 Near-term Climate Change: Projections and Predictability

record heat compared to record cold (Meehl et al., 2009b) show, for one

model, that over the USA the ratio of daily record high temperatures to

daily record low temperatures could increase from an early 2000s value

of roughly 2 to 1 to a mid-century value of about 20 to 1.

In terms of December, January and February (DJF) temperatures (Figure

11.18c), projections show a warming of 0.3°C to 1.8°C, with the larg-

est changes in the N–NE part of Europe. This characteristic pattern of

changes tends to persist to the end of century (van der Linden and

Mitchell, 2009). In contrast to JJA temperatures, daytime high-percen-

tile (i.e., warm) winter temperatures are projected to warm slower than

mean temperatures (compare Figure 11.18c and Figure 11.18d), while

low-percentile (i.e., cold) winter temperatures warm faster than the

mean. This behaviour is indicative of reductions in internal variability,

which may be linked to changes in storm track activity, reductions in

diurnal temperature range and changes in snow cover (e.g., Colle et al.

2013; Dutra et al., 2011).

11.3.2.5.2 Heavy precipitation events

For the 21st century, the AR4 and the SREX concluded that heavy pre-

cipitation events were likely to increase in many areas of the globe

(IPCC, 2007). Since AR4, a larger number of additional studies have

been published using global and regional climate models (Fowler et al.,

2007; Gutowski et al., 2007; Sun et al., 2007; Im et al., 2008; O’Gorman

and Schneider, 2009; Xu et al., 2009; Hanel and Buishand, 2011; Hein-

rich and Gobiet, 2011; Meehl et al., 2012b). For the near term, CMIP5

global projections (Figure 11.17c) conﬁrm a clear tendency for increas-

es in heavy precipitation events in the global mean, but there are sig-

niﬁcant variations across regions (Sillmann et al., 2013). Past observa-

tions have also shown that interannual and decadal variability in mean

and heavy precipitation are large, and are in addition strongly affected

by internal variability (e.g., El Niño), volcanic forcing and anthropogen-

ic aerosol loads (see Section 2.3.1). In general models have difﬁculties

in representing these variations, particularly in the tropics (see Section

9.5.4.2). Thus the frequency and intensity of heavy precipitation events

will likely increase over many land areas in the near term, but this trend

will not be apparent in all regions, because of natural variability and

possible inﬂuences of anthropogenic aerosols.

Simulations with regional climate models demonstrate that the

response in terms of heavy precipitation events to anthropogenic cli-

mate change may become evident in some but not all regions in the

near term. For instance, ENSEMBLES projections for Europe (see Figure

11.18e–h) conﬁrm the previous IPCC results that changes in mean

precipitation as well as heavy precipitation events are characterized

by a pronounced north–south gradient in the extratropics, especially

in the winter season, with precipitation increases in the higher lati-

tudes and decreases in the subtropics. Although this pattern starts to

emerge in the near term, the projected changes are statistically signif-

icant only in a fraction of the domain. The results are affected by both

changes in water vapour content as induced by large-scale warming

and large-scale circulation changes. Figure 11.18e–h also shows that

mid- and high-latitude projections for changes in DJF extremes and

means are qualitatively similar in the near term, at least for the event

size considered.

Previous work reviewed in AR4 has established that extreme

precipitation events may increase substantially stronger than mean

precipitation amounts. More speciﬁcally, extreme events may increase

with the atmospheric water vapour content, that is, up to the rate

of the Clausius–Clapeyron (CC) relationship (e.g., Allen and Ingram,

2002). More recent work suggests that increases beyond this threshold

may occur for short-term events associated with thunderstorms (Len-

derink and Van Meijgaard, 2008; Lenderink and Meijgaard, 2010) and

tropical convection (O’Gorman, 2012). A number of studies showed

strong dependencies on location and season, but conﬁrm the exist-

ence of signiﬁcant deviations from the CC scaling (e.g., Lenderink et

al., 2011; Mishra et al., 2012; Berg et al., 2013). Studies with cloud-re-

solving models generally support the existence of temperature-precip-

itation relations that are close to or above (up to about twice) the CC

relation (Muller et al., 2011; Singleton and Toumi, 2012).

11.3.2.5.3 Tropical cyclones

The projected response of tropical cyclones (TCs) at the end of the 21st

century is summarized in Section 14.6.1 and the IPCC Special Report

on Extremes (SREX) (Seneviratne et al., 2012). Relative to the number

of studies focussing on projections of TC activity at the end of the 21st

century (Section 14.6.1; Knutson et al., 2010; Seneviratne et al., 2012

there are fewer studies that have explored near-term projections of TC

activity (Table 11.2); the North Atlantic (NA) stands out as the basin

with most studies. In the NA, there are mixed projections for basin-

wide TC frequency, suggesting signiﬁcant decreases (Knutson et al.,

2013a) or non-signiﬁcant changes (Villarini et al., 2011; Villarini and

Vecchi, 2012). Multi-model mean projected NA TC frequency chang-

es based on CMIP3 and CMIP5 over the ﬁrst half of the 21st century

were smaller than the overall uncertainty estimated from the Coupled

General Circulation Models (CGCMs), with internal climate variability

being a leading source of uncertainty through the mid-21st century

(Villarini et al., 2011; Villarini and Vecchi, 2012). Therefore, based on

the limited literature available, the conﬂicting near-term projections

in basins with more than one study, the large inﬂuence of internal

variability, the lack of conﬁdently detected/attributed changes in TC

activity (Chapter 10) and the conﬂicting projections for basin-wide TC

frequency even at the end of the 21st century (Chapter 14), there is

currently low conﬁdence in basin-scale and global projections of trends

in tropical cyclone frequency to the mid-21st century.

Exploring different hurricane intensity measures, two studies project

near-term increases of NA hurricane intensity (Knutson et al., 2013a;

Villarini and Vecchi, 2013), driven in large part by projected reductions

in NA tropospheric aerosols in CMIP5 future forcing scenarios. Studies

project near-term increases in the frequency Category 4–5 TCs in the

NA (Knutson et al., 2013a) and southwest Paciﬁc (Leslie et al., 2007).

Published studies agree in the sign of projected mid-century intensity

change (intensiﬁcation), but the only basin with more than one study

exploring intensity is the NA. For the NA, an estimate of the time scale

of emergence of projected changes in intense TC frequency exceeds 60

years (Bender et al., 2010), although that estimate depends crucially

on the amplitude of internal climate variations of intense hurricane fre-

quency (e.g., Emanuel, 2011), which remains poorly constrained at the

moment. Therefore, there is low conﬁdence in near-term TC intensity

projections in all TC basins.

993

Near-term Climate Change: Projections and Predictability Chapter 11

Modes of climate variability that in the past have led to variations in

the intensity, frequency and structure of tropical cyclones across the

globe—such as the ENSO (e.g., Zhang and Delworth, 2006; Wang et

al., 2007; Callaghan and Power, 2011; Chapter 14)—are very likely to

continue inﬂuencing TC activity through the mid-21st century. There-

fore, it is very likely that over the next few decades tropical cyclone

frequency, intensity and spatial distribution globally, and in individual

basins, will vary from year to year and decade to decade.

11.3.3 Near-term Projected Changes in the Ocean

11.3.3.1 Temperature

Globally averaged surface and near-surface ocean temperatures are

projected by AOGCMs to warm over the early 21st century, in response

to both present day atmospheric concentrations of GHGs (‘committed

warming’; e.g., Meehl et al., 2006) and projected future changes in RF

(Figure 11.19). Globally averaged SST shows substantial year-to-year

and decade-to-decade variability (e.g., Knutson et al., 2006; Meehl et

al., 2011), whereas the variability of depth-averaged ocean tempera-

tures is much less (e.g., Meehl et al., 2011; Palmer et al., 2011). The rate

at which globally averaged surface and depth-averaged temperatures

rise in response to a given scenario for RF shows a considerable spread

between models (an example of response uncertainty; see Section

11.2), due to differences in climate sensitivity and ocean heat uptake

(e.g., Gregory and Forster, 2008). In the CMIP5 models under all RCP

forcing scenarios, globally averaged SSTs are projected to be warmer

over the near term relative to 1986–2005 (Figure 11.20).

A key uncertainty in the future evolution of globally averaged oceanic

temperature are possible future large volcanic eruptions, which could

impact the radiative balance of the planet for 2 to 3 years after their

eruption and act to reduce oceanic temperature for decades into the

future (Delworth et al., 2005; Stenchikov et al., 2009; Gregory, 2010).

An estimate using the GFDL-CM2.1 coupled AOGCM (Stenchikov et al.,

2009) suggests that a single Tambora (1815)-like volcano could erase

the projected global ocean depth-averaged temperature increase for

many years to a decade. A Pinatubo (1991)-like volcano could erase

the projected increase for 2 to 10 years. See Section 11.3.6 for further

discussion.

TC Basin

Explored

Projected Change in TC Activity Reported Notes Reference

Global

Reduced global, Northern Hemisphere and Southern Hemisphere frequency

2016–2035 relative to 1986–2005.

High-resolution atmospheric model forced by CMIP3

SRES A1B multi-model SST change 2004–2099.

Sugi and Yoshimura

(2012)

N.W. Paciﬁc

Over ﬁrst half of 21st century: Reduced Activity over South China Sea,

Increased Activity near subtropical Asia

Statistical downscale of ﬁve CMIP3

models under SRES A1B.

Wang et al. (2011)

N.W. Paciﬁc

Over 2001–2040, a decrease in TC frequency in the East China Sea, and a frequen-

cy decrease and increase in intensity of Yangze River Basin landfalling typhoons.

Statistical downscaling of CGCM forced

by CMIP3 SRES A1B scenario.

Orlowsky and

Seneviratne (2012)

S.W. Paciﬁc

Differences of 2000–2050 with 1970–2000. Negligible change in overall

frequency. Signiﬁcant (~15%) increase in number of Category 4–5 TCs.

Dynamical regional downscale of coupled AOGCM

forced with IPCC IS92a increasing CO

scenario.

Leslie et al. (2007)

N. Atlantic

Linear trend in TC frequency 2001–2050: Ensemble-mean non-signiﬁcant

decrease in TC frequency (–5%). Ensemble range of –50% to +30%.

Statistical downscaling of CMIP3

models under A1B scenario.

Villarini et al. (2011)

N. Atlantic

TC frequency averaged 2016–2035 minus 1986–2005: Ensemble-mean non-

signiﬁcant increase for RCP2.6 (4%), non-signiﬁcant decrease for RCP4.5 (–2%)

and RCP8.5 (–1%). Ensemble range of –30% to 27% across all scenarios/models.

Statistical downscaling of CMIP5

RCP2.6, RCP4.5 and RCP8.5

Villarini and

Vecchi (2012)

N. Atlantic

Power Dissipation Index averaged 2016–2035 minus 1986–2005: Ensemble mean

signiﬁcant increase for RCP2.6 (23%) and RCP8.5 (17%), non-signiﬁcant increase

for RCP4.5 (10%). Ensemble range of –43% to 78% across all scenarios/models.

Statistical downscaling of CMIP5

RCP2.6, RCP4.5 and RCP4.5

Villarini and

Vecchi (2013)

N. Atlantic

Difference 2016–2035 minus 1986–2005 averages: Signiﬁcant decrease

(–20%) to overall TC and hurricane frequency. Signiﬁcant increase

(+45%) in number of Category 4–5 TCs. Signiﬁcant increase in pre-

cipitation of hurricanes (11%) and tropical storms (18%).

Double dynamical reﬁnement of CMIP5 RCP4.5

multi-model ensemble projections.

Knutson et al. (2013a)

Table 11.2 | Summary of studies exploring near-term projections of tropical cyclone (TC) activity. First column lists the TC basin explored, the second column summarizes the

changes in TC activity reported in each study, the third column presents notes on the methodology and the fourth column provides a reference to the study.

Global sea surface temperature change

(°C)

Figure 11.19 | Projected changes in annual averaged, globally averaged, surface

ocean temperature based on 12 Atmosphere–Ocean General Circulation Models

(AOGCMs) from the CMIP5 (Meehl et al., 2007b) multi-model ensemble, under 21st

century scenarios RCP2.6, RCP4.5, RCP6.0 and RCP8.5. Shading indicates the 90%

range of projected annual global mean surface temperature anomalies. Anomalies com-

puted against the 1986–2005 average from the historical simulations of each model.

994

Chapter 11 Near-term Climate Change: Projections and Predictability

In the absence of multiple major volcanic eruptions (see Section

11.3.6.2), it is very likely that globally averaged surface and depth-av-

eraged temperatures averaged 2016–2035 will be warmer than those

averaged over 1986–2005.

There are regional variations in the projected amplitude of ocean tem-

perature change (Figure 11.20) which are inﬂuenced by ocean circula-

tion as well as surface heating (Timmermann et al., 2007; Vecchi and

Soden, 2007; DiNezio et al., 2009; Yin et al., 2009; Xie et al., 2010; Yin

et al., 2010), including changes in tropospheric aerosol concentrations

(e.g., Booth et al., 2012; Villarini and Vecchi, 2012). Inter-decadal vari-

ability of upper ocean temperatures is larger in mid-latitudes, particu-

larly in the NH, than in the tropics. A consequence of this contrast is

that it will take longer in the mid-latitudes than in the tropics for the

anthropogenic warming signal to emerge from the noise of internal

variability (Wang et al., 2010).

Projected changes to thermal structure of the tropical Indo-Paciﬁc

are strongly dependent on the future behaviour of the Walker Circu-

lation (Vecchi and Soden, 2007; DiNezio et al., 2009; Timmermann et

al., 2010), in addition to changes in heat transport and changes in

surface heat ﬂuxes. It is likely that internal climate variability will be a

dominant contributor to changes in the depth and tilt of the equatorial

thermocline, and the strength of the east–west gradient of SST across

the Paciﬁc through the mid-21st century; thus it is likely there will be

multi-year periods with increases or decreases in these measures.

11.3.3.2 Salinity

Changes in sea surface salinity are expected in response to changes

in precipitation, evaporation and runoff (see Section 11.3.2.3), as well

as ocean circulation; in general (but not in every region), salty regions

are expected to become saltier and fresh regions fresher (e.g., Durack

et al. 2012; Terray et al. 2012; Figure 11.20). As discussed in Chapter

10 (Section 10.4.2), observation-based and attribution studies have

found some evidence of an emerging anthropogenic signal in salin-

ity change (Section 10.4.2), in particular increases in surface salinity

in the subtropical North Atlantic, and decreases in the west Paciﬁc

warm pool region (Stott et al., 2008; Cravatte et al., 2009; Durack and

Wijffels, 2010; Durack et al., 2012; Pierce et al., 2012; Terray et al.,

2012). Models generally predict increases in salinity in the tropical and

(especially) subtropical Atlantic, and decreases in the western tropical

Paciﬁc over the next few decades (Figure 11.20) (Durack et al., 2012;

Terray et al., 2012). These projected decreases in the Atlantic and in the

western tropical Paciﬁc are considered likely.

Projected near-term increases in freshwater ﬂux into the Arctic Ocean

produce a fresher surface layer and increased transport of fresh water

into the North Atlantic (Holland et al., 2006; Holland et al., 2007; Vavrus

et al., 2012). Such contributions to decreased density of the ocean sur-

face layer in the North Atlantic could act to reduce deep ocean con-

vection there and contribute to a near-term reduction of strength of

Atlantic Meridional Ocean Circulation (AMOC). However, the strength

of the AMOC can also be modulated by changes in temperature, such

as those from changing RF (Delworth and Dixon, 2006).

11.3.3.3 Circulation

As discussed in previous assessment reports, the AMOC is generally

projected to weaken over the next century in response to increase in

atmospheric GHG. However, the rate and magnitude of weakening

is very uncertain. Response uncertainty is a major contributor in the

near term, but the inﬂuence of anthropogenic aerosols and natural RFs

(solar, volcanic) cannot be neglected, and could be as important as the

inﬂuence of GHGs (e.g., Delworth and Dixon, 2006; Stenchikov et al.,

2009). For example, the rate of weakening of the AMOC in two models

with different climate sensitivities is quite different, with the less

sensitive model (CCSM4) showing less weakening and a more rapid

recovery than the more sensitive model (Community Earth System

Model 1/Community Atmosphere Model 5 (CESM1/CAM5; Meehl et

al., 2013c). In addition, the natural variability of the AMOC on dec-

adal time scales is poorly known and poorly understood, and could

dominate any anthropogenic response in the near term (Drijfhout and

Hazeleger, 2007). The AMOC is known to play an important role in the

Figure 11.20 | CMIP5 multi-model ensemble mean of projected changes in sea surface temperature (right panel; °C) and sea surface salinity (left panel; practical salinity units) for

2016–2035 relative to 1986–2005 under RCP4.5. The number of CMIP5 models used is indicated in the upper right corner. Hatching and stippling as in Figure 11.10.

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Near-term Climate Change: Projections and Predictability Chapter 11

decadal variability of the North Atlantic Ocean, but climate models

show large differences in their simulation of both the amplitude and

spectrum of AMOC variability (e.g., Bryan et al., 2006; Msadek et al.,

2010). In some AOGCMs changes in SH surface winds inﬂuence the

evolution of the AMOC on time scales of many decades (Delworth and

Zeng, 2008), so the delayed response to SH wind changes, driven by

the historical reduction in stratospheric ozone along with its projected

recovery, could be an additional confounding issue (Section 11.3.2.3).

Overall, it is likely that there will be some decline in the AMOC by 2050,

but decades during which the AMOC increases are also to be expect-

ed. There is low conﬁdence in projections of when an anthropogenic

inﬂuence on the AMOC might be detected (Baehr et al., 2008; Roberts

and Palmer, 2012).

Projected changes to oceanic circulation in the Indo-Paciﬁc are strongly

dependent on future response of the Walker Circulation (Vecchi and

Soden, 2007; DiNezio et al., 2009), the near-term projected weaken-

ing of which is smaller than the expected variability on time scales of

decades to years (Section 11.3.2.4.3). Taking variability into account,

there is medium conﬁdence in a weakening of equatorial Paciﬁc cir-

culation, including equatorial upwelling and the shallow subtropical

overturning in the Paciﬁc, and the Indonesian Throughﬂow over the

coming decades.

11.3.4 Near-term Projected Changes in the Cryosphere

This section assesses projected near-term changes of elements of the

cryosphere. These consist of sea ice, snow cover and near-surface per-

mafrost (frozen ground), changes to the Arctic Ocean and possible

abrupt changes involving the cryosphere. Glaciers and ice sheets are

addressed in Chapter 13. Here near-term changes in the geographi-

cal coverage of sea ice, snow cover and near-surface permafrost are

assessed.

Trends due to changes in external forcing exist alongside consider-

able interannual and decadal variability. This complicates our ability

to make speciﬁc, precise short-term projections, and delays the emer-

gence of a forced signal above the noise.

11.3.4.1 Sea Ice

Though most of the CMIP5 models project a nearly ice-free Arctic (sea

ice extent less than 1 × 10

for at least 5 consecutive years) at the

end of summer by 2100 in the RCP8.5 scenario (see Section 12.4.6.1),

some show large changes in the near term as well. Some previous

models project an ice-free summer period in the Arctic Ocean by 2040

(Holland et al., 2006), and even as early as the late 2030s using a

criterion of 80% sea ice area loss (e.g., Zhang, 2010). By scaling six

CMIP3 models to recent observed September sea ice changes, a nearly

ice-free Arctic in September is projected to occur by 2037, reaching the

ﬁrst quartile of the distribution for timing of September sea ice loss by

2028 (Wang and Overland, 2009). However, a number of models that

have fairly thick Arctic sea ice produce a slower near-term decrease in

sea ice extent compared to observations (Stroeve et al., 2007). Based

on a linear extrapolation into the future of the recent sea ice volume

trend from a hindcast simulation conducted with a regional model of

the Arctic sea ice–ocean system (Maslowski et al., 2012) projected that

it would take only until about 2016 to reach a nearly ice-free Arctic

Ocean in summer. However, such an approach not only neglects the

effect of year-to-year or longer-term variability (Overland and Wang,

2013) but also ignores the negative feedbacks that can occur when

the sea ice cover becomes thin (Notz, 2009). Mahlstein and Knutti

(2012) estimated the annual mean global surface warming threshold

for nearly ice-free Arctic conditions in September to be ~2°C above the

present derived from both CMIP3 models and observations.

An analysis of CMIP3 model simulations indicates that for near-term

predictions the dominant factor for decreasing sea ice is increased ice

melt, and reductions in ice growth play a secondary role (Holland et

al., 2010). Arctic sea ice has larger volume loss when there is thicker

ice initially across the CMIP3 models, with a projected accumulated

mass loss of about 0.5 m by 2020, and roughly 1.0 m by 2050, with

considerable model spread (Holland et al., 2010). The CMIP3 models

tended to under-estimate the observed rapid decline of summer Arctic

sea ice during the satellite era, but these recent trends are more accu-

rately simulated in the CMIP5 models (see Section 12.4.6.1). For CMIP3

models, results indicate that the changes in Arctic sea ice mass budget

over the 21st century are related to the late 20th century mean sea

ice thickness distribution (Holland et al., 2010), average sea ice thick-

ness (Bitz, 2008; Hodson et al., 2012), fraction of thin ice cover (Boe

et al., 2009) and oceanic heat transport to the Arctic (Mahlstein et al.,

2011). Acceleration of sea ice drift observed over the last three dec-

ades, underestimated in CMIP3 projections (Rampal et al., 2011), and

the presence of fossil-fuel and biofuel soot in the Arctic environment

(Jacobson, 2010), could also contribute to ice-free late summer condi-

tions over the Arctic in the near term. Details on the transition to an

ice-free summer over the Arctic are presented in Chapter 12 (Sections

12.4.6.1 and 12.5.5.7).

The discussion in Section 12.4.6.1 makes the case for assessing near-

term projections of Arctic sea ice by weighting/recalibrating the models

based on their present-day Arctic sea ice simulations, with a credible

underlying physical basis in order to increase conﬁdence in the results,

and accounting for the potentially large imprint of natural variabili-

ty on both observations and model simulations (see Section 9.8.3). A

subselection of a set of CMIP5 models that ﬁts those criteria, following

the methodology proposed by Massonnet et al. (2012), is applied in

Chapter 12 (Section 12.4.6.1) to the full set of models that provid-

ed the CMIP5 database with sea ice output. Among the ﬁve selected

models, four project a nearly ice-free Arctic Ocean in September (sea

ice extent less than 1 × 10

for at least 5 consecutive years) before

2050 for RCP8.5, the earliest and latest years of near disappearance of

the sea ice pack being about 2040 and about 2060, respectively. The

potential irreversibility of the Arctic sea ice loss and the possibility of

an abrupt transition toward an ice-free Arctic Ocean are discussed in

Section 12.5.5.7.

In light of all these results and others discussed in greater detail in Sec-

tion 12.4.6.1, it is very likely that the Arctic sea ice cover will continue

to shrink and thin all year round during the 21st century as the annual

mean global surface temperature rises. It is also likely that the Arctic

Ocean will become nearly ice-free in September before the middle of

the century for high GHG emissions such as those corresponding to

RCP8.5 (medium conﬁdence).

996

Chapter 11 Near-term Climate Change: Projections and Predictability

In early 21st century simulations, Antarctic sea ice cover is projected

to decrease in the CMIP5 models, though CMIP3 and CMIP5 models

simulate recent decreases in Antarctic sea ice extent compared to

slight increases in the observations (Section 12.4.6.1). However, there

is the possibility that melting of the Antarctic ice sheet could be chang-

ing the vertical ocean temperature stratiﬁcation around Antarctica

and encourage sea ice growth (Bintanja et al., 2013). This and other

evidence discussed in Section 12.4.6.1 leads to the assessment that

there is low conﬁdence in Antarctic sea ice model projections that

show near-term decreases of sea ice cover because of the wide range

of model responses and the inability of almost all of the models to

reproduce the mean seasonal cycle, interannual variability and overall

increase of the Antarctic sea ice areal coverage observed during the

satellite era (see Section 9.4.3).

11.3.4.2 Snow Cover

Decreases of snow cover extent (SCE, deﬁned over ice-free land areas)

are strongly connected to a shortening of seasonal snow cover dura-

tion (Brown and Mote, 2009) and are related to both precipitation and

temperature changes (see Section 12.4.6.2). This has implications for

snow on sea ice where loss of sea ice area in autumn delays snowfall

accumulation, with CMIP5 multi-model mean values of snow depth in

April north of 70°N reduced from about 28 cm to roughly 18 cm for

the 2031–2050 period compared to the 1981–2000 average (Hezel et

al., 2012). The snow accumulation season by mid-century in one model

is projected to begin later in autumn, with the melt season initiated

earlier in the spring (Lawrence and Slater, 2010). As discussed in great-

er detail in Section 12.4.6.2, projected increases in snowfall across

much of the northern high latitudes act to increase snow amounts,

but warming reduces the fraction of precipitation that falls as snow.

In addition, the reduction of Arctic sea ice also provides an increased

moisture source for snowfall (Liu et al., 2012). Whether the average

SCE decreases or increases by mid-century depends on the balance

between these competing factors. The dividing line where models tran-

sition from simulating increasing or decreasing maximum snow water

equivalent roughly coincides with the –20°C isotherm in the mid-20th

century November to March mean surface air temperature (Raisanen,

2008). The projected change of SCE over some regions is inconsistent

with that of extreme snowfall, a major contributor to SCE. For instance,

SCE is projected to decrease over northern China by the mid-21st

century (Shi et al., 2011), while the extreme snowfall events over the

region are projected to increase (Sun et al., 2010).

Time series of projected changes in relative SCE (for NH ice-free land

areas) are shown in Figure 12.32. Multi-model averages from the

CMIP5 archive (Brutel-Vuilmet et al., 2013) show percentage decreas-

es of NH SCE ± 1 standard deviation for the 2016–2035 time period

for a March to April average using a 15% extent threshold for the four

RCP scenarios as follows: RCP2.6: –5.2% ± 1.9% (21 models); RCP4.5:

–5.3% ± 1.5% (24 models); RCP6.0: –4.5% ± 1.2% (16 models);

RCP8.5: –6.0% ± 2.0% (24 models).

11.3.4.3 Near Surface Permafrost

Virtually all near-term projections indicate a substantial amount of

near-surface permafrost degradation (typically taking place in the upper

2 to 3 m; see Callaghan et al. (2011) and see glossary for detailed deﬁ-

nition), and thaw depth deepening over much of the permafrost area

(Sushama et al., 2006; Lawrence et al., 2008; Guo and Wang, 2012).

As discussed in more detail in Section 12.4.6.2, these projections have

increased credibility compared to the previous generation of models

assessed in the AR4 because current climate models represent perma-

frost more accurately (Alexeev et al., 2007; Nicolsky et al., 2007; Law-

rence et al., 2008). The reduction in annual mean near-surface perma-

frost area for the 2016–2035 time period compared to the 1986–2005

reference period for the CMIP5 models (Slater and Lawrence, 2013) for

the NH for the four RCP scenarios is 21% ± 5% (RCP2.6), 18% ± 6%

(RCP4.5), 18% ± 3% (RCP6.0) and 20% ± 5% (RCP8.5).

11.3.5 Projections for Atmospheric Composition and Air

Quality to 2100

The future evolution of atmospheric composition is determined by

the chemical–physical processes in the atmosphere, forced primarily

by anthropogenic and natural emissions and by interactions with the

biosphere and ocean (Chapters 2, 6, 7, 8 and 12). Twenty-ﬁrst century

projections of the chemically reactive GHGs, including methane (CH

nitrous oxide (N

O) and ozone (O

), as well as aerosols, are assessed

here (Section 11.3.5.1). Future air pollution, speciﬁcally ground-level

and PM

2.5

(particulate matter with a diameter of less than 2.5 μm,

a measure of aerosol concentration), is also assessed here (Section

11.3.5.2). The impact of changes in natural emissions and deposition

through altered land use (Heald et al., 2008; Chen et al., 2009a; Cook

et al., 2009; Wu et al., 2012) and production of food or biofuels (Chap-

ter 6) on atmospheric composition and air quality are not assessed

here. Projected CO

abundances are discussed in Chapters 6 and 12.

Projections for the 21st century are based predominantly on the CMIP5

models that included atmospheric chemistry and the related ACCMIP

(Atmospheric Chemistry and Climate Model Intercomparison Project)

models, driven by the RCP emission and climate scenarios. These and

the earlier SRES scenarios include only direct anthropogenic emis-

sions. Natural emissions may also change with biosphere feedbacks

in response to climate or land use change (Chapters 6, 8). Emphasis is

placed on evaluating the 21st-century RCP scenarios from emissions

to abundance, summarized in tables in Annex II. For the well-mixed

greenhouse gases (WMGHGs), the effective radiative forcing (ERF) in

both RCP and SRES scenarios increases similarly before 2040 with little

spread (±16% in ERF; see Tables AII.6.1 to AII.6.10), but by 2050 the

RCP2.6 scenario diverges, falling well below the envelope containing

both the SRES and other RCP scenarios.

National and regional regulations implemented on emissions con-

tributing to ground-level ozone and PM

2.5

pollution inﬂuence global

atmospheric chemistry and climate (NRC, 2009; HTAP, 2010a), as was

recognized in the TAR (Jacob et al., 1993; Penner et al., 1993; Johnson

et al., 1999; Prather et al., 2001). Ozone and aerosols are radiatively

active species (Chapters 7 and 8) and many of their precursors serve

as indirect GHGs (e.g., nitrogen oxides (NO

), carbon monoxide (CO),

Non Methane Volatile Organic Compounds (NMVOC)) by changing the

atmospheric oxidative capacity, and thereby the lifetimes and abun-

dances of CH

, hydroﬂuorocarbons (HFCs) and tropospheric O

(Chap-

ter 8). Consequently their evolution can inﬂuence near-term climate

997

Near-term Climate Change: Projections and Predictability Chapter 11

both regionally and globally (Section 11.3.6.1 and FAQ 8.2). The RCP

and SRES scenarios differ greatly in terms of the short-lived air pollut-

ants and aerosol climate forcing. The CMIP3 climate simulations driven

by the SRES scenarios projected a wide range of future air pollutant

trajectories, including unconstrained growth that resulted in very

large tropospheric O

increases (Prather et al., 2003). Subsequently,

the near-term projections of current legislation (CLE) and maximum

feasible reductions (MFR) emissions illustrated the impacts of air pollu-

tion control strategies on air quality, global atmospheric chemistry and

near-term climate (Dentener et al., 2005, 2006; Stevenson et al., 2006).

The RCP scenarios applied in the CMIP5 climate models all assume a

continuation of current trends in air pollution policies (van Vuuren et

al., 2011) and thus do not cover the range of future pollutant emissions

found in the literature, speciﬁcally those with higher pollutant emis-

sions (Dentener et al., 2005; Kloster et al., 2008; Pozzer et al., 2012);

see Chapter 8.

The new RCP emissions are compared to the older SRES and other

published emission scenarios in Annex II (Tables AII.2.1 to AII.2.22) and

Figures 8.2 and 8.SM.1. By 2030 the RCP aerosol and ozone precursor

emissions are smaller than SRES by factors of 1.2 to 3. For these short-

lived air pollutants, the spread across RCPs by 2030 is much smaller

than the range between the CLE and MFR scenarios: ±12% vs. ±31%

for nitrogen oxides; ±17% vs. ±60% for sulphate; ±5% vs. ±11%

for carbon monoxide. BC aerosol emissions also vary little across the

RCPs: ±4% range in 2030; ±15% in 2100. Most of this spread is due to

uncertain projections for the rapidly industrializing nations. From 2000

to 2030, sulphur dioxide (SO

) emissions decline in the RCPs by –15%

to –8% per decade, within the range of the MFR and CLE scenarios

(–23% to +2% per decade), but far below the SRES range (+4% to

+21% per decade). Evaluation of recent trends in SO

emissions shows

a trend similar to the near-term RCP projections (Smith et al., 2011;

Klimont et al., 2013), but independent estimates for recent trends in

other aerosol species are not available. The RCP trend in NO

emissions

(–5% to +2% per decade) is likewise within the CLE-MFR range, but

far below the SRES trends (+10% to +30% per decade). For OC and BC

emissions, the RCP trend lies between the SRES B1/A2 range. A simple

sum of the main four aerosol emissions (N, S, OC, BC; Tables AII.2.18

to AII.2.22) in the SRES vs. RCP scenarios indicates that the CMIP3

simulations driven by the SRES scenarios have about 40% more aero-

sols in 2000 than the CMIP5 simulations driven by the RCP scenarios.

On average, these aerosols increase by 9% per decade in the SRES

scenarios but decrease by 5% per decade in the RCP scenarios over

the near term. By 2030, the CMIP3 models thus include up to three

times more anthropogenic aerosols under the SRES scenarios than the

CMIP5 models driven by the RCP scenarios (high conﬁdence).

11.3.5.1 Reactive Greenhouse Gases and Aerosols

The IPCC has assessed previous emission-based scenarios for future

GHGs and aerosols in the SAR (IS92) and TAR/AR4 (SRES). The new

RCP scenarios are different in that they embed a simple, parametric

model of atmospheric chemistry and biogeochemistry that maps emis-

sions onto atmospheric abundances (the ‘concentration pathways’)

(Lamarque et al., 2011; Meinshausen et al., 2011a, 2011b; van Vuuren

et al., 2011). As an integrated product, the RCP-prescribed emissions,

abundances and RF used in the CMIP5 model ensembles do not reﬂect

the current best understanding of natural and anthropogenic emis-

sions, atmospheric chemistry and biogeochemistry and RF of climate

(Chapters 2, 6 and 8) (see, e.g., Dlugokencky et al., 2011; Prather et al.,

2012; Lamarque et al., 2013; Stevenson et al., 2013; Voulgarakis et al.,

2013; Young et al., 2013). Rather, the best estimates of atmospheric

abundances and associated RF include a more complete atmospheric

chemistry description and a fuller set of uncertainties than considered

in the RCPs provided to the CMIP5 models. While this widens the range

of climate forcing for each individual scenario, this uncertainty general-

ly remains smaller than the range across the four RCP scenarios.

11.3.5.1.1 Methane, nitrous oxide and the ﬂuorinated gases

Kyoto GHG abundances projected to year 2100 are given in Annex II

(Tables AII.4.1–AII.4.15) as both RCP published values (Meinshausen

et al., 2011b) and derived from the RCP anthropogenic emissions path-

ways. The latter includes current best estimates of atmospheric chem-

istry and natural sources, with uncertainties (denoted RCP

). Emissions

of CH

and N

O, primarily from the agriculture, forestry and other land

use sectors (AFOLU) are uncertain, typically by 25% or more (Prather

et al., 2009; NRC, 2010). Following the method of Prather et al. (2012)

a best estimate and uncertainty range for the year 2011 anthropogenic

and natural emissions of CH

and N

O are derived using updated AR5

values (see Chapters 2, 5 and 6). The re-scaled RCP

anthropogenic-on-

ly emissions of CH

and N

O are given in Tables AII.2.2 and AII.2.3 and

differ from the published RCPs by a single scale factor for each species.

An uncertainty range for 2011 values (likely, ±1 standard deviation

in %, based on Prather et al. 2012) is applied to all subsequent years.

Abundances are then integrated using these rescaled RCP

anthropo-

genic emissions, the best estimate for natural emissions, and a model

projecting changes in tropospheric OH (see Holmes et al., 2013; for

details). Similar scaling to match current observational constraints

(harmonization) was done for the SRES emissions (Prather et al., 2001)

and the RCPs (Meinshausen et al., 2011b). However, these earlier har-

monizations used older values for lifetimes and natural sources, and

did not provide estimates of uncertainty.

Combining CH

observations, lifetime estimates for the present day,

the ACCMIP studies, plus estimated limits on changing natural sourc-

es, gives a year 2011 total anthropogenic CH

emission of 354 ± 45

Tg(CH

)

–1

(Montzka et al., 2011; Prather et al., 2012) (Chapters 2,

6 and 8). The RCP total emission lies within 10% of this value, and

thus the scaling factor between the RCP

and RCP total emission, is

small (Table AII.2.2). Projection of the tropospheric OH lifetime of CH

(AII.5.8) is based on the ACCMIP simulations of the RCPs for 2100 time

slice simulations (Voulgarakis et al., 2013), other modelling studies

(Stevenson et al., 2006; John et al., 2012) and multi-model sensitiv-

ity analyses of key factors (Holmes et al., 2013) that includes uncer-

tainties in emissions from agricultural, forest and land use sources, in

atmospheric lifetimes, and in chemical feedbacks and loss. Lifetimes,

and thus future CH

abundances, decrease slowly under RCP2.6 and

RCP4.5, remain almost constant under RCP6.0 and increase slowly

under RCP8.5. Future changes in natural sources of CH

due to land

use and climate change are included in a few CMIP5 models and may

alter future CH

abundances (Chapter 6), but there is limited evidence,

and thus these changes are not included in the RCP

projections.

998

Chapter 11 Near-term Climate Change: Projections and Predictability

The resulting best estimates of total CH

anthropogenic emissions and

abundances (RCP

) are compared with RCP values in Figure 11.21.

For RCP2.6, the CH

abundance is projected to decline continuously

over the century by about 30%, whereas in RCP 4.5 and 6.0 it peaks

mid-century and then declines to below the year 2011 abundance by

the end of the century. Throughout the century, the uncertainty in CH

abundance for an individual scenario is less than range from RCP2.6

to RCP8.5. For example, by year 2020 the spread in CH

abundance

across the RCPs is already large, 1720 to 1920 ppb, with uncertainty in

each scenario estimated at only ±20 ppb. The likely range for RCP

is 30% wider than that in the RCP CH

abundances used to force the

CMIP5 models (Figure 11.21): by year 2100 the likely range of RCP8.5

abundance extends 520 ppb above the single-valued RCP8.5 CH

abundance, and RCP2.6

extends 230 ppb below RCP2.6 CH

Substantial effort has gone into identifying and quantifying individual

sources of N

O (see Chapter 6) but less into evaluating its lifetime and

chemical feedbacks. Recent multi-model, chemistry–climate studies

(CCMVal) project a more vigorous stratospheric overturning by 2100

that is expected to shorten the N

O lifetime (Oman et al., 2010; Strahan

et al., 2011), but no evaluation of the lifetime is reported. Here we com-

bine observations of N

O (pre-industrial, present, and present trends;

Chapter 2), with two modern studies of the lifetime (Hsu and Prather,

2010; Fleming et al., 2011), and a Monte Carlo method (Prather et al.,

2012) to estimate a year 2011 total anthropogenic emission of 6.7 ±

1.3 TgN(N

O) yr

–1

(Table AII.2.3). All RCP N

O (anthropogenic) emis-

sions are reduced by 20% so that year 2011 values are consistent with

an observationally constrained budget using a longer lifetime than

adopted by the RCPs (Table AII.2.3). The N

O lifetime (Table AII.5.9)

is projected to decrease by 2 to 4% by year 2100, due to changing

circulation and chemistry in the stratosphere (Fleming et al., 2011) and

to the negative chemical feedback on its own lifetime (Prather and

Hsu, 2010). In the near term, the spread in N

O across RCP

s is small:

330 to 332 ± 4 ppb in year 2020; 346 to 365 ± 11 ppb in year 2050. By

year 2100, the range of best-estimate N

O concentrations across the

RCP

s (354–425 ppb) is 20% smaller than that across the RCPs (344–

435 ppb), but the likely range in RCP

s encompasses the RCP range.

Recent measurements show some discrepancies with bottom-up

inventories of the industrially produced, synthetic ﬂuorinated (F) gases

(AII.2.4 to AII.2.15). European HFC-23 emissions are greatly under-re-

ported (Keller et al., 2011) while HFC-125 and 152a are roughly con-

sistent with emissions inventories (Brunner et al., 2012). Globally, HFC-

365mfc and HFC-245fa emissions are overestimated (Vollmer et al.,

2011) while SF

appears to be under-reported (Levin et al., 2010). For

HFC-134a, combining current measurements and lifetimes (Table 2.1,

Chapter 8; WMO, 2010; Prather et al., 2012) gives an estimate of 2010

emissions (~150 Gg yr

–1

) that is consistent with the RCP range (139 to

153 Gg yr

–1

). Without clear guidance on how to correct or place uncer-

tainty on the RCP F-gas emissions, the RCP emissions are reported

without uncertainty estimates in Annex II Tables AII.2.4 to AII.2.15. For

the very long-lived SF

and perﬂuorocarbons (CF

, C

) uncer-

tainty in lifetimes does not signiﬁcantly affect the projected abundanc-

es over the 21st century (AII.4.4 to AII.4.7). Projected HFC abundances

depend on the changes in tropospheric OH, which determines their

atmospheric lifetime (Chapter 8). The relative change in hydroxyl rad-

ical (OH), as indicated by the projected OH lifetime of CH

(AII.5.8), is

used to project HFCs including uncertainties (likely range) (AII.4.8 to

AII.4.15) (Prather et al., 2012).

Scenarios for the ozone-depleting GHG under control of the Montreal

Protocol (chloroﬂuorocarbons (CFCs), HCFCs, halons in AII.4.16) follow

scenario A1 of the 2010 WMO Ozone Assessment (WMO, 2010; Table

5-A3). All CFC abundances decline throughout the century, but some

HCFC abundances increase to 2030 before their phase-out and decline.

The summed ERF of all these F-gases is approximately constant (0.35

to 0.39 W m

–2

) up to year 2040 for all RCPs but declines thereafter. In

RCP8.5, the drop in ERF from the Montreal Protocol gases is nearly

made up by the growth in HFCs (Tables AII.6.4 to AII.6.6, Chapter 8).

11.3.5.1.2 Tropospheric and stratospheric O

Projected O

changes are broken into tropospheric and stratospheric

columns (Dobson Unit (DU); see AII.5.1 and AII.5.2) because each has

different driving factors and RF efﬁciencies (Chapter 8). Tropospheric

Figure 11.21 | Projections for CH

(a) anthropogenic emissions (MtCH

–1

) and (b)

atmospheric abundances (ppb) for the four RCP scenarios (2010–2100). Natural emis-

sions in 2010 are estimated to be 202 ± 35 MtCH

–1

(see Chapter 8). The thick solid

lines show the published RCP2.6 (light blue), RCP4.5 (dark blue), RCP6.0 (orange) and

RCP8.5 (red) values. Thin lines with markers show values from this assessment (denot-

ed as RCPn.n

, following methods of Prather et al. (2012) and Holmes et al. (2013):

red plus, RCP8.5; orange square, RCP6.0; light blue circle, RCP4.5; dark blue asterisk,

RCP2.6. The shaded region shows the likely range from the Monte Carlo calculations

that consider uncertainties, including in current anthropogenic emissions.

2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

100

200

300

400

500

600

700

800

900

1000

1100

anthropogenic emissions (Mt CH

-1

)

2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

500

1000

1500

2000

2500

3000

3500

4000

4500

abundance (ppb)

RCP2.0

RCP4.5

RCP6.0

RCP8.5

(b)

(a)

RCP2.6&

RCP4.5&

RCP6.0&

RCP8.5&

999

Near-term Climate Change: Projections and Predictability Chapter 11

changes are driven by anthropogenic emissions of CH

, NO

, CO,

NMVOC (AII.2.2.16 to AII.2.2.18). Small changes (<10%) are project-

ed over the next few decades. By 2100 tropospheric O

decreases in

RCP2.6, 4.5 and 6.0 but increases in RCP8.5 due to CH

increases.

Higher tropospheric temperatures and humidity drive a decline in trop-

ospheric O

, but stratospheric O

recovery and increased stratosphere–

troposphere exchange can counter that (Shindell et al., 2006; Zeng et

al., 2008, 2010; Kawase et al., 2011; Lamarque et al., 2011). The latter

effect is difﬁcult to quantify but it is included in some of the ACCMIP

and CMIP5 models used to project tropospheric O

. Changes in natu-

ral emissions of NO

, particularly soil and lightning NO

, and biogenic

NMVOC may also alter tropospheric O

abundances (Wild, 2007; Wu et

al., 2007). However, global estimates of their change with climate (e.g.,

Kesik et al., 2006; Monson et al., 2007; Butterbach-Bahl et al., 2009;

Price, 2013) remain highly uncertain.

Best estimates for projected tropospheric O

change following the RCP

scenarios (Table AII.5.2) are based on ACCMIP time slice simulations

for 2030 and 2100 with chemistry–climate models (Young et al., 2013)

and the CMIP5 simulations (Eyring et al., 2013). There is high conﬁ-

dence in these results because similar estimates are obtained when

projections are made using the response of tropospheric O

to key

forcing factors that vary across scenarios (Prather et al., 2001; Steven-

son et al., 2006; Oman et al., 2010; Wild et al., 2012). The ACCMIP

models show a wide range in tropospheric O

burden changes from

2000 to 2100: –5 DU (–15%) in RCP2.6 to +5 DU in RCP8.5. The CMIP5

results are similar but not identical: –3 DU (–9%) to +10 DU (+30%).

The 2030 and 2100 multi-model mean estimates are more robust for

ACCMIP which includes 5 to 11 models (range depends on time slice

and scenario) than for CMIP5 (4 models). Tropospheric O

changes in

the near term (2030–2040) are small (±2 DU), except for RCP8.5 (>3

DU), which shows continued growth through to 2100 driven primarily

by CH

increases. The ERF from tropospheric O

changes (AII.6.7b) par-

allels the O

burden change (Stevenson et al., 2013).

Stratospheric O

is being driven by declining chlorine levels, changing

O and CH

, cooler temperatures from increased CO

, and a more

vigorous overturning circulation in the stratosphere driven by more

wave propagation under climate change (Butchart et al., 2006; Eyring

et al., 2010; Oman et al., 2010). Overall stratospheric O

is expected

to increase in the coming decades, reversing the majority of the loss

that occurred between 1980 and 2000. Best estimates for global mean

stratospheric O

change under the RCP scenarios (Table AII.5.1) are

taken from the CMIP5 results (Eyring et al., 2013). By 2100 stratospher-

ic O

columns show a 5 to 7% increase above 2000 levels for all RCPs,

recovering to within 1% of the pre-ozone hole 1980 levels by 2050, but

with latitudinal differences.

11.3.5.1.3 Aerosols

Aerosol species can be emitted directly (mineral dust, sea salt, BC

and some organic carbon (OC)) or indirectly through precursor gases

(SO

, ammonia, nitrogen oxides, hydrocarbons); see Chapter 7. CMIP5

models (Lamarque et al., 2011; Shindell et al., 2013) have projected

changes in aerosol burden (Tg) and aerosol optical depth (AOD) to year

2100 using RCP emissions for anthropogenic source (Tables AII.5.3 to

AII.5.8). Total AOD is dominated by dust and sea salt, but absorbing

aerosol optical depth (AAOD) is primarily of anthropogenic origin

(Chapter 7). Uniformly, anthropogenic aerosols decrease under RCPs

as expected from the declining emissions (11.3.5, Figure 8.2, AII.2.17

to AII.2.22). From years 2010 to 2030 the aerosol burdens decrease

across the RCPs but at varied rates: for sulphate from 6% (RCP8.5)

to 23% (RCP2.6); for BC from 5% (RCP4.5) to 15% (RCP2.6), and for

OC from 0% (RCP6.0) to 11% (RCP4.5). The summed aerosol load-

ing of these three anthropogenic components drop from year 2010 to

year 2030 by 5% to 12% (across RCPs), and by year 2100 this drop is

24% to 39% (Tables AII.5.5 to AII.5.7). These evolving aerosol loadings

reduce the magnitude of the negative aerosol forcing (Chapter 8; Table

AII.6.9) even in the near term (11.3.6.1).

11.3.5.2 Projections of Air Quality for the 21st Century

Future air quality depends on anthropogenic emissions (local, regional

and global), natural biogenic emissions and the physical climate (e.g.,

Steiner et al., 2006, 2010; Meleux et al., 2007; Tao et al., 2007; Wu et al.,

2008; Doherty et al., 2009; Carlton et al., 2010; Tai et al., 2010; Hoyle

et al., 2011). This assessment focuses on O

and PM

2.5

in surface air,

reﬂecting the preponderance of published literature and multi-model

assessments for these air pollutants (e.g., HTAP, 2010a) plus the chem-

istry–climate CMIP5 and ACCMIP model simulations. Nitrogen and acid

deposition is addressed in Chapter 6. Toxic atmospheric species such as

mercury and persistent organic pollutants are outside this assessment

(Jacob and Winner, 2009; NRC, 2009; HTAP, 2010b, 2010c).

The global and continental-scale surface O

and PM

2.5

changes assessed

here include (1) the impact of climate change (Section 11.3.5.2.1), and

(2) the impact of changing global and regional anthropogenic emis-

sions (Section 11.3.5.2.2). Changes in local emissions within a met-

ropolitan region or surrounding air basin on local air quality projec-

tions are not assessed here. Anthropogenic emissions of O

precursors

include NO

, CH

, CO, and NMVOC; PM

2.5

is both directly emitted (OC,

BC) and produced photochemically from precursor emissions (NO

, SO

, NMVOC) (see Tables AII.2.2,16-22). Recent reviews describe

the impact of temperature-driven processes on O

and PM

2.5

air qual-

ity from observational and modelling evidence (Isaksen et al., 2009;

Jacob and Winner, 2009; Fiore et al., 2012). Projecting future air quality

empirically from a mean surface warming using the observed correla-

tion with temperature is problematic, as there is little evidence that

future pollution episodes can be simply modelled as all else being

equal except for a uniform temperature shift. Air quality relationships

with synoptic conditions may be more robust (e.g., Dharshana et al.,

2010; Appelhans et al., 2012; Tai et al., 2012a, 2012b), but require the

ability to project changes in key conditions such as blocking and stag-

nation episodes. The response of blocking frequency to global warming

is complex, with summertime increases possible over some regions,

but models are generally biased compared to observed blocking sta-

tistics, and indicate even larger uncertainty in projecting changes in

blocking intensity and persistence (Box 14.2).

11.3.5.2.1 Climate-driven changes

Projecting regional air quality faces the challenge of simulating

ﬁrst the changes in regional climate and then the feedbacks from

atmospheric chemistry and the biosphere. The air pollution response

1000

Chapter 11 Near-term Climate Change: Projections and Predictability

to climate-driven changes in the biosphere is uncertain as to sign

because of competing effects: for example, plants currently emit more

NMVOC with warmer temperatures; with higher CO

and water stress

plants may emit less; with a warmer climate the vegetation types may

shift to emit either more or less NMVOC; shifting vegetation types

may also alter surface uptake of ozone and aerosols; and our under-

standing of chemical oxidation pathways for biogenic emissions is

incomplete (e.g., Monson et al., 2007; Carlton et al., 2009; Hallquist

et al., 2009; Ito et al., 2009; Paciﬁco et al., 2009, 2012; Paulot et al.,

2009). Although studies have split the cause of air quality changes

into climate versus emissions, these attributions are difﬁcult to assess

for several reasons: the global-to-regional down-scaling of meteorol-

ogy that is model dependent (see Chapters 9 and 14; also Manders

et al., 2012), the brief simulations that preclude clear separation of

climate change from climate variability (Nolte et al., 2008; Fiore et al.,

2012; Langner et al., 2012a), and the lack of systematically explored

standard scenarios for local anthropogenic emissions, land use change

and biogenic emissions.

Ozone

Globally, a warming climate decreases baseline surface O

almost every-

where but increases O

levels in some polluted regions and seasons.

The surface ozone response to climate change alone between 2000

and 2030 is shown in Figure 11.22 (CLIMATE), where the ranges reﬂect

multi-model differences in spatial averages (solid green lines) and spa-

tial variability within a single model (dashed green lines). There is high

conﬁdence that in unpolluted regions, higher water vapour abundances

and temperatures enhance O

destruction, leading to lower baseline O

levels in a warmer climate (e.g., global average in Figure 11.22). Higher

levels such as in RCP8.5 can offset this climate-driven decrease in

baseline O

. Other large-scale factors that could increase baseline O

a warming climate include increased lightning NO

and stratospheric

inﬂux of O

(see Section 11.3.5.1). Evidence and agreement are lim-

ited regarding the impact of climate change on long-range transport

of pollutants (Wu et al., 2008; HTAP, 2010a; Doherty et al., 2013). The

global chemistry-climate models assessed here (Figures 11.22, 11.23ab)

include most of these feedback processes, but a systematic evaluation

of their relative impacts is lacking.

In polluted regions, observations show that high-O

episodes correlate

with high temperatures (e.g., Lin et al., 2001; Bloomer et al., 2009; Ras-

mussen et al., 2012), but these episodes also coincide with cloud-free

enhanced photochemistry and with air stagnation that concentrates

pollution near the surface (e.g., AR4 Box 7.4). Other temperature-re-

lated factors, such as biogenic emissions from vegetation and soils,

volatilization of NMVOC, thermal decomposition of organic nitrates to

and wildﬁre frequency may increase with a warming climate and

are expected to increase surface O

(e.g., Doherty et al., 2013; Skjøth

and Geels, 2013; and as reviewed by Isaksen et al. (2009), Jacob and

Winner (2009) and Fiore et al. (2012)), although some of these process-

es are known to have optimal temperature ranges (e.g., Sillman and

Samson, 1995; Guenther et al., 2006; Steiner et al., 2010). Overall, the

integrated effect of these processes on O

remains poorly understood,

and they have been implemented with varying levels of complexity in

the models assessed here.

Models show that a warmer atmosphere can lead to local O

increas-

es during the peak pollution season (e.g., by 2 to 6 ppb within Cen-

tral Europe by 2030; green dashed line for Europe in Figure 11.22).

Regional models projecting summer daytime statistics tend to simu-

late a wider range of climate-driven changes (e.g., Zhang et al., 2008;

Avise et al., 2012; Kelly et al., 2012), with most studies focusing on

2050 (Fiore et al., 2012) or beyond. For example, summer tempera-

ture extremes over parts of Europe are projected to warm more than

the corresponding mean local temperatures due to enhanced variabil-

ity at interannual to intraseasonal time scales (see Section 12.4.3.3).

Several modelling studies note a longer season for O

pollution in a

warmer world (Nolte et al., 2008; Racherla and Adams, 2008). For some

regions, models agree on the sign of the O

response to a warming

climate (e.g., increases in northeastern USA and southern Europe;

decreases in northern Europe), but they often disagree (e.g., the mid-

west, southeast, and western USA (Jacob and Winner, 2009; Weaver et

al., 2009; Langner et al., 2012a; Langner et al., 2012b; Manders et al.,

2012)). Several studies have suggested a role for changing synoptic

meteorology on future air pollution levels (Leibensperger et al., 2008;

Jacob and Winner, 2009; Weaver et al., 2009; Lang and Waugh, 2011;

Tai et al., 2012a, 2012b; Turner et al., 2013), but projected regional

changes in synoptic conditions are uncertain (see Sections 11.3.2.4,

12.4.3.3 and Box 14.2). Observational and modelling evidence togeth-

er indicate that, all else being equal, a warming climate is expected to

increase surface O

in polluted regions (medium conﬁdence), although

a systematic evaluation of all the factors driving extreme pollution epi-

sodes is lacking.

Aerosols

Evaluations as to whether climate change will worsen or improve

aerosol pollution are model-dependent. Assessments are confound-

ed by opposing inﬂuences on the individual species contributing to

total PM

2.5

and large interannual variability caused by the small-scale

meteorology (e.g., convection and precipitation) that controls aerosol

concentrations (Mahmud et al., 2010). For a full discussion, see Chap-

ter 7. Higher temperatures generally decrease nitrate aerosol through

enhanced volatility but increase sulphate aerosol through faster pro-

duction, although observed PM

2.5

–temperature correlations also reﬂect

humidity and synoptic meteorology (e.g., Aw and Kleeman, 2003; Liao

et al., 2006; Racherla and Adams, 2006; Unger et al., 2006a; Hedegaard

et al., 2008; Jacobson, 2008; Kleeman, 2008; Pye et al., 2009; Tai et

al., 2012b). Natural aerosols may increase with temperature, particu-

larly carbonaceous aerosol from wildﬁres, mineral dust, and biogenic

secondary organic aerosol (SOA; Section 7.3.5; Mahowald and Luo,

2003; Tegen et al., 2004; Jickells et al., 2005; Woodward et al., 2005;

Mahowald et al., 2006; Liao et al., 2007; Mahowald, 2007; Tagaris et

al., 2007; Heald et al., 2008; Spracklen et al., 2009; Jiang et al., 2010;

Yue et al., 2010; Carvalho et al., 2011; Fiore et al., 2012). SOA formation

also depends on anthropogenic emissions and atmospheric oxidizing

capacity (Carlton et al., 2010; Jiang et al., 2010).

Aerosols are scavenged from the atmosphere by precipitation and

direct deposition (see Chapter 7). Hence most components of PM

2.5

are

anti-correlated with precipitation (Tai et al., 2010), and aerosol bur-

dens are expected to decrease on average where precipitation increas-

es (Racherla and Adams, 2006; Liao et al., 2007; Tagaris et al., 2007;

Zhang et al., 2008; Avise et al., 2009; Pye et al., 2009). However, a shift

in the frequency and type of precipitation may be as important as the

change in mean precipitation (see Chapter 7). Seasonal and regional

1001

Near-term Climate Change: Projections and Predictability Chapter 11

differences in aerosol burdens versus precipitation further preclude a

simple scaling of aerosol response to precipitation changes (Kloster et

al., 2010; Fang et al., 2011). Climate-driven changes in the frequency

of drizzle and the mixing depths or ventilation of the surface layer also

inﬂuence projected changes in PM

2.5

(e.g., Kleeman, 2008; Dawson et

al., 2009; Jacob and Winner, 2009; Mahmud et al., 2010), and aerosols

in turn can inﬂuence locally clouds, precipitation and scavenging (e.g.,

Zhang et al., 2010b; see Section 7.6).

While PM

2.5

is expected to decrease in regions where precipitation

increases, the climate variability at these scales results in only low con-

ﬁdence for projections at best. Further, consensus is lacking on the

other factors including climate-driven changes in biogenic and mineral

dust aerosols, leading to no conﬁdence level being attached to the

overall impact of climate change on PM

2.5

distributions.

11.3.5.2.2 Changes driven by regional and global anthropogenic

pollutant emissions

Projections for annual-mean surface O

and PM

2.5

for 2000 through

2100 are shown in Figures 11.23a and 11.25b, respectively. Changes are

spatially averaged over selected world (land-only) regions and include

the combined effects of emission and climate changes under the RCPs.

Results are taken from the ACCMIP models and a subset of the CMIP5

models that included atmospheric chemistry. Large interannual varia-

tions are evident in the CMIP5 transient simulations, and large regional

variations occur in both the CMIP5 and the ACCMIP decadal time slice

simulations (see Lamarque et al., (2013) for ACCMIP overview).

The largest surface O

changes under the RCP scenarios are much

smaller than those projected under the older SRES scenarios (Figures

11.22 and 11.23a; Table AII.7; Lamarque et al., 2011; Wild et al., 2012).

By 2100, global annual multi-model mean surface O

rises by 12 ppb

in SRES A2, but by only 3 ppb in RCP8.5. Much larger O

decreases

are projected to occur by 2030 under the MFR scenario (Figure 11.22),

which assumes that existing control technologies are applied uniform-

ly across the globe (Dentener et al., 2006).

For RCP2.6, RCP4.5 and RCP6.0, the CMIP5/ACCMIP models pro-

ject that continental-scale spatially averaged near-term surface O

decreases or changes little (–4 to +1 ppb) from 2000 to 2030 for all

regions except South Asia, whereas the long-term change to 2100 is

a consistent decrease (–14 to –3 ppb) for all regions (Figure 11.23a;

and Table AII.7.3). For RCP8.5, the CMIP5/ACCMIP models project

continental-scale spatial average surface O

increases of up to +5 ppb

for both 2030 and 2100 (Figure 11.23a; Table AII.7.3). The increas-

es under RCP8.5 reﬂect the prominent rise in methane abundances

(Kawase et al., 2011; Lamarque et al., 2011; Wild et al., 2012), which

by 2100 raise background O

levels by 5 to 14 ppb over continen-

tal-scale regions, and on average by about 8 ppb (25% above current

levels) above RCP4.5 and RCP6.0 which include more stable methane

pathways over the 21st century (high conﬁdence). Earlier studies have

shown that rising CH

abundances (and global NO

emissions) increase

baseline O

and can offset aggressive local emission reductions and

lengthen the O

pollution season (Jacob et al., 1999; Prather et al.,

2001, 2003; Fiore et al., 2002, 2009; Hogrefe et al., 2004; Granier et

al., 2006; Szopa et al., 2006; Tao et al., 2007; Huang et al., 2008; Lin et

al., 2008; Wu et al., 2008; Avise et al., 2009; Chen et al., 2009b;HTAP,

2010a; Wild et al., 2012; Lei et al., 2013).

The O

changes driven by the RCP emissions scenarios with ﬁxed,

present-day climate (Figure 11.22; Wild et al., 2012) are similar to the

changes estimated with the full chemistry–climate models (Figure

11.23a). Although the regions considered are not identical, the evi-

dence supports a major role for global emissions in determining near-

term O

concentrations. Overall, the multi-model ranges associated

with the inﬂuence of near-term climate change on global and regional

air quality are smaller than those across emission scenarios (Figure

11.22; HTAP, 2010a; Wild et al., 2012).

Aerosol changes driven by anthropogenic emissions depend somewhat

on oxidant levels (e.g., Unger et al., 2006a; Kleeman, 2008; Leibens-

perger et al., 2011a), but generally sulphate follows SO

emissions and

carbonaceous aerosols follow the primary elemental and OC emis-

sions. Competition between sulphate and nitrate for ammonium (see

Chapter 7) means that reducing SO

emissions while increasing NH

emissions as in the RCPs (Tables AII.2.19 and AII.2.20) would lead to

near-term nitrate aerosol levels equal to or higher than those of sul-

phate in some regions; see Section 7.3.5.2 (Bauer et al., 2007; Pye et

al., 2009; Bellouin et al., 2011; Henze et al., 2012).

Regional PM

2.5

in the CMIP5 and ACCMIP chemistry–climate models

following the RCP scenarios generally declines over the 21st centu-

ry, with little difference across the individual scenarios except for the

South and East Asia regions (Figure 11.23b). The noisy projections

over Africa, the Middle East and to some extent Australia, reﬂect dust

Figure 11.22 | Changes in surface O

(ppb) between year 2000 and 2030 driven by

climate alone (CLIMATE, green) or driven by emissions alone, following current legisla-

tion (CLE, black), maximum feasible reductions (MFR, grey), SRES (blue) and RCP (red)

emission scenarios. Results are reported globally and for the four northern mid-latitude

source regions used by the Task Force on Hemispheric Transport of Air Pollution (HTAP,

2010a). Where two vertical bars are shown (CLE, MFR, SRES ), they represent the multi-

model standard deviation of the annual mean based on (left bar; SRES includes A2

only) the Atmospheric Composition Change: a European Network (ACCENT)/Photocomp

study (Dentener et al., 2006) and (right bar) the parametric HTAP ensemble (Wild et

al., 2012; four SRES and RCP scenarios included). Under Global, the leftmost (dashed

green) vertical bar denotes the spatial range in climate-only changes from one model

(Stevenson et al., 2005) while the green square shows global annual mean climate-only

changes in another model (Unger et al., 2006b). Under Europe, the dashed green bar

denotes the range of climate-only changes in summer daily maximum O

in one model

(Forkel and Knoche 2006). (Adapted from Figure 3 of Fiore et al., 2012.)

-10

-5

surface O3 change (ppb)

2030 - 2000

CLIMATE SRES RCPCLE MFR

Global N.Am./USA Europe E.Asia S.Asia

1002

Chapter 11 Near-term Climate Change: Projections and Predictability

sources and their strong dependence on interannual meteorological

variability. Over the two Asian regions, different PM

2.5

levels between

the RCPs are due to (1) OC emission trajectories over South Asia and

(2) combined changes in carbonaceous aerosol and SO

over East Asia

(Fiore et al., 2012) (Figure 8.SM.1).

Global emissions of aerosols and precursors can contribute to high-PM

events. For example, dust trans-oceanic transport events are observed

to increase aerosols in downwind regions (Prospero, 1999; Grousset

et al., 2003; Chin et al., 2007; Fairlie et al., 2007; Huang et al., 2008;

Liu et al., 2009; Ramanathan and Feng, 2009; HTAP, 2010a). The bal-

ance between regional and global anthropogenic emissions versus

climate-driven changes for PM

2.5

will vary regionally with future chang-

es in precipitation, wildﬁres, dust and biogenic emissions.

In summary, lower air pollution levels are projected following the

RCP emissions as compared to the SRES emissions in the TAR and

AR4, reﬂecting implementation of air pollution control measures (high

conﬁdence). The range in projections of air quality is driven primarily

by emissions (including CH

) rather than by physical climate change

(medium conﬁdence). The total emission-driven range in air quality—

including the CLE and MFR scenarios—is larger than that spanned by

the RCPs (see Section 11.3.5.1 for comparison of RCPs and SRES).

Figure 11.23a | Projected changes in annual mean surface O

(ppb mole fraction) from 2000 to 2100 following the RCP scenarios (8.5, red; 6.0, orange; 4.5, light blue; 2.6, dark

blue). Results in each box are averaged over the designated coloured land regions. Continuous coloured lines and shading denote the average and full range of four chemistry–cli-

mate models (GFDL-CM3, GISS-E2-R, and NCAR-CAM3.5 from CMIP5 plus LMDz-ORINCA). Coloured dots and vertical black bars denote the average and full range of the ACCMIP

models (CESM-CAM-superfast, CICERO-OsloCTM2, CMAM, EMAC-DLR, GEOSCCM, GFDL-AM3, HadGEM2, MIROC-CHEM, MOCAGE, NCAR-CAM3.5, STOC-HadAM3, UM-CAM)

for decadal time slices centred on 2010, 2030, 2050 and 2100. Participation in the decadal slices ranges from 2 to 12 models (see (Lamarque et al., 2013)). Changes are relative to

the 1986–2005 reference period for the CMIP5 transient simulations, and relative to the average of the 1980 and 2000 decadal time slices for the ACCMIP ensemble. The average

value and model standard deviation for the reference period is shown in the top of each panel for CMIP5 models (left) and ACCMIP models (right). In cases where multiple ensemble

members are available from a single model, they are averaged prior to inclusion in the multi-model mean. (Adapted from Fiore et al., 2012.)

1003

Near-term Climate Change: Projections and Predictability Chapter 11

11.3.5.2.3 Extreme weather and air pollution

Extreme air quality episodes are associated with changing weather

patterns, such as heat waves and stagnation episodes (Logan, 1989;

Vukovich, 1995; Cox and Chu, 1996; Mickley et al., 2004; Stott et

al., 2004). Heat waves are generally associated with poor air quality

(Ordóñez et al., 2005; Vautard et al., 2005; Lee et al., 2006b; Struzewska

and Kaminski, 2008; Tressol et al., 2008; Vieno et al., 2010; Hodnebrog

et al., 2012). Although anthropogenic climate change has increased

the near-term risk of such heat waves (Stott et al., 2004; Clark et al.,

2010; Diffenbaugh and Ashfaq, 2010; Chapter 10; Section 11.3.2.5.1),

projected changes in the frequency of regional air stagnation events,

which are largely driven by blocking events, remain difﬁcult to assess:

the frequency of blocking events with persistent high pressure is

projected to decrease in a warming climate but increases may occur

in some regions, and projected changes in their intensity and duration

remain uncertain (Chapters 9 and 14; Box 14.2). Projections in regional

air pollution extremes are necessarily conditioned on projected chang-

es in these weather patterns. The severity of extreme pollution events

also depends on local emissions (see references in Fiore et al., 2012).

Feedbacks from vegetation (higher biogenic NMVOC emissions, lower

stomatal uptake of O

with higher temperatures) can combine with

similar positive feedbacks via dust and wildﬁres to worsen air pollution

and its impacts during heat waves (Lee et al., 2006a; Jiang et al., 2008;

Royal Society, 2008; Flannigan et al., 2009; Andersson and Engardt,

2010; Vieno et al., 2010; Hodnebrog et al., 2012; Jaffe and Wigder,

2012; Mues et al., 2012).

Figure 11.23b | Projected changes in annual mean surface PM

2.5

(micrograms per cubic metre of aerosols with diameter less than 2.5 μm) from 2000 to 2100 following the RCP

scenarios (8.5 red, 6.0 orange, 4.5 light blue, 2.6 dark blue). PM

2.5

values are calculated as the sum of individual aerosol components (black carbon + organic carbon + sulphate

+ secondary organic aerosol + 0.1*dust + 0.25*sea salt). Nitrate was not reported for most models and is not included here. See Figure 11.23a for details, but note that fewer

models contribute: GISS-E2-R and GFDL-CM3 from CMIP5; CICERO-OsloCTM2, GEOSCCM, GFDL-AM3, HadGEM2, MIROC-CHEM, and NCAR-CAM3.5 from ACCMIP. (Adapted

from Fiore et al., 2012.)

1004

Chapter 11 Near-term Climate Change: Projections and Predictability

There is high agreement across numerous modelling studies projecting

increases in extreme O

pollution events over the USA and Europe,

but the projections do not consistently agree at the regional level

(Kleeman, 2008; Jacob and Winner, 2009; Jacobson and Streets, 2009;

Weaver et al., 2009; Huszar et al., 2011; Katragkou et al., 2011; Langner

et al., 2012b) because they depend on accurate projections of local

emissions, regional climate and poorly understood biospheric feed-

backs. Although observational evidence clearly demonstrates a strong

statistical correlation between extreme temperatures (heat waves) and

pollution events, this temperature correlation reﬂects in part the coin-

cident occurrence of stagnation events and clear skies that also drive

extreme pollution. Mechanistic understanding of biogenic emissions,

deposition and atmospheric chemistry is consistent with a tempera-

ture-driven increase in pollution extremes in already polluted regions,

although these processes may not scale simply with mean tempera-

ture under a changing climate (see Section 11.3.5.2.1), and better pro-

jections of the changing meteorology at regional scales are needed.

Assuming all else is equal (e.g., local anthropogenic emissions) this

collective evidence indicates that uniformly higher temperatures in

polluted environments will trigger regional feedbacks during air stag-

nation episodes that will increase peak pollution (medium conﬁdence).

11.3.6 Additional Uncertainties in Projections of

Near-term Climate

As discussed in Section 11.3.1, most of the projections presented in

Sections 11.3.2 to 11.3.4 are based on the RCP4.5 scenario and rely

on the spread among the CMIP5 ensemble of opportunity as an ad hoc

measure of uncertainty. It is possible that the real world might follow

a path outside (above or below) the range projected by the CMIP5

models. Such an eventuality could arise if there are processes operating

in the real world that are missing from, or inadequately represented in,

the models. Two main possibilities must be considered: (1) Future radi-

ative and other forcings may diverge from the RCP4.5 scenario and,

more generally, could fall outside the range of all the RCP scenarios; (2)

The response of the real climate system to radiative and other forcing

may differ from that projected by the CMIP5 models. A third possibility

is that internal ﬂuctuations in the real climate system are inadequately

simulated in the models. The ﬁdelity of the CMIP5 models in simulating

internal climate variability is discussed in Chapter 9.

Future changes in RF will be caused by anthropogenic and natural

processes. The consequences for near-term climate of uncertainties

in anthropogenic emissions and land use are discussed in Section

11.3.6.1. The uncertainties in natural RF that are most important for

near-term climate are those associated with future volcanic eruptions

and variations in the radiation received from the Sun (solar output),

and are discussed in Section 11.3.6.2. In addition, carbon cycle and

other biogeochemical feedbacks in a warming climate could poten-

tially lead to abundances of CO

and CH

(and hence RF) outside the

range of the RCP scenarios, but these feedbacks are not expected to

play a major role in near term climate—see Chapters 6 and 12 for

further discussion.

The response of the climate system to radiative and other forcing is

inﬂuenced by a very wide range of processes, not all of which are

adequately simulated in the CMIP5 models (Chapter 9). Of particular

concern for projections are mechanisms that could lead to major ‘sur-

prises’ such as an abrupt or rapid change that affects global-to-con-

tinental scale climate. Several such mechanisms are discussed in this

assessment report; these include: rapid changes in the Arctic (Section

11.3.4 and Chapter 12), rapid changes in the ocean’s overturning cir-

culation (Chapter 12), rapid change of ice sheets (Chapter 13) and

rapid changes in regional monsoon systems and hydrological climate

(Chapter 14). Additional mechanisms may also exist as synthesized in

Chapter 12. These mechanisms have the potential to inﬂuence climate

in the near term as well as in the long term, albeit the likelihood of

substantial impacts increases with global warming and is generally

lower for the near term. Section 11.3.6.3 provides an overall assess-

ment of projections for global mean surface air temperature, taking

into account all known quantiﬁable uncertainties.

11.3.6.1 Uncertainties in Future Anthropogenic Forcing

and the Consequences for Near-term Climate

Climate projections for periods prior to year 2050 are not very sensi-

tive to available alternative scenarios for anthropogenic CO

emissions

(see Section 11.3.2.1.1; Stott and Kettleborough, 2002; Meehl et al.,

2007b). Near-term projections, however, may be sensitive to changes

in emissions of climate forcing agents with lifetimes shorter than CO

particularly the GHGs CH

(lifetime of a decade), tropospheric O

(life-

time of weeks), and tropospheric aerosols (lifetime of days). Although

the RCPs and SRES scenarios span a similar range of total effective

radiative forcing (ERF, see Section 7.5, Figure 7.3, Chapter 8), they

include different ranges of ERF from aerosol, CH

, and tropospheric O

(see Section 11.3.5.1, Tables AII.6.2 and AII.6.7 to AII.6.10). From years

2000 to 2030 the change in ERF across the RCPs ranges from –0.05 to

+0.14 W m

–2

for CH

and from –0.04 to +0.08 W m

–2

for tropospheric O

(Tables AII.6.2 and AII.6.7; Stevenson et al., 2013). From years 2000 to

2030 the total aerosol ERF becomes less negative, increasing by +0.26

W m

–2

for RCP8.5 (only RCP evaluated; for ACCMIP results see Table

AII.6.9; Shindell et al., 2013). Total ERF change across scenarios derived

from the CMIP5 ensemble can be compared only beginning in 2010.

For the period 2010 to 2030, total ERF in the CMIP5 decadal averages

increases by +0.5 to +1.0 W m

–2

(RCP2.6 and RCP6.0 to RCP8.5; Table

AII.6.10) while total ERF from the published RCPs increases by +0.7

to +1.1 W m

–2

(RCP2.6 and RCP6.0 to RCP8.5, Table AII.6.8). Here we

re-examine the near-term temperature increases projected from the

RCPs (see Section 11.3.2.1.1) and assess the potential for changes in

near-term anthropogenic forcing to induce climate responses that fall

outside these scenarios.

For the different RCP pathways the increase in global mean surface

temperature by 2026–2035 relative to the reference period 1986-2005

ranges from 0.74°C (RCP2.6 and RCP6.0) to 0.94°C (RCP8.5) (median

of CMIP5 models, see Figure 11.24, Table AII.7.5). This inter-scenario

range of 0.20°C is smaller than the inter-model spread for an indi-

vidual scenario: 0.33°C to 0.52°C (deﬁned as the 17 to 83% range

of the decadal means of the models). This RCP inter-scenario spread

may be too narrow as discussed in Section 11.3.5.1. The temperature

increase of the most rapidly warming scenario (RCP8.5) emerges from

inter-model spread (i.e., becomes greater than two times the 17 to

83% range) by about 2040, due primarily to increasing CH

and CO

By 2050 the inter-scenario spread is 0.8ºC whereas the model spread

1005

Near-term Climate Change: Projections and Predictability Chapter 11

for each scenario is only 0.6ºC. At 2040 the ERF in the published RCPs

ranges from 2.6 (RCP2.6) to 3.6 (RCP8.5) W m

–2

, and about 40% of this

difference is due to the steady increases in CH

and tropospheric O

found only in RCP8.5. RCP6.0 has the lowest ERF and thus warms less

rapidly than other RCPs up to 2030 (Table AII.6.8).

In terms of geographic patterns of warming, differences between

RCP8.5 and RCP2.6 are within ±0.5°C over most of the globe for both

summer and winter seasons for 2016–2035 (Figure 11.24b), but by

2036–2055 RCP8.5 is projected to be warmer than RCP2.6 by 0.5°C

to 1.0°C over most continents, and by more than 1.0°C over the Arctic

in winter. Although studies suggest that the Arctic response is complex

and particularly sensitive to BC aerosols (Flanner et al., 2007; Quinn

et al., 2008; Jacobson, 2010; Ramana et al., 2010; Bond et al., 2013;

Sand et al., 2013), the difference in ERF between RCP2.6 and RCP8.5

is dominated by the GHGs, as the BC atmospheric burden is decreas-

ing through the century with little difference across the RCPs (Table

AII.5.7).

Large changes in emissions of the well-mixed greenhouse gases

(WMGHGs) produce only modest changes in the near term because

these gases are long lived: For example, a 50% cut in Kyoto-gas emis-

sions beginning in 1990 offsets the warming that otherwise would

have occurred by only –0.11°C ± 0.03°C after 12 years (Prather et al.,

2009). In contrast, many studies have noted the large potential for air

pollutant emission reductions to inﬂuence near-term climate because

RF from these species responds almost immediately to changes in

emissions. Decreases in sulphate aerosol have occurred through miti-

gation of both air pollution and fossil-fuel emissions, and are expected

to produce a near-term rise in surface temperatures (e.g., Jacobson and

Streets, 2009; Raes and Seinfeld, 2009; Wigley et al., 2009; Kloster et

al., 2010; Makkonen et al., 2012).

Because global mean aerosol forcing decreases in all RCP scenarios

(AII.5.3 to AII.5.7, AII.6.9; see Section 11.3.5), the potential exists for

a systematic difference between the CMIP3 models forced with the

SRES scenarios and the CMIP5 models forced with the RCP scenarios.

One study directly addressed the impacts of aerosols on climate under

the RCP4.5 scenario, and found that the aerosol emission reductions

induce about a 0.2°C warming in the near term compared with ﬁxed

2005 aerosol levels (more indicative of the SRES CMIP3 aerosols) (Levy

et al., 2013). The cooling over the period 1951–2010 that is attribut-

ed to non-WMGHG anthropogenic forcing in the CMIP5 models (Fig-

ures 10.4 and 10.5) has a likely range of –0.25°C ± 0.35°C compared

to +0.9°C ± 0.4°C for WMGHG. The non-WMGHG forcing generally

includes the inﬂuence of non-aerosol warming agents over the histor-

ical period such as tropospheric ozone, and a simple correction would

give an aerosol-only cooling that is about 50% larger in magnitude

(see ERF components, Chapter 8). The near-term reductions in total

aerosol emissions, however, even under the MFR scenario, are at most

about 50% (AII.2.17 to AII.2.22), indicating a maximum near-term

temperature response of about half that induced by the addition of

aerosols over the last century. Hence, the evidence indicates that dif-

ferences in aerosol loading from the SRES (conservatively assuming

roughly constant aerosols) to the RCP scenarios can increase warming

in the CMIP5 models relative to the CMIP3 models by up to 0.2°C in

the near term for the same WMGHG forcing (medium conﬁdence).

Many studies show that air pollutants inﬂuence climate and identi-

fy approaches to mitigate both air pollution and global warming by

0.0

0.5

1.0

1.5

2.0

2.5

2020 2030 2040 2050

Temperature change (°C w.r.t. 1986-2005)

SRES A1b

RCP 2.6

RCP 4.5

RCP 6.0

RCP 8.5

UNEP-ref

-CH4

1.0

1.5

2.0

2.5

3.0

Temperature change (°C w.r.t. 1850-1900)

Figure 11.24a | Near-term increase in global mean surface air temperatures (°C) across scenarios. Increases in 10-year mean (2016–2025, 2026–2035, 2036–2045 and

2046–2055) relative to the reference period (1986–2005) of the globally averaged surface air temperatures. Results are shown for the CMIP5 model ensembles (see Annex I for

listing of models included) for RCP2.6 (dark blue), RCP4.5 (light blue), RCP6.0 (orange), and RCP8.5 (red) and the CMIP3 model ensemble (22 models) for SRES A1b (black). The

multi-model median (square), 17 to 83% range (wide boxes), 5 to 95% range (whiskers) across all models are shown for each decade and scenario. Values are provided in Table

AII.7.5. Also shown are best estimates for a UNEP scenario (UNEP-ref, grey upward triangles) and one that implements technological controls on methane emissions (UNEP CH4, red

downward-pointing triangles) (UNEP and WMO, 2011; Shindell et al., 2012a). Both UNEP scenarios are adjusted to reﬂect the 1986–2005 reference period. The right-hand ﬂoating

axis shows increases in global mean surface air temperature relative to the early instrumental period (0.61°C), deﬁned from the difference between 1850–1900 and 1986–2005 in

the Hadley Centre/Climate Research Unit gridded surface temperature data set 4 (HadCRUT4) global mean temperature analysis (Chapter 2 and Table AII.1.3). Note that uncertainty

remains on how to match the 1986–2005 reference period in observations with that in CMIP5 results. See discussion of Figure 11.25.

1006

Chapter 11 Near-term Climate Change: Projections and Predictability

decreasing CH

, tropospheric O

and absorbing aerosols, particularly

BC (e.g., Hansen et al., 2000; Fiore et al., 2002, 2008, 2009; Dentener

et al., 2005; West et al., 2006; Royal Society, 2008; Jacobson, 2010;

Penner et al., 2010; UNEP and WMO, 2011; Anenberg et al., 2012; Shin-

dell et al., 2012b; Unger, 2012; Bond et al., 2013). An alternative set of

technologically based scenarios (UNEP and WMO, 2011) that examined

controls on CH

and BC emissions designed to reduce tropospheric CH

and BC also included reductions of co-emitted species (e.g., CO, OC,

). These reductions were applied in two CMIP5 models, and then

those model responses were combined with the AR4 best estimates

for the range of climate sensitivity and for uncertainty estimates for

each component of RF (Shindell et al., 2012a). This approach provided a

near-term best estimate and range of global mean temperature change

for the reference (UNEP-ref) and CH

-mitigation (UNEP-CH4) scenarios

(Figure 11.24a, adjusted to reﬂect the 1986–2005 reference period).

Under UNEP-CH4, anthropogenic CH

emissions decrease by 24% from

2010 to 2030, and global warming is reduced by 0.16°C (best estimate)

at 2030 and by 0.28°C at 2050. A third UNEP scenario (UNEP-BC+CH4;

not shown) adds reductions in BC by 78% onto CH

mitigation and

reduces warming by an additional 0.12°C (best estimate) at 2030. How-

ever, it greatly increases the uncertainty owing to poor understanding

of associated cloud adjustments (i.e., semi-direct and indirect effects)

as well as of the ratio of BC to co-emitted reﬂective OC aerosols, their

size distributions and mixing states (see Chapter 7, Section 7.5). Corre-

sponding BC reductions in the RCPs are only 4 to 11%.

Beyond global mean temperature, shifting magnitudes and geographic

patterns of emissions may induce aerosol-speciﬁc changes in region-

al atmospheric circulation and precipitation. See Chapter 7, especially

Sections 7.6.2 and 7.6.4, for assessment of this work (Roeckner et al.,

2006; Menon and et al., 2008; Ming et al., 2010, 2011; Ott et al., 2010;

Randles and Ramaswamy, 2010; Allen and Sherwood, 2011; Bollasina

et al., 2011; Leibensperger et al., 2011b;Fyfe et al., 2012; Ganguly et

(°C)

al., 2012; Rotstayn et al., 2012; Shindell et al., 2012b; Teng et al., 2012;

Bond et al., 2013). Recent trends in aerosol–fog interactions and snow-

pack decline are implicated in more rapid regional warming in Europe

(van Oldenborgh et al., 2010; Ceppi et al., 2012; Scherrer et al., 2012),

and coupling of aerosols and soil moisture could increase near-term

local warming in the eastern USA (Mickley et al., 2011). Major changes

in the tropical circulation and rainfall have been attributed to increas-

ing aerosols, but studies often disagree in sign (see Section 11.3.2.4.3,

Chapters 10 and 14). The lack of standardization (e.g., different

regions, different mixtures of reﬂecting and absorbing aerosols) and

agreement across studies prevents generalization of these ﬁndings to

project aerosol-induced changes in regional atmospheric circulation or

precipitation in the near term.

Land use and land cover change (LULCC; see Chapter 6), including

deforestation, forest degradation and agricultural expansion for bioen-

ergy (Georgescu et al., 2009; Anderson-Teixeira et al., 2012), can alter

global climate forcing through changing surface albedo (assessed as

ERF; Chapter 8), the hydrological cycle, GHGs (for CO

, see Chapters 6

and 12), or aerosols. The shift from forest to grassland in many places

since the pre-industrial era has been formally attributed as a cause

of regionally lower mean and extreme temperatures (Christidis et al.,

2013). RCP CO

and CH

anthropogenic emissions include land use

changes (Hurtt et al., 2011) that vary with the underlying storylines

and differ across RCPs. These global-scale changes in crop and pasture

land projected over the near term (+2% for RCP2.6 and RCP8.5; –4%

for RCP4.5and RCP6.0) are smaller in magnitude than the 1950–2000

change (+6%) (see Figure 6.23). Overall LULCC has had small impact

on ERF (–0.15 W m

–2

; see AII.1.2) and thus as projected is not a major

factor in near-term climate change on global scales.

Land use changes can also lead to sustained near-term changes in

regional climate through modiﬁcation of the biogeophysical proper-

Figure 11.24b | Global maps of near-term differences in surface air temperature across the RCP scenarios. Differences between (RCP8.5) and low (RCP2.6) scenarios for the CMIP5

model ensemble (31 models) are shown for averages over 2016–2035 (left) and 2036–2055 (right) in boreal winter (December, January and February; top row) and summer (June,

July and August; bottom row).

1007

Near-term Climate Change: Projections and Predictability Chapter 11

ties that alter the water and energy cycles. Local- and regional-scale

climate responses to LULCC can exceed those associated with global

mean warming (Baidya Roy and Avissar, 2002; Findell et al., 2007;

Pitman et al., 2009, 2012; Pielke et al., 2011; Boisier et al., 2012;

de Noblet-Ducoudre et al., 2012; Lee and Berbery, 2012). Examples

of LULCC-driven changes include: Brazilian conversion to sugarcane

induces seasonal shifts of 1 to 2°C (Georgescu et al., 2013); European

forested areas experience less severe heat waves (Teuling et al., 2010);

and deforested regions over the Amazon lack deep convective clouds

(Wang et al., 2009). Systematic assessment of near-term, local-to-re-

gional climate change is beyond the scope here.

In summary, climate projections for the near term are not very sensitive

to the range in anthropogenic emissions of CO

and other WMGHGs. By

the 2040s the CMIP5 median for global mean temperature ranges from

a low of +0.9°C (RCP2.6 and RCP6.0) to a high of +1.3°C (RCP8.5)

above the CMIP5 reference period (Figure 11.24a; Table AII.7.5). See

discussion below regarding possible offsets between the observed and

CMIP5 reference periods. Alternative CH

scenarios incorporating large

emission reductions outside the RCP range would offset near-term

warming by –0.2°C (medium conﬁdence). Aerosols remain a major

source of uncertainty in near-term projections, on both global and

regional scales. Removal of half of the sulphate aerosol, as projected

before 2030 in the MFR scenario and by 2050 in most RCPs, would

increase warming by up to +0.2°C (medium conﬁdence). Actions to

reduce BC aerosol could reduce warming, but the magnitude is highly

uncertain, depending on co-emitted (reﬂective) aerosols and aero-

sol-cloud interactions (Chapter 7; Section 7.5). In addition, near-term

climate change, including extremes and precipitation, may be driven

locally by land use change and shifting geographic patterns of aero-

sols; and these regional climatic effects may exceed those induced by

the global ERF.

11.3.6.2 Uncertainties in Future Natural Radiative Forcing and

the Consequences for Near-term Climate

11.3.6.2.1 The effects of future volcanic eruptions

As discussed in Chapters 8 and 10, explosive volcanic eruptions are the

major cause of natural variations in RF on interannual to decadal time

scales. Most important are large tropical and subtropical eruptions

that inject substantial amounts of SO

directly into the stratosphere.

The subsequent formation of sulphate aerosols leads to a negative RF

of several watts per metre squared, with a typical lifetime of a year

(Robock, 2000). The eruption of Mt Pinatubo in 1991 was one of the

largest in recent times, with a return period of about three times per

century, but dwarfed by Tambora in 1815 (Gao et al., 2008). Mt Pina-

tubo caused a rapid drop in a global mean surface air temperature

of several tenths of a degree Celsius over the following year, but this

signal disappeared over the next ﬁve years (Hansen et al., 1992; Soden

et al., 2002; Bender et al., 2010). In addition to global mean cooling,

there are effects on the hydrological cycle (e.g., Trenberth and Dai,

2007), atmosphere and ocean circulation (e.g., Stenchikov et al., 2006;

Ottera et al., 2010). The surface climate response typically persists for

a few years, but the subsurface ocean response can persist for dec-

ades or centuries, with consequences for sea level rise (Delworth et al.,

2005; Stenchikov et al., 2009; Gregory, 2010; Timmreck, 2012).

Although it is possible to detect when various existing volcanoes

become more active, or are more likely to erupt, the precise timing of

an eruption, the amount of SO

emitted and its distribution in the strat-

osphere are not predictable until after the eruption. Eruptions compa-

rable to Mt Pinatubo can be expected to cause a short-term cooling

of the climate with related effects on surface climate that persist for a

few years before a return to warming trajectories discussed in Section

11.3.2. Larger eruptions, or several eruptions occurring close together

in time, would lead to larger and/or more persistent effects.

11.3.6.2.2 The effects of future changes in solar forcing

Some of the future CMIP5 climate simulations using the RCP scenarios

include an 11-year variation in total solar irradiance (TSI) but no under-

lying trend beyond 2005. Chapter 10 noted that there has been little

observed trend in TSI during a time period of rapid global warming

since the late 1970s, but that the 11-year solar cycle does introduce

a signiﬁcant and measurable pattern of response in the troposphere

(Section 10.3.1.1.3). As discussed in Chapter 8 (Section 8.4.1.3), the

Sun has been in a ‘grand solar maximum’ of magnetic activity on the

multi-decadal time scale. However, the most recent solar minimum was

the lowest and longest since 1920, and some studies (e.g., Lockwood,

2010) suggest there could be a continued decline towards a much qui-

eter period in the coming decades, but there is low conﬁdence in these

projections (Section 8.4.1.3). Nevertheless, if there is such a reduction

in solar activity, there is high conﬁdence that the variations in TSI RF

will be much smaller than the projected increased forcing due to GHGs

(Section 8.4.1.3). In addition, studies that have investigated the effect

of a possible decline in TSI on future climate have shown that the asso-

ciated decrease in global mean surface temperature is much smaller

than the warming expected from increases in anthropogenic GHGs

(Feulner and Rahmstorf, 2010; Jones et al., 2012; Meehl et al., 2013b)

However, regional impacts could be more signiﬁcant (Xoplaki et al.,

2001; Mann et al., 2009; Gray et al., 2010; Ineson et al., 2011).

As discussed in Section 8.4.1, a recent satellite measurement (Harder

et al., 2009) found much greater than expected reduction at ultraviolet

(UV) wavelengths in the recent declining solar cycle phase. Changes

in solar UV drive stratospheric O

chemistry and can change RF. Haigh

et al. (2010) show that if these observations are correct, they imply

the opposite relationship between solar RF and solar activity over that

period than has hitherto been assumed. These new measurements

therefore increase uncertainty in estimates of the sign of solar RF, but

they are not expected to alter estimates of the maximum absolute

magnitude of the solar contribution to RF, which remains small (Chap-

ter 8). However, they do suggest the possibility of a much larger impact

of solar variations on the stratosphere than previously thought, and

some studies have suggested that this may lead to signiﬁcant regional

impacts on climate (as discussed in Section 10.3.1.1.3) that are not

necessarily reﬂected by the RF metric (see Section 8.4.1).

In summary, possible future changes in solar irradiance could inﬂuence

the rate at which global mean surface air temperature increases, but

there is high conﬁdence that this inﬂuence will be small in comparison

to the inﬂuence of increasing concentrations of GHGs in the atmos-

phere. Understanding of the impacts of changes in solar irradiance on

continental and sub-continental scale climate remains low.

1008

Chapter 11 Near-term Climate Change: Projections and Predictability

Frequently Asked Questions

FAQ 11.2 | How Do Volcanic Eruptions Affect Climate and Our Ability to Predict Climate?

Large volcanic eruptions affect the climate by injecting sulphur dioxide gas into the upper atmosphere (also called

stratosphere), which reacts with water to form clouds of sulphuric acid droplets. These clouds reﬂect sunlight back

to space, preventing its energy from reaching the Earth’s surface, thus cooling it, along with the lower atmosphere.

These upper atmospheric sulphuric acid clouds also locally absorb energy from the Sun, the Earth and the lower

atmosphere, which heats the upper atmosphere (see FAQ 11.2, Figure 1). In terms of surface cooling, the 1991

Mt Pinatubo eruption in the Philippines, for example, injected about 20 million tons of sulphur dioxide (SO

) into

the stratosphere, cooling the Earth by about 0.5°C for up to a year. Globally, eruptions also reduce precipitation,

because the reduced incoming shortwave at the surface is compensated by a reduction in latent heating (i.e., in

evaporation and hence rainfall).

For the purposes of predicting climate, an eruption causing signiﬁcant global surface cooling and upper atmo-

spheric heating for the next year or so can be expected. The problem is that, while a volcano that has become more

active can be detected, the precise timing of an eruption, or the amount of SO

injected into the upper atmosphere

and how it might disperse cannot be predicted. This is a source of uncertainty in climate predictions.

Large volcanic eruptions produce lots of particles, called ash or tephra. However, these particles fall out of the

atmosphere quickly, within days or weeks, so they do not affect the global climate. For example, the 1980 Mount

St. Helens eruption affected surface temperatures in the northwest USA for several days but, because it emitted

little SO

into the stratosphere, it had no detectable global climate impacts. If large, high-latitude eruptions inject

sulphur into the stratosphere, they will have an effect only in the hemisphere where they erupted, and the effects

will only last a year at most, as the stratospheric cloud they produce only has a lifetime of a few months.

Tropical or subtropical volcanoes produce more global surface or tropospheric cooling. This is because the resulting

sulphuric acid cloud in the upper atmosphere lasts between one and two years, and can cover much of the globe.

However, their regional climatic impacts are difﬁcult to

predict, because dispersion of stratospheric sulphate

aerosols depends heavily on atmospheric wind condi-

tions at the time of eruption. Furthermore, the surface

cooling effect is typically not uniform: because conti-

nents cool more than the ocean, the summer monsoon

can weaken, reducing rain over Asia and Africa. The cli-

matic response is complicated further by the fact that

upper atmospheric clouds from tropical eruptions also

absorb sunlight and heat from the Earth, which produc-

es more upper atmosphere warming in the tropics than

at high latitudes.

The largest volcanic eruptions of the past 250 years stim-

ulated scientiﬁc study. After the 1783 Laki eruption in

Iceland, there were record warm summer temperatures

in Europe, followed by a very cold winter. Two large

eruptions, an unidentiﬁed one in 1809, and the 1815

Tambora eruption caused the ‘Year Without a Summer’

in 1816. Agricultural failures in Europe and the USA that

year led to food shortages, famine and riots.

The largest eruption in more than 50 years, that of

Agung in 1963, led to many modern studies, including

observations and climate model calculations. Two subse-

quent large eruptions, El Chichón in 1982 and Pinatubo

in 1991, inspired the work that led to our current under-

standing of the effects of volcanic eruptions on climate.

FAQ 11.2, Figure 1 | Schematic of how large tropical or sub-tropical volcanoes

impact upper atmospheric (stratospheric) and lower atmospheric (tropospheric)

temperatures.

Decreased upward ﬂux of

energy due to absorption by

aerosol cloud and emission

at a low temperature

Reﬂected

solar ﬂux

Cooling because

eduction of sunlight

overwhelms any

increased

downward energy

emitted by volcanic

cloud

Increased

downward ﬂux of

energy due to

emission from

aerosol cloud

Reactions

on cloud

particles

destroy ozone

Heating due to

absorption of

energy from the

Earth and lower

atmosphere

Heating due

to absorption

of energy by

cloud

Tropospheric Aerosols

(Lifetime 1-3 Weeks)

Stratospheric Aerosols

(Lifetime 1-2 Years)

(continued on next page)

1009

Near-term Climate Change: Projections and Predictability Chapter 11

11.3.6.3 Synthesis of Near-term Projections of Global Mean

Surface Air Temperature

Figure 11.25 provides a synthesis of near-term projections of global

mean surface air temperature (GMST) from CMIP5, CMIP3 and studies

that have attempted to use observations to quantify projection uncer-

tainty (see Section 11.3.2.1). On the basis of this evidence, an attempt

is made here to assess a likely range for GMST in the period 2016–

2035. Such an overall assessment is not straightforward. The following

points must be taken into account:

1. No likelihoods are associated with the different RCP scenarios. For

this reason, previous IPCC Assessment Reports have only present-

ed projections that are conditional on speciﬁc scenarios. Here we

attempt a broader assessment across all four RCP scenarios. This is

possible only because, as discussed in Section 11.3.6.1, near-term

projections of GMST are not especially sensitive to these different

scenarios.

2. In the near term it is expected that increases in GMST will be

driven by past and future increases in GHG concentrations and

future decreases in anthropogenic aerosols, as found in all the RCP

scenarios. Figure 11.25c shows that in the near term the CMIP3

projections based on the SRES scenarios are generally cooler than

the CMIP5 projections based on the RCP scenarios. This difference

is at least partly attributable to higher aerosol concentrations in

the SRES scenarios (see Section 11.3.6.1).

3. The CMIP3 and CMIP5 projections are ensembles of opportunity,

and it is explicitly recognized that there are sources of uncertain-

ty not simulated by the models. Evidence of this can be seen by

comparing the Rowlands et al. (2012) projections for the A1B sce-

nario, which were obtained using a very large ensemble in which

the physics parameterizations were perturbed in a single climate

model, with the corresponding raw multi-model CMIP3 projec-

tions. The former exhibit a substantially larger likely range than

the latter. A pragmatic approach to addressing this issue, which

was used in the AR4 and is also used in Chapter 12, is to consider

the 5 to 95% CMIP3/5 range as a ‘likely’ rather than ‘very likely’

range.

4. As discussed in Section 11.3.6.2, the RCP scenarios assume no

underlying trend in total solar irradiance and no future volcanic

eruptions. Future volcanic eruptions cannot be predicted and there

is low conﬁdence in projected changes in solar irradiance (Chapter

8). Consequently the possible effects of future changes in natural

forcings are excluded from the assessment here.

FAQ 11.2 (continued)

Volcanic clouds remain in the stratosphere only for a couple of years, so their impact on climate is correspondingly

short. But the impacts of consecutive large eruptions can last longer: for example, at the end of the 13th century

there were four large eruptions—one every ten years. The ﬁrst, in 1258 CE, was the largest in 1000 years. That

sequence of eruptions cooled the North Atlantic Ocean and Arctic sea ice. Another period of interest is the three

large, and several lesser, volcanic events during 1963–1991 (see Chapter 8 for how these eruptions affected atmo-

spheric composition and reduced shortwave radiation at the ground.

Volcanologists can detect when a volcano becomes more active, but they cannot predict whether it will erupt,

or if it does, how much sulphur it might inject into the stratosphere. Nevertheless, volcanoes affect the ability to

predict climate in three distinct ways. First, if a violent eruption injects signiﬁcant volumes of sulphur dioxide into

the stratosphere, this effect can be included in climate predictions. There are substantial challenges and sources of

uncertainty involved, such as collecting good observations of the volcanic cloud, and calculating how it will move

and change during its lifetime. But, based on observations, and successful modelling of recent eruptions, some of

the effects of large eruptions can be included in predictions.

The second effect is that volcanic eruptions are a potential source of uncertainty in our predictions. Eruptions

cannot be predicted in advance, but they will occur, causing short-term climatic impacts on both local and global

scales. In principle, this potential uncertainty can be accounted for by including random eruptions, or eruptions

based on some scenario in our near-term ensemble climate predictions. This area of research needs further explora-

tion. The future projections in this report do not include future volcanic eruptions.

Third, the historical climate record can be used, along with estimates of observed sulphate aerosols, to test the

ﬁdelity of our climate simulations. While the climatic response to explosive volcanic eruptions is a useful analogue

for some other climatic forcings, there are limitations. For example, successfully simulating the impact of one erup-

tion can help validate models used for seasonal and interannual predictions. But in this way not all the mechanisms

involved in global warming over the next century can be validated, because these involve long term oceanic feed-

backs, which have a longer time scale than the response to individual volcanic eruptions.

1010

Chapter 11 Near-term Climate Change: Projections and Predictability

5. As discussed in Section 11.3.2.1.1 observationally constrained

‘ASK’ projections (Gillett et al., 2013; Stott et al., 2013) are 10 to

15% cooler (median values for RCP4.5; 6–10% cooler for RCP8.5),

and have a narrower range, than the corresponding ‘raw’ (unini-

tialized) CMIP5 projections. The reduced rate of warming in the

ASK projections is related to evidence from Chapter 10 (Section

10.3.1) that ‘some CMIP5 models have a higher transient response

to GHGs and a larger response to other anthropogenic forc-

ings (dominated by the effects of aerosols) than the real world

(medium conﬁdence).’ These models may warm too rapidly as

GHGs increase and aerosols decline.

6. Over the last two decades the observed rate of increase in GMST

has been at the lower end of rates simulated by CMIP5 models

(Figure 11.25a). This hiatus in GMST rise is discussed in detail

in Box 9.2 (Chapter 9), where it is concluded that the hiatus is

attributable, in roughly equal measure, to a decline in the rate of

increase in ERF and a cooling contribution from internal variability

(expert judgment, medium conﬁdence). The decline in the rate of

increase in ERF is attributed primarily to natural (solar and vol-

canic) forcing but 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. Concerning the higher rate of warming

in CMIP5 simulations it is concluded that there is a substantial

contribution from internal variability but that errors in ERF and

in model responses may also contribute. There is low conﬁdence

in this assessment because of uncertainties in aerosol forcing in

particular.

The observed hiatus has important implications for near-term pro-

jections of GMST. A basic issue concerns the sensitivity of projec-

tions to the choice of reference period. Figure 11.25b and c shows

the 5 to 95% ranges for CMIP5 projections using a 1986–2005

reference period (light grey), and the same projections using a

2006–2012 reference period (dark grey). The latter projections

are cooler, and the effect of using a more recent reference period

appears similar to the effect of initialization (discussed in Section

11.3.2.1.1 and shown in Figure 11.25c for RCP4.5). Using this more

recent reference period, the 5 to 95% range for the mean GMST

in 2016–2035 relative to 1986–2005 is 0.36°C to 0.79°C (using

all RCP scenarios, weighted to ensure equal weights per model

and using an estimate of the observed GMST anomaly for (2006–

2012)–(1986–2005) of 0.16°C). This range may be compared with

the range of 0.48°C to 1.15°C obtained from the CMIP5 models

using the original 1986–2005 reference period.

7. In view of the sensitivity of projections to the reference period it

is helpful to consider the possible rate of change of GMST in the

near term. The CMIP5 5 to 95% ranges for GMST trends in the

period 2012–2035 are 0.11°C to 0.41°C per decade. This range

is similar to, though slightly narrower than, the range found by

Easterling and Wehner (2009) for the CMIP3 SRES A2 scenario over

the longer period 2000–2050. It may also be compared with recent

rates in the observational record (e.g., ~0.26°C per decade for

1984–1998 and ~0.04°C per decade for hiatus period 1998–2012;

See Box 9.2). The RCP scenarios project that ERF will increase more

rapidly in the near term than occurred over the hiatus period (see

Box 9.2 and Annex II), which is consistent with more rapid warm-

ing. In addition, Box 9.2 includes an assessment that internal vari-

ability is more likely than not to make a positive contribution to the

increase in GMST in the near term. Internal variability is included

in the CMIP5 projections, but because most of the CMIP5 simu-

lations do not reproduce the observed reduction in global mean

surface warming over the last 10 to 15 years, the distribution of

CMIP5 near-term trends will not reﬂect this assessment and might,

as a result, be biased low. This uncertainty, however, is somewhat

counter balanced by the evidence of point 5, which suggests a high

bias in the distribution of near-term trends. A further projection of

GMST for the period 2016–2035 may be obtained by starting from

the observed GMST for 2012 (0.14°C relative to 1986–2005) and

projecting increases at rates between the 5 to 95% CMIP5 range

of 0.11°C to 0.41°C per decade. The resulting range of 0.29°C to

0.69°C, relative to 1986–2005, is shown on Figure 11.25(c).

Overall, in the absence of major volcanic eruptions—which would

cause signiﬁcant but temporary cooling—and, assuming no signiﬁcant

future long term changes in solar irradiance, it is likely (>66% prob-

ability) that the GMST anomaly for the period 2016–2035, relative to

the reference period of 1986–2005 will be in the range 0.3°C to 0.7°C

(expert assessment, to one signiﬁcant ﬁgure; medium conﬁdence). This

range is consistent, to one signiﬁcant ﬁgure, with the range obtained

by using CMIP5 5 to 95% model trends for 2012–2035. It is also con-

sistent with the CMIP5 5 to 95% range for all four RCP scenarios of

0.36°C to 0.79°C, using the 2006–2012 reference period, after the

upper and lower bounds are reduced by 10% to take into account the

evidence noted under point 5 that some models may be too sensitive

to anthropogenic forcing. The 0.3°C to 0.7°C range includes the likely

range of the ASK projections and initialized predictions for RCP4.5. It

corresponds to a rate of change of GMST between 2012 and 2035 in

the range 0.12°C to 0.42°C per decade. The higher rates of change

can be associated with a signiﬁcant positive contribution from internal

variability (Box 9.2) and/or high rates of increase in ERF (e.g., as found

in RCP8.5). Note that an upper limit of 0.8°C on the 2016–2035 GMST

corresponds to a rate of change over the period 2012–2035 of 0.49°C

per decade, which is considered unlikely. The assessed rates of change

are consistent with the AR4 SPM statement that ‘For the next two dec-

ades, a warming of about 0.2°C per decade is projected for a range of

SRES emission scenarios’. However, the implied rates of warming over

the period from 1986–2005 to 2016–2035 are lower as a result of the

hiatus: 0.10°C to 0.23°C per decade, suggesting the AR4 assessment

was near the upper end of current expectations for this speciﬁc time

interval.

The assessment here provides only a likely range for GMST. Possible

reasons why the real world might depart from this range include: RF

departs signiﬁcantly from the RCP scenarios, due to either natural (e.g.,

major volcanic eruptions, changes in solar irradiance) or anthropogenic

(e.g., aerosol or GHG emissions) causes; processes that are poorly sim-

ulated in the CMIP5 models exert a signiﬁcant inﬂuence on GMST. The

latter class includes: a possible strong ‘recovery’ from the recent hiatus

in GMST; the possibility that models might underestimate decadal vari-

ability (but see Section 9.5.3.1); the possibility that model sensitivity to

anthropogenic forcing may differ from that of the real world (see point

1011

Near-term Climate Change: Projections and Predictability Chapter 11

Temperature anomaly (°C)

Global mean temperature near−term projections relative to 1986−2005

RCPsHistorical

(a)

1990 2000 2010 2020 2030 2040 2050

−0.5

0.5

1.5

2.5

Observations (4 datasets)

Historical (42 models)

RCP 2.6 (32 models)

RCP 4.5 (42 models)

RCP 6.0 (25 models)

RCP 8.5 (39 models)

Relative to 1850−1900

RCPsHistorical

(b)

Temperature anomaly (°C)

ALL RCPs Assessed likely range

for 2016−2035 mean

Assuming no future

large volcanic eruptions

1990 2000 2010 2020 2030 2040 2050

−0.5

0.5

1.5

2.5

Indicative likely range for annual means

ALL RCPs (5−95% range, two reference periods)

ALL RCPs min−max (299 ensemble members)

Observational uncertainty (HadCRUT4)

Observations (4 datasets)

B1 A1B A2

SRES CMIP3

2.6 4.5 6.0 8.5 ALL

RCPs CMIP5

Key:

%59%5

17−83%

Obs. Constrained

Meehl & Teng

4.5 4.5 8.5

Stott et al.

Rowlands et al.

A1B

Using trends

Assessed

ALL

(c)

Temperature anomaly (°C)

Projections of 2016−2035 mean

0.5

1.5

Figure 11.25 | Synthesis of near-term projections of global mean surface air temperature (GMST). (a) Simulations and projections of annual mean GMST 1986–2050 (anomalies

relative to 1986–2005). Projections under all RCPs from CMIP5 models (grey and coloured lines, one ensemble member per model), with four observational estimates (Hadley

Centre/Climate Research Unit gridded surface temperature data set 4 (HadCRUT4): Morice et al., 2012); European Centre for Medium range Weather Forecast (ECMWF) interim

reanalysis of the global atmosphere and surface conditions (ERA-Interim): Simmons et al., 2010); Goddard Institute of Space Studies Surface Temperature Analysis (GISTEMP):

Hansen et al., 2010); National Oceanic and Atmospheric Administration (NOAA): Smith et al., 2008)) for the period 1986–2012 (black lines). (b) As (a) but showing the 5 to 95%

range of annual mean CMIP5 projections (using one ensemble member per model) for all RCPs using a reference period of 1986–2005 (light grey shade) and all RCPs using a

reference period of 2006–2012, together with the observed anomaly for (2006–2012) to (1986–2005) of 0.16°C (dark grey shade). The percentiles for 2006 onwards have been

smoothed with a 5-year running mean for clarity. The maximum and minimum values from CMIP5 using all ensemble members and the 1986–2005 reference period are shown

by the grey lines (also smoothed). Black lines show annual mean observational estimates. The red hatched region shows the indicative likely range for annual mean GMST during

the period 2016–2035 based on the ‘ALL RCPs Assessed’ likely range for the 20-year mean GMST anomaly for 2016–2035, which is shown as a black bar in both (b) and (c) (see

text for details). The temperature scale on the right hand side shows changes relative to a reference period of 1850-1900, assuming a warming of GMST between 1850-1900 and

1986-2005 of 0.61°C estimated from HadCRUT4.The temperature scale relative to the 1850-1900 period on the right-hand side assumes a warming of GMST prior to 1986–2005

of 0.61°C estimated from HadCRUT4. (c) A synthesis of projections for the mean GMST anomaly for 2016–2035 relative to 1986–2005. The box and whiskers represent the 66%

and 90% ranges. Shown are unconstrained SRES CMIP3 and RCP CMIP5 projections; observationally constrained projections: Rowlands et al. (2012) for SRES A1B scenario, updated

to remove simulations with large future volcanic eruptions; Meehl and Teng (2012) for RCP4.5 scenario, updated to include 14 CMIP5 models; Stott et al. (2013), based on six CMIP5

models with unconstrained 66% ranges for these six models shown as unﬁlled boxes; unconstrained projections for all four RCP scenarios using two reference periods as in panel

b (light grey and dark grey shades, consistent with panel b); 90% range estimated using CMIP5 trends for the period 2012–2035 and the observed GMST anomaly for 2012; an

overall likely (>66%) assessed range for all RCP scenarios. The dots for the CMIP5 estimates show the maximum and minimum values using all ensemble members. The medians

(or maximum likelihood estimate for Rowlands et al. 2012) are indicated by a grey band.

1012

Chapter 11 Near-term Climate Change: Projections and Predictability

5); and the possibility of abrupt changes in climate (see introduction to

Sections 11.3.6 and 12.5.5).

The assessment here has focused on 20-year mean values of GMST for

the period 2016–2035. There is no unique method to derive a likely

range for annual mean values from the range for 20-year means, so

such calculations necessarily involve additional uncertainties (beyond

those outlined in the previous paragraph), and lower conﬁdence. Nev-

ertheless, it is useful to attempt to estimate a range for annual mean

values, which may be compared with raw model projections and, in

the future, with observations. To do so, the following simple approach

is used: (1) Starting in 2009 from the observed GMST anomaly for

2006–2012 of 0.16°C (relative to 1986–2005), linear trends are pro-

jected over the period 2009–2035 with maximum and minimum gra-

dients selected to be consistent with the 0.3°C to 0.7°C range for the

mean GMST in the period 2016–2035; 2). To take into account the

expected year-to-year variability of annual mean values, the resulting

linear trends are offset by ±0.1°C. The value of 0.1°C is based on the

standard deviation of annual means in CMIP5 control runs (to one sig-

niﬁcant ﬁgure). These calculations provide an indicative likely range for

annual mean GMST, which is shown as the red hatched area in Figure

11.25b. Note that this range does not take into account the expected

impact of any future volcanic eruptions.

The assessed likely range for GMST in the period 2016–2035 may also

be used to assess the likelihood that GMST will cross policy-relevant

levels, relative to earlier time periods (Joshi et al., 2011). Using the

1850–1900 period, and the observed temperature rise between 1850–

1900 and 1986–2005 of 0.61°C (estimated from the HadCRUT4 data

set (Morice et al., 2012) gives a likely range for the GMST anomaly

in 2016–2035 of 0.91°C–1.31°C, and supports the following conclu-

sions: it is more likely than not that the mean GMST for the period

2016–2035 will be more than 1°C above the mean for 1850–1900,

and very unlikely that it will be more than 1.5°C above the 1850–1900

mean (expert assessment, medium conﬁdence). Additional information

about the possibility of GMST crossing speciﬁc temperature levels is

provided in Table 11.3, which shows the percentage of CMIP5 models

for which the projected change in GMST exceeds speciﬁc temperature

levels, under each RCP scenario, in two time periods (early century:

2016–2035 and mid-century: 2046–2065), and also using the two

different reference periods discussed under point 6 and illustrated in

Figure 11.25. However, these percentages should not be interpreted as

likelihoods because—as discussed in this section—there are sources

of uncertainty not captured by the CMIP5 ensemble. Note ﬁnally that it

is very likely that speciﬁc temperature levels will be crossed temporari-

ly in individual years before a permanent crossing is established (Joshi

et al., 2011), but Table 11.3 is based on 20-year mean values.

Scenario Early (2016–2035) Mid (2046–2065)

Temperature +1.0°C

RCP 2.6 100% (84%) 100% (94%)

RCP 4.5 98% (93%) 100% (100%)

RCP 6.0 96% (80%) 100% (100%)

RCP 8.5 100% (100%) 100% (100%)

Temperature +1.5°C

RCP 2.6 22% (0%) 56% (28%)

RCP 4.5 17% (0%) 95% (86%)

RCP 6.0 12% (0%) 92% (88%)

RCP 8.5 33% (5%) 100% (100%)

Temperature +2.0°C

RCP 2.6 0% (0%) 16% (3%)

RCP 4.5 0% (0%) 43% (29%)

RCP 6.0 0% (0%) 32% (20%)

RCP 8.5 0% (0%) 95% (90%)

Temperature +3.0°C

RCP 2.6 0% (0%) 0% (0%)

RCP 4.5 0% (0%) 0% (0%)

RCP 6.0 0% (0%) 0% (0%)

RCP 8.5 0% (0%) 21% (5%)

Table 11.3 | Percentage of CMIP5 models for which the projected change in global

mean surface air temperature, relative to 1850-1900, crosses the speciﬁed temperature

levels, by the speciﬁed time periods and assuming the speciﬁed RCP scenarios. The pro-

jected temperature change relative to the mean temperature in the period 1850-1900 is

calculated using the models’ projected temperature change relative to 1986–2005 plus

the observed temperature change between 1850–1900 and 1986–2005 of 0.61°C esti-

mated from the Hadley Centre/Climate Research Unit gridded surface temperature data

set 4 (HadCRUT4; Morice et al., 2012). The percentages in brackets use an alternative

reference period for the model projections of 2006–2012, together with the observed

temperature difference between 1986–2005 and 2006–2012 of 0.16°C. The deﬁnition

of crossing is that the 20-year mean exceeds the speciﬁed temperature level. Note that

these percentages should not be interpreted as likelihoods because there are other

sources of uncertainty (see discussion in Section 11.3.6.3).

1013

Near-term Climate Change: Projections and Predictability Chapter 11

Box 11.2 | Ability of Climate Models to Simulate Observed Regional Trends

The ability of models to simulate past climate change on regional scales can be used to investigate whether the multi-model ensemble

spread covers the forcing and model uncertainties. Agreement between observed and simulated regional trends, taking natural variabil-

ity and model spread into account, would build conﬁdence in near-term projections. Although large-scale features are simulated well

(see Chapter 10), on sub-continental and smaller scales the observed trends are, in general, more often in the tails of the distribution of

modelled trends than would be expected by chance ﬂuctuations (Bhend and Whetton, 2012; Knutson et al., 2013b; van Oldenborgh et

al., 2013). Natural variability and model spread are larger at smaller scales (Stott et al., 2010), but this is not enough to bridge the gap

between models and observations. Downscaling with Regional Climate Models (RCMs) does not affect seasonal mean trends except

near mountains or coastlines in Europe (van Oldenborgh et al., 2009; van Haren et al., 2012). These results hold for both observed and

modelled estimates of natural variability and for various analyses of the observations. Given the statistical nature of the comparisons, it

is currently not possible to say in which regions observed discrepancies are due to coincidental natural variability and in which regions

they are due to forcing or model deﬁciencies. These results show that in general the Coupled Model Intercomparison Project Phase 5

(CMIP5) ensemble cannot be taken as a reliable regional probability forecast, but that the true uncertainty can be larger than the model

spread indicated in the maps in this chapter and Annex I.

Temperature

Räisänen (2007) and Yokohata et al. (2012) compared regional linear temperature trends during 1955–2005 (1961–2000) with cor-

responding trends in the CMIP3 ensemble. They found that the range of simulated trends captured the observed trend in nearly all

locations. Using another metric, Knutson et al., (2013b) found that CMIP5 models did slightly better than CMIP3 in reproducing linear

trends (see also Figure 10.2, Section 10.3.1.1.2). The linear CMIP5 temperature trends are compared with the observed trends in Box

11.2, Figure 1a–h. The rank histograms show the warm bias in global mean temperature (see Chapter 10) and some overconﬁdence, but

within the inter-model spread. However, the apparent agreement appears to be for the wrong reason. Many of the models that appear

to correctly simulate observed high regional trends do so because they have a high climate response (i.e., the global temperature rises

quickly) and do not simulate the observed spatial pattern of trends (Kumar et al., 2013). To address this, Bhend and Whetton (2012) and

van Oldenborgh et al. (2013) use another deﬁnition of the local trend: the regression of the local temperature on the (low-pass ﬁltered)

global mean temperature. This deﬁnition separates the local temperature response pattern from the global mean climate response.

They ﬁnd highly signiﬁcant discrepancies between the CMIP3 and CMIP5 trend patterns and a variety of estimates of observed trend

estimates. These discrepancies are deﬁned relative to an error model that includes the (modelled or observed) natural variability, model

spread and spatial autocorrelations. In the following, areas where the observed and modelled trends show marked differences are

noted. Areas of agreement are covered in Section 10.3.1.1.4.

In December to February the observed Arctic ampliﬁcation extends further south than modelled in Central Asia and northwestern North

America. In June to August southern Europe and North Africa have warmed signiﬁcantly faster than both CMIP3 and CMIP5 models

simulated (van Oldenborgh et al., 2009); this also holds for the Middle East. The observed Indo-Paciﬁc warm pool trend is signiﬁcantly

higher than the modelled trend year-round (Shin and Sardeshmukh, 2011; Williams and Funk, 2011), and the North Paciﬁc and the

southeastern USA and adjoining ocean trends were lower. Direct causes for many of these discrepancies are known (e.g., December to

February circulation trends that differ between the observation and the models (Gillett et al., 2005; Gillett and Stott, 2009; van Olden-

borgh et al., 2009; Bhend and Whetton, 2012) or teleconnections from other areas with trend biases (Deser and Phillips, 2009; Meehl

et al., 2012a), but the causes of the underlying discrepancies are often unknown. Possibilities include observational uncertainties (note,

however, that the areas where the observations warm more than the models do not correspond to areas of increased urbanization

or irrigation; cf. Section 2.4.1.3), an underestimation of the low-frequency variability (Knutson et al. (2013b) show evidence that this

is probably not the case for temperature outside the tropics), unrealistic local forcing (e.g., aerosols (Ruckstuhl and Norris, 2009)), or

missing or misrepresented processes in models (e.g., fog (Vautard et al., 2009; Ceppi et al., 2012)).

Precipitation

In spite of the larger variability relative to the trends and observational uncertainties (cf. Section 2.5.1.2), annual mean regional linear

precipitation trends have been found to differ signiﬁcantly between observations and CMIP3 models, both in the zonal mean (Allan

and Soden, 2007; Zhang et al., 2007b) and regionally (Räisänen, 2007). The comparison is shown in Box 11.2, Figure 1i–p for the CMIP5

half-year seasons used in Annex I, following van Oldenborgh et al. (2013). In both half years the observations fall more often in the

highest and lowest 5% than expected by chance ﬂuctuations within the ensemble (grey area). The differences larger than the difference

between the CRU and GPCC analyses (cf. Figure 2.29) are noted below. (continued on next page)

1014

Chapter 11 Near-term Climate Change: Projections and Predictability

Box 11.2 (continued)

In Europe there are large-scale differences between observed trends and trends, both in General Circulation Models (GCMs) and RCMs

(Bhend and von Storch, 2008), which are ascribed to circulation change discrepancies in winter and in summer sea surface temperature

(SST) trend biases (Lenderink et al., 2009; van Haren et al., 2012) and the misrepresentation of Summer North Atlantic Oscillation (NAO)

teleconnections (Bladé et al., 2012). Central North America has become much wetter over 1950–2012, especially in winter, which is

not simulated by the CMIP5 models. Larger observed northwest Australian rainfall increases than in CMIP3 in summer are driven by

ozone forcings in two climate models (Kang et al., 2011) and aerosols in another (Rotstayn et al., 2012). The Guinea Coast has become

drier in the observations than in the models. The CMIP5 patterns seem to reproduce the observed patterns somewhat better than the

CMIP3 patterns (Bhend and Whetton, 2012), but the remaining discrepancies imply that CMIP5 projections cannot be used as reliable

precipitation forecasts.

Box 11.2, Figure 1 | (a) Observed linear December to February temperature trend 1950–2012 (Hadley Centre/Climate Research Unit gridded surface temperature

data set 4.1.1.0 (HadCRUT4.1.1.0, °C per century). ( b) The equivalent CMIP5 ensemble mean trend. (c) Quantile of the observed trend in the ensemble, and (d) the

corresponding rank histogram, the grey band denotes the 90% band of intermodel ﬂuctuations (following Annan and Hargreaves, 2010). (e–h) Same for June to

August. (i–l) Same for October to March precipitation (Global Precipitation Climatology Centre (GPCC) v7) 1950–2010, % per century). (m–p) Precipitation in April to

September. Grid boxes where less than 50% of the years have observations are left white. (Based on Räisänen (2007) and van Oldenborgh et al. (2013).)

a b c d

10%

15%

20%

25%

30%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

rank for temperature trends vs HadCRUT4 1950-2011

e f g h

10%

15%

20%

25%

30%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

rank for temperature trends vs HadCRUT4 1950-2011

(°C per century) percentile

percentile

(% per century)

i j k l

10%

15%

20%

25%

30%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

rank for rel. precip. trends vs GPCC Oct-Mar 1950-2010

m n o p

10%

15%

20%

25%

30%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

rank for rel. precip. trends vs GPCC Apr-Sep 1950-2010

Acknowledgements

The authors thank Ed Hawkins (U. Reading, UK) for extensive input to

discussions on the assessment of near-term global temperature and

his work on key synthesis ﬁgures, and Jan Sedlacek (ETH, Switzerland)

for his outstanding work on the production of numerous ﬁgures in this

chapter.

1015

Near-term Climate Change: Projections and Predictability Chapter 11

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