159
2
This chapter should be cited as:
Hartmann, D.L., A.M.G. Klein Tank, M. Rusticucci, L.V. Alexander, S. Brönnimann, Y. Charabi, F.J. Dentener, E.J.
Dlugokencky, D.R. Easterling, A. Kaplan, B.J. Soden, P.W. Thorne, M. Wild and P.M. Zhai, 2013: Observations:
Atmosphere and Surface. 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:
Dennis L. Hartmann (USA), Albert M.G. Klein Tank (Netherlands), Matilde Rusticucci (Argentina)
Lead Authors:
Lisa V. Alexander (Australia), Stefan Brönnimann (Switzerland), Yassine Abdul-Rahman Charabi
(Oman), Frank J. Dentener (EU/Netherlands), Edward J. Dlugokencky (USA), David R. Easterling
(USA), Alexey Kaplan (USA), Brian J. Soden (USA), Peter W. Thorne (USA/Norway/UK), Martin
Wild (Switzerland), Panmao Zhai (China)
Contributing Authors:
Robert Adler (USA), Richard Allan (UK), Robert Allan (UK), Donald Blake (USA), Owen Cooper
(USA), Aiguo Dai (USA), Robert Davis (USA), Sean Davis (USA), Markus Donat (Australia), Vitali
Fioletov (Canada), Erich Fischer (Switzerland), Leopold Haimberger (Austria), Ben Ho (USA),
John Kennedy (UK), Elizabeth Kent (UK), Stefan Kinne (Germany), James Kossin (USA), Norman
Loeb (USA), Carl Mears (USA), Christopher Merchant (UK), Steve Montzka (USA), Colin Morice
(UK), Cathrine Lund Myhre (Norway), Joel Norris (USA), David Parker (UK), Bill Randel (USA),
Andreas Richter (Germany), Matthew Rigby (UK), Ben Santer (USA), Dian Seidel (USA), Tom
Smith (USA), David Stephenson (UK), Ryan Teuling (Netherlands), Junhong Wang (USA),
Xiaolan Wang (Canada), Ray Weiss (USA), Kate Willett (UK), Simon Wood (UK)
Review Editors:
Jim Hurrell (USA), Jose Marengo (Brazil), Fredolin Tangang (Malaysia), Pedro Viterbo (Portugal)
Observations:
Atmosphere and Surface
160
2
Table of Contents
Executive Summary ..................................................................... 161
2.1 Introduction ...................................................................... 164
2.2 Changes in Atmospheric Composition ...................... 165
2.2.1 Well-Mixed Greenhouse Gases ................................. 165
Box 2.1: Uncertainty in Observational Records ..................... 165
2.2.2 Near-Term Climate Forcers ........................................ 170
2.2.3 Aerosols .................................................................... 174
Box 2.2: Quantifying Changes in the Mean:
Trend Models and Estimation .................................................. 179
2.3 Changes in Radiation Budgets .................................... 180
2.3.1 Global Mean Radiation Budget ................................. 181
2.3.2 Changes in Top of the Atmosphere
Radiation Budget ...................................................... 182
2.3.3 Changes in Surface Radiation Budget ....................... 183
Box 2.3: Global Atmospheric Reanalyses ............................... 185
2.4 Changes in Temperature................................................ 187
2.4.1 Land Surface Air Temperature ................................... 187
2.4.2 Sea Surface Temperature and Marine
Air Temperature ........................................................ 190
2.4.3 Global Combined Land and Sea
Surface Temperature ................................................. 192
2.4.4 Upper Air Temperature .............................................. 194
2.5 Changes in Hydrological Cycle .................................... 201
2.5.1 Large-Scale Changes in Precipitation ........................ 201
2.5.2 Streamflow and Runoff ............................................. 204
2.5.3 Evapotranspiration Including Pan Evaporation .......... 205
2.5.4 Surface Humidity ....................................................... 205
2.5.5 Tropospheric Humidity .............................................. 206
2.5.6 Clouds ....................................................................... 208
2.6 Changes in Extreme Events .......................................... 208
2.6.1 Temperature Extremes .............................................. 209
2.6.2 Extremes of the Hydrological Cycle ........................... 213
2.6.3 Tropical Storms ......................................................... 216
2.6.4 Extratropical Storms .................................................. 217
Box 2.4: Extremes Indices ......................................................... 221
2.7 Changes in Atmospheric Circulation and
Patterns of Variability .................................................... 223
2.7.1 Sea Level Pressure ..................................................... 223
2.7.2 Surface Wind Speed .................................................. 224
2.7.3 Upper-Air Winds ........................................................ 226
2.7.4 Tropospheric Geopotential Height and
Tropopause ............................................................... 226
2.7.5 Tropical Circulation ................................................... 226
2.7.6 Jets, Storm Tracks and Weather Types ....................... 229
2.7.7 Stratospheric Circulation ........................................... 230
2.7.8 Changes in Indices of Climate Variability .................. 230
Box 2.5: Patterns and Indices of Climate Variability ............. 232
References .................................................................................. 237
Frequently Asked Questions
FAQ 2.1 How Do We Know the World Has Warmed? ........ 198
FAQ 2.2 Have There Been Any Changes in
Climate Extremes? ................................................. 218
Supplementary Material
Supplementary Material is available in online versions of the report.
161
Observations: Atmosphere and Surface Chapter 2
2
Executive Summary
The evidence of climate change from observations of the atmosphere
and surface has grown significantly during recent years. At the same
time new improved ways of characterizing and quantifying uncertainty
have highlighted the challenges that remain for developing long-term
global and regional climate quality data records. Currently, the obser-
vations of the atmosphere and surface indicate the following changes:
Atmospheric Composition
It is certain that atmospheric burdens of the well-mixed green-
house gases (GHGs) targeted by the Kyoto Protocol increased
from 2005 to 2011. The atmospheric abundance of carbon dioxide
(CO
2
) was 390.5 ppm (390.3 to 390.7)
1
in 2011; this is 40% greater
than in 1750. Atmospheric nitrous oxide (N
2
O) was 324.2 ppb (324.0 to
324.4) in 2011 and has increased by 20% since 1750. Average annual
increases in CO
2
and N
2
O from 2005 to 2011 are comparable to those
observed from 1996 to 2005. Atmospheric methane (CH
4
) was 1803.2
ppb (1801.2 to 1805.2) in 2011; this is 150% greater than before 1750.
CH
4
began increasing in 2007 after remaining nearly constant from
1999 to 2006. Hydrofluorocarbons (HFCs), perfluorocarbons (PFCs),
and sulphur hexafluoride (SF
6
) all continue to increase relatively rapid-
ly, but their contributions to radiative forcing are less than 1% of the
total by well-mixed GHGs. {2.2.1.1}
For ozone-depleting substances (Montreal Protocol gases), it is
certain that the global mean abundances of major chlorofluoro-
carbons (CFCs) are decreasing and HCFCs are increasing. Atmos-
pheric burdens of major CFCs and some halons have decreased since
2005. HCFCs, which are transitional substitutes for CFCs, continue to
increase, but the spatial distribution of their emissions is changing.
{2.2.1.2}
Because of large variability and relatively short data records,
confidence
2
in stratospheric H
2
O vapour trends is low. Near-global
satellite measurements of stratospheric water vapour show substantial
variability but small net changes for 1992–2011. {2.2.2.1}
It is certain that global stratospheric ozone has declined from
pre-1980 values.Most of the decline occurred prior to the mid 1990s;
since then ozone has remained nearly constant at about 3.5% below
the 1964–1980 level. {2.2.2.2}
Confidence is medium in large-scale increases of tropospheric
ozone across the Northern Hemisphere (NH) since the 1970s.
1
Values in parentheses are 90% confidence intervals. Elsewhere in this chapter usually the half-widths of the 90% confidence intervals are provided for the estimated change
from the trend method.
2
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).
3
In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: Virtually certain 99–100% probability, Very likely 90–100%,
Likely 66–100%, About as likely as not 33–66%, Unlikely 0–33%, Very unlikely 0–10%, Exceptionally unlikely 0–1%. Additional terms (Extremely likely: 95–100%, More likely
than not >50–100%, and Extremely unlikely 0–5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very likely (see Section 1.4 and Box TS.1
for more details).
Confidence is low in ozone changes across the Southern Hemi-
sphere (SH) owing to limited measurements. It is likely
3
that sur-
face ozone trends in eastern North America and Western Europe since
2000 have levelled off or decreased and that surface ozone strongly
increased in East Asia since the 1990s. Satellite and surface obser-
vations of ozone precursor gases NO
x
, CO, and non-methane volatile
organic carbons indicate strong regional differences in trends. Most
notably NO
2
has likely decreased by 30 to 50% in Europe and North
America and increased by more than a factor of 2 in Asia since the
mid-1990s. {2.2.2.3, 2.2.2.4}
It is very likely that aerosol column amounts have declined over
Europe and the eastern USA since the mid 1990s and increased
over eastern and southern Asia since 2000. These shifting aerosol
regional patterns have been observed by remote sensingof aerosol
optical depth (AOD), a measure of total atmospheric aerosol load.
Declining aerosol loads over Europe and North America are consistent
with ground-based in situ monitoring of particulate mass. Confidence
in satellite based global average AOD trends is low. {2.2.3}
Radiation Budgets
Satellite records of top of the atmosphere radiation fluxes have
been substantially extended since AR4, and it is unlikely that
significant trends exist in global and tropical radiation budgets
since 2000. Interannual variability in the Earth’s energy imbalance
related to El Niño-Southern Oscillation is consistent with ocean heat
content records within observational uncertainty. {2.3.2}
Surface solar radiation likely underwent widespread decadal
changes after 1950, with decreases (‘dimming’) until the 1980s
and subsequent increases (‘brightening’) observed at many
land-based sites. There is medium confidence for increasing down-
ward thermal and net radiation at land-based observation sites since
the early 1990s. {2.3.3}
Temperature
It is certain that Global Mean Surface Temperature has increased
since the late 19th century. Each of the past three decades has
been successively warmer at the Earth’s surface than all the pre-
vious decades in the instrumental record, and the first decade
of the 21st century has been the warmest. The globally averaged
combined land and ocean surface temperature data as calculated by a
linear trend, show a warming of 0.85 [0.65 to 1.06] °C, over the period
1880–2012, when multiple independently produced datasets exist, and
162
Chapter 2 Observations: Atmosphere and Surface
2
about 0.72°C [0.49°C to 0.89°C] over the period 1951–2012. The total
increase between the average of the 1850–1900 period and the 2003–
2012 period is 0.78 [0.72 to 0.85] °C and the total increase between
the average of the 1850–1900 period and the reference period for pro-
jections, 1986−2005, is 0.61 [0.55 to 0.67] °C, based on the single
longest dataset available. For the longest period when calculation of
regional trends is sufficiently complete (1901–2012), almost the entire
globe has experienced surface warming. In addition to robust multi-
decadal warming, global mean surface temperature exhibits substan-
tial decadal and interannual variability. Owing to natural variability,
trends based on short records are very sensitive to the beginning and
end dates and do not in general reflect long-term climate trends. As one
example, the rate of warming over the past 15 years (1998–2012; 0.05
[–0.05 to +0.15] °C per decade), which begins with a strong El Niño, is
smaller than the rate calculated since 1951 (1951–2012; 0.12 [0.08 to
0.14] °C per decade). Trends for 15-year periods starting in 1995, 1996,
and 1997 are 0.13 [0.02 to 0.24], 0.14 [0.03 to 0.24] and 0.07 [–0.02
to 0.18], respectively. Several independently analyzed data records of
global and regional land-surface air temperature (LSAT) obtained from
station observations are in broad agreement that LSAT has increased.
Sea surface temperatures (SSTs) have also increased. Intercomparisons
of new SST data records obtained by different measurement methods,
including satellite data, have resulted in better understanding of uncer-
tainties and biases in the records. {2.4.1, 2.4.2, 2.4.3; Box 9.2}
It is unlikely that any uncorrected urban heat-island effects and
land use change effects have raised the estimated centennial
globally averaged LSAT trends by more than 10% of the report-
ed trend. This is an average value; in some regions with rapid devel-
opment, urban heat island and land use change impacts on regional
trends may be substantially larger. {2.4.1.3}
Confidence is medium in reported decreases in observed global
diurnal temperature range (DTR), noted as a key uncertainty in
the AR4. Several recent analyses of the raw data on which many pre-
vious analyses were based point to the potential for biases that differ-
ently affect maximum and minimum average temperatures. However,
apparent changes in DTR are much smaller than reported changes in
average temperatures and therefore it is virtually certain that maxi-
mum and minimum temperatures have increased since 1950. {2.4.1.2}
Based on multiple independent analyses of measurements from
radiosondes and satellite sensors it is virtually certain that
globally the troposphere has warmed and the stratosphere has
cooled since the mid-20th century. Despite unanimous agreement
on the sign of the trends, substantial disagreement exists among avail-
able estimates as to the rate of temperature changes, particularly out-
side the NH extratropical troposphere, which has been well sampled
by radiosondes. Hence there is only medium confidence in the rate of
change and its vertical structure in the NH extratropical troposphere
and low confidence elsewhere. {2.4.4}
Hydrological Cycle
Confidence in precipitation change averaged over global land
areas since 1901 is low for years prior to 1951 and medium
afterwards. Averaged over the mid-latitude land areas of the
Northern Hemisphere, precipitation has likely increased since
1901 (medium confidence before and high confidence after
1951). For other latitudinal zones area-averaged long-term positive
or negative trends have low confidence due to data quality, data
completeness or disagreement amongst available estimates. {2.5.1.1,
2.5.1.2}
It is very likely that global near surface and tropospheric air
specific humidity have increased since the 1970s. However,
during recent years the near surface moistening over land has abated
(medium confidence). As a result, fairly widespread decreases in rel-
ative humidity near the surface are observed over the land in recent
years. {2.4.4, 2.5.4, 2.5.5}
While trends of cloud cover are consistent between independent
data sets in certain regions, substantial ambiguity and there-
fore low confidence remains in the observations of global-scale
cloud variability and trends. {2.5.6}
Extreme Events
It is very likely that the numbers of cold days and nights have
decreased and the numbers of warm days and nights have
increased globally since about 1950. There is only medium con-
fidence that the length and frequency of warm spells, including heat
waves, has increased since the middle of the 20th century mostly owing
to lack of data or of studies in Africa and South America. However, it is
likely that heatwave frequency has increased during this period in large
parts of Europe, Asia and Australia. {2.6.1}
It is likely that since about 1950 the number of heavy precipita-
tion events over land has increased in more regions than it has
decreased. Confidence is highest for North America and Europe where
there have been likely increases in either the frequency or intensity of
heavy precipitation with some seasonal and/or regional variation. It is
very likely that there have been trends towards heavier precipitation
events in central North America. {2.6.2.1}
Confidence is low for a global-scale observed trend in drought
or dryness (lack of rainfall) since the middle of the 20th centu-
ry, owing to lack of direct observations, methodological uncer-
tainties and geographical inconsistencies in the trends. Based on
updated studies, AR4 conclusions regarding global increasing trends
in drought since the 1970s were probably overstated. However, this
masks important regional changes: the frequency and intensity of
drought have likely increased in the Mediterranean and West Africa
and likely decreased in central North America and north-west Australia
since 1950. {2.6.2.2}
Confidence remains low for long-term (centennial) changes in
tropical cyclone activity, after accounting for past changes in
observing capabilities. However, it is virtually certain that the fre-
quency and intensity of the strongest tropical cyclones in the North
Atlantic has increased since the 1970s. {2.6.3}
Confidence in large-scale trends in storminess or storminess
proxies over the last century is low owing to inconsistencies
163
Observations: Atmosphere and Surface Chapter 2
2
between studies or lack of long-term data in some parts of the
world (particularly in the SH). {2.6.4}
Because of insufficient studies and data quality issues con-
fidence is also low for trends in small-scale severe weather
events such as hail or thunderstorms. {2.6.2.4}
Atmospheric Circulation and Indices of Variability
It is likely that circulation features have moved poleward since
the 1970s, involving a widening of the tropical belt, a poleward
shift of storm tracks and jet streams, and a contraction of the
northern polar vortex. Evidence is more robust for the NH. It is likely
that the Southern Annular Mode has become more positive since the
1950s. {2.7.5, 2.7.6, 2.7.8; Box 2.5}
Large variability on interannual to decadal time scales hampers
robust conclusions on long-term changes in atmospheric circu-
lation in many instances. Confidence is high that the increase in the
northern mid-latitude westerly winds and the North Atlantic Oscilla-
tion (NAO) index from the 1950s to the 1990s and the weakening of
the Pacific Walker circulation from the late 19th century to the 1990s
have been largely offset by recent changes. {2.7.5, 2.7.8, Box 2.5}
Confidence in the existence of long-term changes in remaining
aspects of the global circulation is low owing to observational
limitations or limited understanding. These include surface winds
over land, the East Asian summer monsoon circulation, the tropical
cold-point tropopause temperature and the strength of the Brewer
Dobson circulation. {2.7.2, 2.7.4, 2.7.5, 2.7.7}
164
Chapter 2 Observations: Atmosphere and Surface
2
2.1 Introduction
This chapter assesses the scientific literature on atmospheric and
surface observations since AR4 (IPCC, 2007). The most likely changes
in physical climate variables or climate forcing agents are identified
based on current knowledge, following the IPCC AR5 uncertainty guid-
ance (Mastrandrea et al., 2011).
As described in AR4 (Trenberth et al., 2007), the climate comprises a
variety of space- and timescales: from the diurnal cycle, to interannual
variability such as the El Niño-Southern Oscillation (ENSO), to mul-
ti-decadal variations. ‘Climate change’ refers to a change in the state
of the climate that can be identified by changes in the mean and/or
the variability of its properties and that persists for an extended period
of time (Annex III: Glossary). In this chapter, climate change is exam-
ined for the period with instrumental observations, since about 1850.
Change prior to this date is assessed in Chapter 5. The word ‘trend’
is used to designate a long-term movement in a time series that may
be regarded, together with the oscillation and random component, as
composing the observed values (Annex III: Glossary). Where numerical
values are given, they are equivalent linear changes (Box 2.2), though
more complex nonlinear changes in the variable will often be clear
from the description and plots of the time series.
In recent decades, advances in the global climate observing system
have contributed to improved monitoring capabilities. In particular, sat-
ellites provide additional observations of climate change, which have
been assessed in this and subsequent chapters together with more
traditional ground-based and radiosonde observations. Since AR4,
substantial developments have occurred including the production of
revised data sets, more digital data records, and new data set efforts.
New dynamical reanalysis data sets of the global atmosphere have
been published (Box 2.3). These various innovations have improved
understanding of data issues and uncertainties (Box 2.1).
Developing homogeneous long-term records from these different
sources remains a challenge. The longest observational series are
land surface air temperatures (LSATs) and sea surface temperatures
(SSTs). Like all physical climate system measurements, they suffer from
non-climatic artefacts that must be taken into account (Box 2.1). The
global combined LSAT and SST remains an important climate change
measure for several reasons. Climate sensitivity is typically assessed
in the context of global mean surface temperature (GMST) responses
to a doubling of CO
2
(Chapter 8) and GMST is thus a key metric in
the climate change policy framework. Also, because it extends back in
time farther than any other global instrumental series, GMST is key to
understanding both the causes of change and the patterns, role and
magnitude of natural variability (Chapter 10). Starting at various points
in the 20th century, additional observations, including balloon-borne
measurements and satellite measurements, and reanalysis products
allow analyses of indicators such as atmospheric composition, radia-
tion budgets, hydrological cycle changes, extreme event characteriza-
tions and circulation indices. A full understanding of the climate system
characteristics and changes requires analyses of all such variables
as well as ocean (Chapter 3) and cryosphere (Chapter 4) indicators.
Through such a holistic analysis, a clearer and more robust assessment
of the changing climate system emerges (FAQ 2.1).
This chapter starts with an assessment of the observations of the abun-
dances of greenhouse gases (GHGs) and of aerosols, the main drivers
of climate change (Section 2.2). Global trends in GHGs are indicative
of the imbalance between sources and sinks in GHG budgets, and play
an important role in emissions verification on a global scale. The radia-
tive forcing (RF) effects of GHGs and aerosols are assessed in Chapter
8. The observed changes in radiation budgets are discussed in Sec-
tion 2.3. Aerosol–cloud interactions are assessed in Chapter 7. Sec-
tion 2.4 provides an assessment of observed changes in surface and
atmospheric temperature. Observed change in the hydrological cycle,
including precipitation and clouds, is assessed in Section 2.5. Changes
in variability and extremes (such as cold spells, heat waves, droughts
and tropical cyclones) are assessed in Section 2.6. Section 2.7 assesses
observed changes in the circulation of the atmosphere and its modes
of variability, which help determine seasonal and longer-term anoma-
lies at regional scales (Chapter 14).
Trends have been assessed where possible for multi-decadal periods
starting in 1880, 1901 (referred to as long-term trends) and in 1951,
1979 (referred to as short-term trends). The time elapsed since AR4
extends the period for trend calculation from 2005 to 2012 for many
variables. The GMST trend since 1998 has also been considered (see
also Box 9.2) as well as the trends for sequential 30-year segments of
the time series. For many variables derived from satellite data, infor-
mation is available for 1979–2012 only. In general, trend estimates
are more reliable for longer time intervals, and trends computed on
short intervals have a large uncertainty. Trends for short intervals are
very sensitive to the start and end years. An exception to this is trends
in GHGs, whose accurate measurement and long lifetimes make them
well-mixed and less susceptible to year-to-year variability, so that
trends computed on relatively short intervals are very meaningful for
these variables. Where possible, the time interval 1961–1990 has been
chosen as the climatological reference period (or normal period) for
averaging. This choice enables direct comparisons with AR4, but is
different from the present-day climate period (1986–2005) used as a
reference in the modelling chapters of AR5 and Annex I: Atlas of Global
and Regional Climate Projections.
It is important to note that the question of whether the observed
changes are outside the possible range of natural internal climate
variability and consistent with the climate effects from changes in
atmospheric composition is not addressed in this chapter, but rather in
Chapter 10. No attempt has been undertaken to further describe and
interpret the observed changes in terms of multi-decadal oscillatory
(or low-frequency) variations, (long-term) persistence and/or secular
trends (e.g., as in Cohn and Lins, 2005; Koutsoyiannis and Montanari,
2007; Zorita et al., 2008; Lennartz and Bunde, 2009; Mills, 2010; Mann,
2011; Wu et al., 2011; Zhou and Tung, 2012; Tung and Zhou, 2013).
In this chapter, the robustness of the observed changes is assessed
in relation to various sources of observational uncertainty (Box 2.1).
In addition, the reported trend significance and statistical confidence
intervals provide an indication of how large the observed trend is
compared to the range of observed variability in a given aspect of the
climate system (see Box 2.2 for a description of the statistical trend
model applied). Unless otherwise stated, 90% confidence intervals
are given. The chapter also examines the physical consistency across
165
Observations: Atmosphere and Surface Chapter 2
2
different observations, which helps to provide additional confidence
in the reported changes. Additional information about data sources
and methods is described in the Supplementary Material to Chapter 2.
2.2 Changes in Atmospheric Composition
2.2.1 Well-Mixed Greenhouse Gases
AR4 (Forster et al., 2007; IPCC, 2007) concluded that increasing atmos-
pheric burdens of well-mixed GHGs resulted in a 9% increase in their
RF from 1998 to 2005. Since 2005, the atmospheric abundances of
many well-mixed GHG increased further, but the burdens of some
ozone-depleting substances (ODS) whose production and use were
controlled by the Montreal Protocol on Substances that Deplete the
Ozone Layer (1987; hereinafter, ‘Montreal Protocol’) decreased.
Based on updated in situ observations, this assessment concludes
that these trends resulted in a 7.5% increase in RF from GHGs from
2005 to 2011, with carbon dioxide (CO
2
) contributing 80%. Of note
is an increase in the average growth rate of atmospheric methane
(CH
4
) from ~0.5 ppb yr
–1
during 1999–2006 to ~6 ppb yr
–1
from 2007
through 2011. Current observation networks are sufficient to quanti-
fy global annual mean burdens used to calculate RF and to constrain
global emission rates (with knowledge of loss rates), but they are not
sufficient for accurately estimating regional scale emissions and how
they are changing with time.
The globally, annually averaged well-mixed GHG mole fractions report-
ed here are used in Chapter 8 to calculate RF. A direct, inseparable con-
nection exists between observed changes in atmospheric composition
and well-mixed GHG emissions and losses (discussed in Chapter 6 for
CO
2
, CH
4
, and N
2
O). A global GHG budget consists of the total atmos-
pheric burden, total global rate of production or emission (i.e., sources),
and the total global rate of destruction or removal (i.e., sinks). Precise,
accurate systematic observations from independent globally distribut-
ed measurement networks are used to estimate global annual mean
well-mixed GHG mole fractions at the Earth’s surface, and these allow
estimates of global burdens. Emissions are predominantly from surface
sources, which are described in Chapter 6 for CO
2
, CH
4
, and N
2
O. Direct
Box 2.1 | Uncertainty in Observational Records
The vast majority of historical (and modern) weather observations were not made explicitly for climate monitoring purposes. Measure-
ments have changed in nature as demands on the data, observing practices and technologies have evolved. These changes almost
always alter the characteristics of observational records, changing their mean, their variability or both, such that it is necessary to
process the raw measurements before they can be considered useful for assessing the true climate evolution. This is true of all observ-
ing techniques that measure physical atmospheric quantities. The uncertainty in observational records encompasses instrumental/
recording errors, effects of representation (e.g., exposure, observing frequency or timing), as well as effects due to physical changes
in the instrumentation (such as station relocations or new satellites). All further processing steps (transmission, storage, gridding,
interpolating, averaging) also have their own particular uncertainties. Because there is no unique, unambiguous, way to identify and
account for non-climatic artefacts in the vast majority of records, there must be a degree of uncertainty as to how the climate system
has changed. The only exceptions are certain atmospheric composition and flux measurements whose measurements and uncertainties
are rigorously tied through an unbroken chain to internationally recognized absolute measurement standards (e.g., the CO
2
record at
Mauna Loa; Keeling et al., 1976a).
Uncertainty in data set production can result either from the choice of parameters within a particular analytical framework—paramet-
ric uncertainty, or from the choice of overall analytical framework— structural uncertainty. Structural uncertainty is best estimated by
having multiple independent groups assess the same data using distinct approaches. More analyses assessed now than in AR4 include
published estimates of parametric or structural uncertainty. It is important to note that the literature includes a very broad range of
approaches. Great care has been taken in comparing the published uncertainty ranges as they almost always do not constitute a like-
for-like comparison. In general, studies that account for multiple potential error sources in a rigorous manner yield larger uncertainty
ranges. This yields an apparent paradox in interpretation as one might think that smaller uncertainty ranges should indicate a better
product. However, in many cases this would be an incorrect inference as the smaller uncertainty range may instead reflect that the pub-
lished estimate considered only a subset of the plausible sources of uncertainty. Within the timeseries figures, where this issue would be
most acute, such parametric uncertainty estimates are therefore not generally included. Consistent with AR4 HadCRUT4 uncertainties
in GMST are included in Figure 2.19, which in addition includes structural uncertainties in GMST.
To conclude, the vast majority of the raw observations used to monitor the state of the climate contain residual non-climatic influences.
Removal of these influences cannot be done definitively and neither can the uncertainties be unambiguously assessed. Therefore, care
is required in interpreting both data products and their stated uncertainty estimates. Confidence can be built from: redundancy in
efforts to create products; data set heritage; and cross-comparisons of variables that would be expected to co-vary for physical reasons,
such as LSATs and SSTs around coastlines. Finally, trends are often quoted as a way to synthesize the data into a single number. Uncer-
tainties that arise from such a process and the choice of technique used within this chapter are described in more detail in Box 2.2.
166
Chapter 2 Observations: Atmosphere and Surface
2
use of observations of well-mixed GHG to model their regional budg-
ets can also play an important role in verifying inventory estimates of
emissions (Nisbet and Weiss, 2010).
Systematic measurements of well-mixed GHG in ambient air began
at various times during the last six decades, with earlier atmospheric
histories being reconstructed from measurements of air stored in air
archives and trapped in polar ice cores or in firn. In contrast to the
physical meteorological parameters discussed elsewhere in this chap-
ter, measurements of well-mixed GHG are reported relative to stand-
ards developed from fundamental SI base units (SI = International
System of Units) as dry-air mole fractions, a unit that is conserved with
changes in temperature and pressure (Box 2.1). This eliminates dilution
by H
2
O vapour, which can reach 4% of total atmospheric composition.
Here, the following abbreviations are used: ppm = µmol mol
–1
; ppb =
nmol mol
–1
; and ppt = pmol mol
–1
. Unless noted otherwise, averag-
es of National Oceanic and Atmospheric Administration (NOAA) and
Advanced Global Atmospheric Gases Experiment (AGAGE) annually
averaged surface global mean mole fractions is described in Section
2.2.1 (see Supplementary Material 2.SM.2 for further species not listed
here).
Table 2.1 summarizes globally, annually averaged well-mixed GHG
mole fractions from four independent measurement programs. Sam-
pling strategies and techniques for estimating global means and their
uncertainties vary among programs. Differences among measurement
programs are relatively small and will not add significantly to uncer-
tainty in RF. Time series of the well-mixed GHG are plotted in Figures 2.1
(CO
2
), 2.2 (CH
4
), 2.3 (N
2
O), and 2.4 (halogen-containing compounds).
2.2.1.1 Kyoto Protocol Gases (Carbon Dioxide, Methane,
Nitrous Oxide, Hydrofluorocarbons, Perfluorocarbons
and Sulphur Hexafluoride)
2.2.1.1.1 Carbon Dioxide
Precise, accurate systematic measurements of atmospheric CO
2
at
Mauna Loa, Hawaii and South Pole were started by C. D. Keeling from
Scripps Institution of Oceanography in the late 1950s (Keeling et al.,
1976a; Keeling et al., 1976b). The 1750 globally averaged abundance
of atmospheric CO
2
based on measurements of air extracted from ice
cores and from firn is 278 ± 2 ppm (Etheridge et al., 1996). Globally
averaged CO
2
mole fractions since the start of the instrumental record
2011 Global Annual Mean Global Increase from 2005 to 2011
Species Lifetime (yr) RE (W m
–2
ppb
–1
) UCI SIO
b
/AGAGE NOAA UCI SIO
b
/AGAGE NOAA
CO
2
(ppm) 1.37 × 10
–5
390.48 ± 0.28 390.44 ± 0.16 11.67 ± 0.37 11.66 ± 0.13
CH
4
(ppb) 9.1 3.63 × 10
–4
1798.1 ± 0.6 1803.1 ± 4.8 1803.2 ± 1.2 26.6 ± 0.9 28.9 ± 6.8 28.6 ± 0.9
N
2
O (ppb) 131 3.03 × 10
–3
324.0 ± 0.1 324.3 ± 0.1 4.7 ± 0.2 5.24 ± 0.14
SF
6
3200 0.575 7.26 ± 0.02 7.31 ± 0.02 1.65 ± 0.03 1.64 ±0.01
CF
4
50,000 0.1 79.0 ± 0.1 4.0 ± 0.2
C
2
F
6
10,000 0.26 4.16 ± 0.02 0.50 ± 0.03
HFC-125 28.2 0.219 9.58 ± 0.04 5.89 ± 0.07
HFC-134a 13.4 0.159 63.4 ± 0.9 62.4 ± 0.3 63.0 ± 0.6 27.7 ± 1.4 28.2 ± 0.4 28.2 ± 0.1
HFC-143a 47.1 0.159 12.04 ± 0.07 6.39 ± 0.10
HFC-152a 1.5 0.094 6.4 ± 0.1 3.0 ± 0.2
HFC-23 222 0.176 24.0 ± 0.3 5.2 ± 0.6
CFC-11 45 0.263 237.9 ± 0.8 236.9 ± 0.1 238.5 ± 0.2 –13.2 ± 0.8 –12.7 ± 0.2 –13.0 ± 0.1
CFC-12 100 0.32 525.3 ± 0.8 529.5 ± 0.2 527.4 ± 0.4 –12.8 ± 0.8 –13.4 ± 0.3 –14.1 ± 0.1
CFC-113 85 0.3 74.9 ± 0.6 74.29 ± 0.06 74.40 ± 0.04 –4.6 ± 0.8 –4.25 ± 0.08 –4.35 ±0.02
HCFC-22 11.9 0.2 209.0 ± 1.2 213.4 ± 0.8 213.2 ± 1.2 41.5 ± 1.4 44.6 ± 1.1 44.3 ± 0.2
HCFC-141b 9.2 0.152 20.8 ± 0.5 21.38 ± 0.09 21.4 ± 0.2 3.7 ± 0.5 3.70 ± 0.1 3.76 ± 0.03
HCFC-142b 17.2 0.186 21.0 ± 0.5 21.35 ± 0.06 21.0 ± 0.1 4.9 ± 0.5 5.72 ± 0.09 5.73 ± 0.04
CCl
4
26 0.175 87.8 ± 0.6 85.0 ± 0.1 86.5 ± 0.3 –6.4 ± 0.5 –6.9 ± 0.2 –7.8 ± 0.1
CH
3
CCl
3
5 0.069 6.8 ± 0.6 6.3 ± 0.1 6.35 ± 0.07 –14.8 ± 0.5 –11.9 ± 0.2 –12.1 ± 0.1
Table 2.1 | Global annual mean surface dry-air mole fractions and their change since 2005 for well-mixed greenhouse gases (GHGs) from four measurement networks. Units are
ppt except where noted. Uncertainties are 90% confidence intervals
a
.
REs (radiative efficiency) and lifetimes (except CH
4
and N
2
O, which are from Prather et al., 2012) are from
Chapter 8.
Notes:
AGAGE = Advanced Global Atmospheric Gases Experiment; NOAA = National Oceanic and Atmospheric Administration, Earth System Research Laboratory, Global Monitoring Division; SIO = Scripps
Institution of Oceanography, University of California, San Diego; UCI = University of California, Irvine, Department of Chemistry. HFC-125 = CHF
2
CF
3
; HFC-134a = CH
2
FCF
3
; HFC-143a = CH
3
CF
3
;
HFC-152a = CH
3
CHF
2
; HFC-23 = CHF
3
; CFC-11 = CCl
3
F; CFC-12 = CCl
2
F
2
; CFC-113 = CClF
2
CCl
2
F; HCFC-22 = CHClF
2
; HCFC-141b = CH
3
CCl
2
F; HCFC-142b = CH
3
CClF
2
.
a
Each program uses different methods to estimate uncertainties.
b
SIO reports only CO
2
; all other values reported in these columns are from AGAGE. SIO CO
2
program and AGAGE are not affiliated with each other.
Budget lifetimes are shown; for CH
4
and N
2
O, perturbation lifetimes (12.4 years for CH
4
and 121 years for N
2
O) are used to estimate global warming potentials (Chapter 8).
Year 1750 values determined from air extracted from ice cores are below detection limits for all species except CO
2
(278 ± 2 ppm), CH
4
(722 ± 25 ppb), N
2
O (270 ± 7 ppb) and CF
4
(34.7 ± 0.2 ppt).
Centennial variations up to 10 ppm CO
2
, 40 ppb CH
4
, and 10 ppb occur throughout the late-Holocene (Chapter 6).
167
Observations: Atmosphere and Surface Chapter 2
2
are plotted in Figure 2.1. The main features in the contemporary CO
2
record are the long-term increase and the seasonal cycle resulting from
photosynthesis and respiration by the terrestrial biosphere, mostly in
the Northern Hemisphere (NH). The main contributors to increasing
atmospheric CO
2
abundance are fossil fuel combustion and land use
change (Section 6.3). Multiple lines of observational evidence indicate
that during the past few decades, most of the increasing atmospheric
burden of CO
2
is from fossil fuel combustion (Tans, 2009). Since the
last year for which the AR4 reported (2005), CO
2
has increased by 11.7
ppm to 390.5 ppm in 2011 (Table 2.1). From 1980 to 2011, the average
annual increase in globally averaged CO
2
(from 1 January in one year
to 1 January in the next year) was 1.7 ppm yr
–1
(1 standard deviation
= 0.5 ppm yr
–1
; 1 ppm globally corresponds to 2.1 PgC increase in the
atmospheric burden). Since 2001, CO
2
has increased at 2.0 ppm yr
–1
(1
standard deviation = 0.3 ppm yr
–1
). The CO
2
growth rate varies from
year to year; since 1980 the range in annual increase is 0.7 ± 0.1 ppm
in 1992 to 2.9 ± 0.1 ppm in 1998. Most of this interannual variability
in growth rate is driven by small changes in the balance between pho-
tosynthesis and respiration on land, each having global fluxes of ~120
PgC yr
–1
(Chapter 6).
2.2.1.1.2 Methane
Globally averaged CH
4
in 1750 was 722 ± 25 ppb (after correction
to the NOAA-2004 CH
4
standard scale) (Etheridge et al., 1998; Dlu-
gokencky et al., 2005), although human influences on the global CH
4
budget may have begun thousands of years earlier than this time that
is normally considered ‘pre-industrial’ (Ruddiman, 2003; Ferretti et al.,
2005; Ruddiman, 2007). In 2011, the global annual mean was 1803
± 2 ppb. Direct atmospheric measurements of CH
4
of sufficient spa-
tial coverage to calculate global annual means began in 1978 and are
plotted through 2011 in Figure 2.2a. This time period is characterized
by a decreasing growth rate (Figure 2.2b) from the early 1980s until
1998, stabilization from 1999 to 2006, and an increasing atmospheric
burden from 2007 to 2011 (Rigby et al., 2008; Dlugokencky et al.,
d(CO
2
)/dt (ppm yr
-1
)
CO
2
(ppm)
380
360
340
320
3
2
1
(a)
(b)
1960 1970 1980 1990 2000 2010
Figure 2.1 | (a) Globally averaged CO
2
dry-air mole fractions from Scripps Institution
of Oceanography (SIO) at monthly time resolution based on measurements from Mauna
Loa, Hawaii and South Pole (red) and NOAA/ESRL/GMD at quasi-weekly time resolution
(blue). SIO values are deseasonalized. (b) Instantaneous growth rates for globally aver-
aged atmospheric CO
2
using the same colour code as in (a). Growth rates are calculated
as the time derivative of the deseasonalized global averages (Dlugokencky et al., 1994).
2009). Assuming no long-term trend in hydroxyl radical (OH) concen-
tration, the observed decrease in CH
4
growth rate from the early 1980s
through 2006 indicates an approach to steady state where total global
emissions have been approximately constant at ~550 Tg (CH
4
) yr
–1
.
Superimposed on the long-term pattern is significant interannual vari-
ability; studies of this variability are used to improve understanding of
the global CH
4
budget (Chapter 6). The most likely drivers of increased
atmospheric CH
4
were anomalously high temperatures in the Arctic in
2007 and greater than average precipitation in the tropics during 2007
and 2008 (Dlugokencky et al., 2009; Bousquet, 2011). Observations of
the difference in CH
4
between zonal averages for northern and south-
ern polar regions (53° to 90°) (Dlugokencky et al., 2009, 2011) suggest
that, so far, it is unlikely that there has been a permanent measureable
increase in Arctic CH
4
emissions from wetlands and shallow sub-sea
CH
4
clathrates.
Reaction with the hydroxyl radical (OH) is the main loss process for
CH
4
(and for hydrofluorocarbons (HFCs) and hydrochlorofluorocarbons
(HCFCs)), and it is the largest term in the global CH
4
budget. Therefore,
trends and interannual variability in OH concentration significantly
impact our understanding of changes in CH
4
emissions. Methyl chloro-
form (CH
3
CCl
3
; Section 2.2.1.2) has been used extensively to estimate
globally averaged OH concentrations (e.g., Prinn et al., 2005). AR4
reported no trend in OH from 1979 to 2004, and there is no evidence
from this assessment to change that conclusion for 2005 to 2011.
Montzka et al. (2011a) exploited the exponential decrease and small
emissions in CH
3
CCl
3
to show that interannual variations in OH con-
centration from 1998 to 2007 are 2.3 ± 1.5%, which is consistent with
estimates based on CH
4
, tetrachloroethene (C
2
Cl
4
), dichloromethane
(CH
2
Cl
2
), chloromethane (CH
3
Cl) and bromomethane (CH
3
Br).
2.2.1.1.3 Nitrous Oxide
Globally averaged N
2
O in 2011 was 324.2 ppb, an increase of 5.0 ppb
over the value reported for 2005 in AR4 (Table 2.1). This is an increase
d(CH
4
)/dt (ppb yr
-1
)
CH
4
(ppb)
1800
1750
1700
1650
1600
1550
25
20
15
10
5
0
-5
(a)
(b)
1980 1990
2000 2010
Figure 2.2 | (a) Globally averaged CH
4
dry-air mole fractions from UCI (green; four
values per year, except prior to 1984, when they are of lower and varying frequency),
AGAGE (red; monthly), and NOAA/ESRL/GMD (blue; quasi-weekly). (b) Instantaneous
growth rate for globally averaged atmospheric CH
4
using the same colour code as in (a).
Growth rates were calculated as in Figure 2.1.
168
Chapter 2 Observations: Atmosphere and Surface
2
of 20% over the estimate for 1750 from ice cores, 270 ± 7 ppb (Prather
et al., 2012). Measurements of N
2
O and its isotopic composition in firn
air suggest the increase, at least since the early 1950s, is dominated
by emissions from soils treated with synthetic and organic (manure)
nitrogen fertilizer (Rockmann and Levin, 2005; Ishijima et al., 2007;
Davidson, 2009; Syakila and Kroeze, 2011). Since systematic measure-
ments began in the late 1970s, N
2
O has increased at an average rate of
~0.75 ppb yr
–1
(Figure 2.3). Because the atmospheric burden of CFC-12
is decreasing, N
2
O has replaced CFC-12 as the third most important
well-mixed GHG contributing to RF (Elkins and Dutton, 2011).
Persistent latitudinal gradients in annually averaged N
2
O are observed
at background surface sites, with maxima in the northern subtropics,
values about 1.7 ppb lower in the Antarctic, and values about 0.4 ppb
lower in the Arctic (Huang et al., 2008). These persistent gradients
contain information about anthropogenic emissions from fertilizer use
at northern tropical to mid-latitudes and natural emissions from soils
and ocean upwelling regions of the tropics. N
2
O time series also con-
tain seasonal variations with peak-to-peak amplitudes of about 1 ppb
in high latitudes of the NH and about 0.4 ppb at high southern and
tropical latitudes. In the NH, exchange of air between the stratosphere
(where N
2
O is destroyed by photochemical processes) and troposphere
is the dominant contributor to observed seasonal cycles, not seasonali-
ty in emissions (Jiang et al., 2007). Nevison et al. (2011) found correla-
tions between the magnitude of detrended N
2
O seasonal minima and
lower stratospheric temperature, providing evidence for a stratospheric
influence on the timing and amplitude of the seasonal cycle at surface
monitoring sites. In the Southern Hemisphere (SH), observed seasonal
cycles are also affected by stratospheric influx, and by ventilation and
thermal out-gassing of N
2
O from the oceans.
2.2.1.1.4 Hydrofluorocarbons, Perfluorocarbons, Sulphur
Hexafluoride and Nitrogen Trifluoride
The budgets of HFCs, PFCs and SF
6
were recently reviewed in Chapter
1 of the Scientific Assessment of Ozone Depletion: 2010 (Montzka et
al., 2011b), so only a brief description is given here. The current atmos-
d(N
2
O)/dt (ppb yr
-1
)N
2
O (ppb)
320
315
310
305
300
1.25
1.00
0.75
0.50
0.25
(a)
(b)
1980 1985 1990 1995 2000 20102005
Figure 2.3 | (a) Globally averaged N
2
O dry-air mole fractions from AGAGE (red) and
NOAA/ESRL/GMD (blue) at monthly resolution. (b) Instantaneous growth rates for glob-
ally averaged atmospheric N
2
O. Growth rates were calculated as in Figure 2.1.
pheric abundances of these species are summarized in Table 2.1 and
plotted in Figure 2.4.
Atmospheric HFC abundances are low and their contribution to RF is
small relative to that of the CFCs and HCFCs they replace (less than 1%
of the total by well-mixed GHGs; Chapter 8). As they replace CFCs and
HCFCs phased out by the Montreal Protocol, however, their contribu-
tion to future climate forcing is projected to grow considerably in the
absence of controls on global production (Velders et al., 2009).
HFC-134a is a replacement for CFC-12 in automobile air conditioners
and is also used in foam blowing applications. In 2011, it reached 62.7
ppt, an increase of 28.2 ppt since 2005. Based on analysis of high-fre-
quency measurements, the largest emissions occur in North America,
Europe and East Asia (Stohl et al., 2009).
HFC-23 is a by-product of HCFC-22 production. Direct measurements
of HFC-23 in ambient air at five sites began in 2007. The 2005 global
annual mean used to calculate the increase since AR4 in Table 2.1, 5.2
ppt, is based on an archive of air collected at Cape Grim, Tasmania
(Miller et al., 2010). In 2011, atmospheric HFC-23 was at 24.0 ppt. Its
growth rate peaked in 2006 as emissions from developing countries
Figure 2.4 | Globally averaged dry-air mole fractions at the Earth’s surface of the
major halogen-containing well-mixed GHG. These are derived mainly using monthly
mean measurements from the AGAGE and NOAA/ESRL/GMD networks. For clarity, only
the most abundant chemicals are shown in different compound classes and results from
different networks have been combined when both are available.
0
100
200
300
400
500
1980 1985 1990 1995 2000 2005 2010
Gas (ppt)
CFC-12
CF
4
CCl
4
CH
3
CCl
3
HCFC-22
CFC-11
HFC-23
HFC-134a
0
3
6
9
12
1980 1985 1990 1995 2000 2005 2010
Gas (ppt)
HFC-125
HFC-143a
HFC-152a
C2F6
SF6
C3F8
C
2
F
6
SF
6
HFC-32
HFC-245fa
HFC-365mfc
C
3
F
8
169
Observations: Atmosphere and Surface Chapter 2
2
increased, then declined as emissions were reduced through abate-
ment efforts under the Clean Development Mechanism (CDM) of the
UNFCCC. Estimates of total global emissions based on atmospheric
observations and bottom-up inventories agree within uncertainties
(Miller et al., 2010; Montzka et al., 2010). Currently, the largest emis-
sions of HFC-23 are from East Asia (Yokouchi et al., 2006; Kim et al.,
2010; Stohl et al., 2010); developed countries emit less than 20% of the
global total. Keller et al. (2011) found that emissions from developed
countries may be larger than those reported to the UNFCCC, but their
contribution is small. The lifetime of HFC-23 was revised from 270 to
222 years since AR4.
After HFC-134a and HFC-23, the next most abundant HFCs are HFC-
143a at 12.04 ppt in 2011, 6.39 ppt greater than in 2005; HFC-125
(O’Doherty et al., 2009) at 9.58 ppt, increasing by 5.89 ppt since 2005;
HFC-152a (Greally et al., 2007) at 6.4 ppt with a 3.0 ppt increase since
2005; and HFC-32 at 4.92 ppt in 2011, 3.77 ppt greater than in 2005.
Since 2005, all of these were increasing exponentially except for HFC-
152a, whose growth rate slowed considerably in about 2007 (Figure
2.4). HFC-152a has a relatively short atmospheric lifetime of 1.5 years,
so its growth rate will respond quickly to changes in emissions. Its
major uses are as a foam blowing agent and aerosol spray propellant
while HFC-143a, HFC-125, and HFC-32 are mainly used in refriger-
ant blends. The reasons for slower growth in HFC-152a since about
2007 are unclear. Total global emissions of HFC-125 estimated from
the observations are within about 20% of emissions reported to the
UNFCCC, after accounting for estimates of unreported emissions from
East Asia (O’Doherty et al., 2009).
CF
4
and C
2
F
6
(PFCs) have lifetimes of 50 kyr and 10 kyr, respective-
ly, and they are emitted as by-products of aluminium production and
used in plasma etching of electronics. CF
4
has a natural lithospheric
source (Deeds et al., 2008) with a 1750 level determined from Green-
land and Antarctic firn air of 34.7 ± 0.2 ppt (Worton et al., 2007; Muhle
et al., 2010). In 2011, atmospheric abundances were 79.0 ppt for CF
4
,
increasing by 4.0 ppt since 2005, and 4.16 ppt for C
2
F
6
, increasing by
0.50 ppt. The sum of emissions of CF
4
reported by aluminium produc-
ers and for non-aluminium production in EDGAR (Emission Database
for Global Atmospheric Research) v4.0 accounts for only about half
of global emissions inferred from atmospheric observations (Muhle et
al., 2010). For C
2
F
6
, emissions reported to the UNFCCC are also sub-
stantially lower than those estimated from atmospheric observations
(Muhle et al., 2010).
The main sources of atmospheric SF
6
emissions are electricity distri-
bution systems, magnesium production, and semi-conductor manufac-
turing. Global annual mean SF
6
in 2011 was 7.29 ppt, increasing by
1.65 ppt since 2005. SF
6
has a lifetime of 3200 years, so its emissions
accumulate in the atmosphere and can be estimated directly from its
observed rate of increase. Levin et al. (2010) and Rigby et al. (2010)
showed that SF
6
emissions decreased after 1995, most likely because
of emissions reductions in developed countries, but then increased
after 1998. During the past decade, they found that actual SF
6
emis-
sions from developed countries are at least twice the reported values.
NF
3
was added to the list of GHG in the Kyoto Protocol with the Doha
Amendment, December, 2012. Arnold et al. (2013) determined 0.59 ppt
for its global annual mean mole fraction in 2008, growing from almost
zero in 1978. In 2011, NF
3
was 0.86 ppt, increasing by 0.49 ppt since
2005. These abundances were updated from the first work to quantify
NF
3
by Weiss et al. (2008). Initial bottom-up inventories underestimat-
ed its emissions; based on the atmospheric observations, NF
3
emissions
were 1.18 ± 0.21Gg in 2011 (Arnold et al., 2013).
In summary, it is certain that atmospheric burdens of well-mixed GHGs
targeted by the Kyoto Protocol increased from 2005 to 2011. The atmos-
pheric abundance of CO
2
was 390.5 ± 0.2 ppm in 2011; this is 40%
greater than before 1750. Atmospheric N
2
O was 324.2 ± 0.2 ppb in
2011 and has increased by 20% since 1750. Average annual increases
in CO
2
and N
2
O from 2005 to 2011 are comparable to those observed
from 1996 to 2005. Atmospheric CH
4
was 1803.2 ± 2.0 ppb in 2011; this
is 150% greater than before 1750. CH
4
began increasing in 2007 after
remaining nearly constant from 1999 to 2006. HFCs, PFCs, and SF
6
all
continue to increase relatively rapidly, but their contributions to RF are
less than 1% of the total by well-mixed GHGs (Chapter 8).
2.2.1.2 Ozone-Depleting Substances (Chlorofluorocarbons,
Chlorinated Solvents, and Hydrochlorofluorocarbons)
CFC atmospheric abundances are decreasing (Figure 2.4) because of the
successful reduction in emissions resulting from the Montreal Protocol.
By 2010, emissions from ODSs had been reduced by ~11 Pg CO
2
-eq
yr
–1
, which is five to six times the reduction target of the first com-
mitment period (2008–2012) of the Kyoto Protocol (2 PgCO
2
-eq yr
–1
)
(Velders et al., 2007). These avoided equivalent-CO
2
emissions account
for the offsets to RF by stratospheric O
3
depletion caused by ODSs and
the use of HFCs as substitutes for them. Recent observations in Arctic
and Antarctic firn air further confirm that emissions of CFCs are entirely
anthropogenic (Martinerie et al., 2009; Montzka et al., 2011b). CFC-12
has the largest atmospheric abundance and GWP-weighted emissions
(which are based on a 100-year time horizon) of the CFCs. Its tropo-
spheric abundance peaked during 2000–2004. Since AR4, its global
annual mean mole fraction declined by 13.8 ppt to 528.5 ppt in 2011.
CFC-11 continued the decrease that started in the mid-1990s, by 12.9
ppt since 2005. In 2011, CFC-11 was 237.7 ppt. CFC-113 decreased by
4.3 ppt since 2005 to 74.3 ppt in 2011. A discrepancy exists between
top-down and bottom-up methods for calculating CFC-11 emissions
(Montzka et al., 2011b). Emissions calculated using top-down methods
come into agreement with bottom-up estimates when a lifetime of 64
years is used for CFC-11 in place of the accepted value of 45 years; this
longer lifetime (64 years) is at the upper end of the range estimated
by Douglass et al. (2008) with models that more accurately simulate
stratospheric circulation. Future emissions of CFCs will largely come
from ‘banks’ (i.e., material residing in existing equipment or stores)
rather than current production.
The mean decrease in globally, annually averaged carbon tetrachlo-
ride (CCl
4
) based on NOAA and AGAGE measurements since 2005 was
7.4 ppt, with an atmospheric abundance of 85.8 ppt in 2011 (Table
2.1). The observed rate of decrease and inter-hemispheric difference
of CCl
4
suggest that emissions determined from the observations are
on average greater and less variable than bottom-up emission esti-
mates, although large uncertainties in the CCl
4
lifetime result in large
uncertainties in the top-down estimates of emissions (Xiao et al., 2010;
170
Chapter 2 Observations: Atmosphere and Surface
2
Montzka et al., 2011b). CH
3
CCl
3
has declined exponentially for about a
decade, decreasing by 12.0 ppt since 2005 to 6.3 ppt in 2011.
HCFCs are classified as ‘transitional substitutes’ by the Montreal Proto-
col. Their global production and use will ultimately be phased out, but
their global production is not currently capped and, based on changes
in observed spatial gradients, there has likely been a shift in emissions
within the NH from regions north of about 30°N to regions south of
30°N (Montzka et al., 2009). Global levels of the three most abundant
HCFCs in the atmosphere continue to increase. HCFC-22 increased by
44.5 ppt since 2005 to 213.3 ppt in 2011. Developed country emissions
of HCFC-22 are decreasing, and the trend in total global emissions is
driven by large increases from south and Southeast Asia (Saikawa et
al., 2012). HCFC-141b increased by 3.7 ppt since 2005 to 21.4 ppt in
2011, and for HCFC-142b, the increase was 5.73 ppt to 21.1 ppt in
2011. The rates of increase in these three HCFCs increased since 2004,
but the change in HCFC-141b growth rate was smaller and less persis-
tent than for the other two, which approximately doubled from 2004
to 2007 (Montzka et al., 2009).
In summary, for ODS, whose production and consumption are con-
trolled by the Montreal Protocol, it is certain that the global mean
abundances of major CFCs are decreasing and HCFCs are increasing.
Atmospheric burdens of CFC-11, CFC-12, CFC-113, CCl
4
, CH
3
CCl
3
and
some halons have decreased since 2005. HCFCs, which are transitional
substitutes for CFCs, continue to increase, but the spatial distribution
of their emissions is changing.
2.2.2 Near-Term Climate Forcers
This section covers observed trends in stratospheric water vapour;
stratospheric and tropospheric ozone (O
3
); the O
3
precursor gases,
nitrogen dioxide (NO
2
) and carbon monoxide (CO); and column and
surface aerosol. Since trend estimates from the cited literature are used
here, issues such as data records of different length, potential lack of
comparability among measurement methods and different trend calcu-
lation methods, add to the uncertainty in assessing trends.
2.2.2.1 Stratospheric Water Vapour
Stratospheric H
2
O vapour has an important role in the Earth’s radi-
ative balance and in stratospheric chemistry. Increased stratospher-
ic H
2
O vapour causes the troposphere to warm and the stratosphere
to cool (Manabe and Strickler, 1964; Solomon et al., 2010), and also
causes increased rates of stratospheric O
3
loss (Stenke and Grewe,
2005). Water vapour enters the stratosphere through the cold tropical
tropopause. As moisture-rich air masses are transported through this
region, most water vapour condenses resulting in extremely dry lower
stratospheric air. Because tropopause temperature varies seasonally,
so does H
2
O abundance there. Other contributions include oxidation
of methane within the stratosphere, and possibly direct injection of
H
2
O vapour in overshooting deep convection (Schiller et al., 2009). AR4
reported that stratospheric H
2
O vapour showed significant long-term
variability and an upward trend over the last half of the 20th century,
but no net increase since 1996. This updated assessment finds large
interannual variations that have been observed by independent meas-
urement techniques, but no significant net changes since 1996.
The longest continuous time series of stratospheric water vapour abun-
dance is from in situ measurements made with frost point hygrome-
ters starting in 1980 over Boulder, USA (40°N, 105°W) (Scherer et al.,
2008), with values ranging from 3.5 to 5.5 ppm, depending on altitude.
These observations have been complemented by long-term global sat-
ellite observations from SAGE II (1984–2005; Stratospheric Aerosol
and Gas Experiment II (Chu et al., 1989)), HALOE (1991–2005; HAL-
ogen Occultation Experiment (Russell et al., 1993)), Aura MLS (2004–
present; Microwave Limb Sounder (Read et al., 2007)) and Envisat
MIPAS (2002-2012; Michelson Interferometer for Passive Atmospheric
Sounding (Milz et al., 2005; von Clarmann et al., 2009)). Discrepancies
in water vapour mixing ratios from these different instruments can be
attributed to differences in the vertical resolution of measurements,
along with other factors. For example, offsets of up to 0.5 ppm in lower
stratospheric water vapour mixing ratios exist between the most cur-
rent versions of HALOE (v19) and Aura MLS (v3.3) retrievals during
their 16-month period of overlap (2004 to 2005), although such biases
can be removed to generate long-term records. Since AR4, new studies
characterize the uncertainties in measurements from individual types
of in situ H
2
O sensors (Vömel et al., 2007b; Vömel et al., 2007a; Wein-
stock et al., 2009), but discrepancies between different instruments
(50 to 100% at H
2
O mixing ratios less than 10 ppm), particularly for
high-altitude measurements from aircraft, remain largely unexplained.
Observed anomalies in stratospheric H
2
O from the near-global com-
bined HALOE+MLS record (1992–2011) (Figure 2.5) include effects
linked to the stratospheric quasi-biennial oscillation (QBO) influence
on tropopause temperatures, plus a step-like drop after 2001 (noted in
AR4), and an increasing trend since 2005. Variability during 2001–2011
was large yet there was only a small net change from 1992 through
2011. These interannual water vapour variations for the satellite record
are closely linked to observed changes in tropical tropopause temper-
atures (Fueglistaler and Haynes, 2005; Randel et al., 2006; Rosenlof
and Reid, 2008; Randel, 2010), providing reasonable understanding of
observed changes. The longer record of Boulder balloon measurements
(since 1980) has been updated and reanalyzed (Scherer et al., 2008;
Hurst et al., 2011), showing deca dal-scale variability and a long-term
stratospheric (16 to 26 km) increase of 1.0 ± 0.2 ppm for 1980–2010.
Agreement between interannual changes inferred from the Boulder
and HALOE+MLS data is good for the period since 1998 but was poor
during 1992–1996. About 30% of the positive trend during 1980–2010
determined from frost point hygrometer data (Fujiwara et al., 2010;
Hurst et al., 2011) can be explained by increased production of H
2
O
from CH
4
oxidation (Rohs et al., 2006), but the remainder cannot be
explained by changes in tropical tropopause temperatures (Fueglistal-
er and Haynes, 2005) or other known factors.
In summary, near-global satellite measurements of stratospheric H
2
O
show substantial variability for 1992–2011, with a step-like decrease
after 2000 and increases since 2005. Because of this large variability
and relatively short time series, confidence in long-term stratospher-
ic H
2
O trends is low. There is good understanding of the relationship
between the satellite-derived H
2
O variations and tropical tropopause
temperature changes. Stratospheric H
2
O changes from temporally
sparse balloon-borne observations at one location (Boulder, Colorado)
are in good agreement with satellite observations from 1998 to the
present, but discrepancies exist for changes during 1992–1996. Long-
171
Observations: Atmosphere and Surface Chapter 2
2
term balloon measurements from Boulder indicate a net increase of
1.0 ± 0.2 ppm over 16 to 26 km for 1980–2010, but these long-term
increases cannot be fully explained by changes in tropical tropopause
temperatures, methane oxidation or other known factors.
2.2.2.2 Stratospheric Ozone
AR4 did not explicitly discuss measured stratospheric ozone trends. For
the current assessment report such trends are relevant because they
are the basis for revising the RF from –0.05 ± 0.10 W m
–2
in 1750 to
–0.10 ± 0.15 W m
–2
in 2005 (Section 8.3.3.2). These values strongly
depend on the vertical distribution of the stratospheric ozone changes.
Total ozone is a good proxy for stratospheric ozone because tropo-
spheric ozone accounts for only about 10% of the total ozone column.
Long-term total ozone changes over various latitudinal belts, derived
from Weber et al. (2012), are illustrated in Figure 2.6 (a–d). Annual-
ly averaged total column ozone declined during the 1980s and early
1990s and has remained constant for the past decade, about 3.5 and
2.5% below the 1964–1980 average for the entire globe (not shown)
and 60°S to 60°N, respectively, with changes occurring mostly outside
the tropics, particularly the SH, where the current extratropical (30ºS
to 60ºS) mean values are 6% below the 1964–1980 average, com-
pared to 3.5% for the NH extratropics (Douglass et al., 2011). In the
NH, the 1993 minimum of about –6% was caused primarily by ozone
loss through heterogeneous reactions on volcanic aerosols from Mt.
Pinatubo.
0.5
0.0
-0.5
Water Vapour Anomaly (ppm)
20
10
0
-10
-20
Water Vapour
Anomaly (%)
HALOE+MLS: 60°S-60°N
-1.5
-1.0
-0.5
0.0
0.5
1.0
Water Vapour Anomaly (ppm)
2010200019901980
-40
-30
-20
-10
0
10
20
30
HALOE+MLS: 30°N-50°
N
NOAA FPH: Boulder (40°N)
(a)
(b)
Water Vapour
Anomaly (%)
Figure 2.5 | Water vapour anomalies in the lower stratosphere (~16 to 19 km) from satellite sensors and in situ measurements normalized to 2000–2011. (a) Monthly mean water
vapour anomalies at 83 hPa for 60°S to 60°N (blue) determined from HALOE and MLS satellite sensors. (b) Approximately monthly balloon-borne measurements of stratospheric
water vapour from Boulder, Colorado at 40°N (green dots; green curve is 15-point running mean) averaged over 16 to 18 km and monthly means as in (a), but averaged over 30°N
to 50°N (black)
Two altitude regions are mainly responsible for long-term changes in
total column ozone (Douglass et al., 2011). In the upper stratosphere
(35 to 45 km), there was a strong and statistically significant decline
(about 10%) up to the mid-1990s and little change or a slight increase
since. The lower stratosphere, between 20 and 25 km over mid-lat-
itudes, also experienced a statistically significant decline (7 to 8%)
between 1979 and the mid-1990s, followed by stabilization or a slight
(2 to 3%) ozone increase.
Springtime averages of total ozone poleward of 60° latitude in the
Arctic and Antarctic are shown in Figure 2.6e. By far the strongest
ozone loss in the stratosphere occurs in austral spring over Antarctica
(ozone hole) and its impact on SH climate is discussed in Chapters
11, 12 and 14. Interannual variability in polar stratospheric ozone
abundance and chemistry is driven by variability in temperature and
transport due to year-to-year differences in dynamics. This variability is
particularly large in the Arctic, where the most recent large depletion
occurred in 2011, when chemical ozone destruction was, for the first
time in the observational record, comparable to that in the Antarctic
(Manney et al., 2011).
In summary, it is certain that global stratospheric ozone has declined
from pre-1980 values. Most of the decline occurred prior to the mid-
1990s; since then there has been little net change and ozone has
remained nearly constant at about 3.5% below the 1964–1980 level.
172
Chapter 2 Observations: Atmosphere and Surface
2
2.2.2.3 Tropospheric Ozone
Tropospheric ozone is a short-lived trace gas that either originates in
the stratosphere or is produced in situ by precursor gases and sunlight
(e.g., Monks et al., 2009). An important GHG with an estimated RF
of 0.40 ± 0.20 W m
–2
(Chapter 8), tropospheric ozone also impacts
human health and vegetation at the surface. Its average atmospheric
lifetime of a few weeks produces a global distribution highly variable
by season, altitude and location. These characteristics and the paucity
of long-term measurements make the assessment of long-term global
ozone trends challenging. However, new studies since AR4 provide
greater understanding of surface and free tropospheric ozone trends
from the 1950s through 2010. An extensive compilation of meas-
ured ozone trends is presented in the Supplementary Material, Figure
2.SM.1 and Table 2.SM.2.
The earliest (1876–1910) quantitative ozone observations are limited
to Montsouris near Paris where ozone averaged 11 ppb (Volz and Kley,
1988). Semiquantitative ozone measurements from more than 40 loca-
tions around the world in the late 1800s and early 1900s range from
5 to 32 ppb with large uncertainty (Pavelin et al., 1999). The low 19th
century ozone values cannot be reproduced by most models (Section
8.2.3.1), and this discrepancy is an important factor contributing to
uncertainty in RF calculations (Section 8.3.3.1). Limited quantitative
measurements from the 1870s to 1950s indicate that surface ozone in
Europe increased by more than a factor of 2 compared to observations
made at the end of the 20th century (Marenco et al., 1994; Parrish et
al., 2012).
Satellite-based tropospheric column ozone retrievals across the tropics
and mid-latitudes reveal a greater burden in the NH than in the SH
(Ziemke et al., 2011). Tropospheric column ozone trend analyses are
few. An analysis by Ziemke et al. (2005) found no trend over the trop-
ical Pacific Ocean but significant positive trends (5 to 9% per decade)
in the mid-latitude Pacific of both hemispheres during 1979–2003. Sig-
nificant positive trends (2 to 9% per decade) were found across broad
regions of the tropical South Atlantic, India, southern China, southeast
Asia, Indonesia and the tropical regions downwind of China (Beig and
Singh, 2007).
Long-term ozone trends at the surface and in the free troposphere (of
importance for calculating RF, Chapter 8) can be assessed only from
in situ measurements at a limited number of sites, leaving large areas
such as the tropics and SH sparsely sampled (Table 2.SM.2, Figure 2.7).
Nineteen predominantly rural surface sites or regions around the globe
have long-term records that stretch back to the 1970s, and in two
cases the 1950s (Lelieveld et al., 2004; Parrish et al., 2012; Oltmans et
al., 2013). Thirteen of these sites are in the NH, and 11 sites have statis-
tically significant positive trends of 1 to 5 ppb per decade, correspond-
ing to >100% ozone increases since the 1950s and 9 to 55% ozone
increases since the 1970s. In the SH, three of six sites have signifi-
cant trends of approximately 2 ppb per decade and three have insig-
nificant trends. Free tropospheric monitoring since the 1970s is more
limited. Significant positive trends since 1971 have been observed
using ozone sondes above Western Europe, Japan and coastal Antarc-
tica (rates of increase range from 1 to 3 ppb per decade), but not at
all levels (Oltmans et al., 2013). In addition, aircraft have measured
WOUDC (Brewer, Dobson, Filter)
GOME/SCIAMACHY/GOME2 (GSG)
BUV/TOMS/SBUV/OMI (MOD V8)
WOUDC (Brewer, Dobson, Filter)
GOME/SCIAMACHY/GOME2 (GSG)
BUV/TOMS/SBUV/OMI (MOD V8)
O
3
(DU) O
3
(DU) O
3
(DU) O
3
(DU) O
3
(DU)
1970 1980 1990 2000 2010
(a)
(b)
(c)
(d)
(e)
Figure 2.6 | Zonally averaged, annual mean total column ozone in Dobson Units
(DU; 1 DU = 2.69 × 10
16
O
3
/cm
2
) from ground-based measurements combining Brewer,
Dobson, and filter spectrometer data WOUDC (red), GOME/SCIAMACHY/GOME-2 GSG
(green) and merged satellite BUV/TOMS/SBUV/OMI MOD V8 (blue) for (a) Non-Polar
Global (60°S to 60°N), (b) NH (30°N to 60°N), (c) Tropics (25°S to 25°N), (d) SH (30°S
to 60°S) and (e) March NH Polar (60°N to 90°N) and October SH Polar. (Adapted from
Weber et al., 2012; see also for abbreviations.)
173
Observations: Atmosphere and Surface Chapter 2
2
significant upper tropospheric trends in one or more seasons above
the north-eastern USA, the North Atlantic Ocean, Europe, the Middle
East, northern India, southern China and Japan (Schnadt Poberaj et
al., 2009). Insignificant free tropospheric trends were found above the
Mid-Atlantic USA (1971–2010) (Oltmans et al., 2013) and in the upper
troposphere above the western USA (1975–2001) (Schnadt Poberaj et
al., 2009). No site or region showed a significant negative trend.
In recent decades ozone precursor emissions have decreased in Europe
and North America and increased in Asia (Granier et al., 2011), impact-
ing ozone production on regional and hemispheric scales (Skeie et al.,
2011). Accordingly, 1990–2010 surface ozone trends vary regionally. In
Europe ozone generally increased through much of the 1990s but since
2000 ozone has either levelled off or decreased at rural and mountain-
top sites, as well as for baseline ozone coming ashore at Mace Head,
Ireland (Tarasova et al., 2009; Logan et al., 2012; Parrish et al., 2012;
Oltmans et al., 2013). In North America surface ozone has increased in
eastern and Arctic Canada, but is unchanged in central and western
Canada (Oltmans et al., 2013). Surface ozone has increased in baseline
air masses coming ashore along the west coast of the USA (Parrish et
al., 2012) and at half of the rural sites in the western USA during spring
(Cooper et al., 2012). In the eastern USA surface ozone has decreased
strongly in summer, is largely unchanged in spring and has increased
in winter (Lefohn et al., 2010; Cooper et al., 2012). East Asian surface
ozone is generally increasing (Table 2.SM.2) and at downwind sites
ozone is increasing at Mauna Loa, Hawaii but decreasing at Minami
Tori Shima in the subtropical western North Pacific (Oltmans et al.,
2013). In the SH ozone has increased at the eight available sites,
although trends are insignificant at four sites (Helmig et al., 2007; Olt-
mans et al., 2013).
Owing to methodological changes, free tropospheric ozone obser-
vations are most reliable since the mid-1990s. Ozone has decreased
above Europe since 1998 (Logan et al., 2012) and is largely unchanged
above Japan (Oltmans et al., 2013). Otherwise the remaining regions
with measurements (North America, North Pacific Ocean, SH) show a
range of positive trends (both significant and insignificant) depending
on altitude, with no site having a negative trend at any altitude (Table
2.SM.2).
In summary, there is medium confidence from limited measurements
in the late 19th through mid-20th century that European surface ozone
more than doubled by the end of the 20th century. There is medium
confidence from more widespread measurements beginning in the
1970s that surface ozone has increased at most (non-urban) sites in
the NH (1 to 5 ppb per decade), while there is low confidence for ozone
increases (2 ppb per decade) in the SH. Since 1990 surface ozone has
likely increased in East Asia, while surface ozone in the eastern USA
and Western Europe has levelled off or is decreasing. Ozone monitor-
ing in the free troposphere since the 1970s is very limited and indicates
a weaker rate of increase than at the surface. Satellite instruments
can now quantify the present-day tropospheric ozone burden on a
near-global basis; significant tropospheric ozone column increases
were observed over extended tropical regions of southern Asia, as well
as mid-latitude regions of the South and North Pacific Ocean since
1979.
Jungfraujoch - 46ºN, 3.6 km
Zugspitze - 47ºN, 3.0 km
Arosa - 47ºN, 1.0 km
Hohenpeissenberg - 48ºN, 1.0 km
Mace Head - 55ºN, 0.2 km
Arkona-Zingst - 54ºN, 0.0 km
Mt. Happo - 36ºN, 1.9 km
Japanese MBL - 38-45ºN, ≤0.1 km
Lassen NP - 41ºN, 1.8 km
U.S. Pacific MBL - 38-48ºN, ≤0.2 km
Summit, Greenland - 72.6ºN, 3.2 km
Barrow, Alaska - 71.3ºN, 0.0 km
Storhofdi, Iceland - 63.3ºN, 1.0 km
Mauna Loa, Hawaii - 19.5ºN, 3.4 km
Samoa - 14.2ºS, 0.1 km
Cape Point, South Africa - 34.4ºS, 0.2 km
Cape Grim, Tasmania - 40.7ºS, 0.1 km
Arrival Heights, Antarctica - 77.8ºS, 0.1 km
South Pole- 90.0ºS, 2.8 km
Europe
Asia
North America
Other
latitudes
60
40
20
0
60
40
20
0
60
40
20
0
O
3
(ppb)O
3
(ppb)O
3
(ppb)
1950 1960 1970 1980 1990 2000 2010
(a)
(b)
(c)
Figure 2.7 | Annual average surface ozone concentrations from regionally representa-
tive ozone monitoring sites around the world. (a) Europe. (b) Asia and North America.
(c) Remote sites in the Northern and Southern Hemispheres. The station name in the
legend is followed by its latitude and elevation. Time series include data from all times of
day and trend lines are linear regressions following the method of Parrish et al. (2012).
Trend lines are fit through the full time series at each location, except for Jungfraujoch,
Zugspitze, Arosa and Hohenpeissenberg where the linear trends end in 2000 (indicated
by the dashed vertical line in (a)). Twelve of these 19 sites have significant positive
ozone trends (i.e., a trend of zero lies outside the 95% confidence interval); the seven
sites with non-significant trends are: Japanese MBL (marine boundary layer), Summit
(Greenland), Barrow (Alaska), Storhofdi (Iceland), Samoa (tropical South Pacific Ocean),
Cape Point (South Africa) and South Pole (Antarctica).
174
Chapter 2 Observations: Atmosphere and Surface
2
2.2.2.4 Carbon Monoxide, Non-Methane Volatile Organic
Compounds and Nitrogen Dioxide
Emissions of carbon monoxide (CO), non-methane volatile organic
compounds (NMVOCs) and NO
x
(NO + NO
2
) do not have a direct effect
on RF, but affect climate indirectly as precursors to tropospheric O
3
and aerosol formation, and their impacts on OH concentrations and
CH
4
lifetime. NMVOCs include aliphatic, aromatic and oxygenated
hydrocarbons (e.g., aldehydes, alcohols and organic acids), and have
atmospheric lifetimes ranging from hours to months. Global cover-
age of NMVOC measurements is poor, except for a few compounds.
Reports on trends generally indicate declines in a range of NMVOCs
in urban and rural regions of North America and Europe on the order
of a few percent to more than 10% yr
–1
. Global ethane levels reported
by Simpson et al. (2012) declined by about 21% from 1986 to 2010.
Measurements of air extracted from firn suggest that NMVOC concen-
trations were growing until 1980 and declined afterwards (Aydin et al.,
2011; Worton et al., 2012). Satellite retrievals of formaldehyde column
abundances from 1997 to 2007 show significant positive trends over
northeastern China (4% yr
–1
) and India (1.6% yr
–1
), possibly related to
strong increases in anthropogenic NMVOC emissions, whereas nega-
tive trends of about –3% yr
–1
are observed over Tokyo, Japan and the
northeast USA urban corridor as a result of pollution regulation (De
Smedt et al., 2010).
The major sources of atmospheric CO are in situ production by oxida-
tion of hydrocarbons (mostly CH
4
and isoprene) and direct emission
resulting from incomplete combustion of biomass and fossil fuels. An
analysis of MOPITT (Measurements of Pollutants in the Troposphere)
and AIRS (Atmospheric Infrared Sounder) satellite data suggesta clear
and consistent decline of CO columns for 2002–2010 over a number
of polluted regions in Europe, North America and Asia with a global
trend of about –1% yr
–1
(Yurganov et al., 2010; Fortems-Cheiney et al.,
2011; Worden et al., 2013). Analysis of satellite data using two more
instruments for recent overlapping years shows qualitatively similar
decreasing trends (Worden et al., 2013), but the magnitude of trends
remains uncertain owing to the presence of instrument biases. Small
CO decreases observed in the NOAA and AGAGE networks are consist-
ent with slight declines in global anthropogenic CO emissions over the
same time (Supplementary Material 2.SM.2).
Due to its short atmospheric lifetime (approximately hours), NO
x
con-
centrations are highly variable in time and space. AR4 described the
potential of satellite observations of NO
2
to verify and improve NO
x
emission inventories and their trends and reported strong NO
2
increas-
es by 50% over the industrial areas of China from 1996 to 2004. An
extension of this analysis reveals increases between a factor of 1.7
and 3.2 over parts of China, while over Europe and the USA NO
2
has
decreased by 30 to 50% between 1996 and 2010 (Hilboll et al., 2013).
Figure 2.8 shows the changes relative to 1996 in satellite-derived trop-
ospheric NO
2
columns, with a strong upward trend over central eastern
China and an overall downward trend in Japan, Europe and the USA.
NO
2
reductions in the USA are very pronounced after 2004, related to
differences in effectiveness of NO
x
emission abatements in the USA and
also to changes in atmospheric chemistry of NO
x
(Russell et al., 2010).
Increasingly, satellite data are used to derive trends in anthropogenic
NO
x
emissions, with Castellanos and Boersma (2012) reporting overall
increases in global emissions, driven by Asian emission increases of
up to 29% yr
–1
(1996–2006), while moderate decreases up to 7% yr
–1
(1996–2006) are reported for North America and Europe.
In summary, satellite and surface observations of ozone precursor
gases NO
x
, CO, and non-methane volatile organic carbons indicate
strong regional differences in trends. Most notably, NO
2
has likely
decreased by 30 to 50% in Europe and North America and increased
by more than a factor of 2 in Asia since the mid-1990s.
2.2.3 Aerosols
This section assesses trends in aerosol resulting from both anthro-
pogenic and natural sources. The significance of aerosol changes for
global dimming and brightening is discussed in Section 2.3. Chapter 7
provides additional discussion of aerosol properties, while Chapter 8
discusses future RF and the ice-core records that contain information
on aerosol changes prior to the 1980s. Chapter 11 assesses air quali-
ty–climate change interactions. Because of the short lifetime (days to
weeks) of tropospheric aerosol, trends have a strong regional signa-
ture. Aerosol from anthropogenic sources (i.e., fossil and biofuel burn-
ing) are confined mainly to populated regions in the NH, whereas aer-
osol from natural sources, such as desert dust, sea salt, volcanoes and
the biosphere, are important in both hemispheres and likely dependent
on climate and land use change (Carslaw et al., 2010). Owing to inter-
annual variability, long-term trends in aerosols from natural sources
are more difficult to identify (Mahowald et al., 2010).
2.2.3.1 Aerosol Optical Depth from Remote Sensing
AOD is a measure of the integrated columnar aerosols load and is an
important parameter for evaluating aerosol–radiation interactions.
AR4 described early attempts to retrieve AOD from satellites but did
not provide estimates of temporal changes in tropospheric aerosol.
Little high-accuracy information on AOD changes exists prior to 1995.
Better satellite sensors and ground-based sun-photometer networks,
CE-U.S.
EC-China
NC-India
Japan
Middle East
cont. U.S.
W-Europe
1996 1998 2000 2002 2004 2006 2008 2010
GOME SCIAMACHY
Figure 2.8 | Relative changes in tropospheric NO
2
column amounts (logarithmic scale)
in seven selected world regions dominated by high NO
x
emissions. Values are normal-
ized for 1996 and derived from the GOME (Global Ozone Monitoring Experiment)
instrument from 1996 to 2002 and SCIAMACHY (Scanning Imaging Spectrometer for
Atmospheric Cartography) from 2003 to 2011 (Hilboll et al., 2013). The regions are
indicated in the map inset.
175
Observations: Atmosphere and Surface Chapter 2
2
along with improved retrieval methods and methodological intercom-
parisons, allow assessment of regional AOD trends since about 1995.
AOD sun photometer measurements at two stations in northern Ger-
many, with limited regional representativity, suggest a long-term
decline of AOD in Europe since 1986 (Ruckstuhl et al., 2008). Ground-
based, cloud-screened solar broadband radiometer measurements
provide longer time-records than spectrally selective sun-photometer
data, but are less specific for aerosol retrieval. Multi-decadal records
over Japan (Kudo et al., 2011) indicate an AOD increase until the mid-
1980s, followed by an AOD decrease until the late 1990s and almost
constant AOD in the 2000s. Similar broad-band solar radiative flux
multi-decadal trends have been observed for urban–industrial regions
of Europe and North America (Wild et al., 2005), and were linked to
successful measures to reduce sulphate (precursor) emissions since the
mid-1980s (Section 2.3). An indirect method to estimate AOD is offered
by ground-based visibility observations. These data are more ambigu-
ous to interpret, but records go further back in time than broadband,
sun photometer and satellite data. A multi-regional analysis for 1973–
2007 (Wang et al., 2009a) shows that prior to the 1990s visibility-de-
rived AOD was relatively constant in most regions analysed (except for
positive trends in southern Asia), but after 1990 positive AOD trends
were observed over Asia, and parts of South America, Australia and
Africa, and mostly negative AOD trends were found over Europe. In
North America, a small stepwise decrease of visibility after 1993 was
likely related to methodological changes (Wang et al., 2012f).
AOD can be determined most accurately with sun photometers that
measure direct solar intensity in the absence of cloud interferences
with an absolute uncertainty of single measurements of ± 0.01%
(Holben et al., 1998). AERONET (AErosol RObotic NETwork) is a global
sun photometer network (Holben et al., 1998), with densest coverage
over Europe and North America. AERONET AOD temporal trends were
examined in independent studies (de Meij et al., 2012; Hsu et al., 2012;
Yoon et al., 2012), using different data selection and statistical meth-
ods. Hsu et al. (2012) investigated AOD trends at 12 AERONET sites
with data coverage of at least 10 years between 1997 and 2010. Yoon
et al. (2012) investigated AOD and size trends at 14 AERONET sites
with data coverage varying between 4 and 12 years between 1997
and 2009. DeMeij et al. (2012) investigated AOD trends between 2000
and 2009 (550 nm; monthly data) at 62 AERONET sites mostly located
in USA and Europe. Each of these studies noted an increase in AOD
over East Asia and reductions in North America and Europe. The only
dense sun photometer network over southern Asia, ARFINET (Aerosol
Radiative Forcing over India NETwork), shows an increase in AOD of
about 2% yr
–1
during the last one to two decades (Krishna Moorthy et
al., 2013), with an absolute uncertainty of ± 0.02 at 500 nm (Krishna
Moorthy et al., 2007). In contrast, negative AOD trends are identified at
more than 80% of examined European and North American AERONET
sites (de Meij et al., 2012). Decreasing AOD is also observed near the
west coast of northern Africa, where aerosol loads are dominated by
Saharan dust outflow. Positive AOD trends are found over the Arabi-
an Peninsula, where aerosol is dominated by dust. Inconsistent AOD
trends reported for stations in central Africa result from the use of rela-
tively short time series with respect to the large interannual variability
caused by wildfires and dust emissions.
Aerosol products from dedicated satellite sensors complement sur-
face-based AOD with better spatial coverage. The quality of the satel-
lite-derived AOD strongly depends on the retrieval’s ability to remove
scenes contaminated by clouds and to accurately account for reflectivi-
ty at the Earth’s surface. Due to relatively weak reflectance of incoming
sunlight by the sea surface, the typical accuracy of retrieved AOD over
oceans (uncertainty of 0.03 +0.05*AOD; Kahn et al. (2007)) is usually
better than over continents (uncertainty of 0.05 +0.15*AOD, Levy et
al. (2010)).
Satellite-based AOD trends at 550 nm over oceans from conservatively
cloud-screened MODIS data (Zhang and Reid, 2010) for 2000–2009 are
presented in Figure 2.9. Strongly positive AOD trends were observed
over the oceans adjacent to southern and eastern Asia. Positive AOD
trends are also observed over most tropical oceans. The negative
MODIS AOD trends observed over coastal regions of Europe and near
the east coast of the USA are in agreement with sun photometer obser-
vations and in situ measurements (Section 2.2.3.2) of aerosol mass
in these regions. These regional changes over oceans are consistent
with analyses of AVHRR (Advanced Very High Resolution Radiometer)
trends for 1981–2005 (Mishchenko et al., 2007; Cermak et al., 2010;
Zhao et al., 2011), except over the Southern Ocean (45°S to 60°S),
where negative AOD trends of AVHRR retrievals are neither confirmed
by MODIS after 2001 (Zhang and Reid, 2010) nor by ATSR-2 (Along
Track Scanning Radiometer) for 1995–2001 (Thomas et al., 2010).
Satellite-based AOD changes for both land and oceans (Figure 2.9b)
were examined with re-processed SeaWiFS (Sea-viewing Wide Field-
of-view Sensor) AOD data for 1998–2010 (Hsu et al., 2012). A small
positive global average AOD trend is reported, which is likely influ-
enced by interannual natural aerosol emissions variability (e.g., related
to ENSO or North Atlantic Oscillation (NAO); Box 2.5), and compen-
sating larger positive and negative regional AOD trends. In addition,
temporal changes in aerosol composition are ignored in the retrieval
algorithms, giving more uncertain trends than suggested by statisti-
cal analysis alone (Mishchenko et al., 2012). Thus, confidence is low
for global satellite derived AOD trends over these relatively short time
periods.
The sign and magnitude of SeaWiFS regional AOD trends over conti-
nents are in agreement with most AOD trends by ground-based sun
photometer data (see above) and with MODIS trends (Figure 2.9). The
strong positive AOD trend over the Arabian Peninsula occurs mainly
during spring (MAM) and summer (JJA), during times of dust transport,
and is also visible in MODIS data (Figure 2.9). The positive AOD trend
over southern and eastern Asia is strongest during the dry seasons (DJF,
MAM), when reduced wet deposition allows anthropogenic aerosol to
accumulate in the troposphere. AOD over the Saharan outflow region
off western Africa displays the strongest seasonal AOD trend differ-
ences, with AOD increases only in spring, but strong AOD decreases
during the other seasons. SeaWifs AOD decreases over Europe and the
USA and increases over southern and eastern Asia (especially during
the dry season) are in agreement with reported temporal trends in
anthropogenic emissions, and surface observations (Section 2.2.3.2).
In summary, based on satellite- and surface-based remote sensing
it is very likely that AOD has decreased over Europe and the eastern
176
Chapter 2 Observations: Atmosphere and Surface
2
Aerosol Optical Depth trend (yr
-1
)
-0.04
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
0.04
Aerosol Optical Depth trend x 100 (yr
-1
)
-1.2 -0.4 0
0.4
0.8
1.21.6
(a)
(b)
Figure 2.9 | (a) Annual average aerosol optical depth (AOD) trends at 0.55 μm for 2000–2009, based on de-seasonalized, conservatively cloud-screened MODIS aerosol data over
oceans (Zhang and Reid, 2010). Negative AOD trends off Mexico are due to enhanced volcanic activity at the beginning of the record. Most non-zero trends are significant (i.e., a
trend of zero lies outside the 95% confidence interval). (b) Seasonal average AOD trends at 0.55 μm for 1998–2010 using SeaWiFS data (Hsu et al., 2012). White areas indicate
incomplete or missing data. Black dots indicate significant trends (i.e., a trend of zero lies outside the 95% confidence interval).
USA since the mid 1990s and increased over eastern and southern
Asia since 2000. In the 2000s dust-related AOD has been increasing
over the Arabian Peninsula and decreasing over the North Atlantic
Ocean. Aerosol trends over other regions are less strong or not signifi-
cant during this period owing to relative strong interannual variability.
Overall, confidence in satellite-based global average AOD trends is low.
2.2.3.2 In Situ Surface Aerosol Measurements
AR4 did not report trends in long-term surface-based in situ meas-
urements of particulate matter, its components or its properties. This
section summarizes reported trends of PM
10
, PM
2.5
(particulate matter
with aerodynamic diameters <10 and <2.5 μm, respectively), sulphate
and equivalent black carbon/elemental carbon, from regionally repre-
sentative measurement networks. An overview of current networks
and definitions pertinent to aerosol measurements is given in Sup-
plementary Material 2.SM.2.3. Studies reporting trends representa-
tive of regional changes are presented in Table 2.2. Long-term data
are almost entirely from North America and Europe, whereas a few
individual studies on aerosol trends in India and China are reported
in Supplementary Material 2.SM.2.3. Figure 2.10 gives an overview
of observed PM
10
, PM
2.5
, and sulphate trends in North America and
Europe for 1990–2009 and 2000–2009.
177
Observations: Atmosphere and Surface Chapter 2
2
In Europe, strong downward trends are observed for PM
10
, PM
2.5
and
sulphate from the rural stations in the EMEP (European Monitoring
and Evaluation Programme) network. For 2000–2009, PM
2.5
shows
an average reduction of 3.9% yr
–1
for the six stations with significant
trends, while trends are not significant at seven other stations. Over
2000–2009, PM
10
at 12 (out of 24) sites shows significant downward
trend of on average 2.6% yr
–1
. Similarly sulphate strongly decreased
at 3.1% yr
–1
from 1990 to 2009 with 26 of 30 sites having signifi-
cant reductions. The largest decrease occurred before 2000, while for
2000–2009, the trends were weaker and less robust. This is consistent
with reported emission reductions of 65% from 1990 to 2000 and 28%
from 2001 to 2009 (Yttri et al., 2011; Torseth et al., 2012). Model anal-
ysis (Pozzoli et al., 2011) attributed the trends in large part to emission
changes.
In the USA, the largest reductions in PM and sulphate are observed in
the 2000s, rather than the 1990s as in Europe. IMPROVE (U.S. Inter-
agency Monitoring of Protected Visual Environments Network) PM
2.5
measurements (Hand et al., 2011) show significant downward trends
averaging 4.0% yr
–1
for 2000–2009 at sites with significant trends,
(a) PM
10
, 2000-2009
(b) PM
2.5
, 2000-2009
(c) Sulphate, 2000-2009
(d) PM
2.5
, 1990-2009
Trend (% yr
-1
)
(e) Sulphate, 1990-2009
Figure 2.10 | Trends in particulate matter (PM
10
and PM
2.5
with aerodynamic diameters <10 and <2.5 μm, respectively) and sulphate in Europe and USA for two overlapping
periods 2000–2009 (a, b, c) and 1990–2009 (d, e). The trends are based on measurements from the EMEP (Torseth et al., 2012) and IMPROVE (Hand et al., 2011) networks in
Europe and USA, respectively. Sites with significant trends (i.e., a trend of zero lies outside the 95% confidence interval) are shown in colour; black dots indicate sites with non-
significant trends.
and 2.1% yr
–1
at all sites, and PM
10
decreases of 3.1% yr
–1
for 2000–
2009. Declines of PM
2.5
and SO
4
2–
in Canada are very similar (Hidy and
Pennell, 2010), with annual mean PM
2.5
at urban measurement sites
decreasing by 3.6% yr
–1
during 1985–2006 (Canada, 2012).
In the eastern and southwestern USA, IMPROVE data show strong sul-
phate declines, which range from 2 to 6% yr
–1
, with an average of
2.3% yr
–1
for the sites with significant negative trends for 1990–2009.
However, four IMPROVE sites show strong SO
4
2–
increases from 2000
to 2009, amounting to 11.9% yr
–1
, at Hawaii (1225 m above sea level),
and 4 to 7% yr
–1
at three sites in southwest Alaska.
A recent study on long-term trends in aerosol optical properties from
24 globally distributed background sites (Collaud Coen et al., 2013)
reported statistically significant trends at 16 locations, but the sign and
magnitude of the trends varied largely with the aerosol property con-
sidered and geographical region (Table 2.3). Among the sites, this study
reported strong increases in absorption and scattering coefficients in
the free troposphere at Mauna Loa, Hawaii (3400 m above sea level),
which is a regional feature also evident in the satellite-based AOD
178
Chapter 2 Observations: Atmosphere and Surface
2
Aerosol
variable
Trend, % yr
–1
(1σ,
standard deviation)
Period Reference Comments
Europe
PM
2.5
–2.9 (1.31)
–3.9 (0.87)
b
2000–2009
(Adapted from Torseth et al., 2012)
Regional background sites
13 sites available, 6 sites show statistically significant results. Average change was
–0.37 and –0.52
b
mg m
–3
yr
–1
.
PM
10
–1.9 (1.43)
–2.6 (1.19)
b
2000–2009
24 sites available, 12 sites show statistically significant results. Average change was
–0.29 and –0.40
b
mg m
–3
yr
–1
.
SO
4
2–
–3.0 (0.82)
–3.1 (0.72)
b
1990–2009
30 sites available, 26 sites show statistically significant results. Average change was
–0.04 and –0.04
b
mg m
–3
yr
–1
.
SO
4
2–
– 1.5 (1.41)
–2.0 (1.8)
b
2000–2009
30 sites available, 10 sites show statistically significant results. Average change was
–0.01 and –0.01
b
mg m
–3
yr
–1
.
PM
10
–1.9 1991–2008
(Barmpadimos et al., 2012)
Rural and urban sites
10 sites in Switzerland. The trend is adjusted for change in meteorology—unadjusted
data did not differ strongly. The average change was –0.51 mg m
–3
yr
–1
.
USA
PM
2.5
–2.1 (2.08)
–4.0 (1.01)
b
2000–2009
Adapted from (Hand et al., 2011)
Regional background sites
153 sites available, 52 sites show statistically significant negative results. Only 1 site
shows statisticallly positive trend.
PM
2.5
–1.5 (1.25)
–2.1 (0.97)
b
1990–2009 153 sites available, 39 sites show statistically significant results.
PM
10
–1.7 (2.00)
–3.1 (1.65)
b
2000–2009 154 sites available, 37 sites show statistically significant results.
SO
4
2–
–3.0 (2.86)
–3.0 (0.62)
b
2000–2009
154 sites available, 83 sites show statistically significant negative results. 4 sites
showed statistical positive trend.
SO
4
2–
–2.0 (1.07)
–2.3 (0.85)
b
1990–2009 103 sites available, 41 sites show statistically significant results.
Total Carbon –2.5 to –7.5 1989–2008
(Hand et al., 2011)
Regional background sites
The trend interval includes about 50 sites mainly located along the East and West
Coasts of the USA; fewer sites were situated in the central part of the continent.
Arctic
EBC
a
–3.8 (0.7)
c
1989–2008 (Hirdman et al., 2010) Alert, Canada 62.3°W 82.5°N
SO
4
2–
–3.0 (0.6)
c
1985–2006
EBC
a
Not sig.
c
1998–2008 Barrow, Alaska, 156.6°W 71.3° N
SO
4
2–
Not sig.
c
1997–2008
EBC
a
–9.0 (5.0)
c
2002–2009 Zeppelin, Svalbard, 11.9°E 78.9° N
SO
4
2–
–1.9 (1.7)
c
1990–2008
Table 2.2 | Trend estimates for various aerosol variables reported in the literature, using data sets with at least 10 years of measurements. Unless otherwise noted, trends of indi-
vidual stations were reported in % yr
–1
, and 95% confidence intervals. The standard deviation (in parentheses) is determined from the individual trends of a set of regional stations.
Notes:
a
Equivalent black carbon.
b
Trend numbers indicated refer to the subset of stations with significant changes over time—generally in regions strongly influenced by anthropogenic emissions (Figure 2.10).
c
Trend values significant at 1% level.
trends (illustrated in Figure 2.9). Possible explanations for these chang-
es include the influence of increasing Asian emissions and changes
in clouds and removal processes. More and longer Asian time series,
coupled with transport analyses, are needed to corroborate these find-
ings. Aerosol number concentrations (Asmi et al., 2013) are declining
significantly at most sites in Europe, North America, the Pacific and the
Caribbean, but increasing at South Pole based on a study of 17 globally
distributed remote sites.
Total carbon (= light absorbing carbon + organic carbon) measure-
ments indicate highly significant downward trends between 2.5 and
7.5% yr
–1
along the east and west coasts of the USA, and smaller and
less significant trends in other regions of the USA from 1989 to 2008
(Hand et al., 2011; Murphy et al., 2011).
In Europe, Torseth et al. (2012) suggest a slight reduction in elemental
carbon concentrations at two stations from 2001 to 2009, subject to
large interannual variability. Collaud Coen et al. (2013) reported con-
sistent negative trends in the aerosol absorption coefficient at stations
in the continental USA, Arctic and Antarctica, but mostly insignificant
trends in Europe over the last decade.
In the Arctic, changes in aerosol impact the atmosphere’s radiative bal-
ance as well as snow and ice albedo. Similar to Europe and the USA,
Hirdman et al. (2010) reported downward trends in equivalent black
carbon and SO
4
2–
for two out of total three Arctic stations and attribut-
ed them to emission changes.
In summary, declining AOD in Europe and North America is corrobo-
rated by very likely downward trends in ground-based in situ particu-
late matter measurements since the mid-1980s. Robust evidence from
around 200 regional background sites with in situ ground based aer-
osol measurements indicate downward trends in the last two decades
of PM
2.5
in parts of Europe (2 to 6% yr
–1
) and the USA (1 to 2.5% yr
–1
),
179
Observations: Atmosphere and Surface Chapter 2
2
Region
Trend, % yr
–1
(1σ,
standard deviation)
Period Reference Comments
Scattering coefficient
Europe (4/1)
+0.6 (1.9)
+2.7
a
2001–2010
Adapted from (Collaud Coen et al.,
2013) Regional background sites
Trend study including 24 regional background sites with more than 10
years of observations. Regional averages for last 10 years are included
here. Values in parenthesis show total number of sites/number of sites with
significant trend.
USA (14/10)
–2.0 (2.5)
–2.9 (2.4)
a
Mauna Loa (1/1) +2.7
Arctic (1/0) +2.4
Antarctica (1/0) +2.5
Absorption coefficient
Europe (3/0) +0.3 (0.4)
2001–2010
Adapted from (Collaud Coen et al.,
2013) Regional background sites
Trend study of aerosol optical properties including 24 regional background
sites with more than 10 years of observations. Regional averages for last
10 years are included here. Values in parenthesis show total number of
sites and number of sites with significant trend.
USA (1/1) –2.0
Mauna Loa (1/1) +9.0
Arctic (1/1) –6.5
Antarctica (1/1) –0.1
Particle number concentration
Europe (4/2)
–0.9 (1.8)
–2.3 (1.0)
a
2001–2010
Adapted from (Asmi et al., 2013)
Regional background sites
Trend study of particle number concentration (N) and size distribution
including 17 regional background sites. Regional averages of particle
number concentration for last 10 years are included here. Values in
parentheses show total number of sites and number of sites with
significant trend.
North America and
Caribbean (3/3)
–5.3 (2.8)
–6.6 (1.1)
a
Mauna Loa (1/1) –3.5
Arctic (1/0) –1.3
Antarctica (2/2) +2.7 (1.4)
Table 2.3 | Summary table of aerosol optical property trends reported in the literature, using data sets with at least 10 years of measurements. Otherwise as in Table 2.2.
Box 2.2 | Quantifying Changes in the Mean: Trend Models and Estimation
Many statistical methods exist for estimating trends in environmental time series (see Chandler and Scott, 2011 for a review). The
assessment of long-term changes in historical climate data requires trend models that are transparent and robust, and that can provide
credible uncertainty estimates.
Linear Trends
Historical climate trends are frequently described and quantified by estimating the linear component of the change over time (e.g.,
AR4). Such linear trend modelling has broad acceptance and understanding based on its frequent and widespread use in the published
research assessed in this report, and its strengths and weaknesses are well known (von Storch and Zwiers, 1999; Wilks, 2006). Chal-
lenges exist in assessing the uncertainty in the trend and its dependence on the assumptions about the sampling distribution (Gaussian
or otherwise), uncertainty in the data, dependency models for the residuals about the trend line, and treating their serial correlation
(Von Storch, 1999; Santer et al., 2008).
The quantification and visualization of temporal changes are assessed in this chapter using a linear trend model that allows for first-
order autocorrelation in the residuals (Santer et al., 2008; Supplementary Material 2.SM.3). Trend slopes in such a model are the same as
ordinary least squares trends; uncertainties are computed using an approximate method. The 90% confidence interval quoted is solely
that arising from sampling uncertainty in estimating the trend. Structural uncertainties, to the extent sampled, are apparent from the
range of estimates from different data sets. Parametric and other remaining uncertainties (Box 2.1), for which estimates are provided with
some data sets, are not included in the trend estimates shown here, so that the same method can be applied to all data sets considered.
Nonlinear Trends
There is no a priori physical reason why the long-term trend in climate variables should be linear in time. Climatic time series often
have trends for which a straight line is not a good approximation (e.g., Seidel and Lanzante, 2004). The residuals from a linear fit in time
often do not follow a simple autoregressive or moving average process, and linear trend estimates can easily change when recalculated
(continued on next page)
Notes:
a
Trend numbers indicated refer to the subset of stations with significant changes over time—generally in regions strongly influenced by anthropogenic emissions (Figure 2.10).
180
Chapter 2 Observations: Atmosphere and Surface
2
for shorter or longer time periods or when new data are
added. When linear trends for two parts of a longer time
series are calculated separately, the trends calculated for two
shorter periods may be very different (even in sign) from the
trend in the full period, if the time series exhibits significant
nonlinear behavior in time (Box 2.2, Table 1).
Many methods have been developed for estimating the long-
term change in a time series without assuming that the change
is linear in time (e.g., Wu et al., 2007; Craigmile and Guttorp,
2011). Box 2.2, Figure 1 shows the linear least squares and
a nonlinear trend fit to the GMST values from the HadCRUT4
data set (Section 2.4.3). The nonlinear trend is obtained by
fitting a smoothing spline trend (Wood, 2006; Scinocca et
al., 2010) while allowing for first-order autocorrelation in
the residuals (Supplementary Material 2.SM.3). The results
indicate that there are significant departures from linearity
in the trend estimated this way.
Box 2.2, Table 1 shows estimates of the change in the GMST
from the two methods. The methods give similar estimates
with 90% confidence intervals that overlap one another.
Smoothing methods that do not assume the trend is linear
can provide useful information on the structure of change
that is not as well treated with linear fits. The linear trend fit
is used in this chapter because it can be applied consistently
to all the data sets, is relatively simple, transparent and
easily comprehended, and is frequently used in the published
research assessed here.
and also for SO
4
2–
(2 to 5% yr
–1
). The strongest decreases were in the
1990s in Europe and in the 2000s in the USA. There is robust evidence
for downward trends of light absorbing aerosol in the USA and the
Arctic, while elsewhere in the world in situ time series are lacking or
not long enough to reach statistical significance.
2.3 Changes in Radiation Budgets
The radiation budget of the Earth is a central element of the climate
system. On average, radiative processes warm the surface and cool the
atmosphere, which is balanced by the hydrological cycle and sensible
Box 2.2, Figure 1 | (a) Global mean surface temperature (GMST) anomalies relative
to a 1961–1990 climatology based on HadCRUT4 annual data. The straight black
lines are least squares trends for 1901–2012, 1901–1950 and 1951–2012. (b) Same
data as in (a), with smoothing spline (solid curve) and the 90% confidence interval on
the smooth curve (dashed lines). Note that the (strongly overlapping) 90% confidence
intervals for the least square lines in (a) are omitted for clarity. See Figure 2.20 for the
other two GMST data products.
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
(a)
1850 1900
1950
2000
Temperature anomaly (ºC)
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
(b)
Box 2.2 (continued)
heating. Spatial and temporal energy imbalances due to radiation and
latent heating produce the general circulation of the atmosphere and
oceans. Anthropogenic influence on climate occurs primarily through
perturbations of the components of the Earth radiation budget.
The radiation budget at the top of the atmosphere (TOA) includes the
absorption of solar radiation by the Earth, determined as the difference
between the incident and reflected solar radiation at the TOA, as well
as the thermal outgoing radiation emitted to space. The surface radia-
tion budget takes into account the solar fluxes absorbed at the Earth’s
surface, as well as the upward and downward thermal radiative fluxes
emitted by the surface and atmosphere, respectively. In view of new
Trends in °C per decade
Method 1901–2012 1901–1950 1951–2012
Least squares 0.075 ± 0.013 0.107 ± 0.026 0.106 ± 0.027
Smoothing spline 0.081 ± 0.010 0.070 ± 0.016 0.090 ± 0.018
Box 2.2, Table 1 | Estimates of the mean change in global mean surface temperature (GMST) between 1901 and 2012, 1901 and 1950, and 1951 and 2012,
obtained from the linear (least squares) and nonlinear (smoothing spline) trend models. Half-widths of the 90% confidence intervals are also provided for the estimated
changes from the two trend methods.
181
Observations: Atmosphere and Surface Chapter 2
2
observational evidence since AR4, the mean state as well as multi-dec-
adal changes of the surface and TOA radiation budgets are assessed
in the following.
2.3.1 Global Mean Radiation Budget
Since AR4, knowledge on the magnitude of the radiative energy fluxes
in the climate system has improved, requiring an update of the global
annual mean energy balance diagram (Figure 2.11). Energy exchanges
between Sun, Earth and Space are observed from space-borne plat-
forms such as the Clouds and the Earth’s Radiant Energy System (CERES,
Wielicki et al., 1996) and the Solar Radiation and Climate Experiment
(SORCE, Kopp and Lawrence, 2005) which began data collection in
2000 and 2003, respectively. The total solar irradiance (TSI) incident at
the TOA is now much better known, with the SORCE Total Irradiance
Monitor (TIM) instrument reporting uncertainties as low as 0.035%,
compared to 0.1% for other TSI instruments (Kopp et al., 2005). During
the 2008 solar minimum, SORCE/TIM observed a solar irradiance of
1360.8 ± 0.5 W m
–2
compared to 1365.5 ± 1.3 W m
–2
for instruments
launched prior to SORCE and still operating in 2008 (Section 8.4.1.1).
Kopp and Lean (2011) conclude that the SORCE/TIM value of TSI is the
most credible value because it is validated by a National Institute of
Standards and Technology calibrated cryogenic radiometer. This revised
TSI estimate corresponds to a solar irradiance close to 340 W m
–2
glob-
ally averaged over the Earth’s sphere (Figure 2.11).
100
(96, 100)
solar absorbed
surface
thermal
up surface
thermal
down surface
solar absorbed
atmosphere
thermal outgoing
TOA
evapo-
ration
sensible
heat
solar reflected
TOA
incoming
solar TOA
solar
reflected
surface
greenhouse
gases
atmospheric
window
latent heat
340
(340, 341)
161
(154, 166)
79
(74, 91)
239
(236, 242)
398
(394, 400)
342
(338, 348)
imbalance
84
(70, 85)
20
(15, 25)
24
(22,26)
solar
down
surface
185
(179, 189)
0.6
(0.2, 1.0)
Units (Wm
-2
)
Figure 2.11: | Global mean energy budget under present-day climate conditions. Numbers state magnitudes of the individual energy fluxes in W m
–2
, adjusted within their
uncertainty ranges to close the energy budgets. Numbers in parentheses attached to the energy fluxes cover the range of values in line with observational constraints. (Adapted
from Wild et al., 2013.)
The estimate for the reflected solar radiation at the TOA in Figure 2.11,
100 W m
–2
, is a rounded value based on the CERES Energy Balanced
and Filled (EBAF) satellite data product (Loeb et al., 2009, 2012b) for
the period 2001–2010. This data set adjusts the solar and thermal TOA
fluxes within their range of uncertainty to be consistent with inde-
pendent estimates of the global heating rate based on in situ ocean
observations (Loeb et al., 2012b). This leaves 240 W m
–2
of solar radia-
tion absorbed by the Earth, which is nearly balanced by thermal emis-
sion to space of about 239 W m
–2
(based on CERES EBAF), considering
a global heat storage of 0.6 W m
–2
(imbalance term in Figure 2.11)
based on Argo data from 2005 to 2010 (Hansen et al., 2011; Loeb et
al., 2012b; Box 3.1). The stated uncertainty in the solar reflected TOA
fluxes from CERES due to uncertainty in absolute calibration alone is
about 2% (2-sigma), or equivalently 2 W m
–2
(Loeb et al., 2009). The
uncertainty of the outgoing thermal flux at the TOA as measured by
CERES due to calibration is ~3.7 W m
–2
(2σ). In addition to this, there is
uncertainty in removing the influence of instrument spectral response
on measured radiance, in radiance-to-flux conversion, and in time–
space averaging, which adds up to another 1 W m
–2
(Loeb et al., 2009).
The components of the radiation budget at the surface are generally
more uncertain than their counterparts at the TOA because they cannot
be directly measured by passive satellite sensors and surface measure-
ments are not always regionally or globally representative. Since AR4,
new estimates for the downward thermal infrared (IR) radiation at
182
Chapter 2 Observations: Atmosphere and Surface
2
the surface have been established that incorporate critical information
on cloud base heights from space-borne radar and lidar instruments
(L’Ecuyer et al., 2008; Stephens et al., 2012a; Kato et al., 2013). In line
with studies based on direct surface radiation measurements (Wild et
al., 1998, 2013) these studies propose higher values of global mean
downward thermal radiation than presented in previous IPCC assess-
ments and typically found in climate models, exceeding 340 W m
–2
(Figure 2.11). This aligns with the downward thermal radiation in the
ERA-Interim and ERA-40 reanalyses (Box 2.3), of 341 and 344 W m
–2
,
respectively (Berrisford et al., 2011). Estimates of global mean down-
ward thermal radiation computed as a residual of the other terms of
the surface energy budget (Kiehl and Trenberth, 1997; Trenberth et al.,
2009) are lower (324 to 333 W m
–2
), highlighting remaining uncertain-
ties in estimates of both radiative and non-radiative components of the
surface energy budget.
Estimates of absorbed solar radiation at the Earth’s surface include
considerable uncertainty. Published global mean values inferred from
satellite retrievals, reanalyses and climate models range from below
160 W m
–2
to above 170 W m
–2
. Recent studies taking into account
surface observations as well as updated spectroscopic parameters
and continuum absorption for water vapor favour values towards
the lower bound of this range, near 160 W m
–2
, and an atmospheric
solar absorption around 80 W m
–2
(Figure 2.11) (Kim and Ramanathan,
2008; Trenberth et al., 2009; Kim and Ramanathan, 2012; Trenberth
and Fasullo, 2012b; Wild et al., 2013). The ERA-Interim and ERA-40
reanalyses further support an atmospheric solar absorption of this
magnitude (Berrisford et al., 2011). Latest satellite-derived estimates
constrained by CERES now also come close to these values (Kato et
al., in press). Recent independently derived surface radiation estimates
favour therefore a global mean surface absorbed solar flux near 160
W m
–2
and a downward thermal flux slightly above 340 W m
–2
, respec-
tively (Figure 2.11).
The global mean latent heat flux is required to exceed 80 W m
–2
to
close the surface energy balance in Figure 2.11, and comes close to the
85 W m
–2
considered as upper limit by Trenberth and Fasullo (2012b)
in view of current uncertainties in precipitation retrieval in the Global
Precipitation Climatology Project (GPCP, Adler et al., 2012) (the latent
heat flux corresponds to the energy equivalent of evaporation, which
globally equals precipitation; thus its magnitude may be constrained
by global precipitation estimates). This upper limit has recently been
challenged by Stephens et al. (2012b). The emerging debate reflects
potential remaining deficiencies in the quantification of the radiative
and non-radiative energy balance components and associated uncer-
tainty ranges, as well as in the consistent representation of the global
mean energy and water budgets (Stephens et al., 2012b; Trenberth and
Fasullo, 2012b; Wild et al., 2013). Relative uncertainty in the globally
averaged sensible heat flux estimate remains high owing to the very
limited direct observational constraints (Trenberth et al., 2009; Ste-
phens et al., 2012b).
In summary, newly available observations from both space-borne and
surface-based platforms allow a better quantification of the Global
Energy Budget, even though notable uncertainties remain, particu-
larly in the estimation of the non-radiative surface energy balance
components.
2.3.2 Changes in Top of the Atmosphere Radiation
Budget
While the previous section emphasized the temporally-averaged state
of the radiation budget, the focus in the following is on the temporal
(multi-decadal) changes of its components. Variations in TSI are dis-
cussed in Section 8.4.1. AR4 reported large changes in tropical TOA
radiation between the 1980s and 1990s based on observations from
the Earth Radiation Budget Satellite (ERBS) (Wielicki et al., 2002;
Wong et al., 2006). Although the robust nature of the large decadal
changes in tropical radiation remains to be established, several studies
have suggested links to changes in atmospheric circulation (Allan and
Slingo, 2002; Chen et al., 2002; Clement and Soden, 2005; Merrifield,
2011) (Section 2.7).
Since AR4, CERES enabled the extension of satellite records of TOA
fluxes into the 2000s (Loeb et al., 2012b). The extended records from
CERES suggest no noticeable trends in either the tropical or global
radiation budget during the first decade of the 21st century (e.g.,
Andronova et al., 2009; Harries and Belotti, 2010; Loeb et al., 2012a,
2012b). Comparisons between ERBS/CERES thermal radiation and that
derived from the NOAA High Resolution Infrared Radiation Sounder
(HIRS) (Lee et al., 2007) show good agreement until approximate-
ly 1998, corroborating the rise of 0.7 W m
–2
between the 1980s and
1990s reported in AR4. Thereafter the HIRS thermal fluxes show much
higher values, likely due to changes in the channels used for HIRS/3
instruments launched after October 1998 compared to earlier HIRS
instruments (Lee et al., 2007).
On a global scale, interannual variations in net TOA radiation and
ocean heating rate (OHR) should correspond, as oceans have a much
larger effective heat capacity than land and atmosphere, and therefore
serve as the main reservoir for heat added to the Earth–atmosphere
system (Box 3.1). Wong et al. (2006) showed that interannual varia-
tions in these two data sources are in good agreement for 1992–2003.
In the ensuing 5 years, however, Trenberth and Fasullo (2010) note
that the two diverge with ocean in situ measurements (Levitus et al.,
2009), indicating a decline in OHR, in contrast to expectations from the
observed net TOA radiation. The divergence after 2004 is referred to as
‘missing energy’’ by Trenberth and Fasullo (2012b), who further argue
that the main sink of the missing energy likely occurs at ocean depths
below 275 m. Loeb et al. (2012b) compared interannual variations in
CERES net radiation with OHRs derived from three independent ocean
heat content anomaly analyses and included an error analysis of both
CERES and the OHRs. They conclude that the apparent decline in OHR
is not statistically robust and that differences between interannual var-
iations in OHR and satellite net TOA flux are within the uncertainty
of the measurements (Figure 2.12). They further note that between
January 2001 and December 2012, the Earth has been steadily accu-
mulating energy at a rate of 0.50 ± 0.43 W m
–2
(90% CI). Hansen et al.
(2011) obtained a similar value for 2005–2010 using an independent
analysis of the ocean heat content anomaly data (von Schuckmann
and Le Traon, 2011). The variability in the Earth’s energy imbalance is
strongly influenced by ocean circulation changes relating to the ENSO
(Box 2.5); during cooler La Niña years (e.g., 2009) less thermal radia-
tion is emitted and the climate system gains heat while the reverse is
true for warmer El Niño years (e.g., 2010) (Figure 2.12).
183
Observations: Atmosphere and Surface Chapter 2
2
In summary, satellite records of TOA radiation fluxes have been sub-
stantially extended since AR4. It is unlikely that significant trends exist
in global and tropical radiation budgets since 2000. Interannual vari-
ability in the Earth’s energy imbalance related to ENSO is consistent
with ocean heat content records within observational uncertainty.
2.3.3 Changes in Surface Radiation Budget
2.3.3.1 Surface Solar Radiation
Changes in radiative fluxes at the surface can be traced further back
in time than the satellite-based TOA fluxes, although only at selected
terrestrial locations where long-term records exist. Monitoring of radi-
ative fluxes from land-based stations began on a widespread basis in
the mid-20th century, predominantly measuring the downward solar
component, also known as global radiation or surface solar radiation
(SSR).
AR4 reported on the first indications for substantial decadal changes
in observational records of SSR. Specifically, a decline of SSR from the
beginning of widespread measurements in the 1950s until the mid-
1980s has been observed at many land-based sites (popularly known
as ‘global dimming’; Stanhill and Cohen, 2001; Liepert, 2002), as well
as a partial recovery from the 1980s onward (‘brightening’; Wild et al.,
2005) (see the longest available SSR series of Stockholm, Sweden, in
Figure 2.13 as an illustrative example).
Figure 2.12 | Comparison of net top of the atmosphere (TOA) flux and upper ocean heating rates (OHRs). Global annual average (July to June) net TOA flux from CERES observa-
tions (based on the EBAF-TOA_Ed2.6r product) (black line) and 0–700 (blue) and 0–1800 m (red) OHR from the Pacific Marine Environmental Laboratory/Jet Propulsion Laboratory/
Joint Institute for Marine and Atmospheric Research (PMEL/JPL/JIMAR), 0–700 m OHR from the National Oceanic Data Center (NODC) (green; Levitus et al., 2009), and 0–700 m
OHR from the Hadley Center (brown; Palmer et al., 2007). The length of the coloured bars corresponds to the magnitude of OHR. Thin vertical lines are error bars, corresponding to
the magnitude of uncertainties. Uncertainties for all annual OHR are given at one standard error derived from ocean heat content anomaly uncertainties (Lyman et al., 2010). CERES
net TOA flux uncertainties are given at the 90% confidence level determined following Loeb et al. (2012b). (Adapted from Loeb et al., 2012b.)
Since AR4, numerous studies have substantiated the findings of sig-
nificant decadal SSR changes observed both at worldwide distributed
terrestrial sites (Dutton et al., 2006; Wild et al., 2008; Gilgen et al.,
2009; Ohmura, 2009; Wild, 2009 and references therein) as well as in
specific regions. In Europe, Norris and Wild (2007) noted a dimming
between 1971 and 1986 of 2.0 to 3.1 W m
–2
per decade and subse-
quent brightening of 1.1 to 1.4 W m
–2
per decade from 1987 to 2002
in a pan-European time series comprising 75 sites. Similar tendencies
were found at sites in northern Europe (Stjern et al., 2009), Estonia
(Russak, 2009) and Moscow (Abakumova et al., 2008). Chiacchio and
Wild (2010) pointed out that dimming and subsequent brightening in
Europe is seen mainly in spring and summer. Brightening in Europe
from the 1980s onward was further documented at sites in Switzer-
land, Germany, France, the Benelux, Greece, Eastern Europe and the
Iberian Peninsula (Ruckstuhl et al., 2008; Wild et al., 2009; Zerefos et
al., 2009; Sanchez-Lorenzo et al., 2013). Concurrent brightening of 2
to 8 W m
–2
per decade was also noted at continental sites in the USA
(Long et al., 2009; Riihimaki et al., 2009; Augustine and Dutton, 2013).
The general pattern of dimming and consecutive brightening was fur-
ther found at numerous sites in Japan (Norris and Wild, 2009; Ohmura,
2009; Kudo et al., 2011) and in the SH in New Zealand (Liley, 2009).
Analyses of observations from sites in China confirmed strong declines
in SSR from the 1960s to 1980s on the order of 2 to 8 W m
–2
per decade,
which also did not persist in the 1990s (Che et al., 2005; Liang and
Xia, 2005; Qian et al., 2006; Shi et al., 2008; Norris and Wild, 2009;
Xia, 2010a). On the other hand, persistent dimming since the mid-20th
184
Chapter 2 Observations: Atmosphere and Surface
2
century with no evidence for a trend reversal was noted at sites in India
(Wild et al., 2005; Kumari et al., 2007; Kumari and Goswami, 2010;
Soni et al., 2012) and in the Canadian Prairie (Cutforth and Judiesch,
2007). Updates on latest SSR changes observed since 2000 provide
a less coherent picture (Wild, 2012). They suggest a continuation of
brightening at sites in Europe, USA, and parts of Asia, a levelling off at
sites in Japan and Antarctica, and indications for a renewed dimming
in parts of China (Wild et al., 2009; Xia, 2010a).
The longest observational SSR records, extending back to the 1920s
and 1930s at a few sites in Europe, further indicate some brightening
during the first half of the 20th century, known as ‘early brightening’
(cf. Figure 2.13) (Ohmura, 2009; Wild, 2009). This suggests that the
decline in SSR, at least in Europe, was confined to a period between
the 1950s and 1980s.
A number of issues remain, such as the quality and representativeness
of some of the SSR data as well as the large-scale significance of the
phenomenon (Wild, 2012). The historic radiation records are of varia-
ble quality and rigorous quality control is necessary to avoid spurious
trends (Dutton et al., 2006; Shi et al., 2008; Gilgen et al., 2009; Tang
et al., 2011; Wang et al., 2012e; Sanchez-Lorenzo et al., 2013). Since
the mid-1990s, high-quality data are becoming increasingly available
from new sites of the Baseline Surface Radiation Network (BSRN) and
Atmospheric Radiation Measurement (ARM) Program, which allow the
determination of SSR variations with unprecedented accuracy (Ohmura
et al., 1998). Alpert et al. (2005) and Alpert and Kishcha (2008) argued
that the observed SSR decline between 1960 and 1990 was larger in
densely populated than in rural areas. The magnitude of this ‘urbani-
zation effect’ in the radiation data is not yet well quantified. Dimming
and brightening is, however, also notable at remote and rural sites
(Dutton et al., 2006; Karnieli et al., 2009; Liley, 2009; Russak, 2009;
Wild, 2009; Wang et al., 2012d).
Globally complete satellite estimates have been available since the
early 1980s (Hatzianastassiou et al., 2005; Pinker et al., 2005; Hin-
kelman et al., 2009). Because satellites do not directly measure the
surface fluxes, they have to be inferred from measurable TOA signals
using empirical or physical models to remove atmospheric pertur-
bations. Available satellite-derived products qualitatively agree on a
brightening from the mid-1980s to 2000 averaged globally as well as
over oceans, on the order of 2 to 3 W m
–2
per decade (Hatzianastas-
siou et al., 2005; Pinker et al., 2005; Hinkelman et al., 2009). Averaged
over land, however, trends are positive or negative depending on the
respective satellite product (Wild, 2009). Knowledge of the decadal
variation of aerosol burdens and optical properties, required in satel-
lite retrievals of SSR and considered relevant for dimming/brightening
particularly over land, is very limited (Section 2.2.3). Extensions of sat-
ellite-derived SSR beyond 2000 indicate tendencies towards a renewed
dimming at the beginning of the new millennium (Hinkelman et al.,
2009; Hatzianastassiou et al., 2012).
Reconstructions of SSR changes from more widely measured mete-
orological variables can help to increase their spatial and temporal
coverage. Multi-decadal SSR changes have been related to observed
changes in sunshine duration, atmospheric visibility, diurnal tempera-
ture range (DTR; Section 2.4.1.2) and pan evaporation (Section 2.5.3).
Overall, these proxies provide independent evidence for the existence
of large-scale multi-decadal variations in SSR. Specifically, widespread
observations of declines in pan evaporation from the 1950s to the
1980s were related to SSR dimming amongst other factors (Roderick
and Farquhar, 2002). The observed decline in DTR over global land
surfaces from the 1950s to the 1980s (Section 2.4.1.2), and its stabi-
lisation thereafter fits to a large-scale dimming and subsequent bright-
ening, respectively (Wild et al., 2007). Widespread brightening after
1980 is further supported by reconstructions from sunshine duration
records (Wang et al., 2012e). Over Europe, SSR dimming and subse-
quent brightening is consistent with concurrent declines and increas-
es in sunshine duration (Sanchez-Lorenzo et al., 2008), evaporation
in energy limited environments (Teuling et al., 2009), visibility records
(Vautard et al., 2009; Wang et al., 2009b) and DTR (Makowski et al.,
2009). The early brightening in the 1930s and 1940s seen in a few
European SSR records is in line with corresponding changes in sun-
shine duration and DTR (Sanchez-Lorenzo et al., 2008; Wild, 2009;
Sanchez-Lorenzo and Wild, 2012). In China, the levelling off in SSR in
the 1990s after decades of decline coincides with similar tendencies
in the pan evaporation records, sunshine duration and DTR (Liu et al.,
2004a; Liu et al., 2004b; Qian et al., 2006; Ding et al., 2007; Wang et al.,
2012d). Dimming up to the 1980s and subsequent brightening is also
indicated in a set of 237 sunshine duration records in South America
(Raichijk, 2011).
2.3.3.2 Surface Thermal and Net Radiation
Thermal radiation, also known as longwave, terrestrial or far-IR radi-
ation is sensitive to changes in atmospheric GHGs, temperature and
humidity. Long-term measurements of the thermal surface com-
ponents as well as surface net radiation are available at far fewer
sites than SSR. Downward thermal radiation observations started to
become available during the early 1990s at a limited number of glob-
ally distributed terrestrial sites. From these records, Wild et al. (2008)
determined an overall increase of 2.6 W m
–2
per decade over the 1990s,
in line with model projections and the expectations of an increasing
greenhouse effect. Wang and Liang (2009) inferred an increase in
downward thermal radiation of 2.2 W m
–2
per decade over the period
1973–2008 from globally available terrestrial observations of temper-
ature, humidity and cloud fraction. Prata (2008) estimated a slightly
lower increase of 1.7 W m
–2
per decade for clear sky conditions over
the earlier period 1964–1990, based on observed temperature and
1940 1960 1980 2000
Radiation (Wm
-2
)
90
100
110
120
130
1920
Figure 2.13 | Annual mean Surface Solar Radiation (SSR) as observed at Stockholm,
Sweden, from 1923 to 2010. Stockholm has the longest SSR record available world-
wide. (Updated from Wild (2009) and Ohmura (2009).)
185
Observations: Atmosphere and Surface Chapter 2
2
humidity profiles from globally distributed land-based radiosonde sta-
tions and radiative transfer calculations. Philipona et al. (2004; 2005)
and Wacker et al. (2011) noted increasing downward thermal fluxes
recorded in the Swiss Alpine Surface Radiation Budget (ASRB) network
since the mid-1990s, corroborating an increasing greenhouse effect.
For mainland Europe, Philipona et al. (2009) estimated an increase of
downward thermal radiation of 2.4 to 2.7 W m
–2
per decade for the
period 1981–2005.
There is limited observational information on changes in surface net
radiation, in large part because measurements of upward fluxes at the
surface are made at only a few sites and are not spatially representa-
tive. Wild et al. (2004, 2008) inferred a decline in land surface net radi-
ation on the order of 2 W m
–2
per decade from the 1960s to the 1980s,
and an increase at a similar rate from the 1980s to 2000, based on esti-
mated changes of the individual radiative components that constitute
the surface net radiation. Philipona et al. (2009) estimated an increase
in surface net radiation of 1.3 to 2 W m
–2
per decade for central Europe
and the Alps between 1981 and 2005.
2.3.3.3 Implications from Observed Changes in Related
Climate Elements
The observed multi-decadal SSR variations cannot be explained by
changes in TSI, which are an order of magnitude smaller (Willson and
Mordvinov, 2003). They therefore have to originate from alterations
in the transparency of the atmosphere, which depends on the pres-
ence of clouds, aerosols and radiatively active gases (Kvalevag and
Myhre, 2007; Kim and Ramanathan, 2008). Cloud cover changes (Sec-
tion 2.5.7) effectively modulate SSR on an interannual basis, but their
contribution to the longer-term SSR trends is ambiguous. Although
cloud cover changes were found to explain the trends in some areas
(e.g., Liley, 2009), this is not always the case, particularly in relatively
polluted regions (Qian et al., 2006; Norris and Wild, 2007, 2009; Wild,
2009; Kudo et al., 2012). SSR dimming and brightening has also been
observed under cloudless atmospheres at various locations, pointing to
a prominent role of atmospheric aerosols (Wild et al., 2005; Qian et al.,
2007; Ruckstuhl et al., 2008; Sanchez-Lorenzo et al., 2009; Wang et al.,
2009b; Zerefos et al., 2009).
Box 2.3 | Global Atmospheric Reanalyses
Dynamical reanalyses are increasingly used for assessing weather and climate phenomena. Given their more frequent use in this
assessment compared to AR4, their characteristics are described in more detail here.
Reanalyses are distinct from, but complement, more ‘traditional’ statistical approaches to assessing the raw observations. They aim to
produce continuous reconstructions of past atmospheric states that are consistent with all observations as well as with atmospheric
physics as represented in a numerical weather prediction model, a process termed data assimilation. Unlike real-world observations,
reanalyses are uniform in space and time and provide non-observable variables (e.g., potential vorticity).
Several groups are actively pursuing reanalysis development at the global scale, and many of these have produced several generations
of reanalyses products (Box 2.3, Table 1). Since the first generation of reanalyses produced in the 1990s, substantial development has
taken place. The NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) and ERA-Interim reanalyses show
improved tropical precipitation and hence better represent the global hydrological cycle (Dee et al., 2011b). The NCEP/CFSR reanalysis
(continued on next page)
Institution Reanalysis Period
Approximate Resolution
at Equator
Reference
Cooperative Institute for Research in Environmental Sciences (CIRES),
National Oceanic and Atmospheric Administration (NOAA), USA
20th Century Reanalysis,
Vers. 2 (20CR)
1871–2010 320 km Compo et al. (2011)
National Centers for Environmental Prediction (NCEP) and National
Center for Atmospheric Research (NCAR), USA
NCEP/NCAR R1 (NNR) 1948– 320 km Kistler et al. (2001)
European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-40 1957–2002 125 km Uppala et al. (2005)
Japan Meteorological Agency (JMA) JRA-55 1958– 60 km Ebita et al. (2011)
National Centers for Environmental Prediction (NCEP), US Department
of Energy, USA
NCEP/DOE R2 1979– 320 km Kanamitsu et al. (2002)
Japan Meteorological Agency (JMA) JRA-25 1979– 190 km Onogi et al. (2007)
National Aeronautics and Space Administration (NASA), USA MERRA 1979– 75 km Rienecker et al. (2011)
European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim 1979– 80 km Dee et al. (2011b)
National Centers for Environmental Prediction (NCEP), USA CFSR 1979– 50 km Saha et al. (2010)
Box 2.3, Table 1 | Overview of global dynamical reanalysis data sets (ranked by start year; the period extends to present if no end year is provided). A further
description of reanalyses and their technical derivation is given in pp. S33–35 of Blunden et al. (2011). Approximate resolution is calculated as 1000 km * 20/N
(with N denoting the spectral truncation, Laprise, 1992).
186
Chapter 2 Observations: Atmosphere and Surface
2
Aerosols can directly attenuate SSR by scattering and absorbing solar
radiation, or indirectly, through their ability to act as cloud condensa-
tion nuclei, thereby changing cloud reflectivity and lifetime (Chapter
7). SSR dimming and brightening is often reconcilable with trends in
anthropogenic emission histories and atmospheric aerosol loadings
(Stern, 2006; Streets et al., 2006; Mishchenko et al., 2007; Ruckstuhl et
al., 2008; Ohvril et al., 2009; Russak, 2009; Streets et al., 2009; Cermak
et al., 2010; Wild, 2012). Recent trends in aerosol optical depth derived
from satellites indicate a decline in Europe since 2000 (Section 2.2.3),
in line with evidence from SSR observations. However, direct aerosol
effects alone may not be able to account for the full extent of the
observed SSR changes in remote regions with low pollution levels
(Dutton and Bodhaine, 2001; Schwartz, 2005). Aerosol indirect effects
have not yet been well quantified, but have the potential to amplify
aerosol-induced SSR trends, particularly in relatively pristine environ-
ments, such as over oceans (Wild, 2012).
SSR trends are also qualitatively in line with observed multi-decadal
surface warming trends (Chapter 10), with generally smaller warm-
ing rates during phases of declining SSR, and larger warming rates
in phases of increasing SSR (Wild et al., 2007). This is seen more pro-
nounced for the relatively polluted NH than the more pristine SH (Wild,
2012). For Europe, Vautard et al. (2009) found that a decline in the fre-
quency of low-visibility conditions such as fog, mist and haze over the
past 30 years and associated SSR increase may be responsible for 10
to 20% of Europe’s recent daytime warming, and 50% of Eastern Euro-
pean warming. Philipona (2012) noted that both warming and bright-
ening are weaker in the European Alps compared to the surrounding
lowlands with stronger aerosol declines since 1981.
Reanalyses and observationally based methods have been used to
show that increased atmospheric moisture with warming (Willett et
al., 2008; Section 2.5) enhances thermal radiative emission of the
atmosphere to the surface, leading to reduced net thermal cooling of
the surface (Prata, 2008; Allan, 2009; Philipona et al., 2009; Wang and
Liang, 2009).
In summary, the evidence for widespread multi-decadal variations in
solar radiation incident on land surfaces has been substantiated since
AR4, with many of the observational records showing a decline from
the 1950s to the 1980s (‘dimming’), and a partial recovery thereafter
(‘brightening’). Confidence in these changes is high in regions with
high station densities such as over Europe and parts of Asia. These
likely changes are generally supported by observed changes in related,
but more widely measured variables, such as sunshine duration, DTR
and hydrological quantities, and are often in line with aerosol emission
patterns. Over some remote land areas and over the oceans, confi-
dence is low owing to the lack of direct observations, which hamper a
truly global assessment. Satellite-derived SSR fluxes support the exist-
ence of brightening also over oceans, but are less consistent over land
surface where direct aerosol effects become more important. There are
also indications for increasing downward thermal and net radiation
at terrestrial stations since the early 1990s with medium confidence.
uses a coupled ocean–atmosphere–land-sea–ice model (Saha et al., 2010). The 20th Century Reanalyses (20CR, Compo et al., 2011) is a
56-member ensemble and covers 140 years by assimilating only surface and sea level pressure (SLP) information. This variety of groups
and approaches provides some indication of the robustness of reanalyses when compared. In addition to the global reanalyses, several
regional reanalyses exist or are currently being produced.
Reanalyses products provide invaluable information on time scales ranging from daily to interannual variability. However, they may
often be unable to characterize long-term trends (Trenberth et al., 2011). Although reanalyses projects by definition use a ‘frozen’
assimilation system, there are many other sources of potential errors. In addition to model biases, changes in the observational systems
(e.g., coverage, introduction of satellite data) and time-dependent errors in the underlying observations or in the boundary conditions
lead to step changes in time, even in latest generation reanalyses (Bosilovich et al., 2011).
Errors of this sort were ubiquitous in early generation reanalyses and rendered them of limited value for trend characterization (Thorne
and Vose, 2010). Subsequent products have improved and uncertainties are better understood (Dee et al., 2011a), but artefacts are
still present. As a consequence, trend adequacy depends on the variable under consideration, the time period and the region of inter-
est. For example, surface air temperature and humidity trends over land in the ERA-Interim reanalysis compare well with observations
(Simmons et al., 2010), but polar tropospheric temperature trends in ERA-40 disagree with trends derived from radiosonde and satel-
lite observations (Bitz and Fu, 2008; Grant et al., 2008; Graversen et al., 2008; Thorne, 2008; Screen and Simmonds, 2011) owing to
problems that were resolved in ERA-Interim (Dee et al., 2011a).
Studies based on reanalyses are used cautiously in AR5 and known inadequacies are pointed out and referenced. Later generation
reanalyses are preferred where possible; however, literature based on these new products is still sparse.
Box 2.3 (continued)
187
Observations: Atmosphere and Surface Chapter 2
2
Particular controversy since AR4 has surrounded the LSAT record over
the United States, focussed on siting quality of stations in the US His-
torical Climatology Network (USHCN) and implications for long-term
trends. Most sites exhibit poor current siting as assessed against offi-
cial WMO siting guidance, and may be expected to suffer potentially
large siting-induced absolute biases (Fall et al., 2011). However, overall
biases for the network since the 1980s are likely dominated by instru-
ment type (owing to replacement of Stevenson screens with maximum
minimum temperature systems (MMTS) in the 1980s at the majori-
ty of sites), rather than siting biases (Menne et al., 2010; Williams et
al., 2012). A new automated homogeneity assessment approach (also
used in GHCNv3, Menne and Williams, 2009) was developed that has
been shown to perform as well or better than other contemporary
approaches (Venema et al., 2012). This homogenization procedure
likely removes much of the bias related to the network-wide changes
in the 1980s (Menne et al., 2010; Fall et al., 2011; Williams et al., 2012).
Williams et al. (2012) produced an ensemble of data set realizations
using perturbed settings of this procedure and concluded through
assessment against plausible test cases that there existed a propensity
to under-estimate adjustments. This propensity is critically dependent
upon the (unknown) nature of the inhomogeneities in the raw data
records. Their homogenization increases both minimum temperature
and maximum temperature centennial-time-scale USA average LSAT
trends. Since 1979 these adjusted data agree with a range of reanalysis
products whereas the raw records do not (Fall et al., 2010; Vose et al.,
2012a).
Regional analyses of LSAT have not been limited to the United States.
Various national and regional studies have undertaken assessments for
Europe (Winkler, 2009; Bohm et al., 2010; Tietavainen et al., 2010; van
2.4 Changes in Temperature
2.4.1 Land Surface Air Temperature
2.4.1.1 Large-Scale Records and Their Uncertainties
AR4 concluded global land-surface air temperature (LSAT) had
increased over the instrumental period of record, with the warming
rate approximately double that reported over the oceans since 1979.
Since AR4, substantial developments have occurred including the pro-
duction of revised data sets, more digital data records, and new data
set efforts. These innovations have improved understanding of data
issues and uncertainties, allowing better quantification of regional
changes. This reinforces confidence in the reported globally averaged
LSAT time series behaviour.
Global Historical Climatology Network Version 3 (GHCNv3) incorpo-
rates many improvements (Lawrimore et al., 2011) but was found to
be virtually indistinguishable at the global mean from version 2 (used
in AR4). Goddard Institute of Space Studies (GISS) continues to provide
an estimate based upon primarily GHCN, accounting for urban impacts
through nightlights adjustments (Hansen et al., 2010). CRUTEM4
(Jones et al., 2012) incorporates additional station series and also
newly homogenized versions of many individual station records. A new
data product from a group based predominantly at Berkeley (Rohde
et al., 2013a) uses a method that is substantially distinct from ear-
lier efforts (further details on all the data sets and data availability
are given in Supplementary Material 2.SM.4). Despite the range of
approaches, the long-term variations and trends broadly agree among
these various LSAT estimates, particularly after 1900. Global LSAT has
increased (Figure 2.14, Table 2.4).
Since AR4, various theoretical challenges have been raised over the
verity of global LSAT records (Pielke et al., 2007). Globally, sam-
pling and methodological independence has been assessed through
sub-sampling (Parker et al., 2009; Jones et al., 2012), creation of an
entirely new and structurally distinct product (Rohde et al., 2013b) and
a complete reprocessing of GHCN (Lawrimore et al., 2011). None of
these yielded more than minor perturbations to the global LSAT records
since 1900. Willett et al. (2008) and Peterson et al. (2011) explicitly
showed that changes in specific and relative humidity (Section 2.5.5)
were physically consistent with reported temperature trends, a result
replicated in the ERA reanalyses (Simmons et al., 2010). Various inves-
tigators (Onogi et al., 2007; Simmons et al., 2010; Parker, 2011; Vose et
al., 2012a) showed that LSAT estimates from modern reanalyses were
in quantitative agreement with observed products.
-0.5
1.0
0.5
0.0
-1.0
1850 1900
1950
2000
Temperature anomaly (ºC)
GHCN
Berkeley
CRUTEM
GISS
Figure 2.14 | Global annual average land-surface air temperature (LSAT) anomalies
relative to a 1961–1990 climatology from the latest versions of four different data sets
(Berkeley, CRUTEM, GHCN and GISS).
Table 2.4: | Trend estimates and 90% confidence intervals (Box 2.2) for LSAT global average values over five common periods.
Data Set
Trends in °C per decade
1880–2012 1901–2012 1901–1950 1951–2012 1979–2012
CRUTEM4.1.1.0 (Jones et al., 2012) 0.086 ± 0.015 0.095 ± 0.020 0.097 ± 0.029 0.175 ± 0.037 0.254 ± 0.050
GHCNv3.2.0 (Lawrimore et al., 2011) 0.094 ± 0.016 0.107 ± 0.020 0.100 ± 0.033 0.197 ± 0.031 0.273 ± 0.047
GISS (Hansen et al., 2010) 0.095 ± 0.015 0.099 ± 0.020 0.098 ± 0.032 0.188 ± 0.032 0.267 ± 0.054
Berkeley (Rohde et al., 2013) 0.094 ± 0.013 0.101 ± 0.017 0.111 ± 0.034 0.175 ± 0.029 0.254 ± 0.049
188
Chapter 2 Observations: Atmosphere and Surface
2
der Schrier et al., 2011), China (Li et al., 2009; Zhen and Zhong-Wei,
2009; Li et al., 2010a; Tang et al., 2010), India (Jain and Kumar, 2012),
Australia (Trewin, 2012), Canada (Vincent et al., 2012), South America,
(Falvey and Garreaud, 2009) and East Africa (Christy et al., 2009). These
analyses have used a range of methodologies and, in many cases, more
data and metadata than available to the global analyses. Despite the
range of analysis techniques they are generally in broad agreement
with the global products in characterizing the long-term changes in
mean temperatures. This includes some regions, such as the Pacific
coast of South America, that have exhibited recent cooling (Falvey and
Garreaud, 2009). Of specific importance for the early global records,
large (>1°C) summer time warm bias adjustments for many European
19th century and early 20th century records were revisited and broadly
confirmed by a range of approaches (Bohm et al., 2010; Brunet et al.,
2011).
Since AR4 efforts have also been made to interpolate Antarctic records
from the sparse, predominantly coastal ground-based network (Chap-
man and Walsh, 2007; Monaghan et al., 2008; Steig et al., 2009;
O’Donnell et al., 2011). Although these agree that Antarctica as a
whole has warmed since the late 1950s, substantial multi-annual to
multi-decadal variability and uncertainties in reconstructed magnitude
and spatial trend structure yield only low confidence in the details of
pan-Antarctic regional LSAT changes.
In summary, it is certain that globally averaged LSAT has risen since the
late 19th century and that this warming has been particularly marked
since the 1970s. Several independently analyzed global and regional
LSAT data products support this conclusion. There is low confidence
in changes prior to 1880 owing to the reduced number of estimates,
non-standardized measurement techniques, the greater spread among
the estimates and particularly the greatly reduced observational sam-
pling. Confidence is also low in the spatial detail and magnitude of
LSAT trends in sparsely sampled regions such as Antarctica. Since AR4
significant efforts have been undertaken to identify and adjust for data
issues and new estimates have been produced. These innovations have
further strengthened overall understanding of the global LSAT records.
2.4.1.2 Diurnal Temperature Range
In AR4 diurnal temperature range (DTR) was found, globally, to have
narrowed since 1950, with minimum daily temperatures increasing
faster than maximum daily temperatures. However, significant mul-
ti-decadal variability was highlighted including a recent period from
1997 to 2004 of no change, as both maximum and minimum temper-
atures rose at similar rates. The Technical Summary of AR4 highlight-
ed changes in DTR and their causes as a key uncertainty. Since AR4,
uncertainties in DTR and its physical interpretation have become even
more apparent.
No dedicated global analysis of DTR has been undertaken subsequent
to Vose et al. (2005a), although global behaviour has been discussed
in two broader ranging analyses. Rohde et al. (2012) and Wild et al.
(2007) note an apparent reversal since the mid-1980s; with DTR sub-
sequently increasing. This decline and subsequent increase in DTR over
global land surfaces is qualitatively consistent with the dimming and
subsequent brightening noted in Section 2.3.3.1. Donat et al. (2013c)
using HadEX2 (Section 2.6) find significant decreasing DTR trends
in more than half of the land areas assessed but less than 10% of
land with significant increases since 1951. Available trend estimates
(–0.04 ± 0.01°C per decade over 1950–2011 (Rohde et al., 2013b)
and –0.066°C per decade over 1950–2004 (Vose et al., 2005a)) are
much smaller than global mean LSAT average temperature trends
over 1951–2012 (Table 2.4). It therefore logically follows that globally
averaged maximum and minimum temperatures over land have both
increased by in excess of 0.1°C per decade since 1950.
Regionally, Makowski et al. (2008) found that DTR behaviour in Europe
over 1950 to 2005 changed from a decrease to an increase in the
1970s in Western Europe and in the 1980s in Eastern Europe. Sen Roy
and Balling (2005) found significant increases in both maximum and
minimum temperatures for India, but little change in DTR over 1931–
2002. Christy et al. (2009) reported that for East Africa there has been
no pause in the narrowing of DTR in recent decades. Zhou and Ren
(2011) reported a significant decrease in DTR over mainland China of
–0.15°C per decade during 1961–2008.
Various investigators (e.g., Christy et al. (2009), Pielke and Matsui
(2005), Zhou and Ren (2011)) have raised doubts about the physical
interpretation of minimum temperature trends, hypothesizing that
microclimate and local atmospheric composition impacts are more
apparent because the dynamical mixing at night is much reduced.
Parker (2006) investigated this issue arguing that if data were affected
in this way, then a trend difference would be expected between calm
and windy nights. However, he found no such minimum temperature
differences on a global average basis. Using more complex boundary
layer modelling techniques, Steeneveld et al. (2011) and McNider et al.
(2012) showed much lower sensitivity to windspeed variations than
posited by Pielke and Matsui but both concluded that boundary layer
understanding was key to understanding the minimum temperature
changes. Data analysis and long-term side-by-side instrumentation
field studies show that real non-climatic data artefacts certainly affect
maximum and minimum differently in the raw records for both recent
(Fall et al., 2011; Williams et al., 2012) and older (Bohm et al., 2010;
Brunet et al., 2011) records. Hence there could be issues over interpre-
tation of apparent DTR trends and variability in many regions (Christy
et al., 2006, 2009; Fall et al., 2011; Zhou and Ren, 2011; Williams et
al., 2012), particularly when accompanied by regional-scale land-use/
land-cover (LULC) changes (Christy et al., 2006).
In summary, confidence is medium in reported decreases in observed
global DTR, noted as a key uncertainty in AR4. Several recent analyses
of the raw data on which many previous analyses were based point to
the potential for biases that differently affect maximum and minimum
average temperatures. However, apparent changes in DTR are much
smaller than reported changes in average temperatures and therefore
it is virtually certain that maximum and minimum temperatures have
increased since 1950.
2.4.1.3 Land Use Change and Urban Heat Island Effects
In AR4 Urban Heat Island (UHI) effects were concluded to be real local
phenomena with negligible impact on large-scale trends. UHI and
land-use land-cover change (LULC) effects arise mainly because the
189
Observations: Atmosphere and Surface Chapter 2
2
modified surface affects the storage and transfer of heat, water and
airflow. For single discrete locations these impacts may dominate all
other factors.
Regionally, most attention has focused on China. A variety of investi-
gations have used methods as diverse as SST comparisons (e.g., Jones
et al., 2008), urban minus rural (e.g., Ren et al., 2008; Yang et al., 2011),
satellite observations (Ren and Ren, 2011) and observations minus rea-
nalysis (e.g., Hu et al., 2010; Yang et al., 2011). Interpretation is com-
plicated because often studies have used distinct versions of station
series. For example, the effect in Beijing is estimated at 80% (Ren et
al., 2007) or 40% (Yan et al., 2010) of the observed trend depending
on data corrections applied. A representative sample of these stud-
ies suggest the effect of UHI and LULC is approximately 20% of the
trend in Eastern China as a whole and of the order 0.1°C per decade
nationally (Table 1 in Yang et al., 2011) over the last 30 years, but with
very substantial uncertainties. These effects have likely been partially
or completely accounted for in many homogenized series (e.g., Li et
al., 2010b; Yan et al., 2010). Fujibe (2009) ascribes about 25% of Jap-
anese warming trends in 1979–2006 to UHI effects. Das et al. (2011)
confirmed that many Japanese sites have experienced UHI warming
but that rural stations show unaffected behaviour when compared to
nearby SSTs.
There is an important distinction to be made between UHI trend effects
in regions underseeing rapid development and those that have been
developed for a long time. Jones and Lister (2009) and Wilby et al.
(2011) using data from London (UK) concluded that some sites that
have always been urban and where the UHI has not grown in mag-
nitude will exhibit regionally indicative trends that agree with nearby
rural locations and that in such cases the time series may exhibit mul-
ti-decadal trends driven primarily by synoptic variations. A lack of obvi-
ous time-varying UHI influences was also noted for Sydney, Melbourne
and Hobart in Australia by Trewin (2012). The impacts of urbanization
also will be dependent on the natural LULC characteristics that they
replace. Zhang et al. (2010) found no evidence for urban influences in
the desert North West region of China despite rapid urbanization.
Global adjusted data sets likely account for much of the UHI effect pres-
ent in the raw data. For the US network, Hausfather et al. (2013) showed
that the adjustments method used in GHCNv3 removed much of an
apparent systematic difference between urban and rural locations, con-
cluding that this arose from adjustment of biased urban location data.
Globally, Hansen et al. (2010) used satellite-based nightlight radiances
to estimate the worldwide influence on LSAT of local urban develop-
ment. Adjustments reduced the global 1900–2009 temperature change
(averaged over land and ocean) only from 0.71°C to 0.70°C. Wickham
et al. (2013) also used satellite data and found that urban locations in
the Berkeley data set exhibited even less warming than rural stations,
although not statistically significantly so, over 1950 to 2010.
Studies of the broader effects of LULC since AR4 have tended to focus
on the effects of irrigation on temperatures, with a large number of
studies in the Californian central belt (Christy et al., 2006; Kueppers et
al., 2007; Bonfils et al., 2008; Lo and Famiglietti, 2013). They find cooler
average temperatures and a marked reduction in DTR in areas of active
irrigation and ascribe this to increased humidity; effectively a repar-
titioning of moist and dry energy terms. Reanalyses have also been
used to estimate the LULC signature in LSAT trends. Fall et al. (2010)
found that the North American Regional Reanalysis generated over-
all surface air temperature trends for 1979–2003 similar to observed
records. Observations-minus-reanalysis trends were most positive for
barren and urban areas, in accord with the results of Lim et al. (2008)
using the NCEP/NCAR and ERA-40 reanalyses, and negative in agricul-
tural areas.
McKitrick and Michaels (2004) and de Laat and Maurellis (2006)
assessed regression of trends with national socioeconomic and geo-
graphical indicators, concluding that UHI and related LULC have
caused much of the observed LSAT warming. AR4 concluded that
this correlation ceases to be statistically significant if one takes into
account the fact that the locations of greatest socioeconomic devel-
opment are also those that have been most warmed by atmospheric
circulation changes but provided no explicit evidence for this overall
assessment result. Subsequently McKitrick and Michaels (2007) con-
cluded that about half the reported warming trend in global-average
land surface air temperature in 1980–2002 resulted from local land
surface changes and faults in the observations. Schmidt (2009) under-
took a quantitative analysis that supported AR4 conclusions that much
of the reported correlation largely arose due to naturally occurring
climate variability and model over-fitting and was not robust. Taking
these factors into account, modified analyses by McKitrick (2010) and
McKitrick and Nierenberg (2010) still yielded significant evidence for
such contamination of the record.
In marked contrast to regression based studies, several studies have
shown the methodologically diverse set of modern reanalysis products
and the various LSAT records at global and regional levels to be similar
since at least the mid-20th century (Simmons et al., 2010; Parker, 2011;
Ferguson and Villarini, 2012; Jones et al., 2012; Vose et al., 2012a).
These reanalyses do not directly assimilate the LSAT measurements but
rather infer LSAT estimates from an observational constraint provided
by much of the rest of the global observing system, thus representing
an independent estimate. A hypothesized residual significant warming
artefact argued for by regression-based analyses is therefore physical-
ly inconsistent with many other components of the global observing
system according to a broad range of state-of-the-art data assimilation
models (Box 2.3). Further, Efthymiadis and Jones (2010) estimated an
absolute upper limit on urban influence globally of 0.02°C per decade,
or about 15% of the total LSAT trends, in 1951–2009 from trends of
coastal land and SST.
In summary, it is indisputable that UHI and LULC are real influenc-
es on raw temperature measurements. At question is the extent to
which they remain in the global products (as residual biases in broader
regionally representative change estimates). Based primarily on the
range of urban minus rural adjusted data set comparisons and the
degree of agreement of these products with a broad range of rea-
nalysis products, it is unlikely that any uncorrected urban heat-island
effects and LULC change effects have raised the estimated centennial
globally averaged LSAT trends by more than 10% of the reported trend
(high confidence, based on robust evidence and high agreement). This
is an average value; in some regions with rapid development, UHI and
LULC change impacts on regional trends may be substantially larger.
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Chapter 2 Observations: Atmosphere and Surface
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2.4.2 Sea Surface Temperature and Marine Air
Temperature
AR4 concluded that ‘recent’ warming (since the 1950s) is strongly evi-
dent at all latitudes in SST over each ocean. Prominent spatio-temporal
structures including the ENSO and decadal variability patterns in the
Pacific Ocean (Box 2.5) and a hemispheric asymmetry in the Atlantic
Ocean were highlighted as contributors to the regional differences in
surface warming rates, which in turn affect atmospheric circulation.
Since AR4 the availability of metadata has increased, data complete-
ness has improved and a number of new SST products have been pro-
duced. Intercomparisons of data obtained by different measurement
methods, including satellite data, have resulted in better understand-
ing of errors and biases in the record.
2.4.2.1 Advances in Assembling Data Sets and in
Understanding Data Errors
2.4.2.1.1 In situ data records
Historically, most SST observations were obtained from moving ships.
Buoy measurements comprise a significant and increasing fraction
of in situ SST measurements from the 1980s onward (Figure 2.15).
Improvements in the understanding of uncertainty have been expe-
dited by the use of metadata (Kent et al., 2007) and the recovery of
1920 1940
0.0
0.2
0.4
0.6
0.8
1.0
Fractional contribution to
global average SST
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
SST anomaly (°C) relative
to 1961-1990
1960 1980 2000
1960 1970 19801950
(a)
(b)
1990 2000
All
ERI/Hull contact sensors
Bucket
Buoy
Figure 2.15 | Temporal changes in the prevalence of different measurement methods
in the International Comprehensive Ocean-Atmosphere Data Set (ICOADS). (a) Fraction-
al contributions of observations made by different measurement methods: bucket obser-
vations (blue), engine room intake (ERI) and hull contact sensor observations (green),
moored and drifting buoys (red), and unknown (yellow). (b) Global annual average sea
surface temperature (SST) anomalies based on different kinds of data: ERI and hull
contact sensor (green), bucket (blue), buoy (red), and all (black). Averages are computed
over all 5° × 5° grid boxes where both ERI/hull and bucket measurements, but not
necessarily buoy data, were available. (Adapted from Kennedy et al., 2011a.)
observer instructions and other related documents. Early data were
systematically cold biased because they were made using canvas or
wooden buckets that, on average, lost heat to the air before the meas-
urements were taken. This effect has long been recognized (Brooks,
1926), and prior to AR4 represented the only artefact adjusted in grid-
ded SST products, such as HadSST2 (Rayner et al., 2006) and ERSST
(Smith et al., 2005, 2008), which were based on ‘bucket correction’
methods by Folland and Parker (1995) and Smith and Reynolds (2002),
respectively. The adjustments, made using ship observations of Night
Marine Air Temperature (NMAT) and other sources, had a striking effect
on the SST global mean estimates: note the difference in 1850–1941
between HadSST2 and International Comprehensive Ocean-Atmos-
phere Data Set (ICOADS) curves in Figure 2.16 (a brief description of
SST and NMAT data sets and their methods is given in Supplementary
Material 2.SM.4.3).
Buckets of improved design and measurement methods with smaller,
on average, biases came into use after 1941 (Figure 2.15, top); aver-
age biases were reduced further in recent decades, but not eliminated
(Figure 2.15, bottom). Increasing density of SST observations made
possible the identification (Reynolds et al., 2002, 2010; Kennedy et al.,
2012) and partial correction of more recent period biases (Kennedy et
al., 2011a). In particular, it is hypothesized that the proximity of the
hot engine often biases engine room intake (ERI) measurements warm
(Kent et al., 2010). Because of the prevalence of the ERI measurements
among SST data from ships, the ship SSTs are biased warm by 0.12°C
to 0.18°C on average compared to the buoy data (Reynolds et al., 2010;
Kennedy et al., 2011a, 2012). An assessment of the potential impact
of modern biases can be ascertained by considering the difference
HadSST2
ICOADS
HadSST3
HadNMAT2
1850 1900 1950 2000
Temperature anomaly (ºC)
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
Figure 2.16 | Global annual average sea surface temperature (SST) and Night Marine
Air Temperature (NMAT) relative to a 1961–1990 climatology from gridded data sets
of SST observations (HadSST2 and its successor HadSST3), the raw SST measurement
archive (ICOADS, v2.5) and night marine air temperatures data set HadNMAT2 (Kent et
al., 2013). HadSST2 and HadSST3 both are based on SST observations from versions of
the ICOADS data set, but differ in degree of measurement bias correction.
Data Set
Trends in °C per decade
1880–2012 1901–2012 1901–1950 1951–2012 1979–2012
HadSST3 (Kennedy et al., 2011a) 0.054 ± 0.012 0.067 ± 0.013 0.117 ± 0.028 0.074 ± 0.027 0.124 ± 0.030
HadSST2 (Rayner et al., 2006) 0.051 ± 0.015 0.069 ± 0.012 0.084 ± 0.055 0.098 ± 0.017 0.121 ± 0.033
Table 2.5 | Trend estimates and 90% confidence intervals (Box 2.2) for two subsequent versions of the HadSST data set over five common periods. HadSST2 has been used in
AR4; HadSST3 is used in this chapter.
191
Observations: Atmosphere and Surface Chapter 2
2
between HadSST3 (bias corrections applied throughout) and HadSST2
(bucket corrections only) global means (Figure 2.16): it is particularly
prominent in 1945–1970 period, when rapid changes in prevalence of
ERI and bucket measurements during and after the World War II affect
HadSST2 owing to the uncorrected measurement biases (Thompson et
al., 2008), while these are corrected in HadSST3. Nevertheless, for peri-
ods longer than a century the effect of HadSST3-HadSST2 differences
on linear trend slopes is small relative to the trend uncertainty (Table
2.5). Some degree of independent check on the validity of HadSST3
adjustments comes from a comparison to sub-surface temperature
data (Gouretski et al., 2012) (see Section 3.2).
The traditional approach to modeling random error of in situ SST
data assumed the independence of individual measurements. Kent
and Berry (2008) identified the need to account for error correlation
for measurements from the same “platform” (i.e., an individual ship
or buoy), while measurement errors from different platforms remain
independent.. Kennedy et al. (2011b) achieved that by introducing
platform-dependent biases, which are constant within the same plat-
form, but change randomly from one platform to another. Accounting
for such correlated errors in HadSST3 resulted in estimated error for
global and hemispheric monthly means that are more than twice the
estimates given by HadSST2. The uncertainty in many, but not all, com-
ponents of the HadSST3 product is represented by the ensemble of its
realizations (Figure 2.17).
Data sets of marine air temperatures (MATs) have traditionally been
restricted to nighttime series only (NMAT data sets) due to the direct
solar heating effect on the daytime measurements, although corrected
daytime MAT records for 1973–present are already available (Berry
and Kent, 2009). Other major biases, affecting both nighttime and day-
time MAT are due to increasing deck height with the general increase
in the size of ships over time and non-standard measurement prac-
tices. Recently these biases were re-examined and explicit uncertainty
calculation undertaken for NMAT by Kent et al. (2013), resulting in the
HadNMAT2 data set.
2.4.2.1.2 Satellite SST data records
Satellite SST data sets are based on measuring electromagnetic radia-
tion that left the ocean surface and got transmitted through the atmos-
phere. Because of the complexity of processes involved, the majority
of such data has to be calibrated on the basis of in situ observations.
The resulting data sets, however, provide a description of global SST
fields with a level of spatial detail unachievable by in situ data only.
The principal IR sensor is the Advanced Very High Resolution Radiom-
eter (AVHRR). Since AR4, the AVHRR time series has been reprocessed
consistently back to March 1981 (Casey et al., 2010) to create the
AVHRR Pathfinder v5.2 data set. Passive microwave data sets of SST
are available since 1997 equatorward of 40° and near-globally since
2002 (Wentz et al., 2000; Gentemann et al., 2004). They are generally
less accurate than IR-based SST data sets, but their superior coverage
in areas of persistent cloudiness provides SST estimates where the IR
record has none (Reynolds et al., 2010).
The (Advanced) Along Track Scanning Radiometer (A)ATSR) series of
three sensors was designed for climate monitoring of SST; their com-
bined record starts in August 1991 and exceeds two decades (it stopped
with the demise of the ENVISAT platform in 2012). The (A)ATSRs are
‘dual-view’ IR radiometers intended to allow atmospheric effects
removal without the use of in situ observations. Since AR4, (A)ATSR
observations have been reprocessed with new estimation techniques
(Embury and Merchant, 2011). The resulting SST products seem to be
more accurate than many in situ observations (Embury et al., 2011). In
terms of monthly global means, the agreement is illustrated in Figure
2.17. By analyzing (A)ATSR and in situ data together, Kennedy at al.
(2012) verified and extended existing models for biases and random
errors of in situ data.
2.4.2.2 Interpolated SST Products and Trends
SST data sets form a major part of global surface temperature anal-
yses considered in this assessment report. To use an SST data set as
a boundary condition for atmospheric reanalyses products (Box 2.3)
or in atmosphere-only climate simulations (considered in Chapter 9
onwards), gridded data sets with complete coverage over the global
ocean are typically needed. These are usually based on a special form
of kriging (optimal interpolation) procedure that retains large-scale
correlation structures and can accommodate very sparse data cover-
age. For the pre-satellite era (generally, before October 1981) only in
situ data are used; for the latter period some products also use AVHRR
data. Figure 2.18 compares interpolated SST data sets that extend
back to the 19th century with the uninterpolated HadSST3 and Had-
NMAT2 products. Linear trend estimates for global mean SSTs from
those products updated through 2012 are presented in Table 2.6. Dif-
ferences between the trends from different data sets are larger when
the calculation period is shorter (1979–2012) or has lower quality
data (1901–1950); these are due mainly to different data coverage of
underlying observational data sets and bias correction methods used
in these products.
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
-0.2
0.0
0.2
0.4
0.6
SST anomaly (°C)
ARC D3
ARC D2
HadSST3
ATSR-1 ATSR-2 AATSR
Figure 2.17 | Global monthly mean sea surface temperature (SST) anomalies relative
to a 1961–1990 climatology from satellites (ATSRs) and in situ records (HadSST3). Black
lines: the 100-member HadSST3 ensemble. Red lines: ATSR-based nighttime subsurface
temperature at 0.2 m depth (SST
0.2m
) estimates from the ATSR Reprocessing for Climate
(ARC) project. Retrievals based on three spectral channels (D3, solid line) are more
accurate than retrievals based on only two (D2, dotted line). Contributions of the three
different ATSR missions to the curve shown are indicated at the bottom. The in situ and
satellite records were co-located within 5° × 5° monthly grid boxes: only those where
both data sets had data for the same month were used in the comparison. (Adapted
from Merchant et al. 2012.)
192
Chapter 2 Observations: Atmosphere and Surface
2
In summary, it is certain that global average sea surface temperatures
(SSTs) have increased since the beginning of the 20th century. Since
AR4, major improvements in availability of metadata and data com-
pleteness have been made, and a number of new global SST records
have been produced. Intercomparisons of new SST data records
obtained by different measurement methods, including satellite data,
have resulted in better understanding of uncertainties and biases in
the records. Although these innovations have helped highlight and
quantify uncertainties and affect our understanding of the character of
changes since the mid-20th century, they do not alter the conclusion
that global SSTs have increased both since the 1950s and since the
late 19th century.
2.4.3 Global Combined Land and Sea Surface
Temperature
AR4 concluded that the GMST had increased, with the last 50 years
increasing at almost double the rate of the last 100 years. Subsequent
developments in LSAT and SST have led to better understanding of the
data and their uncertainties as discussed in preceding sections. This
improved understanding has led to revised global products.
Changes have been made to all three GMST data sets that were used
in AR4 (Hansen et al., 2010; Morice et al., 2012; Vose et al., 2012b).
These are now in somewhat better agreement with each other over
recent years, in large part because HadCRUT4 now better samples the
NH high latitude land regions (Jones et al., 2012; Morice et al., 2012)
which comparisons to reanalyses had shown led to a propensity for
HadCRUT3 to underestimate recent warming (Simmons et al., 2010).
Table 2.6 | Trend estimates and 90% confidence intervals (Box 2.2) for interpolated SST data sets (uninterpolated state-of-the-art HadSST3 data set is included for comparison).
Dash indicates not enough data available for trend calculation.
Data Set
Trends in °C per decade
1880–2012 1901–2012 1901–1950 1951–2012 1979–2012
HadISST (Rayner et al., 2003) 0.042 ± 0.007 0.052 ± 0.007 0.067 ± 0.024 0.064 ± 0.015 0.072 ± 0.024
COBE-SST (Ishii et al., 2005) 0.058 ± 0.007 0.066 ± 0.032 0.071 ± 0.014 0.073 ± 0.020
ERSSTv3b (Smith et al., 2008) 0.054 ± 0.015 0.071 ± 0.011 0.097 ± 0.050 0.088 ± 0.017 0.105 ± 0.031
HadSST3 (Kennedy et al., 2011a) 0.054 ± 0.012 0.067 ± 0.013 0.117 ± 0.028 0.074 ± 0.027 0.124 ± 0.030
0.4
0.2
0.0
-0.2
-0.4
-0.6
Temperature anomaly (ºC)
1850 1900
1950
2000
COBE HadISST
HadNMAT2
ERSST
HadSST3
Figure 2.18 | Global annual average sea surface temperature (SST) and Night Marine
Air Temperature (NMAT) relative to a 1961–1990 climatology from state of the art data
sets. Spatially interpolated products are shown by solid lines; non-interpolated products
by dashed lines.
Starting in the 1980s each decade has been significantly warmer at
the Earth’s surface than any preceding decade since the 1850s in Had-
CRUT4, a data set that explicitly quantifies a large number of sources
of uncertainty (Figure 2.19). Each of the last three decades is also the
warmest in the other two GMST data sets, but these have substan-
tially less mature and complete uncertainty estimates, precluding such
an assessment of significance of their decadal differences. The GISS
and MLOST data sets fall outside the 90% CI of HadCRUT4 for several
decades in the 20th century (Figure 2.19). These decadal differences
could reflect residual biases in one or more data set, an incomplete
treatment of uncertainties in HadCRUT4.1 or a combination of these
effects (Box 2.1). The data sets utilize different LSAT (Section 2.4.1)
and SST (Section 2.4.2) component records (Supplementary Material
2.SM.4.3.4) that in the case of SST differ somewhat in their multi-dec-
adal trend behaviour (Table 2.6 compare HadSST3 and ERSSTv3b).
-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6
Temperature difference from 1961-1990 (
o
C)
1850s
1860s
1870s
1880s
1890s
1900s
1910s
1920s
1930s
1940s
1950s
1960s
1970s
1980s
1990s
2000s
MLOST
GISS
HadCRUT4
Figure 2.19 | Decadal global mean surface temperature (GMST) anomalies (white
vertical lines in grey blocks) and their uncertainties (90% confidence intervals as grey
blocks) based upon the land-surface air temperature (LSAT) and sea surface tempera-
ture (SST) combined HadCRUT4 (v4.1.1.0) ensemble (Morice et al., 2012). Anomalies
are relative to a 1961–1990 climatology. 1850s indicates the period 1850-1859, and
so on. NCDC MLOST and GISS data set best-estimates are also shown.
193
Observations: Atmosphere and Surface Chapter 2
2
All ten of the warmest years have occurred since 1997, with 2010 and
2005 effectively tied for the warmest year on record in all three prod-
ucts. However, uncertainties on individual annual values are sufficient-
ly large that the ten warmest years are statistically indistinguishable
from one another. The global-mean trends are significant for all data
sets and multi-decadal periods considered in Table 2.7. Using Had-
CRUT4 and its uncertainty estimates, the warming from 1850–1900 to
1986–2005 (reference period for the modelling chapters and Annex I)
is 0.61 [0.55 to 0.67] °C (90% confidence interval), and the warming
from 1850–1900 to 2003–2012 (the most recent decade) is 0.78 [0.72
to 0.85] °C (Supplementary Material 2.SM.4.3.3).
Differences between data sets are much smaller than both interannual
variability and the long-term trend (Figure 2.20). Since 1901 almost the
whole globe has experienced surface warming (Figure 2.21). Warming
has not been linear; most warming occurred in two periods: around
1900 to around 1940 and around 1970 onwards (Figure 2.22. Shorter
periods are noisier and so proportionately less of the sampled globe
exhibits statistically significant trends at the grid box level (Figure
2.22). The two periods of global mean warming exhibit very distinct
spatial signatures. The early 20th century warming was largely a NH
mid- to high-latitude phenomenon, whereas the more recent warm-
ing is more global in nature. These distinctions may yield important
information as to causes (Chapter 10). Differences between data sets
are larger in earlier periods (Figures 2.19, 2.20), particularly prior to
the 1950s when observational sampling is much more geographically
incomplete (and many of the well sampled areas may have been glob-
ally unrepresentative (Brönnimann, 2009)), data errors and subsequent
methodological impacts are larger (Thompson et al., 2008), and differ-
ent ways of accounting for data void regions are more important (Vose
et al., 2005b).
Table 2.7 | Same as Table 2.4, but for global mean surface temperature (GMST) over five common periods.
Data Set
Trends in °C per decade
1880–2012 1901–2012 1901–1950 1951–2012 1979–2012
HadCRUT4 (Morice et al., 2012) 0.062 ± 0.012 0.075 ± 0.013 0.107 ± 0.026 0.106 ± 0.027 0.155 ± 0.033
NCDC MLOST (Vose et al., 2012b) 0.064 ± 0.015 0.081 ± 0.013 0.097 ± 0.040 0.118 ± 0.021 0.151 ± 0.037
GISS (Hansen et al., 2010) 0.065 ± 0.015 0.083 ± 0.013 0.090 ± 0.034 0.124 ± 0.020 0.161 ± 0.033
1850 1900
1950
2000
Temperature anomaly (ºC)
GISSHadCRUT4MLOST
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
Trend (ºC over period)
-0.6 -0.4 -0.2 00.2 0.40.6 0.81.1.251.5 1.75 2.5
HadCRUT4 1901-2012
MLOST 1901-2012
GISS 1901-2012
Figure 2.20 | Annual global mean surface temperature (GMST) anomalies relative to a
1961–1990 climatology from the latest version of the three combined land-surface air
temperature (LSAT) and sea surface temperature (SST) data sets (HadCRUT4, GISS and
NCDC MLOST). Published data set uncertainties are not included for reasons discussed
in Box 2.1.
Figure 2.21 | Trends in surface temperature from the three data sets of Figure 2.20
for 1901–2012. White areas indicate incomplete or missing data. Trends have been
calculated only for those grid boxes with greater than 70% complete records and more
than 20% data availability in first and last decile of the period. Black plus signs (+)
indicate grid boxes where trends are significant (i.e., a trend of zero lies outside the 90%
confidence interval). Differences in coverage primarily reflect the degree of interpolation
to account for data void regions undertaken by the data set providers ranging from none
beyond grid box averaging (HadCRUT4) to substantial (GISS).
Much interest has focussed on the period since 1998 and an observed
reduction in warming trend, most marked in NH winter (Cohen et al.,
2012). Various investigators have pointed out the limitations of such
short-term trend analysis in the presence of auto-correlated series var-
iability and that several other similar length phases of no warming
exist in all the observational records and in climate model simulations
194
Chapter 2 Observations: Atmosphere and Surface
2
MLOST 1911-1940
MLOST 1951-1980
MLOST 1981-2012
Trend (°C per decade)
-1.25 -1
-0.8 -0.6
-0.5
-0.4
-0.3
-0.2
-0.1 0 0.1 0.2
0.3
0.4 0.5
0.6
0.8 1 1.25
Figure 2.22 | Trends in surface temperature from NCDC MLOST for three non-
consectutive shorter periods (1911–1940; 1951–1980; 1981–2012). White areas
indicate incomplete or missing data. Trends and significance have been calculated as
in Figure 2.21.
(Easterling and Wehner, 2009; Peterson et al., 2009; Liebmann et al.,
2010; Foster and Rahmstorf, 2011; Santer et al., 2011). This issue is
discussed in the context of model behaviour, forcings and natural var-
iability in Box 9.2 and Section 10.3.1. Regardless, all global combined
LSAT and SST data sets exhibit a statistically non-significant warming
trend over 1998–2012 (0.042°C ± 0.093°C per decade (HadCRUT4);
0.037°C ± 0.085°C per decade (NCDC MLOST); 0.069°C ± 0.082°C per
decade (GISS)). An average of the trends from these three data sets
yields an estimated change for the 1998–2012 period of 0.05 [–0.05 to
+0.15] °C per decade. Trends of this short length are very sensitive to
the precise period selection with trends calculated in the same manner
for the 15-year periods starting in 1995, 1996, and 1997 being 0.13
[0.02 to 0.24], 0.14 [0.03 to 0.24] and 0.07 [–0.02 to 0.18] (all °C per
decade), respectively.
In summary, it is certain that globally averaged near surface temper-
atures have increased since the late 19th century. Each of the past
three decades has been warmer than all the previous decades in
the instrumental record, and the decade of the 2000s has been the
warmest. The globally averaged combined land and ocean surface
temperature data as calculated by a linear trend, show a warming
of 0.85 [0.65 to 1.06] °C, over the period 1880–2012, when multiple
independently produced datasets exist, about 0.89°C [0.69 to 1.08] °C
over the period 1901–2012, and about 0.72 [0.49° to 0.89] °C over
the period 1951–2012. The total increase between the average of the
1850–1900 period and the 2003–2012 period is 0.78 [0.72 to 0.85] °C
and the total increase between the average of the 1850–1900 period
and the reference period for projections 1986−2005 is 0.61 [0.55 to
0.67] °C, based on the single longest dataset available. For the lon-
gest period when calculation of regional trends is sufficiently complete
(1901–2012), almost the entire globe has experienced surface warm-
ing. In addition to robust multi-decadal warming, global mean surface
temperature exhibits substantial decadal and interannual variability.
Owing to natural variability, trends based on short records are very
sensitive to the beginning and end dates and do not in general reflect
long-term climate trends. As one example, the rate of warming over
the past 15 years (1998–2012; 0.05 [–0.05 to +0.15] °C per decade),
which begins with a strong El Niño, is smaller than the rate calculated
since 1951 (1951–2012; 0.12 [0.08 to 0.14] °C per decade)Trends for
15-year periods starting in 1995, 1996, and 1997 are 0.13 [0.02 to
0.24], 0.14 [0.03 to 0.24] and 0.07 [–0.02 to 0.18], respectively..
2.4.4 Upper Air Temperature
AR4 summarized that globally the troposphere had warmed at a rate
greater than the GMST over the radiosonde record, while over the
shorter satellite era the GMST and tropospheric warming rates were
indistinguishable. Trends in the tropics were more uncertain than
global trends although even this region was concluded to be warming.
Globally, the stratosphere was reported to be cooling over the satellite
era starting in 1979. New advances since AR4 have highlighted the
substantial degree of uncertainty in both satellite and balloon-borne
radiosonde records and led to some revisions and improvements in
existing products and the creation of a number of new data products.
2.4.4.1 Advances in Multi-Decadal Observational Records
The major global radiosonde records extend back to 1958, with tem-
peratures, measured as the balloon ascends, reported at mandatory
pressure levels. Satellites have monitored tropospheric and lower strat-
ospheric temperature trends since late 1978 through the Microwave
Sounding Unit (MSU) and its follow-on Advanced Microwave Sound-
ing Unit (AMSU) since 1998. These measures of upwelling radiation
represent bulk (volume averaged) atmospheric temperature (Figure
2.23). The ‘Mid-Tropospheric’ (MT) MSU channel that most directly cor-
responds to the troposphere has 10 to 15% of its signal from both the
skin temperature of the Earth’s surface and the stratosphere. Two alter-
native approaches have been suggested for removing the stratospheric
component based on differencing of view angles (LT) and statistical
recombination (*G) with the ‘Lower Stratosphere’ (LS) channel (Spen-
cer and Christy, 1992; Fu et al., 2004). The MSU satellite series also
included a Stratospheric Sounding Unit (SSU) that measured at higher
altitudes (Seidel et al., 2011).
195
Observations: Atmosphere and Surface Chapter 2
2
At the time of AR4 there were only two ‘global’ radiosonde data sets
that included treatment of homogeneity issues: RATPAC (Free et al.,
2005) and HadAT (Thorne et al., 2005). Three additional estimates
have appeared since AR4 based on novel and distinct approaches.
A group at the University of Vienna have produced RAOBCORE and
RICH (Haimberger, 2007; Haimberger et al., 2008, 2012) using ERA
reanalysis products (Box 2.3). Sherwood and colleagues developed an
iterative universal kriging approach for radiosonde data to create IUK
(Sherwood et al., 2008) and concluded that non-climatic data issues
leading to spurious cooling remained in the deep tropics even after
homogenization. The HadAT group created an automated version,
undertook systematic experimentation and concluded that the para-
metric uncertainty (Box 2.1) was of the same order of magnitude as
the apparent climate signal (McCarthy et al., 2008; Titchner et al.,
2009; Thorne et al., 2011). A similar ensemble approach has also been
applied to the RICH product (Haimberger et al., 2012). These various
ensembles and new products exhibit more tropospheric warming / less
stratospheric cooling than pre-existing products at all levels. Globally
the radiosonde records all imply the troposphere has warmed and the
stratosphere cooled since 1958 but with uncertainty that grows with
height and is much greater outside the better-sampled NH extra-trop-
ics (Thorne et al., 2011; Haimberger et al., 2012), where it is of the
order 0.1°C per decade.
For MSU, AR4 considered estimates produced from three groups: UAH
(University of Alabama in Huntsville); RSS (Remote Sensing Systems)
and VG2 (now no longer updated). A new product has been creat-
ed by NOAA labelled STAR, using a fundamentally distinct approach
for the critical inter-satellite warm target calibration step (Zou et al.,
2006a). STAR exhibits more warming/less cooling at all levels than
UAH and RSS. For MT and LS, Zou and Wang (2010) concluded that
this does not relate primarily to use of their inter-satellite calibration
technique but rather differences in other processing steps. RSS also
produced a parametric uncertainty ensemble (Box 2.1) employing a
Monte Carlo approach allowing methodological inter-dependencies to
be fully expressed (Mears et al., 2011). For large-scale trends dominant
effects were inter-satellite offset determinations and, for tropospheric
channels, diurnal drift. Uncertainties were concluded to be of the order
0.1°C per decade at the global mean for both tropospheric channels
(where it is of comparable magnitude to the long-term trends) and the
stratospheric channel.
SSU provides the only long-term near-global temperature data above
the lower stratosphere, with the series terminating in 2006. Some
AMSU-A channels have replaced this capability and efforts to under-
stand the effect of changed measurement properties have been under-
taken (Kobayashi et al., 2009). Until recently only one SSU data set
existed (Nash and Edge, 1989), updated by Randel et al. (2009). Liu and
Weng (2009) have produced an intermediate analysis for Channels 25
and 26 (but not Channel 27). Wang et al. (2012g), building on insights
from several of these recent studies, have produced a more complete
analysis. Differences between the independent estimates are much
larger than differences between MSU records or radiosonde records
at lower levels, with substantial inter-decadal time series behaviour
departures, zonal trend structure, and global trend differences of the
order 0.5°C per decade (Seidel et al., 2011; Thompson et al., 2012;
Wang et al., 2012g). Although all SSU data sets agree that the strato-
sphere is cooling, there is therefore low confidence in the details above
the lower stratosphere.
In summary, many new data sets have been produced since AR4 from
radiosondes and satellites with renewed interest in satellite measure-
ments above the lower stratosphere. Several studies have attempted
to quantify the parametric uncertainty (Box 2.1) more rigorously. These
various data sets and analyses have served to highlight the degree of
uncertainty in the data and derived products.
2.4.4.2 Intercomparisons of Various Long-Term Radiosonde
and MSU Products
Since AR4 there have been a large number of intercomparisons between
radiosonde and MSU data sets. Interpretation is complicated, as most
studies considered data set versions that have since been superseded.
Several studies compared UAH and RSS products to local, regional or
global raw/homogenized radiosonde data (Christy and Norris, 2006,
2009; Christy et al., 2007, 2010, 2011; Randall and Herman, 2008;
Mears et al., 2012; Po-Chedley and Fu, 2012). Early studies focussed on
the time of transition from NOAA-11 to NOAA-12 (early 1990s) which
indicated an apparent issue in RSS. Christy et al. (2007) noted that this
coincided with the Mt Pinatubo eruption and that RSS was the only
product, either surface or tropospheric, that exhibited tropical warm-
ing immediately after the eruption when cooling would be expected.
Using reanalysis data Bengtsson and Hodges (2011) also found evi-
dence of a potential jump in RSS in 1993 over the tropical oceans.
Mears et al. (2012) cautioned that an El Niño event quasi-simultane-
ous with Pinatubo complicates interpretation. They also highlighted
several other periods of disagreement between radiosonde records
and MSU records. All MSU records were most uncertain when satellite
orbits are drifting rapidly (Christy and Norris, 2006, 2009). Mears et
al. (2011) found that trend differences between RSS and other data
sets could not be explained in many cases by parametric uncertainties
in RSS alone. It was repeatedly cautioned that there were potential
common biases (of varying magnitude) between the different MSU
Pressure (hPa)
30
25
20
15
10
5
Height (km)
Weight
0.1
1.0
10
100
1000
LT
*G
MT LS SSU 25 SSU 26 SSU 27
Tropopause
Level
Poles
Stratosphere
60
50
40
Troposphere
Mesosphere
Tr
opics
Surface
Figure 2.23 | Vertical weighting functions for those satellite temperature retrievals
discussed in this chapter (modified from Seidel et al. (2011)). The dashed line indicates
the typical maximum altitude achieved in the historical radiosonde record. The three SSU
channels are denoted by the designated names 25, 26 and 27. LS (Lower Stratosphere)
and MT (Mid Troposphere) are two direct MSU measures and LT (Lower Troposphere)
and *G (Global Troposphere) are derived quantities from one or more of these that
attempt to remove the stratospheric component from MT.
196
Chapter 2 Observations: Atmosphere and Surface
2
records or between the different radiosonde records which complicate
intercomparisons (Christy and Norris, 2006, 2009; Mears et al., 2012).
In summary, assessment of the large body of studies comparing var-
ious long-term radiosonde and MSU products since AR4 is hampered
by data set version changes, and inherent data uncertainties. These
factors substantially limit the ability to draw robust and consistent
inferences from such studies about the true long-term trends or the
value of different data products.
2.4.4.3 Additional Evidence from Other Technologies
and Approaches
Global Positioning System (GPS) radio occultation (RO) currently repre-
sents the only self-calibrated SI traceable raw satellite measurements
(Anthes et al., 2008; Anthes, 2011). The fundamental observation is
time delay of the occulted signal’s phase traversing the atmosphere.
The time delay is a function of several atmospheric physical state vari-
ables. Subsequent analysis converts the time delay to temperature and
other parameters, which inevitably adds some degree of uncertainty to
the derived temperature data. Intercomparisons of GPS-RO products
show that differences are largest for derived geophysical parameters
(including temperature), but are still small relative to other observing
technologies (Ho et al., 2012). Comparisons to MSU and radiosondes
(Kuo et al., 2005; Ho et al., 2007, 2009a, 2009b; He et al., 2009; Bar-
inger et al., 2010; Sun et al., 2010; Ladstadter et al., 2011) show sub-
stantive agreement in interannual behaviour, but also some multi-year
drifts that require further examination before this additional data
source can usefully arbitrate between different MSU and radiosonde
trend estimates.
Atmospheric winds are driven by thermal gradients. Radiosonde winds
are far less affected by time-varying biases than their temperatures
(Gruber and Haimberger, 2008; Sherwood et al., 2008; Section 2.7.3).
Allen and Sherwood (2007) initially used radiosonde wind to infer
temperatures within the Tropical West Pacific warm pool region, then
extended this to a global analysis (Allen and Sherwood, 2008) yielding
a distinct tropical upper tropospheric warming trend maximum within
the vertical profile, but with large uncertainty. Winds can only quan-
tify relative changes and require an initialization (location and trend
at that location) (Allen and Sherwood, 2008). The large uncertainty
range was predominantly driven by this initialization choice, a finding
later confirmed by Christy et al. (2010), who in addition questioned
the stability given the sparse geographical sampling, particularly in the
tropics, and possible systematic sampling effects amongst other poten-
tial issues. Initializing closer to the tropics tended to reduce or remove
the appearance of a tropical upper tropospheric warming trend maxi-
mum (Allen and Sherwood, 2008; Christy et al., 2010). There is only low
confidence in trends inferred from ‘thermal winds’ given the relative
immaturity of the analyses and their large uncertainties.
In summary, new technologies and approaches have emerged since
AR4. However, these new technologies and approaches either consti-
tute too short a record or are too immature to inform assessments of
long-term trends at the present time.
2.4.4.4 Synthesis of Free Atmosphere Temperature Estimates
Global-mean lower tropospheric temperatures have increased since the
mid-20th century (Figure 2.24, bottom). Structural uncertainties (Box
2.1) are larger than at the surface but it can still be concluded that glob-
ally the troposphere has warmed (Table 2.8). On top of this long-term
trend are superimposed short-term variations that are highly correlated
with those at the surface but of somewhat greater amplitude. Global
mean lower stratospheric temperatures have decreased since the mid-
20th century punctuated by short-lived warming events associated with
explosive volcanic activity (Figure 2.24a). However, since the mid-1990s
little net change has occurred. Cooling rates are on average greater
from radiosonde data sets than MSU data sets. This very likely relates
to widely recognized cooling biases in radiosondes (Mears et al., 2006)
which all data set producers explicitly caution are likely to remain to
some extent in their final products (Free and Seidel, 2007; Haimberger
et al., 2008; Sherwood et al., 2008; Thorne et al., 2011).
In comparison to the surface (Figure 2.22), tropospheric layers exhibit
smoother geographic trends (Figure 2.25) with warming dominating
cooling north of approximately 45°S and greatest warming in high
northern latitudes. The lower stratosphere cooled almost everywhere
but this cooling exhibits substantial large-scale structure. Cooling is
greatest in the highest southern latitudes and smallest in high northern
latitudes. There are also secondary stratospheric cooling maxima in the
mid-latitude regions of each hemisphere.
Available global and regional trends from radiosondes since 1958
(Figure 2.26) show agreement that the troposphere has warmed and
the stratosphere cooled. While there is little ambiguity in the sign of the
changes, the rate and vertical structure of change are distinctly data
set dependent, particularly in the stratosphere. Differences are greatest
in the tropics and SH extra-tropics where the historical radiosonde data
coverage is poorest. Not shown in the figure for clarity are estimates
of parametric data set uncertainties or trend-fit
uncertainties—both of
which are of the order of at least 0.1°C per decade (Section 2.4.4.1).
Differences in trends between available radiosonde data sets are
greater during the satellite era than for the full radiosonde period of
record in all regions and at most levels (Figure 2.27; cf. Figure 2.26). The
RAOBCORE product exhibits greater vertical trend gradients than other
data sets and it has been posited that this relates to its dependency
on reanalysis fields (Sakamoto and Christy, 2009; Christy et al., 2010).
MSU trend estimates in the troposphere are generally bracketed by the
radiosonde range. In the stratosphere MSU deep layer estimates tend
to show slightly less cooling. Over both 1958–2011 and 1979–2011
there is some evidence in the radiosonde products taken as a whole
that the tropical tropospheric trends increase with height. But the mag-
nitude and the structure is highly data set dependent.
In summary, based on multiple independent analyses of measurements
from radiosondes and satellite sensors it is virtually certain that global-
ly the troposphere has warmed and the stratosphere has cooled since
the mid-20th century. Despite unanimous agreement on the sign of the
trends, substantial disagreement exists among available estimates as
to the rate of temperature changes, particularly outside the NH extra-
tropical troposphere, which has been well sampled by radiosondes.
197
Observations: Atmosphere and Surface Chapter 2
2
Figure 2.24 | Global annual average lower stratospheric (top) and lower tropospheric (bottom) temperature anomalies relative to a 1981–2010 climatology from different data
sets. STAR does not produce a lower tropospheric temperature product. Note that the y-axis resolution differs between the two panels.
1.5
1.0
0.5
0.0
-0.5
(a)
(b)
Temperature anomaly (ºC)
1960 1970 1980 1990 2000 2010
Lower stratosphere
Lower troposphere
Temperature anomaly (ºC)
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
HadAT2
RAOBCORE 1.5
UAH
RICH - obs
RATPACB
STAR
RICH - tau
RSS
Table 2.8 | Trend estimates and 90% confidence intervals (Box 2.2) for radiosonde and MSU data set global average values over the radiosonde (1958–2012) and satellite periods
(1979–2012). LT indicates Lower Troposphere, MT indicates Mid Troposphere and LS indicates Lower Stratosphere (Figure 2.23. Satellite records start only in 1979 and STAR do
not produce an LT product.
Data Set
Trends in °C per decade
1958–2012 1979–2012
LT MT LS LT MT LS
HadAT2 (Thorne et al., 2005) 0.159 ± 0.038 0.095 ± 0.034 –0.339 ± 0.086 0.162 ± 0.047 0.079 ± 0.057 –0.436 ± 0.204
RAOBCORE 1.5 (Haimberger et al., 2012) 0.156 ± 0.031 0.109 ± 0.029 –0.186 ± 0.087 0.139 ± 0.049 0.079 ± 0.054 –0.266 ± 0.227
RICH-obs (Haimberger et al., 2012) 0.162 ± 0.031 0.102 ± 0.029 –0.285 ± 0.087 0.158 ± 0.046 0.081 ± 0.052 –0.331 ± 0.241
RICH-tau (Haimberger et al., 2012) 0.168 ± 0.032 0.111 ± 0.030 –0.280 ± 0.085 0.160 ± 0.046 0.083 ± 0.052 –0.345 ± 0.238
RATPAC (Free et al., 2005) 0.136 ± 0.028 0.076 ± 0.028 –0.338 ± 0.092 0.128 ± 0.044 0.039 ± 0.051 –0.468 ± 0.225
UAH (Christy et al., 2003) 0.138 ± 0.043 0.043 ± 0.042 –0.372 ± 0.201
RSS (Mears and Wentz, 2009a, 2009b) 0.131 ± 0.045 0.079 ± 0.043 –0.268 ± 0.177
STAR (Zou and Wang, 2011) 0.123 ± 0.047 –0.320 ± 0.175
198
Chapter 2 Observations: Atmosphere and Surface
2
Frequently Asked Questions
FAQ 2.1 | How Do We Know the World Has Warmed?
Evidence for a warming world comes from multiple independent climate indicators, from high up in the atmosphere
to the depths of the oceans. They include changes in surface, atmospheric and oceanic temperatures; glaciers; snow
cover; sea ice; sea level and atmospheric water vapour. Scientists from all over the world have independently veri-
fied this evidence many times. That the world has warmed since the 19th century is unequivocal.
Discussion about climate warming often centres on potential residual biases in temperature records from land-
based weather stations. These records are very important, but they only represent one indicator of changes in the
climate system. Broader evidence for a warming world comes from a wide range of independent physically consis-
tent measurements of many other, strongly interlinked, elements of the climate system (FAQ 2.1, Figure 1).
A rise in global average surface temperatures is the best-known indicator of climate change. Although each year and
even decade is not always warmer than the last, global surface temperatures have warmed substantially since 1900.
Warming land temperatures correspond closely with the observed warming trend over the oceans. Warming oce-
anic air temperatures, measured from aboard ships, and temperatures of the sea surface itself also coincide, as
borne out by many independent analyses.
The atmosphere and ocean are both fluid bodies, so warming at the surface should also be seen in the lower atmo-
sphere, and deeper down into the upper oceans, and observations confirm that this is indeed the case. Analyses of
measurements made by weather balloon radiosondes and satellites consistently show warming of the troposphere,
the active weather layer of the atmosphere. More than 90% of the excess energy absorbed by the climate system
since at least the 1970s has been stored in the oceans as can be seen from global records of ocean heat content
going back to the 1950s. (continued on next page)
FAQ 2.1, Figure 1 | Independent analyses of many components of the climate system that would be expected to change in a warming world exhibit trends
consistent with warming (arrow direction denotes the sign of the change), as shown in FAQ 2.1, Figure 2.
Temperature
Over Land
Ocean Heat Content
Water Vapor
Air Temperature
in the lowest few Km (troposphere)
Marine Air Temperature
Snow Cover
Sea Level
Glacier Volume
Sea Surface Temperature
Sea Ice Area
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As the oceans warm, the water itself expands. This expansion is one of the main drivers of the independently
observed rise in sea levels over the past century. Melting of glaciers and ice sheets also contribute, as do changes in
storage and usage of water on land.
A warmer world is also a moister one, because warmer air can hold more water vapour. Global analyses show that
specific humidity, which measures the amount of water vapour in the atmosphere, has increased over both the land
and the oceans.
The frozen parts of the planet—known collectively as the cryosphere—affect, and are affected by, local changes
in temperature. The amount of ice contained in glaciers globally has been declining every year for more than 20
years, and the lost mass contributes, in part, to the observed rise in sea level. Snow cover is sensitive to changes in
temperature, particularly during the spring, when snow starts to melt. Spring snow cover has shrunk across the NH
since the 1950s. Substantial losses in Arctic sea ice have been observed since satellite records began, particularly at
the time of the mimimum extent, which occurs in September at the end of the annual melt season. By contrast, the
increase in Antarctic sea ice has been smaller.
Individually, any single analysis might be unconvincing, but analysis of these different indicators and independent
data sets has led many independent research groups to all reach the same conclusion. From the deep oceans to the
top of the troposphere, the evidence of warmer air and oceans, of melting ice and rising seas all points unequivo-
cally to one thing: the world has warmed since the late 19th century (FAQ 2.1, Figure 2).
FAQ 2.1, Figure 2 | Multiple independent indicators of a changing global climate. Each line represents an independently derived estimate of change in the climate
element. In each panel all data sets have been normalized to a common period of record. A full detailing of which source data sets go into which panel is given in the
Supplementary Material 2.SM.5.
FAQ 2.1 (continued)
Land surface air temperature: 4 datasets
Mass balance (10
15
GT)
1.0
0.5
0.0
-0.5
-1.0
0.4
0.2
0.0
-0.2
-0.4
-0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
12
10
8
6
4
10
5
0
-5
-10
-15
0.4
0.2
0.0
-0.2
20
10
0
-10
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
100
50
0
-50
-100
-150
-200
Tropospheric temperature:
7 datasets
Ocean heat content(0-700m):
5 datasets
Specific humidity:
4 datasets
Glacier mass balance:
3 datasets
Sea-surface temperature: 5 datasets
Marine air temperature: 2 datasets
Sea level: 6 datasets
1850 1900 1950 2000
1940 1960 1980 2000
Summer arctic sea-ice extent: 6 datasets
Sea level
anomaly (mm)
Temperature
anomaly (ºC)
Temperature
anomaly (ºC)
Temperature
anomaly (ºC)
Temperature
anomaly (ºC)
Ocean heat content
anomaly (10
22
J)
Specific humidity
anomaly (g/kg)
Extent anomaly (10
6
km
2
)
6
4
2
0
-2
-4
-6
Extent (10
6
km
2
)
Year Year
Northern hemisphere (March-
April) snow cover: 2 datasets
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Chapter 2 Observations: Atmosphere and Surface
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UAH LS
UAH LT RSS LT
RSS LS
Trend (°C per decade)
-1.25-1.0-0.8-0.6-0.5-0.4-0.3-0.2-0.10 0.20.3 0.40.60.50.8 1.01.250.1
Figure 2.25 | Trends in MSU upper air temperature over 1979–2012 from UAH (left-hand panels) and RSS (right-hand panels) and for LS (top row) and LT (bottom row). Data
are temporally complete within the sampled domains for each data set. White areas indicate incomplete or missing data. Black plus signs (+) indicate grid boxes where trends are
significant (i.e., a trend of zero lies outside the 90% confidence interval).
Radiosonde Datasets
HadAT2
RICH-obs
RAOBCORE1.5
RICH-tau
RATPACB
Global
SH Extra-Tropics NH Extra-TropicsTropics
Trend (ºC per decade) Trend (ºC per decade)
Trend (ºC per decade)
Trend (ºC per decade)
-1.0 -0.8 -0.6 -0.4 -0.2 0.00.2 0.4-1.0-0.8-0.6-0.4-0.20.0 0.20.4 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4
-1.0 -0.8 -0.6 -0.4 -0.2 0.00.2 0.4
LS
MT
LT
LS
MT
LT
LS
MT
LT
LS
MT
LT
30
50
70
100
150
200
250
300
400
500
700
850
30
50
70
100
150
200
250
300
400
500
700
850
30
50
70
100
150
200
250
300
400
500
700
850
Pressure (hPa)
Pressure (hPa)
Pressure (hPa)
Pressure (hPa)
30
50
70
100
150
200
250
300
400
500
700
850
Figure 2.26 | Trends in upper air temperature for all available radiosonde data products that contain records for 1958–2012 for the globe (top) and tropics (20°N to 20°S) and
extra-tropics (bottom). The bottom panel trace in each case is for trends on distinct pressure levels. Note that the pressure axis is not linear. The top panel points show MSU layer
equivalent measure trends. MSU layer equivalents have been processed using the method of Thorne et al. (2005). No attempts have been made to sub-sample to a common data
mask.
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Observations: Atmosphere and Surface Chapter 2
2
Radiosonde Datasets
HadAT2
RICH-obs
RAOBCORE1.5
RICH-tau
RATPACB
Satellite Datasets
RSS
UAH
STAR
Global
SH Extra-Tropics NH Extra-TropicsTropics
Trend (ºC per decade) Trend (ºC per decade)
Trend (ºC per decade)
Trend (ºC per decade)
-1.0 -0.8 -0.6 -0.4 -0.2 0.00.2 0.4-1.0-0.8-0.6-0.4-0.20.0 0.2 0.4 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.20.4
-1.0 -0.8 -0.6 -0.4 -0.2 0.00.2 0.4
LS
MT
LT
LS
MT
LT
LS
MT
LT
LS
MT
LT
30
50
70
100
150
200
250
300
400
500
700
850
30
50
70
100
150
200
250
300
400
500
700
850
30
50
70
100
150
200
250
300
400
500
700
850
Pressure (hPa)
Pressure (hPa)
Pressure (hPa)
Pressure (hPa)
30
50
70
100
150
200
250
300
400
500
700
850
Figure 2.27 | As Figure 2.26 except for the satellite era 1979–2012 period and including MSU products (RSS, STAR and UAH).
Hence there is only medium confidence in the rate of change and its
vertical structure in the NH extratropical troposphere and low confi-
dence elsewhere.
2.5 Changes in Hydrological Cycle
This section covers the main aspects of the hydrological cycle, including
large-scale average precipitation, stream flow and runoff, soil mois-
ture, atmospheric water vapour, and clouds. Meteorological drought is
assessed in Section 2.6. Ocean precipitation changes are assessed in
Section 3.4.3 and changes in the area covered by snow in Section 4.5.
2.5.1 Large-Scale Changes in Precipitation
2.5.1.1 Global Land Areas
AR4 concluded that precipitation has generally increased over land
north of 30°N over the period 1900–2005 but downward trends dom-
inate the tropics since the 1970s. AR4 included analysis of both the
GHCN (Vose et al., 1992) and CRU (Mitchell and Jones, 2005) gauge-
based precipitation data sets for the globally averaged annual pre-
cipitation over land. For both data sets the overall linear trend from
1900 to 2005 (1901–2002 for CRU) was positive but not statistically
significant (Table 3.4 from AR4). Other periods covered in AR4 (1951–
2005 and 1979–2005) showed a mix of negative and positive trends
depending on the data set.
Since AR4, existing data sets have been updated and a new data set
developed. Figure 2.28 shows the century-scale variations and trends
on globally and zonally averaged annual precipitation using five data
sets: GHCN V2 (updated through 2011; Vose et al., 1992), Global Pre-
cipitation Climatology Project V2.2 (GPCP) combined raingauge–satel-
lite product (Adler et al., 2003), CRU TS 3.10.01 (updated from Mitchell
and Jones, 2005), Global Precipitation Climatology Centre V6 (GPCC)
data set (Becker et al., 2013) and a reconstructed data set by Smith et
al. (2012). Each data product incorporates a different number of station
series for each region. The Smith et al. product is a statistical recon-
struction using Empirical Orthogonal Functions, similar to the NCDC
MLOST global temperature product (Section 2.4.3) that does provide
coverage for most of the global surface area although only land is
included here. The data sets based on in situ observations only start in
1901, but the Smith et al. data set ends in 2008, while the other three
data sets contain data until at least 2010.
For the longest common period of record (1901–2008) all datasets
exhibit increases in globally averaged precipitation, with three of the
four showing statistically significant changes (Table 2.9). However,
there is a factor of almost three spread in the magnitude of the change
which serves to create low confidence. Global trends for the shorter
period (1951–2008) show a mix of statistically non-significant positive
and negative trends amongst the four data sets with the infilled Smith
et al. (2012) analysis showing increases and the remainder decreases.
These differences among data sets indicate that long-term increases
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Chapter 2 Observations: Atmosphere and Surface
2
in global precipitation discussed in AR4 are uncertain, owing in part
to issues in data coverage in the early part of the 20th century (Wan
et al., 2013).
In summary, confidence in precipitation change averaged over global
land areas is low for the years prior to 1950 and medium afterwards
because of insufficient data, particularly in the earlier part of the record.
Available globally incomplete records show mixed and non-significant
long-term trends in reported global mean changes. Further, when vir-
tually all the land area is filled in using a reconstruction method, the
resulting time series shows less change in land-based precipitation
since 1900.
-90
-60
-30
0
30
60
-60
-30
0
30
-90
-60
-30
0
30
60
90
120
Precipitation anomaly (mm yr
-1
)
-90
-60
-30
0
30
60
90
120
1900 1910 1920 1930 1940
1950 1960
1970 1980 1990 2000 2010
-60
-30
0
30
60
CRU
GHCN
GPCC
Smith et al.
GPCP
Global
60ºN-90ºN
30ºN-60ºN
30ºS-30ºN
60ºS-30ºS
CRU
Figure 2.28 | Annual precipitation anomalies averaged over land areas for four
latitudinal bands and the globe from five global precipitation data sets relative to a
1981–2000 climatology.
Table 2.9 | Trend estimates and 90% confidence intervals (Box 2.2) for annual precipitation for each time series in Figure 2.28 over two common periods of record.
Data Set Area
Trends in mm yr
–1
per decade
1901–2008 1951–2008
CRU TS 3.10.01 (updated from Mitchell and Jones, 2005) Global 2.77 ± 1.46 –2.12 ± 3.52
GHCN V2 (updated through 2011; Vose et al., 1992) Global 2.08 ± 1.66 –2.77 ± 3.92
GPCC V6 (Becker et al., 2013) Global 1.48 ± 1.65 –1.54 ± 4.50
Smith et al. (2012) Global 1.01 ± 0.64 0.68 ± 2.07
2.5.1.2 Spatial Variability of Observed Trends
The latitude band plots in Figure 2.28 suggest that precipitation over
tropical land areas (30°S to 30°N) has increased over the last decade
reversing the drying trend that occurred from the mid-1970s to mid-
1990s. As a result the period 1951–2008 shows no significant overall
trend in tropical land precipitation in any of the datasets (Table 2.10).
Longer term trends (1901–2008) in the tropics, shown in Table 2.10,
are also non-significant for each of the four data sets. The mid-latitudes
of the NH (30°N to 60°N) show an overall increase in precipitation
from 1901 to 2008 with statistically significant trends for each data
set. For the shorter period (1951–2008) the trends are also positive
but non-significant for three of the four data sets. For the high lat-
itudes of the NH (60°N to 90°N) where data completeness permits
trend calculations solely for the 1951–2008 period, all datasets show
increases but there is a wide range of magnitudes and the infilled
Smith et al. series shows small and insignificant trends (Table 2.10).
Fewer data from high latitude stations make these trends less certain
and yield low confidence in resulting zonal band average estimates.
In the mid-latitudes of the SH (60°S to 30°S) there is limited evidence
of long-term increases with three data sets showing significant trends
for the 1901–2008 period but GHCN having negative trends that are
not significant. For the 1951–2008 period changes in SH mid-latitude
precipitation are less certain, with one data set showing a significant
trend towards drying, two showing non-significant drying trends and
the final dataset suggesting increases in precipitation. All data sets
show an abrupt decline in SH mid-latitude precipitation in the early
2000s (Figure 2.28) consistent with enhanced drying that has very
recently recovered. These results for latitudinal changes are broadly
consistent with the global satellite observations for the 1979–2008
period (Allan et al., 2010) and land-based gauge measurements for the
1950–1999 period (Zhang et al., 2007a).
In AR4, maps of observed trends of annual precipitation for 1901–2005
were calculated using GHCN interpolated to a 5° × 5° latitude/longi-
tude grid. Trends (in percent per decade) were calculated for each grid
box and showed statistically significant changes, particularly increas-
es in eastern and northwestern North America, parts of Europe and
Russia, southern South America and Australia, declines in the Sahel
region of Africa, and a few scattered declines elsewhere.
Figure 2.29 shows the spatial variability of long-term trends (1901–
2010) and more recent trends (1951–2010) over land in annual precip-
itation using the CRU, GHCN and GPCC data sets. The trends are com-
puted from land-only grid box time series using each native data set
grid resolution. The patterns of these absolute trends (in mm yr
–1
per
decade) are broadly similar to the trends (in percent per decade) relative
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Observations: Atmosphere and Surface Chapter 2
2
Table 2.10 | Trend estimates and 90% confidence intervals (Box 2.2) for annual precipitation for each time series in Figure 2.28 over two periods. Dashes indicate not enough
data available for trend calculation. For the latitudinal band 90°S to 60°S not enough data exist for each product in either period.
Data Set
Area
Trends in mm yr
–1
per decade
1901–2008 1951–2008
CRU TS 3.10.01 (updated from Mitchell and
Jones, 2005)
60°N–90°N 5.82 ± 2.72
30°N–60°N 3.82 ± 1.14 1.13 ± 2.01
30°S–30°N 0.89 ± 2.89 –4.22 ± 8.27
60°S–30°S 3.88 ± 2.28 –3.73 ± 5.94
GHCN V2 (updated through 2011; Vose et al., 1992)
60°N–90°N 4.52 ± 2.64
30°N–60°N 3.23 ± 1.10 1.39 ± 1.98
30°S–30°N 1.01 ± 3.00 –5.15 ± 7.28
60°S–30°S –0.57 ± 2.27 –8.01 ± 5.63
GPCC V6 (Becker et al., 2013)
60°N–90°N 2.69 ± 2.54
30°N–60°N 3.14 ± 1.05 1.50 ± 1.93
30°S–30°N –0.48 ± 3.35 –4.16 ± 9.65
60°S–30°S 2.40 ± 2.01 –0.51 ± 5.45
Smith et al. (2012)
60°N–90°N 0.63 ± 1.27
30°N–60°N 1.44 ± 0.50 0.97 ± 0.88
30°S–30°N 0.43 ± 1.48 0.67 ± 4.75
60°S–30°S 2.94 ± 1.40 0.78 ± 3.31
Trend (mm yr
-1
per decade)
CRU 1901-2010
GHCN 1901-2010 GHCN 1951-2010
GPCC 1901-2010 GPCC 1951-2010
CRU 1951-2010
Figure 2.29 | Trends in annual precipitation over land from the CRU, GHCN and GPCC data sets for 1901–2010 (left-hand panels) and 1951–2010 (right-hand panels). Trends
have been calculated only for those grid boxes with greater than 70% complete records and more than 20% data availability in first and last decile of the period. White areas
indicate incomplete or missing data. Black plus signs (+) indicate grid boxes where trends are significant (i.e., a trend of zero lies outside the 90% confidence interval).
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Chapter 2 Observations: Atmosphere and Surface
2
to local climatology (Supplementary Material 2.SM.6.1). Increases for
the period 1901–2010 are seen in the mid- and higher-latitudes of
both the NH and SH consistent with the reported changes for latitu-
dinal bands. At the grid box scale, statistically significant trends occur
in most of the same areas, in each data set but are far more limited
than for temperature over a similar length period (cf. Figure 2.21). The
GPCC map shows the most areas with significant trends. Comparing
the maps in Figure 2.29, most areas for which trends can be calculated
for both periods show similar trends between the 1901–2010 period
and the 1951–2010 period with few exceptions (e.g., South Eastern
Australia, ). Trends over shorter periods can differ from those implied
for the longest periods. For example, since the late 1980s trends in the
Sahel region have been significantly positive (not shown).
In summary, when averaged over the land areas of the mid-latitudes
of the NH, all datasets show a likely overall increase in precipitation
(medium confidence since 1901, but high confidence after 1951).
For all other zones one or more of data sparsity, quality, or a lack
of quantitative agreement amongst available estimates yields low
confidence in characterisation of such long-term trends in zonally
averaged precipitation. Nevertheless, changes in some more regional
or shorter-term recent changes can be quantified. It is likely there was
an abrupt decline in SH mid-latitude precipitation in the early 2000s
consistent with enhanced drying that has very recently recovered. Pre-
cipitation in the tropical land areas has increased (medium confidence)
over the last decade, reversing the drying trend that occurred from the
mid-1970s to mid-1990s reported in AR4.
2.5.1.3 Changes in Snowfall
AR4 draws no conclusion on global changes in snowfall. Changes
in snowfall are discussed on a region-by-region basis, but focussed
mainly on North America and Eurasia. Statistically significant increases
were found in most of Canada, parts of northern Europe and Russia. A
number of areas showed a decline in the number of snowfall events,
especially those where climatological averaged temperatures were
close to 0°C and where warming led to earlier onset of spring. Also,
an increase in lake-effect snowfall was found for areas near the North
American Great Lakes.
Since AR4, most published literature has considered again changes in
snowfall in North America. These studies have confirmed that more
winter-time precipitation is falling as rain rather than snow in the
western USA (Knowles et al., 2006), the Pacific Northwest and Central
USA (Feng and Hu, 2007). Kunkel et al. (2009) analyzed trends using
a specially quality-controlled data set of snowfall observations over
the contiguous USA and found that snowfall has been declining in the
western USA, northeastern USA and southern margins of the season-
al snow region, but increasing in the western Great Plains and Great
Lakes regions. Snowfall in Canada has increased mainly in the north
while a significant decrease was observed in the southwestern part of
the country for 1950–2009 (Mekis and Vincent, 2011).
Other regions that have been analyzed include Japan (Takeuchi et al.,
2008), where warmer winters in the heavy snowfall areas on Honshu
are associated with decreases in snowfall and precipitation in general.
Shekar et al. (2010) found declines in total seasonal snowfall along
with increases in maximum and minimum temperatures in the west-
ern Himalaya. Serquet et al. (2011) analyzed snowfall and rainfall days
since 1961 and found the proportion of snowfall days to rainfall days
in Switzerland was declining in association with increasing temper-
atures. Scherrer and Appenzeller (2006) found a trend in a pattern
of variability of snowfall in the Swiss Alps that indicated decreasing
snow at low altitudes relative to high altitudes, but with large decadal
variability in key snow indicators (Scherrer et al., 2013). Van Ommen
and Morgan (2010) draw a link between increased snowfall in coastal
East Antarctica and increased southwest Western Australia drought.
However, Monaghan and Bromwich (2008) found an increase in snow
accumulation over all Antarctica from the late 1950s to 1990, then a
decline to 2004. Thus snowfall changes in Antarctica remain uncertain.
In summary, in most regions analyzed, it is likely that decreasing num-
bers of snowfall events are occurring where increased winter temper-
atures have been observed (North America, Europe, Southern and East
Asia). Confidence is low for the changes in snowfall over Antarctica.
2.5.2 Streamflow and Runoff
AR4 concluded that runoff and river discharge generally increased at
high latitudes, with some exceptions. No consistent long-term trend in
discharge was reported for the world’s major rivers on a global scale.
River discharge is unique among water cycle components in that it
both spatially and temporally integrates surplus waters upstream
within a catchment (Shiklomanov et al., 2010) which makes it well
suited for in situ monitoring (Arndt et al., 2010). The most recent com-
prehensive analyses (Milliman et al., 2008; Dai et al., 2009) do not sup-
port earlier work (Labat et al., 2004) that reported an increasing trend
in global river discharge associated with global warming during the
20th century. It must be noted that many if not most large rivers, espe-
cially those for which a long-term streamflow record exists, have been
impacted by human influences such as dam construction or land use,
so results must be interpreted with caution. Dai et al. (2009) assem-
bled a data set of 925 most downstream stations on the largest rivers
monitoring 80% of the global ocean draining land areas and capturing
73% of the continental runoff. They found that discharges in about
one-third of the 200 largest rivers (including the Congo, Mississippi,
Yenisey, Paraná, Ganges, Colombia, Uruguay and Niger) show sta-
tistically significant trends during 1948–2004, with the rivers having
downward trends (45) outnumbering those with upward trends (19).
Decreases in streamflow were found over many low and mid-latitude
river basins such as the Yellow River in northern China since 1960s
(Piao et al., 2010) where precipitation has decreased. Increases in
streamflow during the latter half of the 20th century also have been
reported over regions with increased precipitation, such as parts of
the USA (Groisman et al., 2004), and in the Yangtze River in southern
China (Piao et al., 2010). In the Amazon basin an increase of discharge
extremes is observed over recent decades (Espinoza Villar et al., 2009).
For France, Giuntoli et al. (2013) found that the sign of the temporal
trends in natural streamflows varies with period studied. In that case
study, significant correlations between median to low flows and the
Atlantic Multidecadal Oscillation (AMO; Section 2.7.8) result in long
quasi-periodic oscillations.
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Observations: Atmosphere and Surface Chapter 2
2
At high latitudes, increasing winter base flow and mean annual
stream flow resulting from possible permafrost thawing were report-
ed in northwest Canada (St. Jacques and Sauchyn, 2009). Rising min-
imum daily flows also have been observed in northern Eurasian rivers
(Smith et al., 2007). For ocean basins other than the Arctic, and for
the global ocean as a whole, the data for continental discharge show
small or downward trends, which are statistically significant for the
Pacific (–9.4 km
3
yr
–1
). Precipitation is a major driver for the discharge
trends and for the large interannual-to-decadal variations (Dai et al.,
2009). However, for the Arctic drainage areas, Adam and Lettenmaier
(2008) found that upward trends in streamflow are not accompanied
by increasing precipitation, especially over Siberia, based on available
observations. Zhang et al. (2012a) argued that precipitation measure-
ments are sparse and exhibit large cold-season biases in the Arctic
drainage areas and hence there would be large uncertianties using
these data to investigate their influence on streamflow.
Recently, Stahl et al. (2010) and Stahl and Tallaksen (2012) investigat-
ed streamflow trends based on a data set of near-natural streamflow
records from more than 400 small catchments in 15 countries across
Europe for 1962–2004. A regional coherent pattern of annual stream-
flow trends was revealed with negative trends in southern and eastern
regions, and generally positive trends elsewhere. Subtle regional dif-
ferences in the subannual changes in various streamflow metrics also
can be captured in regional studies such as by Monk et al. (2011) for
Canadian rivers.
In summary, the most recent comprehensive analyses lead to the con-
clusion that confidence is low for an increasing trend in global river
discharge during the 20th century.
2.5.3 Evapotranspiration Including Pan Evaporation
AR4 concluded that decreasing trends were found in records of pan
evaporation over recent decades over the USA, India, Australia, New
Zealand, China and Thailand and speculated on the causes including
decreased surface solar radiation, sunshine duration, increased spe-
cific humidity and increased clouds. However, AR4 also reported that
direct measurements of evapotranspiration over global land areas
are scarce, and concluded that reanalysis evaporation fields are not
reliable because they are not well constrained by precipitation and
radiation.
Since AR4 gridded data sets have been developed that estimate actual
evapotranspiration from either atmospheric forcing and thermal
remote sensing, sometimes in combination with direct measurements
(e.g., from FLUXNET, a global network of flux towers), or interpolation
of FLUXNET data using regression techniques, providing an unprec-
edented look at global evapotranspiration (Mueller et al., 2011). On
a global scale, evapotranspiration over land increased from the early
1980s up to the late 1990s (Wild et al., 2008; Jung et al., 2010; Wang et
al., 2010) and Wang et al. (2010) found that global evapotranspiration
increased at a rate of 0.6 W m
–2
per decade for the period 1982–2002.
After 1998, a lack of moisture availability in SH land areas, particularly
decreasing soil moisture, has acted as a constraint to further increase
of global evapotranspiration (Jung et al., 2010).
Zhang et al. (2007b) found decreasing pan evaporation at stations
across the Tibetan Plateau, even with increasing air temperature. Sim-
ilarly, decreases in pan evaporation were also found for northeast-
ern India (Jhajharia et al., 2009) and the Canadian Prairies (Burn and
Hesch, 2007). A continuous decrease in reference and pan evapora-
tion for the period 1960–2000 was reported by Xu et al. (2006a) for a
humid region in China, consistent with reported continuous increase
in aerosol levels over China (Qian et al., 2006; Section 2.2.4). Rod-
erick et al. (2007) examined the relationship between pan evapora-
tion changes and many of the possible causes listed above using a
physical model and conclude that many of the decreases (USA, China,
Tibetan Plateau, Australia) cited previously are related to declining
wind speeds and to a lesser extent decreasing solar radiation. Fu et al.
(2009) provided an overview of pan evaporation trends and conclud-
ed the major possible causes, changes in wind speed, humidity and
solar radiation, have been occurring, but that the importance of each
is regionally dependent.
The recent increase in incoming shortwave radiation in regions with
decreasing aerosol concentrations (Section 2.2.3) can explain positive
evapotranspiration trends only in the humid part of Europe. In semiarid
and arid regions, trends in evapotranspiration largely follow trends in
precipitation (Jung et al., 2010). Trends in surface winds (Section 2.7.2)
and CO
2
(Section 2.2.1.1.1) also alter the partitioning of available
energy into evapotranspiration and sensible heat. While surface wind
trends may explain pan evaporation trends over Australia (Rayner,
2007; Roderick et al., 2007), their impact on actual evapotranspiration
is limited due to the compensating effect of boundary-layer feedbacks
(van Heerwaarden et al., 2010). In vegetated regions, where a large
part of evapotranspiration comes from transpiration through plants’
stomata, rising CO
2
concentrations can lead to reduced stomatal open-
ing and evapotranspiration (Idso and Brazel, 1984; Leakey et al., 2006).
Additional regional effects that impact evapotranspiration trends are
lengthening of the growing season and land use change.
In summary, there is medium confidence that pan evaporation contin-
ued to decline in most regions studied since AR4 related to changes
in wind speed, solar radiation and humidity. On a global scale, evapo-
transpiration over land increased (medium confidence) from the early
1980s up to the late 1990s. After 1998, a lack of moisture availability
in SH land areas, particularly decreasing soil moisture, has acted as a
constraint to further increase of global evapotranspiration.
2.5.4 Surface Humidity
AR4 reported widespread increases in surface air moisture content
since 1976, along with near-constant relative humidity over large
scales though with some significant changes specific to region, time
of day or season.
In good agreement with previous analysis from Dai (2006), Willett et al.
(2008) show widespread increasing specific humidity across the globe
from the homogenized gridded monthly mean anomaly product Had-
CRUH (1973–2003). Both Dai and HadCRUH products that are blended
land and ocean data products end in 2003 but HadISDH (1973–2012)
(Willett et al., 2013) and the NOCS product (Berry and Kent, 2009) are
available over the land and ocean respectively through 2012. There
206
Chapter 2 Observations: Atmosphere and Surface
2
are some small isolated but coherent areas of drying over some of the
more arid land regions (Figure 2.30a). Moistening is largest in the trop-
ics and in the extratropics during summer over both land and ocean.
Large uncertainty remains over the SH where data are sparse. Global
specific humidity is sensitive to large-scale phenomena such as ENSO
(Figure 2.30b; Box 2.5). It is strongly correlated with land surface tem-
perature averages over the 23 Giorgi and Francisco (2000) regions for
the period 1973–1999 and exhibits increases mostly at or above the
1973–2012
Specific humidity anomaly (g kg
-1
)
0.4
0.2
0.0
-0.2
-0.4
1980 1990 2000 2010
-0.5 -0.25-0.2-0.15 -0.1 -0.050.0.050.1 0.15 0.20.250.5
Trend (g kg
-1
per decade)
HadISDH
ERA-InterimHadCRUH
Dai
(a)
(b)
Figure 2.30 | (a) Trends in surface specific humidity from HadISDH and NOCS over
1973–2012. Trends have been calculated only for those grid boxes with greater than
70% complete records and more than 20% data availability in first and last decile
of the period. White areas indicate incomplete or missing data. Black plus signs (+)
indicate grid boxes where trends are significant (i.e., a trend of zero lies outside the
90% confidence interval). (b) Global annual average anomalies in land surface spe-
cific humidity from Dai (2006; red), HadCRUH (Willett et al., 2013; orange), HadISDH
(Willett et al., 2013; black), and ERA-Interim (Simmons et al., 2010; blue). Anomalies
are relative to the 1979–2003 climatology.
Table 2.11 | Trend estimates and 90% confidence intervals (Box 2.2) for surface humidity over two periods.
Data Set
Trends in % per decade
1976–2003 1973–2012
Land
HadISDH (Willett et al., 2008) 0.127 ± 0.037 0.091 ± 0.023
HadCRUH_land (Willett et al., 2008) 0.128 ± 0.043
Dai_land (Dai, 2006) 0.099 ± 0.046
Ocean
NOCS (Berry and Kent, 2009) 0.114 ± 0.064 0.090 ± 0.033
HadCRUH_marine (Willett et al., 2008) 0.065 ± 0.049
Dai_marine (Dai, 2006) 0.058 ± 0.044
increase expected from the Clausius–Clapeyron relation (about 7%
°C
–1
; Annex III: Glossary) with high confidence (Willett et al., 2010).
Land surface humidity trends are similar in ERA-Interim to observed
estimates of homogeneity-adjusted data sets (Simmons et al., 2010;
Figure 2.30b).
Since 2000 surface specific humidity over land has remained largely
unchanged (Figure 2.30) whereas land areas have on average warmed
slightly (Figure 2.14), implying a reduction in land region relative
humidity. This may be linked to the greater warming of the land surface
relative to the ocean surface (Joshi et al., 2008). The marine specific
humidity (Berry and Kent, 2009), like that over land, shows widespread
increases that correlate strongly with SST. However, there is a marked
decline in marine relative humidity around 1982. This is reported in Wil-
lett et al. (2008) where its origin is concluded to be a non-climatic data
issue owing to a change in reporting practice for dewpoint temperature.
In summary, it is very likely that global near surface air specific humidi-
ty has increased since the 1970s. However, during recent years the near
surface moistening over land has abated (medium confidence). As a
result, fairly widespread decreases in relative humidity near the surface
are observed over the land in recent years.
2.5.5 Tropospheric Humidity
As reported in AR4, observations from radiosonde and GPS meas-
urements over land, and satellite measurements over ocean indicate
increases in tropospheric water vapour at near-global spatial scales
which are consistent with the observed increase in atmospheric tem-
perature over the last several decades. Tropospheric water vapour
plays an important role in regulating the energy balance of the surface
and TOA, provides a key feedback mechanism and is essential to the
formation of clouds and precipitation.
2.5.5.1 Radiosonde
Radiosonde humidity data for the troposphere were used sparingly
in AR4, noting a renewed appreciation for biases with the operation-
al radiosonde data that had been highlighted by several major field
campaigns and intercomparisons. Since AR4 there have been three
distinct efforts to homogenize the tropospheric humidity records from
operational radiosonde measurements (Durre et al., 2009; McCarthy
et al., 2009; Dai et al., 2011) (Supplementary Material 2.SM.6.1, Table
2.SM.9). Over the common period of record from 1973 onwards, the
resulting estimates are in substantive agreement regarding specific
207
Observations: Atmosphere and Surface Chapter 2
2
humidity trends at the largest geographical scales. On average, the
impact of the correction procedures is to remove an artificial temporal
trend towards drying in the raw data and indicate a positive trend in
free tropospheric specific humidity over the period of record. In each
analysis, the rate of increase in the free troposphere is concluded to
be largely consistent with that expected from the Clausius– Clapeyron
relation (about 7% per degree Celsius). There is no evidence for a
significant change in free tropospheric relative humidity, although
a decrease in relative humidity at lower levels is observed (Section
2.5.5). Indeed, McCarthy et al. (2009) show close agreement between
their radiosonde product at the lowest levels and HadCRUH (Willett et
al., 2008).
2.5.5.2 Global Positioning System
Since the early 1990s, estimates of column integrated water vapour
have been obtained from ground-based Global Positioning System
(GPS) receivers. An international network started with about 100
stations in 1997 and has currently been expanded to more than 500
(primarily land-based) stations. Several studies have compiled GPS
water vapour data sets for climate studies (Jin et al., 2007; Wang et
al., 2007; Wang and Zhang, 2008, 2009). Using such data, Mears et al.
(2010) demonstrated general agreement of the interannual anomalies
between ocean-based satellite and land-based GPS column integrat-
ed water vapour data. The interannual water vapour anomalies are
closely tied to the atmospheric temperature changes in a manner con-
sistent with that expected from the Clausius–Clapeyron relation. Jin
et al. (2007) found an average column integrated water vapour trend
of about 2 kg m
–2
per decade during 1994–2006 for 150 (primarily
land-based) stations over the globe, with positive trends at most NH
stations and negative trends in the SH. However, given the short length
(about 10 years) of the GPS records, the estimated trends are very sen-
sitive to the start and end years and the analyzed time period (Box 2.2).
2.5.5.3 Satellite
AR4 reported positive decadal trends in lower and upper tropospheric
water vapour based on satellite observations for the period 1988–2004.
Since AR4, there has been continued evidence for increases in lower
tropospheric water vapour from microwave satellite measurements of
column integrated water vapour over oceans (Santer et al., 2007; Wentz
et al., 2007) and globally from satellite measurements of spectrally
resolved reflected solar radiation (Mieruch et al., 2008). The interannual
variability and longer-term trends in column-integrated water vapour
over oceans are closely tied to changes in SST at the global scale and
interannual anomalies show remarkable agreement with low-level spe-
cific humidity anomalies from HadCRUH (O’Gorman et al., 2012). The
rate of moistening at large spatial scales over oceans is close to that
expected from the Clausius–Clapeyron relation (about 7% per degree
Celsius) with invariant relative humidity (Figure 2.31). Satellite meas-
urements also indicate that the globally averaged upper tropospheric
relative humidity has changed little over the period 1979–2010 while
the troposphere has warmed, implying an increase in the mean water
vapour mass in the upper troposphere (Shi and Bates, 2011).
Interannual variations in temperature and upper tropospheric water
vapour from IR satellite data are consistent with a constant RH
behavior at large spatial scales (Dessler et al., 2008; Gettelman and
Fu, 2008; Chung et al., 2010). On decadal time-scales, increased GHG
concentrations reduce clear-sky outgoing long-wave radiation (Allan,
2009; Chung and Soden, 2010), thereby influencing inferred relation-
ships between moisture and temperature. Using Meteosat IR radianc-
es, Brogniez et al. (2009) demonstrated that interannual variations in
free tropospheric humidity over subtropical dry regions are heavily
influenced by meridional mixing between the deep tropics and the
extra tropics. Regionally, upper tropospheric humidity changes in the
tropics were shown to relate strongly to the movement of the ITCZ
based upon microwave satellite data (Xavier et al., 2010). Shi and
Bates (2011) found an increase in upper tropospheric humidity over
the equatorial tropics from 1979 to 2008. However there was no signif-
icant trend found in tropical-mean or global-mean averages, indicating
that on these time and space scales the upper troposphere has seen
little change in relative humidity over the past 30 years. While micro-
wave satellite measurements have become increasingly relied upon for
studies of upper tropospheric humidity, the absence of a homogenized
data set across multiple satellite platforms presents some difficulty in
documenting coherent trends from these records (John et al., 2011).
1998 - 2012
Trend (g kg
-1
per decade)
Water vapour (kg m
-2
per decade)
1990 1995 2000 2005 2010
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
-10 -5 -2.5 -1 -0.5 -0.250. 0.25 0.512.5 5 10
(a)
(b)
Figure 2.31 | (a) Trends in column integrated water vapour over ocean surfaces from
Special Sensor Microwave Imager (Wentz et al., 2007) for the period 1988–2010.
Trends have been calculated only for those grid boxes with greater than 70% complete
records and more than 20% data availability in first and last decile of the period. Black
plus signs (+) indicate grid boxes where trends are significant (i.e., a trend of zero lies
outside the 90% confidence interval). (b) Global annual average anomalies in column
integrated water vapour averaged over ocean surfaces. Anomalies are relative to the
1988–2007 average.
208
Chapter 2 Observations: Atmosphere and Surface
2
2.5.5.4 Reanalyses
Using NCEP reanalyses for the period 1973–2007, Paltridge et al.
(2009) found negative trends in specific humidity above 850 hPa over
both the tropics and southern mid-latitudes, and above 600 hPa in the
NH mid-latitudes. However, as noted in AR4, reanalysis products suffer
from time dependent biases and have been shown to simulate unreal-
istic trends and variability over the ocean (Mears et al., 2007; John et
al., 2009) (Box 2.3). Some reanalysis products do reproduce observed
variability in low level humidity over land (Simmons et al., 2010), more
complete assesments of multiple reanalysis products yield substan-
tially different and even opposing trends in free tropospheric specific
humidity (Chen et al., 2008; Dessler and Davis, 2010). Consequently,
reanalysis products are still considered to be unsuitable for the analysis
of tropospheric water vapour trends (Sherwood et al., 2010).
In summary, radiosonde, GPS and satellite observations of tropospher-
ic water vapour indicate very likely increases at near global scales
since the 1970s occurring at a rate that is generally consistent with
the Clausius-Clapeyron relation (about 7% per degree Celsius) and
the observed increase in atmospheric temperature. Significant trends
in tropospheric relative humidity at large spatial scales have not been
observed, with the exception of near-surface air over land where rela-
tive humidity has decreased in recent years (Section 2.5.5).
2.5.6 Clouds
2.5.6.1 Surface Observations
AR4 reported that surface-observed total cloud cover may have
increased over many land areas since the middle of the 20th centu-
ry, including the USA, the former USSR, Western Europe, mid-latitude
Canada and Australia. A few regions exhibited decreases, including
China and central Europe. Trends were less globally consistent since
the early 1970s, with regional reductions in cloud cover reported for
western Asia and Europe but increases over the USA.
Analyses since AR4 have indicated decreases in cloud occurrence/cover
in recent decades over Poland (Wibig, 2008), China and the Tibetan
Plateau (Duan and Wu, 2006; Endo and Yasunari, 2006; Xia, 2010b),
in particular for upper level clouds (Warren et al., 2007) and also over
Africa, Eurasia and in particular South America (Warren et al., 2007).
Increased frequency of overcast conditions has been reported for some
regions, such as Canada, from 1953 to 2002 (Milewska, 2004), with
no statistically significant trends evident over Australia (Jovanovic et
al., 2011) and North America (Warren et al., 2007). A global analysis
of surface observations spanning the period 1971–2009 (Eastman and
Warren, 2012) indicates a small decline in total cloud cover of about
0.4% per decade which is largely attributed to declining mid- and
high-level cloud cover and is most prominent in the middle latitudes.
Regional variability in surface-observed cloudiness over the ocean
appeared more credible than zonal and global mean variations in AR4.
Multidecadal changes in upper-level cloud cover and total cloud cover
over particular areas of the tropical Indo-Pacific Ocean were consist-
ent with island precipitation records and SST variability. This has been
extended more recently by Deser et al. (2010a), who found that an
eastward shift in tropical convection and total cloud cover from the
western to central equatorial Pacific occurred over the 20th century
and attributed it to a long-term weakening of the Walker circulation
(Section 2.7.5). Eastman et al. (2011) report that, after the remov-
al of apparently spurious globally coherent variability, cloud cover
decreased in all subtropical stratocumulus regions from 1954 to 2008.
2.5.6.2 Satellite Observations
Satellite cloud observations offer the advantage of much better spa-
tial and temporal coverage compared to surface observations. How-
ever they require careful efforts to identify and correct for temporal
discontinuities in the data sets associated with orbital drift, sensor
degradation, and inter-satellite calibration differences. AR4 noted that
there were substantial uncertainties in decadal trends of cloud cover
in all satellite data sets available at the time and concluded that there
was no clear consensus regarding the decadal changes in total cloud
cover. Since AR4 there has been continued effort to assess the quality
of and develop improvements to multi-decadal cloud products from
operational satellite platforms (Evan et al., 2007; O’Dell et al., 2008;
Heidinger and Pavolonis, 2009).
Several satellite data sets offer multi-decadal records of cloud cover
(Stubenrauch et al., 2013). AR4 noted that there were discrepancies in
global cloud cover trends between ISCCP and other satellite data prod-
ucts, notably a large downward trend of global cloudiness in ISCCP
since the late 1980s which is inconsistent with PATMOS-x and surface
observations (Baringer et al., 2010). Recent work has confirmed the
conclusion of AR4, that much of the downward trend in ISCCP is spuri-
ous and an artefact of changes in satellite viewing geometry (Evan et
al., 2007). An assesment of long-term variations in global-mean cloud
amount from nine different satellite data sets by Stubenrauch et al.
(2013) found differences between data sets were comparable in mag-
nitude to the interannual variability (2.5 to 3.5%). Such inconsistencies
result from differnces in sampling as well as changes in instrument
calibration and inhibit an accurate assessment of global-scale cloud
cover trends.
Satellite observations of low-level marine clouds suggest no long-term
trends in cloud liquid water path or optical properties (O’Dell et al.,
2008; Rausch et al., 2010). On regional scales, trends in cloud proper-
ties over China have been linked to changes in aerosol concentrations
(Qian et al., 2009; Bennartz et al., 2011) (Section 2.2.3).
In summary, surface-based observations show region- and height-spe-
cific variations and trends in cloudiness but there remains substantial
ambiguity regarding global-scale cloud variations and trends, especial-
ly from satellite observations. Although trends of cloud cover are con-
sistent between independent data sets in certain regions, substantial
ambiguity and therefore low confidence remains in the observations of
global-scale cloud variability and trends.
2.6 Changes in Extreme Events
AR4 highlighted the importance of understanding changes in extreme
climate events (Annex III: Glossary) because of their disproportionate
209
Observations: Atmosphere and Surface Chapter 2
2
impact on society and ecosystems compared to changes in mean cli-
mate (see also IPCC Working Group II). More recently a comprehensive
assessment of observed changes in extreme events was undertaken by
the IPCC Special Report on Managing the Risks of Extreme Events and
Disasters to Advance Climate Change Adaptation (SREX) (Seneviratne
et al., 2012; Section 1.3.3).
Data availability, quality and consistency especially affect the statistics
of extremes and some variables are particularly sensitive to chang-
ing measurement practices over time. For example, historical tropical
cyclone records are known to be heterogeneous owing to changing
observing technology and reporting protocols (Section 14.6.1) and
when records from multiple ocean basins are combined to explore
global trends, because data quality and reporting protocols vary sub-
stantially between regions (Knapp and Kruk, 2010). Similar problems
have been discovered when analysing wind extremes, because of the
sensitivity of measurements to changing instrumentation and observ-
ing practice (e.g., Smits et al., 2005; Wan et al., 2010).
Numerous regional studies indicate that changes observed in the
frequency of extremes can be explained or inferred by shifts in the
overall probability distribution of the climate variable (Griffiths et al.,
2005; Ballester et al., 2010; Simolo et al., 2011). However, it should be
noted that these studies refer to counts of threshold exceedance—
frequency, duration—which closely follow mean changes. Departures
from high percentiles/return periods (intensity, severity, magnitude)
are highly sensitive to changes in the shape and scale parameters of
the distribution (Schär et al., 2004; Clark et al., 2006; Della-Marta et
al., 2007a, 2007b; Fischer and Schär, 2010) and geographical location.
Debate continues over whether variance as well as mean changes are
affecting global temperature extremes (Hansen et al., 2012; Rhines and
Huybers, 2013) as illustrated in Figure 1.8 and FAQ 2.2, Figure 1. In
the following sections the conclusions from both AR4 and SREX are
reviewed along with studies subsequent to those assessments.
2.6.1 Temperature Extremes
AR4 concluded that it was very likely that a large majority of global
land areas had experienced decreases in indices of cold extremes and
increases in indices of warm extremes, since the middle of the 20th
century, consistent with warming of the climate. In addition, global-
ly averaged multi-day heat events had likely exhibited increases over
a similar period. SREX updated AR4 but came to similar conclusions
while using the revised AR5 uncertainty guidance (Seneviratne et al.,
2012). Further evidence since then indicates that the level of confi-
dence that the majority of warm and cool extremes show warming
remains high.
A large amount of evidence continues to support the conclusion that
most global land areas analysed have experienced significant warming
of both maximum and minimum temperature extremes since about
1950 (Donat et al., 2013c). Changes in the occurrence of cold and
warm days (based on daily maximum temperatures) are generally
less marked (Figure 2.32). ENSO (Box 2.5) influences both maximum
and minimum temperature variability especially around the Pacific
Rim (e.g., Kenyon and Hegerl, 2008; Alexander et al., 2009) but often
affecting cold and warm extremes differently. Different data sets using
different gridding methods and/or input data (Supplementary Mate-
rial 2.SM.7) indicate large coherent trends in temperature extremes
globally, associated with warming (Figure 2.32). The level of quality
control varies between these data sets. For example, HadEX2 (Donat
et al., 2013c) uses more rigorous quality control which leads to a
reduced station sample compared to GHCNDEX (Donat et al., 2013a)
or HadGHCND (Caesar et al., 2006). However, despite these issues
data sets compare remarkably consistently even though the station
networks vary through time (Figure 2.32; Table 2.12). Other data sets
that have assessed these indices, but cover a shorter period, also agree
very well over the period of overlapping data, e.g., HadEX (Alexander
et al., 2006) and Duke (Morak et al., 2011, 2013).
The shift in the distribution of nighttime temperatures appears great-
er than daytime temperatures although whether distribution changes
are simply linked to increases in the mean or other moments is an
active area of research (Ballester et al., 2010; Simolo et al., 2011; Donat
and Alexander, 2012; Hansen et al., 2012). Indeed, all data sets exam-
ined (Duke, GHCNDEX, HadEX, HadEX2 and HadGHCND), indicate a
faster increase in minimum temperature extremes than maximum
temperature extremes. While DTR declines have only been assessed
with medium confidence (Section 2.4.1.2), confidence of accelerated
increases in minimum temperature extremes compared to maximum
temperature extremes is high due to the more consistent patterns of
warming in minimum temperature extremes globally.
Regional changes in a range of climate indices are assessed in Table
2.13. These indicate likely increases across most continents in unusu-
ally warm days and nights and/or reductions in unusually cold days
and nights including frosts. Some regions have experienced close to a
doubling of the occurrence of warm and a halving of the occurrence
of cold nights, for example, parts of the Asia-Pacific region (Choi et
al., 2009) and parts of Eurasia (Klein Tank et al., 2006; Donat et al.,
2013a, 2013c) since the mid-20th century. Changes in both local and
global SST patterns (Section 2.4.2) and large scale circulation patterns
(Section 2.7) have been shown to be associated with regional changes
in temperature extremes (Barrucand et al., 2008; Scaife et al., 2008;
Table 2.12 | Trend estimates and 90% confidence intervals (Box 2.2) for global values of cold nights (TN10p), cold days (TX10p), warm nights (TN90p) and warm days (TX90p)
over the periods 1951–2010 and 1979–2010 (see Box 2.4, Table 1 for more information on indices).
Data Set
Trends in % per decade
TN10p TX10p TN90p TX90p
1951–2010 1979–2010 1951–2010 1979–2010 1951–2010 1979–2010 1951–2010 1979–2010
HadEX2 (Donat et al., 2013c) –3.9 ± 0.6 –4.2 ± 1.2 –2.5 ± 0.7 –4.1 ± 1.4 4.5 ± 0.9 6.8 ± 1.8 2.9 ± 1.2 6.3 ± 2.2
HadGHCND (Caesar et al., 2006) –4.5 ± 0.7 –4.0 ± 1.5 –3.3 ± 0.8 –5.0 ± 1.6 5.8 ± 1.3 8.6 ± 2.3 4.2 ± 1.8 9.4 ± 2.7
GHCNDEX (Donat et al., 2013a) –3.9 ± 0.6 –3.9 ± 1.3 –2.6 ± 0.7 –3.9 ± 1.4 4.3 ± 0.9 6.3 ± 1.8 2.9 ± 1.2 6.1 ± 2.2
210
Chapter 2 Observations: Atmosphere and Surface
2
Figure 2.32 | Trends in annual frequency of extreme temperatures over the period 1951–2010, for (a) cold nights (TN10p), (b) cold days (TX10p), (c) warm nights (TN90p) and (d)
warm days (TX90p) (Box 2.4, Table 1). Trends were calculated only for grid boxes that had at least 40 years of data during this period and where data ended no earlier than 2003.
Grey areas indicate incomplete or missing data. Black plus signs (+) indicate grid boxes where trends are significant (i.e., a trend of zero lies outside the 90% confidence interval).
The data source for trend maps is HadEX2 (Donat et al., 2013c) updated to include the latest version of the European Climate Assessment data set (Klok and Tank, 2009). Beside
each map are the near-global time series of annual anomalies of these indices with respect to 1961–1990 for three global indices data sets: HadEX2 (red); HadGHCND (Caesar et
al., 2006; blue) and updated to 2010 and GHCNDEX (Donat et al., 2013a; green). Global averages are only calculated using grid boxes where all three data sets have at least 90%
of data over the time period. Trends are significant (i.e., a trend of zero lies outside the 90% confidence interval) for all the global indices shown.
Trend (days per decade)
(a) Cold Nights
(b) Cold Days
(c) Warm Nights
(d) Warm Days
Trend (days per decade)
Trend (days per decade)
Trend (days per decade)
20
10
0
-10
-20
1950 1960 1970 1980 1990 2000 2010
20
10
0
-10
-20
1950 1960 1970 1980 1990 2000 2010
30
20
10
0
-10
1950 1960 1970 1980 1990 2000 2010
30
20
10
0
-10
1950 1960 1970 1980 1990 2000 2010
Anomaly (days)Anomaly (days)Anomaly (days)Anomaly (days)
HadEX2
HadGHCND
GHCNDEX
211
Observations: Atmosphere and Surface Chapter 2
2
Region
Warm Days
(e.g., TX90p
a
)
Cold Days
(e.g., TX10p
a
)
Warm Nights
(e.g., TN90p
a
, TR
a
)
Cold Nights/Frosts
(e.g., TN10p
a
, FD
a
)
Heat Waves /
Warm Spells
g
Extreme
Precipitation
(e.g., RX1day
a
,
R95p
a
, R99p
a
)
Dryness (e.g,.
CDD
a
) / Drought
h
North America
and Central
America
High confidence:
Likely overall
increase but spatially
varying trends
1,2
High confidence:
Likely overall
decrease but
with spatially
varying trends
1,2
High confidence:
Likely overall
increase
1,2
High confidence:
Likely overall
decrease
1,2
Medium confidence:
increases in more
regions than
decreases
1,3
but 1930s
dominates longer term
trends in the USA
4
High confidence:
Likely overall
increase
1,2,5
but some
spatial variation
High confidence:
Very likely increase
central North
America
6,7
Medium confidence:
decrease
1
but
spatially varying
trends
High confidence
b
:
Likely decrease
central North
America
4
South America
Medium
confidence
b
:
Overall increase
8
Medium
confidence
b
:
Overall decrease
8
Medium
confidence
b
:
Overall increase
8
Medium
confidence
b
:
Overall decrease
8
Low confidence:
insufficient evidence
(lack of literature)
and spatially varying
trends but some
evidence of increases
in more areas
than decreases
8
Medium
confidence
b
:
Increases in
more regions
than decreases
8,9
but spatially
varying trends
Low confidence:
limited literature
and spatially
varying trends
8
Europe and
Mediterranean
High confidence:
Likely overall
increase
10,11,12
High confidence:
Likely overall
decrease
11,12
High confidence:
Likely overall
increase
11,12
High confidence:
Likely overall
decrease
10,11,12
High confidence
b
:
Likely increases in
most regions
3,13
High confidence
b,c
:
Likely increases
in more regions
than decreases
5,15,16
but regional and
seasonal
variation
Medium confidence:
spatially varying
trends
High confidence
b
:
Likely increase in
Mediterranean
17,18
Africa and
Middle East
Low to medium
confidence
b,d
:
limited data in
many regions but
increases in most
regions assessed
Medium
confidence
b
:
increase North
Africa and
Middle East
19,20
High confidence
b
:
Likely increase
southern
Africa
21,22,23
Low to medium
confidence
b,d
:
limited data in
many regions but
decreases in most
regions assessed
Medium
confidence
b
:
decrease North
Africa and
Middle East
19,20
High confidence
b
:
Likely decrease
southern Africa
21,22,23
Medium
confidence
b,d
:
limited data in
many regions but
increases in most
regions assessed
Medium
confidence
b
:
increase North
Africa and
Middle East
19,20
High confidence
b
:
Likely increase
southern
Africa
21,22,23
Medium
confidence
b,d
:
limited data in
many regions but
decreases in most
regions assessed
Medium
confidence
b
:
decrease North
Africa and
Middle East
19,20
High confidence
b
:
Likely decrease
southern
Africa
21,22,23
Low confidence
d
:
insufficient evidence
(lack of literature)
Medium confidence:
increase in North
Africa and
Middle East and
southern
Africa
3,19,21,22
Low confidence
d
:
insufficient evidence
and spatially
varying trends
Medium
confidence
b
:
increases in more
regions than
decreases in
southern Africa but
spatially varying
trends depending
on index
5,21,22
Medium confidence
d
:
increase
19,22,24
High confidence
b
:
Likely increase in
West Africa
25,26
although 1970s
Sahel drought
dominates
the trend
Asia (excluding
South-east
Asia)
High confidence
b,e
:
Likely overall
increase
27,28,29,30,31,32
High confidence
b,e
:
Likely overall
decrease
27,28,29,30,31,32
High confidence
b,e
:
Likely overall
increase
27,28,29,30,31,32
High confidence
b,e
:
Likely overall
increase
27,28,29,30,31,32
Medium
confidence
b,e
:
Spatially varying
trends and
insufficient data
in some regions
High confidence
b,c
:
Likely more areas
of increases than
decreases
3,28,33
Low to medium
confidence
b,e
:
Low confidence
due to insufficient
evidence or spatially
varying trends.
Medium confidence:
increases in more
regions than
decreases
5,34,35,36
Low to medium
confidence
b,e
Medium
confidence:
Increase in
eastern Asia
36,37
South-east Asia
and Oceania
High confidence
b,f
:
Likely overall
increase
27,38,39,40
High confidence
b,f
:
Likely overall
decrease
27,38,39
High confidence
b,f
:
Likely overall
increase
27,38,39,40
High confidence
b,f
:
Likely overall
decrease
27,38,39
Low confidence (due
lack of literature)
to high confidence
b,f
depending on region
High confidence
2
:
Likely overall
increase in
Australia
3,14,41
Low confidence
(lack of literature)
to high confidence
b,f
High confidence:
Likely decrease in
southern Australia
42,43
but index and
season dependent
Low to medium
confidence
b,f
:
inconsistent trends
between studies
in SE Asia. Overall
increase in dryness
in southern and
eastern Australia
High confidence
b
:
Likely decrease
northwest
Australia
25,26,44
Table 2.13 | Regional observed changes in a range of climate indices since the middle of the 20th century. Assessments are based on a range of ‘global’ studies and assessments
(Groisman et al., 2005; Alexander et al., 2006; Caesar et al., 2006; Sheffield and Wood, 2008; Dai, 2011a, 2011b, 2013; Seneviratne et al., 2012; Sheffield et al., 2012; Donat et al.,
2013a, 2013c; van der Schrier et al., 2013) and selected regional studies as indicated. Bold text indicates where the assessment is somewhat different to SREX Table 3-2. In each
such case a footnote explains why the assessment is different. See also Figures 2.32 and 2.33.
(continued on next page)
212
Chapter 2 Observations: Atmosphere and Surface
2
(Table 2.13 continued)
Notes:
a
See Table 1 in Box 2.4, for definitions.
b
More recent literature updates the assessment from SREX Table 3-2 (including ‘global’ studies).
c
This represents a measure of the area affected which is different from what was assessed in SREX Table 3-2.
d
This represents a slightly different region than that assessed in SREX Table 3-2 as it includes the Middle East.
e
This represents a slightly different region than that assessed in SREX Table 3-2 as it excludes Southeast Asia.
f
This represents a slightly different region than that assessed in SREX Table 3-2 as it combines SE Asia and Oceania.
g
Definitions for warm spells and heat waves vary (Perkins and Alexander, 2012) but here we are commonly assessing the Warm Spell Duration Index (WSDI; Zhang et al., 2011) or other heat wave
indices (e.g., HWF, HWM; (Fischer and Schär, 2010; Perkins et al., 2012) that have defined multi-day heat extremes relative to either daily maximum or minimum temperatures (or both) above a
high (commonly 90th) percentile relative to a late-20th century reference period.
h
See Box 2.4 and Section 2.6.1 for definitions.
1
Kunkel et al. (2008),
2
Peterson et al. (2008),
3
Perkins et al. (2012),
4
Peterson et al. (2013),
5
Westra et al. (2013),
6
Groisman et al. (2012),
7
Villarini et al. (2013),
8
Skansi et al. (2013),
9
Haylock
et al. (2006),
10
Andrade et al. (2012),
11
Efthymiadis et al. (2011),
12
Moberg et al. (2006),
13
Della-Marta et al. (2007a),
14
Perkins and Alexander (2012),
15
Van den Besselaar et al. (2012),
16
Zolina
et al. (2009),
17
Sousa et al. (2011),
18
Hoerling et al. (2012),
19
Donat et al. (2013b),
20
Zhang et al. (2005),
21
Kruger and Sekele (2013),
22
New et al. (2006),
23
Vincent et al. (2011),
24
Aguilar et al.
(2009),
25
Dai (2013),
26
Sheffield et al. (2012),
27
Choi et al. (2009),
28
Rahimzadeh et al. (2009),
29
Revadekar et al. (2012),
30
Tank et al. (2006),
31
You et al. (2010),
32
Zhou and Ren (2011),
33
Ding
et al. (2010),
34
Krishna Moorthy et al. (2009),
35
Pattanaik and Rajeevan (2010),
36
Wang et al. (2012b),
37
Fischer et al. (2011),
38
Caesar et al. (2011),
39
Chambers and Griffiths (2008),
40
Wang et
al. (2013),
41
Tryhorn and Risbey (2006),
42
Gallant et al. (2007),
43
King et al. (2013),
44
Jones et al. (2009).
Alexander et al., 2009; Li et al., 2012), particularly in regions around
the Pacific Rim (Kenyon and Hegerl, 2008). Globally, there is evidence
of large-scale warming trends in the extremes of temperature, espe-
cially minimum temperature, since the beginning of the 20th century
(Donat et al., 2013c).
There are some exceptions to this large-scale warming of temperature
extremes including central North America, eastern USA (Alexander et
al., 2006; Kunkel et al., 2008; Peterson et al., 2008) and some parts of
South America (Alexander et al., 2006; Rusticucci and Renom, 2008;
Skansi et al., 2013) which indicate changes consistent with cooling in
these locations. However, these exceptions appear to be mostly associ-
ated with changes in maximum temperatures (Donat et al., 2013c). The
so-called ‘warming hole’ in central North America and eastern USA,
where temperatures have cooled relative to the significant warming
elsewhere in the region, is associated with observed changes in the
hydrological cycle and land–atmosphere interaction (Pan et al., 2004;
Portmann et al., 2009a; Portmann et al., 2009b; Misra et al., 2012)
and decadal and multi-decadal variability linked with the Atlantic and
Pacific Oceans (Meehl et al., 2012; Weaver, 2012).
Since AR4 many studies have analysed local to regional changes in
multi-day temperature extremes in more detail, specifically address-
ing different heat wave aspects such as frequency, intensity, duration
and spatial extent (Box 2.4, FAQ 2.2). Several high-profile heat waves
have occurred in recent years (e.g., in Europe in 2003 (Beniston, 2004),
Australia in 2009 (Pezza et al., 2012), Russia in 2010 (Barriopedro et
al., 2011; Dole et al., 2011; Trenberth and Fasullo, 2012a) and USA in
2010/2011 (Hoerling et al., 2012) (Section 10.6.2) which have had
severe impacts (see WGII). Heat waves are often associated with qua-
si-stationary anticyclonic circulation anomalies that produce prolonged
hot conditions at the surface (Black and Sutton, 2007; Garcia-Herrera
et al., 2010), but long-term changes in the persistence of these anom-
alies are still relatively poorly understood (Section 2.7). Heat waves
can also be amplified by pre-existing dry soil conditions in transitional
climate zones (Ferranti and Viterbo, 2006; Fischer et al., 2007; Senevi-
ratne et al., 2010; Mueller and Seneviratne, 2012) and the persistence
of those soil-mositure anomalies (Lorenz et al., 2010). Dry soil-mois-
ture conditions are either induced by precipitation deficits (Della-Mar-
ta et al., 2007b; Vautard et al., 2007), or evapotranspiration excesses
(Black and Sutton, 2007; Fischer et al., 2007), or a combination of both
(Seneviratne et al., 2010). This amplification of soil moisture–temper-
ature feedbacks is suggested to have partly enhanced the duration of
extreme summer heat waves in southeastern Europe during the latter
part of the 20th century (Hirschi et al., 2011), with evidence emerging
of a signature in other moisture-limited regions (Mueller and Senevi-
ratne, 2012).
Table 2.13 shows that there has been a likely increasing trend in the
frequency of heatwaves since the middle of the 20th century in Europe
and Australia and across much of Asia where there are sufficient data.
However, confidence on a global scale is medium owing to lack of
studies over Africa and South America but also in part owing to dif-
ferences in trends depending on how heatwaves are defined (Perkins
et al., 2012). Using monthly means as a proxy for heatwaves Coumou
et al. (2013) and Hansen et al. (2012) indicate that record-breaking
temperatures in recent decades substantially exceed what would be
expected by chance but caution is required when making inferences
between these studies and those that deal with multi-day events and/
or use more complex definitions for heatwave events. There is also
evidence in some regions that periods prior to the 1950s had more
heatwaves (e.g., over the USA, the decade of the 1930s stands out
and is also associated with extreme drought conditions (Peterson et
al., 2013) whereas conversely in other regions heatwave trends may
have been underestimated owing to poor quality and/or consistency of
data (e.g., Della-Marta et al. (2007a) over Western Europe; Kuglitsch et
al. (2009, 2010) over the Mediterranean). Recent available studies also
suggest that the number of cold spells has reduced significantly since
the 1950s (Donat et al., 2013a, 2013c).
In summary, new analyses continue to support the AR4 and SREX
conclusions that since about 1950 it is very likely that the numbers
of cold days and nights have decreased and the numbers of warm
days and nights have increased overall on the global scale, that is, for
land areas with sufficient data. It is likely that such changes have also
occurred across most of North America, Europe, Asia and Australia.
There is low to medium confidence in historical trends in daily
temperature extremes in Africa and South America as there is either
213
Observations: Atmosphere and Surface Chapter 2
2
insufficient data or trends vary across these regions. This, combined
with issues with defining events, leads to the assessment that there
is medium confidence that globally the length and frequency of warm
spells, including heat waves, has increased since the middle of the 20th
century although it is likely that heatwave frequency has increased
during this period in large parts of Europe, Asia and Australia.
2.6.2 Extremes of the Hydrological Cycle
In Section 2.5 mean state changes in different aspects of the hydrolog-
ical cycle are discussed. In this section we focus on the more extreme
aspects of the cycle including extreme rainfall, severe local weather
events like hail, flooding and droughts. Extreme events associated with
tropical and extratropical storms are discussed in Sections 2.6.3 and
2.6.4 respectively.
2.6.2.1 Precipitation Extremes
AR4 concluded that substantial increases are found in heavy precipi-
tation events. It was likely that annual heavy precipitation events had
disproportionately increased compared to mean changes between
1951 and 2003 over many mid-latitude regions, even where there had
been a reduction in annual total precipitation. Rare precipitation (such
as the highest annual daily precipitation total) events were likely to
have increased over regions with sufficient data since the late 19th
century. SREX supported this view, as have subsequent analyses, but
noted large spatial variability within and between regions (Table 3.2 of
Seneviratne et al., 2012).
Given the diverse climates across the globe, it has been difficult to pro-
vide a universally valid definition of ‘extreme precipitation’. However,
Box 2.4 Table 1 indicates some of the common definitions that are used
in the scientific literature. In general, statistical tests indicate changes
in precipitation extremes are consistent with a wetter climate (Sec-
tion 7.6.5), although with a less spatially coherent pattern of change
than temperature, in that there are large areas that show increasing
trends and large areas that show decreasing trends and a lower level
of statistical significance than for temperature change (Alexander et
al., 2006; Donat et al., 2013a, 2013c). Using R95p and SDII indices (Box
2.4), Figures 2.33a and 2.33b show these areas for heavy precipitation
amounts and precipitation intensity where sufficient data are available
in the HadEX2 data set (Donat et al., 2013c) although there are more
areas showing significant increases than decreases. Although chang-
es in large-scale circulation patterns have a substantial influence on
precipitation extremes globally (Alexander et al., 2009; Kenyon and
Hegerl, 2010), Westra et al. (2013) showed, using in situ data over land,
that trends in the wettest day of the year indicate more increases than
would be expected by chance. Over the tropical oceans satellite meas-
urements show an increase in the frequency of the heaviest rainfall
during warmer (El Niño) years (Allan and Soden, 2008).
Regional trends in precipitation extremes since the middle of the 20th
century are varied (Table 2.13). In most continents confidence in trends
is not higher than medium except in North America and Europe where
there have been likely increases in either the frequency or intensity of
heavy precipitation. This assessment increases to very likely for cen-
tral North America. For North America it is also likely that increases
have occurred during the whole of the 20th century (Pryor et al., 2009;
Donat et al., 2013c; Villarini et al., 2013). For South America the most
recent integrative studies indicate heavy rain events are increasing in
frequency and intensity over the contient as a whole (Donat et al.,
2013c; Skansi et al., 2013). For Europe and the Mediterranean, the
assessment masks some regional and seasonal variation. For example,
much of the increase reported in Table 2.13 is found in winter although
with decreasing trends in some other regions such as northern Italy,
Poland and some Mediterranean coastal sites (Pavan et al., 2008; Lupi-
kasza, 2010; Toreti et al., 2010). There are mixed regional trends across
Asia and Oceania but with some indication that increases are being
observed in more regions than decreases while recent studies focused
on Africa, in general, have not found significant trends in extreme pre-
cipitation (see Chapter 14 for more on regional variations and trends).
The above studies generally use indices which reflect ‘moderate’
extremes, for example, events occurring as often as 5% or 10% of the
time (Box 2.4). Only a few regions have sufficient data to assess trends
in rarer precipitation events reliably, for example, events occurring on
average once in several decades. Using Extreme Value Theory, DeGae-
tano (2009) showed a 20% reduction in the return period for extreme
precipitation events over large parts of the contiguous USA from 1950
to 2007. For Europe from 1951 to 2010, Van den Besselaar et al. (2012)
reported a median reduction in 5- to 20-year return periods of 21%,
with a range between 2% and 58% depending on the subregion and
season. This decrease in return times for rare extremes is qualitatively
similar to the increase in moderate extremes for these regions reported
above, and also consistent with earlier local results for the extreme tail
of the distribution reported in AR4.
The aforementioned studies refer to daily precipitation extremes,
although rainfall will often be limited to part of the day only. The litera-
ture on sub-daily scales is too limited for a global assessment although
it is clear that analysis and framing of questions regarding sub-daily
precipitation extremes is becoming more critical (Trenberth, 2011).
Available regional studies have shown results that are even more com-
plex than for daily precipitation and with variations in the spatial pat-
terns of trends depending on event formulation and duration. However,
regional studies show indications of more increasing than decreasing
trends (Sen Roy, 2009; for India) (Sen Roy and Rouault, 2013; for South
Africa) (Westra and Sisson, 2011; for Australia). Some studies present
evidence of scaling of sub-daily precipitation with temperature that
is outside that expected from the Clausius–Clapeyron relation (about
7%
per degree Celsius) (Lenderink and Van Meijgaard, 2008; Haerter
et al., 2010; Jones et al., 2010; Lenderink et al., 2011; Utsumi et al.,
2011), but scaling beyond that expected from thermodynamic theories
is controversial (Section 7.6.5).
In summary, further analyses continue to support the AR4 and SREX
conclusions that it is likely that since 1951 there have been statistically
significant increases in the number of heavy precipitation events (e.g.,
above the 95th percentile) in more regions than there have been sta-
tistically significant decreases, but there are strong regional and sub-
regional variations in the trends. In particular, many regions present
statistically non-significant or negative trends, and, where seasonal
changes have been assessed, there are also variations between seasons
(e.g., more consistent trends in winter than in summer in Europe). The
214
Chapter 2 Observations: Atmosphere and Surface
2
overall most consistent trends towards heavier precipitation events are
found in central North America (very likely increase) but assessment for
Europe shows likely increases in more regions than decreases.
2.6.2.2 Floods
AR4 WGI Chapter 3 (Trenberth et al., 2007) did not assess changes in
floods but AR4 WGII concluded that there was not a general global
trend in the incidence of floods (Kundzewicz et al., 2007). SREX went
further to suggest that there was low agreement and thus low confi-
dence at the global scale regarding changes in the magnitude or fre-
quency of floods or even the sign of changes.
AR5 WGII assesses floods in regional detail accounting for the fact
that trends in floods are strongly influenced by changes in river man-
agement (see also Section 2.5.2). Although the most evident flood
trends appear to be in northern high latitudes, where observed warm-
ing trends have been largest, in some regions no evidence of a trend
in extreme flooding has been found, for example, over Russia based
on daily river discharge (Shiklomanov et al., 2007). Other studies for
Europe (Hannaford and Marsh, 2008; Renard et al., 2008; Petrow and
Merz, 2009; Stahl et al., 2010) and Asia (Jiang et al., 2008; Delgado
et al., 2010) show evidence for upward, downward or no trend in the
magnitude and frequency of floods, so that there is currently no clear
and widespread evidence for observed changes in flooding except for
the earlier spring flow in snow-dominated regions (Seneviratne et al.,
2012).
In summary, there continues to be a lack of evidence and thus low con-
fidence regarding the sign of trend in the magnitude and/or frequency
of floods on a global scale.
2.6.2.3 Droughts
AR4 concluded that droughts had become more common, especial-
ly in the tropics and sub-tropics since about 1970. SREX provided a
comprehensive assessment of changes in observed droughts (Section
3.5.1 and Box 3.3 of SREX), updated the conclusions provided by AR4
and stated that the type of drought considered and the complexities
in defining drought (Annex III: Glossary) can substantially affect the
conclusions regarding trends on a global scale (Chapter 10). Based on
evidence since AR4, SREX concluded that there were not enough direct
observations of dryness to suggest high confidence in observed trends
globally, although there was medium confidence that since the 1950s
some regions of the world have experienced more intense and longer
droughts. The differences between AR4 and SREX are due primarily to
analyses post-AR4, differences in how both assessments considered
drought and updated IPCC uncertainty guidance.
There are very few direct measurements of drought related variables,
such as soil moisture (Robock et al., 2000), so drought proxies (e.g.,
PDSI, SPI, SPEI; Box 2.4) and hydrological drought proxies (e.g., Vidal
et al., 2010; Dai, 2011b) are often used to assess drought. The chosen
proxy (e.g., precipitation, evapotranspiration, soil moisture or stream-
flow) and time scale can strongly affect the ranking of drought events
(Sheffield et al., 2009; Vidal et al., 2010). Analyses of these indirect
indices come with substantial uncertainties. For example, PDSI may not
be comparable across climate zones. A self-calibrating (sc-) PDSI can
replace the fixed empirical constants in PDSI with values representa-
tive of the local climate (Wells et al., 2004). Furthermore, for studies
using simulated soil moisture, the type of potential evapotranspiration
model used can lead to significant differences in the estimation of the
regions affected and the areal extent of drought (Sheffield et al., 2012),
but the overall effect of a more physically realistic parameterisation is
debated (van der Schrier et al., 2013).
Because drought is a complex variable and can at best be incompletely
represented by commonly used drought indices, discrepancies in the
interpretation of changes can result. For example, Sheffield and Wood
(2008) found decreasing trends in the duration, intensity and severity
of drought globally. Conversely, Dai (2011a,b) found a general global
increase in drought, although with substantial regional variation and
individual events dominating trend signatures in some regions (e.g.,
the 1970s prolonged Sahel drought and the 1930s drought in the USA
and Canadian Prairies). Studies subsequent to these continue to pro-
vide somewhat different conclusions on trends in global droughts and/
or dryness since the middle of the 20th century (Sheffield et al., 2012;
Dai, 2013; Donat et al., 2013c; van der Schrier et al., 2013).
Van der Schrier et al. (2013), using monthly sc-PDSI, found no strong
case either for notable drying or moisture increase on a global scale
over the periods 1901–2009 or 1950–2009, and this largely agrees
with the results of Sheffield et al. (2012) over the latter period. A
comparison between the sc-PDSI calculated by van der Schrier et al.
(2013) and that of Dai (2011a) shows that the dominant mode of
variability is very similar, with a temporal evolution suggesting a trend
toward drying. However, the same analysis for the 1950–2009 period
shows an initial increase in drying in the Van der Schrier et al. data set,
followed by a decrease from the mid-1980s onwards, while the Dai
data show a continuing increase until 2000. The difference in trends
between the sc-PDSI data set of Van der Schrier et al. and Dai appears
to be due to the different calibration periods used, the shorter 1950–
1979 period in the latter study resulting in higher index values from
1980 onwards, although the associated spatial patterns are similar. In
addition, the observed precipitation forcing data set differs between
studies, with van der Schrier et al. (2013) and Sheffield et al. (2012)
using CRU TS 3.10.01 (updated from Mitchell and Jones, 2005). This
data set uses fewer stations and has been wetter than some other
precipitation products in the last couple of decades (Figure 2.29,
Table 2.9), although the best data set to use is still an open question.
Despite this, a measure of sc-PDSI with potential evapotranspiration
estimated using the Penman–Montieth equation shows an increase
in the percentage of land area in drought since 1950 (Sheffield et
al., 2012; Dai, 2013), while van der Schrier et al. (2013) also finds a
slight increase in the percentage of land area in severe drought using
the same measure. This is qualitatively consistent with the trends
in surface soil moisture found for the shorter period 1988–2010 by
Dorigo et al. (2012) using a new multi-satellite data set and changes
in observed streamflow (Dai, 2011b). However all these studies draw
somewhat different conclusions and the compelling arguments both
for (Dai, 2011b, 2013) and against (Sheffield et al., 2012; van der
Schrier et al., 2013) a significant increase in the land area experienc-
ing drought has hampered global assessment.
215
Observations: Atmosphere and Surface Chapter 2
2
Studies that support an increasing trend towards the land area affect-
ed by drought seem to be at odds with studies that look at trends
in dryness (i.e., lack of rainfall). For example, Donat et al. (2013c)
found that the annual maximum number of consecutive dry days
has declined since the 1950s in more regions than it has increased
(Figure 2.33c). However, only regions in Russia and the USA indicate
significant changes and there is a lack of information for this index
over large regions, especially Africa. Most other studies focussing on
global dryness find similar results, with decadal variability dominating
longer-term trends (Frich et al., 2002; Alexander et al., 2006; Donat et
al., 2013a). However, Giorgi et al. (2011) indicate that ‘hydroclimatic
intensity’ (Box 2.4, Chapter 7), a measure which combines both dry
spell length and precipitation intensity, has increased over the latter
part of the 20th century in response to a warming climate. They show
that positive trends (reflecting an increase in the length of drought
and/or extreme precipitation events) are most marked in Europe,
India, parts of South America and East Asia although trends appear to
have decreased (reflecting a decrease in the length of drought and/or
extreme precipitation events) in Australia and northern South America
(Figure 2.33c). Data availability, quality and length of record remain
issues in drawing conclusions on a global scale, however.
Despite differences between the conclusions drawn by global studies,
there are some areas in which they agree. Table 2.13 indicates that
there is medium confidence of an increase in dryness or drought in East
Asia with high confidence that this is the case in the Mediterannean
and West Africa. There is also high confidence of decreases in dryness
or drought in central North America and north-west Australia.
In summary, the current assessment concludes that there is not enough
evidence at present to suggest more than low confidence in a glob-
al-scale observed trend in drought or dryness (lack of rainfall) since the
middle of the 20th century, owing to lack of direct observations, geo-
graphical inconsistencies in the trends, and dependencies of inferred
trends on the index choice. Based on updated studies, AR4 conclusions
regarding global increasing trends in drought since the 1970s were
probably overstated. However, it is likely that the frequency and inten-
sity of drought has increased in the Mediterranean and West Africa
and decreased in central North America and north-west Australia since
1950.
Figure 2.33 | Trends in (a) annual amount of precipitation from days >95th percentile (R95p), (b) daily precipitation intensity (SDII) and (c) frequency of the annual maximum
number of consecutive dry days (CDD) (Box 2.4, Table 1). Trends are shown as relative values for better comparison across different climatic regions. Trends were calculated only
for grid boxes that had at least 40 years of data during this period and where data ended no earlier than 2003. Grey areas indicate incomplete or missing data. Black plus signs
(+) indicate grid boxes where trends are significant (i.e., a trend of zero lies outside the 90% confidence interval). The data source for trend maps is HadEX2 (Donat et al., 2013a)
updated to include the latest version of the European Climate Assessment data set (Klok and Tank, 2009). (d) Trends (normalized units) in hydroclimatic intensity (HY-INT: a multipli-
cative measure of length of dry spell and precipitation intensity) over the period 1976–2000 (adapted from Giorgi et al., 2011). An increase (decrease) in HY-INT reflects an increase
(decrease) in the length of drought and /or extreme precipitation events.
(b) SDII 1951-2010
Trend (% per decade)
(a) R95p 1951-2010
Trend (% per decade)
(d) HY-INT 1976-2000
-0.8 - 0.4 -0.2 0 0.2 0.4 0.8
(c) CDD 1951-2010
Trend (% per decade)
-10 -5 0510 15-15-20 20
Trend (Normalised units)
-10 -5 0510 15-15-20 20
-10 -5 0510 15-15-20 20
216
Chapter 2 Observations: Atmosphere and Surface
2
2.6.2.4 Severe Local Weather Events
Another extreme aspect of the hydrological cycle is severe local
weather phenomena such as hail or thunder storms. These are not well
observed in many parts of the world because the density of surface
meteorological observing stations is too coarse to measure all such
events. Moreover, homogeneity of existing reporting is questionable
(Verbout et al., 2006; Doswell et al., 2009). Alternatively, measures of
severe thunderstorms or hailstorms can be derived by assessing the
environmental conditions that are favourable for their formation but
this method is very uncertain (Seneviratne et al., 2012). SREX high-
lighted studies such as those of Brooks and Dotzek (2008), who found
significant variability but no clear trend in the past 50 years in severe
thunderstorms in a region east of the Rocky Mountains in the USA,
Cao (2008), who found an increasing frequency of severe hail events in
Ontario, Canada during the period 1979–2002 and Kunz et al. (2009),
who found that hail days significantly increased during the period
1974–2003 in southwest Germany. Hailpad studies from Italy (Eccel et
al., 2012) and France (Berthet et al., 2011) suggest slight increases in
larger hail sizes and a correlation between the fraction of precipitation
falling as hail with average summer temperature while in Argentina
between 1960 and 2008 the annual number of hail events was found
to be increasing in some regions and decreasing in others (Mezher et
al., 2012). In China between 1961 and 2005, the number of hail days
has been found to generally decrease, with the highest occurrence
between 1960 and 1980 but with a sharp drop since the mid-1980s
(CMA, 2007; Xie et al., 2008). However, there is little consistency in hail
size changes in different regions of China since 1980 (Xie et al., 2010).
Remote sensing offers a potential alterative to surface-based meteor-
ological networks for detecting changes in small scale severe weather
phenomenon such as proxy measurements of lightning from satel-
lites (Zipser et al., 2006) but there remains little convincing evidence
that changes in severe thunderstorms or hail have occurred since the
middle of the 20th century (Brooks, 2012).
In summary, there is low confidence in observed trends in small-scale
severe weather phenomena such as hail and thunderstorms because
of historical data inhomogeneities and inadequacies in monitoring
systems.
2.6.3 Tropical Storms
AR4 concluded that it was likely that an increasing trend had occurred
in intense tropical cyclone activity since 1970 in some regions but that
there was no clear trend in the annual numbers of tropical cyclones.
Subsequent assessments, including SREX and more recent literature
indicate that it is difficult to draw firm conclusions with respect to the
confidence levels associated with observed trends prior to the satellite
era and in ocean basins outside of the North Atlantic.
Section 14.6.1 discusses changes in tropical storms in detail. Current data
sets indicate no significant observed trends in global tropical cyclone
frequency over the past century and it remains uncertain whether any
reported long-term increases in tropical cyclone frequency are robust,
after accounting for past changes in observing capabilities (Knutson
et al., 2010). Regional trends in tropical cyclone frequency and the fre-
quency of very intense tropical cyclones have been identified in the
North Atlantic and these appear robust since the 1970s (Kossin et al.
2007) (very high confidence). However, argument reigns over the cause
of the increase and on longer time scales the fidelity of these trends
is debated (Landsea et al., 2006; Holland and Webster, 2007; Land-
sea, 2007; Mann et al., 2007b) with different methods for estimating
undercounts in the earlier part of the record providing mixed conclu-
sions (Chang and Guo, 2007; Mann et al., 2007a; Kunkel et al., 2008;
Vecchi and Knutson, 2008, 2011). No robust trends in annual numbers
of tropical storms, hurricanes and major hurricanes counts have been
identified over the past 100 years in the North Atlantic basin. Measures
of land-falling tropical cyclone frequency (Figure 2.34) are generally
considered to be more reliable than counts of all storms which tend
to be strongly influenced by those that are weak and/or short lived.
Callaghan and Power (2011) find a statistically significant decrease
in Eastern Australia land-falling tropical cyclones since the late 19th
century although including 2010/2011 season data this trend becomes
non-significant (i.e., a trend of zero lies just inside the 90% confidence
interval). Significant trends are not found in other oceans on shorter
time scales (Chan and Xu, 2009; Kubota and Chan, 2009; Mohapatra
et al., 2011; Weinkle et al., 2012), although Grinsted et al. (2012) find
a significant positive trend in eastern USA using tide-guage data from
1923–2008 as a proxy for storm surges associated with land-falling
hurricanes. Differences between tropical cyclone studies highlight the
challenges that still lie ahead in assessing long-term trends.
(b)
(c)
(a)
1900
1950
2000
Landfalling Tropical Cyclones, Eastern Australia
Landfalling Hurricanes, United States
Landfalling Typhoons, China
Normalized units Normalized units Normalized units
Figure 2.34 | Normalized 5-year running means of the number of (a) adjusted land
falling eastern Australian tropical cyclones (adapted from Callaghan and Power (2011)
and updated to include 2010//2011 season) and (b) unadjusted land falling U.S. hur-
ricanes (adapted from Vecchi and Knutson (2011) and (c) land-falling typhoons in China
(adapted from CMA, 2011). Vertical axis ticks represent one standard deviation, with all
series normalized to unit standard deviation after a 5-year running mean was applied.
217
Observations: Atmosphere and Surface Chapter 2
2
Arguably, storm frequency is of limited usefulness if not considered
in tandem with intensity and duration measures. Intensity measures
in historical records are especially sensitive to changing technology
and improving methodology. However, over the satellite era, increases
in the intensity of the strongest storms in the Atlantic appear robust
(Kossin et al., 2007; Elsner et al., 2008) but there is limited evidence
for other regions and the globe. Time series of cyclone indices such
as power dissipation, an aggregate compound of tropical cyclone
frequency, duration and intensity that measures total wind energy
by tropical cyclones, show upward trends in the North Atlantic and
weaker upward trends in the western North Pacific since the late 1970s
(Emanuel, 2007), but interpretation of longer-term trends is again con-
strained by data quality concerns (Landsea et al., 2011).
In summary, this assessment does not revise the SREX conclusion of
low confidence that any reported long-term (centennial) increases in
tropical cyclone activity are robust, after accounting for past changes
in observing capabilities. More recent assessments indicate that it is
unlikely that annual numbers of tropical storms, hurricanes and major
hurricanes counts have increased over the past 100 years in the North
Atlantic basin. Evidence, however, is for a virtually certain increase in
the frequency and intensity of the strongest tropical cyclones since the
1970s in that region.
2.6.4 Extratropical Storms
AR4 noted a likely net increase in frequency/intensity of NH extreme
extratropical cyclones and a poleward shift in storm tracks since the
1950s. SREX further consolidated the AR4 assessment of poleward
shifting storm tracks, but revised the assessment of the confidence
levels associated with regional trends in the intensity of extreme extra-
tropical cyclones.
Studies using reanalyses continue to support a northward and eastward
shift in the Atlantic cyclone activity during the last 60 years with both
more frequent and more intense wintertime cyclones in the high-lati-
tude Atlantic (Schneidereit et al., 2007; Raible et al., 2008; Vilibic and
Sepic, 2010) and fewer in the mid-latitude Atlantic (Wang et al., 2006b;
Raible et al., 2008). Some studies show an increase in intensity and
number of extreme Atlantic cyclones (Paciorek et al., 2002; Lehmann
et al., 2011) while others show opposite trends in eastern Pacific and
North America (Gulev et al., 2001). Comparisons between studies are
hampered because of the sensitivities in identification schemes and/
or different definitions for extreme cyclones (Ulbrich et al., 2009; Neu
et al., 2012). The fidelity of research findings also rests largely with
the underlying reanalyses products that are used (Box 2.3). See also
Section 14.6.2.
Over longer periods studies of severe storms or storminess have been
performed for Europe where long running in situ pressure and wind
observations exist. Direct wind speed measurements, however, either
have short records or are hampered by inconsistencies due to changing
instrumentation and observing practice over time (Smits et al., 2005;
Wan et al., 2010). In most cases, therefore wind speed or storminess
proxies are derived from in situ pressure measurements or reanalyses
data, the quality and consistency of which vary. In situ observations
indicate no clear trends over the past century or longer (Hanna et al.,
2008; Matulla et al., 2008; Allan et al., 2009; Barring and Fortuniak,
2009), with substantial decadal and longer fluctuations but with some
regional and seasonal trends (Wang et al., 2009c, 2011). Figure 2.35
shows some of these changes for boreal winter using geostrophic wind
speeds indicating that decreasing trends outnumber increasing trends
(Wang et al., 2011), although with few that are statistically significant.
Although Donat et al. (2011) and Wang et al. (2012h) find significant
increases in both the strength and frequency of wintertime storms for
large parts of Europe using the 20CR (Compo et al., 2011), there is
debate over whether this is an artefact of the changing number of
assimilated observations over time (Cornes and Jones, 2011; Krueger
et al., 2013) even though Wang et al. (2012h) find good agreement
between the 20CR trends and those derived from geostropic wind
extremes in the North Sea region.
SREX noted that available studies using reanalyses indicate a decrease
in extratropical cyclone activity (Zhang et al., 2004) and intensity
(Zhang et al., 2004; Wang et al., 2009d) over the last 50 years has been
reported for northern Eurasia (60°N to 40°N) linked to a possible north-
ward shift with increased cyclone frequency in the higher latitudes and
decrease in the lower latitudes. The decrease at lower latitudes was
also found in East Asia (Wang et al., 2012h) and is also supported by a
study of severe storms by Zou et al. (2006b) who used sub-daily in situ
pressure data from a number of stations across China.
SREX also notes that, based on reanalyses, North American cyclone
numbers have increased over the last 50 years, with no statistically
significant change in cyclone intensity (Zhang et al., 2004). Hourly
SLP data from Canadian stations showed that winter cyclones have
become significantly more frequent, longer lasting, and stronger in
the lower Canadian Arctic over the last 50 years (1953–2002), but
less frequent and weaker in the south, especially along the southeast
and southwest Canadian coasts (Wang et al., 2006a). Further south, a
tendency toward weaker low-pressure systems over the past few dec-
ades was found for U.S. east coast winter cyclones using reanalyses,
but no statistically significant trends in the frequency of occurrence of
systems (Hirsch et al., 2001).
Using the 20CR (Compo et al., 2011), Wang et al. (2012h) found sub-
stantial increases in extratropical cyclone activity in the SH (20°S to
90°S). However, for southeast Australia, a decrease in activity is found
and this agrees well with geostrophic wind extremes derived from
in situ surface pressure observations (Alexander et al., 2011). This
strengthens the evidence of a southward shift in storm tracks previous-
ly noted using older reanalyses products (Fyfe, 2003; Hope et al., 2006).
Frederiksen and Frederiksen (2007) linked the reduction in cyclogenesis
at 30°S and southward shift to a decrease in the vertical mean meridi-
onal temperature gradient. There is some inconsistency among reanal-
ysis products for the SH regarding trends in the frequency of intense
extratropical cyclones (Lim and Simmonds, 2007; Pezza et al., 2007;
Lim and Simmonds, 2009) although studies tend to agree on a trend
towards more intense systems, even when inhomogeneities associated
with changing numbers of observations have been taken into account
(Wang et al., 2012h). However, further undetected contamination of
these trends owing to issues with the reanalyses products cannot be
ruled out (Box 2.3) and this lowers our confidence in long-term trends.
Links between extratropical cyclone activity and large-scale variability
are discussed in Sections 2.7 and 14.6.2.
218
Chapter 2 Observations: Atmosphere and Surface
2
Frequently Asked Questions
FAQ 2.2 | Have There Been Any Changes in Climate Extremes?
There is strong evidence that warming has lead to changes in temperature extremes—including heat waves—since
the mid-20th century. Increases in heavy precipitation have probably also occurred over this time, but vary by
region. However, for other extremes, such as tropical cyclone frequency, we are less certain, except in some limited
regions, that there have been discernable changes over the observed record.
From heat waves to cold snaps or droughts to flooding rains, recording and analysing climate extremes poses
unique challenges, not just because these events are rare, but also because they invariably happen in conjunction
with disruptive conditions. Furthermore, there is no consistent definition in the scientific literature of what consti-
tutes an extreme climatic event, and this complicates comparative global assessments.
Although, in an absolute sense, an extreme climate event will vary from place to place—a hot day in the tropics,
for instance, may be a different temperature to a hot day in the mid-latitudes—international efforts to monitor
extremes have highlighted some significant global changes.
For example, using consistent definitions for cold
(<10th percentile) and warm (>90th percentile) days
and nights it is found that warm days and nights have
increased and cold days and nights have decreased for
most regions of the globe; a few exceptions being cen-
tral and eastern North America, and southern South
America but mostly only related to daytime tempera-
tures. Those changes are generally most apparent in
minimum temperature extremes, for example, warm
nights. Data limitations make it difficult to establish
a causal link to increases in average temperatures,
but FAQ 2.2, Figure 1 indicates that daily global tem-
perature extremes have indeed changed. Whether
these changes are simply associated with the average
of daily temperatures increasing (the dashed lines in
FAQ 2.2, Figure 1) or whether other changes in the
distribution of daytime and nighttime temperatures
have occurred is still under debate.
Warm spells or heat waves, that is, periods contain-
ing consecutive extremely hot days or nights, have
also been assessed, but there are fewer studies of
heat wave characteristics than those that compare
changes in merely warm days or nights. Most global
land areas with available data have experienced more
heat waves since the middle of the 20th century. One
exception is the south-eastern USA, where heat wave
frequency and duration measures generally show
decreases. This has been associated with a so-called
‘warming hole’ in this region, where precipitation
has also increased and may be related to interactions
between the land and the atmosphere and long-term
variations in the Atlantic and Pacific Oceans. Howev-
er, for large regions, particularly in Africa and South
America, information on changes in heatwaves is
limited.
For regions such as Europe, where historical temperature reconstructions exist going back several hundreds of
years, indications are that some areas have experienced a disproportionate number of extreme heat waves in
recent decades. (continued on next page)
Daily Maximum
Temperatures
Daily Minimum
Temperatures
Probability Probability
0.08
0.06
0.04
0.02
Temperature Anomaly (ºC)
0.08
0.06
0.04
0.02
-15-10 -5
05
10 15
(a)
(b)
FAQ 2.2, Figure 1 | Distribution of (a) daily minimum and (b) daily maxi-
mum temperature anomalies relative to a 1961–1990 climatology for two peri-
ods: 1951–1980 (blue) and 1981–2010 (red) using the HadGHCND data set.
The shaded blue and red areas represent the coldest 10% and warmest 10%
respectively of (a) nights and (b) days during the 1951–1980 period. The darker
shading indicates by how much the number of the coldest days and nights has
reduced (dark blue) and by how much the number of the warmest days and
nights has increased (dark red) during the 1981–2010 period compared to the
1951–1980 period.
219
Observations: Atmosphere and Surface Chapter 2
2
Changes in extremes for other climate variables are generally less coherent than those observed for temperature,
owing to data limitations and inconsistencies between studies, regions and/or seasons. However, increases in pre-
cipitation extremes, for example, are consistent with a warmer climate. Analyses of land areas with sufficient data
indicate increases in the frequency and intensity of extreme precipitation events in recent decades, but results vary
strongly between regions and seasons. For instance, evidence is most compelling for increases in heavy precipitation
in North America, Central America and Europe, but in some other regions—such as southern Australia and western
Asia—there is evidence of decreases. Likewise, drought studies do not agree on the sign of the global trend, with
regional inconsistencies in trends also dependent on how droughts are defined. However, indications exist that
droughts have increased in some regions (e.g., the Mediterranean) and decreased in others (e.g., central North
America) since the middle of the 20th century.
Considering other extremes, such as tropical cyclones, the latest assessments show that due to problems with past
observing capabilities, it is difficult to make conclusive statements about long-term trends. There is very strong evi-
dence, however, that storm activity has increased in the North Atlantic since the 1970s.
Over periods of a century or more, evidence suggests slight decreases in the frequency of tropical cyclones making
landfall in the North Atlantic and the South Pacific, once uncertainties in observing methods have been considered.
Little evidence exists of any longer-term trend in other ocean basins. For extratropical cyclones, a poleward shift is
evident in both hemispheres over the past 50 years, with further but limited evidence of a decrease in wind storm
frequency at mid-latitudes. Several studies suggest an increase in intensity, but data sampling issues hamper these
assessments.
FAQ 2.2, Figure 2 summarizes some of the observed changes in climate extremes. Overall, the most robust global
changes in climate extremes are seen in measures of daily temperature, including to some extent, heat waves.
Precipitation extremes also appear to be increasing, but there is large spatial variability, and observed trends in
droughts are still uncertain except in a few regions. While robust increases have been seen in tropical cyclone fre-
quency and activity in the North Atlantic since the 1970s, the reasons for this are still being debated. There is limited
evidence of changes in extremes associated with other climate variables since the mid-20th century.
Hot Days and Nights;
Warm Spells and Heat Waves
Strongest Tropical Cyclones North Atlantic
Droughts Mediterranean,
West Africa
Cold Days and Nights
Droughts Central North America
Northwest Australia
Heavy Precipitation Events
FAQ 2.2, Figure 2 | Trends in the frequency (or intensity) of various climate extremes (arrow direction denotes the sign of the change) since the middle of the 20th
century (except for North Atlantic storms where the period covered is from the 1970s).
FAQ 2.2 (continued)
220
Chapter 2 Observations: Atmosphere and Surface
2
45N
Stockholm
Jan Mayen
Milan
Torshavn
Paris-Orly
Lisboa
Valentia
Aberdeen
Kremsmuenster
de Bilt
Bodo
Bergen
Vestervig
Madrid
La_corunya
Gibraltar
Barcelona
15W
-1
0
1
1900 1950 2000
-1
0
1
1900
1950
2000
-1
0
1
1900 1950 2000
-1
0
1
1900 1950 2000
-1
0
1
1900 1950 2000
-1
0
1
1900 1950 2000
-1
0
1
1900 1950 2000
-1
0
1
1900 1950 2000
-1
0
1
1900 1950 2000
-1
0
1
1900 1950 2000
-1
0
1
1900 1950 2000
-1
0
1
1900 1950 2000
-1
0
1
1900 1950 2000
-1
0
1
1900 1950 2000
-1
0
1
1900 1950 2000
-1
0
1
1900 1950 2000
-1
0
1
1900 1950 2000
-1
0
1
1900 1950 2000
-1
0
1
1900 1950 2000
-1
0
1
1900 1950 2000
-1
0
1
1900 1950 2000
-1
0
1
1900 1950 2000
-1
0
1
1900
1950 2000
-1
0
1
1900 1950
2000
Figure 2.35 | 99th percentiles of geostrophic wind speeds for winter (DJF). Triangles show regions where geostrophic wind speeds have been calculated from in situ surface pres-
sure observations. Within each pressure triangle, Gaussian low-pass filtered curves and estimated linear trends of the 99th percentile of these geostrophic wind speeds for winter
are shown. The ticks of the time (horizontal) axis range from 1875 to 2005, with an interval of 10 years. Disconnections in lines show periods of missing data. Red (blue) trend lines
indicate upward (downward) significant trends (i.e., a trend of zero lies outside the 95% confidence interval). (From Wang et al., 2011.)
Studies that have examined trends in wind extremes from observa-
tions or regional reanalysis products tend to point to declining trends
in extremes in mid-latitudes (Pirazzoli and Tomasin, 2003; Smits et al.,
2005; Pryor et al., 2007; Zhang et al., 2007b) and increasing trends
in high latitudes (Lynch et al., 2004; Turner et al., 2005; Hundecha et
al., 2008; Stegall and Zhang, 2012). Other studies have compared the
trends from observations with reanalysis data and reported differing
or even opposite trends in the reanalysis products (Smits et al., 2005;
McVicar et al., 2008). On the other hand, declining trends reported
by Xu et al. (2006b) over China between 1969 and 2000 were gener-
ally consistent with trends in NCEP reanalysis. Trends extracted from
reanalysis products must be treated with caution however, although
usually with later generation products providing improvements over
older products (Box 2.3).
In summary, confidence in large scale changes in the intensity of
extreme extratropical cyclones since 1900 is low. There is also low con-
fidence for a clear trend in storminess proxies over the last century
due to inconsistencies between studies or lack of long-term data in
some parts of the world (particularly in the SH). Likewise, confidence in
trends in extreme winds is low, owing to quality and consistency issues
with analysed data.
221
Observations: Atmosphere and Surface Chapter 2
2
Box 2.4 | Extremes Indices
As SREX highlighted, there is no unique definition of what constitutes a climate extreme in the scientific literature given variations in
regions and sectors affected (Stephenson et al., 2008). Much of the available research is based on the use of so-called ‘extremes indices’
(Zhang et al., 2011). These indices can either be based on the probability of occurrence of given quantities or on absolute or percentage
threshold exceedances (relative to a fixed climatological period) but also include more complex definitions related to duration, intensity
and persistence of extreme events. For example, the term ‘heat wave’ can mean very different things depending on the index formula-
tion for the application for which it is required (Perkins and Alexander, 2012).
Box 2.4, Table 1 lists a number of specific indices that appear widely in the literature and have been chosen to provide some consistency
across multiple chapters in AR5 (along with the location of associated figures and text). These indices have been generally chosen for
their robust statistical properties and their applicability across a wide range of climates. Another important criterion is that data for
these indices are broadly available over both space and time. The existing near-global land-based data sets cover at least the post-1950
period but for regions such as Europe, North America, parts of Asia and Australia much longer analyses are available. The same indices
used in observational studies (this chapter) are also used to diagnose climate model output (Chapters 9, 10, 11 and 12).
The types of indices discussed here do not include indices such as NIÑO3 representing positive and negative phases of ENSO (Box 2.5),
nor do they include extremes such as 1 in 100 year events. Typically extreme indices assessed here reflect more ‘moderate’ extremes,
for example, events occurring as often as 5% or 10% of the time (Box 2.4, Table 1). Predefined extreme indices are usually easier to
obtain than the underlying daily climate data, which are not always freely exchanged by meteorological services. However, some of
these indices do represent rarer events, for example, annual maxima or minima. Analyses of these and rarer extremes (e.g., with longer
Box 2.4, Table 1 | Definitions of extreme temperature and precipitation indices used in IPCC (after Zhang et al., 2011). The most common units are shown but these
may be shown as normalized or relative depending on application in different chapters.
Index Descriptive name Definition Units Figures/Tables Section
TXx Warmest daily Tmax Seasonal/annual maximum value of daily maximum
temperature
ºC Box 2.4, Figure 1, Figures 9.37,
10.17, 12.13
Box 2.4, 9.5.4.1, 10.6.1.1,
12.4.3.3
TNx Warmest daily Tmin Seasonal/annual maximum value of daily minimum
temperature
ºC Figures 9.37, 10.17 9.5.4.1, 10.6.1.1
TXn Coldest daily Tmax Seasonal/annual minimum value of daily maximum
temperature
ºC Figures 9.37, 10.17, 12.13 9.5.4.1, 10.6.1.1, 12.4.3.3
TNn Coldest daily Tmin Seasonal/annual minimum value of daily minimum
temperature
ºC Figures 9.37, 10.17, 12.13 9.5.4.1, 10.6.1.1
TN10p Cold nights Days (or fraction of time) when daily minimum
temperature <10th percentile
Days (%) Figures 2.32, 9.37, 10.17
Tables 2.11, 2.12
2.6.1, 9.5.4.1, 10.6.1.1,
11.3.2.5.1
TX10p Cold days Days (or fraction of time) when daily maximum
temperature <10th percentile
Days (%) Figures 2.32, 9.37, 10.17, 11.17 2.6.1, 9.5.4.1, 10.6.1.1,
11.3.2.5.1,
TN90p Warm nights Days (or fraction of time) when daily minimum
temperature >90th percentile
Days (%) Figures 2.32, 9.37, 10.17
Tables 2.11, 2.12
2.6.1, 9.5.4.1, 10.6.1.1,
11.3.2.5.1
TX90p Warm days Days (or fraction of time) when daily maximum
temperature >90th percentile
Days (%) Figures 2.32, 9.37, 10.17, 11.17
Tables 2.11, 2.12
2.6.1, 9.5.4.1, 10.6.1.1,
11.3.2.5.1,
FD Frost days Frequency of daily minimum temperature <0°C Days Figures 9.37, 12.13
Table 2.12
2.6.1, 9.5.4.1, 10.6.1.1,
12.4.3.3
TR Tropical nights Frequency of daily minimum temperature >20°C Days Figures 9.37, 12.13 9.5.4.1, 12.4.3.3
RX1day Wettest day Maximum 1-day precipitation mm Figures 9.37, 10.10
Table 2.12, 12.27
2.6.2.1, 9.5.4.1, 10.6.1.2,
12.4.5.5
RX5day Wettest consecutive five days Maximum of consecutive 5-day precipitation mm Figures 9.37, 12.26, 14.1 9.5.4.1, 10.6.1.2, 12.4.5.5,
14.2.1
SDII Simple daily intensity index Ratio of annual total precipitation to the number of
wet days (≥1 mm)
mm day
–1
Figures 2.33, 9.37, 14.1 2.6.2.1, 9.5.4.1, 14.2.1
R95p Precipitation from very wet
days
Amount of precipitation from days >95th percentile mm Figures 2.33, 9.37, 11.17
Table 2.12
2.6.2.1, 9.5.4.1, 11.3.2.5.1
CDD Consecutive dry days Maximum number of consecutive days when
precipitation <1 mm
Days Figures 2.33, 9.37, 12.26, 14.1 2.6.2.3, 9.5.4.1, 12.4.5.5,
14.2.1
(continued on next page)
222
Chapter 2 Observations: Atmosphere and Surface
2
return period thresholds) are making their way into a
growing body of literature which, for example, are using
Extreme Value Theory (Coles, 2001) to study climate
extremes (Zwiers and Kharin, 1998; Brown et al., 2008;
Sillmann et al., 2011; Zhang et al., 2011; Kharin et al.,
2013).
Extreme indices are more generally defined for daily
temperature and precipitation characteristics (Zhang
et al., 2011) although research is developing on the
analysis of sub-daily events but mostly only on regional
scales (Sen Roy, 2009; Shiu et al., 2009; Jones et al.,
2010; Jakob et al., 2011; Lenderink et al., 2011; Shaw
et al., 2011). Temperature and precipitation indices
are sometimes combined to investigate ‘extremeness’
(e.g., hydroclimatic intensity, HY-INT; Giorgi et al., 2011)
and/or the areal extent of extremes (e.g., the Climate
Extremes Index (CEI) and its variants (Gleason et al.,
2008; Gallant and Karoly, 2010; Ren et al., 2011). Indi-
ces rarely include other weather and climate variables,
such as wind speed, humidity or physical impacts (e.g.,
streamflow) and phenomena. Some examples are avail-
able in the literature for wind-based (Della-Marta et al.,
2009) and pressure-based (Beniston, 2009) indices, for
health-relevant indices combining temperature and rel-
ative humidity characteristics (Diffenbaugh et al., 2007;
Fischer and Schär, 2010) and for a range of dryness or
drought indices (e.g., Palmer Drought Severity Index
(PDSI) Palmer, 1965; Standardised Precipitation Index
(SPI), Standardised Precipitation Evapotranspiration
Index (SPEI) Vicente-Serrano et al., 2010) and wetness
indices (e.g., Standardized Soil Wetness Index (SSWI);
Vidal et al., 2010). (continued on next page)
In addition to the complication of defining an index,
the results depend also on the way in which indices
are calculated (to create global averages, for example).
This is due to the fact that different algorithms may be
employed to create grid box averages from station data,
or that extremes indices may be calculated from grid-
ded daily data or at station locations and then gridded.
All of these factors add uncertainty to the calculation of
an extreme. For example, the spatial patterns of trends
in the hottest day of the year differ slightly between
data sets, although when globally averaged, trends are
similar over the second half of the 20th century (Box
2.4, Figure 1). Further discussion of the parametric and
structural uncertainties in data sets is given in Box 2.1.
Box 2.4 (continued)
Box 2.4, Figure 1 | Trends in the warmest day of the year using different data sets for
the period 1951–2010. The data sets are (a) HadEX2 (Donat et al., 2013c) updated to
include the latest version of the European Climate Assessment data set (Klok and Tank,
2009), (b) HadGHCND (Caesar et al., 2006) using data updated to 2010 (Donat et al.,
2013a) and (c) Globally averaged annual warmest day anomalies for each data set.
Trends were calculated only for grid boxes that had at least 40 years of data during this
period and where data ended no earlier than 2003. Grey areas indicate incomplete or
missing data. Black plus signs (+) indicate grid boxes where trends are significant (i.e., a
trend of zero lies outside the 90% confidence interval). Anomalies are calculated using
grid boxes only where both data sets have data and where 90% of data are available.
(a) HadEX2 1951-2010
(b) HadGHCND 1951-2010
(c) Global land average
Trend (°C per decade)
2.0
1.5
1.0
0.5
0.0
-0.5
1950
-1 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 1
1960 1970 1980 1990 2000
HadEX2
HadGHCND
2010
Temperature anomaly (ºC)
223
Observations: Atmosphere and Surface Chapter 2
2
2.7 Changes in Atmospheric Circulation and
Patterns of Variability
Changes in atmospheric circulation and indices of climate variability, as
expressed in sea level pressure (SLP), wind, geopotential height (GPH),
and other variables were assessed in AR4. Substantial multi-decadal
variability was reported in the large-scale atmospheric circulation over
the Atlantic and the Pacific. With respect to trends, a decrease was
found in tropospheric GPH over high latitudes of both hemispheres
and an increase over the mid-latitudes in boreal winter for the period
1979–2001. These changes were found to be associated with an inten-
sification and poleward displacement of Atlantic and southern mid-lat-
itude jet streams and enhanced storm track activity in the NH from the
1960s to at least the 1990s. Changes in the North Atlantic Oscillation
(NAO) and the Southern Annular Mode (SAM) towards their positive
phases were observed, but it was noted that the NAO returned to its
long-term mean state from the mid-1990s to the early 2000s.
Since AR4, more and improved observational data sets and reanalysis
data sets (Box 2.3) have been published. Uncertainties and inaccuracies
in all data sets are better understood (Box 2.1). The studies since AR4
assessed in this section support the poleward movement of circulation
features since the 1970s and the change in the SAM. At the same time,
large decadal-to-multidecadal variability in atmospheric circulation is
found that partially offsets previous trends in other circulation features
such as the NAO or the Pacific Walker circulation.
This section assesses observational evidence for changes in atmos-
pheric circulation in fields of SLP, GPH, and wind, in circulation features
(such as the Hadley and Walker circulation, monsoons, or jet streams;
Annex III: Glossary), as well as in circulation variability modes. Regional
climate effects of the circulation changes are discussed in Chapter 14.
2.7.1 Sea Level Pressure
AR4 concluded that SLP in December to February decreased between
1948 and 2005 in the Arctic, Antarctic and North Pacific. More recent
studies using updated data for the period 1949–2009 (Gillett and Stott,
2009) also find decreases in SLP in the high latitudes of both hemi-
spheres in all seasons and increasing SLP in the tropics and subtropics
most of the year. However, due to decadal variability SLP trends are
sensitive to the choice of the time period (Box 2.2), and they depend
on the data set.
The spatial distribution of SLP represents the distribution of atmos-
pheric mass, which is the surface imprint of the atmospheric circula-
tion. Barometric measurements are made in weather stations or on
board ships. Fields are produced from the observations by interpolation
or using data assimilation into weather models. One of the most widely
used observational data sets is HadSLP2 (Allan and Ansell, 2006),
which integrates 2228 historical global terrestrial stations with marine
observations from the ICOADS on a 5° × 5°grid. Other observation
products (e.g., Trenberth and Paolino, 1980; for the extratropical NH)
or reanalyses are also widely used to address changes in SLP. Although
the quality of SLP data is considered good, there are discrepancies
between gridded SLP data sets in regions with sparse observations,
e.g., over Antarctica (Jones and Lister, 2007).
Van Haren et al. (2012) found a strong SLP decrease over the Mediter-
ranean in January to March from 1961 to 2000. For the more recent
period (1979–2012) trends in SLP, consistent across different data sets
(shown in Figure 2.36 for ERA-Interim), are negative in the tropical
and northern subtropical Atlantic during most of the year as well as,
in May to October, in northern Siberia. Positive trends are found year-
round over the North and South Pacific and South Atlantic. Trends in
Trend (hPa per decade)
-1.2 -0.6 0 0.6 1.2
Trend (gpm per decade)
-30 -15 015 30
-40 -20 02040
Trend (gpm per decade)
Sea-level pressure 500hPa geopotential height 100hPa geopotential height
Nov-AprMay-Oct
Figure 2.36 | Trends in (left) sea level pressure (SLP), (middle) 500 hPa geopotential height (GPH) and (right) 100 hPa GPH in (top) November to April 1979/1980 to 2011/2012
and (bottom) May to October 1979 to 2011 from ERA-Interim data. Trends are shown only if significant (i.e., a trend of zero lies outside the 90% confidence interval).
224
Chapter 2 Observations: Atmosphere and Surface
2
the equatorial Pacific zonal SLP gradient during the 20th century (e.g.,
Vecchi et al., 2006; Power and Kociuba, 2011a, 2011b) are discussed
in Section 2.7.5.
The position and strength of semi-permanent pressure centres show
no clear evidence for trends since 1951. However, prominent variability
is found on decadal time scales (Figure 2.37). Consistent across differ-
ent data sets, the Azores high and the Icelandic low in boreal winter,
as captured by the high and low SLP contours, were both small in the
1960s and 1970s, large in the 1980s and 1990s, and again smaller in
the 2000s. Favre and Gershunov (2006) find an eastward shift of the
Aleutian low from the mid-1970s to 2001, which persisted during the
2000s (Figure 2.37). The Siberian High exhibits pronounced decad-
al-to-multidecadal variability (Panagiotopoulos et al., 2005; Huang et
al., 2010), with a recent (1998 to 2012) strengthening and northwest-
ward expansion (Zhang et al., 2012b). In boreal summer, the Atlantic and
Pacific high-pressure systems extended more westward in the 1960s
and 1970s than later. On interannual time scales, variations in pressure
centres are related to modes of climate variability. Trends in the indices
that capture the strength of these modes are reported in Section 2.7.8,
their characteristics and impacts are discussed in Chapter 14.
In summary, sea level pressure has likely decreased from 1979 to 2012
over the tropical Atlantic and increased over large regions of the Pacific
and South Atlantic, but trends are sensitive to the time period analysed
owing to large decadal variability.
2.7.2 Surface Wind Speed
AR4 concluded that mid-latitude westerly winds have general-
ly increased in both hemispheres. Because of shortcomings in the
observations, SREX stated that confidence in surface wind trends is
low. Further studies assessed here confirm this assessment.
1004 hPa 1020.5 hPa
November-April
May-October
1961-1970
1971-1980
1981-1990
1991-2000
2001-2010
Figure 2.37 | Decadal averages of sea level pressure (SLP) from the 20th Century Reanalysis (20CR) for (left) November of previous year to April and (right) May to October shown
by two selected contours: 1004 hPa (dashed lines) and 1020.5 hPa (solid lines). Topography above 2 km above mean sea level in 20CR is shaded in dark grey.
Surface wind measurements over land and ocean are based on largely
separate observing systems. Early marine observations were based on
ship speed through the water or sails carried or on visual estimates
of sea state converted to the wind speed using the Beaufort scale.
Anemometer measurements were introduced starting in the 1950s.
The transition from Beaufort to measured winds introduced a spurious
trend, compounded by an increase in mean anemometer height over
time (Kent et al., 2007; Thomas et al., 2008). ICOADS release 2.5 (Wood-
ruff et al., 2011) contains information on measurement methods and
wind measurement heights, permitting adjustment for these effects.
The ICOADS-based data set WASWind (1950–2010; Tokinaga and Xie,
2011a) and the interpolated product NOCS v.2.0 (1973–present; Berry
and Kent, 2011) include such corrections, among other improvements.
Marine surface winds are also measured from space using various
microwave range instruments: scatterometers and synthetic aper-
ture radars retrieve wind vectors, while altimeters and passive radi-
ometers measure wind speed only (Bourassa et al., 2010). The latter
type provides the longest continuous record, starting in July 1987.
Satellite-based interpolated marine surface wind data sets use objec-
tive analysis methods to blend together data from different satellites
and atmospheric reanalyses. The latter provide wind directions as in
Blended Sea Winds (BSW; Zhang et al., 2006), or background fields
as in Cross-Calibrated Multi-Platform winds (CCMP; Atlas et al., 2011)
and OAFlux (Yu and Weller, 2007). CCMP uses additional dynamical
constraints, in situ data and a recently homogenized data set of SSM/I
observations (Wentz et al., 2007), among other satellite sources.
Figure 2.38 compares 1988–2010 linear trends in surface wind speeds
from interpolated data sets based on satellite data, from interpolat-
ed and non-interpolated data sets based on in situ data, and from
atmospheric reanalyses. Note that these trends over a 23-year-long
period primarily reflect decadal variability in winds, rather than long-
225
Observations: Atmosphere and Surface Chapter 2
2
term climate change (Box 2.2). Kent et al. (2012) recently intercom-
pared several of these data sets and found large differences. The differ-
ences in trend patterns in Figure 2.38 are large as well. Nevertheless,
some statistically significant features are present in most data sets,
including a pattern of positive and negative trend bands across the
North Atlantic Ocean (Section 2.7.6.2.) and positive trends along the
west coast of North America. Strengthening of the Southern Ocean
winds, consistent with the increasing trend in the SAM (Section 2.7.8)
and with the observed changes in wind stress fields described in Sec-
tion 3.4.4, can be seen in satellite-based analyses and atmospheric
reanalyses in Figure 2.38. Alternating Southern Ocean trend signs in
the NOCS v.2.0 panel are due to interpolation of very sparse in situ
data (cf. the panel for the uninterpolated WASWind product).
Surface winds over land have been measured with anemometers on a
global scale for decades, but until recently the data have been rarely
used for trend analysis. Global data sets lack important meta informa-
tion on instrumentation and siting (McVicar et al., 2012). Long, homog-
enized instrumental records are rare (e.g., Usbeck et al., 2010; Wan et
al., 2010). Moreover, wind speed trends are sensitive to the anemome-
ter height (Troccoli et al., 2012). Winds near the surface can be derived
from reanalysis products (Box 2.3), but discrepancies are found when
comparing trends therein with trends for land stations (Smits et al.,
2005; McVicar et al., 2008).
Over land, a weakening of seasonal and annual mean as well as max-
imum winds is reported for many regions from around the 1960s or
1970s to the early 2000s (a detailed review is given in McVicar et al.
(2012)), including China and the Tibetan Plateau (Xu et al., 2006b; Guo
et al., 2010) (but levelling off since 2000; Lin et al., 2012), Western and
southern Europe (e.g., Earl et al., 2013), much of the USA (Pryor et
al., 2007), Australia (McVicar et al., 2008) and southern and western
(a) CCMP (b) OAFlux (c) BSW
(d) ERA−Interim (e) NNR (f) 20CR
(g) NOCS v2.0 (h) WASWind (i) Surface winds on the land
Trend (m s
-1
per decade)
−0.5 −0.4 −0.3 −0.2 −0.1 0 0.1 0.2 0.3 0.4 0.5
Figure 2.38 | Trends in surface wind speed for 1988–2010. Shown in the top row are data sets based on the satellite wind observations: (a) Cross-Calibrated Multi-Platform wind
product (CCMP; Atlas et al., 2011); (b) wind speed from the Objectively Analyzed Air-Sea Heat Fluxes data set, release 3 (OAFlux); (c) Blended Sea Winds (BSW; Zhang et al., 2006); in
the middle row are data sets based on surface observations: (d) ERA-Interim; (e) NCEP-NCAR, v.1 (NNR); (f) 20th Century Reanalysis (20CR, Compo et al., 2011), and in the bottom
row are surface wind speeds from atmospheric reanalyses: (g) wind speed from the Surface Flux Data set, v.2, from NOC, Southampton, UK (Berry and Kent, 2009); (h) Wave- and
Anemometer-based Sea Surface Wind (WASWind; Tokinaga and Xie, 2011a)); and (i) Surface Winds on the Land (Vautard et al., 2010). Wind speeds correspond to 10 m heights
in all products. Land station winds (panel f) are also for 10 m (but anemometer height is not always reported) except for the Australian data where they correspond to 2 m height.
To improve readability of plots, all data sets (including land station data) were averaged to the 4° × 4° uniform longitude-latitude grid. Trends were computed for the annually
averaged timeseries of 4° × 4° cells. For all data sets except land station data, an annual mean was considered available only if monthly means for no less than eight months were
available in that calendar year. Trend values were computed only if no less than 17 years had values and at least 1 year was available among the first and last 3 years of the period.
White areas indicate incomplete or missing data. Black plus signs (+) indicate grid boxes where trends are significant (i.e., a trend of zero lies outside the 90% confidence interval).
226
Chapter 2 Observations: Atmosphere and Surface
2
Canada (Wan et al., 2010). Increasing wind speeds were found at high
latitudes in both hemispheres, namely in Alaska from 1921 to 2001
(Lynch et al., 2004), in the central Canadian Arctic and Yukon from the
1950 to the 2000s (Wan et al., 2010) and in coastal Antarctica over the
second half of the 20th century (Turner et al., 2005). A global review
of 148 studies showed that near-surface terrestrial wind speeds are
declining in the Tropics and the mid-latitudes of both hemispheres at
a rate of −0.14 m s
−1
per decade (McVicar et al., 2012). Vautard et al.
(2010), analysing a global land surface wind data set from 1979 to
2008, found negative trends on the order of –0.1 m s
–1
per decade
over large portions of NH land areas. The wind speed trend pattern
over land inferred from their data (1988–2010, Figure 2.38) has many
points with magnitudes much larger than those in the reanalysis prod-
ucts, which appear to underestimate systematically the wind speed
over land, as well as in coastal regions (Kent et al., 2012).
In summary, confidence is low in changes in surface wind speed over
the land and over the oceans owing to remaining uncertainties in data
sets and measures used.
2.7.3 Upper-Air Winds
In contrast to surface winds, winds above the planetary bounda-
ry layer have received little attention in AR4. Radiosondes and pilot
balloon observations are available from around the 1930s (Stickler et
al., 2010). Temporal inhomogeneities in radiosonde wind records are
less common, but also less studied, than those in radiosonde temper-
ature records (Gruber and Haimberger, 2008; Section 2.4.4.3). Upper
air winds can also be derived from tracking clouds or water vapour
in satellite imagery (Menzel, 2001) or from measurements using wind
profilers, aircraft or thermal observations, all of which serve as an input
to reanalyses (Box 2.3).
In the past few years, interest in an accurate depiction of upper air
winds has grown, as they are essential for estimating the state and
changes of the general atmospheric circulation and for explaining
changes in the surface winds (Vautard et al., 2010). Allen and Sher-
wood (2008), analysing wind shear from radiosonde data, found sig-
nificant positive zonal mean zonal wind trends in the northern extrat-
ropics in the upper troposphere and stratosphere and negative trends
in the tropical upper troposphere for the period 1979–2005. Vautard
et al. (2010) find increasing wind speed in radiosonde observations
in the lower and middle troposphere from 1979 to 2008 over Europe
and North America and decreasing wind speeds over Central and East
Asia. However, systematic global trend analyses of radiosonde winds
are rare, prohibiting an assessment of upper-air wind trends (specific
features such as monsoons, jet streams and storms are discussed in
Sections 2.7.5, 2.7.6 and 2.6, respectively).
In summary, upper-air winds are less studied than other aspects of the
circulation, and less is known about the quality of data products, hence
confidence in upper-air wind trends is low.
2.7.4 Tropospheric Geopotential Height and Tropopause
AR4 concluded that over the NH between 1960 and 2000, boreal
winter and annual means of tropospheric GPH decreased over high
latitudes and increased over the mid-latitudes. AR4 also reported an
increase in tropical tropopause height and a slight cooling of the trop-
ical cold-point tropopause.
Changes in GPH, which can be addressed using radiosonde data or
reanalysis data (Box 2.3), reflect SLP and temperature changes in the
atmospheric levels below. The spatial gradients of the trend indicate
changes in the upper-level circulation. As for SLP, tropopsheric GPH
trends strongly depend on the period analysed due to pronounced dec-
adal variability. For the 1979–2012 period, trends for 500 hPa GPH
from the ERA-Interim reanalysis (Figure 2.36) as well as for other rea-
nalyses show a significant decrease only at southern high latitudes in
November to April, but significant positive GPH trends in the subtrop-
ics and northern high latitudes. Hence the change in the time period
leads to a different trend pattern as compared to AR4. The seasonality
and spatial dependence of 500 hPa GPH trends over Antarctica was
highlighted by Neff et al. (2008), based upon radiosonde data over the
period 1957–2007.
Minimum temperatures near the tropical tropopause (and therefore
tropical tropopause height) are important as they affect the water
vapour input into the stratosphere (Section 2.2.2.1). Studies since AR4
confirm the increase in tropopause height (Wang et al., 2012c). For
tropical tropopause temperatures, studies based on radiosonde data
and reanalyses partly support a cooling between the 1990s and the
early 2000s (Randel et al., 2006; Randel and Jensen, 2013), but uncer-
tainties in long-term trends of the tropical cold-point tropopause tem-
perature from radiosondes (Wang et al., 2012c; Randel and Jensen,
2013) and reanalyses (Gettelman et al., 2010) are large and confidence
is therefore low.
In summary, tropospheric geopotential height likely decreased from
1979 to 2012 at SH high latitudes in austral summer and increased
in the subtropics and NH high latitudes. Confidence in trends of the
tropical cold-point tropopause is low owing to remaining uncertainties
in the data.
2.7.5 Tropical Circulation
In AR4, large interannual variability of the Hadley and Walker circula-
tion was highlighted, as well as the difficulty in addressing changes in
these features in the light of discrepancies between data sets. AR4 also
found that rainfall in many monsoon systems exhibits decadal chang-
es, but that data uncertainties restrict confidence in trends. SREX also
attributed low confidence to observed trends in monsoons.
Observational evidence for trends and variability in the strength of the
Hadley and Walker circulations (Annex III: Glossary), the monsoons, and
the width of the tropical belt is based on radiosonde and
reanalyses
data (Box 2.3). In addition, changes in the tropical circulation imprint
on other fields that are observed from space (e.g., total ozone, outgo-
ing longwave radiation). Changes in the average state of the tropical
circulation are constrained to some extent by changes in the water
227
Observations: Atmosphere and Surface Chapter 2
2
cycle (Held and Soden, 2006; Schneider et al., 2010). Changes in the
monsoon systems are expressed through altered circulation, moisture
transport and convergence, and precipitation. Only a few monsoon
studies address circulation changes, while most work focuses on pre-
cipitation.
Several studies report a weakening of the global monsoon circulations
as well as a decrease of global land monsoon rainfall or of the number
of precipitation days over the past 40 to 50 years (Zhou et al., 2008,
see also SREX; Liu et al., 2011). Concerning the East Asian Monsoon,
a year-round decrease is reported for wind speeds over China at the
surface and in the lower troposphere based on surface observations
and radiosonde data (Guo et al., 2010; Jiang et al., 2010; Vautard et
al., 2010; Xu et al., 2010). The changes in wind speed are concomi-
tant with changes in pressure centres such as a westward extension
of the Western Pacific Subtropical High (Gong and Ho, 2002; Zhou et
al., 2009b). A weakening of the East Asian summer monsoon since the
1920s is also found in SLP gradients (Zhou et al., 2009a). However,
trends derived from wind observations and circulation trends from
reanalysis data carry large uncertainties (Figure 2.38), and monsoon
rainfall trends depend, for example, on the definition of the monsoon
area (Hsu et al., 2011). For instance, using a new definition of monsoon
area, an increase in northern hemispheric and global summer monsoon
(land and ocean) precipitation is reported from 1979 to 2008 (Hsu et
al., 2011; Wang et al., 2012a).
The additional data sets that became available since AR4 confirm the
large interannual variability of the Hadley and Walker circulation. The
strength of the northern Hadley circulation (Figure 2.39) in boreal
winter and of the Pacific Walker circulation in boreal fall and winter is
largely related to the ENSO (Box 2.5). This association dominates inter-
annual variability and affects trends. Data sets do not agree well with
respect to trends in the Hadley circulation (Figure 2.39). Two widely
used reanalysis data sets, NNR and ERA-40, both have demonstrated
Hadley Circulation indices
Pacific Walker circulation indices
1880 1900 1920 1940 1960 1980 2000
4
2
0
-2
-4
-2
-1
0
1
2
3
2
1
0
-1
-2
0.04
0.02
0
-0.02
-0.04
-0.06
Reanalyses (individual and spread) Reconstructions SSMI ICOADS/WASWIND HADSPL2
Ψ
max
(10
10
kg s
-1
)
ω (Pa s
-1
) SLP (hPa)
-0.5
0.0
0.5
c (octas)
-0.6
-0.4
-0.2
0.0
V
E
(m s
-1
)
u (m s
-1
)
(a)
(b)
Figure 2.39 | (a) Indices of the strength of the northern Hadley circulation in December to March (Ψ
max
is the maximum of the meridional mass stream function at 500 hPa
between the equator and 40°N). (b) Indices of the strength of the Pacific Walker circulation in September to January (Δω is the difference in the vertical velocity between [10°S
to 10°N, 180°W to 100°W] and [10°S to 10°N, 100°E to 150°E] as in Oort and Yienger (1996), Δc is the difference in cloud cover between [6°N to 12°S, 165°E to 149°W] and
[18°N to 6°N, 165°E to 149°W] as in Deser et al. (2010a), v
E
is the effective wind index from SSM/I satellite data, updated from Sohn and Park (2010), u is the zonal wind at 10
m averaged in the region [10°S to 10°N, 160°E to 160°W], ΔSLP is the SLP difference between [5°S to 5°N, 160°W to 80°W] and [5°S to 5°N, 80°E to 160°E] as in Vecchi et
al. (2006)). Reanalysis data sets include 20CR, NCEP/NCAR, ERA-Interim, JRA-25, MERRA, and CFSR, except for the zonal wind at 10 m (20CR, NCEP/NCAR, ERA-Interim), where
available until January 2013. ERA-40 and NCEP2 are not shown as they are outliers with respect to the strength trend of the northern Hadley circulation (Mitas and Clement, 2005;
Song and Zhang, 2007; Hu et al., 2011; Stachnik and Schumacher, 2011). Observation data sets include HadSLP2 (Section 2.7.1), ICOADS (Section 2.7.2; only 1957–2009 data
are shown) and WASWIND (Section 2.7.2), reconstructions are from Brönnimann et al. (2009). Where more than one time series was available, anomalies from the 1980/1981 to
2009/2010 mean values of each series are shown.
228
Chapter 2 Observations: Atmosphere and Surface
2
shortcomings with respect to tropical circulation; hence their increases
in the Hadley circulation strength since the 1970s might be artificial
(Mitas and Clement, 2005; Song and Zhang, 2007; Hu et al., 2011;
Stachnik and Schumacher, 2011). Later generation reanalysis data sets
including ERA-Interim (Brönnimann et al., 2009; Nguyen et al., 2013)
as well as satellite humidity data (Sohn and Park, 2010) also suggest
a strengthening from the mid 1970s to present, but the magnitude is
strongly data set dependent.
Consistent changes in different observed variables suggest a weaken-
ing of the Pacific Walker circulation during much of the 20th century
that has been largely offset by a recent strengthening. A weakening
is indicated by trends in the zonal SLP gradient across the equato-
rial Pacific (Section 2.7.1, Table 2.14) from 1861 to 1992 (Vecchi et
al., 2006), or from 1901 to 2004 (Power and Kociuba, 2011b). Boreal
spring and summer contribute most strongly to the centennial trend
(Nicholls, 2008; Karnauskas et al., 2009), as well as to the trend in the
second half of the 20th century (Tokinaga et al., 2012). For boreal fall
and winter, when the circulation is strongest, no trend is found in the
Pacific Walker circulation based on the vertical velocity at 500 hPa from
reanalyses (Compo et al., 2011), equatorial Pacific 10 m zonal winds, or
SLP in Darwin (Nicholls, 2008; Figure 2.39). However, there are incon-
sistencies between ERA-40 and NNR (Chen et al., 2008). Deser et al.
(2010a) find changes in marine air temperature and cloud cover over
the Pacific that are consistent with a weakening of the Walker circu-
lation during most of the 20th century (Section 2.5.7.1 and Yu and
Zwiers, 2010). Tokinaga et al. (2012) find robust evidence for a weak-
ening of the Walker circulation (most notably over the Indian Ocean)
from 1950 to 2008 based on observations of cloud cover, surface wind,
and SLP. Since the 1980s or 1990s, however, trends in the Pacific Walker
Tropical belt width
1980 1990 2000 2010 1980 1990 2000 2010
1980 1990 2000 2010
38
34
30
68
67
66
80
75
70
65
70
65
60
68
76
64
75
73
71
-30
-34
-38
Northern Hemisphere
Tropical edge latitude (°N)
Tropical edge latitude (°S)
Total tropical width (°lat)
Southern Hemisphere
Ozone
Jet Stream
Hadley Cell
Tropopause
Outgoing Longwave Radiation
circulation have reversed (Figure 2.39; Luo et al., 2012). This is evident
from changes in SLP (see equatorial Southern Oscillation Index (SOI)
trends in Table 2.14 and Box 2.5, Figure 1), vertical velocity (Compo et
al., 2011), water vapour flux from satellite and reanalysis data (Sohn
and Park, 2010), or sea level height (Merrifield, 2011). It is also con-
sistent with the SST trend pattern since 1979 (Meng et al., 2012; see
also Figure 2.22).
Observed changes in several atmospheric parameters suggest that the
width of the tropical belt has increased at least since 1979 (Seidel et
al., 2008; Forster et al., 2011; Hu et al., 2011). Since AR4, wind, tem-
perature, radiation, and ozone information from radiosondes, satellites,
and reanalyses had been used to diagnose the tropical belt width and
estimate their trends. Annual mean time series of the tropical belt
width from various sources are shown in Figure 2.40.
Since 1979 the region of low column ozone values typical of the tropics
has expanded in the NH (Hudson et al., 2006; Hudson, 2012). Based on
radiosonde observations and reanalyses, the region of the high tropical
tropopause has expanded since 1979, and possibly since 1960 (Seidel
and Randel, 2007; Birner, 2010; Lucas et al., 2012), although widening
estimates from different reanalyses and using different methodologies
show a range of magnitudes (Seidel and Randel, 2007; Birner, 2010).
Several lines of evidence indicate that climate features at the edges
of the Hadley cell have also moved poleward since 1979. Subtropi-
cal jet metrics from reanalysis zonal winds (Strong and Davis, 2007,
2008; Archer and Caldeira, 2008b, 2008a) and layer-average satellite
temperatures (Fu et al., 2006; Fu and Lin, 2011) also indicate widening,
although 1979–2009 wind-based trends (Davis and Rosenlof, 2011)
Figure 2.40 | Annual average tropical belt width (left) and tropical edge latitudes in each hemisphere (right). The tropopause (red), Hadley cell (blue), and jet stream (green)
metrics are based on reanalyses (NCEP/NCAR, ERA-40, JRA25, ERA-Interim, CFSR, and MERRA, see Box 2.3); outgoing longwave radiation (orange) and ozone (black) metrics are
based on satellite measurements. The ozone metric refers to equivalent latitude (Hudson et al., 2006; Hudson, 2012). Adapted and updated from Seidel et al. (2008) using data
presented in Davis and Rosenlof (2011) and Hudson (2012). Where multiple data sets are available for a particular metric, all are shown as light solid lines, with shading showing
their range and a heavy solid line showing their median.
229
Observations: Atmosphere and Surface Chapter 2
2
are not statistically significant. Changes in subtropical outgoing long-
wave radiation, a surrogate for high cloud, also suggest widening (Hu
and Fu, 2007), but the methodology and results are disputed (Davis
and Rosenlof, 2011). Widening of the tropical belt is also found in pre-
cipitation patterns (Hu and Fu, 2007; Davis and Rosenlof, 2011; Hu et
al., 2011; Kang et al., 2011; Zhou et al., 2011), including in SH regions
(Cai et al., 2012).
The qualitative consistency of these observed changes in independent
data sets suggests a widening of the tropical belt between at least
1979 and 2005 (Seidel et al., 2008), and possibly longer. Widening esti-
mates range between around 0° and 3° latitude per decade, but their
uncertainties have been only partially explored (Birner, 2010; Davis and
Rosenlof, 2011).
In summary, large interannual-to-decadal variability is found in the
strength of the Hadley and Walker circulation. The confidence in trends
in the strength of the Hadley circulation is low due to uncertainties in
reanalysis data sets. Recent strengthening of the Pacific Walker circu-
lation has largely offset the weakening trend from the 19th century
to the 1990s (high confidence). Several lines of independent evidence
indicate a widening of the tropical belt since the 1970s. The suggested
weakening of the East Asian monsoon has low confidence, given the
nature and quality of the evidence.
2.7.6 Jets, Storm Tracks and Weather Types
2.7.6.1 Mid-latitude and Subtropical Jets and Storm Track
Position
AR4 reported a poleward displacement of Atlantic and southern polar
front jet streams from the 1960s to at least the mid-1990s and a pole-
ward shift of the northern hemispheric storm tracks. However, it was
also noted that uncertainties are large and that NNR and ERA-40 dis-
agree in important aspects. SREX also reported a poleward shift of NH
and SH storm tracks. Studies since AR4 confirm that in the NH, the jet
core has been migrating towards the pole since the 1970s, but trends
in the jet speed are uncertain. Additional studies assessed here further
support the poleward shift of the North Atlantic storm track from the
1950s to the early 2000s.
Subtropical and mid-latitude jet streams are three-dimensional enti-
ties that vary meridionally, zonally, and vertically. The position of the
mid-latitude jet streams is related to the position of the mid-latitude
storm tracks; regions of enhanced synoptic activity due to the pas-
sage of cyclones (Section 2.6). Jet stream winds can be determined
from radiosonde measurements of GPH using quasi-geostrophic flow
assumptions. Using reanalysis data sets (Box 2.3), it is possible to track
three-dimensional jet variations by identifying a surface of maximum
wind (SMW), although a high vertical resolution is required for identi-
fication of jets.
Various new analyses based on NCEP/NCAR and ERA-40 reanalyses as
well as MSU/AMSU lower stratospheric temperatures (Section 2.4.4)
confirm that the jet streams (mid-latitude and subtropical) have been
moving poleward in most regions in the NH over the last three decades
(Fu et al., 2006; Hu and Fu, 2007; Strong and Davis, 2007; Archer and
Caldeira, 2008a; Fu and Lin, 2011) but no clear trend is found in the SH
(Swart and Fyfe, 2012). There is inconsistency with respect to jet speed
trends based upon whether one uses an SMW-based or isobaric-based
approach (Strong and Davis, 2007, 2008; Archer and Caldeira, 2008b,
2008a) and the choice of analysis periods due to inhomogeneities in
reanalyses (Archer and Caldeira, 2008a). In general, jets have become
more common (and jet speeds have increased) over the western and
central Pacific, eastern Canada, the North Atlantic and Europe (Strong
and Davis, 2007; Barton and Ellis, 2009), trends that are concomitant
with regional increases in GPH gradients and circumpolar vortex con-
traction (Frauenfeld and Davis, 2003; Angell, 2006). From a climate
dynamics perspective, these trends are driven by regional patterns of
tropospheric and lower stratospheric warming or cooling and thus are
coupled to large-scale circulation variability.
The North Atlantic storm track is closely associated with the NAO
(Schneidereit et al., 2007). Studies based on ERA-40 reanalysis (Schnei-
dereit et al., 2007), SLP measurements from ships (Chang, 2007), sea
level time series (Vilibic and Sepic, 2010), and cloud analyses (Bender
et al., 2012) support a poleward shift and intensification of the North
Atlantic cyclone tracks from the 1950s to the early 2000s (Sorteberg
and Walsh, 2008; Cornes and Jones, 2011).
2.7.6.2 Weather Types and Blocking
In AR4, weather types were not assessed as such, but an increase in
blocking frequency in the Western Pacific and a decrease in North
Atlantic were noted.
Changes in the frequency of weather types are of interest since weath-
er extremes are often associated with specific weather types. For
instance, persistent blocking of the westerly flow was essential in the
development of the 2010 heat wave in Russia (Dole et al., 2011) (Sec-
tion 9.5.2.2 and Box 14.2). Synoptic classifications or statistical clus-
tering (Philipp et al., 2007) are commonly used to classify the weather
on a given day. Feature-based methods are also used (Croci-Maspoli
et al., 2007a). All these methods require daily SLP or upper-level fields.
Trends in synoptic weather types have been best analysed for central
Europe since the mid-20th century, where several studies describe an
increase in westerly or cyclonic weather types in winter but an increase
of anticyclonic, dry weather types in summer (Philipp et al., 2007;
Werner et al., 2008; Trnka et al., 2009). An eastward shift of blocking
events over the North Atlantic (fewer cases of blocking over Green-
land and more frequent blocking over the eartern North Atlantic) and
the North Pacific was found by Davini et al. (2012) using NCEP/NCAR
reanalysis since 1951 and by Croci-Maspoli et al. (2007a) in ERA-40
reanalysis during the period 1957–2001. Mokhov et al. (2013) find an
increase in blocking duration over the NH year-round since about 1990
in a study based on NCEP/NCAR reanalysis data from 1969–2011. For
the SH, Dong et al. (2008) found a decrease in number of blocking days
but increase in intensity of blocking over the period 1948–1999. Dif-
ferences in blocking index definitions, the sensitivity of some indices to
changes in the mean field, and strong interannual variability in all sea-
sons (Kreienkamp et al., 2010), partly related to circulation variability
modes (Croci-Maspoli et al., 2007b), complicate a global assessment
of blocking trends.
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Chapter 2 Observations: Atmosphere and Surface
2
In summary, there is evidence for a poleward shift of storm tracks and
jet streams since the 1970s. Based on the consistency of these trends
with the widening of the tropical belt (Section 2.7.5), trends that are
based on many different data sets, variables, and approaches, it is likely
that circulation features have moved poleward since the 1970s. Meth-
odological differences between studies mean there is low confidence
in characterizing the global nature of any change in blocking.
2.7.7 Stratospheric Circulation
Changes in the polar vortices were assessed in AR4. A significant
decrease in lower-stratospheric GPH in summer over Antarctica since
1969 was found, whereas trends in the Northern Polar Vortex were
considered uncertain owing to its large variability.
The most important characteristics of the stratospheric circulation for
climate and for trace gas distribution are the winter and spring polar
vortices and Sudden Stratospheric Warmings (rapid warmings of the
middle stratosphere that may lead to a collapse of the Polar Vortex),
the Quasi-Biennial Oscillation (an oscillation of equatorial zonal winds
with a downward phase propagation) and the Brewer-Dobson circu-
lation (BDC, the meridional overturning circulation transporting air
upward in the tropics, poleward to the winter hemisphere, and down-
ward at polar and subpolar latitudes; Annex III: Glossary). Radiosonde
observations, reanalysis data sets and space-borne temperature or
trace gas observations are used to address changes in the stratospher-
ic circulation, but all of these sources of information carry large trend
uncertainties.
The AR4 assessment was corroborated further in Forster et al. (2011)
and in updated 100 hPa GPH trends from ERA-Interim reanalysis (Box
2.3, Figure 2.36). There is high confidence that lower stratospheric GPH
over Antarctica has decreased in spring and summer at least since
1979. Cohen et al. (2009) reported an increase in the number of Arctic
sudden stratospheric warmings during the last two decades. However,
interannual variability in the Arctic Polar Vortex is large, uncertainties in
reanalysis products are high (Tegtmeier et al., 2008), and trends depend
strongly on the time period analysed (Langematz and Kunze, 2008).
The BDC is only indirectly observable via wave activity diagnostics
(which represent the main driving mechanism of the BDC), via tem-
peratures or via the distribution of trace gases which may allow the
determination of the ‘age of air’ (i.e., the time an air parcel has resided
in the stratosphere after its entry from the troposphere). Randel et al.
(2006), found a sudden decrease in global lower stratospheric water
vapour and ozone around 2001 that is consistent with an increase in
the mean tropical upwelling, that is, the tropical branch of the BDC
(Rosenlof and Reid, 2008; Section 2.2.2.1; Lanzante, 2009; Randel and
Jensen, 2013). On the other hand, Engel et al. (2009) found no statisti-
cally significant change in the age of air in the 24-35 km layer over the
NH mid-latitudes from measurements of chemically inert trace gases
from 1975 to 2005. However, this does not rule out trends in the lower
stratospheric branch of the BDC or trends in mid to low latitude mixing
(Bonisch et al., 2009; Ray et al., 2010). All of these methods are subject
to considerable uncertainties, and they might shed light only on some
aspects of the BDC. Confidence in trends in the BDC is therefore low.
In summary, it is likely that lower-stratopheric geopotential height over
Antarctica has decreased in spring and summer at least since 1979.
Owing to uncertainties in the data and approaches used, confidence in
trends in the Brewer–Dobson circulation is low.
2.7.8 Changes in Indices of Climate Variability
AR4 assessed changes in indices of climate variability. The NAO and
SAM were found to exhibit positive trends (strengthened mid-latitude
westerlies) from the 1960s to 1990s, but the NAO has returned to its
long-term mean state since then.
Indices of climate variability describe the state of the climate system
with regards to individual modes of climate variability. Together with
corresponding spatial patterns, they summarize large fractions of spa-
tio-temporal climate variability. Inferences about significant trends in
indices are generally hampered by relative shortness of climate records,
their uncertainties and the presence of large variability on decadal and
multidecadal time scales.
Table 2.14 summarizes observed changes in well-known indices of
climate variability (see Box 2.5, Table 1 for precise definitions). Even
the indices that explicitly include detrending of the entire record (e.g.,
Deser et al., 2010b), can exhibit statistically significant trends over
shorter sub-periods. Confidence intervals in Table 2.14 that do not con-
tain zero indicate trend significance at 10% level; however, the trends
significant at 5% and 1% levels are emphasized in the discussion that
follows. Chapter 14 discusses the main features and physical meaning
of individual climate modes.
The NAO index reached very low values in the winter of 2010 (Osborn,
2011). As a result, with the exception of the principal component
(PC) -based NAO index, which still shows a 5% significant positive
trend from 1951 to present, other NAO or North Annular Mode (NAM)
indices do not show significant trends of either sign for the periods
presented in Table 2.14. In contrast, the SAM maintained the upward
trend (Table 2.14). Fogt et al. (2009) found a positive trend in the SAM
index from 1957 to 2005. Visbeck (2009), in a station-based index,
found an increase in recent decades (1970s to 2000s).
The observed detrended multidecadal SST anomaly averaged over the
North Atlantic Ocean area is often called Atlantic Multi-decadal Oscil-
lation Index (AMO; see Box 2.5, Table 1, Figure 1). The warming trend
in the “revised” AMO index since 1979 is significant at 1% level (Table
2.14) but cannot be readily interpreted because of the difficulty with
reliable removal of the SST warming trend from it (Deser et al., 2010b).
On decadal and inter-decadal time scales the Pacific climate shows
an irregular oscillation with long periods of persistence in individu-
al stages and prominent shifts between them. Pacific Decadal Oscil-
lation (PDO), Inter-decadal Pacific Oscillation (IPO) and North Pacific
Index (NPI) indices characterize this variability for both hemispheres
and agree well with each other (Box 2.5, Figure 1). While AR4 noted
climate impacts of the 1976–1977 PDO phase transition, the shift in
the opposite direction, both in PDO and IPO, may have occurred at the
end of 1990s (Cai and van Rensch, 2012; Dai, 2012). Significance of
1979–2012 trends in PDO and NPI then would be an artefact of this
231
Observations: Atmosphere and Surface Chapter 2
2
Index Name
Trends in standard deviation units per decade
1901–2012 1951–2012 1979–2012
(–1)*SOI from CPC
0.004 ± 0.103 –0.243 ± 0.233
(–1)*SOI Troup from BOM records
0.012 ± 0.039 0.018 ± 0.104 –0.247 ± 0.236
SOI Darwin from BOM records
0.028 ± 0.036 0.082 ± 0.085 –0.116 ± 0.195
(–1)*EQSOI
0.001 ± 0.051 –0.076 ± 0.143 –0.558
b
± 0.297
NIÑO3.4
–0.003 ± 0.042 0.012 ± 0.105 –0.156 ± 0.274
NIÑO3.4 (ERSST v.3b)
0.067
a
± 0.045 0.054 ± 0.103 –0.085 ± 0.259
NIÑO3.4 (COBE SST)
0.024 ± 0.041 0.008 ± 0.107 –0.154 ± 0.289
NIÑO3
0.007 ± 0.039 0.043 ± 0.095 –0.143 ± 0.256
NIÑO3 (ERSST v.3b)
0.069 ± 0.039 0.098 ± 0.092 –0.073 ± 0.236
NIÑO3 (COBE SST)
0.034 ± 0.036 0.054 ± 0.096 –0.113 ± 0.258
NIÑO4
0.026 ± 0.054 0.068 ± 0.145 –0.102 ± 0.380
EMI
–0.059 ± 0.061 –0.119 ± 0.189 –0.131 ± 0.580
(–1)*TNI
0.019 ± 0.052 0.066 ± 0.167 0.030 ± 0.550
PDO from Mantua et al. (1997)
–0.017 ± 0.071 0.112 ± 0.189 –0.460
a
± 0.284
(–1)*NPI
–0.026
a
± 0.022 0.010 ± 0.046 –0.169
a
± 0.105
AMO revised
–0.001 ± 0.111 –0.012 ± 0.341 0.779
b
± 0.291
NAO stations from Jones et al. (1997)
–0.044 ± 0.056 0.095 ± 0.149 –0.136 ± 0.394
NAO stations from Hurrell (1995)
–0.001 ± 0.066 0.171 ± 0.179 –0.214 ± 0.400
NAO PC from Hurrell (1995)
0.012 ± 0.059 0.198
a
± 0.148 –0.037 ± 0.401
NAM PC
0.003 ± 0.048 0.141 ± 0.123 0.029 ± 0.360
SAM Z850 PC
0.268
b
± 0.063 0.100 ± 0.109
SAM SLP grid 40°S to 70°S
0.139
b
± 0.026 0.198
b
± 0.052 0.294
b
± 0.131
SAM SLP stations from Marshall (2003)
0.128
a
± 0.097
PNA CoA
0.113 ± 0.114 –0.103 ± 0.298
PNA RPC from CPC
0.202
b
± 0.111 0.019 ± 0.271
PSA Karoly (1989) CoA definition
–0.267
b
± 0.079 –0.233
a
± 0.174
(–1)*PSA Yuan and Li (2008) CoA definition
–0.211
b
± 0.069 –0.208 ± 0.189
PSA1 PC
–0.163
a
± 0.103 –0.368
a
± 0.245
PSA2 PC
0.200
b
± 0.066 0.036 ± 0.156
ATL3
0.035 ± 0.043 0.125
a
± 0.088 0.186 ± 0.193
AONM PC
0.064
a
± 0.051 0.138
a
± 0.109 0.327
a
± 0.230
AMM PC
0.019 ± 0.058 –0.015 ± 0.155 0.309 ± 0.324
IOBM PC
0.075
a
± 0.051 0.314
b
± 0.082 0.201 ± 0.206
BMI
0.072
a
± 0.050 0.294
b
± 0.083 0.189 ± 0.206
IODM PC
–0.016 ± 0.034 –0.031 ± 0.093 –0.052 ± 0.203
DMI
0.030 ± 0.033 0.080 ± 0.090 0.211 ± 0.210
Table 2.14 | Trends for selected indices listed in Box 2.5, Table 1. Each index was standardized for its longest available period contained within the 1870–2012 interval. Standard-
ization was done on the December-to-March (DJFM) means for the NAO, NAM and Pacific-North American pattern (PNA), on seasonal anomalies for Pacific-South American patterns
(PSA1,PSA2) and on monthly anomalies for all other indices. Standardized monthly and seasonal anomalies were further averaged to annual means. Trend values computed for
annual or DJFM means are given in standard deviation per decade with their 90% confidence intervals. Index records where the source is not explicitly indicated were computed
from either HadISST1 (for SST-based indices), or HadSLP2r (for SLP-based indices) or NNR fields of 500 hPa or 850 hPa geopotential height. CoA stands for ‘Centers of Action’ index
definitions. Linear trends for 1870–2012 were removed from ATL3, BMI and DMI.
Notes:
a
Trend values significant at the 5% level.
b
Trend values significant at the 1% level.
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Chapter 2 Observations: Atmosphere and Surface
2
change; incidentally, no significant trends in these indices were seen
for longer periods (Table 2.14). Nevertheless, Pacific changes since the
1980s (positive for NPI and negative for PDO and IPO) are consist-
ent with the observed SLP changes (Section 2.7.1) and with reversing
trends in the Walker Circulation (Section 2.7.5), which was reported to
be slowing down during much of the 20th century but sped up again
since the 1990s. Equatorial SOI shows an increasing trend since 1979
at 1% significance; more traditionally defined SOI indices do not show
significant trends (Table 2.14).
NIÑO3.4 and NIÑO3 show a century-scale warming trend significant at
5% level, if computed from the ERSSTv3b data set (Section 2.4.2) but
not if calculated from other data sets (Table 2.14). Furthermore, the
sign (and significance) of the trend in east–west SST gradient across
the Pacific remains ambiguous (Vecchi and Soden, 2007; Bunge and
Clarke, 2009; Karnauskas et al., 2009; Deser et al., 2010a) (Section
14.4.1).
In addition to changes in the mean values of climate indices, changes
in the associated spatial patterns are also possible. In particular, the
diversity of detail of different ENSO events and possible distinction
between their “flavors” have received significant attention (Section
14.4.2). These efforts also intensified the discussion of useful ENSO
indices in the literature. Starting from the work of Trenberth and Stepa-
niak (2001), who proposed to characterize the evolution of ENSO
events with the Trans-Niño Index (TNI), which is virtually uncorrelated
with the standard ENSO index NIÑO3.4, other alternative ENSO indices
have been introduced and proposals were made for classifying ENSO
events according to the indices they primarily maximize. While a tradi-
tional, ‘canonical’ El Niño event type (Rasmusson and Carpenter, 1982)
is viewed as the ‘eastern Pacific’ type, some of the alternative indices
purport to identify events that have central Pacific maxima and are
called dateline El Niño (Larkin and Harrison, 2005), Modoki (Ashok et
al., 2007), or Central Pacific El Niño (Kao and Yu, 2009). However, no
consensus has been reached regarding the appropriate classification
of ENSO events. Takahashi et al. (2011) and Ren and Jin (2011) have
presented many of the popular ENSO indices as elements in a two-di-
mensional linear space spanned by a pair of such indices. ENSO indices
that involve central and western Pacific SST (NIÑO4, EMI, TNI) show no
significant trends.
Significant positive PNA trends and negative and positive trends in
the first and second PSA modes respectively are observed over the
last 60 years (Table 2.14). However, the level of significance of these
trends depends on the index definition and on the data set used. The
positive trend in the Atlantic Ocean ‘Niño’ mode (AONM) index and in
ATL3 are due to the intensified warming in the eastern Tropical Atlantic
that causes the the weakening of the Atlantic equatorial cold tongue:
these changes were noticed by Tokinaga and Xie (2011b) with regards
to the last 60-year period. The Indian Ocean Basin Mode (IOBM) has a
strong warming trend (significant at 1% since the middle of the 20th
century). This phenomenon is well-known (Du and Xie, 2008) and its
consequences for the regional climate is a subject of active research
(Du et al., 2009; Xie et al., 2009).
In summary, large variability on interannual to decadal time scales and
remaining differences between data sets precludes robust conclusions
on long-term changes in indices of climate variability. Confidence is
high that the increase in the NAO index from the 1950s to the 1990s
has been largely offset by recent changes. It is likely that the SAM
index has become more positive since the 1950s.
Box 2.5 | Patterns and Indices of Climate Variability
Much of the spatial structure of climate variability can be described as a combination of ‘preferred’ patterns. The most prominent of
these are known as modes of climate variability and they impact weather and climate on many spatial and temporal scales (Chapter
14). Individual climate modes historically have been identified through spatial teleconnections: correlations between regional climate
variations at widely separated, geographically fixed spatial locations. An index describing temporal variations of the climate mode in
question can be formed, for example, by adding climate anomalies calculated from meteorological records at stations exhibiting the
strongest correlation with the mode and subtracting anomalies at stations exhibiting anticorrelation. By regressing climate records
from other places on this index, one derives a spatial climate pattern characterizing this mode. Patterns of climate variability have also
been derived using a variety of mathematical techniques such as principal component analysis (PCA). These patterns and their indices
are useful both because they efficiently describe climate variability in terms of a few preferred modes and also because they can provide
clues about how the variablility is sustained (Box 14.1 provides formal definitions of these terms).
Box 2.5, Table 1 lists some prominent modes of large-scale climate variability and indices used for defining them. Changes in these
indices are associated with large-scale climate variations on interannual and longer time scales. With some exceptions, indices shown
have been used by a variety of authors. They are defined relatively simply from raw or statistically analyzed observations of a single
climate variable, which has a history of surface observations. For most of these indices at least a century-long record is available for
climate research. (continued on next page)
233
Observations: Atmosphere and Surface Chapter 2
2
Climate Phenomenon Index Name Index Definition Primary Reference(s)
El Niño –
Southern
Oscillation
(ENSO)
Traditional
indices of
ENSO-related
Tropical
Pacific
variability
NIÑO1+2 SSTa averaged over [10°S–0°, 90°W–80°W] Rasmusson and Wallace
(1983), Cane (1986)
NIÑO3 Same as above but for [5°S–5°N, 150°W–90°W]
NIÑO4 Same as above but for [5°S–5°N, 160°E–150°W]
NIÑO3.4 Same as above but for [5°S–5°N, 170°W–120°W] Trenberth (1997)
Troup Southern Oscilla-
tion Index (SOI)
Standardized for each calendar month SLP
a
difference: Tahiti minus Darwin,
×10
Troup (1965)
SOI Standardized difference of SLP
sa
: Tahiti minus Darwin Trenberth (1984); Ropelewski
and Jones (1987)
Darwin SOI Darwin SLP
sa
Trenberth and Hoar (1996)
Equatorial SOI (EQSOI) Standardized difference of standardized averages of SLP
a
over equatorial
[5°S–5°N] Pacific Ocean areas: [130°W–80°W] minus [90°E–140°E]
Bell and Halpert (1998)
Indices of
ENSO events
evolution and
type
Trans-Niño Index (TNI) NIÑO1+2
s
minus NIÑO4
s
Trenberth and Stepaniak (2001)
El Niño Modoki Index
(EMI)
SST
a
[165°E–140°W, 10°S–10°N] minus ½*[110°W–70°W, 15°S–5°N] minus
½*[125°E–145°E, 10°S–20°N]
Ashok et al. (2007)
Pacific Decadal and
Interdecadal Variability
Pacific Decadal Oscillation
(PDO)
1st PC of monthly N. Pacific SST
a
field [20°N–70°N] with subtracted global
mean
Mantua et al. (1997); Zhang et
al. (1997)
Inter-decadal Pacific
Oscillation (IPO)
Projection of a global SST
a
onto the IPO pattern, which is found as one of the
leading Empirical Orthogonal Functions of a low-pass filtered global SST
a
field
Folland et al. (1999); Power et
al. (1999); Parker et al. (2007)
North Pacific Index (NPI) SLP
a
averaged over [30°N–65°N; 160°E–140°W] Trenberth and Hurrell (1994)
North Atlantic Oscillation
(NAO)
Azores-Iceland NAO Index SLP
sa
difference: Lisbon/Ponta Delgada minus Stykkisholmur/ Reykjavik Hurrell (1995)
PC-based NAO Index Leading PC of SLP
a
over the Atlantic sector Hurrell (1995)
Gibraltar – South-west
Iceland NAO Index
Standardized for each calendar month SLP
a
difference: Gibraltar minus SW
Iceland / Reykjavik
Jones et al. (1997)
Annular
modes
Northern
Annular
Mode (NAM)
PC-based NAM or Arctic
Oscillation (AO) index
1st PC of the monthly mean SLP
a
poleward of 20°N Thompson and Wallace (1998,
2000)
Southern
Annular
Mode (SAM)
PC-based SAM or Ant-
arctic Oscillation (AAO)
index
1st PC of Z850
a
or Z700
a
south of 20°S Thompson and Wallace (2000)
Grid-based SAM index:
40°S–70°S difference
Difference between standardized zonally averaged SLP
a
at 40°S and 70°S,
using gridded SLP fields
Nan and Li (2003)
Station-based SAM index:
40°S–65°S
Difference in standardized zonal mean SLP
a
at 40°S and 65°S, using station
data
Marshall (2003)
Pacific/North America (PNA)
atmospheric teleconnection
PNA index based on
centers of action
¼[(20°N, 160°W) – (45°N, 165°W) + (55°N, 115°W) – (30°N, 85°W)] in the
Z500
sa
field
Wallace and Gutzler (1981)
PNA from rotated PCA Rotated PC (RPC) from the analysis of the NH Z500
a
field Barnston and Livezey (1987)
Pacific/South America (PSA)
atmospheric teleconnection
PSA1 and PSA2 mode
indices (PC-based)
2nd and 3rd PCs respectively of SH seasonal Z500
a
Mo and Paegle (2001)
PSA index (centers of
action)
[–(35°S, 150°W) + (60°S, 120°W) – (45°S, 60°W)] in the Z500
a
field Karoly (1989)
[(45°S, 170°W) – (67.5°S, 120°W) + (50°S, 45°W)]/3 in the Z500
a
field Yuan and Li (2008)
Atlantic Ocean Multidecadal
Variability
Atlantic Multi-decadal
Oscillation (AMO) index
10-year running mean of linearly detrended Atlantic mean SST
a
[0°–70°N] Enfield et al. (2001)
Revised AMO index As above, but detrended by subtracting SST
a
[60°S–60°N] mean Trenberth and Shea (2006)
Tropical
Atlantic
Ocean
Variability
Atlantic
Ocean
Niño Mode
(AONM)
ATL3 SST
a
averaged over [3°S–3°N, 20°W–0°] Zebiak (1993)
PC-based AONM 1st PC of the detrended tropical Atlantic monthly SST
a
(20°S–20°N) Deser et al. (2010b)
Tropical
Atlantic
Meridional
Mode (AMM)
PC-based AMM Index 2nd PC of the detrended tropical Atlantic monthly SST
a
(20°S–20°N)
Tropical
Indian
Ocean
Variability
Indian Ocean
Basin Mode
(IOBM)
Basin mean index (BMI) SST
a
averaged over [40°–110°E, 20°S–20°N] Yang et al. (2007)
IOBM, PC-based Index The first PC of the IO detrended SST
a
(40°E–110° E, 20°S–20°N) Deser et al. (2010b)
Indian Ocean
Dipole Mode
(IODM)
PC-based IODM index The second PC of the IO detrended SST
a
(40°E–110° E, 20°S–20°N)
Dipole Mode Index (DMI) SST
a
difference: [50°E–70°E, 10°S–10°N] minus [90°E–110°E, 10°S–0°] Saji et al. (1999)
Box 2.5, Table 1 | Established indices of climate variability with global or regional influence. Z500, Z700 and Z850 denote geopotential height at the 500, 700
and 850 hPa levels, respectively. The subscripts s and a denote “standardized” and “anomalies”, respectively. Further information is given in Supplementary Material
2.SM.8. Climate impacts of these modes are listed in Box 14.1. (continued on next page)
234
Chapter 2 Observations: Atmosphere and Surface
2
1880 1900 1920 1940 1960 1980 2000
−2
0
2
NINO3.4 (
o
C), SOIs (s.d.)
Traditional indices of ENSO
)r2PLSdaH()1TSSIdaH(
NINO3.4
Troup (−1)*SOI (−1)*EQSOI
1
880
1
900
1
9
2
0
1
9
4
0
1
960
1
980
2
000
−2
0
2
Indices (s.d.)
Additional indices of ENSO (HadISST1)
NINO4
(−1)*TNI EMI
1
880
1
900
1
9
2
0
1
9
4
0
1
960
1
980
2
000
−2
0
2
4
Indices (s.d.)
Indices of Pacific Decadal/Interdecadal Variability
r.m. (HadSLP2r)
PDO (−1)*NPI, 24−mon IPO, 11−yr LPF
1
880
1
900
1
9
2
0
1
9
4
0
1
960
1
980
2
000
−0.4
−0.2
0
0.2
0.4
0.6
Indices (
o
C)
AMO indices (HadISST1): annual & 10−yr r.m.
AMO 10−yr r.m. Revised 10−yr r.m.
1
880
1
900
1
9
2
0
1
9
4
0
1
960
1
980
2
000
−4
−2
0
2
4
Indices (s.d.)
Indices of NAO and NAM/AO, DJFM means
(Hurrell,1995)
NAO stations
NAO PC NAM (HadSLP2r)
1
880
1
900
1
9
2
0
1
9
4
0
1
960
1
980
2
000
−2
0
2
Indices (s.d.)
Indices of SAM/AAO
(NNR) (HadSLP2r)40S−65S
Z850 PC grid 40S−70S stations
1
880
1
900
1
9
2
0
1
9
4
0
1
960
1
980
2
000
−2
−1
0
1
2
Indices (s.d.)
PNA indices, DJFM means
Centers of action (NNR)
RPC
1
880
1
900
1
9
2
0
1
9
4
0
1
960
1
980
2
000
−2
−1
0
1
2
Indices (s.d.)
PSA1 mode indices (NNR), 24−mon r.m.
PSA1 PC
Karoly PSA Yuan&Li (−1)*PSA
1
880
1
900
1
9
2
0
1
9
4
0
1
960
1
980
2
000
−2
−1
0
1
2
3
Indices (s.d.)
Atlantic Ocean Nino Mode indices (HadISST1)
AONM PC
ATL3 (detrended)
1
880
1
900
1
9
2
0
1
9
4
0
1
960
1
980
2
000
−1
0
1
2
3
AMM index (s.d.)
Tropical Atlantic Meridional Mode index (HadISST1)
AMM PC
1
880
1
900
1
9
2
0
1
9
4
0
1
960
1
980
2
000
−2
0
2
Indices (s.d.)
Indian Ocean Basin Mode indices (HadISST1)
IOBM PC
BMI (detrended)
1
880
1
900
1
9
2
0
1
9
4
0
1
960
1
980
2
000
−2
−1
0
1
2
Indices (s.d.)
Indian Ocean Dipole Mode indices (HadISST1)
IODM PC
DMI (detrended)
(a)
(b)
(c) (d)
(g) (h)
(i) (j)
(k) (l)
(e) (f)
Box 2.5, Figure 1 | Some indices of climate variability, as defined in Box 2.5, Table 1, plotted in the 1870–2012 interval. Where ‘HadISST1’, ‘HadSLP2r’, or ‘NNR’ are
indicated, the indices were computed from the sea surface temperature (SST) or sea level pressure (SLP) values of the former two data sets or from 500 or 850 hPa
geopotential height fields from the NNR. Data set references given in the panel titles apply to all indices shown in that panel. Where no data set is specified, a publicly
available version of an index from the authors of a primary reference given in Box 2.5, Table 1 was used. All indices were standardized with regard to 1971–2000 period
except for NIÑO3.4 (centralized for 1971–2000) and AMO indices (centralized for 1901–1970). Indices marked as “detrended” had their linear trend for 1870–2012
removed. All indices are shown as 12-month running means except when the temporal resolution is explicitly indicated (e.g., ‘DJFM’ for December-to-March averages)
or smoothing level (e.g., 11-year LPF for a low-pass filter with half-power at 11 years).
Box 2.5 (continued)
Most climate modes are illustrated by several indices (Box 2.5, Figure 1), which often behave similarly to each other. Spatial pat-
terns of SST or SLP associated with these climate modes are illustrated in Box 2.5, Figure 2. They can be interpreted as a change
in the SST or SLP field associated with one standard deviation change in the index. (continued on next page)
235
Observations: Atmosphere and Surface Chapter 2
2
El Niño – Southern Oscillation
Decadal to Multi-decadal Variability of Pacific and Atlantic Oceans
SST change per index s.d. (
º
C per s.d.)
−0.6
−0.4 −0.2 0 0.2 0.4 0.6
Hemispheric-Scale Modes of Atmospheric Variability
MSLP change per index s.d. (hPa per s.d.)
−2 −1 0 1 2
Tropical Variability of Atlantic and Indian Oceans
−0.4
−0.3 −0.2 −0.1 0 0.1 0.2 0.3 0.4
NINO3.4 (-1)*TNI
PDO AMO (revised)
NAO
Stations (Hurrell)
SAM
Z850 PC
PNA
Centers of Action
PSA1 & PSA2
PC
AONM ATL3 AMM PC IOBM BMI IODM DMI
SST change per index s.d. (
º
C per s.d.)
(a) (b)
(c) (d)
(e) (f) (g) (h)
(i) (j) (k) (l)
Box 2.5, Figure 2 | Spatial patterns of climate modes listed in Box 2.5, Table 1. All patterns shown here are obtained by regression of either sea surface temperature
(SST) or sea level pressure (SLP) fields on the standardized index of the climate mode. For each climate mode one of the specific indices shown in Box 2.5, Figure 1 was
used, as identified in the panel subtitles. SST and SLP fields are from HadISST1 and HadSLP2r data sets (interpolated gridded products based on data sets of historical
observations). Regressions were done on monthly means for all patterns except for NAO and PNA, which were done with the DJFM means, and for PSA1 and PSA2,
where seasonal means were used. Each regression was done for the longest period within the 1870-2012 interval when the index was available. For each pattern the
time series was linearly de-trended over the entire regression interval. All patterns are shown by color plots, except for PSA2, which is shown by white contours over
the PSA1 color plot (contour steps are 0.5 hPa, zero contour is skipped, negative values are indicated by dash).
Box 2.5 (continued)
The difficulty of identifying a universally ‘best’ index for any particular climate mode is due to the fact that no simply defined indicator
can achieve a perfect separation of the target phenomenon from all other effects occurring in the climate system. As a result, each index
is affected by many climate phenomena whose relative contributions may change with the time period and the data set used. Limited
length and quality of the observational record further compound this problem. Thus the choice of index is always application specific.
236
Chapter 2 Observations: Atmosphere and Surface
2
Acknowledgements
The authors of Chapter 2 wish to thank Wenche Aas (NILU, Kjeller),
Erika Coppola (ICTP, Trieste), Ritesh Gautam (NASA GSFC, Greenbelt),
Jenny Hand (CIRA, Fort Collins), Andreas Hilboll (U. Bremen, Bremen),
Glenn Hyatt (NOAA NCDC, Asheville), David Parrish (NOAA ESRL-CSD,
Boulder), Deborah Misch (LMI, Inc, Asheville), Jared Rennie (CICS-
NC, Asheville), Deborah Riddle (NOAA NCDC, Asheville), Sara Veasey
(NOAA NCDC, Asheville), Mark Weber (U. Bremen, Bremen), Yin Xun-
gang (STG Inc., Asheville), Teresa Young (STG, Asheville) and Jianglong
Zhang (U. North Dakota, Grand Forks) for their critical contributions to
the production of figures in this work.
237
Observations: Atmosphere and Surface Chapter 2
2
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