This chapter should be cited as:
Cubasch, U., D. Wuebbles, D. Chen, M.C. Facchini, D. Frame, N. Mahowald, and J.-G. Winther, 2013: Introduction.
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:
Ulrich Cubasch (Germany), Donald Wuebbles (USA)
Lead Authors:
Deliang Chen (Sweden), Maria Cristina Facchini (Italy), David Frame (UK/New Zealand), Natalie
Mahowald (USA), Jan-Gunnar Winther (Norway)
Contributing Authors:
Achim Brauer (Germany), Lydia Gates (Germany), Emily Janssen (USA), Frank Kaspar
(Germany), Janina Körper (Germany), Valérie Masson-Delmotte (France), Malte Meinshausen
(Australia/Germany), Matthew Menne (USA), Carolin Richter (Switzerland), Michael Schulz
(Germany), Uwe Schulzweida (Germany), Bjorn Stevens (Germany/USA), Rowan Sutton (UK),
Kevin Trenberth (USA), Murat Türkeş (Turkey), Daniel S. Ward (USA)
Review Editors:
Yihui Ding (China), Linda Mearns (USA), Peter Wadhams (UK)
Table of Contents
Executive Summary ..................................................................... 121
1.1 Chapter Preview .............................................................. 123
1.2 Rationale and Key Concepts of the
WGI Contribution ............................................................ 123
1.2.1 Setting the Stage for the Assessment ........................ 123
1.2.2 Key Concepts in Climate Science ............................... 123
1.2.3 Multiple Lines of Evidence for Climate Change ......... 129
1.3 Indicators of Climate Change ...................................... 130
1.3.1 Global and Regional Surface Temperatures ............... 131
1.3.2 Greenhouse Gas Concentrations ............................... 132
1.3.3 Extreme Events ......................................................... 134
1.3.4 Climate Change Indicators ........................................ 136
1.4 Treatment of Uncertainties .......................................... 138
1.4.1 Uncertainty in Environmental Science ....................... 138
1.4.2 Characterizing Uncertainty ........................................ 138
1.4.3 Treatment of Uncertainty in IPCC .............................. 139
1.4.4 Uncertainty Treatment in This Assessment................. 139
1.5 Advances in Measurement and Modelling
Capabilities ....................................................................... 142
1.5.1 Capabilities of Observations ..................................... 142
1.5.2 Capabilities in Global Climate Modelling .................. 144
Box 1.1: Description of Future Scenarios ............................... 147
1.6 Overview and Road Map to the Rest of
the Report ......................................................................... 151
1.6.1 Topical Issues ............................................................ 151
References .................................................................................. 152
Appendix 1.A: Notes and Technical Details on Figures
Displayed in Chapter 1 ............................................................... 155
Frequently Asked Questions
FAQ 1.1 If Understanding of the Climate System Has
Increased, Why Hasn’t the Range of
Temperature Projections Been Reduced? ........... 140
Introduction Chapter 1
Executive Summary
Human Effects on Climate
Human activities are continuing to affect the Earth’s energy
budget by changing the emissions and resulting atmospheric
concentrations of radiatively important gases and aerosols and
by changing land surface properties. Previous assessments have
already shown through multiple lines of evidence that the climate is
changing across our planet, largely as a result of human activities. The
most compelling evidence of climate change derives from observations
of the atmosphere, land, oceans and cryosphere. Unequivocal evidence
from in situ observations and ice core records shows that the atmos-
pheric concentrations of important greenhouse gases such as carbon
dioxide (CO
), methane (CH
), and nitrous oxide (N
O) have increased
over the last few centuries. {1.2.2, 1.2.3}
The processes affecting climate can exhibit considerable natural
variability. Even in the absence of external forcing, periodic and
chaotic variations on a vast range of spatial and temporal scales
are observed. Much of this variability can be represented by simple
(e.g., unimodal or power law) distributions, but many components of
the climate system also exhibit multiple states—for instance, the gla-
cial–interglacial cycles and certain modes of internal variability such
as El Niño-Southern Oscillation (ENSO). Movement between states can
occur as a result of natural variability, or in response to external forc-
ing. The relationship among variability, forcing and response reveals
the complexity of the dynamics of the climate system: the relationship
between forcing and response for some parts of the system seems rea-
sonably linear; in other cases this relationship is much more complex.
Multiple Lines of Evidence for Climate Change
Global mean surface air temperatures over land and oceans
have increased over the last 100 years. Temperature measure-
ments in the oceans show a continuing increase in the heat content
of the oceans. Analyses based on measurements of the Earth’s radi-
ative budget suggest a small positive energy imbalance that serves
to increase the global heat content of the Earth system. Observations
from satellites and in situ measurements show a trend of significant
reductions in the mass balance of most land ice masses and in Arctic
sea ice. The oceans’ uptake of CO
is having a significant effect on
the chemistry of sea water. Paleoclimatic reconstructions have helped
place ongoing climate change in the perspective of natural climate var-
iability. {1.2.3; Figure 1.3}
Observations of CO
concentrations, globally averaged temper-
ature and sea level rise are generally well within the range of
the extent of the earlier IPCC projections. The recently observed
increases in CH
and N
O concentrations are smaller than those
assumed in the scenarios in the previous assessments. Each
IPCC assessment has used new projections of future climate change
that have become more detailed as the models have become more
advanced. Similarly, the scenarios used in the IPCC assessments have
themselves changed over time to reflect the state of knowledge. The
range of climate projections from model results provided and assessed
in the first IPCC assessment in 1990 to those in the 2007 AR4 provides
an opportunity to compare the projections with the actually observed
changes, thereby examining the deviations of the projections from the
observations over time. {1.3.1, 1.3.2, 1.3.4; Figures 1.4, 1.5, 1.6, 1.7,
Climate change, whether driven by natural or human forcing,
can lead to changes in the likelihood of the occurrence or
strength of extreme weather and climate events or both. Since
the AR4, the observational basis has increased substantially, so that
some extremes are now examined over most land areas. Furthermore,
more models with higher resolution and a greater number of regional
models have been used in the simulations and projections of extremes.
{1.3.3; Figure 1.9}
Treatment of Uncertainties
For AR5, the three IPCC Working Groups use two metrics to com-
municate the degree of certainty in key findings: (1) Confidence
is a qualitative measure of the validity of a finding, based on the type,
amount, quality and consistency of evidence (e.g., data, mechanis-
tic understanding, theory, models, expert judgment) and the degree
of agreement
; and (2) Likelihood provides a quantified measure of
uncertainty in a finding expressed probabilistically (e.g., based on sta-
tistical analysis of observations or model results, or both, and expert
. {1.4; Figure 1.11}
Advances in Measurement and Modelling Capabilities
Over the last few decades, new observational systems, especial-
ly satellite-based systems, have increased the number of obser-
vations of the Earth’s climate by orders of magnitude. Tools to
analyse and process these data have been developed or enhanced to
cope with this large increase in information, and more climate proxy
data have been acquired to improve our knowledge of past chang-
es in climate. Because the Earth’s climate system is characterized on
multiple spatial and temporal scales, new observations may reduce
the uncertainties surrounding the understanding of short timescale
In this Report, the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium, or high.
A level of confidence is expressed using five qualifiers: very low, low, medium, high, and very high, and typeset in italics, e.g., medium confidence. For a given evidence and
agreement statement, different confidence levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence (see
Section 1.4 and Box TS.1 for more details).
In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: Virtually certain 99–100% probability, Very likely 90–100%,
Likely 66–100%, About as likely as not 33–66%, Unlikely 0–33%, Very unlikely 0–10%, Exceptionally unlikely 0–1%. Additional terms (Extremely likely: 95–100%, More likely
than not >50–100%, 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).
Chapter 1 Introduction
processes quite rapidly. However, processes that occur over longer
timescales may require very long observational baselines before much
progress can be made. {1.5.1; Figure 1.12}
Increases in computing speed and memory have led to the
development of more sophisticated models that describe phys-
ical, chemical and biological processes in greater detail. Model-
ling strategies have been extended to provide better estimates of the
uncertainty in climate change projections. The model comparisons with
observations have pushed the analysis and development of the models.
The inclusion of ‘long-term’ simulations has allowed incorporation
of information from paleoclimate data to inform projections. Within
uncertainties associated with reconstructions of past climate variables
from proxy record and forcings, paleoclimate information from the Mid
Holocene, Last Glacial Maximum, and Last Millennium have been used
to test the ability of models to simulate realistically the magnitude and
large-scale patterns of past changes. {1.5.2; Figures 1.13, 1.14}
As part of the process of getting model analyses for a range of alter-
native images of how the future may unfold, four new scenarios for
future emissions of important gases and aerosols have been developed
for the AR5, referred to as Representative Concentration Pathways
(RCPs). {Box 1.1}
Introduction Chapter 1
1.1 Chapter Preview
This introductory chapter serves as a lead-in to the science presented in
the Working Group I (WGI) contribution to the Intergovernmental Panel
on Climate Change (IPCC) Fifth Assessment Report (AR5). Chapter 1 in
the IPCC Fourth Assessment Report (AR4) (Le Treut et al., 2007) provid-
ed a historical perspective on the understanding of climate science and
the evidence regarding human influence on the Earth’s climate system.
Since the last assessment, the scientific knowledge gained through
observations, theoretical analyses, and modelling studies has contin-
ued to increase and to strengthen further the evidence linking human
activities to the ongoing climate change. In AR5, Chapter 1 focuses on
the concepts and definitions applied in the discussions of new findings
in the other chapters. It also examines several of the key indicators for
a changing climate and shows how the current knowledge of those
indicators compares with the projections made in previous assess-
ments. The new scenarios for projected human-related emissions used
in this assessment are also introduced. Finally, the chapter discusses
the directions and capabilities of current climate science, while the
detailed discussion of new findings is covered in the remainder of the
WGI contribution to the AR5.
1.2 Rationale and Key Concepts of the
WGI Contribution
1.2.1 Setting the Stage for the Assessment
The IPCC was set up in 1988 by the World Meteorological Organiza-
tion and the United Nations Environment Programme to provide gov-
ernments with a clear view of the current state of knowledge about
the science of climate change, potential impacts, and options for
adaptation and mitigation through regular assessments of the most
recent information published in the scientific, technical and socio-eco-
nomic literature worldwide. The WGI contribution to the IPCC AR5
assesses the current state of the physical sciences with respect to cli-
mate change. This report presents an assessment of the current state
of research results and is not a discussion of all relevant papers as
would be included in a review. It thus seeks to make sure that the
range of scientific views, as represented in the peer-reviewed literature,
is considered and evaluated in the assessment, and that the state of
the science is concisely and accurately presented. A transparent review
process ensures that disparate views are included (IPCC, 2012a).
As an overview, Table 1.1 shows a selection of key findings from earlier
IPCC assessments. This table provides a non-comprehensive selection
of key assessment statements from previous assessment reports—
IPCC First Assessment Report (FAR, IPCC, 1990), IPCC Second Assess-
ment Report (SAR, IPCC, 1996), IPCC Third Assessment Report (TAR,
IPCC, 2001) and IPCC Fourth Assessment Report (AR4, IPCC, 2007)
with a focus on policy-relevant quantities that have been evaluated in
each of the IPCC assessments.
Scientific hypotheses are contingent and always open to revision in
light of new evidence and theory. In this sense the distinguishing fea-
tures of scientific enquiry are the search for truth and the willingness
to subject itself to critical re-examination. Modern research science
conducts this critical revision through processes such as the peer
review. At conferences and in the procedures that surround publica-
tion in peer-reviewed journals, scientific claims about environmental
processes are analysed and held up to scrutiny. Even after publication,
findings are further analysed and evaluated. That is the self-correcting
nature of the scientific process (more details are given in AR4 Chapter
1 and Le Treut et al., 2007).
Science strives for objectivity but inevitably also involves choices and
judgements. Scientists make choices regarding data and models, which
processes to include and which to leave out. Usually these choices
are uncontroversial and play only a minor role in the production of
research. Sometimes, however, the choices scientists make are sources
of disagreement and uncertainty. These are usually resolved by fur-
ther scientific enquiry into the sources of disagreement. In some cases,
experts cannot reach a consensus view. Examples in climate science
include how best to evaluate climate models relative to observations,
how best to evaluate potential sea level rise and how to evaluate prob-
abilistic projections of climate change. In many cases there may be no
definitive solution to these questions. The IPCC process is aimed at
assessing the literature as it stands and attempts to reflect the level of
reasonable scientific consensus as well as disagreement.
To assess areas of scientific controversy, the peer-reviewed literature is
considered and evaluated. Not all papers on a controversial point can
be discussed individually in an assessment, but every effort has been
made here to ensure that all views represented in the peer-reviewed
literature are considered in the assessment process. A list of topical
issues is given in Table 1.3.
The Earth sciences study the multitude of processes that shape our
environment. Some of these processes can be understood through
idealized laboratory experiments, by altering a single element and then
tracing through the effects of that controlled change. However, as in
other natural and the social sciences, the openness of environmental
systems, in terms of our lack of control of the boundaries of the system,
their spatially and temporally multi-scale character and the complexity
of interactions, often hamper scientists’ ability to definitively isolate
causal links. This in turn places important limits on the understand-
ing of many of the inferences in the Earth sciences (e.g., Oreskes et
al., 1994). There are many cases where scientists are able to make
inferences using statistical tools with considerable evidential support
and with high degrees of confidence, and conceptual and numerical
modelling can assist in forming understanding and intuition about the
interaction of dynamic processes.
1.2.2 Key Concepts in Climate Science
Here, some of the key concepts in climate science are briefly described;
many of these were summarized more comprehensively in earlier IPCC
assessments (Baede et al., 2001). We focus only on a certain number of
them to facilitate discussions in this assessment.
First, it is important to distinguish the meaning of weather from cli-
mate. Weather describes the conditions of the atmosphere at a cer-
tain place and time with reference to temperature, pressure, humid-
ity, wind, and other key parameters (meteorological elements); the
Chapter 1 Introduction
Topic FAR SPM Statement SAR SPM Statement TAR SPM Statement AR4 SPM Statement
Human and Natural
Drivers of Climate Change
There is a natural greenhouse effect which already keeps
the Earth warmer than it would otherwise be. Emissions
resulting from human activities are substantially increasing
the atmospheric concentrations of the greenhouse gases
carbon dioxide, methane, chlorofluorocarbons and nitrous
oxide. These increases will enhance the greenhouse effect,
resulting on average in an additional warming of the Earth’s
Continued emissions of these gases at present rates would
commit us to increased concentrations for centuries ahead.
Greenhouse gas concentrations have
continued to increase. These trends
can be attributed largely to human
activities, mostly fossil fuel use, land
use change and agriculture.
Anthropogenic aerosols are short-
lived and tend to produce negative
radiative forcing.
Emissions of greenhouse gases and aerosols
due to human activities continue to alter the
atmosphere in ways that are expected to affect
the climate. The atmospheric concentration of
has increased by 31% since 1750 and that
of methane by 151%.
Anthropogenic aerosols are short-lived and
mostly produce negative radiative forcing by
their direct effect. There is more evidence for
their indirect effect, which is negative, although
of very uncertain magnitude.
Natural factors have made small contributions
to radiative forcing over the past century.
Global atmospheric concentrations of carbon dioxide, methane
and nitrous oxide have increased markedly as a result of human
activities since 1750 and now far exceed pre-industrial values
determined from ice cores spanning many thousands of years.
The global increases in carbon dioxide concentration are due
primarily to fossil fuel use and land use change, while those of
methane and nitrous oxide are primarily due to agriculture.
Very high confidence that the global average net effect of human
activities since 1750 has been one of warming, with a radiative
forcing of +1.6 [+0.6 to +2.4] W m
of Recent
Global mean surface air temperature has increased by 0.3°C
to 0.6°C over the last 100 years, with the five global-aver-
age warmest years being in the 1980s.
Climate has changed over the past
century. Global mean surface tem-
perature has increased by between
about 0.3 and 0.6°C since the late
19th century. Recent years have been
among the warmest since 1860, de-
spite the cooling effect of the 1991
Mt. Pinatubo volcanic eruption.
An increasing body of observations gives a col-
lective picture of a warming world and other
changes in the climate system.
The global average temperature has increased
since 1861. Over the 20th century the increase
has been 0.6°C.
Some important aspects of climate appear not
to have changed.
Warming of the climate system is unequivocal, as is now evident
from observations of increases in global average air and ocean
temperatures, widespread melting of snow and ice, and rising
global average sea level.
Eleven of the last twelve years (1995–2006) rank among the 12
warmest years in the instrumental record of global surface tem-
perature (since 1850). The updated 100-year linear trend (1906
to 2005) of 0.74°C [0.56°C to 0.92°C] is therefore larger than the
corresponding trend for 1901 to 2000 given in the TAR of 0.6°C
[0.4°C to 0.8°C].
Some aspects of climate have not been observed to change.
Sea Level
Over the same period global sea level has increased by 10
to 20 cm These increases have not been smooth with time,
nor uniform over the globe.
Global sea level has risen by between
10 and 25 cm over the past 100 years
and much of the rise may be related
to the increase in global mean tem-
Tide gauge data show that global average sea
level rose between 0.1 and 0.2 m during the
20th century.
Global average sea level rose at an average rate of 1.8 [1.3 to
2.3] mm per year over 1961 to 2003. The rate was faster over
1993 to 2003: about 3.1 [2.4 to 3.8] mm per year. The total 20th
century rise is estimated to be 0.17 [0.12 to 0.22] m.
A Palaeoclimatic
Climate varies naturally on all timescales from hundreds
of millions of years down to the year-to-year. Prominent in
the Earth’s history have been the 100,000 year glacial–in-
terglacial cycles when climate was mostly cooler than at
present. Global surface temperatures have typically varied
by 5°C to 7°C through these cycles, with large changes in
ice volume and sea level, and temperature changes as great
as 10°C to 15°C in some middle and high latitude regions
of the Northern Hemisphere. Since the end of the last ice
age, about 10,000 years ago, global surface temperatures
have probably fluctuated by little more than 1°C. Some fluc-
tuations have lasted several centuries, including the Little
Ice Age which ended in the nineteenth century and which
appears to have been global in extent.
The limited available evidence from
proxy climate indicators suggests
that the 20th century global mean
temperature is at least as warm as
any other century since at least 1400
AD. Data prior to 1400 are too sparse
to allow the reliable estimation of
global mean temperature.
New analyses of proxy data for the Northern
Hemisphere indicate that the increase in tem-
perature in the 20th century is likely to have
been the largest of any century during the past
1,000 years. It is also likely that, in the Northern
Hemisphere, the 1990s was the warmest decade
and 1998 the warmest year. Because less data
are available, less is known about annual aver-
ages prior to 1,000 years before present and for
conditions prevailing in most of the Southern
Hemisphere prior to 1861.
Palaeoclimatic information supports the interpretation that the
warmth of the last half century is unusual in at least the previous
1,300 years.
The last time the polar regions were significantly warmer than
present for an extended period (about 125,000 years ago), re-
ductions in polar ice volume led to 4 to 6 m of sea level rise.
Table 1.1 | Historical overview of major conclusions of previous IPCC assessment reports. The table provides a non-comprehensive selection of key statements from previous assessment reports—IPCC First Assessment Report (FAR; IPCC,
1990), IPCC Second Assessment Report (SAR; IPCC, 1996), IPCC Third Assessment Report (TAR; IPCC, 2001) and IPCC Fourth Assessment Report (AR4; IPCC, 2007)—with a focus on global mean surface air temperature and sea level change
as two policy relevant quantities that have been covered in IPCC since the first assessment report.
(continued on next page)
Introduction Chapter 1
(Table 1.1 continued)
Topic FAR SPM Statement SAR SPM Statement TAR SPM Statement AR4 SPM Statement
Understanding and
Attributing Climate
The size of this warming is broadly consistent with predic-
tions of climate models, but it is also of the same magnitude
as natural climate variability. Thus the observed increase
could be largely due to this natural variability; alternatively
this variability and other human factors could have offset
a still larger human-induced greenhouse warming. The un-
equivocal detection of the enhanced greenhouse effect from
observations is not likely for a decade or more.
The balance of evidence suggests a
discernible human influence on glob-
al climate. Simulations with coupled
atmosphere–ocean models have pro-
vided important information about
decade to century timescale natural
internal climate variability.
There is new and stronger evidence that most
of the warming observed over the last 50 years
is attributable to human activities. There is a
longer and more scrutinized temperature record
and new model estimates of variability. Recon-
structions of climate data for the past 1,000
years indicate this warming was unusual and is
unlikely to be entirely natural in origin.
Most of the observed increase in global average temperatures
since the mid-20th century is very likely due to the observed
increase in anthropogenic greenhouse gas concentrations.
Discernible human influences now extend to other aspects of
climate, including ocean warming, continental-average tempera-
tures, temperature extremes and wind patterns.
of Future
Changes in
Under the IPCC Business-as-Usual emissions of greenhouse
gases, a rate of increase of global mean temperature during
the next century of about 0.3°C per decade (with an uncer-
tainty range of 0.2°C to 0.5°C per decade); this is greater
than that seen over the past 10,000 years.
Climate is expected to continue to
change in the future. For the mid-
range IPCC emission scenario, IS92a,
assuming the ‘best estimate’ value of
climate sensitivity and including the
effects of future increases in aerosols,
models project an increase in global
mean surface air temperature rela-
tive to 1990 of about 2°C by 2100.
Global average temperature and sea level are
projected to rise under all IPCC SRES scenarios.
The globally averaged surface temperature is
projected to increase by 1.4°C to 5.8°C over the
period 1990 to 2100.
Confidence in the ability of models to project
future climate has increased.
Anthropogenic climate change will persist for
many centuries.
For the next two decades, a warming of about 0.2°C per decade
is projected for a range of SRES emission scenarios. Even if the
concentrations of all greenhouse gases and aerosols had been
kept constant at year 2000 levels, a further warming of about
0.1°C per decade would be expected.
There is now higher confidence in projected patterns of warm-
ing and other regional-scale features, including changes in wind
patterns, precipitation and some aspects of extremes and of ice.
Anthropogenic warming and sea level rise would continue for
centuries, even if greenhouse gas concentrations were to be
Sea Level
An average rate of global mean sea level rise of about 6 cm
per decade over the next century (with an uncertainty range
of 3 to 10 cm per decade) is projected.
Models project a sea level rise of 50
cm from the present to 2100.
Global mean sea level is projected to rise by
0.09 to 0.88 m between 1990 and 2100.
Global sea level rise for the range of scenarios is projected as
0.18 to 0.59 m by the end of the 21st century.
Chapter 1 Introduction
Radiation (SWR)
SWR Absorbed by
the Atmosphere
SWR Reected by
the Atmosphere
Outgoing Longwave
Radiation (OLR)
SWR Absorbed by
the Surface
SWR Reected by
the Surface
Heat Flux
Heat Flux
Emission of
and Aerosols
Vegetation Changes
Ice/Snow Cover
Ocean Color
Wave Height
Gases and
Large Aerosols
Fluctuations in
Solar Output
Figure 1.1 | Main drivers of climate change. The radiative balance between incoming solar shortwave radiation (SWR) and outgoing longwave radiation (OLR) is influenced by
global climate ‘drivers’. Natural fluctuations in solar output (solar cycles) can cause changes in the energy balance (through fluctuations in the amount of incoming SWR) (Section
2.3). Human activity changes the emissions of gases and aerosols, which are involved in atmospheric chemical reactions, resulting in modified O
and aerosol amounts (Section 2.2).
and aerosol particles absorb, scatter and reflect SWR, changing the energy balance. Some aerosols act as cloud condensation nuclei modifying the properties of cloud droplets
and possibly affecting precipitation (Section 7.4). Because cloud interactions with SWR and LWR are large, small changes in the properties of clouds have important implications
for the radiative budget (Section 7.4). Anthropogenic changes in GHGs (e.g., CO
, CH
, N
O, O
, CFCs) and large aerosols (>2.5 μm in size) modify the amount of outgoing LWR
by absorbing outgoing LWR and re-emitting less energy at a lower temperature (Section 2.2). Surface albedo is changed by changes in vegetation or land surface properties, snow
or ice cover and ocean colour (Section 2.3). These changes are driven by natural seasonal and diurnal changes (e.g., snow cover), as well as human influence (e.g., changes in
vegetation types) (Forster et al., 2007).
presence of clouds, precipitation; and the occurrence of special phe-
nomena, such as thunderstorms, dust storms, tornados and others.
Climate in a narrow sense is usually defined as the average weather,
or more rigorously, as the statistical description in terms of the mean
and variability of relevant quantities over a period of time ranging from
months to thousands or millions of years. The relevant quantities are
most often surface variables such as temperature, precipitation and
wind. Classically the period for averaging these variables is 30 years,
as defined by the World Meteorological Organization. Climate in a
wider sense also includes not just the mean conditions, but also the
associated statistics (frequency, magnitude, persistence, trends, etc.),
often combining parameters to describe phenomena such as droughts.
Climate change refers to a change in the state of the climate that can
be identified (e.g., by using statistical tests) by changes in the mean
and/or the variability of its properties, and that persists for an extended
period, typically decades or longer.
The Earth’s climate system is powered by solar radiation (Figure 1.1).
Approximately half of the energy from the Sun is supplied in the vis-
ible part of the electromagnetic spectrum. As the Earth’s tempera-
ture has been relatively constant over many centuries, the incoming
solar energy must be nearly in balance with outgoing radiation. Of
the incoming solar shortwave radiation (SWR), about half is absorbed
by the Earth’s surface. The fraction of SWR reflected back to space
by gases and aerosols, clouds and by the Earth’s surface (albedo) is
approximately 30%, and about 20% is absorbed in the atmosphere.
Based on the temperature of the Earth’s surface the majority of the
outgoing energy flux from the Earth is in the infrared part of the spec-
trum. The longwave radiation (LWR, also referred to as infrared radi-
ation) emitted from the Earth’s surface is largely absorbed by certain
atmospheric constituents—water vapour, carbon dioxide (CO
), meth-
ane (CH
), nitrous oxide (N
O) and other greenhouse gases (GHGs);
see Annex III for Glossary—and clouds, which themselves emit LWR
into all directions. The downward directed component of this LWR adds
heat to the lower layers of the atmosphere and to the Earth’s surface
(greenhouse effect). The dominant energy loss of the infrared radiation
from the Earth is from higher layers of the troposphere. The Sun pro-
vides its energy to the Earth primarily in the tropics and the subtropics;
this energy is then partially redistributed to middle and high latitudes
by atmospheric and oceanic transport processes.
Introduction Chapter 1
Changes in the global energy budget derive from either changes in
the net incoming solar radiation or changes in the outgoing longwave
radiation (OLR). Changes in the net incoming solar radiation derive
from changes in the Sun’s output of energy or changes in the Earth’s
albedo. Reliable measurements of total solar irradiance (TSI) can be
made only from space, and the precise record extends back only to
1978. The generally accepted mean value of the TSI is about 1361 W
(Kopp and Lean, 2011; see Chapter 8 for a detailed discussion on
the TSI); this is lower than the previous value of 1365 W m
used in the
earlier assessments. Short-term variations of a few tenths of a percent
are common during the approximately 11-year sunspot solar cycle (see
Sections 5.2 and 8.4 for further details). Changes in the outgoing LWR
can result from changes in the temperature of the Earth’s surface or
atmosphere or changes in the emissivity (measure of emission effi-
ciency) of LWR from either the atmosphere or the Earth’s surface. For
the atmosphere, these changes in emissivity are due predominantly to
changes in cloud cover and cloud properties, in GHGs and in aerosol
concentrations. The radiative energy budget of the Earth is almost in
balance (Figure 1.1), but ocean heat content and satellite measure-
ments indicate a small positive imbalance (Murphy et al., 2009; Tren-
berth et al., 2009; Hansen et al., 2011) that is consistent with the rapid
changes in the atmospheric composition.
In addition, some aerosols increase atmospheric reflectivity, whereas
others (e.g., particulate black carbon) are strong absorbers and also
modify SWR (see Section 7.2 for a detailed assessment). Indirectly, aer-
osols also affect cloud albedo, because many aerosols serve as cloud
condensation nuclei or ice nuclei. This means that changes in aerosol
types and distribution can result in small but important changes in
cloud albedo and lifetime (Section 7.4). Clouds play a critical role in
climate because they not only can increase albedo, thereby cooling
the planet, but also because of their warming effects through infra-
red radiative transfer. Whether the net radiative effect of a cloud is
one of cooling or of warming depends on its physical properties (level
of occurrence, vertical extent, water path and effective cloud particle
size) as well as on the nature of the cloud condensation nuclei pop-
ulation (Section 7.3). Humans enhance the greenhouse effect direct-
ly by emitting GHGs such as CO
, CH
, N
O and chlorofluorocarbons
(CFCs) (Figure 1.1). In addition, pollutants such as carbon monoxide
(CO), volatile organic compounds (VOC), nitrogen oxides (NO
) and
sulphur dioxide (SO
), which by themselves are negligible GHGs, have
an indirect effect on the greenhouse effect by altering, through atmos-
pheric chemical reactions, the abundance of important gases to the
amount of outgoing LWR such as CH
and ozone (O
), and/or by acting
as precursors of secondary aerosols. Because anthropogenic emission
sources simultaneously can emit some chemicals that affect climate
and others that affect air pollution, including some that affect both,
atmospheric chemistry and climate science are intrinsically linked.
In addition to changing the atmospheric concentrations of gases and
aerosols, humans are affecting both the energy and water budget of
the planet by changing the land surface, including redistributing the
balance between latent and sensible heat fluxes (Sections 2.5, 7.2, 7.6
and 8.2). Land use changes, such as the conversion of forests to culti-
vated land, change the characteristics of vegetation, including its colour,
seasonal growth and carbon content (Houghton, 2003; Foley et al.,
2005). For example, clearing and burning a forest to prepare agricultural
land reduces carbon storage in the vegetation, adds CO
to the atmos-
phere, and changes the reflectivity of the land (surface albedo), rates of
evapotranspiration and longwave emissions (Figure 1.1).
Changes in the atmosphere, land, ocean, biosphere and cryosphere—
both natural and anthropogenic—can perturb the Earth’s radiation
budget, producing a radiative forcing (RF) that affects climate. RF is
a measure of the net change in the energy balance in response to an
external perturbation. The drivers of changes in climate can include, for
example, changes in the solar irradiance and changes in
trace gas and aerosol concentrations (Figure 1.1). The concept of RF
cannot capture the interactions of anthropogenic aerosols and clouds,
for example, and thus in addition to the RF as used in previous assess-
ments, Sections 7.4 and 8.1 introduce a new concept, effective radi-
ative forcing (ERF), that accounts for rapid response in the climate
system. ERF is defined as the change in net downward flux at the top
of the atmosphere after allowing for atmospheric temperatures, water
vapour, clouds and land albedo to adjust, but with either sea surface
temperatures (SSTs) and sea ice cover unchanged or with global mean
surface temperature unchanged.
Once a forcing is applied, complex internal feedbacks determine the
eventual response of the climate system, and will in general cause this
response to differ from a simple linear one (IPCC, 2001, 2007). There
are many feedback mechanisms in the climate system that can either
amplify (‘positive feedback’) or diminish (‘negative feedback’) the
effects of a change in climate forcing (Le Treut et al., 2007) (see Figure
1.2 for a representation of some of the key feedbacks). An example of
a positive feedback is the water vapour feedback whereby an increase
in surface temperature enhances the amount of water vapour pres-
ent in the atmosphere. Water vapour is a powerful GHG: increasing
its atmospheric concentration enhances the greenhouse effect and
leads to further surface warming. Another example is the ice albedo
feedback, in which the albedo decreases as highly reflective ice and
snow surfaces melt, exposing the darker and more absorbing surfaces
below. The dominant negative feedback is the increased emission of
energy through LWR as surface temperature increases (sometimes also
referred to as blackbody radiation feedback). Some feedbacks oper-
ate quickly (hours), while others develop over decades to centuries;
in order to understand the full impact of a feedback mechanism, its
timescale needs to be considered. Melting of land ice sheets can take
days to millennia.
A spectrum of models is used to project quantitatively the climate
response to forcings. The simplest energy balance models use one
box to represent the Earth system and solve the global energy bal-
ance to deduce globally averaged surface air temperature. At the other
extreme, full complexity three-dimensional climate models include
the explicit solution of energy, momentum and mass conservation
equations at millions of points on the Earth in the atmosphere, land,
ocean and cryosphere. More recently, capabilities for the explicit sim-
ulation of the biosphere, the carbon cycle and atmospheric chemistry
have been added to the full complexity models, and these models are
called Earth System Models (ESMs). Earth System Models of Interme-
diate Complexity include the same processes as ESMs, but at reduced
resolution, and thus can be simulated for longer periods (see Annex III
for Glossary and Section 9.1).
Chapter 1 Introduction
An equilibrium climate experiment is an experiment in which a cli-
mate model is allowed to adjust fully to a specified change in RF. Such
experiments provide information on the difference between the initial
and final states of the model simulated climate, but not on the time-de-
pendent response. The equilibrium response in global mean surface air
temperature to a doubling of atmospheric concentration of CO
pre-industrial levels (e.g., Arrhenius, 1896; see Le Treut et al., 2007 for
a comprehensive list) has often been used as the basis for the concept
of equilibrium climate sensitivity (e.g., Hansen et al., 1981; see Meehl
et al., 2007 for a comprehensive list). For more realistic simulations of
climate, changes in RF are applied gradually over time, for example,
using historical reconstructions of the CO
, and these simulations are
called transient simulations. The temperature response in these tran-
sient simulations is different than in an equilibrium simulation. The
transient climate response is defined as the change in global surface
temperature at the time of atmospheric CO
doubling in a global cou-
pled ocean–atmosphere climate model simulation where concentra-
tions of CO
were increased by 1% yr
. The transient climate response
is a measure of the strength and rapidity of the surface temperature
response to GHG forcing. It can be more meaningful for some problems
as well as easier to derive from observations (see Figure 10.20; Sec-
tion 10.8; Chapter 12; Knutti et al., 2005; Frame et al., 2006; Forest et
al., 2008), but such experiments are not intended to replace the more
realistic scenario evaluations.
Climate change commitment is defined as the future change to which
the climate system is committed by virtue of past or current forcings.
The components of the climate system respond on a large range of
timescales, from the essentially rapid responses that characterise some
radiative feedbacks to millennial scale responses such as those associ-
ated with the behaviour of the carbon cycle (Section 6.1) and ice sheets
(see Figure 1.2 and Box 5.1). Even if anthropogenic emissions were
immediately ceased (Matthews and Weaver, 2010) or if climate forcings
were fixed at current values (Wigley, 2005), the climate system would
continue to change until it came into equilibrium with those forcings
(Section 12.5). Because of the slow response time of some components
Lapse rate
Emission of non-CO
greenhouse gases
and aerosols
Air-sea CO
Air-land CO
and biogeochemical
Peat and permafrost
Longwave rad.
Snow/sea ice albedo
Lapse rate
Water vapor
Air-land CO
GHG and aerosols
Air-sea CO
Land ice
Ocean circ.
Figure 1.2 | Climate feedbacks and timescales. The climate feedbacks related to increasing CO
and rising temperature include negative feedbacks (–) such as LWR, lapse
rate (see Glossary in Annex III), and air–sea carbon exchange and positive feedbacks (+) such as water vapour and snow/ice albedo feedbacks. Some feedbacks may be
positive or negative (±): clouds, ocean circulation changes, air–land CO
exchange, and emissions of non-GHGs and aerosols from natural systems. In the smaller box, the
large difference in timescales for the various feedbacks is highlighted.
Introduction Chapter 1
of the climate system, equilibrium conditions will not be reached for
many centuries. Slow processes can sometimes be constrained only by
data collected over long periods, giving a particular salience to paleo-
climate data for understanding equilibrium processes. Climate change
commitment is indicative of aspects of inertia in the climate system
because it captures the ongoing nature of some aspects of change.
A summary of perturbations to the forcing of the climate system from
changes in solar radiation, GHGs, surface albedo and aerosols is pre-
sented in Box 13.1. The energy fluxes from these perturbations are bal-
anced by increased radiation to space from a warming Earth, reflection
of solar radiation and storage of energy in the Earth system, principally
the oceans (Box 3.1, Box 13.1).
The processes affecting climate can exhibit considerable natural var-
iability. Even in the absence of external forcing, periodic and chaotic
variations on a vast range of spatial and temporal scales are observed.
Much of this variability can be represented by simple (e.g., unimodal or
power law) distributions, but many components of the climate system
also exhibit multiple states—for instance, the glacial-interglacial
cycles and certain modes of internal variability such as El Niño-South-
ern Oscillation (ENSO) (see Box 2.5 for details on patterns and indices
of climate variability). Movement between states can occur as a result
of natural variability, or in response to external forcing. The relation-
ship between variability, forcing and response reveals the complexity
of the dynamics of the climate system: the relationship between forc-
ing and response for some parts of the system seems reasonably linear;
in other cases this relationship is much more complex, characterised by
hysteresis (the dependence on past states) and a non-additive combi-
nation of feedbacks.
Related to multiple climate states, and hysteresis, is the concept of
irreversibility in the climate system. In some cases where multiple
states and irreversibility combine, bifurcations or ‘tipping points’ can
been reached (see Section 12.5). In these situations, it is difficult if not
impossible for the climate system to revert to its previous state, and the
change is termed irreversible over some timescale and forcing range.
A small number of studies using simplified models find evidence for
global-scale ‘tipping points’ (e.g., Lenton et al., 2008); however, there
is no evidence for global-scale tipping points in any of the most com-
prehensive models evaluated to date in studies of climate evolution in
the 21st century. There is evidence for threshold behaviour in certain
aspects of the climate system, such as ocean circulation (see Section
12.5) and ice sheets (see Box 5.1), on multi-centennial-to-millennial
timescales. There are also arguments for the existence of regional tip-
ping points, most notably in the Arctic (e.g., Lenton et al., 2008; Duarte
et al., 2012; Wadhams, 2012), although aspects of this are contested
(Armour et al., 2011; Tietsche et al., 2011).
1.2.3 Multiple Lines of Evidence for Climate Change
While the first IPCC assessment depended primarily on observed
changes in surface temperature and climate model analyses, more
recent assessments include multiple lines of evidence for climate
change. The first line of evidence in assessing climate change is based
on careful analysis of observational records of the atmosphere, land,
ocean and cryosphere systems (Figure 1.3). There is incontroverti-
ble evidence from in situ observations and ice core records that the
atmospheric concentrations of GHGs such as CO
, CH
, and N
O have
increased substantially over the last 200 years (Sections 6.3 and 8.3).
In addition, instrumental observations show that land and sea sur-
face temperatures have increased over the last 100 years (Chapter 2).
Satellites allow a much broader spatial distribution of measurements,
especially over the last 30 years. For the upper ocean temperature the
observations indicate that the temperature has increased since at least
1950 (Willis et al., 2010; Section 3.2). Observations from satellites and
in situ measurements suggest reductions in glaciers, Arctic sea ice and
ice sheets (Sections 4.2, 4.3 and 4.4). In addition, analyses based on
measurements of the radiative budget and ocean heat content sug-
gest a small imbalance (Section 2.3). These observations, all published
in peer-reviewed journals, made by diverse measurement groups in
multiple countries using different technologies, investigating various
climate-relevant types of data, uncertainties and processes, offer a
wide range of evidence on the broad extent of the changing climate
throughout our planet.
Conceptual and numerical models of the Earth’s climate system offer
another line of evidence on climate change (discussions in Chapters
5 and 9 provide relevant analyses of this evidence from paleoclimat-
ic to recent periods). These use our basic understanding of the cli-
mate system to provide self-consistent methodologies for calculating
impacts of processes and changes. Numerical models include the cur-
rent knowledge about the laws of physics, chemistry and biology, as
well as hypotheses about how complicated processes such as cloud
formation can occur. Because these models can represent only the
existing state of knowledge and technology, they are not perfect; they
are, however, important tools for analysing uncertainties or unknowns,
for testing different hypotheses for causation relative to observations,
and for making projections of possible future changes.
One of the most powerful methods for assessing changes occurring in
climate involves the use of statistical tools to test the analyses from
models relative to observations. This methodology is generally called
detection and attribution in the climate change community (Section
10.2). For example, climate models indicate that the temperature
response to GHG increases is expected to be different than the effects
from aerosols or from solar variability. Radiosonde measurements
and satellite retrievals of atmospheric temperature show increases
in tropospheric temperature and decreases in stratospheric tempera-
tures, consistent with the increases in GHG effects found in climate
model simulations (e.g., increases in CO
, changes in O
), but if the
Sun was the main driver of current climate change, stratospheric and
tropospheric temperatures would respond with the same sign (Hegerl
et al., 2007).
Resources available prior to the instrumental period—historical
sources, natural archives, and proxies for key climate variables (e.g.,
tree rings, marine sediment cores, ice cores)—can provide quantita-
tive information on past regional to global climate and atmospheric
composition variability and these data contribute another line of evi-
dence. Reconstructions of key climate variables based on these data
sets have provided important information on the responses of the
Earth system to a variety of external forcings and its internal variabil-
ity over a wide range of timescales (Hansen et al., 2006; Mann et al.,
Chapter 1 Introduction
2008). Paleoclimatic reconstructions thus offer a means for placing
the current changes in climate in the perspective of natural climate
variability (Section 5.1). AR5 includes new information on external RFs
caused by variations in volcanic and solar activity (e.g., Steinhilber
et al., 2009; see Section 8.4). Extended data sets on past changes
in atmospheric concentrations and distributions of atmospheric GHG
concentrations (e.g., Lüthi et al., 2008; Beerling and Royer, 2011) and
mineral aerosols (Lambert et al., 2008) have also been used to attrib-
ute reconstructed paleoclimate temperatures to past variations in
external forcings (Section 5.2).
1.3 Indicators of Climate Change
There are many indicators of climate change. These include physical
responses such as changes in the following: surface temperature,
atmospheric water vapour, precipitation, severe events, glaciers, ocean
and land ice, and sea level. Some key examples of such changes in
important climate parameters are discussed in this section and all are
assessed in much more detail in other chapters.
As was done to a more limited extent in AR4 (Le Treut et al., 2007), this
section provides a test of the planetary-scale hypotheses of climate
change against observations. In other words, how well do the projec-
tions used in the past assessments compare with observations to date?
Seven additional years of observations are now available to evaluate
earlier model projections. The projected range that was given in each
assessment is compared to observations. The largest possible range
of scenarios available for a specific variable for each of the previous
assessment reports is shown in the figures.
Based on the assessment of AR4, a number of the key climate and
associated environmental parameters are presented in Figure 1.3,
which updates the similar figure in the Technical Summary (TS) of IPCC
(2001). This section discusses the recent changes in several indicators,
while more thorough assessments for each of these indicators are
Ocean Land Ice
Near Surface
Cooling Stratospheric temperature (Chapter 2.4).
Changes in winter polar vortex strength (Chapter 2.7).
Increasing concentration of CO2 and other greenhouse
gases from human activities (Chapter 2.2).
Changes in cloud cover (Chapter 2.5).
Increasing tropospheric water vapour (Chapter 2.5).
Changes in aerosole burden and ozone concentrations
(Chapter 2.2)
Rising global average near surface
temperature (Chapter 2.4).
Increasing surface humidity (Chapter 2.5).
Warming throughout much of the
worlds ocean (Chapter 3.2).
Increasing rates of global mean
sea level rise (Chapter 3.7).
Changes in ocean
salinity (Chapter 3.3).
Acidification of the oceans
(Chapter 3.8).
More frequent warm days and nights. Fewer
cold days and nights (Chapter 2.6).
Reductions in the number of frost days
(Chapter 2.6).
Decreasing snow cover in most regions
(Chapter 4.5).
Degrading permafrost in areal
extent and thickness (Chapter 4.6).
Large scale precipitation changes (Chapter 2.5).
Increase in the number of heavy precipitation
events (Chapter 2.6).
Shrinking annual average
Arctic sea ice extent
(Chapter 4.2).
Widespread glacier
retreat (Chapter 4.3).
Changes in ice sheets in
Greenland and Antarctica
(Chapter 4.4).
Warming from the surface through much of the
troposphere (Chapter 2.4).
Long-term changes in the large-scale atmospheric
circulation, including a poleward shift of jet
streams (Chapter 2.7).
Warming of sea surface
temperatures (Chapter 2.4).
Observations of Climate Changes from AR4 (points to AR5)
Figure 1.3 | Overview of observed climate change indicators as listed in AR4. Chapter numbers indicate where detailed discussions for these indicators are found in AR5
(temperature: red; hydrological: blue; others: black).
Introduction Chapter 1
Figure 1.4 | Estimated changes in the observed globally and annually averaged surface temperature anomaly relative to 1961–1990 (in °C) since 1950 compared with the range
of projections from the previous IPCC assessments. Values are harmonized to start from the same value in 1990. Observed global annual mean surface air temperature anomaly,
relative to 1961–1990, is shown as squares and smoothed time series as solid lines (NASA (dark blue), NOAA (warm mustard), and the UK Hadley Centre (bright green) reanalyses).
The coloured shading shows the projected range of global annual mean surface air temperature change from 1990 to 2035 for models used in FAR (Figure 6.11 in Bretherton et al.,
1990), SAR (Figure 19 in the TS of IPCC, 1996), TAR (full range of TAR Figure 9.13(b) in Cubasch et al., 2001). TAR results are based on the simple climate model analyses presented
and not on the individual full three-dimensional climate model simulations. For the AR4 results are presented as single model runs of the CMIP3 ensemble for the historical period
from 1950 to 2000 (light grey lines) and for three scenarios (A2, A1B and B1) from 2001 to 2035. The bars at the right-hand side of the graph show the full range given for 2035
for each assessment report. For the three SRES scenarios the bars show the CMIP3 ensemble mean and the likely range given by –40% to +60% of the mean as assessed in Meehl
et al. (2007). The publication years of the assessment reports are shown. See Appendix 1.A for details on the data and calculations used to create this figure.
provided in other chapters. Also shown in parentheses in Figure 1.3 are
the chapter and section where those indicators of change are assessed
in AR5.
Note that projections presented in the IPCC assessments are not pre-
dictions (see the Glossary in Annex III); the analyses in the discussion
below only examine the short-term plausibility of the projections up to
AR4, including the scenarios for future emissions and the models used
to simulate these scenarios in the earlier assessments. Model results
from the Coupled Model Intercomparison Project Phase 5 (CMIP5)
(Taylor et al., 2012) used in AR5 are therefore not included in this sec-
tion; Chapters 11 and 12 describe the projections from the new mod-
elling studies. Note that none of the scenarios examined in the IPCC
assessments were ever intended to be short-term predictors of change.
1.3.1 Global and Regional Surface Temperatures
Observed changes in global mean surface air temperature since 1950
(from three major databases, as anomalies relative to 1961–1990)
are shown in Figure 1.4. As in the prior assessments, global climate
models generally simulate global temperatures that compare well with
o bservations over climate timescales (Section 9.4). Even though the
projections from the models were never intended to be predictions
over such a short timescale, the observations through 2012 generally
fall within the projections made in all past assessments. The 1990–
2012 data have been shown to be consistent with the FAR projections
(IPCC, 1990), and not consistent with zero trend from 1990, even in
the presence of substantial natural variability (Frame and Stone, 2013).
The scenarios were designed to span a broad range of plausible
futures, but are not aimed at predicting the most likely outcome. The
scenarios considered for the projections from the earlier reports (FAR,
SAR) had a much simpler basis than those of the Special Report on
Emission Scenarios (SRES) (IPCC, 2000) used in the later assessments.
For example, the FAR scenarios did not specify future aerosol distribu-
tions. AR4 presented a multiple set of projections that were simulated
using comprehensive ocean–atmosphere models provided by CMIP3
and these projections are continuations of transient simulations of the
20th century climate. These projections of temperature provide in addi-
tion a measure of the natural variability that could not be obtained
Chapter 1 Introduction
from the earlier projections based on models of intermediate complex-
ity (Cubasch et al., 2001).
Note that before TAR the climate models did not include natural forc-
ing (such as volcanic activity and solar variability). Even in AR4 not all
models included natural forcing and some also did not include aero-
sols. Those models that allowed for aerosol effects presented in the
AR4 simulated, for example, the cooling effects of the 1991 Mt Pinatu-
bo eruption and agree better with the observed temperatures than the
previous assessments that did not include those effects.
The bars on the side for FAR, SAR and TAR represent the range of
results for the scenarios at the end of the time period and are not error
bars. In contrast to the previous reports, the AR4 gave an assessment
of the individual scenarios with a mean estimate (cross bar; ensemble
mean of the CMIP3 simulations) and a likely range (full bar; –40% to
+60% of the mean estimate) (Meehl et al., 2007).
In summary, the trend in globally averaged surface temperatures falls
within the range of the previous IPCC projections. During the last
decade the trend in the observations is smaller than the mean of the
projections of AR4 (see Section 9.4.1, Box 9.2 for a detailed assessment
of the hiatus in global mean surface warming in the last 15 years).
As shown by Hawkins and Sutton (2009), trends in the observations
during short-timescale periods (decades) can be dominated by natural
variability in the Earth’s climate system. Similar episodes are also seen
in climate model experiments (Easterling and Wehner, 2009). Due to
their experimental design these episodes cannot be duplicated with
the same timing as the observed episodes in most of the model simu-
lations; this affects the interpretation of recent trends in the scenario
evaluations (Section 11.2). Notwithstanding these points, there is evi-
dence that early forecasts that carried formal estimates of uncertainty
have proved highly consistent with subsequent observations (Allen et
al., 2013). If the contributions of solar variability, volcanic activity and
ENSO are removed from the observations the remaining trend of sur-
face air temperature agree better with the modelling studies (Rahm-
storf et al., 2012).
1.3.2 Greenhouse Gas Concentrations
Key indicators of global climate change also include the changing con-
centrations of the radiatively important GHGs that are significant driv-
ers for this change (e.g., Denman et al., 2007; Forster et al., 2007). Fig-
ures 1.5 through 1.7 show the recent globally and annually averaged
observed concentrations for the gases of most concern, CO
, CH
, and
O (see Sections 2.2, 6.3 and 8.3 for more detailed discussion of these
and other key gases). As discussed in the later chapters, accurate meas-
urements of these long-lived gases come from a number of monitoring
stations throughout the world. The observations in these figures are
compared with the projections from the previous IPCC assessments.
The model simulations begin with historical emissions up to 1990. The
further evolution of these gases was described by scenario projections.
TAR and AR4 model concentrations after 1990 are based on the SRES
Figure 1.5 | Observed globally and annually averaged CO
concentrations in parts per million (ppm) since 1950 compared with projections from the previous IPCC assessments.
Observed global annual CO
concentrations are shown in dark blue. The shading shows the largest model projected range of global annual CO
concentrations from 1950 to 2035
from FAR (Figure A.3 in the Summary for Policymakers of IPCC, 1990); SAR (Figure 5b in the Technical Summary of IPCC, 1996); TAR (Appendix II of IPCC, 2001); and from the A2,
A1B and B1 scenarios presented in the AR4 (Figure 10.26 in Meehl et al., 2007). The bars at the right-hand side of the graph show the full range given for 2035 for each assessment
report. The publication years of the assessment reports are shown. See Appendix 1.A for details on the data and calculations used to create this figure.
Introduction Chapter 1
Figure 1.7 | Observed globally and annually averaged N
O concentrations in parts per billion (ppb) since 1950 compared with projections from the previous IPCC assessments.
Observed global annual N
O concentrations are shown in dark blue. The shading shows the largest model projected range of global annual N
O concentrations from 1950 to 2035
from FAR (Figure A3 in the Annex of IPCC, 1990), SAR (Table 2.5b in Schimel et al., 1996), TAR (Appendix II of IPCC, 2001), and from the A2, A1B and B1 scenarios presented in
the AR4 (Figure 10.26 in Meehl et al., 2007). The bars at the right hand side of the graph show the full range given for 2035 for each assessment report. The publication years of
the assessment reports are shown. See Appendix 1.A for details on the data and calculations used to create this figure.
Figure 1.6 | Observed globally and annually averaged CH
concentrations in parts per billion (ppb) since 1950 compared with projections from the previous IPCC assessments.
Estimated observed global annual CH
concentrations are shown in dark blue. The shading shows the largest model projected range of global annual CH
concentrations from 1950
to 2035 from FAR (Figure A.3 of the Annex of IPCC, 1990); SAR (Table 2.5a in Schimel et al., 1996); TAR (Appendix II of IPCC, 2001); and from the A2, A1B and B1 scenarios pre-
sented in the AR4 (Figure 10.26 in Meehl et al., 2007). The bars at the right-hand side of the graph show the full range given for 2035 for each assessment report. The publication
years of the assessment reports are shown. See Appendix 1.A for details on the data and calculations used to create this figure.
Chapter 1 Introduction
scenarios but those model results may also account for historical emis-
sions analyses. The recent observed trends in CO
concentrations tend
to be in the middle of the scenarios used for the projections (Figure
As discussed in Dlugokencky et al. (2009), trends in CH
showed a
stabilization from 1999 to 2006, but CH
concentrations have been
increasing again starting in 2007 (see Sections 2.2 and 6.3 for more
discussion on the budget and changing concentration trends for CH
Because at the time the scenarios were developed (e.g., the SRES
scenarios were developed in 2000), it was thought that past trends
would continue, the scenarios used and the resulting model projec-
tions assumed in FAR through AR4 all show larger increases than those
observed (Figure 1.6).
Concentrations of N
O have continued to increase at a nearly constant
rate (Elkins and Dutton, 2010) since about 1970 as shown in Figure
1.7. The observed trends tend to be in the lower part of the projections
for the previous assessments.
1.3.3 Extreme Events
Climate change, whether driven by natural or human forcings, can lead
to changes in the likelihood of the occurrence or strength of extreme
weather and climate events such as extreme precipitation events or
warm spells (see Chapter 3 of the IPCC Special Report on Managing
the Risks of Extreme Events and Disasters to Advance Climate Change
Adaptation (SREX); Seneviratne et al., 2012). An extreme weather
event is one that is rare at a particular place and/or time of year. Defi-
nitions of ‘rare’ vary, but an extreme weather event would normally
be as rare as or rarer than the 10th or 90th percentile of a probabili-
ty density function estimated from observations (see also Glossary in
Annex III and FAQ 2.2). By definition, the characteristics of what is
called extreme weather may vary from place to place in an absolute
sense. At present, single extreme events cannot generally be directly
attributed to anthropogenic influence, although the change in likeli-
hood for the event to occur has been determined for some events by
accounting for observed changes in climate (see Section 10.6). When
a pattern of extreme weather persists for some time, such as a season,
it may be classified as an extreme climate event, especially if it yields
an average or total that is itself extreme (e.g., drought or heavy rainfall
over a season). For some climate extremes such as drought, floods and
heat waves, several factors such as duration and intensity need to be
combined to produce an extreme event (Seneviratne et al., 2012).
The probability of occurrence of values of a climate or weather variable
can be described by a probability density function (PDF) that for some
variables (e.g., temperature) is shaped similar to a Gaussian curve. A
PDF is a function that indicates the relative chances of occurrence of
different outcomes of a variable. Simple statistical reasoning indicates
that substantial changes in the frequency of extreme events (e.g., the
maximum possible 24-hour rainfall at a specific location) can result
from a relatively small shift in the distribution of a weather or climate
variable. Figure 1.8a shows a schematic of such a PDF and illustrates
the effect of a small shift in the mean of a variable on the frequency of
extremes at either end of the distribution. An increase in the frequency
of one extreme (e.g., the number of hot days) can be accompanied by
Figure 1.8 | Schematic representations of the probability density function of daily tem-
perature, which tends to be approximately Gaussian, and daily precipitation, which has
a skewed distribution. Dashed lines represent a previous distribution and solid lines a
changed distribution. The probability of occurrence, or frequency, of extremes is denoted
by the shaded areas. In the case of temperature, changes in the frequencies of extremes
are affected by changes (a) in the mean, (b) in the variance or shape, and (c) in both
the mean and the variance. (d) In a skewed distribution such as that of precipitation, a
change in the mean of the distribution generally affects its variability or spread, and thus
an increase in mean precipitation would also imply an increase in heavy precipitation
extremes, and vice-versa. In addition, the shape of the right-hand tail could also change,
affecting extremes. Furthermore, climate change may alter the frequency of precipita-
tion and the duration of dry spells between precipitation events. (Parts a–c modified
from Folland et al., 2001, and d modified from Peterson et al., 2008, as in Zhang and
Zwiers, 2012.)
Average HotCold
(d) Change in skewness
(c) Increase in mean and variance
(b) Increase in variance
(a) Increase in mean
More heavy precipitation
More hot extremes
Fewer cold extremes
More hot extremes
More cold extremes
More hot extremes
More/Fewer cold extremes
Introduction Chapter 1
Figure 1.9 | Change in the confidence levels for extreme events based on prior IPCC assessments: TAR, AR4 and SREX. Types of extreme events discussed in all three reports are
highlighted in green. Confidence levels are defined in Section 1.4. Similar analyses for AR5 are discussed in later chapters. Please note that the nomenclature for confidence level
changed from AR4 to SREX and AR5.
a decline in the opposite extreme (in this case the number of cold days
such as frost days). Changes in the variability, skewness or the shape
of the distribution can complicate this simple picture (Figure 1.8b, c
and d).
While the SAR found that data and analyses of extremes related to cli-
mate change were sparse, improved monitoring and data for changes
in extremes were available for the TAR, and climate models were being
analysed to provide projections of extremes. In AR4, the observation-
al basis of analyses of extremes had increased substantially, so that
some extremes were now examined over most land areas (e.g., rainfall
extremes). More models with higher resolution, and a larger number
of regional models have been used in the simulation and projection of
extremes, and ensemble integrations now provide information about
PDFs and extremes.
Since the TAR, climate change studies have especially focused on
changes in the global statistics of extremes, and observed and pro-
jected changes in extremes have been compiled in the so-called
‘Extremes’-Table (Figure 1.9). This table has been modified further to
account for the SREX assessment. For some extremes (‘higher maximum
temperature’, ‘higher minimum temperature’, ‘precipitation extremes’,
‘droughts or dryness’), all of these assessments found an increasing
trend in the observations and in the projections. In the observations for
More intense precipitation events
² Heavy precipitation events. Frequency (or proportion of total rainfall from heavy falls) increases
³ Statistically significant trends in the number of heavy precipitation events in some regions. It is likely that more of these regions have experienced increases than decreases.
See SREX Table 3-3 for details on precipitation extremes for the different regions.
Increased summer continental drying and associated risk of drought
Area affected by droughts increases
Some areas include southern Europe and the Mediterranean region, central Europe, central North America and Mexico, northeast Brazil and southern Africa
Increase in tropical cyclone peak wind intensities
Increase in intense tropical cyclone activity
In any observed long-term (i.e., 40 years or more) after accounting for past changes in observing capabilities (see SREX, section 3.4.4)
Increase in average tropical cyclone maximum wind speed is, although not in all ocean basins; either decrease or no change in the global frequency of tropical cyclones
Increase in extreme coastal high water worldwide related to increases in mean sea level in the late 20th century
Mean sea level rise will contribute to upward trends in extreme coastal high water levels
Chapter 1 Introduction
the ‘higher maximum temperature’ the likelihood level was raised from
likely in the TAR to very likely in SREX. While the diurnal temperature
range was assessed in the Extremes-Table of the TAR, it was no longer
included in the Extremes-Table of AR4, since it is not considered a cli-
mate extreme in a narrow sense. Diurnal temperature range was, how-
ever, reported to decrease for 21st century projections in AR4 (Meehl
et al., 2007). In projections for precipitation extremes, the spatial rel-
evance has been improved from very likely ‘over many Northern Hemi-
sphere mid-latitudes to high latitudes land areas’ from the TAR to very
likely for all regions in AR4 (these ‘uncertainty labels’ are discussed in
Section 1.4). However,