119
1
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)
Introduction
1
120
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
1
Introduction Chapter 1
121
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
2
), methane (CH
4
), and nitrous oxide (N
2
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.
{1.2.2}
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
2
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
2
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
4
and N
2
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,
1.10}
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
1
; 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
judgement)
2
. {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
1
In this Report, the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium, or high.
A level of confidence is expressed using five qualifiers: very low, low, medium, high, and very high, and typeset in italics, e.g., medium confidence. For a given evidence and
agreement statement, different confidence levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence (see
Section 1.4 and Box TS.1 for more details).
2
In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: Virtually certain 99–100% probability, Very likely 90–100%,
Likely 66–100%, About as likely as not 33–66%, Unlikely 0–33%, Very unlikely 0–10%, Exceptionally unlikely 0–1%. Additional terms (Extremely likely: 95–100%, More likely
than not >50–100%, and Extremely unlikely 0–5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very likely (see Section 1.4 and Box TS.1
for more details).
1
Chapter 1 Introduction
122
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}
1
Introduction Chapter 1
123
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
1
Chapter 1 Introduction
124
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
surface.
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
CO
2
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
–2
.
Direct
Observations
of Recent
Climate
Change
Temperature
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-
perature.
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
Perspective
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)
1
Introduction Chapter 1
125
(Table 1.1 continued)
Topic FAR SPM Statement SAR SPM Statement TAR SPM Statement AR4 SPM Statement
Understanding and
Attributing Climate
Change
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.
Projections
of Future
Changes in
Climate
Temperature
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
stabilised.
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.
1
Chapter 1 Introduction
126
SWR
SWR, LWR SWR, LWR
SWR
SWR
LWR
Incoming
Shortwave
Radiation (SWR)
SWR Absorbed by
the Atmosphere
Aerosol/cloud
Interactions
SWR Reected by
the Atmosphere
Outgoing Longwave
Radiation (OLR)
SWR Absorbed by
the Surface
SWR Reected by
the Surface
Latent
Heat Flux
Sensible
Heat Flux
Back
Longwave
Radiation
(LWR)
LWR
Emitted
from
Surface
Chemical
Reactions
Chemical
Reactions
Emission of
Gases
and Aerosols
Vegetation Changes
Ice/Snow Cover
Ocean Color
Wave Height
Surface
Albedo
Changes
Aerosols
Clouds
Ozone
Greenhouse
Gases and
Large Aerosols
Natural
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
3
and aerosol amounts (Section 2.2).
O
3
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
2
, CH
4
, N
2
O, O
3
, 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
2
), meth-
ane (CH
4
), nitrous oxide (N
2
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.
1
Introduction Chapter 1
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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
m
−2
(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
−2
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
2
, CH
4
, N
2
O and chlorofluorocarbons
(CFCs) (Figure 1.1). In addition, pollutants such as carbon monoxide
(CO), volatile organic compounds (VOC), nitrogen oxides (NO
x
) and
sulphur dioxide (SO
2
), 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
4
and ozone (O
3
), 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
2
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
atmospheric
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).
1
Chapter 1 Introduction
128
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
2
above
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
2
, 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
2
doubling in a global cou-
pled ocean–atmosphere climate model simulation where concentra-
tions of CO
2
were increased by 1% yr
–1
. 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
Snow/ice
albedo
Longwave
radiation
Lapse rate
Clouds
Water
vapor
Emission of non-CO
2
greenhouse gases
and aerosols
Air-sea CO
2
exchange
Air-land CO
2
exchange
and biogeochemical
processes
Biogeophysical
processes
Peat and permafrost
decomposition
Ocean
circulation
HOURS
DAYS YEARS
CENTURIES
Longwave rad.
Snow/sea ice albedo
Lapse rate
Water vapor
Clouds
Air-land CO
2
exchange
Biogeophysics
Non-CO
2
GHG and aerosols
Air-sea CO
2
exchange
Peat/Permafrost
Land ice
Ocean circ.
Figure 1.2 | Climate feedbacks and timescales. The climate feedbacks related to increasing CO
2
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
2
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.
1
Introduction Chapter 1
129
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
2
, CH
4
, and N
2
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
2
, changes in O
3
), 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.,
1
Chapter 1 Introduction
130
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
Troposphere
Near Surface
Stratosphere
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).
Atmosphere
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).
1
Introduction Chapter 1
131
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
1
Chapter 1 Introduction
132
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
2
, CH
4
, and
N
2
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
2
concentrations in parts per million (ppm) since 1950 compared with projections from the previous IPCC assessments.
Observed global annual CO
2
concentrations are shown in dark blue. The shading shows the largest model projected range of global annual CO
2
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.
1
Introduction Chapter 1
133
Figure 1.7 | Observed globally and annually averaged N
2
O concentrations in parts per billion (ppb) since 1950 compared with projections from the previous IPCC assessments.
Observed global annual N
2
O concentrations are shown in dark blue. The shading shows the largest model projected range of global annual N
2
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
4
concentrations in parts per billion (ppb) since 1950 compared with projections from the previous IPCC assessments.
Estimated observed global annual CH
4
concentrations are shown in dark blue. The shading shows the largest model projected range of global annual CH
4
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.
1
Chapter 1 Introduction
134
scenarios but those model results may also account for historical emis-
sions analyses. The recent observed trends in CO
2
concentrations tend
to be in the middle of the scenarios used for the projections (Figure
1.5).
As discussed in Dlugokencky et al. (2009), trends in CH
4
showed a
stabilization from 1999 to 2006, but CH
4
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
4
).
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
2
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.)
Precipitation
Temperature
Temperature
Temperature
AverageLight
Average HotCold
HotAverageCold
HotAverageCold
Heavy
(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
1
Introduction Chapter 1
135
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
1
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.
4
See SREX Table 3-3 for details on precipitation extremes for the different regions.
5
Increased summer continental drying and associated risk of drought
6
Area affected by droughts increases
7
Some areas include southern Europe and the Mediterranean region, central Europe, central North America and Mexico, northeast Brazil and southern Africa
8
Increase in tropical cyclone peak wind intensities
9
Increase in intense tropical cyclone activity
10
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)
11
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
12
Increase in extreme coastal high water worldwide related to increases in mean sea level in the late 20th century
13
Mean sea level rise will contribute to upward trends in extreme coastal high water levels
1
Chapter 1 Introduction
136
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, likelihood in trends in projected precipitation
extremes was downscaled to likely in the SREX as a result of a percep-
tion of biases and a fairly large spread in the precipitation projections
in some regions. SREX also had less confidence than TAR and AR4 in
the trends for droughts and dryness, ‘due to lack of direct observations,
some geographical inconsistencies in the trends, and some dependen-
cies of inferred trends on the index choice’ (IPCC, 2012b).
For some extremes (e.g., ‘changes in tropical cyclone activity’) the defi-
nition changed between the TAR and the AR4. Whereas the TAR only
made a statement about the peak wind speed of tropical cyclones,
the AR4 also stressed the overall increase in intense tropical cyclone
activity. The ‘low confidence’ for any long term trend (>40 years) in the
observed changes of the tropical cyclone activities is due to uncertain-
ties in past observational capabilities (IPCC, 2012b). The ‘increase in
extreme sea level’ has been added in the AR4. Such an increase is likely
according to the AR4 and the SREX for observed trends, and very likely
for the climate projections reported in the SREX.
The assessed likelihood of anthropogenic contributions to trends is
lower for variables where the assessment is based on indirect evidence.
Especially for extremes that are the result of a combination of factors
such as droughts, linking a particular extreme event to specific causal
relationships is difficult to determine (e.g., difficult to establish the
clear role of climate change in the event) (see Section 10.6 and Peter-
son et al., 2012). In some cases (e.g., precipitation extremes), however,
it may be possible to estimate the human-related contribution to such
changes in the probability of occurrence of extremes (Pall et al., 2011;
Seneviratne et al., 2012).
1.3.4 Climate Change Indicators
Climate change can lead to other effects on the Earth’s physical system
that are also indicators of climate change. Such integrative indicators
include changes in sea level (ocean warming + land ice melt), in ocean
acidification (ocean uptake of CO
2
) and in the amount of ice on ocean
and land (temperature and hydrological changes). See Chapters 3, 4
and 13 for detailed assessment.
1.3.4.1 Sea Level
Global mean sea level is an important indicator of climate change (Sec-
tion 3.7 and Chapter 13). The previous assessments have all shown
that observations indicate that the globally averaged sea level is rising.
Direct observations of sea level change have been made for more
than 150 years with tide gauges, and for more than 20 years with
satellite radar altimeters. Although there is regional variability from
non-uniform density change, circulation changes, and deformation of
ocean basins, the evidence indicates that the global mean sea level is
rising, and that this is likely (according to AR4 and SREX) resulting from
global climate change (ocean warming plus land ice melt; see Chapter
13 for AR5 findings). The historical tide gauge record shows that the
average rate of global mean sea level rise over the 20th century was
1.7 ± 0.2 mm yr
–1
(e.g., Church and White, 2011). This rate increased
to 3.2 ± 0.4 mm yr
–1
since 1990, mostly because of increased thermal
expansion and land ice contributions (Church and White, 2011; IPCC,
2012b). Although the long-term sea level record shows decadal and
multi-decadal oscillations, there is evidence that the rate of global
mean sea level rise during the 20th century was greater than during
the 19th century.
All of the previous IPCC assessments have projected that global sea
level will continue to rise throughout this century for the scenarios
examined. Figure 1.10 compares the observed sea level rise since 1950
with the projections from the prior IPCC assessments. Earlier models
had greater uncertainties in modelling the contributions, because of
limited observational evidence and deficiencies in theoretical under-
standing of relevant processes. Also, projections for sea level change in
the prior assessments are scenarios for the response to anthropogenic
forcing only; they do not include unforced or natural interannual vari-
ability. Nonetheless, the results show that the actual change is in the
middle of projected changes from the prior assessments, and towards
the higher end of the studies from TAR and AR4.
1.3.4.2 Ocean Acidification
The observed decrease in ocean pH resulting from increasing concen-
trations of CO
2
is another indicator of global change. As discussed
in AR4, the ocean’s uptake of CO
2
is having a significant impact on
the chemistry of sea water. The average pH of ocean surface waters
has fallen by about 0.1 units, from about 8.2 to 8.1 (total scale) since
1765 (Section 3.8). Long time series from several ocean sites show
ongoing declines in pH, consistent with results from repeated pH
measurements on ship transects spanning much of the globe (Sec-
tions 3.8 and 6.4; Byrne et al., 2010; Midorikawa et al., 2010). Ocean
time-series in the North Atlantic and North Pacific record a decrease in
pH ranging between –0.0015 and –0.0024 per year (Section 3.8). Due
to the increased storage of carbon by the ocean, ocean acidification
will increase in the future (Chapter 6). In addition to other impacts
of global climate change, ocean acidification poses potentially serious
threats to the health of the world’s oceans ecosystems (see AR5 WGII
assessment).
1.3.4.3 Ice
Rapid sea ice loss is one of the most prominent indicators of Arctic
climate change (Section 4.2). There has been a trend of decreasing
Northern Hemisphere sea ice extent since 1978, with the summer of
2012 being the lowest in recorded history (see Section 4.2 for details).
The 2012 minimum sea ice extent was 49% below the 1979 to 2000
average and 18% below the previous record from 2007. The amount of
multi-year sea ice has been reduced, i.e., the sea ice has been thinning
and thus the ice volume is reduced (Haas et al., 2008; Kwok et al.,
2009). These changes make the sea ice less resistant to wind forcing.
1
Introduction Chapter 1
137
AR4TARSARFAR
1950 1960 1970 1980 1990 2000 2010 2020 2030
−5
0
5
10
15
20
25
30
35
Year
Global mean sea level rise (cm)
FAR
SAR
TAR
A1B
A2
B1
Church et al.
(2011)
Estimates derived from sea-surface altimetry
Estimates derived from tide-gauge data
}
Figure 1.10 | Estimated changes in the observed global annual mean sea level (GMSL) since 1950 relative to 1961–1990. Estimated changes in global annual sea level anomalies
are presented based on tide gauge data (warm mustard: Jevrejeva et al., 2008; dark blue: Church and White, 2011; dark green: Ray and Douglas, 2011) and based on sea surface
altimetry (light blue). The altimetry data start in 1993 and are harmonized to start from the mean 1993 value of the tide gauge data. Squares indicate annual mean values and
solid lines smoothed values. The shading shows the largest model projected range of global annual sea level rise from 1950 to 2035 for FAR (Figures 9.6 and 9.7 in Warrick and
Oerlemans, 1990), SAR (Figure 21 in TS of IPCC, 1996), TAR (Appendix II of IPCC, 2001) and for Church et al. (2011) based on the Coupled Model Intercomparison Project Phase
3 (CMIP3) model results not assessed at the time of AR4 using the SRES B1, A1B and A2 scenarios. Note that in the AR4 no full range was given for the sea level projections for
this period. Therefore, the figure shows results that have been published subsequent to the AR4. The bars at the right-hand side of the graph show the full range given for 2035 for
each assessment report. For Church et al. (2011) the mean sea level rise is indicated in addition to the full range. See Appendix 1.A for details on the data and calculations used
to create this figure.
Sea ice extent has been diminishing significantly faster than projected
by most of the AR4 climate models (SWIPA, 2011). While AR4 found no
consistent trends in Antarctica sea ice, more recent studies indicate a
small increase (Section 4.2). Various studies since AR4 suggest that this
has resulted in a deepening of the low-pressure systems in West Ant-
arctica that in turn caused stronger winds and enhanced ice production
in the Ross Sea (Goosse et al., 2009; Turner and Overland, 2009).
AR4 concluded that taken together, the ice sheets in Greenland and
Antarctica have very likely been contributing to sea level rise. The
Greenland Ice Sheet has lost mass since the early 1990s and the rate
of loss has increased (see Section 4.4). The interior, high-altitude areas
are thickening due to increased snow accumulation, but this is more
than counterbalanced by the ice loss due to melt and ice discharge
(AMAP, 2009; Ettema et al., 2009). Since 1979, the area experiencing
surface melting has increased significantly (Tedesco, 2007; Mernild et
al., 2009), with 2010 breaking the record for surface melt area, runoff,
and mass loss, and the unprecedented areal extent of surface melt of
the Greenland Ice Sheet in 2012 (Nghiem et al., 2012). Overall, the
Antarctic continent now experiences a net loss of ice (Section 4.4).
Significant mass loss has been occurring in the Amundsen Sea sector
of West Antarctica and the northern Antarctic Peninsula. The ice sheet
on the rest of the continent is relatively stable or thickening slightly
(Lemke et al., 2007; Scott et al., 2009; Turner et al., 2009). Since AR4,
there have been improvements in techniques of measurement, such as
gravity, altimetry and mass balance, and understanding of the change
(Section 4.4).
As discussed in the earlier assessments, most glaciers around the globe
have been shrinking since the end of the Little Ice Age, with increasing
rates of ice loss since the early 1980s (Section 4.3). The vertical profiles
of temperature measured through the entire thickness of mountain
glaciers, or through ice sheets, provide clear evidence of a warming
climate over recent decades (e.g., Lüthi and Funk, 2001; Hoelzle et al.,
2011). As noted in AR4, the greatest mass losses per unit area in the
last four decades have been observed in Patagonia, Alaska, northwest
USA, southwest Canada, the European Alps, and the Arctic. Alaska and
the Arctic are especially important regions as contributors to sea level
rise (Zemp et al., 2008, 2009).
1
Chapter 1 Introduction
138
1.4 Treatment of Uncertainties
1.4.1 Uncertainty in Environmental Science
Science always involves uncertainties. These arise at each step of the
scientific method: in the development of models or hypotheses, in
measurements and in analyses and interpretation of scientific assump-
tions. Climate science is not different in this regard from other areas of
science. The complexity of the climate system and the large range of
processes involved bring particular challenges because, for example,
gaps in direct measurements of the past can be filled only by recon-
structions using proxy data.
Because the Earth’s climate system is characterized by multiple spatial
and temporal scales, uncertainties do not usually reduce at a single,
predictable rate: for example, new observations may reduce the uncer-
tainties surrounding short-timescale processes quite rapidly, while
longer timescale processes may require very long observational base-
lines before much progress can be made. Characterization of the inter-
action between processes, as quantified by models, can be improved
by model development, or can shed light on new areas in which uncer-
tainty is greater than previously thought. The fact that there is only
a single realization of the climate, rather than a range of different
climates from which to draw, can matter significantly for certain lines
of enquiry, most notably for the detection and attribution of causes of
climate change and for the evaluation of projections of future states.
1.4.2 Characterizing Uncertainty
‘Uncertainty’ is a complex and multifaceted property, sometimes orig-
inating in a lack of information, and at other times from quite funda-
mental disagreements about what is known or even knowable (Moss
and Schneider, 2000). Furthermore, scientists often disagree about the
best or most appropriate way to characterize these uncertainties: some
can be quantified easily while others cannot. Moreover, appropriate
characterization is dependent on the intended use of the information
and the particular needs of that user community.
Scientific uncertainty can be partitioned in various ways, in which the
details of the partitioning usually depend on the context. For instance,
the process and classifications used for evaluating observational
uncertainty in climate science is not the same as that employed to
evaluate projections of future change. Uncertainty in measured quan-
tities can arise from a range of sources, such as statistical variation,
variability, inherent randomness, inhomogeneity, approximation, sub-
jective judgement, and linguistic imprecision (Morgan et al., 1990),
or from calibration methodologies, instrumental bias or instrumental
limitations (JCGM, 2008).
In the modelling studies that underpin projections of future climate
change, it is common to partition uncertainty into four main catego-
ries: scenario uncertainty, due to uncertainty of future emissions of
GHGs and other forcing agents; ‘model uncertainty’ associated with
climate models; internal variability and initial condition uncertainty;
and forcing and boundary condition uncertainty for the assessment of
historical and paleoclimate simulations (e.g., Collins and Allen, 2002;
Yip et al., 2011).
Model uncertainty is an important contributor to uncertainty in cli-
mate predictions and projections. It includes, but is not restricted to,
the uncertainties introduced by errors in the model’s representation
of dynamical and physical and bio-geochemical aspects of the climate
system as well as in the model’s response to external forcing. The
phrase ‘model uncertainty’ is a common term in the climate change
literature, but different studies use the phrase in different senses: some
use it to represent the range of behaviours observed in ensembles of
climate model (model spread), while others use it in more comprehen-
sive senses (see Sections 9.2, 11.2 and 12.2). Model spread is often
used as a measure of climate response uncertainty, but such a measure
is crude as it takes no account of factors such as model quality (Chap-
ter 9) or model independence (e.g., Masson and Knutti, 2011; Pennell
and Reichler, 2011), and not all variables of interest are adequately
simulated by global climate models.
To maintain a degree of terminological clarity this report distinguishes
between ‘model spread’ for this narrower representation of climate
model responses and ‘model uncertainty’ which describes uncertainty
about the extent to which any particular climate model provides an
accurate representation of the real climate system. This uncertainty
arises from approximations required in the development of models.
Such approximations affect the representation of all aspects of the cli-
mate including the response to external forcings.
Model uncertainty is sometimes decomposed further into parametric
and structural uncertainty, comprising, respectively, uncertainty in the
values of model parameters and uncertainty in the underlying model
structure (see Section 12.2). Some scientific research areas, such as
detection and attribution and observationally-constrained model pro-
jections of future climate, incorporate significant elements of both
observational and model-based science, and in these instances both
sets of relevant uncertainties need to be incorporated.
Scenario uncertainty refers to the uncertainties that arise due to limita-
tions in our understanding of future emissions, concentration or forcing
trajectories. Scenarios help in the assessment of future developments
in complex systems that are either inherently unpredictable, or that
have high scientific uncertainties (IPCC, 2000). The societal choices
defining future climate drivers are surrounded by considerable uncer-
tainty, and these are explored by examining the climate response to
a wide range of possible futures. In past reports, emissions scenarios
from the SRES (IPCC, 2000) were used as the main way of exploring
uncertainty in future anthropogenic climate drivers. Recent research
has made use of Representative Concentration Pathways (RCP) (van
Vuuren et al., 2011a, 2011b).
Internal or natural variability, the natural fluctuations in climate, occur
in the absence of any RF of the Earth’s climate (Hawkins and Sutton,
2009). Climate varies naturally on nearly all time and space scales, and
quantifying precisely the nature of this variability is challenging, and
is characterized by considerable uncertainty. The analysis of internal
and forced contributions to recent climate is discussed in Chapter 10.
The fractional contribution of internal variability compared with other
forms of uncertainty varies in time and in space, but usually diminish-
es with time as other sources of uncertainty become more significant
(Hawkins and Sutton, 2009; see also Chapter 11 and FAQ 1.1).
1
Introduction Chapter 1
139
In the WGI contribution to the AR5, uncertainty is quantified using
90% uncertainty intervals unless otherwise stated. The 90% uncer-
tainty interval, reported in square brackets, is expected to have a 90%
likelihood of covering the value that is being estimated. The value that
is being estimated has a 5% likelihood of exceeding the upper end-
point of the uncertainty interval, and the value has a 5% likelihood of
being less than that the lower endpoint of the uncertainty interval. A
best estimate of that value is also given where available. Uncertainty
intervals are not necessarily symmetric about the corresponding best
estimate.
In a subject as complex and diverse as climate change, the information
available as well as the way it is expressed, and often the interpreta-
tion of that material, varies considerably with the scientific context. In
some cases, two studies examining similar material may take different
approaches even to the quantification of uncertainty. The interpretation
of similar numerical ranges for similar variables can differ from study
to study. Readers are advised to pay close attention to the caveats
and conditions that surround the results presented in peer- reviewed
studies, as well as those presented in this assessment. To help readers
in this complex and subtle task, the IPCC draws on specific, calibrat-
ed language scales to express uncertainty (Mastrandrea et al., 2010),
as well as specific procedures for the expression of uncertainty (see
Table 1.2). The aim of these structures is to provide tools through which
chapter teams might consistently express uncertainty in key results.
1.4.3 Treatment of Uncertainty in IPCC
In the course of the IPCC assessment procedure, chapter teams review
the published research literature, document the findings (including
uncertainties), assess the scientific merit of this information, identify
the key findings, and attempt to express an appropriate measure of
the uncertainty that accompanies these findings using a shared guid-
ance procedure. This process has changed over time. The early Assess-
ment Reports (FAR and SAR) were largely qualitative. As the field has
grown and matured, uncertainty is being treated more explicitly, with
a greater emphasis on the expression, where possible and appropriate,
of quantified measures of uncertainty.
Although IPCC’s treatment of uncertainty has become more sophis-
ticated since the early reports, the rapid growth and considerable
diversity of climate research literature presents ongoing challenges. In
the wake of the TAR the IPCC formed a Cross-Working Group team
charged with identifying the issues and compiling a set of Uncertainty
Guidance Notes that could provide a structure for consistent treatment
of uncertainty across the IPCC’s remit (Manning et al., 2004). These
expanded on the procedural elements of Moss and Schneider (2000)
and introduced calibrated language scales designed to enable chap-
ter teams to use the appropriate level of precision to describe find-
ings. These notes were revised between the TAR and AR4 and again
between AR4 and AR5 (Mastrandrea et al., 2010).
Recently, increased engagement of social scientists (e.g., Patt and
Schrag, 2003; Kandlikar et al., 2005; Risbey and Kandlikar, 2007;
Broomell and Budescu, 2009; Budescu et al., 2009; CCSP, 2009) and
expert advisory panels (CCSP, 2009; InterAcademy Council, 2010) in
the area of uncertainty and climate change has helped clarify issues
and procedures to improve presentation of uncertainty. Many of the
recommendations of these groups are addressed in the revised Guid-
ance Notes. One key revision relates to clarification of the relation-
ship between the ‘confidence’ and ‘likelihood’ language, and pertains
to demarcation between qualitative descriptions of ‘confidence’ and
the numerical representations of uncertainty that are expressed by
the likelihood scale. In addition, a finding that includes a probabilistic
measure of uncertainty does not require explicit mention of the level
of confidence associated with that finding if the level of confidence is
high or very high. This is a concession to stylistic clarity and readabil-
ity: if something is described as having a high likelihood, then in the
absence of additional qualifiers it should be inferred that it also has
high or very high confidence.
1.4.4 Uncertainty Treatment in This Assessment
All three IPCC Working Groups in the AR5 have agreed to use two met-
rics for communicating the degree of certainty in key findings (Mas-
trandrea et al., 2010):
Confidence in the validity of a finding, based on the type, amount,
quality, and consistency of evidence (e.g., data, mechanistic under-
standing, theory, models, expert judgment) and the degree of
agreement. Confidence is expressed qualitatively.
Quantified measures of uncertainty in a finding expressed proba-
bilistically (based on statistical analysis of observations or model
results, or expert judgement).
A level of confidence synthesizes the Chapter teams’ judgements about
the validity of findings as determined through evaluation of the availa-
ble evidence and the degree of scientific agreement. The evidence and
agreement scale underpins the assessment, as it is on the basis of evi-
dence and agreement that statements can be made with scientific con-
fidence (in this sense, the evidence and agreement scale replaces the
‘level of scientific understanding’ scale used in previous WGI assess-
ments). There is flexibility in this relationship; for a given evidence and
agreement statement, different confidence levels could be assigned,
but increasing levels of evidence and degrees of agreement are cor-
related with increasing confidence. Confidence cannot necessarily be
assigned for all combinations of evidence and agreement, but where
key variables are highly uncertain, the available evidence and scientific
agreement regarding that variable are presented and discussed. Confi-
dence should not be interpreted probabilistically, and it is distinct from
‘statistical confidence’.
The confidence level is based on the evidence (robust, medium and
limited) and the agreement (high, medium and low). A combination of
different methods, e.g., observations and modelling, is important for
evaluating the confidence level. Figure 1.11 shows how the combined
evidence and agreement results in five levels for the confidence level
used in this assessment.
The qualifier ‘likelihood’ provides calibrated language for describ-
ing quantified uncertainty. It can be used to express a probabilistic
e stimate of the occurrence of a single event or of an outcome, for
example, a climate parameter, observed trend, or projected change
1
Chapter 1 Introduction
140
Frequently Asked Questions
FAQ 1.1 | If Understanding of the Climate System Has Increased, Why Hasn’t the Range of
Temperature Projections Been Reduced?
The models used to calculate the IPCC’s temperature projections agree on the direction of future global change,
but the projected size of those changes cannot be precisely predicted. Future greenhouse gas (GHG) emission rates
could take any one of many possible trajectories, and some underlying physical processes are not yet completely
understood, making them difficult to model. Those uncertainties, combined with natural year-to-year climate
variability, produce an ‘uncertainty range’ in temperature projections.
The uncertainty range around projected GHG and aerosol precursor emissions (which depend on projections of
future social and economic conditions) cannot be materially reduced. Nevertheless, improved understanding and
climate models—along with observational constraints—may reduce the uncertainty range around some factors that
influence the climate’s response to those emission changes. The complexity of the climate system, however, makes
this a slow process. (FAQ1.1, Figure 1)
Climate science has made many important advances since the last IPCC assessment report, thanks to improvements
in measurements and data analysis in the cryosphere, atmosphere, land, biosphere and ocean systems. Scientists
also have better understanding and tools to model the role of clouds, sea ice, aerosols, small-scale ocean mixing,
the carbon cycle and other processes. More observations mean that models can now be evaluated more thoroughly,
and projections can be better constrained. For example, as models and observational analysis have improved,
projections of sea level rise have become more accurate, balancing the current sea level rise budget.
Despite these advances, there is still a range in plausible projections for future global and regional climate—
what scientists call an ‘uncertainty range’. These uncertainty ranges are specific to the variable being considered
(precipitation vs. temperature, for instance) and the spatial and temporal extent (such as regional vs. global
averages). Uncertainties in climate projections arise from natural variability and uncertainty around the rate of
future emissions and the climate’s response to them. They can also occur because representations of some known
processes are as yet unrefined, and because some processes are not included in the models.
There are fundamental limits to just how precisely annual temperatures can be projected, because of the chaotic
nature of the climate system. Furthermore, decadal-scale projections are sensitive to prevailing conditions—such
as the temperature of the deep ocean—that are less well known. Some natural variability over decades arises from
interactions between the ocean, atmosphere, land, biosphere and cryosphere, and is also linked to phenomena such
as the El Niño-Southern Oscillation (ENSO) and the North Atlantic Oscillation (see Box 2.5 for details on patterns and
indices of climate variability).
Volcanic eruptions and variations in the sun’s output also contribute to natural variability, although they are
externally forced and explainable. This natural variability can be viewed as part of the ‘noise’ in the climate record,
which provides the backdrop against which the ‘signal’ of anthropogenic climate change is detected.
Natural variability has a greater influence on uncertainty at regional and local scales than it does over continental
or global scales. It is inherent in the Earth system, and more knowledge will not eliminate the uncertainties it brings.
However, some progress is possible—particularly for projections up to a few years ahead—which exploit advances
in knowledge of, for instance, the cryosphere or ocean state and processes. This is an area of active research. When
climate variables are averaged over decadal timescales or longer, the relative importance of internal variability
diminishes, making the long-term signals more evident (FAQ1.1, Figure 1). This long-term perspective is consistent
with a common definition of climate as an average over 30 years.
A second source of uncertainty stems from the many possible trajectories that future emission rates of GHGs
and aerosol precursors might take, and from future trends in land use. Nevertheless, climate projections rely on
input from these variables. So to obtain these estimates, scientists consider a number of alternative scenarios for
future human society, in terms of population, economic and technological change, and political choices. They then
estimate the likely emissions under each scenario. The IPCC informs policymaking, therefore climate projections
for different emissions scenarios can be useful as they show the possible climatic consequences of different policy
choices. These scenarios are intended to be compatible with the full range of emissions scenarios described in the
current scientific literature, with or without climate policy. As such, they are designed to sample uncertainty in
future scenarios. (continued on next page)
1
Introduction Chapter 1
141
FAQ 1.1 (continued)
Projections for the next few years and decades are sensitive to emissions of short-lived compounds such as aerosols
and methane. More distant projections, however, are more sensitive to alternative scenarios around long-lived GHG
emissions. These scenario-dependent uncertainties will not be reduced by improvements in climate science, and will
become the dominant uncertainty in projections over longer timescales (e.g., 2100) (FAQ 1.1, Figure 1).
The final contribution to the uncertainty range comes from our imperfect knowledge of how the climate will
respond to future anthropogenic emissions and land use change. Scientists principally use computer-based global
climate models to estimate this response. A few dozen global climate models have been developed by different
groups of scientists around the world. All models are built on the same physical principles, but some approximations
are needed because the climate system is so complex. Different groups choose slightly different approximations
to represent specific processes in the atmosphere, such as clouds. These choices produce differences in climate
projections from different models. This contribution to the uncertainty range is described as ‘response uncertainty’
or ‘model uncertainty’.
The complexity of the Earth system means that future climate could follow many different scenarios, yet still
be consistent with current understanding and models. As observational records lengthen and models improve,
researchers should be able, within the limitations of the range of natural variability, to narrow that range in
probable temperature in the next few decades (FAQ 1.1, Figure 1). It is also possible to use information about the
current state of the oceans and cryosphere to produce better projections up to a few years ahead.
As science improves, new geophysical processes can be added to climate models, and representations of those
already included can be improved. These developments can appear to increase model-derived estimates of climate
response uncertainty, but such increases merely reflect the quantification of previously unmeasured sources of
uncertainty (FAQ1.1, Figure 1). As more and more important processes are added, the influence of unquantified
processes lessens, and there can be more confidence in the projections.
Year
1960
1980 2000 2020
2040
2060
2080
2100
0
0.5
1
1.5
2
2.5
3
3.5
4
Global average temperature change (°C)
Decadal mean temperature anomalies
Observations
Natural variability
Climate response uncertainty
Emission uncertainty
Historical GCM uncertainty
All 90% uncertainty ranges
(a)
Year
1960
1980 2000 2020
2040
2060
2080
2100
(b)
0
0.5
1
1.5
2
2.5
3
3.5
4
Global average temperature change (°C)
Year
1960
1980 2000 2020
2040
2060
2080
2100
(c)
0
0.5
1
1.5
2
2.5
3
3.5
4
Global average temperature change (°C)
FAQ 1.1, Figure 1 | Schematic diagram showing the relative importance of different uncertainties, and their evolution in time. (a) Decadal mean surface temperature
change (°C) from the historical record (black line), with climate model estimates of uncertainty for historical period (grey), along with future climate projections and
uncertainty. Values are normalised by means from 1961 to 1980. Natural variability (orange) derives from model interannual variability, and is assumed constant with
time. Emission uncertainty (green) is estimated as the model mean difference in projections from different scenarios. Climate response uncertainty (blue-solid) is based
on climate model spread, along with added uncertainties from the carbon cycle, as well as rough estimates of additional uncertainty from poorly modelled processes.
Based on Hawkins and Sutton (2011) and Huntingford et al. (2009). (b) Climate response uncertainty can appear to increase when a new process is discovered to be
relevant, but such increases reflect a quantification of previously unmeasured uncertainty, or (c) can decrease with additional model improvements and observational
constraints. The given uncertainty range of 90% means that the temperature is estimated to be in that range, with a probability of 90%.
1
Chapter 1 Introduction
142
lying in a given range. Statements made using the likelihood scale
may be based on statistical or modelling analyses, elicitation of expert
views, or other quantitative analyses. Where sufficient information is
available it is preferable to eschew the likelihood qualifier in favour
of the full probability distribution or the appropriate probability range.
See Table 1.2 for the list of ‘likelihood’ qualifiers to be used in AR5.
Many social sciences studies have found that the interpretation of
uncertainty is contingent on the presentation of information, the con-
text within which statements are placed and the interpreter’s own
lexical preferences. Readers often adjust their interpretation of prob-
abilistic language according to the magnitude of perceived potential
consequences (Patt and Schrag, 2003; Patt and Dessai, 2005). Further-
more, the framing of a probabilistic statement impinges on how it is
interpreted (Kahneman and Tversky, 1979): for example, a 10% chance
of dying is interpreted more negatively than a 90% chance of surviving.
In addition, work examining expert judgement and decision making
shows that people—including scientific experts—are prone to a range
of heuristics and biases that affect their judgement (e.g., Kahneman
et al., 1982). For example, in the case of expert judgements there
is a tendency towards overconfidence both at the individual level
(Morgan et al., 1990) and at the group level as people converge on a
view and draw confidence in its reliability from each other. However,
in an assessment of the state of scientific knowledge across a field
such as climate change—characterized by complexity of process and
heterogeneity of data constraints—some degree of expert judgement
is inevitable (Mastrandrea et al., 2010).
These issues were brought to the attention of chapter teams so that
contributors to the AR5 might be sensitized to the ways presentation,
framing, context and potential biases might affect their own assess-
ments and might contribute to readers’ understanding of the infor-
mation presented in this assessment. There will always be room for
debate about how to summarize such a large and growing literature.
The uncertainty guidance is aimed at providing a consistent, cali-
brated set of words through which to communicate the uncertainty,
confidence and degree of consensus prevailing in the scientific litera-
ture. In this sense the guidance notes and practices adopted by IPCC
for the presentation of uncertainties should be regarded as an inter-
disciplinary work in progress, rather than as a finalized, comprehensive
approach. Moreover, one precaution that should be considered is that
translation of this assessment from English to other languages may
lead to a loss of precision.
1.5 Advances in Measurement and
Modelling Capabilities
Since AR4, measurement capabilities have continued to advance. The
models have been improved following the progress in the understand-
ing of physical processes within the climate system. This section illus-
trates some of those developments.
1.5.1 Capabilities of Observations
Improved understanding and systematic monitoring of Earth’s climate
requires observations of various atmospheric, oceanic and terrestrial
parameters and therefore has to rely on various technologies (ranging
from ground-based instruments to ships, buoys, ocean profilers, bal-
loons, aircraft, satellite-borne sensors, etc.). The Global Climate Observ-
ing System (GCOS, 2009) defined a list of so-called Essential Climate
Variables, that are technically and economically feasible to observe,
but some of the associated observing systems are not yet operated in
a systematic manner. However, during recent years, new observational
systems have increased the number of observations by orders of mag-
nitude and observations have been made at places where there have
been no data before (see Chapters 2, 3 and 4 for an assessment of
changes in observations). Parallel to this, tools to analyse and process
the data have been developed and enhanced to cope with the increase
of information and to provide a more comprehensive picture of the
Earth’s climate. At the same time, it should be kept in mind that there
has been some limited progress in developing countries in filling gaps
in their in situ observing networks, but developed countries have made
little progress in ensuring long-term continuity for several important
observing systems (GCOS, 2009). In addition, more proxy (non-instru-
mental) data have been acquired to provide a more comprehensive
picture of climate changes in the past (see Chapter 5). Efforts are also
occurring to digitize historic observations, mainly of ground-station
data from periods prior to the second half of the 20th century (Brunet
and Jones, 2011).
High agreement
Limited evidence
High agreement
Medium evidence
High agreement
Robust evidence
Medium agreement
Robust evidence
Medium agreement
Medium evidence
Medium agreement
Limited evidence
Low agreement
Limited evidence
Low agreement
Medium evidence
Low agreement
Robust evidence
Evidence (type, amount, quality, consistency)
Agreement
Confidence
Scale
Figure 1.11 | The basis for the confidence level is given as a combination of evidence
(limited, medium, robust) and agreement (low, medium and high) (Mastrandrea et al.,
2010).
Term Likelihood of the Outcome
Virtually certain 99−100% probability
Very likely 90−100% probability
Likely 66−100% probability
About as likely as not 33−66% probability
Unlikely 0−33% probability
Very unlikely 0−10% probability
Exceptionally unlikely 0−1% probability
Notes:
Additional terms that were used in limited circumstances in the AR4 (extremely likely =
95−100% probability, more likely than not = >50−100% probability, and extremely unlikely =
0−5% probability) may also be used in the AR5 when appropriate.
Table 1.2 | Likelihood terms associated with outcomes used in the AR5.
1
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19601940192019001880
satellites
rockets
radiosonde
International
Geophysical Year
ozone sonde
aircraft
aircraft (chemistry)
kites
pilot balloons
total ozone / remote sensing
in-situ air chemistry
cloud obs.
surface stations (temperature, pressure, wind, radiation, turbidity, carbon flux)
marine observations (e.g., sea surface temperature, precipitation)
1980 2000
First Year of Temperature Record
in GHCN−Daily Database
1900
1910
1920
1930
1940
1950
1960
1970
1980
1990
2000
2010
1996
0
10
20
30
40
50
60
70
2000 2005 2010
Number of satellite data sources used
60
1980
2000
96
0
60
1
9
9
Figure 1.12 | Development of capabilities of observations. Top: Changes in the mix and increasing diversity of observations over time create challenges for a consistent climate
record (adapted from Brönnimann et al., 2008). Bottom left: First year of temperature data in Global Historical Climatology Network (GHCN) daily database (available at http://
www.ncdc.noaa.gov/oa/climate/ghcn-daily/; Menne et al., 2012). Bottom right: Number of satellite instruments from which data have been assimilated in the European Centre
for Medium-Range Weather Forecasts production streams for each year from 1996 to 2010. This figure is used as an example to demonstrate the fivefold increase in the usage of
satellite data over this time period.
Reanalysis is a systematic approach to produce gridded dynamically
consistent data sets for climate monitoring and research by assimilat-
ing all available observations with help of a climate model (Box 2.3).
Model-based reanalysis products play an important role in obtaining
a consistent picture of the climate system. However, their usefulness
in detecting long-term climate trends is currently limited by changes
over time in observational coverage and biases, linked to the presence
of biases in the assimilating model (see also Box 2.3 in Chapter 2).
Because AR4 both the quantity and quality of the observations that
are assimilated through reanalysis have increased (GCOS, 2009). As
an example, there has been some overall increase in mostly atmos-
pheric observations assimilated in European Centre for Medium-Range
Weather Forecasts Interim Reanalysis since 2007 (Dee et al., 2011).
The overwhelming majority of the data, and most of the increase over
recent years, come from satellites (Figure 1.12) (GCOS, 2011). For
example, information from Global Positioning System radio occultation
measurements has increased significantly since 2007. The increases in
data from fixed stations are often associated with an increased fre-
quency of reporting, rather than an increase in the number of stations.
Increases in data quality come from improved instrument design or
from more accurate correction in the ground-station processing that is
applied before the data are transmitted to users and data centres. As
an example for in situ data, temperature biases of radiosonde measure-
ments from radiation effects have been reduced over recent years. The
new generation of satellite sensors such as the high spectral resolution
infrared sounders (such as the Atmospheric Infrared Sounder and the
Infrared Atmospheric Sounding Interferometer) are instrumental to
achieving a better temporal stability for recalibrating sensors such
as the High-Resolution Infrared Radiation Sounder. Few instruments
(e.g., the Advanced Very High Resolution Radiometer) have now been
1
Chapter 1 Introduction
144
in orbit for about three decades, but these were not originally designed
for climate applications and therefore require careful re-calibration.
A major achievement in ocean observation is due to the implementa-
tion of the Argo global array of profiling floats system (GCOS, 2009).
Deployment of Argo floats began in 2000, but it took until 2007 for
numbers to reach the design target of 3000 floats. Since 2000 the ice-
free upper 2000 m of the ocean have been observed systematically
for temperature and salinity for the first time in history, because both
the Argo profiling float and surface drifting buoy arrays have reached
global coverage at their target numbers (in January 2009, there were
3291 floats operating). Biases in historical ocean data have been iden-
tified and reduced, and new analytical approaches have been applied
(e.g., Willis et al., 2009). One major consequence has been the reduc-
tion of an artificial decadal variation in upper ocean temperature and
heat content that was apparent in the observational assessment for
AR4 (see Section 3.2). The spatial and temporal coverage of bioge-
ochemical measurements in the ocean has also expanded. Satellite
observations for sea level (Sections 3.7 and 13.2), sea surface salinity
(Section 3.3), sea ice (Section 4.2) and ocean colour have also been
further developed over the past few years.
Progress has also been made with regard to observation of terrestri-
al Essential Climate Variables. Major advances have been achieved in
remote sensing of soil moisture due to the launch of the Soil Moisture
and Oceanic Salinity mission in 2009 but also due to new retrieval
techniques that have been applied to data from earlier and ongoing
missions (see Seneviratne et al., 2010 for a detailed review). However,
these measurements have limitations. For example, the methods fail
under dense vegetation and they are restricted to the surface soil.
Updated Advanced Very High Resolution Radiometer-based Normalized
Differenced Vegetation Index data provide new information on the
change in vegetation. During the International Polar Year 2007–2009
the number of borehole sites was significantly increased and therefore
allows a better monitoring of the large-scale permafrost features (see
Section 4.7).
1.5.2 Capabilities in Global Climate Modelling
Several developments have especially pushed the capabilities in mod-
elling forward over recent years (see Figure 1.13 and a more detailed
discussion in Chapters 6, 7 and 9).
A
tmosphere
Land
Surface
Ocean &
Sea Ice
Aerosols
Carbon Cycle
Dynamic
Vegetation
Atmospheric
Chemistry
Mid-1970s Mid-1980s TARSARFA
RA
R4
C
O
U
P
L
E
D
C
L
I
M
A
T
E
M
O
D
E
L
Land Ice
AR5
Mid-1970s Mid-1980s TARSARFA
RA
R4 AR5
Figure 1.13 | The development of climate models over the last 35 years showing how the different components were coupled into comprehensive climate models over time. In
each aspect (e.g., the atmosphere, which comprises a wide range of atmospheric processes) the complexity and range of processes has increased over time (illustrated by growing
cylinders). Note that during the same time the horizontal and vertical resolution has increased considerably e.g., for spectral models from T21L9 (roughly 500 km horizontal resolu-
tion and 9 vertical levels) in the 1970s to T95L95 (roughly 100 km horizontal resolution and 95 vertical levels) at present, and that now ensembles with at least three independent
experiments can be considered as standard.
1
Introduction Chapter 1
145
87.5 km x 87.5 km
30.0 km x 30.0 km
a)
b)
Figure 1.14 | Horizontal resolutions considered in today’s higher resolution models and in the very high resolution models now being tested: (a) Illustration of the European
topography at a resolution of 87.5 × 87.5 km; (b) same as (a) but for a resolution of 30.0 × 30.0 km.
There has been a continuing increase in horizontal and vertical resolu-
tion. This is especially seen in how the ocean grids have been refined,
and sophisticated grids are now used in the ocean and atmosphere
models making optimal use of parallel computer architectures. More
models with higher resolution are available for more regions. Figure
1.14a and 1.14b show the large effect on surface representation from
a horizontal grid spacing of 87.5 km (higher resolution than most cur-
rent global models and similar to that used in today’s highly resolved
models) to a grid spacing of 30.0 km (similar to the current regional
climate models).
Representations of Earth system processes are much more extensive
and improved, particularly for the radiation and the aerosol cloud inter-
actions and for the treatment of the cryosphere. The representation of
the carbon cycle was added to a larger number of models and has been
improved since AR4. A high-resolution stratosphere is now included in
many models. Other ongoing process development in climate models
includes the enhanced representation of nitrogen effects on the carbon
cycle. As new processes or treatments are added to the models, they
are also evaluated and tested relative to available observations (see
Chapter 9 for more detailed discussion).
1
Chapter 1 Introduction
146
Ensemble techniques (multiple calculations to increase the statistical
sample, to account for natural variability, and to account for uncertainty
in model formulations) are being used more frequently, with larger
samples and with different methods to generate the samples (different
models, different physics, different initial conditions). Coordinated
projects have been set up to generate and distribute large samples
(ENSEMBLES, climateprediction.net, Program for Climate Model Diag-
nosis and Intercomparison).
The model comparisons with observations have pushed the analysis
and development of the models. CMIP5, an important input to the AR5,
has produced a multi-model data set that is designed to advance our
understanding of climate variability and climate change. Building on
previous CMIP efforts, such as the CMIP3 model analysis reported in
AR4, CMIP5 includes ‘long-term’ simulations of 20th century climate
and projections for the 21st century and beyond. See Chapters 9, 10, 11
and 12 for more details on the results derived from the CMIP5 archive.
Since AR4, the incorporation of ‘long-term’ paleoclimate simulations
in the CMIP5 framework has allowed incorporation of information
from paleoclimate data to inform projections. Within uncertainties
associated with reconstructions of past climate variables from proxy
records 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 (Section 5.3, Box 5.1 and 9.4).
The capabilities of ESMs continue to be enhanced. For example, there
are currently extensive efforts towards developing advanced treat-
ments for the processes affecting ice sheet dynamics. Other enhance-
ments are being aimed at land surface hydrology, and the effects of
agriculture and urban environments.
As part of the process of getting model analyses for a range of alter-
native assumptions about how the future may unfold, scenarios for
future emissions of important gases and aerosols have been generated
for the IPCC assessments (e.g., see the SRES scenarios used in TAR
and AR4). The emissions scenarios represent various development
pathways based on well-defined assumptions. The scenarios are used
to calculate future changes in climate, and are then archived in the
Climate Model Intercomparison Project (e.g., CMIP3 for AR4; CMIP5
for AR5). For CMIP5, four new scenarios, referred to as Representative
Concentration Pathways (RCPs) were developed (Section 12.3; Moss et
al., 2010). See Box 1.1 for a more thorough discussion of the RCP sce-
narios. Because results from both CMIP3 and CMIP5 will be presented
in the later chapters (e.g., Chapters 8, 9, 11 and 12), it is worthwhile
considering the differences and similarities between the SRES and the
RCP scenarios. Figure 1.15, acting as a prelude to the discussion in Box
1.1, shows that the RF for several of the SRES and RCP scenarios are
similar over time and thus should provide results that can be used to
compare climate modelling studies.
1950 1975 2000 2025 2050 2075 2100
0
1
2
3
4
5
6
7
8
9
Year
RF total (Wm
-2
)
RCP2.6
RCP4.5
RCP6.0
RCP8.5
SRES A1B
SRES A2
SRES B1
IS92a
Figure 1.15 | Historical and projected total anthropogenic RF (W m
–2
) relative to preindustrial (about 1765) between 1950 and 2100. Previous IPCC assessments (SAR IS92a, TAR/
AR4 SRES A1B, A2 and B1) are compared with representative concentration pathway (RCP) scenarios (see Chapter 12 and Box 1.1 for their extensions until 2300 and Annex II for
the values shown here). The total RF of the three families of scenarios, IS92, SRES and RCP, differ for example, for the year 2000, resulting from the knowledge about the emissions
assumed having changed since the TAR and AR4.
1
Introduction Chapter 1
147
Box 1.1 | Description of Future Scenarios
Long-term climate change projections require assumptions on human activities or natural effects that could alter the climate over
decades and centuries. Defined scenarios are useful for a variety of reasons, e.g., assuming specific time series of emissions, land use,
atmospheric concentrations or RF across multiple models allows for coherent climate model intercomparisons and synthesis. Scenarios
can be formed in a range of ways, from simple, idealized structures to inform process understanding, through to comprehensive
scenarios produced by Integrated Assessment Models (IAMs) as internally consistent sets of assumptions on emissions and socio-
economic drivers (e.g., regarding population and socio-economic development).
Idealized Concentration Scenarios
As one example of an idealized concentration scenario, a 1% yr
–1
compound increase of atmospheric CO
2
concentration until a doubling
or a quadrupling of its initial value has been widely used in the past (Covey et al., 2003). An exponential increase of CO
2
concentrations
induces an essentially linear increase in RF (Myhre et al., 1998) due to a ‘saturation effect’ of the strong absorbing bands. Such a linear
ramp function is highly useful for comparative diagnostics of models’ climate feedbacks and inertia. The CMIP5 intercomparison project
again includes such a stylized pathway up to a quadrupling of CO
2
concentrations, in addition to an instantaneous quadrupling case.
The Socio-Economic Driven SRES Scenarios
The SRES suite of scenarios were developed using IAMs and resulted from specific socio-economic scenarios from storylines about future
demographic and economic development, regionalization, energy production and use, technology, agriculture, forestry and land use
(IPCC, 2000). The climate change projections undertaken as part of CMIP3 and discussed in AR4 were based primarily on the SRES A2,
A1B and B1 scenarios. However, given the diversity in models’ carbon cycle and chemistry schemes, this approach implied differences in
models’ long lived GHG and aerosol concentrations for the same emissions scenario. As a result of this and other shortcomings, revised
scenarios were developed for AR5 to allow atmosphere-ocean general circulation model (AOGCM) (using concentrations) simulations
to be compared with those ESM simulations that use emissions to calculate concentrations.
Representative Concentration Pathway Scenarios and Their Extensions
Representative Concentration Pathway (RCP) scenarios (see Section 12.3 for a detailed description of the scenarios; Moss et al., 2008;
Moss et al., 2010; van Vuuren et al., 2011b) are new scenarios that specify concentrations and corresponding emissions, but are not
directly based on socio-economic storylines like the SRES scenarios. The RCP scenarios are based on a different approach and include
more consistent short-lived gases and land use changes. They are not necessarily more capable of representing future developments
than the SRES scenarios. Four RCP scenarios were selected from the published literature (Fujino et al., 2006; Smith and Wigley, 2006;
Riahi et al., 2007; van Vuuren et al., 2007; Hijioka et al., 2008; Wise et al., 2009) and updated for use within CMIP5 (Masui et al.,
2011; Riahi et al., 2011; Thomson et al., 2011; van Vuuren et al., 2011a). The four scenarios are identified by the 21st century peak or
stabilization value of the RF derived by the reference model (in W m
–2
) (Box 1.1, Figure 1): the lowest RCP, RCP2.6 (also referred to as
Box 1.1, Figure 1 | Total RF (anthropogenic plus natural) for RCPs and extended concentration pathways (ECP)—for RCP2.6, RCP4.5, and RCP6, RCP8.5, as well as a
supplementary extension RCP6 to 4.5 with an adjustment of emissions after 2100 to reach RCP4.5 concentration levels in 2250 and thereafter. Note that the stated RF
levels refer to the illustrative default median estimates only. There is substantial uncertainty in current and future RF levels for any given scenario. Short-term variations
in RF are due to both volcanic forcings in the past (1800–2000) and cyclical solar forcing assuming a constant 11-year solar cycle (following the CMIP5 recommenda-
tion), except at times of stabilization. (Reproduced from Figure 4 in Meinshausen et al., 2011.)
1800 1900 2000 2100 2200 2300 2400 2500
−2
0
2
4
6
8
10
12
14
History RCPs ECPs
~3.0 (Wm
-2
)
~4.5 (Wm
-2
)
~6 (Wm
-2
)
~8.5 (Wm
-2
)
RCP2.6
RCP4.5
RCP6
RCP8.5
SCP6to4.5
Year
Radiative forcing (Wm
-2
)
(continued on next page)
1
Chapter 1 Introduction
148
RCP3-PD) which peaks at 3 W m
–2
and then declines to approximately 2.6 W m
–2
by 2100; the medium-low RCP4.5 and the medium-
high RCP6 aiming for stabilization at 4.5 and 6 W m
–2
, respectively around 2100; and the highest one, RCP8.5, which implies a RF of
8.5 W m
–2
by 2100, but implies rising RF beyond that date (Moss et al., 2010). In addition there is a supplementary extension SCP6to4.5
with an adjustment of emissions after 2100 to reach RCP 4.5 concentration levels in 2250 and thereafter. The RCPs span the full range
of RF associated with emission scenarios published in the peer-reviewed literature at the time of the development of the RCPs, and the
two middle scenarios where chosen to be roughly equally spaced between the two extremes (2.6 and 8.5 W m
–2
). These forcing values
should be understood as comparative labels representative of the forcing associated with each scenario, which will vary somewhat
from model to model. This is because concentrations or emissions (rather than the RF) are prescribed in the CMIP5 climate model runs.
Various steps were necessary to turn the selected ‘raw’ RCPs into emission scenarios from IAMs and to turn these into data sets usable
by the climate modelling community, including the extension with historical emissions (Granier et al., 2011; Meinshausen et al., 2011),
the harmonization (smoothly connected historical reconstruction) and gridding of land use data sets (Hurtt et al., 2011), the provision
of atmospheric chemistry modelling studies, particularly for tropospheric ozone (Lamarque et al., 2011), analyses of 2000–2005 GHG
emission levels, and extension of GHG concentrations with historical GHG concentrations and harmonization with analyses of 2000–
2005 GHG concentrations levels (Meinshausen et al., 2011). The final RCP data sets comprise land use data, harmonized GHG emissions
and concentrations, gridded reactive gas and aerosol emissions, as well as ozone and aerosol abundance fields ( Figures 2, 3, and 4 in
Box 1.1). (continued on next page)
Box 1.1, Figure 2 | Concentrations of GHG following the 4 RCPs and their extensions (ECP) to 2300. (Reproduced from Figure 5 in Meinshausen et al., 2011.) Also
see Annex II Table AII.4.1 for CO
2
, Table AII.4.2 for CH
4
, Table AII.4.3 for N
2
O.
1500
2000
ppm
a) Carbon
Dioxide
History RCPs ECPs
RCP8.5
300
400
500
600
700
800
900
1000
RCP2.6
RCP4.5
RCP6
SCP6to4.5
1
800
1
900
2
000
21
00
22
00
2
300
0
100
200
300
400
500
600
700
800
900
1000
ppt
d) CFC12−eq
1
800
1
900
2
000
21
00
22
00
2
300
250
300
350
400
450
500
ppb
c) Nitrous
Oxide
RCP8.5
RCP2.6
RCP4.5
RCP6
SCP6to4.5
500
1000
1500
2000
2500
3000
3500
ppb
b) Methane
HistoryRCPsECPs
RCP8.5
RCP2.6
RCP4.5
RCP6
Box 1.1 (continued)
1
Introduction Chapter 1
149
To aid model understanding of longer-term climate change implications, these RCPs were extended until 2300 (Meinshausen et al.,
2011) under reasonably simple and somewhat arbitrary assumptions regarding post-2100 GHG emissions and concentrations. In order
to continue to investigate a broad range of possible climate futures, the two outer RCPs, RCP2.6 and RCP8.5 assume constant emissions
after 2100, while the two middle RCPs aim for a smooth stabilization of concentrations by 2150. RCP8.5 stabilizes concentrations
only by 2250, with CO
2
concentrations of approximately 2000 ppm, nearly seven times the pre-industrial levels. As the RCP2.6 implies
netnegative CO
2
emissions after around 2070 and throughout the extension, CO
2
concentrations are slowly reduced towards 360 ppm
by 2300.
Comparison of SRES and RCP Scenarios
The four RCP scenarios used in CMIP5 lead to RF values that span a range larger than that of the three SRES scenarios used in CMIP3
(Figure 12.3). RCP4.5 is close to SRES B1, RCP6 is close to SRES A1B (more after 2100 than during the 21st century) and RCP8.5 is
somewhat higher than A2 in 2100 and close to the SRES A1FI scenario (Figure 3 in Box 1.1). RCP2.6 is lower than any of the SRES
scenarios (see also Figure 1.15). (continued on next page)
Box 1.1 (continued)
Box 1.1, Figure 3 | (a) Equivalent CO
2
concentration and (b) CO
2
emissions (except land use emissions) for the four RCPs and their ECPs as well as some SRES
scenarios.
1800 1850 1900 1950 2000 2050 2100 2150 2200 2250 2300
300
400
500
600
700
800
900
1000
a)
History RCPs ECPs
RCP2.6
RCP4.5
RCP6
RCP8.5
SCP6to4.5
SRES A1B
SRES A1FI
SRES A1T
SRES A2
SRES B1
SRES B2
CO
2
equivalent (CO
2
-eq ppm)
1800 1850 1900 1950 2000 2050 2100 2150 2200 2250 2300
−5
0
5
10
15
20
25
30
b)
History RCPs ECPs
RCP2.6
RCP4.5
RCP6
RCP8.5
SCP6to4.5
SRES
A1B
SRES
A1FI
SRES
A1T
SRES
A2
SRES
B1
SRES
B2
Global CO
2
(fossil & ind.) emissions (GtC yr
-1
)
1
Chapter 1 Introduction
150
Box 1.1 (continued)
Box 1.1, Figure 4 | (a) Anthropogenic BC emissions (Annex II Table AII.2.22), (b) anthropogenic NO
x
emissions (Annex II Table AII.2.18), and (c) anthropogenic SO
x
emissions (Annex II Table II.2.20).
2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
0
5
10
15
20
25
30
a)
Anthropogenic BC emissions
2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
15
30
45
60
75
90
105
120
b)
Anthropogenic NO
X
emissions
(TgN yr
-1
)(
Tg yr
-1
)(TgS yr
-1
)
RCP2.6
RCP4.5
RCP6.0
RCP8.5
SRES A2
SRES B1
2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
15
30
45
60
75
90
105
120
c)
Anthropogenic SO
X
emissions
Note: Primary anthropogenic sulphur emissions as SO
2
measured here as Tg of S
(see Annex II Table AII.2.20)
1
Introduction Chapter 1
151
1.6 Overview and Road Map to the Rest
of the Report
As this chapter has shown, understanding of the climate system and
the changes occurring in it continue to advance. The notable scientific
advances and associated peer-reviewed publications since AR4 provide
the basis for the assessment of the science as found in Chapters 2 to
14. Below a quick summary of these chapters and their objectives is
provided.
Observations and Paleoclimate Information (Chapters 2, 3, 4 and
5): These chapters assess information from all climate system compo-
nents on climate variability and change as obtained from instrumental
records and climate archives. This group of chapters covers all relevant
aspects of the atmosphere including the stratosphere, the land surface,
the oceans and the cryosphere. Information on the water cycle, includ-
ing evaporation, precipitation, runoff, soil moisture, floods, drought,
etc. is assessed. Timescales from daily to decades (Chapters 2, 3 and
4) and from centuries to many millennia (Chapter 5) are considered.
Process Understanding (Chapters 6 and 7): These chapters cover
all relevant aspects from observations and process understanding, to
projections from global to regional scale. Chapter 6 covers the carbon
cycle and its interactions with other biogeochemical cycles, in particular
the nitrogen cycle, as well as feedbacks on the climate system. Chapter
7 treats in detail clouds and aerosols, their interactions and chemistry,
the role of water vapour, as well as their role in feedbacks on the cli-
mate system.
From Forcing to Attribution of Climate Change (Chapters 8, 9
and 10): In these chapters, all the information on the different drivers
(natural and anthropogenic) of climate change is collected, expressed
in terms of RF, and assessed (Chapter 8). As part of this, the science of
metrics commonly used in the literature to compare radiative effects
from a range of agents (Global Warming Potential, Global Temperature
Change Potential and others) is covered. In Chapter 9, the hierarchy of
climate models used in simulating past and present climate change is
assessed. Information regarding detection and attribution of changes
on global to regional scales is assessed in Chapter 10.
Future Climate Change and Predictability (Chapters 11 and 12):
These chapters assess projections of future climate change derived from
climate models on timescales from decades to centuries at both global
and regional scales, including mean changes, variability and extremes.
Fundamental questions related to the predictability of climate as well
as long-term climate change, climate change commitments and inertia
in the climate system are addressed.
Integration (Chapters 13 and 14): These chapters integrate all rel-
evant information for two key topics in WGI AR5: sea level change
(Chapter 13) and climate phenomena across the regions (Chapter 14).
Chapter 13 assesses information on sea level change ranging from
observations and process understanding to projections from global
to regional scales. Chapter 14 assesses the most important modes of
variability in the climate system and extreme events. Furthermore, this
chapter deals with interconnections between the climate phenome-
na, their regional expressions, and their relevance for future regional
climate change. Maps produced and assessed in Chapter 14, together
with Chapters 11 and 12, form the basis of the Atlas of Global and
Regional Climate Projections in Annex I. RFs and estimates of future
atmospheric concentrations from Chapters 7, 8, 11 and 12 form the
basis of the Climate System Scenario Tables in Annex II.
1.6.1 Topical Issues
A number of topical issues are discussed throughout the assessment.
These issues include those of areas where there is contention in the
peer-reviewed literature and where questions have been raised that
are being addressed through ongoing research. Table 1.3 provides a
non-comprehensive list of many of these and the chapters where they
are discussed.
Topic Section
Abrupt change and irreversibility 5.7, 12.5, 13.4
Aerosols 6.4, 7.3, 7.4, 7.5, 7.6, 8.3, 11.3, 14.1
Antarctic climate change 5.8, 9.4, 10.3, 13.3
Arctic sea ice change 4.2, 5.5, 9.4, 10.3, 11.3, 12.4
Hydrological cycle changes 2.5, 2.6, 3.3, 3.4, 3.5, 7.6, 10.3, 12.4
Carbon-climate feedbacks 6.4, 12.4
Climate sensitivity 5.3, 9.7, 10.8, 12.5
Climate stabilization 6.3, 6.4, 12.5
Cloud feedbacks 5.3, 7.2, 9.7, 11.3, 12.4
Cosmic ray effects on clouds 7.4
Decadal climate variability 5.3, 9.5, 10.3
Earth’s Energy (trends, distribution and
budget)
2.3, 3.2, 13.3
El Niño-Southern Oscillation 2.7, 5.4, 9.4, 9.5, 14.4
Geo-engineering 6.4, 7.7
Glacier change 4.3, 5.5, 10.5, 13.3
Ice sheet dynamics and mass balance
assessment
4.4, 5.3, 5.6, 10.5, 13.3
Monsoons 2.7, 5.5, 9.5, 14.2
Ocean acidification 3.8, 6.4
Permafrost change 4.7, 6.3, 10.5
Solar effects on climate change 5.2, 8.4
Sea level change, including regional effects 3.7, 5.6, 13.1
Temperature trends since 1998 2.4, 3.2, 9.4
Tropical cyclones 2.6, 10.6, 14.6
Upper troposphere temperature trends 2.4, 9.4
Table 1.3 | Key topical issues discussed in the assessment.
1
Chapter 1 Introduction
152
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155
Appendix 1.A:
Notes and Technical Details on Figures Displayed
in Chapter 1
Figure 1.4: Documentation of Data Sources
Observed Temperature
NASA GISS evaluation of the observations: Hansen et al. (2010) updat-
ed: The data were downloaded from http://data.giss.nasa.gov/gistemp/
tabledata_v3/GLB.Ts+dSST.txt. Annual means are used (January to
December) and anomalies are calculated relative to 1961–1990.
NOAA NCDC evaluation of the observations: Smith et al. (2008)
updated: The data were downloaded from ftp://ftp.ncdc.noaa.gov/pub/
data/anomalies/annual.land_ocean.90S.90N.df_1901–2000mean.dat.
Annual mean anomalies are calculated relative to 1961–1990.
Hadley Centre evaluation of the observations: Morice et al. (2012): The
data were downloaded from http://www.metoffice.gov.uk/hadobs/
hadcrut4/data/current/download.html#regional_series. Annual mean
anomalies are calculated relative to 1961–1990 based on the ensem-
ble median.
IPCC Range of Projections
Table 1.A.1 | FAR: The data have been digitized using a graphics tool from FAR Chap-
ter 6, Figure 6.11 (Bretherton et al., 1990) in 5-year increments as anomalies relative
to 1990 (°C).
Year
Lower Bound
(Scenario D)
Upper Bound
(Business as Usual)
1990 0.00 0.00
1995 0.09 0.14
2000 0.15 0.30
2005 0.23 0.53
2010 0.28 0.72
2015 0.33 0.91
2020 0.39 1.11
2025 0.45 1.34
2030 0.52 1.58
2035 0.58 1.86
Year
Lower Bound
(IS92c/1.5)
Upper Bound
(IS92e/4.5)
1990 0.00 0.00
1995 0.05 0.09
2000 0.11 0.17
2005 0.16 0.28
2010 0.19 0.38
2015 0.23 0.47
2020 0.27 0.57
2025 0.31 0.67
2030 0.36 0.79
2035 0.41 0.92
Table 1.A.2 | SAR: The data have been digitized using a graphics tool from Figure 19
of the TS (IPCC, 1996) in 5-year increments as anomalies relative to 1990. The scenarios
include changes in aerosols beyond 1990 (°C).
Table 1.A.3 | TAR: The data have been digitized using a graphics tool from Figure
9.13(b) (Cubasch et al., 2001) in 5-year increments based on the GFDL_R15_a and DOE
PCM parameter settings (°C).
Year Lower Bound Upper Bound
1990 0.00 0.00
1995 0.05 0.09
2000 0.11 0.20
2005 0.14 0.34
2010 0.17 0.52
2015 0.22 0.70
2020 0.28 0.87
2025 0.37 1.08
2030 0.43 1.28
2035 0.52 1.50
AR4: The temperature projections of the AR4 are presented for three
SRES scenarios: B1, A1B and A2. Annual mean anomalies relative to
1961–1990 of the individual CMIP3 ensemble simulations (as used in
AR4 SPM Figure SPM5) are shown. One outlier has been eliminated
based on the advice of the model developers because of the model
drift that leads to an unrealistic temperature evolution. As assessed
by Meehl et al. (2007), the likely range for the temperature change is
given by the ensemble mean temperature change +60% and –40%
of the ensemble mean temperature change. Note that in the AR4 the
uncertainty range was explicitly estimated for the end of the 21st cen-
tury results. Here, it is shown for 2035. The time dependence of this
range has been assessed in Knutti et al. (2008). The relative uncertainty
is approximately constant over time in all estimates from different
sources, except for the very early decades when natural variability is
being considered (see Figure 3 in Knutti et al., 2008).
Data Processing
Observations
The observations are shown from 1950 to 2012 as annual mean anom-
aly relative to 1961–1990 (squares). For smoothing, first, the trend of
each of the observational data sets was calculated by locally weighted
scatter plot smoothing (Cleveland, 1979; f = 1/3). Then, the 11-year
running means of the residuals were determined with reflected ends
for the last 5 years. Finally, the trend was added back to the 11-year
running means of the residuals.
Projections
For FAR, SAR and TAR, the projections have been harmonized to match
the average of the three smoothed observational data sets at 1990.
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Chapter 1 Introduction
156
Figure 1.5: Documentation of Data Sources
Observed CO
2
Concentrations
Global annual mean CO
2
concentrations are presented as annual mean
values from Annex II Table AII.1.1a.
IPCC Range of Projections
Table 1.A.4 | FAR: The data have been digitized using a graphics tool from Figure A.3
(Annex, IPCC, 1990) as anomalies compared to 1990 in 5-year increments (ppm) and
the observed 1990 value (353.6) has been added.
Year
Lower Bound
(Scenario D)
Upper Bound
(Business as Usual)
1990 353.6 353.6
1995 362.8 363.7
2000 370.6 373.3
2005 376.5 386.5
2010 383.2 401.5
2015 390.2 414.3
2020 396.6 428.8
2025 401.5 442.0
2030 406.0 460.7
2035 410.0 480.3
Year
Lower Bound
(IS92c)
Upper Bound
(IS92e)
1990 353.6 353.6
1995 358.4 359.0
2000 366.8 369.2
2005 373.7 380.4
2010 382.3 392.9
2015 391.4 408.0
2020 400.7 423.0
2025 408.0 439.6
2030 416.9 457.7
2035 424.5 477.7
Table 1.A.5 | SAR: The data have been digitized using a graphics tool from Figure 5b
in the TS (IPCC, 1996) in 5-year increments (ppm) as anomalies compared to 1990 and
the observed 1990 value (353.6) has been added.
TAR: The data were taken in 10-year increments from table Appendix
II (IPCC, 2001) SRES Data Tables Table II.2.1 (ISAM model high and
low setting). The scenarios that give the upper bound or lower bound
respectively vary over time.
AR4: The data used was obtained from Figure 10.26 in Chapter 10
of AR4 (Meehl et al., 2007, provided by Malte Meinshausen). Annual
means are used.
Data Processing
The projections have been harmonized to start from the observed value
in 1990.
Figure 1.6: Documentation of Data Sources
Observed CH
4
Concentrations
Global annual mean CH
4
concentrations are presented as annual mean
values from Annex II Table AII.1.1a.
IPCC Range of Projections
Table 1.A.6 | FAR: The data have been digitized using a graphics tool from FAR SPM
Figure 5 (IPCC, 1990) in 5-year increments (ppb) as anomalies compared to 1990 the
observed 1990 value (1714.4) has been added.
Year
Lower Bound
(Scenario D)
Upper Bound
(Business as Usual)
1990 1714.4 1714.4
1995 1775.7 1816.7
2000 1809.7 1938.7
2005 1819.0 2063.8
2010 1823.1 2191.1
2015 1832.3 2314.1
2020 1847.7 2441.3
2025 1857.9 2562.3
2030 1835.3 2691.6
2035 1819.0 2818.8
SAR: The data were taken in 5-year increments from Table 2.5a (Schimel
et al., 1996). The scenarios that give the upper bound or lower bound
respectively vary over time.
TAR: The data were taken in 10-year increments from Appendix II SRES
Data Tables Table II.2.2 (IPCC, 2001). The upper bound is given by the
A1p scenario, the lower bound by the B1p scenario.
AR4: The data used was obtained from Figure 10.26 in Chapter 10
of AR4 (Meehl et al., 2007, provided by Malte Meinshausen). Annual
means are used.
Data Processing
The observations are shown as annual means. The projections have
been harmonized to start from the same value in 1990.
1
Introduction Chapter 1
157
Figure 1.7: Documentation of Data Sources
Observed N
2
O Concentrations
Global annual mean N
2
O concentrations are presented as annual mean
values from Annex II Table AII.1.1a.
IPCC Range of Projections
Table 1.A.7: FAR | The data have been digitized using a graphics tool from FAR A.3
(Annex, IPCC, 1990) in 5-year increments (ppb) as anomalies compared to 1990 and the
observed 1990 value (308.7) has been added.
Year
Lower Bound
(Scenario D)
Upper Bound
(Business as Usual)
1990 308.7 308.7
1995 311.7 313.2
2000 315.4 317.7
2005 318.8 322.9
2010 322.1 328.0
2015 325.2 333.0
2020 328.2 337.9
2025 331.7 343.0
2030 334.0 348.9
2035 336.1 354.1
SAR: The data were taken in 5-year increments from Table 2.5b (Schimel
et al., 1996). The upper bound is given by the IS92e and IS92f scenario,
the lower bound by the IS92d scenario.
TAR: The data were taken in 10-year increments from Appendix II SRES
Data Tables Table II.2.3 (IPCC, 2001). The upper bound is given by the
A1FI scenario, the lower bound by the B2 and A1T scenario.
AR4: The data used was obtained from Figure 10.26 in Chapter 10
of AR4 (Meehl et al., 2007, provided by Malte Meinshausen). Annual
means are used.
Data Processing
The observations are shown as annual means. No smoothing is applied.
The projections have been harmonized to start from the same value in
1990.
Figure 1.10: Documentation of Data Sources
Observed Global Mean Sea Level Rise
Three data sets based on tide gauge measurements are presented:
Church and White (2011), Jevrejeva et al. (2008), and Ray and Douglas
(2011). Annual mean anomalies are calculated relative to 1961–1990.
Estimates based on sea surface altimetry are presented as the ensem-
ble mean of five different data sets (Section 3.7, Figure 3.13, Section
13.2, Figure 13.3) from 1993 to 2012. Annual means have been calcu-
lated. The data are harmonized to start from the mean of the three tide
gauge based estimates (see above) at 1993.
IPCC Range of Projections
Table 1.A.8 | FAR: The data have been digitized using a graphics tool from Chapter
9, Figure 9.6 for the upper bound and Figure 9.7 for the lower bound (Warrick and
Oerlemans, 1990) in 5-year increments as anomalies relative to 1990 (cm) and the
observed anomaly relative to 1961–1990 (2.0 cm) has been added.
Table 1.A.9 | SAR: The data have been digitized using a graphics tool from Figure
21 (TS, IPCC, 1996) in 5-year increments as anomalies relative to 1990 (cm) and the
observed anomaly relative to 1961–1990 (2.0 cm) has been added.
Year
Lower Bound
(Scenario D)
Upper Bound
(Business as Usual)
1990 2.0 2.0
1995 2.7 5.0
2000 3.7 7.9
2005 4.6 11.3
2010 5.5 15.0
2015 6.3 18.7
2020 6.9 22.8
2025 7.7 26.7
2030 8.4 30.9
2035 9.2 35.4
Year
Lower Bound
(IS92c/1.5)
Upper Bound
(IS92e/4.5)
1990 2.0 2.0
1995 2.4 4.3
2000 2.7 6.5
2005 3.1 9.0
2010 3.4 11.7
2015 3.8 14.9
2020 4.4 18.3
2025 5.1 21.8
2030 5.7 25.4
2035 6.4 29.2
TAR: The data are given in Table II.5.1 in 10-year increments. They are
harmonized to start from mean of the observed anomaly relative to
1961–1990 at 1990 (2.0 cm).
1
Chapter 1 Introduction
158
AR4: The AR4 did not give a time-dependent estimate of sea level rise.
These analyses have been conducted post AR4 by Church et al. (2011)
based on the CMIP3 model results that were available at the time of
AR4. Here, the SRES B1, A1B and A2 scenarios are shown from Church
et al. (2011). The data start in 2001 and are given as anomalies with
respect to 1990. They are displayed from 2001 to 2035, but the anoma-
lies are harmonized to start from mean of the observed anomaly rela-
tive to 1961–1990 at 1990 (2.0 cm).
Data Processing
The observations are shown from 1950 to 2012 as the annual mean
anomaly relative to 1961–1990 (squares) and smoothed (solid lines).
For smoothing, first, the trend of each of the observational data sets
was calculated by locally weighted scatterplot smoothing (Cleveland,
1979; f = 1/3). Then, the 11-year running means of the residuals were
determined with reflected ends for the last 5 years. Finally, the trend
was added back to the 11-year running means of the residuals.