119

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

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

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

), methane (CH

), and nitrous oxide (N

O) have increased

over the last few centuries. {1.2.2, 1.2.3}

The processes affecting climate can exhibit considerable natural

variability. Even in the absence of external forcing, periodic and

chaotic variations on a vast range of spatial and temporal scales

are observed. Much of this variability can be represented by simple

(e.g., unimodal or power law) distributions, but many components of

the climate system also exhibit multiple states—for instance, the gla-

cial–interglacial cycles and certain modes of internal variability such

as El Niño-Southern Oscillation (ENSO). Movement between states can

occur as a result of natural variability, or in response to external forc-

ing. The relationship among variability, forcing and response reveals

the complexity of the dynamics of the climate system: the relationship

between forcing and response for some parts of the system seems rea-

sonably linear; in other cases this relationship is much more complex.

{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

is having a signiﬁcant effect on

the chemistry of sea water. Paleoclimatic reconstructions have helped

place ongoing climate change in the perspective of natural climate var-

iability. {1.2.3; Figure 1.3}

Observations of CO

concentrations, globally averaged temper-

ature and sea level rise are generally well within the range of

the extent of the earlier IPCC projections. The recently observed

increases in CH

and N

O concentrations are smaller than those

assumed in the scenarios in the previous assessments. Each

IPCC assessment has used new projections of future climate change

that have become more detailed as the models have become more

advanced. Similarly, the scenarios used in the IPCC assessments have

themselves changed over time to reﬂect the state of knowledge. The

range of climate projections from model results provided and assessed

in the ﬁrst IPCC assessment in 1990 to those in the 2007 AR4 provides

an opportunity to compare the projections with the actually observed

changes, thereby examining the deviations of the projections from the

observations over time. {1.3.1, 1.3.2, 1.3.4; Figures 1.4, 1.5, 1.6, 1.7,

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

; 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)

. {1.4; Figure 1.11}

Advances in Measurement and Modelling Capabilities

Over the last few decades, new observational systems, especial-

ly satellite-based systems, have increased the number of obser-

vations of the Earth’s climate by orders of magnitude. Tools to

analyse and process these data have been developed or enhanced to

cope with this large increase in information, and more climate proxy

data have been acquired to improve our knowledge of past chang-

es in climate. Because the Earth’s climate system is characterized on

multiple spatial and temporal scales, new observations may reduce

the uncertainties surrounding the understanding of short timescale

In this Report, the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium, or high.

A level of conﬁdence is expressed using ﬁve qualiﬁers: very low, low, medium, high, and very high, and typeset in italics, e.g., medium conﬁdence. For a given evidence and

agreement statement, different conﬁdence levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing conﬁdence (see

Section 1.4 and Box TS.1 for more details).

In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: Virtually certain 99–100% probability, Very likely 90–100%,

Likely 66–100%, About as likely as not 33–66%, Unlikely 0–33%, Very unlikely 0–10%, Exceptionally unlikely 0–1%. Additional terms (Extremely likely: 95–100%, More likely

than not >50–100%, and Extremely unlikely 0–5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very likely (see Section 1.4 and Box TS.1

for more details).

Chapter 1 Introduction

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}

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

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

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)

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.

Chapter 1 Introduction

126

SWR

SWR, LWR SWR, LWR

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

and aerosol amounts (Section 2.2).

and aerosol particles absorb, scatter and reﬂect SWR, changing the energy balance. Some aerosols act as cloud condensation nuclei modifying the properties of cloud droplets

and possibly affecting precipitation (Section 7.4). Because cloud interactions with SWR and LWR are large, small changes in the properties of clouds have important implications

for the radiative budget (Section 7.4). Anthropogenic changes in GHGs (e.g., CO

, CH

, N

O, O

, CFCs) and large aerosols (>2.5 μm in size) modify the amount of outgoing LWR

by absorbing outgoing LWR and re-emitting less energy at a lower temperature (Section 2.2). Surface albedo is changed by changes in vegetation or land surface properties, snow

or ice cover and ocean colour (Section 2.3). These changes are driven by natural seasonal and diurnal changes (e.g., snow cover), as well as human inﬂuence (e.g., changes in

vegetation types) (Forster et al., 2007).

presence of clouds, precipitation; and the occurrence of special phe-

nomena, such as thunderstorms, dust storms, tornados and others.

Climate in a narrow sense is usually deﬁned as the average weather,

or more rigorously, as the statistical description in terms of the mean

and variability of relevant quantities over a period of time ranging from

months to thousands or millions of years. The relevant quantities are

most often surface variables such as temperature, precipitation and

wind. Classically the period for averaging these variables is 30 years,

as deﬁned by the World Meteorological Organization. Climate in a

wider sense also includes not just the mean conditions, but also the

associated statistics (frequency, magnitude, persistence, trends, etc.),

often combining parameters to describe phenomena such as droughts.

Climate change refers to a change in the state of the climate that can

be identiﬁed (e.g., by using statistical tests) by changes in the mean

and/or the variability of its properties, and that persists for an extended

period, typically decades or longer.

The Earth’s climate system is powered by solar radiation (Figure 1.1).

Approximately half of the energy from the Sun is supplied in the vis-

ible part of the electromagnetic spectrum. As the Earth’s tempera-

ture has been relatively constant over many centuries, the incoming

solar energy must be nearly in balance with outgoing radiation. Of

the incoming solar shortwave radiation (SWR), about half is absorbed

by the Earth’s surface. The fraction of SWR reﬂected back to space

by gases and aerosols, clouds and by the Earth’s surface (albedo) is

approximately 30%, and about 20% is absorbed in the atmosphere.

Based on the temperature of the Earth’s surface the majority of the

outgoing energy ﬂux from the Earth is in the infrared part of the spec-

trum. The longwave radiation (LWR, also referred to as infrared radi-

ation) emitted from the Earth’s surface is largely absorbed by certain

atmospheric constituents—water vapour, carbon dioxide (CO

), meth-

ane (CH

), nitrous oxide (N

O) and other greenhouse gases (GHGs);

see Annex III for Glossary—and clouds, which themselves emit LWR

into all directions. The downward directed component of this LWR adds

heat to the lower layers of the atmosphere and to the Earth’s surface

(greenhouse effect). The dominant energy loss of the infrared radiation

from the Earth is from higher layers of the troposphere. The Sun pro-

vides its energy to the Earth primarily in the tropics and the subtropics;

this energy is then partially redistributed to middle and high latitudes

by atmospheric and oceanic transport processes.

Introduction Chapter 1

127

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

−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

, CH

, N

O and chloroﬂuorocarbons

(CFCs) (Figure 1.1). In addition, pollutants such as carbon monoxide

(CO), volatile organic compounds (VOC), nitrogen oxides (NO

) and

sulphur dioxide (SO

), which by themselves are negligible GHGs, have

an indirect effect on the greenhouse effect by altering, through atmos-

pheric chemical reactions, the abundance of important gases to the

amount of outgoing LWR such as CH

and ozone (O

), and/or by acting

as precursors of secondary aerosols. Because anthropogenic emission

sources simultaneously can emit some chemicals that affect climate

and others that affect air pollution, including some that affect both,

atmospheric chemistry and climate science are intrinsically linked.

In addition to changing the atmospheric concentrations of gases and

aerosols, humans are affecting both the energy and water budget of

the planet by changing the land surface, including redistributing the

balance between latent and sensible heat ﬂuxes (Sections 2.5, 7.2, 7.6

and 8.2). Land use changes, such as the conversion of forests to culti-

vated land, change the characteristics of vegetation, including its colour,

seasonal growth and carbon content (Houghton, 2003; Foley et al.,

2005). For example, clearing and burning a forest to prepare agricultural

land reduces carbon storage in the vegetation, adds CO

to the atmos-

phere, and changes the reﬂectivity of the land (surface albedo), rates of

evapotranspiration and longwave emissions (Figure 1.1).

Changes in the atmosphere, land, ocean, biosphere and cryosphere—

both natural and anthropogenic—can perturb the Earth’s radiation

budget, producing a radiative forcing (RF) that affects climate. RF is

a measure of the net change in the energy balance in response to an

external perturbation. The drivers of changes in climate can include, for

example, changes in the solar irradiance and changes in

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).

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

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

, and these simulations are

called transient simulations. The temperature response in these tran-

sient simulations is different than in an equilibrium simulation. The

transient climate response is deﬁned as the change in global surface

temperature at the time of atmospheric CO

doubling in a global cou-

pled ocean–atmosphere climate model simulation where concentra-

tions of CO

were increased by 1% yr

–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

greenhouse gases

and aerosols

Air-sea CO

exchange

Air-land CO

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

exchange

Biogeophysics

Non-CO

GHG and aerosols

Air-sea CO

exchange

Peat/Permafrost

Land ice

Ocean circ.

Figure 1.2 | Climate feedbacks and timescales. The climate feedbacks related to increasing CO

and rising temperature include negative feedbacks (–) such as LWR, lapse

rate (see Glossary in Annex III), and air–sea carbon exchange and positive feedbacks (+) such as water vapour and snow/ice albedo feedbacks. Some feedbacks may be

positive or negative (±): clouds, ocean circulation changes, air–land CO

exchange, and emissions of non-GHGs and aerosols from natural systems. In the smaller box, the

large difference in timescales for the various feedbacks is highlighted.

Introduction Chapter 1

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

, CH

, and N

O have

increased substantially over the last 200 years (Sections 6.3 and 8.3).

In addition, instrumental observations show that land and sea sur-

face temperatures have increased over the last 100 years (Chapter 2).

Satellites allow a much broader spatial distribution of measurements,

especially over the last 30 years. For the upper ocean temperature the

observations indicate that the temperature has increased since at least

1950 (Willis et al., 2010; Section 3.2). Observations from satellites and

in situ measurements suggest reductions in glaciers, Arctic sea ice and

ice sheets (Sections 4.2, 4.3 and 4.4). In addition, analyses based on

measurements of the radiative budget and ocean heat content sug-

gest a small imbalance (Section 2.3). These observations, all published

in peer-reviewed journals, made by diverse measurement groups in

multiple countries using different technologies, investigating various

climate-relevant types of data, uncertainties and processes, offer a

wide range of evidence on the broad extent of the changing climate

throughout our planet.

Conceptual and numerical models of the Earth’s climate system offer

another line of evidence on climate change (discussions in Chapters

5 and 9 provide relevant analyses of this evidence from paleoclimat-

ic to recent periods). These use our basic understanding of the cli-

mate system to provide self-consistent methodologies for calculating

impacts of processes and changes. Numerical models include the cur-

rent knowledge about the laws of physics, chemistry and biology, as

well as hypotheses about how complicated processes such as cloud

formation can occur. Because these models can represent only the

existing state of knowledge and technology, they are not perfect; they

are, however, important tools for analysing uncertainties or unknowns,

for testing different hypotheses for causation relative to observations,

and for making projections of possible future changes.

One of the most powerful methods for assessing changes occurring in

climate involves the use of statistical tools to test the analyses from

models relative to observations. This methodology is generally called

detection and attribution in the climate change community (Section

10.2). For example, climate models indicate that the temperature

response to GHG increases is expected to be different than the effects

from aerosols or from solar variability. Radiosonde measurements

and satellite retrievals of atmospheric temperature show increases

in tropospheric temperature and decreases in stratospheric tempera-

tures, consistent with the increases in GHG effects found in climate

model simulations (e.g., increases in CO

, changes in O

), but if the

Sun was the main driver of current climate change, stratospheric and

tropospheric temperatures would respond with the same sign (Hegerl

et al., 2007).

Resources available prior to the instrumental period—historical

sources, natural archives, and proxies for key climate variables (e.g.,

tree rings, marine sediment cores, ice cores)—can provide quantita-

tive information on past regional to global climate and atmospheric

composition variability and these data contribute another line of evi-

dence. Reconstructions of key climate variables based on these data

sets have provided important information on the responses of the

Earth system to a variety of external forcings and its internal variabil-

ity over a wide range of timescales (Hansen et al., 2006; Mann et al.,

Chapter 1 Introduction

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).

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

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

, CH

, and

O (see Sections 2.2, 6.3 and 8.3 for more detailed discussion of these

and other key gases). As discussed in the later chapters, accurate meas-

urements of these long-lived gases come from a number of monitoring

stations throughout the world. The observations in these ﬁgures are

compared with the projections from the previous IPCC assessments.

The model simulations begin with historical emissions up to 1990. The

further evolution of these gases was described by scenario projections.

TAR and AR4 model concentrations after 1990 are based on the SRES

Figure 1.5 | Observed globally and annually averaged CO

concentrations in parts per million (ppm) since 1950 compared with projections from the previous IPCC assessments.

Observed global annual CO

concentrations are shown in dark blue. The shading shows the largest model projected range of global annual CO

concentrations from 1950 to 2035

from FAR (Figure A.3 in the Summary for Policymakers of IPCC, 1990); SAR (Figure 5b in the Technical Summary of IPCC, 1996); TAR (Appendix II of IPCC, 2001); and from the A2,

A1B and B1 scenarios presented in the AR4 (Figure 10.26 in Meehl et al., 2007). The bars at the right-hand side of the graph show the full range given for 2035 for each assessment

report. The publication years of the assessment reports are shown. See Appendix 1.A for details on the data and calculations used to create this ﬁgure.

Introduction Chapter 1

133

Figure 1.7 | Observed globally and annually averaged N

O concentrations in parts per billion (ppb) since 1950 compared with projections from the previous IPCC assessments.

Observed global annual N

O concentrations are shown in dark blue. The shading shows the largest model projected range of global annual N

O concentrations from 1950 to 2035

from FAR (Figure A3 in the Annex of IPCC, 1990), SAR (Table 2.5b in Schimel et al., 1996), TAR (Appendix II of IPCC, 2001), and from the A2, A1B and B1 scenarios presented in

the AR4 (Figure 10.26 in Meehl et al., 2007). The bars at the right hand side of the graph show the full range given for 2035 for each assessment report. The publication years of

the assessment reports are shown. See Appendix 1.A for details on the data and calculations used to create this ﬁgure.

Figure 1.6 | Observed globally and annually averaged CH

concentrations in parts per billion (ppb) since 1950 compared with projections from the previous IPCC assessments.

Estimated observed global annual CH

concentrations are shown in dark blue. The shading shows the largest model projected range of global annual CH

concentrations from 1950

to 2035 from FAR (Figure A.3 of the Annex of IPCC, 1990); SAR (Table 2.5a in Schimel et al., 1996); TAR (Appendix II of IPCC, 2001); and from the A2, A1B and B1 scenarios pre-

sented in the AR4 (Figure 10.26 in Meehl et al., 2007). The bars at the right-hand side of the graph show the full range given for 2035 for each assessment report. The publication

years of the assessment reports are shown. See Appendix 1.A for details on the data and calculations used to create this ﬁgure.

Chapter 1 Introduction

134

scenarios but those model results may also account for historical emis-

sions analyses. The recent observed trends in CO

concentrations tend

to be in the middle of the scenarios used for the projections (Figure

1.5).

As discussed in Dlugokencky et al. (2009), trends in CH

showed a

stabilization from 1999 to 2006, but CH

concentrations have been

increasing again starting in 2007 (see Sections 2.2 and 6.3 for more

discussion on the budget and changing concentration trends for CH

Because at the time the scenarios were developed (e.g., the SRES

scenarios were developed in 2000), it was thought that past trends

would continue, the scenarios used and the resulting model projec-

tions assumed in FAR through AR4 all show larger increases than those

observed (Figure 1.6).

Concentrations of N

O have continued to increase at a nearly constant

rate (Elkins and Dutton, 2010) since about 1970 as shown in Figure

1.7. The observed trends tend to be in the lower part of the projections

for the previous assessments.

1.3.3 Extreme Events

Climate change, whether driven by natural or human forcings, can lead

to changes in the likelihood of the occurrence or strength of extreme

weather and climate events such as extreme precipitation events or

warm spells (see Chapter 3 of the IPCC Special Report on Managing

the Risks of Extreme Events and Disasters to Advance Climate Change

Adaptation (SREX); Seneviratne et al., 2012). An extreme weather

event is one that is rare at a particular place and/or time of year. Deﬁ-

nitions of ‘rare’ vary, but an extreme weather event would normally

be as rare as or rarer than the 10th or 90th percentile of a probabili-

ty density function estimated from observations (see also Glossary in

Annex III and FAQ 2.2). By deﬁnition, the characteristics of what is

called extreme weather may vary from place to place in an absolute

sense. At present, single extreme events cannot generally be directly

attributed to anthropogenic inﬂuence, although the change in likeli-

hood for the event to occur has been determined for some events by

accounting for observed changes in climate (see Section 10.6). When

a pattern of extreme weather persists for some time, such as a season,

it may be classiﬁed as an extreme climate event, especially if it yields

an average or total that is itself extreme (e.g., drought or heavy rainfall

over a season). For some climate extremes such as drought, ﬂoods and

heat waves, several factors such as duration and intensity need to be

combined to produce an extreme event (Seneviratne et al., 2012).

The probability of occurrence of values of a climate or weather variable

can be described by a probability density function (PDF) that for some

variables (e.g., temperature) is shaped similar to a Gaussian curve. A

PDF is a function that indicates the relative chances of occurrence of

different outcomes of a variable. Simple statistical reasoning indicates

that substantial changes in the frequency of extreme events (e.g., the

maximum possible 24-hour rainfall at a speciﬁc location) can result

from a relatively small shift in the distribution of a weather or climate

variable. Figure 1.8a shows a schematic of such a PDF and illustrates

the effect of a small shift in the mean of a variable on the frequency of

extremes at either end of the distribution. An increase in the frequency

of one extreme (e.g., the number of hot days) can be accompanied by

Figure 1.8 | Schematic representations of the probability density function of daily tem-

perature, which tends to be approximately Gaussian, and daily precipitation, which has

a skewed distribution. Dashed lines represent a previous distribution and solid lines a

changed distribution. The probability of occurrence, or frequency, of extremes is denoted

by the shaded areas. In the case of temperature, changes in the frequencies of extremes

are affected by changes (a) in the mean, (b) in the variance or shape, and (c) in both

the mean and the variance. (d) In a skewed distribution such as that of precipitation, a

change in the mean of the distribution generally affects its variability or spread, and thus

an increase in mean precipitation would also imply an increase in heavy precipitation

extremes, and vice-versa. In addition, the shape of the right-hand tail could also change,

affecting extremes. Furthermore, climate change may alter the frequency of precipita-

tion and the duration of dry spells between precipitation events. (Parts a–c modiﬁed

from Folland et al., 2001, and d modiﬁed from Peterson et al., 2008, as in Zhang and

Zwiers, 2012.)

Precipitation

Temperature

AverageLight

Average HotCold

HotAverageCold

Heavy

(d) Change in skewness

(b) Increase in variance

(a) Increase in mean

More heavy precipitation

More hot extremes

Fewer cold extremes

More hot extremes

More cold extremes

More hot extremes

More/Fewer cold extremes

Introduction Chapter 1

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

More intense precipitation events

² Heavy precipitation events. Frequency (or proportion of total rainfall from heavy falls) increases

³ Statistically signiﬁcant trends in the number of heavy precipitation events in some regions. It is likely that more of these regions have experienced increases than decreases.

See SREX Table 3-3 for details on precipitation extremes for the different regions.

Increased summer continental drying and associated risk of drought

Area affected by droughts increases

Some areas include southern Europe and the Mediterranean region, central Europe, central North America and Mexico, northeast Brazil and southern Africa

Increase in tropical cyclone peak wind intensities

Increase in intense tropical cyclone activity

In any observed long-term (i.e., 40 years or more) after accounting for past changes in observing capabilities (see SREX, section 3.4.4)

Increase in average tropical cyclone maximum wind speed is, although not in all ocean basins; either decrease or no change in the global frequency of tropical cyclones

Increase in extreme coastal high water worldwide related to increases in mean sea level in the late 20th century

Mean sea level rise will contribute to upward trends in extreme coastal high water levels

Chapter 1 Introduction

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

) 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

is another indicator of global change. As discussed

in AR4, the ocean’s uptake of CO

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.

Introduction Chapter 1

137

AR4TARSARFAR

1950 1960 1970 1980 1990 2000 2010 2020 2030

−5

Year

Global mean sea level rise (cm)

FAR

SAR

TAR

A1B

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).

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).

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

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)

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

0.5

1.5

2.5

3.5

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)

0.5

1.5

2.5

3.5

Global average temperature change (°C)

Year

1960

1980 2000 2020

2040

2060

2080

2100

(c)

0.5

1.5

2.5

3.5

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%.

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.

Introduction Chapter 1

143

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

2000 2005 2010

Number of satellite data sources used

1980

2000

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

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).

tmosphere

Land

Surface

Ocean &

Sea Ice

Aerosols

Carbon Cycle

Dynamic

Vegetation

Atmospheric

Chemistry

Mid-1970s Mid-1980s TARSARFA

Land Ice

AR5

Mid-1970s Mid-1980s TARSARFA

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.

Introduction Chapter 1

145

87.5 km x 87.5 km

30.0 km x 30.0 km

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).

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

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.

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

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

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

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

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)

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

, Table AII.4.2 for CH

, Table AII.4.3 for N

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

800

900

000

300

100

200

300

400

500

600

700

800

900

1000

ppt

d) CFC12−eq

800

900

000

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)

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

concentrations of approximately 2000 ppm, nearly seven times the pre-industrial levels. As the RCP2.6 implies

netnegative CO

emissions after around 2070 and throughout the extension, CO

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

concentration and (b) CO

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

History RCPs ECPs

RCP2.6

RCP4.5

RCP6

RCP8.5

SCP6to4.5

SRES A1B

SRES A1FI

SRES A1T

SRES A2

SRES B1

SRES B2

equivalent (CO

-eq ppm)

1800 1850 1900 1950 2000 2050 2100 2150 2200 2250 2300

−5

History RCPs ECPs

RCP2.6

RCP4.5

RCP6

RCP8.5

SCP6to4.5

SRES

A1B

SRES

A1FI

SRES

A1T

SRES

Global CO

(fossil & ind.) emissions (GtC yr

-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

emissions (Annex II Table AII.2.18), and (c) anthropogenic SO

emissions (Annex II Table II.2.20).

2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Anthropogenic BC emissions

2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

105

120

Anthropogenic NO

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

105

120

Anthropogenic SO

emissions

Note: Primary anthropogenic sulphur emissions as SO

measured here as Tg of S

(see Annex II Table AII.2.20)

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.

Chapter 1 Introduction

152

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Introduction Chapter 1

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.

Chapter 1 Introduction

156

Figure 1.5: Documentation of Data Sources

Observed CO

Concentrations

Global annual mean CO

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

Concentrations

Global annual mean CH

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.

Introduction Chapter 1

157

Figure 1.7: Documentation of Data Sources

Observed N

O Concentrations

Global annual mean N

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).

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.