Chapter.9_FINAL.5.9.07.indd

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Chapter 9

Table of Contents

Executive Summary

.................................................... 665

9.1 Introduction

......................................................... 667

9.1.1 What are Climate Change and

Climate

Variability?

.............................................. 667

9.1.2 What are Climate Change Detection

and

Attribution?

................................................... 667

9.1.3 The Basis from which We Begin .......................... 669

9.2 Radiative Forcing and Climate

Response

............................................................... 670

9.2.1 Radiative Forcing Estimates Used to

Simulate Climate Change .................................... 671

9.2.2 Spatial and Temporal Patterns of the Response

to Different Forcings and their Uncertainties ....... 674

9.2.3 Implications for Understanding 20th-Century

Climate

Change

................................................... 678

9.2.4 Summary

............................................................. 678

9.3 Understanding Pre-Industrial

Climate

Change

................................................... 679

9.3.1 Why Consider Pre-Industrial Climate
Change?

.............................................................. 679

9.3.2 What can be Learned from the Last Glacial

Maximum and the Mid-Holocene? ...................... 679

9.3.3 What can be Learned from the Past
1,000

Years?

........................................................ 680

9.3.4 Summary

............................................................. 683

9.4 Understanding

Air

Temperature

Change During the Industrial Era

.............. 683

9.4.1 Global-Scale Surface Temperature Change ........ 683

9.4.2 Continental and Sub-continental Surface
Temperature

Change

........................................... 693

9.4.3 Surface Temperature Extremes ........................... 698

9.4.4 Free Atmosphere Temperature ............................ 699

9.4.5 Summary

............................................................. 704

9.5 Understanding of Change in Other

Variables during the Industrial Era

............. 705

9.5.1 Ocean

Climate

Change

....................................... 705

9.5.2 Sea

Level

............................................................. 707

9.5.3 Atmospheric

Circulation

Changes

....................... 709

9.5.4 Precipitation

......................................................... 712

9.5.5 Cryosphere

Changes

........................................... 716

9.5.6 Summary

............................................................. 717

9.6 Observational Constraints on

Climate

Sensitivity

............................................. 718

9.6.1 Methods to Estimate Climate Sensitivity ............. 718

9.6.2 Estimates of Climate Sensitivity Based on
Instrumental

Observations

.................................. 719

9.6.3 Estimates of Climate Sensitivity Based on
Palaeoclimatic

Data

............................................. 724

9.6.4 Summary of Observational Constraints for
Climate

Sensitivity

............................................... 725

9.7 Combining Evidence of Anthropogenic

Climate

Change

................................................... 727

Frequently Asked Questions

FAQ 9.1:

Can Individual Extreme Events be Explained

by Greenhouse Warming?

..................................... 696

FAQ 9.2:

Can the Warming of the 20th Century be

Explained by Natural Variability?

......................... 702

References

........................................................................ 733

Appendix 9.A: Methods Used to Detect

Externally Forced Signals

.......................................... 744

Supplementary Material

The following supplementary material is available on CD-ROM and
in on-line versions of this report.

Appendix 9.B:

Methods Used to Estimate Climate Sensitivity and Aerosol

Forcing

Appendix 9.C:

Notes and technical details on Figures displayed in Chapter 9

Appendix 9.D:

Additional Figures and Tables

References for Appendices 9.B to 9.D

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Chapter 9

Understanding and Attributing Climate Change

Executive Summary

Evidence of the effect of external in

uences on the climate

system has continued to accumulate since the Third Assessment
Report (TAR). The evidence now available is substantially
stronger and is based on analyses of widespread temperature
increases throughout the climate system and changes in other
climate variables.

Human-induced warming of the climate system is

widespread.

Anthropogenic warming of the climate system can

be detected in temperature observations taken at the surface,
in the troposphere and in the oceans. Multi-signal detection
and attribution analyses, which quantify the contributions
of different natural and anthropogenic forcings to observed
changes, show that greenhouse gas forcing alone during the
past half century would

likely

have resulted in greater than the

observed warming if there had not been an offsetting cooling
effect from aerosol and other forcings.

It is

extremely unlikely

(<5%) that the global pattern of

warming during the past half century can be explained without
external forcing, and

very unlikely

that it is due to known

natural external causes alone. The warming occurred in both the
ocean and the atmosphere and took place at a time when natural
external forcing factors would

likely

have produced cooling.

Greenhouse gas forcing has

very likely

caused most of the

observed global warming over the last 50 years. This conclusion
takes into account observational and forcing uncertainty, and
the possibility that the response to solar forcing could be
underestimated by climate models. It is also robust to the use
of different climate models, different methods for estimating
the responses to external forcing and variations in the analysis
technique.

Further evidence has accumulated of an anthropogenic

uence on the temperature of the free atmosphere as

measured by radiosondes and satellite-based instruments. The
observed pattern of tropospheric warming and stratospheric
cooling is

very likely

due to the in

uence of anthropogenic

forcing, particularly greenhouse gases and stratospheric ozone
depletion. The combination of a warming troposphere and a
cooling stratosphere has

likely

led to an increase in the height

of the tropopause. It is

likely

that anthropogenic forcing has

contributed to the general warming observed in the upper
several hundred meters of the ocean during the latter half of
the 20th century. Anthropogenic forcing, resulting in thermal
expansion from ocean warming and glacier mass loss, has

very likely

contributed to sea level rise during the latter half

of the 20th century. It is dif

cult to quantify the contribution

of anthropogenic forcing to ocean heat content increase and
glacier melting with presently available detection and attribution
studies.

It is

likely

that there has been a substantial anthropogenic

contribution to surface temperature increases in every
continent except Antarctica since the middle of the 20th

century.

Anthropogenic in

uence has been detected in

every continent except Antarctica (which has insuf

cient

observational coverage to make an assessment), and in some
sub-continental land areas. The ability of coupled climate models
to simulate the temperature evolution on continental scales and
the detection of anthropogenic effects on each of six continents
provides stronger evidence of human in

uence on the global

climate than was available at the time of the TAR. No climate
model that has used natural forcing only has reproduced the
observed global mean warming trend or the continental mean
warming trends in all individual continents (except Antarctica)
over the second half of the 20th century.

Dif

culties remain in attributing temperature changes on

smaller than continental scales and over time scales of less than
50 years. Attribution at these scales, with limited exceptions,
has not yet been established. Averaging over smaller regions
reduces the natural variability less than does averaging over
large regions, making it more dif

cult to distinguish between

changes expected from different external forcings, or between
external forcing and variability. In addition, temperature changes
associated with some modes of variability are poorly simulated
by models in some regions and seasons. Furthermore, the small-
scale details of external forcing, and the response simulated by
models are less credible than large-scale features.

Surface temperature extremes have

likely

been affected

by anthropogenic forcing.

Many indicators of climate extremes

and variability, including the annual numbers of frost days,
warm and cold days, and warm and cold nights, show changes
that are consistent with warming. An anthropogenic in

uence

has been detected in some of these indices, and there is evidence
that anthropogenic forcing may have substantially increased the
risk of extremely warm summer conditions regionally, such as
the 2003 European heat wave.

There is evidence of anthropogenic in

uence in other

parts of the climate system

Anthropogenic forcing has

likely

contributed to recent decreases in arctic sea ice extent and to
glacier retreat. The observed decrease in global snow cover
extent and the widespread retreat of glaciers are consistent
with warming, and there is evidence that this melting has

likely

contributed to sea level rise.

Trends over recent decades in the Northern and Southern

Annular Modes, which correspond to sea level pressure
reductions over the poles, are

likely

related in part to human

activity, affecting storm tracks, winds and temperature
patterns in both hemispheres. Models reproduce the sign of
the Northern Annular Mode trend, but the simulated response
is smaller than observed. Models including both greenhouse
gas and stratospheric ozone changes simulate a realistic trend
in the Southern Annular Mode, leading to a detectable human
in

uence on global sea level pressure patterns.

The response to volcanic forcing simulated by some models

is detectable in global annual mean land precipitation during
the latter half of the 20th century. The latitudinal pattern of
change in land precipitation and observed increases in heavy

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Chapter 9

precipitation over the 20th century appear to be consistent with
the anticipated response to anthropogenic forcing. It is

more

likely than not

that anthropogenic in

uence has contributed to

increases in the frequency of the most intense tropical cyclones.
Stronger attribution to anthropogenic factors is not possible at
present because the observed increase in the proportion of such
storms appears to be larger than suggested by either theoretical
or modelling studies and because of inadequate process
knowledge, insuf

cient understanding of natural variability,

uncertainty in modelling intense cyclones and uncertainties in
historical tropical cyclone data.

Analyses of palaeoclimate data have increased

con

dence in the role of external in

uences on climate.

Coupled climate models used to predict future climate have
been used to understand past climatic conditions of the Last
Glacial Maximum and the mid-Holocene. While many aspects
of these past climates are still uncertain, key features have been
reproduced by climate models using boundary conditions and
radiative forcing for those periods. A substantial fraction of the
reconstructed Northern Hemisphere inter-decadal temperature
variability of the seven centuries prior to 1950 is

very likely

attributable to natural external forcing, and it is

likely

that

anthropogenic forcing contributed to the early 20th-century
warming evident in these records.

Estimates of the climate sensitivity are now better

constrained by observations.

Estimates based on observational

constraints indicate that it is

very likely

that the equilibrium

climate sensitivity is larger than 1.5°C with a most likely value
between 2°C and 3°C. The upper 95% limit remains dif

cult

to constrain from observations. This supports the overall
assessment based on modelling and observational studies that
the equilibrium climate sensitivity is

likely

2°C to 4.5°C with

a most likely value of approximately 3°C (Box 10.2). The
transient climate response, based on observational constraints,
is

very likely

larger than 1°C and

very unlikely

to be greater than

3.5°C at the time of atmospheric CO

doubling in response to a

1% yr

–1

increase in CO

, supporting the overall assessment that

the transient climate response is

very unlikely

greater than 3°C

(Chapter 10).

Overall consistency of evidence.

Many observed changes

in surface and free atmospheric temperature, ocean temperature
and sea ice extent, and some large-scale changes in the
atmospheric circulation over the 20th century are distinct from
internal variability and consistent with the expected response
to anthropogenic forcing. The simultaneous increase in energy
content of all the major components of the climate system as
well as the magnitude and pattern of warming within and across
the different components supports the conclusion that the cause
of the warming is

extremely unlikely

(<5%) to be the result of

internal processes. Qualitative consistency is also apparent in
some other observations, including snow cover, glacier retreat
and heavy precipitation.

Remaining uncertainties.

Further improvements in models

and analysis techniques have led to increased con

dence in the

understanding of the in

uence of external forcing on climate

since the TAR. However, estimates of some radiative forcings
remain uncertain, including aerosol forcing and inter-decadal
variations in solar forcing. The net aerosol forcing over the 20th
century from inverse estimates based on the observed warming
likely ranges between –1.7 and –0.1 W m

–2

. The consistency

of this result with forward estimates of total aerosol forcing
(Chapter 2) strengthens con

dence in estimates of total aerosol

forcing, despite remaining uncertainties. Nevertheless, the
robustness of surface temperature attribution results to forcing
and response uncertainty has been evaluated with a range of
models, forcing representations and analysis procedures.
The potential impact of the remaining uncertainties has been
considered, to the extent possible, in the overall assessment of
every line of evidence listed above. There is less con

dence in

the understanding of forced changes in other variables, such
as surface pressure and precipitation, and on smaller spatial
scales.

Better understanding of instrumental and proxy climate

records, and climate model improvements, have increased
con

dence in climate model-simulated internal variability.

However, uncertainties remain. For example, there are apparent
discrepancies between estimates of ocean heat content variability
from models and observations. While reduced relative to the
situation at the time of the TAR, uncertainties in the radiosonde
and satellite records still affect con

dence in estimates of the

anthropogenic contribution to tropospheric temperature change.
Incomplete global data sets and remaining model uncertainties
still restrict understanding of changes in extremes and attribution
of changes to causes, although understanding of changes in the
intensity, frequency and risk of extremes has improved.

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9.1 Introduction

The objective of this chapter is to assess scienti

understanding about the extent to which the observed climate
changes that are reported in Chapters 3 to 6 are expressions
of natural internal climate variability and/or externally forced
climate change. The scope of this chapter includes ‘detection
and attribution’ but is wider than that of previous detection and
attribution chapters in the Second Assessment Report (SAR;
Santer et al., 1996a) and the Third Assessment Report (TAR;
Mitchell et al., 2001). Climate models, physical understanding of
the climate system and statistical tools, including formal climate
change detection and attribution methods, are used to interpret
observed changes where possible. The detection and attribution
research discussed in this chapter includes research on regional
scales, extremes and variables other than temperature. This
new work is placed in the context of a broader understanding
of a changing climate. However, the ability to interpret some
changes, particularly for non-temperature variables, is limited
by uncertainties in the observations, physical understanding
of the climate system, climate models and external forcing
estimates. Research on the impacts of these observed climate
changes is assessed by Working Group II of the IPCC.

9.1.1

What are Climate Change and Climate
Variability?

‘Climate change’ refers to a change in the state of the climate

that can be identi

ed (e.g., 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 (see
Glossary). Climate change may be due to internal processes
and/or external forcings. Some external in

uences, such as

changes in solar radiation and volcanism, occur naturally and
contribute to the total natural variability of the climate system.
Other external changes, such as the change in composition of
the atmosphere that began with the industrial revolution, are
the result of human activity. A key objective of this chapter is to
understand climate changes that result from anthropogenic and
natural external forcings, and how they may be distinguished
from changes and variability that result from internal climate
system processes.

Internal variability is present on all time scales. Atmospheric

processes that generate internal variability are known to
operate on time scales ranging from virtually instantaneous
(e.g., condensation of water vapour in clouds) up to years (e.g.,
troposphere-stratosphere or inter-hemispheric exchange). Other
components of the climate system, such as the ocean and the
large ice sheets, tend to operate on longer time scales. These
components produce internal variability of their own accord and
also integrate variability from the rapidly varying atmosphere
(Hasselmann, 1976). In addition, internal variability is produced
by coupled interactions between components, such as is the case
with the El-Niño Southern Oscillation (ENSO; see Chapters 3
and 8).

Distinguishing between the effects of external in

uences

and internal climate variability requires careful comparison
between observed changes and those that are expected to result
from external forcing. These expectations are based on physical
understanding of the climate system. Physical understanding is
based on physical principles. This understanding can take the
form of conceptual models or it might be quanti

ed with climate

models that are driven with physically based forcing histories.
An array of climate models is used to quantify expectations in
this way, ranging from simple energy balance models to models
of intermediate complexity to comprehensive coupled climate
models (Chapter 8) such as those that contributed to the multi-
model data set (MMD) archive at the Program for Climate
Model Diagnosis and Intercomparison (PCMDI). The latter
have been extensively evaluated by their developers and a broad
investigator community. The extent to which a model is able to
reproduce key features of the climate system and its variations,
for example the seasonal cycle, increases its credibility for
simulating changes in climate.

The comparison between observed changes and those that are

expected is performed in a number of ways. Formal detection
and attribution (Section 9.1.2) uses objective statistical tests to
assess whether observations contain evidence of the expected
responses to external forcing that is distinct from variation
generated within the climate system (internal variability). These
methods generally do not rely on simple linear trend analysis.
Instead, they attempt to identify in observations the responses
to one or several forcings by exploiting the time and/or spatial
pattern of the expected responses. The response to forcing does
not necessarily evolve over time as a linear trend, either because
the forcing itself may not evolve in that way, or because the
response to forcing is not necessarily linear.

The comparison between model-simulated and observed

changes, for example, in detection and attribution methods
(Section 9.1.2), also carefully accounts for the effects of
changes over time in the availability of climate observations to
ensure that a detected change is not an artefact of a changing
observing system. This is usually done by evaluating climate
model data only where and when observations are available,
in order to mimic the observational system and avoid possible
biases introduced by changing observational coverage.

9.1.2

What are Climate Change Detection and
Attribution?

The concepts of climate change ‘detection’ and ‘attribution’

used in this chapter remain as they were de

ned in the TAR

(IPCC, 2001; Mitchell et al., 2001). ‘Detection’ is the process
of demonstrating that climate has changed in some de

ned

statistical sense, without providing a reason for that change (see
Glossary). In this chapter, the methods used to identify change
in observations are based on the expected responses to external
forcing (Section 9.1.1), either from physical understanding or as
simulated by climate models. An identi

ed change is ‘detected’

in observations if its likelihood of occurrence by chance due to
internal variability alone is determined to be small. A failure to

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Chapter 9

detect a particular response might occur for a number of reasons,
including the possibility that the response is weak relative to
internal variability, or that the metric used to measure change
is insensitive to the expected change. For example, the annual
global mean precipitation may not be a sensitive indicator of
the in

uence of increasing greenhouse concentrations given the

expectation that greenhouse forcing would result in moistening
at some latitudes that is partially offset by drying elsewhere
(Chapter 10; see also Section 9.5.4.2). Furthermore, because
detection studies are statistical in nature, there is always some
small possibility of spurious detection. The risk of such a
possibility is reduced when corroborating lines of evidence
provide a physically consistent view of the likely cause for the
detected changes and render them less consistent with internal
variability (see, for example, Section 9.7).

Many studies use climate models to predict the expected

responses to external forcing, and these predictions are usually
represented as patterns of variation in space, time or both (see
Chapter 8 for model evaluation). Such patterns, or ‘

ngerprints’,

are usually derived from changes simulated by a climate model
in response to forcing. Physical understanding can also be
used to develop conceptual models of the anticipated pattern
of response to external forcing and the consistency between
responses in different variables and different parts of the
climate system. For example, precipitation and temperature are
ordinarily inversely correlated in some regions, with increases
in temperature corresponding to drying conditions. Thus,
a warming trend in such a region that is not associated with
rainfall change may indicate an external in

uence on the climate

of that region (Nicholls et al., 2005; Section 9.4.2.3). Purely
diagnostic approaches can also be used. For example, Schneider
and Held (2001) use a technique that discriminates between
slow changes in climate and shorter time-scale variability to
identify in observations a pattern of surface temperature change
that is consistent with the expected pattern of change from
anthropogenic forcing.

The spatial and temporal scales used to analyse climate

change are carefully chosen so as to focus on the spatio-temporal
scale of the response,

lter out as much internal variability as

possible (often by using a metric that reduces the in

uence of

internal variability, see Appendix 9.A) and enable the separation
of the responses to different forcings. For example, it is expected
that greenhouse gas forcing would cause a large-scale pattern
of warming that evolves slowly over time, and thus analysts
often smooth data to remove small-scale variations. Similarly,
when

ngerprints from Atmosphere-Ocean General Circulation

Models (AOGCMs) are used, averaging over an ensemble of
coupled model simulations helps separate the model’s response
to forcing from its simulated internal variability.

Detection does not imply attribution of the detected change

to the assumed cause. ‘Attribution’ of causes of climate
change is the process of establishing the most likely causes
for the detected change with some de

ned level of con

dence

(see Glossary). As noted in the SAR (IPCC, 1996) and the
TAR (IPCC, 2001), unequivocal attribution would require
controlled experimentation with the climate system. Since that

is not possible, in practice attribution of anthropogenic climate
change is understood to mean demonstration that a detected
change is ‘consistent with the estimated responses to the given
combination of anthropogenic and natural forcing’ and ‘not
consistent with alternative, physically plausible explanations of
recent climate change that exclude important elements of the
given combination of forcings’ (IPCC, 2001).

The consistency between an observed change and the

estimated response to a hypothesised forcing is often determined
by estimating the amplitude of the hypothesised pattern of
change from observations and then assessing whether this
estimate is statistically consistent with the expected amplitude
of the pattern. Attribution studies additionally assess whether
the response to a key forcing, such as greenhouse gas increases,
is distinguishable from that due to other forcings (Appendix
9.A). These questions are typically investigated using a
multiple regression of observations onto several

ngerprints

representing climate responses to different forcings that, ideally,
are clearly distinct from each other (i.e., as distinct spatial
patterns or distinct evolutions over time; see Section 9.2.2). If
the response to this key forcing can be distinguished, and if
even rescaled combinations of the responses to other forcings
do not suf

ciently explain the observed climate change, then

the evidence for a causal connection is substantially increased.
For example, the attribution of recent warming to greenhouse
gas forcing becomes more reliable if the in

uences of other

external forcings, for example solar forcing, are explicitly
accounted for in the analysis. This is an area of research with
considerable challenges because different forcing factors may
lead to similar large-scale spatial patterns of response (Section
9.2.2). Note that another key element in attribution studies is
the consideration of the physical consistency of multiple lines
of evidence.

Both detection and attribution require knowledge of the

internal climate variability on the time scales considered,
usually decades or longer. The residual variability that remains
in instrumental observations after the estimated effects of
external forcing have been removed is sometimes used to
estimate internal variability. However, these estimates are
uncertain because the instrumental record is too short to give
a well-constrained estimate of internal variability, and because
of uncertainties in the forcings and the estimated responses.
Thus, internal climate variability is usually estimated from long
control simulations from coupled climate models. Subsequently,
an assessment is usually made of the consistency between the
residual variability referred to above and the model-based
estimates of internal variability; analyses that yield implausibly
large residuals are not considered credible (for example, this
might happen if an important forcing is missing, or if the
internal variability from the model is too small). Con

dence is

further increased by systematic intercomparison of the ability
of models to simulate the various modes of observed variability
(Chapter 8), by comparisons between variability in observations
and climate model data (Section 9.4) and by comparisons
between proxy reconstructions and climate simulations of the
last millennium (Chapter 6 and Section 9.3).

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Studies where the estimated pattern amplitude is substantially

different from that simulated by models can still provide some
understanding of climate change but need to be treated with
caution (examples are given in Section 9.5). If this occurs for
variables where con

dence in the climate models is limited,

such a result may simply re

ect weaknesses in models. On the

other hand, if this occurs for variables where con

dence in the

models is higher, it may raise questions about the forcings, such
as whether all important forcings have been included or whether
they have the correct amplitude, or questions about uncertainty
in the observations.

Model and forcing uncertainties are important considerations

in attribution research. Ideally, the assessment of model
uncertainty should include uncertainties in model parameters
(e.g., as explored by multi-model ensembles), and in the
representation of physical processes in models (structural
uncertainty). Such a complete assessment is not yet available,
although model intercomparison studies (Chapter 8) improve
the understanding of these uncertainties. The effects of forcing
uncertainties, which can be considerable for some forcing agents
such as solar and aerosol forcing (Section 9.2), also remain
dif

cult to evaluate despite advances in research. Detection and

attribution results based on several models or several forcing
histories do provide information on the effects of model and
forcing uncertainty. Such studies suggest that while model
uncertainty is important, key results, such as attribution of a
human in

uence on temperature change during the latter half of

the 20th century, are robust.

Detection of anthropogenic in

uence is not yet possible for

all climate variables for a variety of reasons. Some variables
respond less strongly to external forcing, or are less reliably
modelled or observed. In these cases, research that describes
observed changes and offers physical explanations, for example,
by demonstrating links to sea surface temperature changes,
contributes substantially to the understanding of climate change
and is therefore discussed in this chapter.

The approaches used in detection and attribution research

described above cannot fully account for all uncertainties,
and thus ultimately expert judgement is required to give a
calibrated assessment of whether a speci

c cause is responsible

for a given climate change. The assessment approach used in
this chapter is to consider results from multiple studies using a
variety of observational data sets, models, forcings and analysis
techniques. The assessment based on these results typically
takes into account the number of studies, the extent to which
there is consensus among studies on the signi

cance of detection

results, the extent to which there is consensus on the consistency
between the observed change and the change expected from
forcing, the degree of consistency with other types of evidence,
the extent to which known uncertainties are accounted for
in and between studies, and whether there might be other
physically plausible explanations for the given climate change.
Having determined a particular likelihood assessment, this was
then further downweighted to take into account any remaining
uncertainties, such as, for example, structural uncertainties or
a limited exploration of possible forcing histories of uncertain

forcings. The overall assessment also considers whether several
independent lines of evidence strengthen a result.

While the approach used in most detection studies assessed

in this chapter is to determine whether observations exhibit the
expected response to external forcing, for many decision makers
a question posed in a different way may be more relevant. For
instance, they may ask, ‘Are the continuing drier-than-normal
conditions in the Sahel due to human causes?’ Such questions
are dif

cult to respond to because of a statistical phenomenon

known as ‘selection bias’. The fact that the questions are ‘self
selected’ from the observations (only large observed climate
anomalies in a historical context would be likely to be the subject
of such a question) makes it dif

cult to assess their statistical

signi

cance from the same observations (see, e.g., von Storch

and Zwiers, 1999). Nevertheless, there is a need for answers to
such questions, and examples of studies that attempt to do so
are discussed in this chapter (e.g., see Section 9.4.3.3).

9.1.3

The Basis from which We Begin

Evidence of a human in

uence on the recent evolution of the

climate has accumulated steadily during the past two decades.
The

rst IPCC Assessment Report (IPCC, 1990) contained little

observational evidence of a detectable anthropogenic in

uence

on climate. However, six years later the IPCC Working Group
I SAR (IPCC, 1996) concluded that ‘the balance of evidence’
suggested there had been a ‘discernible’ human in

uence on

the climate of the 20th century. Considerably more evidence
accumulated during the subsequent

ve years, such that the TAR

(IPCC, 2001) was able to draw a much stronger conclusion,
not just on the detectability of a human in

uence, but on its

contribution to climate change during the 20th century.

The evidence that was available at the time of the TAR was

considerable. Using results from a range of detection studies of
the instrumental record, which was assessed using

ngerprints

and estimates of internal climate variability from several climate
models, it was found that the warming over the 20th century
was ‘very unlikely to be due to internal variability alone as
estimated by current models’.

Simulations of global mean 20th-century temperature

change that accounted for anthropogenic greenhouse gases and
sulphate aerosols as well as solar and volcanic forcing were
found to be generally consistent with observations. In contrast,
a limited number of simulations of the response to known
natural forcings alone indicated that these may have contributed
to the observed warming in the

rst half of the 20th century, but

could not provide an adequate explanation of the warming in
the second half of the 20th century, nor the observed changes in
the vertical structure of the atmosphere.

Attribution studies had begun to use techniques to

determine whether there was evidence that the responses
to several different forcing agents were simultaneously
present in observations, mainly of surface temperature and of
temperature in the free atmosphere. A distinct greenhouse gas
signal was found to be detectable whether or not other external
in

uences were explicitly considered, and the amplitude of the

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Chapter 9

simulated greenhouse gas response was generally found to be
consistent with observationally based estimates on the scales
that were considered. Also, in most studies, the estimated rate
and magnitude of warming over the second half of the 20th
century due to increasing greenhouse gas concentrations alone
was comparable with, or larger than, the observed warming.
This result was found to be robust to attempts to account for
uncertainties, such as observational uncertainty and sampling
error in estimates of the response to external forcing, as well as
differences in assumptions and analysis techniques.

The TAR also reported on a range of evidence of qualitative

consistencies between observed climate changes and
model responses to anthropogenic forcing, including global
temperature rise, increasing land-ocean temperature contrast,
diminishing arctic sea ice extent, glacial retreat and increases in
precipitation at high northern latitudes.

A number of uncertainties remained at the time of the

TAR. For example, large uncertainties remained in estimates
of internal climate variability. However, even substantially
in

ated (doubled or more) estimates of model-simulated

internal variance were found unlikely to be large enough to
nullify the detection of an anthropogenic in

uence on climate.

Uncertainties in external forcing were also reported, particularly
in anthropogenic aerosol, solar and volcanic forcing, and in
the magnitude of the corresponding climate responses. These
uncertainties contributed to uncertainties in detection and
attribution studies. Particularly, estimates of the contribution to
the 20th-century warming by natural forcings and anthropogenic
forcings other than greenhouse gases showed some discrepancies
with climate simulations and were model dependent. These
results made it dif

cult to attribute the observed climate change

to one speci

c combination of external in

uences.

Based on the available studies and understanding of the

uncertainties, the TAR concluded that ‘in the light of new
evidence and taking into account the remaining uncertainties,
most of the observed warming over the last 50 years is likely to
have been due to the increase in greenhouse gas concentrations’.
Since the TAR, a larger number of model simulations using
more complete forcings have become available, evidence on a
wider range of variables has been analysed and many important
uncertainties have been further explored and in many cases
reduced. These advances are assessed in this chapter.

9.2

Radiative Forcing and Climate
Response

This section brie

y summarises the understanding of radiative

forcing based on the assessment in Chapter 2, and of the climate
response to forcing. Uncertainties in the forcing and estimates of
climate response, and their implications for understanding and
attributing climate change are also discussed. The discussion
of radiative forcing focuses primarily on the period since 1750,
with a brief reference to periods in the more distant past that

are also assessed in the chapter, such as the last millennium, the
Last Glacial Maximum and the mid-Holocene.

Two basic types of calculations have been used in detection

and attribution studies. The

rst uses best estimates of forcing

together with best estimates of modelled climate processes to
calculate the effects of external changes in the climate system
(forcings) on the climate (the response). These ‘forward
calculations’ can then be directly compared to the observed
changes in the climate system. Uncertainties in these simulations
result from uncertainties in the radiative forcings that are used,
and from model uncertainties that affect the simulated response
to the forcings. Forward calculations are explored in this chapter
and compared to observed climate change.

Results from forward calculations are used for formal

detection and attribution analyses. In such studies, a climate
model is used to calculate response patterns (‘

ngerprints’) for

individual forcings or sets of forcings, which are then combined
linearly to provide the best

t to the observations. This

procedure assumes that the amplitude of the large-scale pattern
of response scales linearly with the forcing, and that patterns
from different forcings can be added to obtain the total response.
This assumption may not hold for every forcing, particularly
not at smaller spatial scales, and may be violated when forcings
interact nonlinearly (e.g., black carbon absorption decreases
cloudiness and thereby decreases the indirect effects of sulphate
aerosols). Generally, however, the assumption is expected to
hold for most forcings (e.g., Penner et al., 1997; Meehl et al.,
2004). Errors or uncertainties in the magnitude of the forcing
or the magnitude of a model’s response to the forcing should
not affect detection results provided that the space-time pattern
of the response is correct. However, for the linear combination
of responses to be considered consistent with the observations,
the scaling factors for individual response patterns should
indicate that the model does not need to be rescaled to match
the observations (Sections 9.1.2, 9.4.1.4 and Appendix 9.A)
given uncertainty in the amplitude of forcing, model response
and estimate due to internal climate variability. For detection
studies, if the space-time pattern of response is incorrect, then
the scaling, and hence detection and attribution results, will be
affected.

In the second type of calculation, the so-called ‘inverse’

calculations, the magnitude of uncertain parameters in the
forward model (including the forcing that is applied) is varied in
order to provide a best

t to the observational record. In general,

the greater the degree of

a priori

uncertainty in the parameters of

the model, the more the model is allowed to adjust. Probabilistic
posterior estimates for model parameters and uncertain forcings
are obtained by comparing the agreement between simulations
and observations, and taking into account prior uncertainties
(including those in observations; see Sections 9.2.1.2, 9.6 and
Supplementary Material, Appendix 9.B).

671

Chapter 9

Understanding and Attributing Climate Change

9.2.1

Radiative Forcing Estimates Used to
Simulate Climate Change

9.2.1.1

Summary of ‘Forward’ Estimates of Forcing for
the Instrumental Period

Estimates of the radiative forcing (see Section 2.2 for a

nition) since 1750 from forward model calculations and

observations are reviewed in detail in Chapter 2 and provided in
Table 2.12. Chapter 2 describes estimated forcing resulting from
increases in long-lived greenhouse gases (carbon dioxide (CO

methane, nitrous oxide, halocarbons), decreases in stratospheric
ozone, increases in tropospheric ozone, sulphate aerosols,
nitrate aerosols, black carbon and organic matter from fossil
fuel burning, biomass burning aerosols, mineral dust aerosols,
land use change, indirect aerosol effects on clouds, aircraft cloud
effects, solar variability, and stratospheric and tropospheric
water vapour increases from methane

and irrigation. An

example of one model’s implemented set of forcings is given in
Figure 2.23. While some members of the MMD at PCMDI have
included a nearly complete list of these forcings for the purpose
of simulating the 20th-century climate (see Supplementary
Material, Table S9.1), most detection studies to date have used
model runs with a more limited set of forcings. The combined
anthropogenic forcing from the estimates in Section 2.9.2 since
1750 is 1.6 W m

–2

, with a 90% range of 0.6 to 2.4 W m

–2

indicating that it is extremely

likely that humans have exerted a

substantial warming in

uence on climate over that time period.

The combined forcing by greenhouse gases plus ozone is 2.9 ±
0.3 W m

–2

and the total aerosol forcing (combined direct and

indirect ‘cloud albedo’ effect) is virtually certain to be negative
and estimated to be –1.3 (90% uncertainty range of –2.2 to –0.5
W m

–2

; see Section 2.9). In contrast, the direct radiative forcing

due to increases in solar irradiance is estimated to be +0.12
(90% range from 0.06 to 0.3) W m

–2

. In addition, Chapter 2

concludes that it is exceptionally unlikely that the combined
natural (solar and volcanic) radiative forcing has had a warming
in

uence comparable to that of the combined anthropogenic

forcing over the period 1950 to 2005. As noted in Chapter 2,
the estimated global average surface temperature response
from these forcings may differ for a particular magnitude of
forcing since all forcings do not have the same ‘ef

cacy’ (i.e.,

effectiveness at changing the surface temperature compared to
CO

; see Section 2.8). Thus, summing these forcings does not

necessarily give an adequate estimate of the response in global
average surface temperature.

9.2.1.2

Summary of ‘Inverse’ Estimates of Net Aerosol
Forcing

Forward model approaches to estimating aerosol forcing are

based on estimates of emissions and models of aerosol physics
and chemistry. They directly resolve the separate contributions
by various aerosol components and forcing mechanisms. This

must be borne in mind when comparing results to those from
inverse calculations (see Section 9.6 and Supplementary
Material, Appendix 9.B for details), which, for example, infer the
net aerosol forcing required to match climate model simulations
with observations. These methods can be applied using a global
average forcing and response, or using the spatial and temporal
patterns of the climate response in order to increase the ability
to distinguish between responses to different external forcings.
Inverse methods have been used to constrain one or several
uncertain radiative forcings (e.g., by aerosols), as well as climate
sensitivity (Section 9.6) and other uncertain climate parameters
(Wigley, 1989; Schlesinger and Ramankutty, 1992; Wigley et
al., 1997; Andronova and Schlesinger, 2001; Forest et al., 2001,
2002; Harvey and Kaufmann, 2002; Knutti et al., 2002, 2003;
Andronova et al., 2007; Forest et al., 2006; see Table 9.1 – Stott
et al., 2006c). The reliability of the spatial and temporal patterns
used is discussed in Sections 9.2.2.1 and 9.2.2.2.

In the past, forward calculations have been unable to rule

out a total net negative radiative forcing over the 20th century
(Boucher and Haywood, 2001). However, Section 2.9 updates
the Boucher and Haywood analysis for current radiative forcing
estimates since 1750 and shows that it is extemely likely that
the combined anthropogenic RF is both positive and substantial
(best estimate: +1.6 W m

–2

). A net forcing close to zero would

imply a very high value of climate sensitivity, and would be very
dif

cult to reconcile with the observed increase in temperature

(Sections 9.6 and 9.7). Inverse calculations yield only the
‘net forcing’, which includes all forcings that project on the

ngerprint of the forcing that is estimated. For example, the

response to tropospheric ozone forcing could project onto that for
sulphate aerosol forcing. Therefore, differences between forward
estimates and inverse estimates may have one of several causes,
including (1) the magnitude of the forward model calculation
is incorrect due to inadequate physics and/or chemistry, (2) the
forward calculation has not evaluated all forcings and feedbacks
or (3) other forcings project on the

ngerprint of the forcing that

is estimated in the inverse calculation.

Studies providing inverse estimates of aerosol forcing are

compared in Table 9.1. One type of inverse method uses the
ranges of climate change

ngerprint scaling factors derived from

detection and attribution analyses that attempt to separate the
climate response to greenhouse gas forcing from the response to
aerosol forcing and often from natural forcing as well (Gregory
et al., 2002a; Stott et al., 2006c; see also Section 9.4.1.4).
These provide the range of

ngerprint magnitudes (e.g., for the

combined temperature response to different aerosol forcings) that
are consistent with observed climate change, and can therefore
be used to infer the likely range of forcing that is consistent with
the observed record. The separation between greenhouse gas and
aerosol

ngerprints exploits the fact that the forcing from well-

mixed greenhouse gases is well known, and that errors in the
model’s transient sensitivity can therefore be separated from errors
in aerosol forcing in the model (assuming that there are similar
errors in a model’s sensitivity to greenhouse gas and aerosol

672

Understanding and Attributing Climate Change

Chapter 9

Table 9.1.

Inverse estimates of aerosol forcing from detection and attribution studies and studies estimating equilibrium climate sensitivity (see Section 9.6 and Table 9.3 for

details on studies). The 5 to 95% estimates for the range of aerosol forcing relate to total or net fossil-fuel related aerosol forcing (in W m

–2

Forest et al.

(2006)

Andronova and

Schlesinger

(2001)

Knutti et al.

(2002, 2003)

Gregory et al.

(2002a)

Stott et al.

(2006c)

Harvey and

Kaufmann (2002)

Observational
data used to
constrain aerosol
forcing

Upper air, surface
and deep ocean
space-time
temperature,
latter half of 20th
century

Global mean
and hemispheric
difference in
surface air
temperature 1856
to 1997

Global mean
ocean heat
uptake 1955
to 1995, global
mean surface
air temperature
increase 1860 to
2000

Surface air
temperature
space-time
patterns, one
AOGCM

Surface air
temperature
space-time
patterns, three
AOGCMs

Global mean
and hemispheric
difference in
surface air
temperature 1856
to 2000

Forcings
considered

G, Sul, Sol, Vol,
OzS, land surface
changes

G, OzT, Sul, Sol,
Vol

G, Sul, Suli, OzT,
OzS, BC+OM,
stratospheric
water vapour, Vol,
Sol

G, Sul, Suli, Sol,
Vol

G, Sul, Suli, OzT,
OzS, Sol, Vol

G, Sul, biomass
aerosol, Sol, Vol

Year

1980s

1990

2000

1990

Aerosol forcing
(W m

–2

)

–0.14 to –0.74
–0.07 to –0.65
with expert prior

–0.54 to –1.3

0 to –1.2 indirect
aerosol
–0.6 to –1.7 total
aerosol

–0.4 to –1.6
total aerosol

–0.4 to –1.4
total aerosol

Fossil fuel aerosol
unlikely < –1,
biomass plus dust
unlikely < –0.5

Notes:

G: greenhouse gases; Sul: direct sulphate aerosol effect; Suli: (fi rst) indirect sulphate aerosol effect; OzT: tropospheric ozone; OzS: stratospheric ozone; Vol:

volcanic forcing; Sol: solar forcing; BC+OM: black carbon and organic matter from fossil fuel and biomass burning.

Year(s) for which aerosol forcing is calculated, relative to pre-industrial conditions.

5 to 95% inverse estimate of the total aerosol forcing in the year given relative to pre-industrial forcing. The aerosol range refers to the net fossil-fuel related aerosol

range, which tends to be all forcings not directly accounted for that project onto the pattern associated with fossil fuel aerosols, and includes all unknown forcings
and those not explicitly considered (for example, OzT and BC+OM in several of the studies).

Explores IPCC TAR range of climate sensitivity (i.e., 1.5°C to 4.5°C), while other studies explore wider ranges

forcing; see Gregory et al., 2002a; Table 9.1). By scaling spatio-
temporal patterns of response up or down, this technique takes
account of gross model errors in climate sensitivity and net aerosol
forcing but does not fully account for modelling uncertainty in the
patterns of temperature response to uncertain forcings.

Another approach uses the response of climate models,

most often simple climate models or Earth System Models of
Intermediate Complexity (EMICs, Table 8.3

)

to explore the

range of forcings and climate parameters that yield results
consistent with observations (Andronova and Schlesinger, 2001;
Forest et al., 2002; Harvey and Kaufmann, 2002; Knutti et al.,
2002, 2003; Forest et al., 2006). Like detection methods, these
approaches seek to

t the space-time patterns, or spatial means

in time, of observed surface, atmospheric or ocean temperatures.
They determine the probability of combinations of climate
sensitivity and net aerosol forcing based on the

t between

simulations and observations (see Section 9.6 and Supplementary
Material, Appendix 9.B for further discussion). These are often
based on Bayesian approaches, where prior assumptions about

ranges of external forcing are used to constrain the estimated net
aerosol forcing and climate sensitivity. Some of these studies
use the difference between Northern and Southern Hemisphere
mean temperature to separate the greenhouse gas and aerosol
forcing effects (e.g., Andronova and Schlesinger, 2001; Harvey
and Kaufmann, 2002). In these analyses, it is necessary to
accurately account for hemispheric asymmetry in tropospheric
ozone forcing in order to infer the hemispheric aerosol forcing.
Additionally, aerosols from biomass burning could cause an
important fraction of the total aerosol forcing although this
forcing shows little hemispheric asymmetry. Since it therefore
projects on the greenhouse gas forcing, it is dif

cult to separate

in an inverse calculation. Overall, results will be only as good
as the spatial or temporal pattern that is assumed in the analysis.
Missing forcings or lack of knowledge about uncertainties, and
the highly parametrized spatial distribution of response in some
of these models may hamper the interpretation of results.

Aerosol forcing appears to have grown rapidly during

the period from 1945 to 1980, while greenhouse gas forcing

673

Chapter 9

Understanding and Attributing Climate Change

grew more slowly (Ramaswamy et al., 2001). Global sulphur
emissions (and thus sulphate aerosol forcing) appear to have
decreased after 1980

(Stern, 2005), further rendering the

temporal evolution of aerosols and greenhouse gases distinct.
As long as the temporal pattern of variation in aerosol forcing is
approximately correct, the need to achieve a reasonable

t to the

temporal variation in global mean temperature and the difference
between Northern and Southern Hemisphere temperatures can
provide a useful constraint on the net aerosol radiative forcing
(as demonstrated, e.g., by Harvey and Kaufmann, 2002; Stott
et al., 2006c).

The inverse estimates summarised in Table 9.1 suggest that

to be consistent with observed warming, the net aerosol forcing
over the 20th century should be negative with likely ranges
between –1.7 and –0.1 W m

–2

. This assessment accounts for the

probability of other forcings projecting onto the

ngerprints.

These results typically provide a somewhat smaller upper limit
for the total aerosol forcing than the estimates given in Chapter 2,
which are derived from forward calculations and range between
–2.2 and –0.5 W m

–2

(5 to 95% range, median –1.3 W m

–2

). Note

that the uncertainty ranges from inverse and forward calculations
are different due to the use of different information, and that
they are affected by different uncertainties. Nevertheless, the
similarity between results from inverse and forward estimates
of aerosol forcing strengthens con

dence in estimates of total

aerosol forcing, despite remaining uncertainties. Harvey and
Kaufmann (2002), who use an approach that focuses on the TAR
range of climate sensitivity, further conclude that global mean
forcing from fossil-fuel related aerosols was probably less than
–1.0 W m

–2

in 1990 and that global mean forcing from biomass

burning and anthropogenically enhanced soil dust aerosols is
‘unlikely’ to have exceeded –0.5 W m

–2

in 1990.

9.2.1.3

Radiative Forcing of Pre-Industrial Climate
Change

Here we brie

y discuss the radiative forcing estimates used

for understanding climate during the last millennium, the mid-
Holocene and the Last Glacial Maximum (LGM) (Section 9.3)
and in estimates of climate sensitivity based on palaeoclimatic
records (Section 9.6.3).

Regular variation in the Earth’s orbital parameters has been

identi

ed as the pacemaker of climate change on the glacial to

interglacial time scale (see Berger, 1988 for a review). These
orbital variations, which can be calculated from astronomical laws
(Berger, 1978), force climate variations by changing the seasonal
and latitudinal distribution of solar radiation (Chapter 6).

Insolation at the time of the LGM (21 ka) was similar to

today. Nonetheless, the LGM climate remained cold due to the
presence of large ice sheets in the Northern Hemisphere (Peltier,
1994, 2004) and reduced atmospheric CO

concentration (185

ppm according to recent ice core estimates, see Monnin et al.,
2001). Most modelling studies of this period do not treat ice
sheet extent and elevation or CO

concentration prognostically,

but specify them as boundary conditions. The LGM radiative
forcing from the reduced atmospheric concentrations of
well-mixed greenhouse gases is likely to have been about
–2.8 W m

–2

(see Figure 6.5). Ice sheet albedo forcing is estimated

to have caused a global mean forcing of about –3.2 W m

–2

(based on a range of several LGM simulations) and radiative
forcing from increased atmospheric aerosols (primarily dust
and vegetation) is estimated to have been about –1 W m

–2

each.

Therefore, the total annual and global mean radiative forcing
during the LGM is likely to have been approximately –8 W m

–2

relative to 1750, with large seasonal and geographical variations
and signi

cant uncertainties (see Section 6.4.1).

The major mid-Holocene forcing relative to the present

was due to orbital perturbations that led to large changes in the
seasonal cycle of insolation. The Northern Hemisphere (NH)
seasonal cycle was about 27 W m

–2

greater, whereas there was

only a negligible change in NH annual mean solar forcing. For
the Southern Hemisphere (SH), the seasonal forcing was –6.5
W m

–2

. In contrast, the global and annual mean net forcing was

only 0.011 W m

–2

Changes in the Earth’s orbit have had little impact on annual

mean insolation over the past millennium. Summer insolation
decreased by 0.33 W m

–2

at 45°N over the millennium, winter

insolation increased by 0.83 W m

–2

(Goosse et al., 2005), and the

magnitude of the mean seasonal cycle of insolation in the NH
decreased by 0.4 W m

–2

. Changes in insolation are also thought

to have arisen from small variations in solar irradiance, although
both timing and magnitude of past solar radiation

uctuations

are highly uncertain (see Chapters 2 and 6; Lean et al., 2002;
Gray et al., 2005; Foukal et al., 2006). For example, sunspots
were generally missing from approximately 1675 to 1715 (the
so-called Maunder Minimum) and thus solar irradiance is
thought to have been reduced during this period. The estimated
difference between the present-day solar irradiance cycle mean
and the Maunder Minimum is 0.08% (see Section 2.7.1.2.2),
which corresponds to a radiative forcing of about 0.2 W m

–2

which is substantially lower than estimates used in the TAR
(Chapter 2).

Natural external forcing also results from explosive volcanism

that introduces aerosols into the stratosphere (Section 2.7.2),
leading to a global negative forcing during the year following the
eruption. Several reconstructions are available for the last two
millennia and have been used to force climate models (Section
6.6.3). There is close agreement on the timing of large eruptions
in the various compilations of historic volcanic activity, but
large uncertainty in the magnitude of individual eruptions
(Figure 6.13). Different reconstructions identify similar periods
when eruptions happened more frequently. The uncertainty in
the overall amplitude of the reconstruction of volcanic forcing
is also important for quantifying the in

uence of volcanism on

temperature reconstructions over longer periods, but is dif

cult

to quantify and may be a substantial fraction of the best estimate
(e.g., Hegerl et al., 2006a).

674

Understanding and Attributing Climate Change

Chapter 9

9.2.2

Spatial and Temporal Patterns of the
Response to Different Forcings and their
Uncertainties

9.2.2.1

Spatial and Temporal Patterns of Response

The ability to distinguish between climate responses to

different external forcing factors in observations depends
on the extent to which those responses are distinct (see, e.g.,
Section 9.4.1.4 and Appendix 9.A). Figure 9.1 illustrates the
zonal average temperature response in the PCM model (see
Table 8.1 for model details) to several different forcing agents
over the last 100 years, while Figure 9.2 illustrates the zonal
average temperature response in the Commonwealth Scienti

and Industrial Research Organisation (CSIRO) atmospheric
model (when coupled to a simple mixed layer ocean model) to
fossil fuel black carbon and organic matter, and to the combined
effect of these forcings together with biomass burning aerosols
(Penner et al., 2007). These

gures indicate that the modelled

vertical and zonal average signature of the temperature response
should depend on the forcings. The major features shown in
Figure 9.1 are robust to using different climate models. On the
other hand, the response to black carbon forcing has not been
widely examined and therefore the features in Figure 9.2 may
be model dependent. Nevertheless, the response to black carbon
forcings appears to be small.

Greenhouse gas forcing is expected to produce warming in

the troposphere, cooling in the stratosphere, and, for transient
simulations, somewhat more warming near the surface in the
NH due to its larger land fraction, which has a shorter surface
response time to the warming than do ocean regions (Figure
9.1c). The spatial pattern of the transient surface temperature
response to greenhouse gas forcing also typically exhibits a
land-sea pattern of stronger warming over land, for the same
reason (e.g., Cubasch et al., 2001). Sulphate aerosol forcing
results in cooling throughout most of the globe, with greater
cooling in the NH due to its higher aerosol loading (Figure
9.1e; see Chapter 2), thereby partially offsetting the greater
NH greenhouse-gas induced warming. The combined effect
of tropospheric and stratospheric ozone forcing (Figure 9.1d)
is expected to warm the troposphere, due to increases in
tropospheric ozone, and cool the stratosphere, particularly
at high latitudes where stratospheric ozone loss has been
greatest. Greenhouse gas forcing is also expected to change the
hydrological cycle worldwide, leading to disproportionately
greater increases in heavy precipitation (Chapter 10 and Section
9.5.4), while aerosol forcing can in

uence rainfall regionally

(Section 9.5.4).

The simulated responses to natural forcing are distinct from

those due to the anthropogenic forcings described above. Solar
forcing results in a general warming of the atmosphere (Figure
9.1a) with a pattern of surface warming that is similar to that
expected from greenhouse gas warming, but in contrast to the
response to greenhouse warming, the simulated solar-forced
warming extends throughout the atmosphere (see, e.g., Cubasch

et al., 1997). A number of independent analyses have identi

tropospheric changes that appear to be associated with the solar
cycle (van Loon and Shea, 2000; Gleisner and Thejll, 2003;
Haigh, 2003; White et al., 2003; Coughlin and Tung, 2004;
Labitzke, 2004; Crooks and Gray, 2005), suggesting an overall
warmer and moister troposphere during solar maximum. The
peak-to-trough amplitude of the response to the solar cycle
globally is estimated to be approximately 0.1°C near the
surface. Such variations over the 11-year solar cycle make it
is necessary to use several decades of data in detection and
attribution studies. The solar cycle also affects atmospheric
ozone concentrations with possible impacts on temperatures
and winds in the stratosphere, and has been hypothesised to
in

uence clouds through cosmic rays (Section 2.7.1.3). Note

that there is substantial uncertainty in the identi

cation of

climate response to solar cycle variations because the satellite
period is short relative to the solar cycle length, and because the
response is dif

cult to separate from internal climate variations

and the response to volcanic eruptions (Gray et al., 2005).

Volcanic sulphur dioxide (SO

) emissions ejected into the

stratosphere form sulphate aerosols and lead to a forcing that
causes a surface and tropospheric cooling and a stratospheric
warming that peak several months after a volcanic eruption
and last for several years. Volcanic forcing also likely leads
to a response in the atmospheric circulation in boreal winter
(discussed below) and a reduction in land precipitation (Robock
and Liu, 1994; Broccoli et al., 2003; Gillett et al., 2004b). The
response to volcanic forcing causes a net cooling over the 20th
century because of variations in the frequency and intensity of
volcanic eruptions. This results in stronger volcanic forcing
towards the end of the 20th century than early in the 20th
century. In the PCM, this increase results in a small warming
in the lower stratosphere and near the surface at high latitudes,
with cooling elsewhere (Figure 9.1b).

The net effect of all forcings combined is a pattern of NH

temperature change near the surface that is dominated by the
positive forcings (primarily greenhouse gases), and cooling in
the stratosphere that results predominantly from greenhouse gas
and stratospheric ozone forcing (Figure 9.1f). Results obtained
with the CSIRO model (Figure 9.2) suggest that black carbon,
organic matter and biomass aerosols would slightly enhance the
NH warming shown in Figure 9.1f. On the other hand, indirect
aerosol forcing from fossil fuel aerosols may be larger than
the direct effects that are represented in the CSIRO and PCM
models, in which case the NH warming could be somewhat
diminished. Also, while land use change may cause substantial
forcing regionally and seasonally, its forcing and response are
expected to have only a small impact at large spatial scales
(Sections 9.3.3.3 and 7.2.2; Figures 2.20 and 2.23).

The spatial signature of a climate model’s response is seldom

very similar to that of the forcing, due in part to the strength of
the feedbacks relative to the initial forcing. This comes about
because climate system feedbacks vary spatially and because
the atmospheric and ocean circulation cause a redistribution of
energy over the globe. For example, sea ice albedo feedbacks

676

Understanding and Attributing Climate Change

Chapter 9

tend to enhance the high-latitude response of both a positive
forcing, such as that of CO

, and a negative forcing such as

that of sulphate aerosol (e.g., Mitchell et al., 2001; Rotstayn
and Penner, 2001). Cloud feedbacks can affect both the spatial
signature of the response to a given forcing and the sign of
the change in temperature relative to the sign of the radiative
forcing (Section 8.6). Heating by black carbon, for example,
can decrease cloudiness (Ackerman et al., 2000). If the black
carbon is near the surface, it may increase surface temperatures,
while at higher altitudes it may reduce surface temperatures
(Hansen et al., 1997; Penner et al., 2003). Feedbacks can also
lead to differences in the response of different models to a given
forcing agent, since the spatial response of a climate model to
forcing depends on its representation of these feedbacks and
processes. Additional factors that affect the spatial pattern of
response include differences in thermal inertia between land
and sea areas, and the lifetimes of the various forcing agents.
Shorter-lived agents, such as aerosols, tend to have a more
distinct spatial pattern of forcing, and can therefore be expected
to have some locally distinct response features.

The pattern of response to a radiative forcing can also be

altered quite substantially if the atmospheric circulation is
affected by the forcing. Modelling studies and data comparisons
suggest that volcanic aerosols (e.g., Kirchner et al., 1999;
Shindell et al., 1999; Yang and Schlesinger, 2001; Stenchikov
et al., 2006) and greenhouse gas changes (e.g., Fyfe et al., 1999;
Shindell et al., 1999; Rauthe et al., 2004) can alter the North
Atlantic Oscillation (NAO) or the Northern Annular Mode
(NAM). For example, volcanic eruptions, with the exception of
high-latitude eruptions, are often followed by a positive phase
of the NAM or NAO (e.g., Stenchikov et al.,
2006) leading to Eurasian winter warming
that may reduce the overall cooling effect
of volcanic eruptions on annual averages,
particularly over Eurasia (Perlwitz and Graf,
2001; Stenchikov et al., 2002; Shindell et
al., 2003; Stenchikov et al., 2004; Oman et
al., 2005; Rind et al., 2005a; Miller et al.,
2006; Stenchikov et al., 2006). In contrast,
NAM or NAO responses to solar forcing
vary between studies, some indicating a
response, perhaps with dependence of the
response on season or other conditions, and
some

nding no changes (Shindell et al.,

2001a,b; Ruzmaikin and Feynman, 2002;
Tourpali et al., 2003; Egorova et al., 2004;
Palmer et al., 2004; Stendel et al., 2006; see
also review in Gray et al., 2005).

In addition to the spatial pattern, the

temporal evolution of the different forcings
(Figure 2.23) generally helps to distinguish
between the responses to different forcings.
For example, Santer et al. (1996b,c)
point out that a temporal pattern in the
hemispheric temperature contrast would
be expected in the second half of the 20th

century with the SH warming more than the NH for the

rst two

decades of this period and the NH subsequently warming more
than the SH, as a result of changes in the relative strengths of
the greenhouse gas and aerosol forcings. However, it should
be noted that the integrating effect of the oceans (Hasselmann,
1976) results in climate responses that are more similar in time
between different forcings than the forcings are to each other,
and that there are substantial uncertainties in the evolution of
the hemispheric temperature contrasts associated with sulphate
aerosol forcing.

9.2.2.2

Aerosol Scattering and Cloud Feedback in Models
and Observations

One line of observational evidence that re

ective aerosol

forcing has been changing over time comes from satellite
observations of changes in top-of-atmosphere outgoing
shortwave radiation

ux. Increases in the outgoing shortwave

radiation

ux can be caused by increases in re

ecting aerosols,

increases in clouds or a change in the vertical distribution
of clouds and water vapour, or increases in surface albedo.
Increases in aerosols and clouds can cause decreases in surface
radiation

uxes and decreases in surface warming. There has

been continuing interest in this possibility (Gilgen et al., 1998;
Stanhill and Cohen, 2001; Liepert, 2002). Sometimes called
‘global dimming’, this phenomena has reversed since about
1990 (Pinker et al., 2005; Wielicki et al., 2005; Wild et al., 2005;
Section 3.4.3), but over the entire period from 1984 to 2001,
surface solar radiation has increased by about 0.16 W m

–2

–1

on average (Pinker et al., 2005). Figure 9.3 shows the top-of-

Figure 9.3.

Comparison of outgoing shortwave radiation fl ux anomalies (in W m

–2

, calculated relative to the

entire time period) from several models in the MMD archive at PCMDI (coloured curves) with ERBS satellite data
(black with stars; Wong et al., 2006) and with the ISCCP fl ux data set (black with squares; Zhang et al., 2004).
Models shown are CCSM3, CGCM3.1(T47), CGCM3.1(T63), CNRM-CM3, CSIRO-MK3.0, FGOALS-g1.0, GFDL-
CM2.0, GFDL-CM2.1, GISS-AOM, GISS-EH, GISS-ER, INM-CM3.0, IPSL-CM4, and MRI-CGCM2.3.2 (see Table 8.1
for model details). The comparison is restricted to 60°S to 60°N because the ERBS data are considered more
accurate in this region. Note that not all models included the volcanic forcing from Mt. Pinatubo (1991–1993)
and so do not predict the observed increase in outgoing solar radiation. See Supplementary Material, Appendix
9.C for additional information.

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Understanding and Attributing Climate Change

atmosphere outgoing shortwave radiation

ux anomalies from

the MMD at PCMDI, compared to that measured by the Earth
Radiation Budget Satellite (ERBS; Wong et al., 2006) and
inferred from International Satellite Cloud Climatology Project
(ISCCP)

ux data (FD) (Zhang et al., 2004). The downward

trend in outgoing solar radiation is consistent with the long-
term upward trend in surface radiation found by Pinker et al.
(2005). The effect of the eruption of Mt. Pinatubo in 1991
results in an increase in the outgoing shortwave radiation

ux (and a corresponding dimming at the surface) and its

effect has been included in most (but not all) models in the
MMD. The ISCCP

ux anomaly for the Mt. Pinatubo signal

is almost 2 W m

–2

larger than that for ERBS, possibly due to

the aliasing of the stratospheric aerosol signal into the ISCCP
cloud properties. Overall, the trends from the ISCCP FD
(–0.18 with 95% con

dence limits of ±0.11 W m

–2

–1

) and the

ERBS data (–0.13 ± 0.08 W m

–2

–1

) from 1984 to 1999 are not

signi

cantly different from each other at the 5% signi

cance

level, and are in even better agreement if only tropical latitudes
are considered (Wong et al., 2006). These observations suggest
an overall decrease in aerosols and/or clouds, while estimates
of changes in cloudiness are uncertain (see Section 3.4.3). The
model-predicted trends are also negative over this time period,
but are smaller in most models than in the ERBS observations
(which are considered more accurate than the ISCCP FD).
Wielicki et al. (2002) explain the observed downward trend by
decreases in cloudiness, which are not well represented in the
models on these decadal time scales (Chen et al., 2002; Wielicki
et al., 2002).

9.2.2.3

Uncertainty in the Spatial Pattern of Response

Most detection methods identify the magnitude of the

space-time patterns of response to forcing (sometimes called
‘

ngerprints’) that provide the best

t to the observations.

The

ngerprints are typically estimated from ensembles of

climate model simulations forced with reconstructions of past
forcing. Using different forcing reconstructions and climate
models in such studies provides some indication of forcing
and model uncertainty. However, few studies have examined
how uncertainties in the spatial pattern of forcing explicitly
contribute to uncertainties in the spatial pattern of the response.
For short-lived components, uncertainties in the spatial pattern
of forcing are related to uncertainties in emissions patterns,
uncertainties in the transport within the climate model or
chemical transport model and, especially for aerosols,
uncertainties in the representation of relative humidities or
clouds. These uncertainties affect the spatial pattern of the
forcing. For example, the ratio of the SH to NH indirect aerosol
forcing associated with the total aerosol forcing ranges from
–0.12 to 0.63 (best guess 0.29) in different studies, and that
between ocean and land forcing ranges from 0.03 to 1.85 (see
Figure 7.21; Rotstayn and Penner, 2001; Chuang et al., 2002;
Kristjansson, 2002; Lohmann and Lesins, 2002; Menon et al.,
2002a; Rotstayn and Liu, 2003; Lohmann and Feichter, 2005).

9.2.2.4

Uncertainty in the Temporal Pattern of Response

Climate model studies have also not systematically explored

the effect of uncertainties in the temporal evolution of forcings.
These uncertainties depend mainly on the uncertainty in the
spatio-temporal expression of emissions, and, for some forcings,
fundamental understanding of the possible change over time.

The increasing forcing by greenhouse gases is relatively

well known. In addition, the global temporal history of SO

emissions, which have a larger overall forcing than the other
short-lived aerosol components, is quite well constrained.
Seven different reconstructions of the temporal history of
global anthropogenic sulphur emissions up to 1990 have a
relative standard deviation of less than 20% between 1890 and
1990, with better agreement in more recent years. This robust
temporal history increases con

dence in results from detection

and attribution studies that attempt to separate the effects of
sulphate aerosol and greenhouse gas forcing (Section 9.4.1).

In contrast, there are large uncertainties related to the

anthropogenic emissions of other short-lived compounds and
their effects on forcing. For example, estimates of historical
emissions from fossil fuel combustion do not account for
changes in emission factors (the ratio of the emitted gas or
aerosol to the fuel burned) of short-lived species associated
with concerns over urban air pollution (e.g., van Aardenne et
al., 2001). Changes in these emission factors would have slowed
the emissions of nitrogen oxides as well as carbon monoxide
after about 1970 and slowed the accompanying increase in
tropospheric ozone compared to that represented by a single
emission factor for fossil fuel use. In addition, changes in the
height of SO

emissions associated with the implementation of

tall stacks would have changed the lifetime of sulphate aerosols
and the relationship between emissions and effects. Another
example relates to the emissions of black carbon associated with
the burning of fossil fuels. The spatial and temporal emissions
of black carbon by continent reconstructed by Ito and Penner
(2005) are signi

cantly different from those reconstructed

using the methodology of Novakov et al. (2003). For example,
the emissions in Asia grow signi

cantly faster in the inventory

based on Novakov et al. (2003) compared to those based on
Ito and Penner (2005). In addition, before 1988 the growth in
emissions in Eastern Europe using the Ito and Penner (2005)
inventory is faster than the growth based on the methodology of
Novakov et al. (2003). Such spatial and temporal uncertainties
will contribute to both spatial and temporal uncertainties in
the net forcing and to spatial and temporal uncertainties in the
distribution of forcing and response.

There are also large uncertainties in the magnitude of low-

frequency changes in forcing associated with changes in total
solar radiation as well as its spectral variation, particularly on
time scales longer than the 11-year cycle. Previous estimates
of change in total solar radiation have used sunspot numbers
to calculate these slow changes in solar irradiance over the last
few centuries, but these earlier estimates are not necessarily
supported by current understanding and the estimated

678

Understanding and Attributing Climate Change

Chapter 9

magnitude of low-frequency changes has been substantially
reduced since the TAR (Lean et al., 2002; Foukal et al., 2004,
2006; Sections 6.6.3.1 and 2.7.1.2). In addition, the magnitude
of radiative forcing associated with major volcanic eruptions is
uncertain and differs between reconstructions (Sato et al., 1993;
Andronova et al., 1999; Ammann et al., 2003), although the
timing of the eruptions is well documented.

9.2.3

Implications for Understanding 20th-Century
Climate Change

Any assessment of observed climate change that compares

simulated and observed responses will be affected by errors and
uncertainties in the forcings prescribed in a climate model and
its corresponding responses. As noted above, detection studies
scale the response patterns to different forcings to obtain the
best match to observations. Thus, errors in the magnitude of the
forcing or in the magnitude of the model response to a forcing
(which is approximately, although not exactly, a function of
climate sensitivity), should not affect detection results provided
that the large-scale space-time pattern of the response is
correct. Attribution studies evaluate the consistency between
the model-simulated amplitude of response and that inferred
from observations. In the case of uncertain forcings, scaling
factors provide information about the strength of the forcing
(and response) needed to reproduce the observations, or about
the possibility that the simulated pattern or strength of response
is incorrect. However, for a model simulation to be considered
consistent with the observations given forcing uncertainty, the
forcing used in the model should remain consistent with the
uncertainty bounds from forward model estimates of forcing.

Detection and attribution approaches that try to distinguish

the response to several external forcings simultaneously may
be affected by similarities in the pattern of response to different
forcings and by uncertainties in forcing and response.
Similarities between the responses to different forcings,
particularly in the spatial patterns of response, make it more
dif

cult to distinguish between responses to different external

forcings, but also imply that the response patterns will be
relatively insensitive to modest errors in the magnitude and
distribution of the forcing. Differences between the temporal
histories of different kinds of forcing (e.g., greenhouse gas
versus sulphate aerosol) ameliorate the problem of the similarity
between the spatial patterns of response considerably. For
example, the spatial response of surface temperature to solar
forcing resembles that due to anthropogenic greenhouse gas
forcing (Weatherall and Manabe, 1975; Nesme-Ribes et al.,
1993; Cubasch et al., 1997; Rind et al., 2004; Zorita et al.,
2005). Distinct features of the vertical structure of the responses
in the atmosphere to different types of forcing further help to
distinguish between the different sources of forcing. Studies
that interpret observed climate in subsequent sections use such
strategies, and the overall assessment in this chapter uses results
from a range of climate variables and observations.

Many detection studies attempt to identify in observations

both temporal and spatial aspects of the temperature response to a

given set of forcings because the combined space-time responses
tend to be more distinct than either the space-only or the time-
only patterns of response. Because the emissions and burdens of
different forcing agents change with time, the net forcing and its
rate of change vary with time. Although explicit accounting for
uncertainties in the net forcing is not available (see discussion
in Sections 9.2.2.3 and 9.2.2.4), models often employ different
implementations of external forcing. Detection and attribution
studies using such simulations suggest that results are not very
sensitive to moderate forcing uncertainties. A further problem
arises due to spurious temporal correlations between the responses
to different forcings that arise from sampling variability. For
example, spurious correlation between the climate responses to
solar and volcanic forcing over parts of the 20th century (North
and Stevens, 1998) can lead to misidenti

cation of one as the

other, as in Douglass and Clader (2002).

The spatial pattern of the temperature response to aerosol

forcing is quite distinct from the spatial response pattern to CO

in some models and diagnostics (Hegerl et al., 1997), but less so
in others (Reader and Boer, 1998; Tett et al., 1999; Hegerl et al.,
2000; Harvey, 2004). If it is not possible to distinguish the spatial
pattern of greenhouse warming from that of fossil-fuel related
aerosol cooling, the observed warming over the last century
could be explained by large greenhouse warming balanced
by large aerosol cooling or alternatively by small greenhouse
warming with very little or no aerosol cooling. Nevertheless,
estimates of the amplitude of the response to greenhouse
forcing in the 20th century from detection studies are quite
similar, even though the simulated responses to aerosol forcing
are model dependent (Gillett et al., 2002a; Hegerl and Allen,
2002). Considering three different climate models, Stott et al.
(2006c) conclude that an important constraint on the possible
range of responses to aerosol forcing is the temporal evolution
of the global mean and hemispheric temperature contrast as was
suggested by Santer et al. (1996a; see also Section 9.4.1.5).

9.2.4 Summary

The uncertainty in the magnitude and spatial pattern of

forcing differs considerably between forcings. For example,
well-mixed greenhouse gas forcing is relatively well constrained
and spatially homogeneous. In contrast, uncertainties are large
for many non-greenhouse gas forcings. Inverse model studies,
which use methods closely related to those used in climate
change detection research, indicate that the magnitude of
the total net aerosol forcing has a likely range of –1.7 to –0.1
W m

–2

. As summarised in Chapter 2, forward calculations of

aerosol radiative forcing, which do not depend on knowledge
of observed climate change or the ability of climate models
to simulate the transient response to forcings, provide results
(–2.2 to –0.5 W m

–2

; 5 to 95%) that are quite consistent with

inverse estimates; the uncertainty ranges from inverse and
forward calculations are different due to the use of different
information. The large uncertainty in total aerosol forcing makes
it more dif

cult to accurately infer the climate sensitivity from

observations (Section 9.6). It also increases uncertainties in

679

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Understanding and Attributing Climate Change

The discussion here is restricted to several periods in the

past for which modelling and observational evidence can
be compared to test understanding of the climate response
to external forcings. One such period is the last millennium,
which places the recent instrumental record in a broader
context (e.g., Mitchell et al., 2001). The analysis of the past 1
kyr focuses mainly on the climate response to natural forcings
(changes in solar radiation and volcanism) and on the role of
anthropogenic forcing during the most recent part of the record.
Two time periods analysed in the Paleoclimate Modelling
Intercomparison Project (PMIP, Joussaume and Taylor, 1995;
PMIP2, Harrison et al., 2002) are also considered, the mid-
Holocene (6 ka) and the LGM (21 ka). Both periods had a
substantially different climate compared to the present, and
there is relatively good information from data synthesis and
model simulation experiments (Braconnot et al., 2004; Cane et
al., 2006). An increased number of simulations using EMICs
or Atmosphere-Ocean General Circulation Models (AOGCMs)
that are the same as, or related to, the models used in simulations
of the climates of the 20th and 21st centuries are available for
these periods.

9.3.2

What Can be Learned from the Last Glacial
Maximum and the Mid-Holocene?

Relatively high-quality global terrestrial climate

reconstructions exist for the LGM and the mid-Holocene and
as part of the Global Palaeovegetation Mapping (BIOME
6000) project (Prentice and Webb, 1998; Prentice and Jolly,
2000). The Climate: Long-range Investigation, Mapping and
Prediction (CLIMAP, 1981) reconstruction of LGM sea surface
temperatures has also been improved (Chapter 6). The LGM
climate was colder and drier than at present as is indicated by
the extensive tundra and steppe vegetation that existed during
this period. Most LGM proxy data suggest that the tropical
oceans were colder by about 2°C than at present, and that
the frontal zones in the SH and NH were shifted equatorward
(Kucera et al., 2005), even though large differences are found
between temperature estimates from the different proxies in the
North Atlantic.

Several new AOGCM simulations of the LGM have been

produced since the TAR. These simulations show a global
cooling of approximately 3.5°C to 5.2°C when LGM greenhouse
gas and ice sheet boundary conditions are speci

ed (Chapter 6),

which is within the range (–1.8°C to –6.5°C) of PMIP results
from simpler models that were discussed in the TAR (McAvaney
et al., 2001). Only one simulation exhibits a very strong response
with a cooling of approximately 10°C (Kim et al., 2002). All of
these simulations exhibit a strongly damped hydrological cycle
relative to that of the modern climate, with less evaporation over
the oceans and continental-scale drying over land. Changes in
greenhouse gas concentrations may account for about half of
the simulated tropical cooling (Shin et al., 2003), and for the
production of colder and saltier water found at depth in the
Southern Ocean (Liu et al., 2005). Most LGM simulations with
coupled models shift the deep-water formation in the North

results that attribute cause to observed climate change (Section
9.4.1.4), and is in part responsible for differences in probabilistic
projections of future climate change (Chapter 10). Forcings
from black carbon, fossil fuel organic matter and biomass
burning aerosols, which have not been considered in most
detection studies performed to date, are likely small but with
large uncertainties relative to the magnitudes of the forcings.

Uncertainties also differ between natural forcings and

sometimes between different time scales for the same forcing.
For example, while the 11-year solar forcing cycle is well
documented, lower-frequency variations in solar forcing are
highly uncertain. Furthermore, the physics of the response to
solar forcing and some feedbacks are still poorly understood.
In contrast, the timing and duration of forcing due to aerosols
ejected into the stratosphere by large volcanic eruptions is well
known during the instrumental period, although the magnitude
of that forcing is uncertain.

Differences in the temporal evolution and sometimes the

spatial pattern of climate response to external forcing make
it possible, with limitations, to separate the response to these
forcings in observations, such as the responses to greenhouse gas
and sulphate aerosol forcing. In contrast, the climate response
and temporal evolution of other anthropogenic forcings is
more uncertain, making the simulation of the climate response
and its detection in observations more dif

cult. The temporal

evolution, and to some extent the spatial and vertical pattern, of
the climate response to natural forcings is also quite different
from that of anthropogenic forcing. This makes it possible to
separate the climate response to solar and volcanic forcing from
the response to anthropogenic forcing despite the uncertainty in
the history of solar forcing noted above.

9.3 Understanding Pre-Industrial

Climate Change

9.3.1

Why Consider Pre-Industrial Climate
Change?

The Earth system has experienced large-scale climate

changes in the past (Chapter 6) that hold important lessons for
the understanding of present and future climate change. These
changes resulted from natural external forcings that, in some
instances, triggered strong feedbacks as in the case of the LGM
(see Chapter 6). Past periods offer the potential to provide
information not available from the instrumental record, which
is affected by anthropogenic as well as natural external forcings
and is too short to fully understand climate variability and
major climate system feedbacks on inter-decadal and longer
time scales. Indirect indicators (‘proxy data’ such as tree ring
width and density) must be used to infer climate variations
(Chapter 6) prior to the instrumental era (Chapter 3). A complete
description of these data and of their uncertainties can be found
in Chapter 6.

680

Understanding and Attributing Climate Change

Chapter 9

Atlantic southward, but large differences exist between models
in the intensity of the Atlantic meridional overturning circulation.
Including vegetation changes appears to improve the realism of
LGM simulations (Wyputta and McAvaney, 2001). Furthermore,
including the physiological effect of the atmospheric CO

concentration on vegetation has a non-negligible impact (Levis et
al., 1999) and is necessary to properly represent changes in global
forest (Harrison and Prentice, 2003) and terrestrial carbon storage
(e.g., Kaplan et al., 2002; Joos et al., 2004; see also Chapter 6).
To summarise, despite large uncertainties, LGM simulations
capture the broad features found in palaeoclimate data, and better
agreement is obtained with new coupled simulations using more
recent models and more complete feedbacks from ocean, sea
ice and land surface characteristics such as vegetation and soil
moisture (Chapter 6).

Closer to the present, during the mid-Holocene, one of the

most noticeable indications of climate change is the northward
extension of northern temperate forest (Bigelow et al., 2003),
which re

ects warmer summers than at present. In the tropics

the more vegetated conditions inferred from pollen records in
the now dry sub-Saharan regions indicate wetter conditions due
to enhanced summer monsoons (see Braconnot et al., 2004 for
a review). Simulations of the mid-Holocene with AOGCMs
(see Section 9.2.1.3 for forcing) produce an ampli

cation

of the mean seasonal cycle of temperature of approximately
0.5°C to 0.7°C. This range is slightly smaller than that obtained
using atmosphere-only models in PMIP1 (~0.5°C to ~1.2°C)
due to the thermal response of the ocean (Braconnot et al.,
2000). Simulated changes in the ocean circulation have strong
seasonal features with an ampli

cation of the sea surface

temperature (SST) seasonal cycle of 1°C to 2°C in most
places within the tropics (Zhao et al., 2005), in

uencing the

Indian and African monsoons. Over West Africa, AOGCM-
simulated changes in annual mean precipitation are about 5
to 10% larger than for atmosphere-only simulations, and in
better agreement with data reconstructions (Braconnot et al.,
2004). Results for the Indian and Southwest Asian monsoon
are less consistent between models.

As noted in the TAR (McAvaney et al., 2001), vegetation

change during the mid-Holocene likely triggered changes in the
hydrological cycle, explaining the wet conditions that prevailed
in the Sahel region that were further enhanced by ocean
feedbacks (Ganopolski et al., 1998; Braconnot et al., 1999),
although soil moisture may have counteracted some of these
feedbacks (Levis et al., 2004). Wohlfahrt et al. (2004) show that
at middle and high latitudes the vegetation and ocean feedbacks
enhanced the warming in spring and autumn by about 0.8°C.
However, models have a tendency to overestimate the mid-
continental drying in Eurasia, which is further ampli

ed when

vegetation feedbacks are included (Wohlfahrt et al., 2004).

A wide range of proxies containing information about ENSO

variability during the mid-Holocene is now also available
(Section 6.5.3). These data suggest that ENSO variability was
weaker than today prior to approximately 5 kyr before present
(Moy et al., 2002 and references therein; Tudhope and Collins,
2003). Several studies have attempted to analyse these changes

in interannual variability from model simulations. Even though
some results are controversial, a consistent picture has emerged
for the mid-Holocene, for which simulations produce reduced
variability in precipitation over most ocean regions in the
tropics (Liu et al., 2000; Braconnot et al., 2004; Zhao et al.,
2005). Results obtained with the Cane-Zebiak model suggest
that the Bjerknes (1969) feedback mechanism may be a key
element of the ENSO response in that model. The increased
mid-Holocene solar heating in boreal summer leads to more
warming in the western than in the eastern Paci

c, which

strengthens the trade winds and inhibits the development of
ENSO (Clement et al., 2000, 2004). Atmosphere-Ocean General
Circulation Models also tend to simulate less intense ENSO
events, in qualitative agreement with data, although there are
large differences in magnitude and proposed mechanisms, and
inconsistent responses of the associated teleconnections (Otto-
Bliesner, 1999; Liu et al., 2000; Kitoh and Murakami, 2002;
Otto-Bliesner et al., 2003).

9.3.3

What Can be Learned from the Past
1,000 Years?

External forcing relative to the present is generally small

for the last millennium when compared to that for the mid-
Holocene and LGM. Nonetheless, there is evidence that climatic
responses to forcing, together with natural internal variability
of the climate system, produced several well-de

ned climatic

events, such as the cool conditions during the 17th century or
relatively warm periods early in the millennium.

9.3.3.1

Evidence of External In

uence on the Climate

Over the Past 1,000 Years

A substantial number of proxy reconstructions of annual

or decadal NH mean surface temperature are now available
(see Figure 6.11, and the reviews by Jones et al., 2001 and
Jones and Mann, 2004). Several new reconstructions have
been published, some of which suggest larger variations over
the last millennium than assessed in the TAR, but uncertainty
remains in the magnitude of inter-decadal to inter-centennial
variability. This uncertainty arises because different studies
rely on different proxy data or use different reconstruction
methods (Section 6.6.1). Nonetheless, NH mean temperatures
in the second half of the 20th century were likely warmer than
in any other 50-year period in the last 1.3 kyr (Chapter 6), and
very likely warmer than any such period in the last 500 years.
Temperatures subsequently decreased, and then rose rapidly
during the most recent 100 years. This long-term tendency is
punctuated by substantial shorter-term variability (Figure 6.10).
For example, cooler conditions with temperatures 0.5°C to 1°C
below the 20th-century mean value are found in the 17th and
early 18th centuries.

A number of simulations of the last millennium (Figure 6.13)

have been performed using a range of models, including some
simulations with AOGCMs (e.g., Crowley, 2000; Goosse and
Renssen, 2001; Bertrand et al., 2002; Bauer et al., 2003; Gerber

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Chapter 9

Understanding and Attributing Climate Change

et al., 2003; see also Gonzalez-Rouco et al., 2003; Jones and
Mann, 2004; Zorita et al., 2004; Weber, 2005; Tett et al., 2007).
These simulations use different reconstructions of external
forcing, particularly solar, volcanic and greenhouse gas forcing,
and often include land use changes (e.g., Bertrand et al., 2002;
Stendel et al., 2006; Tett et al., 2007). While the use of different
models and forcing reconstructions leads to differences, the
simulated evolution of the NH annual mean surface temperature
displays some common characteristics between models that
are consistent with the broad features of the data (Figures 6.13
and 9.4). For example, all simulations show relatively cold
conditions during the period around 1675 to 1715 in response
to natural forcing, which is in qualitative agreement with the
proxy reconstructions. In all simulations shown in Figure
6.13, the late 20th century is warmer than any other multi-
decadal period during the last millennium. In addition, there
is signi

cant correlation between simulated and reconstructed

variability (e.g., Yoshimori et al., 2005). By comparing
simulated and observed atmospheric CO

concentration during

the last 1 kyr, Gerber et al. (2003) suggest that the amplitude
of the temperature evolution simulated by simple climate
models and EMICs is consistent with the observed evolution
of CO

. Since reconstructions of external forcing are virtually

independent from the reconstructions of past temperatures, this
broad consistency increases con

dence in the broad features of

the reconstructions and the understanding of the role of external
forcing in recent climate variability. The simulations also
show that it is not possible to reproduce the large 20th-century
warming without anthropogenic forcing regardless of which
solar or volcanic forcing reconstruction is used (Crowley, 2000;
Bertrand et al., 2002; Bauer et al., 2003; Hegerl et al., 2003,
2007), stressing the impact of human activity on the recent
warming.

While there is broad qualitative agreement between

simulated and reconstructed temperatures, it is dif

cult to fully

assess model-simulated variability because of uncertainty in
the magnitude of historical variations in the reconstructions and
differences in the sensitivity to external forcing (Table 8.2). The
role of internal variability has been found to be smaller than that
of the forced variability for hemispheric temperature means at
decadal or longer time scales (Crowley, 2000; Hegerl et al., 2003;
Goosse et al., 2004; Weber et al., 2004; Hegerl et al., 2007; Tett
et al., 2007), and thus internal variability is a relatively small
contributor to differences between different simulations of NH
mean temperature. Other sources of uncertainty in simulations
include model ocean initial conditions, which, for example,
explain the warm conditions found in the Zorita et al. (2004)
simulation during the

rst part of the millennium (Goosse et al.,

2005; Osborn et al., 2006).

9.3.3.2

Role of Volcanism and Solar Irradiance

Volcanic eruptions cause rapid decreases in hemispheric and

global mean temperatures followed by gradual recovery over
several years (Section 9.2.2.1) in climate simulations driven
by volcanic forcing (Figure 6.13; Crowley, 2000; Bertrand

et al., 2002; Weber, 2005; Yoshimori et al., 2005; Tett et al.,
2007). These simulated changes appear to correspond to
cool episodes in proxy reconstructions (Figure 6.13). This
suggestive correspondence has been con

rmed in comparisons

between composites of temperatures following multiple
volcanic eruptions in simulations and reconstructions (Hegerl
et al., 2003; Weber, 2005). In addition, changes in the frequency
of large eruptions result in climate variability on decadal and
possibly longer time scales (Crowley, 2000; Briffa et al., 2001;
Bertrand et al., 2002; Bauer et al., 2003; Weber, 2005). Hegerl
et al. (2003; 2007), using a multi-regression approach based
on Energy Balance Model (EBM) simulated

ngerprints of

solar, volcanic and greenhouse gas forcing (Appendix 9.A.1;
see also Section 9.4.1.4 for the 20th century), simultaneously
detect the responses to volcanic and greenhouse gas forcing
in a number of proxy reconstructions of average NH mean
annual and growing season temperatures (Figure 9.4) with high
signi

cance. They

nd that a high percentage of decadal variance

in the reconstructions used can be explained by external forcing
(between 49 and 70% of decadal variance depending upon the
reconstruction).

There is more uncertainty regarding the in

uence of solar

forcing. In addition to substantial uncertainty in the timing and
amplitude of solar variations on time scales of several decades
to centuries, which has increased since the TAR although the
estimate of solar forcing has been revised downwards (Sections
9.2.1.3 and 2.7.1), uncertainty also arises because the spatial
response of surface temperature to solar forcing resembles that
due to greenhouse gas forcing (Section 9.2.3). Analyses that
make use of differences in the temporal evolution of solar and
volcanic forcings are better able to distinguish between the two
(Section 9.2.3; see also Section 9.4.1.5 for the 20th century).
In such an analysis, solar forcing can only be detected and
distinguished from the effect of volcanic and greenhouse gas
forcing over some periods in some reconstructions (Hegerl et
al., 2003, 2007), although the effect of solar forcing has been
detected over parts of the 20th century in some time-space
analyses (Section 9.4.1.5) and there are similarities between
regressions of solar forcing on model simulations and several
proxy reconstructions (Weber, 2005; see also Waple, 2002).
A model simulation (Shindell et al., 2003) suggests that solar
forcing may play a substantial role in regional anomalies due to
dynamical feedbacks. These uncertainties in the contribution of
different forcings to climatic events during the last millennium
re

ect substantial uncertainty in knowledge about past solar

and volcanic forcing, as well as differences in the way these
effects are taken into account in model simulations.

Overall, modelling and detection and attribution studies

con

rm a role of volcanic, greenhouse gas and probably solar

forcing in explaining the broad temperature evolution of the last
millennium, although the role of solar forcing has recently been
questioned (Foukal et al., 2006). The variability that remains
in proxy reconstructions after estimates of the responses to
external forcing have been removed is broadly consistent with
AOGCM-simulated internal variability (e.g., Hegerl et al., 2003,
2007), providing a useful check on AOGCMs even though

682

Understanding and Attributing Climate Change

Chapter 9

uncertainties are large. Such studies also help to explain episodes
during the climate of the last millennium. For example, several
modelling studies suggest that volcanic activity has a dominant
role in explaining the cold conditions that prevailed from 1675
to 1715 (Andronova et al., 2007; Yoshimori et al., 2005). In
contrast, Rind et al. (2004) estimate from model simulations
that the cooling relative to today was primarily associated with
reduced greenhouse gas forcing, with a substantial contribution
from solar forcing.

There is also some evidence from proxy data that the response

to external forcing may in

uence modes of climate variability.

For example, Cobb et al. (2003), using fossil corals, attempt
to extend the ENSO record back through the last millennium.
They

nd that ENSO events may have been as frequent and

intense during the mid-17th century as during the instrumental
period, with events possibly rivalling the strong 1997–1998
event. On the other hand, there are periods during the 12th and
14th centuries when there may have been signi

cantly less

ENSO variability, a period during which there were also cooler
conditions in the northeast Paci

c (MacDonald and Case, 2005)

and evidence of droughts in central North America (Cook et al.,
2004). Cobb et al. (2003)

nd that

uctuations in reconstructed

ENSO variability do not appear to be correlated in an obvious
way with mean state changes in the tropical Paci

c or global

mean climate, while Adams et al. (2003)

nd statistical evidence

for an El Niño-like anomaly during the

rst few years following

explosive tropical volcanic eruptions. The Cane-Zebiak model
simulates changes similar to those in the Cobb et al. (2003) data
when volcanism and solar forcing are accounted for, supporting
the link with volcanic forcing over the past millennium (Mann
et al., 2005). However, additional studies with different models
are needed to fully assess this relationship, since previous work
was less conclusive (Robock, 2000).

Extratropical variability also appears to respond to volcanic

forcing. During the winter following a large volcanic eruption,
the zonal circulation may be more intense, causing a relative
warming over the continents during the cold season that could
partly offset the direct cooling due to the volcanic aerosols
(Sections 9.2.2.1 and 8.4.1; Robock, 2000; Shindell et al.,
2003). A tendency towards the negative NAO state during
periods of reduced solar input is found in some reconstructions
of this pattern for the NH (Shindell et al., 2001b; Luterbacher
et al., 2002, 2004; Stendel et al., 2006), possibly implying a
solar forcing role in some long-term regional changes, such
as the cooling over the NH continents around 1700 (Shindell
et al., 2001b; Section 9.2.2). Indications of changes in ENSO
variability during the low solar irradiance period of the 17th
to early 18th centuries are controversial (e.g., D’Arrigo et al.,
2005).

9.3.3.3

Other Forcings and Sources of Uncertainties

In addition to forcing uncertainties discussed above, a

number of other uncertainties affect the understanding of pre-
industrial climate change. For example, land cover change may
have in

uenced the pre-industrial climate (Bertrand et al., 2002;

Bauer et al., 2003), leading to a regional cooling of 1°C to 2°C
in winter and spring over the major agricultural regions of North
America and Eurasia in some model simulations, when pre-
agriculture vegetation was replaced by present-day vegetation
(Betts, 2001). The largest anthropogenic land cover changes
involve deforestation (Chapter 2). The greatest proportion of
deforestation has occurred in the temperate regions of the NH
(Ramankutty and Foley, 1999; Goldewijk, 2001). Europe had
cleared about 80% of its agricultural area by 1860, but over

Figure 9.4.

Contribution of external forcing to several high-variance

reconstructions of NH temperature anomalies, (Esper et al., 2002; Briffa et al., 2001;
Hegerl et al., 2007, termed CH-blend and CH-blend long; and Moberg et al., 2005).
The top panel compares reconstructions to an EBM simulation (equilibrium climate
sensitivity of 2.5°C) of NH 30°N to 90°N average temperature, forced with volcanic,
solar and anthropogenic forcing. All timeseries are centered on the 1500-1925
average. Instrumental temperature data are shown by a green line (centered to
agree with CH-blend average over the period 1880-1960). The displayed data are
low-pass fi ltered (20-year cutoff) for clarity. The bottom panel shows the estimated
contribution of the response to volcanic (blue lines with blue uncertainty shade), solar
(green) and greenhouse gas (GHG) and aerosol forcing (red line with yellow shades,
aerosol only in 20th century) to each reconstruction (all timeseries are centered
over the analysis period). The estimates are based on multiple regression of the
reconstructions on fi ngerprints for individual forcings. The contributions to different
reconstructions are indicated by different line styles (Briffa et al.: solid, fat; Esper et
al.: dotted; Moberg: dashed; CH-blend: solid, thin; with shaded 90% confi dence limits
around best estimates for each detectable signal). All reconstructions show a highly
signifi cant volcanic signal, and all but Moberg et al. (which ends in 1925) show a
detectable greenhouse gas signal at the 5% signifi cance level. The latter shows a
detectable greenhouse gas signal with less signifi cance. Only Moberg et al. contains
a detectable solar signal (only shown for these data and CH-blend, where it is not
detectable). All data are decadally averaged. The reconstructions represent slightly
different regions and seasons: Esper et al. (2002) is calibrated to 30°N to 90°N land
temperature, CH-blend and CH-blend long (Hegerl et al., 2007) to 30°N to 90°N
mean temperature and Moberg et al. (2005) to 0° to 90°N temperature. From Hegerl
et al. (2007).

683

Chapter 9

Understanding and Attributing Climate Change

half of the forest removal in North America took place after
1860 (Betts, 2001), mainly in the late 19th century (Stendel et
al., 2006). During the past two decades, the CO

ux caused by

land use changes has been dominated by tropical deforestation
(Section 7.3.2.1.2). Climate model simulations suggest that the
effect of land use change was likely small at hemispheric and
global scales, estimated variously as –0.02°C relative to natural
pre-agricultural vegetation (Betts, 2001), less than –0.1°C since
1700 (Stendel et al., 2006) and about –0.05°C over the 20th
century and too small to be detected statistically in observed
trends (Matthews et al., 2004). However, the latter authors did

nd a larger cooling effect since 1700 of between –0.06°C

and –0.22°C when they explored the sensitivity to different
representations of land cover change.

Oceanic processes and ocean-atmosphere interaction may

also have played a role in the climate evolution during the
last millennium (Delworth and Knutson, 2000; Weber et al.,
2004; van der Schrier and Barkmeijer, 2005). Climate models
generally simulate a weak to moderate increase in the intensity
of the oceanic meridional overturning circulation in response to
a decrease in solar irradiance (Cubasch et al., 1997; Goosse and
Renssen, 2004; Weber et al., 2004). A delayed response to natural
forcing due to the storage and transport of heat anomalies by the
deep ocean has been proposed to explain the warm Southern
Ocean around the 14th to 15th centuries (Goosse et al., 2004).

9.3.4 Summary

Considerable progress has been made since the TAR in

understanding the response of the climate system to external
forcings. Periods like the mid-Holocene and the LGM are
now used as benchmarks for climate models that are used
to simulate future climate (Chapter 6). While considerable
uncertainties remain in the climate reconstructions for these
periods, and in the boundary conditions used to force climate
models, comparisons between simulated and reconstructed
conditions in the LGM and mid-Holocene demonstrate that
models capture the broad features of changes in the temperature
and precipitation patterns. These studies have also increased
understanding of the roles of ocean and vegetation feedbacks in
determining the response to solar and greenhouse gas forcing.
Moreover, although proxy data on palaeoclimatic interannual
to multi-decadal variability during these periods remain very
uncertain, there is an increased appreciation that external
forcing may, in the past, have affected climatic variability such
as that associated with ENSO.

The understanding of climate variability and change, and

its causes during the past 1 kyr, has also improved since the
TAR (IPCC, 2001). There is consensus across all millennial
reconstructions on the timing of major climatic events, although
their magnitude remains somewhat uncertain. Nonetheless, the
collection of reconstructions from palaeodata, which is larger
and more closely scrutinised than that available for the TAR,
indicates that it is likely that NH average temperatures during
the second half of the 20th century were warmer than any
other 50-year period during the past 1.3 kyr (Chapter 6). While

uncertainties remain in temperature and forcing reconstructions,
and in the models used to estimate the responses to external
forcings, the available detection studies, modelling and other
evidence support the conclusion that volcanic and possibly
solar forcings have very likely affected NH mean temperature
over the past millennium and that external in

uences explain

a substantial fraction of inter-decadal temperature variability
in the past. The available evidence also indicates that natural
forcing may have in

uenced the climatic conditions of individual

periods, such as the cooler conditions around 1700. The climate
response to greenhouse gas increases can be detected in a range
of proxy reconstructions by the end of the records.

When driven with estimates of external forcing for the

last millennium, AOGCMs simulate changes in hemispheric
mean temperature that are in broad agreement with proxy
reconstructions (given their uncertainties), increasing
con

dence in the forcing reconstructions, proxy climate

reconstructions and models. In addition, the residual variability
in the proxy climate reconstructions that is not explained by
forcing is broadly consistent with AOGCM-simulated internal
variability. Overall, the information on temperature change over
the last millennium is broadly consistent with the understanding
of climate change in the instrumental era.

9.4

Understanding of Air Temperature
Change During the Industrial Era

9.4.1

Global-Scale Surface Temperature Change

9.4.1.1 Observed

Changes

Six additional years of observations since the TAR (Chapter

3) show that temperatures are continuing to warm near the
surface of the planet. The annual global mean temperature for
every year since the TAR has been among the 10 warmest years
since the beginning of the instrumental record. The global mean
temperature averaged over land and ocean surfaces warmed by
0.76°C ± 0.19°C between the

rst 50 years of the instrumental

record (1850–1899) and the last 5 years (2001–2005) (Chapter
3; with a linear warming trend of 0.74°C ± 0.18°C over the last
100 years (1906–2005)). The rate of warming over the last 50
years is almost double that over the last 100 years (0.13°C ±
0.03°C vs 0.07°C ± 0.02°C per decade; Chapter 3). The larger
number of proxy reconstructions from palaeodata than were
available for the TAR indicate that it is very likely that average
NH temperatures during the second half of the 20th century
were warmer than any other 50-year period in the last 500 years
and it is likely that this was the warmest period in the past 1.3
kyr (Chapter 6). Global mean temperature has not increased
smoothly since 1900 as would be expected if it were in

uenced

only by forcing from increasing greenhouse gas concentrations
(i.e., if natural variability and other forcings did not have a role;
see Section 9.2.1; Chapter 2). A rise in near-surface temperatures

684

Understanding and Attributing Climate Change

Chapter 9

also occurred over several decades during the

rst half of the

20th century, followed by a period of more than three decades
when temperatures showed no pronounced trend (Figure 3.6).
Since the mid-1970s, land regions have warmed at a faster rate
than oceans in both hemispheres (Figure 3.8) and warming over
the SH was smaller than that over the NH during this period
(Figure 3.6), while warming rates during the early 20th century
were similar over land and ocean.

9.4.1.2

Simulations of the 20th Century

There are now a greater number of climate simulations from

AOGCMs for the period of the global surface instrumental
record than were available for the TAR, including a greater
variety of forcings in a greater variety of combinations. These
simulations used models with different climate sensitivities,
rates of ocean heat uptake and magnitudes and types of forcings
(Supplementary Material, Table S9.1). Figure 9.5 shows that
simulations that incorporate anthropogenic forcings, including
increasing greenhouse gas concentrations and the effects of
aerosols, and that also incorporate natural external forcings
provide a consistent explanation of the observed temperature
record, whereas simulations that include only natural forcings do
not simulate the warming observed over the last three decades.
A variety of different forcings is used in these simulations. For
example, some anthropogenically forced simulations include
both the direct and indirect effects of sulphate aerosols whereas
others include just the direct effect, and the aerosol forcing that
is calculated within models differs due to differences in the
representation of physics. Similarly, the effects of tropospheric
and stratospheric ozone changes are included in some
simulations but not others, and a few simulations include the
effects of carbonaceous aerosols and land use changes, while the
naturally forced simulations include different representations
of changing solar and volcanic forcing. Despite this additional
uncertainty, there is a clear separation in Figure 9.5 between the
simulations with anthropogenic forcings and those without.

Global mean and hemispheric-scale temperatures on multi-

decadal time scales are largely controlled by external forcings
(Stott et al., 2000). This external control is demonstrated
by ensembles of model simulations with identical forcings
(whether anthropogenic or natural) whose members exhibit
very similar simulations of global mean temperature on multi-
decadal time scales (e.g., Stott et al., 2000; Broccoli et al., 2003;
Meehl et al., 2004). Larger interannual variations are seen in the
observations than in the ensemble mean model simulation of
the 20th century because the ensemble averaging process

lters

out much of the natural internal interannual variability that
is simulated by the models. The interannual variability in the
individual simulations that is evident in Figure 9.5 suggests that
current models generally simulate large-scale natural internal
variability quite well, and also capture the cooling associated
with volcanic eruptions on shorter time scales. Section 9.4.1.3
assesses the variability of near surface temperature observations
and simulations.

The fact that climate models are only able to reproduce

observed global mean temperature changes over the 20th century
when they include anthropogenic forcings, and that they fail to
do so when they exclude anthropogenic forcings, is evidence
for the in

uence of humans on global climate. Further evidence

is provided by spatial patterns of temperature change. Figure
9.6 compares observed near-surface temperature trends over the

Figure 9.5.

Comparison between global mean surface temperature anomalies (°C)

from observations (black) and AOGCM simulations forced with (a) both anthropogenic
and natural forcings and (b) natural forcings only. All data are shown as global
mean temperature anomalies relative to the period 1901 to 1950, as observed
(black, Hadley Centre/Climatic Research Unit gridded surface temperature data
set (HadCRUT3); Brohan et al., 2006) and, in (a) as obtained from 58 simulations
produced by 14 models with both anthropogenic and natural forcings. The multi-
model ensemble mean is shown as a thick red curve and individual simulations are
shown as thin yellow curves. Vertical grey lines indicate the timing of major volcanic
events. Those simulations that ended before 2005 were extended to 2005 by using
the fi rst few years of the IPCC Special Report on Emission Scenarios (SRES) A1B
scenario simulations that continued from the respective 20th-century simulations,
where available. The simulated global mean temperature anomalies in (b) are from
19 simulations produced by fi ve models with natural forcings only. The multi-model
ensemble mean is shown as a thick blue curve and individual simulations are shown
as thin blue curves. Simulations are selected that do not exhibit excessive drift in
their control simulations (no more than 0.2°C per century). Each simulation was
sampled so that coverage corresponds to that of the observations. Further details of
the models included and the methodology for producing this fi gure are given in the
Supplementary Material, Appendix 9.C. After Stott et al. (2006b).

685

Chapter 9

Understanding and Attributing Climate Change

globe (top row) with those simulated
by climate models when they
include anthropogenic and natural
forcing (second row) and the same
trends simulated by climate models
when only natural forcings are
included (third row). The observed
trend over the entire 20th century
(Figure 9.6, top left panel) shows
warming almost everywhere with
the exception of the southeastern
USA, northern North Atlantic, and
isolated grid boxes in Africa and
South America (see also Figure
3.9). Such a pattern of warming is
not associated with known modes
of internal climate variability.
For example, while El Niño or
El Niño-like decadal variability
results in unusually warm annual
temperatures, the spatial pattern
associated with such a warming is
more structured, with cooling in
the North Paci

c and South Paci

(see, e.g., Zhang et al., 1997).
In contrast, the trends in climate
model simulations that include
anthropogenic and natural forcing
(Figure 9.6, second row) show a
pattern of spatially near-uniform
warming similar to that observed.
There is much greater similarity
between the general evolution of
the warming in observations and
that simulated by models when
anthropogenic and natural forcings
are included than when only natural
forcing is included (Figure 9.6,
third row). Figure 9.6 (fourth row)
shows that climate models are only
able to reproduce the observed
patterns of zonal mean near-surface
temperature trends over the 1901
to 2005 and 1979 to 2005 periods
when they include anthropogenic
forcings and fail to do so when they
exclude anthropogenic forcings.
Although there is less warming at
low latitudes than at high northern
latitudes, there is also less internal
variability at low latitudes, which results in a greater separation
of the climate simulations with and without anthropogenic
forcings.

Climate simulations are consistent in showing that the global

mean warming observed since 1970 can only be reproduced
when models are forced with combinations of external forcings

that include anthropogenic forcings (Figure 9.5). This conclusion
holds despite a variety of different anthropogenic forcings and
processes being included in these models (e.g., Tett et al., 2002;
Broccoli et al., 2003; Meehl et al., 2004; Knutson et al., 2006).
In all cases, the response to forcing from well-mixed greenhouse
gases dominates the anthropogenic warming in the model. No

Figure 9.6.

Trends in observed and simulated temperature changes (°C) over the 1901 to 2005 (left column) and 1979

to 2005 (right column) periods. First row:

trends in observed temperature changes (Hadley Centre/Climatic Research

Unit gridded surface temperature data set (HadCRUT3), Brohan et al., 2006). Second row: average trends in 58 historical
simulations from 14 climate models including both anthropogenic and natural forcings. Third row: average trends in 19
historical simulations from fi ve climate models including natural forcings only. Grey shading in top three rows indicates
regions where there are insuffi cient observed data to calculate a trend for that grid box (see Supplementary Material,
Appendix 9.C for further details of data exclusion criteria). Fourth row: average trends for each latitude; observed
trends are indicated by solid black curves. Red shading indicates the middle 90% range of trend estimates from the
58 simulations including both anthropogenic and natural forcings (estimated as the range between 4th and 55th of the
58 ranked simulations); blue shading indicates the middle 90% range of trend estimates from the 19 simulations with
natural forcings only (estimated as the range between 2nd and 18th of the 19 ranked simulations); for comparison, the
dotted black curve in the right-hand plot shows the observed 1901 to 2005 trend. Note that scales are different between
columns. The ‘ALL’ simulations were extended to 2005 by adding their IPCC Special Report on Emission Scenarios
(SRES) A1B continuation runs where available. Where not available, and in the case of the ‘NAT’ simulations, the mean
for the 1996 to 2005 decade was estimated using model output from 1996 to the end of the available runs. In all plots,
each climate simulation was sampled so that coverage corresponds to that of the observations. Further details of the
models included and the methodology for producing this fi gure are given in the Supplementary Material, Appendix 9.C.

686

Understanding and Attributing Climate Change

Chapter 9

climate model using natural forcings alone has reproduced the
observed global warming trend in the second half of the 20th
century. Therefore, modelling studies suggest that late 20th-
century warming is much more likely to be anthropogenic than
natural in origin, a

nding which is con

rmed by studies relying

on formal detection and attribution methods (Section 9.4.1.4).

Modelling studies are also in moderately good agreement

with observations during the

rst half of the 20th century

when both anthropogenic and natural forcings are considered,
although assessments of which forcings are important differ,
with some studies

nding that solar forcing is more important

(Meehl et al., 2004) while other studies

nd that volcanic forcing

(Broccoli et al., 2003) or internal variability (Delworth and
Knutson, 2000) could be more important. Differences between
simulations including greenhouse gas forcing only and those
that also include the cooling effects of sulphate aerosols (e.g.,
Tett et al., 2002) indicate that the cooling effects of sulphate
aerosols may account for some of the lack of observational
warming between 1950 and 1970, despite increasing greenhouse
gas concentrations, as was proposed by Schwartz (1993).
In contrast, Nagashima et al. (2006)

nd that carbonaceous

aerosols are required for the MIROC model (see Table 8.1 for a
description) to provide a statistically consistent representation
of observed changes in near-surface temperature in the middle
part of the 20th century. The mid-century cooling that the
model simulates in some regions is also observed, and is caused
in the model by regional negative surface forcing from organic
and black carbon associated with biomass burning. Variations
in the Atlantic Multi-decadal Oscillation (see Section 3.6.6
for a more detailed discussion) could account for some of the
evolution of global and hemispheric mean temperatures during
the instrumental period (Schlesinger and Ramankutty, 1994;
Andronova and Schlesinger, 2000; Delworth and Mann, 2000);
Knight et al. (2005) estimate that variations in the Atlantic
Multi-decadal Oscillation could account for up to 0.2°C peak-
to-trough variability in NH mean decadal temperatures.

9.4.1.3

Variability of Temperature from Observations
and Models

Year-to-year variability of global mean temperatures

simulated by the most recent models compares reasonably well
with that of observations, as can be seen by comparing observed
and modelled variations in Figure 9.5a. A more quantitative
evaluation of modelled variability can be carried out by
comparing the power spectra of observed and modelled global
mean temperatures. Figure 9.7 compares the power spectrum
of observations with the power spectra of transient simulations
of the instrumental period. This avoids the need to compare
variability estimated from long control runs of models with
observed variability, which is dif

cult because observations

are likely to contain a response to external forcings that cannot
be reliably removed by subtracting a simple linear trend. The
simulations considered contain both anthropogenic and natural
forcings, and include most 20th Century Climate in Coupled
Models (20C3M) simulations in the MMD at PCMDI. Figure

9.7 shows that the models have variance at global scales that
is consistent with the observed variance at the 5% signi

cance

level on the decadal to inter-decadal time scales important
for detection and attribution. Figure 9.8 shows that this is
also generally the case at continental scales, although model
uncertainty is larger at smaller scales (Section 9.4.2.2).

Detection and attribution studies routinely assess if the

residual variability unexplained by forcing is consistent
with the estimate of internal variability (e.g., Allen and Tett,
1999; Tett et al., 1999; Stott et al., 2001; Zwiers and Zhang,
2003). Furthermore, there is no evidence that the variability in
palaeoclimatic reconstructions that is not explained by forcing
is stronger than that in models, and simulations of the last 1 kyr
show similar variability to reconstructions (Section 9.3.3.2).
Chapter 8 discusses the simulation of major modes of variability
and the extent to which they are simulated by models (including
on decadal to inter-decadal time scales).

Figure 9.7.

Comparison of variability as a function of time scale of annual global

mean temperatures (°C

–1

) from the observed record (Hadley Centre/Climatic

Research Unit gridded surface temperature data set (HadCRUT3), Brohan et al.,
2006) and from AOGCM simulations including both anthropogenic and natural
forcings. All power spectra are estimated using a Tukey-Hanning fi lter of width
97 years. The model spectra displayed are the averages of the individual spectra
estimated from individual ensemble members. The same 58 simulations and 14
models are used as in Figure 9.5a. All models simulate variability on decadal time
scales and longer that is consistent with observations at the 10% signifi cance
level. Further details of the method of calculating the spectra are given in the
Supplementary Material, Appendix 9.C.

688

Understanding and Attributing Climate Change

Chapter 9

Figure 9.9.

Estimated contribution from greenhouse gas (red), other anthropogenic

(green) and natural (blue) components to observed global mean surface temperature
changes, based on ‘optimal’ detection analyses (Appendix 9.A). (a) 5 to 95%
uncertainty limits on scaling factors (dimensionless) based on an analysis over
the 20th century, (b) the estimated contribution of forced changes to temperature
changes over the 20th century, expressed as the difference between 1990 to 1999
mean temperature and 1900 to 1909 mean temperature (°C) and (c) estimated
contribution to temperature trends over 1950 to 1999 (°C per 50 years). The
horizontal black lines in (b) and (c) show the observed temperature changes from
the Hadley Centre/Climatic Research Unit gridded surface temperature data set
(HadCRUT2v; Parker et al., 2004). The results of full space-time optimal detection
analyses (Nozawa et al., 2005; Stott et al., 2006c) using a total least squares
algorithm (Allen and Stott, 2003) from ensembles of simulations containing each
set of forcings separately are shown for four models, MIROC3.2(medres), PCM,
UKMO-HadCM3 and GFDL-R30. Also shown, labelled ‘EIV’, is an optimal detection
analysis using the combined spatio-temporal patterns of response from three models
(PCM, UKMO-HadCM3 and GFDL-R30) for each of the three forcings separately, thus
incorporating inter-model uncertainty (Huntingford et al., 2006).

with a signi

cant net cooling from other anthropogenic forcings

over that period, dominated by aerosols. Stott et al. (2006c)
compare results over the 20th century obtained using the
UKMO-HadCM3, PCM (see Table 8.1 for model descriptions)
and Geophysical Fluid Dynamics Laboratory (GFDL) R30
models. They

nd consistent estimates for the greenhouse

gas attributable warming over the century, expressed as the

difference between temperatures in the last and

rst decades of

the century, of 0.6°C to 1.3°C

(5 to 95%) offset by cooling from

other anthropogenic factors associated mainly with cooling
from aerosols of 0.1°C to 0.7°C and a small net contribution
from natural factors over the century of –0.1°C to 0.1°C (Figure
9.9b). Scaling factors for the model response to three forcings
are shown in Figure 9.9a. A similar analysis for the MIROC3.2
model (see Table 8.1 for a description)

nds a somewhat larger

warming contribution from greenhouse gases of 1.2°C to 1.5°C
offset by a cooling of 0.6°C to 0.8°C from other anthropogenic
factors and a very small net natural contribution (Figure 9.9b).
In all cases, the

fth percentile of the warming attributable to

greenhouse gases is greater than the observed warming over the
last 50 years of the 20th century (Figure 9.9c).

The detection and estimation of a greenhouse gas signal is

also robust to accounting more fully for model uncertainty. An
analysis that combines results from three climate models and
thereby incorporates uncertainty in the response of these three
models (by including an estimate of the inter-model covariance
structure in the regression method; Huntingford et al., 2006),
supports the results from each of the models individually that
it is likely that greenhouse gases would have caused more
warming than was observed over the 1950 to 1999 period
(Figure 9.9, results labelled ‘EIV’). These results are consistent
with the results of an earlier analysis, which calculated the
mean response patterns from

ve models and included a simpler

estimate of model uncertainty (obtained by a simple rescaling
of the variability estimated from a long control run, thereby
assuming that inter-model uncertainty has the same covariance
structure as internal variability; Gillett et al., 2002c). Both the
results of Gillett et al. (2002c) and Huntingford et al. (2006)
indicate that inter-model differences do not greatly increase
detection and attribution uncertainties and that averaging

ngerprints improves detection results.

A robust anthropogenic signal is also found in a wide range

of climate models that do not have the full range of simulations
required to directly estimate the responses to individual forcings
required for the full multi-signal detection and attribution
analyses (Stone et al., 2007a,b). In these cases, an estimate
of the model’s pattern of response to each individual forcing
can be diagnosed by

tting a series of EBMs, one for each

forcing, to the mean coupled model response to all the forcings
to diagnose the time-dependent response in the global mean
for each individual forcing. The magnitude of these time-only
signals can then be inferred from observations using detection
methods (Stone et al., 2007a,b). When applied to 13 different
climate models that had transient simulations of 1901 to 2005
temperature change, Stone et al. (2007a)

nd a robust detection

across the models of greenhouse gas warming over this period,
although uncertainties in attributable temperature changes due
to the different forcings are larger than when considering spatio-
temporal patterns. By tuning an EBM to the observations, and
using an AOGCM solely to estimate internal variability, Stone
and Allen (2005b) detect the effects of greenhouse gases and
tropospheric sulphate aerosols in the observed 1900 to 2004
record, but not the effects of volcanic and solar forcing.

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The detection of an anthropogenic signal is also robust to

using different methods. For example, Bayesian detection
analyses (Appendix 9.A.2) robustly detect anthropogenic
in

uence on near-surface temperature changes (Smith et

al., 2003; Schnur and Hasselmann, 2005; Min and Hense,
2006a,b). In these studies, Bayes Factors (ratios of posterior to
prior odds) are used to assess evidence supporting competing
hypotheses (Kass and Raftery, 1995; see Appendix 9.A.2).
A Bayesian analysis of seven climate models (Schnur and
Hasselman, 2005) and Bayesian analyses of MMD 20C3M
simulations (Min and Hense, 2006a,b)

nd decisive evidence

for the in

uence of anthropogenic forcings. Lee et al. (2005),

using an approach suggested by Berliner et al. (2000), evaluate
the evidence for the presence of the combined greenhouse gas
and sulphate aerosol (GS) signal, estimated from CGCM1 and
CGCM2 (Table 8.1; McAvaney et al., 2001), in observations for
several

ve-decade windows, beginning with 1900 to 1949 and

ending with 1950 to 1999. Very strong evidence was found in
support of detection of the forced response during both halves
of the 20th century regardless of the choice of prior distribution.
However, evidence for attribution in that approach is based on
the extent to which observed data narrow the prior uncertainty
on the size of the anthropogenic signal. That evidence was not
found to be very strong, although Lee et al. (2005) estimate that

strong evidence for attribution as de

ned in their approach may

emerge within the next two decades as the anthropogenic signal
strengthens.

In a further study, Lee et al. (2006) assess whether

anthropogenic forcing has enhanced the predictability of
decadal global-scale temperature changes; a forcing-related
enhancement in predictability would give a further indication
of its role in the evolution of the 20th-century climate. Using an
ensemble of simulations of the 20th century with GS forcing,
they use Bayesian tools similar to those of Lee et al. (2005)
to produce, for each decade beginning with 1930 to 1939, a
forecast of the probability of above-normal temperatures where
‘normal’ is de

ned as the mean temperature of the preceding

three decades. These hindcasts become skilful during the last
two decades of the 20th century as indicated both by their Brier
skill scores, a standard measure of the skill of probabilistic
forecasts, and by the con

dence bounds on hindcasts of global

mean temperature anomalies (Figure 9.10). This indicates that
greenhouse gas forcing contributes to predictability of decadal
temperature changes during the latter part of the 20th century.

Another type of analysis is a Granger causality analysis

of the lagged covariance structure of observed hemispheric
temperatures (Kaufmann and Stern, 2002), which also
provides evidence for an anthropogenic signal, although such

Figure 9.10.

Observed and hindcast decadal mean surface temperature anomalies (°C) expressed, for each decade, relative to the preceding three decades. Observed

anomalies are represented by horizontal black lines. Hindcast decadal anomalies and their uncertainties (5 to 95% confi dence bounds) are displayed as vertical bars. Hindcasts
are based on a Bayesian detection analysis using the estimated response to historical external forcing. Hindcasts made with CGCM2, HadCM2 (see Table 8.1 of the TAR) and
HadCM3 (see Table 8.1, this report) use the estimated response to anthropogenic forcing only (left hand column of legend) while those made with selected MMD 20C3M
models used anthropogenic and natural forcings (centre column of legend; see Table 8.1 for model descriptions). Hindcasts made with the ensemble mean of the selected
20C3M models are indicated by the thick green line. A hindcast based on persisting anomalies from the previous decade is also shown. The hindcasts agree well with
observations from the 1950s onward. Hindcasts for the decades of the 1930s and 1940s are sensitive to the details of the hindcast procedure. A forecast for the decadal global
mean anomaly for the decade 2000 to 2009, relative to the 1970 to 1999 climatology, based on simulations performed with the Canadian Centre for Climate Modelling and
Analysis Coupled Global Climate Model (CGCM2) is also displayed. From Lee et al. (2006).

690

Understanding and Attributing Climate Change

Chapter 9

evidence may not be conclusive on its own without additional
information from climate models (Triacca, 2001). Consistently,
a neural network model is unable to reconstruct the observed
global temperature record from 1860 to 2000 if anthropogenic
forcings are not taken into account (Pasini et al., 2006). Further,
an assessment of recent climate change relative to the long-
term persistence of NH mean temperature as diagnosed from a
range of reconstructed temperature records (Rybski et al., 2006)
suggests that the recent warming cannot be explained solely in
terms of natural factors, regardless of the reconstruction used.
Similarly, Fomby and Vogelsang (2002), using a test of trend
that accounts for the effects of serial correlation,

nd that the

increase in global mean temperature over the 20th century is
statistically signi

cant even if it is assumed that natural climate

variability has strong serial correlation.

9.4.1.5 The

uence of Other Anthropogenic and

Natural Forcings

A signi

cant cooling due to other anthropogenic factors,

dominated by aerosols, is a robust feature of a wide range of
detection analyses. These analyses indicate that it is likely
that greenhouse gases alone would have caused more than the
observed warming over the last 50 years of the 20th century,
with some warming offset by cooling from natural and other
anthropogenic factors, notably aerosols, which have a very
short residence time in the atmosphere relative to that of well-
mixed greenhouse gases (Schwartz, 1993). A key factor in
identifying the aerosol

ngerprint, and therefore the amount

of aerosol cooling counteracting greenhouse warming, is the
change through time of the hemispheric temperature contrast,
which is affected by the different evolution of aerosol forcing
in the two hemispheres as well as the greater thermal inertia of
the larger ocean area in the SH (Santer et al., 1996b,c; Hegerl
et al., 2001; Stott et al., 2006c). Regional and seasonal aspects
of the temperature response may help to distinguish further
the response to greenhouse gas increases from the response
to aerosols (e.g., Ramanathan et al., 2005; Nagashima et al.,
2006).

Results on the importance and contribution from

anthropogenic forcings other than greenhouse gases vary more
between different approaches. For example, Bayesian analyses
differ in the strength of evidence they

nd for an aerosol effect.

Schnur and Hasselman (2005), for example, fail to

nd decisive

evidence for the in

uence of aerosols. They postulate that

this could be due to taking account of modelling uncertainty
in the response to aerosols. However, two other studies using
frequentist methods that also include modelling uncertainty

nd a clear detection of sulphate aerosols, suggesting that

the use of multiple models helps to reduce uncertainties and
improves detection of a sulphate aerosol effect (Gillett et al.,
2002c; Huntingford et al., 2006). Similarly, a Bayesian study
of hemispheric mean temperatures from 1900 to 1996

nds

decisive evidence for an aerosol cooling effect (Smith et al.,
2003). Differences in the separate detection of sulphate aerosol
in

uences in multi-signal approaches can also re

ect differences

in the diagnostics applied (e.g., the space-time analysis of Tett et
al. (1999) versus the space-only analysis of Hegerl et al. (1997,
2000)) as was shown by Gillett et al. (2002a).

Recent estimates (Figure 9.9) indicate a relatively small

combined effect of natural forcings on the global mean
temperature evolution of the second half of the 20th century,
with a small net cooling from the combined effects of solar
and volcanic forcings. Coupled models simulate much less
warming over the 20th century in response to solar forcing
alone than to greenhouse gas forcing (Cubasch et al., 1997;
Broccoli et al., 2003; Meehl et al., 2004), independent of which
solar forcing reconstruction is used (Chapter 2). Several studies
have attempted to estimate the individual contributions from
solar and volcanic forcings separately, thus allowing for the
possibility of enhancement of the solar response in observations
due to processes not represented in models. Optimal detection
studies that attempt to separate the responses to solar and other
forcings in observations can also account for gross errors in the
overall magnitude of past solar forcing, which remains uncertain
(Chapter 2), by scaling the space-time patterns of response
(Section 9.2.2.1). Using such a method, Tett et al. (1999)
estimate that the net anthropogenic warming in the second half
of the 20th century was much greater than any possible solar
warming, even when using the solar forcing reconstruction
by Hoyt and Schatten (1993), which indicates larger solar
forcing and a different evolution over time than more recent
reconstructions (Section 2.7.1). However, Stott et al. (2003b),
using the same solar reconstruction but a different model, are
not able to completely rule out the possibility that solar forcing
might have caused more warming than greenhouse gas forcing
over the 20th century due to dif

culties in distinguishing

between the patterns of response to solar and greenhouse forcing.
This was not the case when using the response to solar forcing
based on the alternative reconstruction of Lean et al. (1995), in
which case they

nd a very small likelihood (less than 1%, as

opposed to approximately 10%) that solar warming could be
greater than greenhouse warming since 1950. Note that recent
solar forcing reconstructions show a substantially decreased
magnitude of low-frequency variations in solar forcing (Section
2.7.1) compared to Lean et al. (1995) and particularly Hoyt and
Schatten (1993).

The conclusion that greenhouse warming dominates

over solar warming is supported further by a detection and
attribution analysis using 13 models from the MMD at PCMDI
(Stone et al., 2007a) and an analysis of the National Center for
Atmospheric Research (NCAR) Community Climate System
Model (CCSM1.4; Stone et al., 2007b). In both these analyses,
the response to solar forcing in the model was inferred by

tting

a series of EBMs to the mean coupled model response to the
combined effects of anthropogenic and natural forcings. In
addition, a combined analysis of the response at the surface and
through the depth of the atmosphere using HadCM3 and the
solar reconstruction of Lean et al. (1995) concluded that the near-
surface temperature response to solar forcing over 1960 to 1999
is much smaller than the response to greenhouse gases (Jones
et al., 2003). This conclusion is also supported by the vertical

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pattern of climate change, which is more consistent with the
response to greenhouse gas than to solar forcing (Figure 9.1).
Further evidence against a dominant solar role arises from older
analyses targeted at detecting the solar response (e.g., North and
Stevens, 1998). Based on these detection results, which allow
for possible ampli

cation of the solar in

uence by processes

not represented in climate models, we conclude that it is very
likely that greenhouse gases caused more global warming over
the last 50 years than changes in solar irradiance.

Detection and attribution as well as modelling studies

indicate more uncertainty regarding the causes of early 20th-
century warming than the recent warming. A number of studies
detect a signi

cant natural contribution to early 20th-century

warming (Tett et al., 2002; Stott et al., 2003b; Nozawa et al.,
2005; Shiogama et al., 2006). Some studies

nd a greater role

for solar forcing than other forcings before 1950 (Stott et al.,
2003b), although one detection study

nds a roughly equal

role for solar and volcanic forcing (Shiogama et al., 2006), and
others

nd that volcanic forcing (Hegerl et al., 2003, 2007) or a

substantial contribution from natural internal variability (Tett et
al., 2002; Hegerl et al., 2007) could be important. There could
also be an early expression of greenhouse warming in the early
20th century (Tett et al., 2002; Hegerl et al., 2003, 2007).

9.4.1.6

Implications for Transient Climate Response

Quanti

cation of the likely contributions of greenhouse

gases and other forcing factors to past temperature change
(Section 9.4.1.4) in turn provides observational constraints on
the transient climate response, which determines the rapidity
and strength of a global temperature response to external forcing
(see Glossary and Sections 9.6.2.3 and 8.6.2.1 for detailed
de

nitions) and therefore helps to constrain likely future rates

of warming. Scaling factors derived from detection analyses
can be used to scale predictions of future change by assuming
that the fractional error in model predictions of global mean
temperature change is constant (Allen et al., 2000, 2002; Allen
and Stainforth, 2002; Stott and Kettleborough, 2002). This
linear relationship between past and future fractional error in
temperature change has been found to be suf

ciently robust

over a number of realistic forcing scenarios to introduce little
additional uncertainty (Kettleborough et al., 2007). In this
approach based on detection and attribution methods, which
is compared with other approaches for producing probabilistic
projections in Section 10.5.4.5, different scaling factors are
applied to the greenhouse gases and to the response to other
anthropogenic forcings (notably aerosols); these separate
scaling factors are used to account for possible errors in
the models and aerosol forcing. Uncertainties calculated
in this way are likely to be more reliable than uncertainty
ranges derived from simulations by coupled AOGCMs that
happen to be available. Such ensembles could provide a
misleading estimate of forecast uncertainty because they do
not systematically explore modelling uncertainty (Allen et
al., 2002; Allen and Stainforth, 2002). Stott et al. (2006c)
compare observationally constrained predictions from three

coupled climate models with a range of sensitivities and show
that predictions made in this way are relatively insensitive
to the particular choice of model used to produce them.
The robustness to choice of model of such observationally
constrained predictions was also demonstrated by Stone
et al. (2007a) for the MMD ensemble. The observationally
constrained transient climate response at the time of doubling
of atmospheric CO

following a 1% per year increase in CO

was estimated by Stott et al. (2006c) to lie between 1.5°C and
2.8°C (Section 9.6.2, Figure 9.21). Such approaches have also
been used to provide observationally constrained predictions
of global mean (Stott and Kettleborough, 2002; Stone et al.,
2007a) and continental-scale temperatures (Stott et al., 2006a)
following the IPCC Special Report on Emission Scenarios
(SRES) emissions scenarios, and these are discussed in
Sections 10.5.4.5 and 11.10.

9.4.1.7

Studies of Indices of Temperature Change

Another method for identifying

ngerprints of climate

change in the observational record is to use simple indices
of surface air temperature patterns that re

ect features of the

anticipated response to anthropogenic forcing (Karoly and
Braganza, 2001; Braganza et al., 2003). By comparing modelled
and observed changes in such indices, which include the global
mean surface temperature, the land-ocean temperature contrast,
the temperature contrast between the NH and SH, the mean
magnitude of the annual cycle in temperature over land and the
mean meridional temperature gradient in the NH mid-latitudes,
Braganza et al. (2004) estimate that anthropogenic forcing
accounts for almost all of the warming observed between 1946
and 1995 whereas warming between 1896 and 1945 is explained
by a combination of anthropogenic and natural forcing and
internal variability. These results are consistent with the results
from studies using space-time detection techniques (Section
9.4.1.4).

Diurnal temperature range (DTR) has decreased over

land by about 0.4°C over the last 50 years, with most of
that change occurring prior to 1980 (Section 3.2.2.1). This
decreasing trend has been shown to be outside the range of
natural internal variability estimated from models. Hansen et
al. (1995) demonstrate that tropospheric aerosols plus increases
in continental cloud cover, possibly associated with aerosols,
could account for the observed decrease in DTR. However,
although models simulate a decrease in DTR when they include
anthropogenic changes in greenhouse gases and aerosols, the
observed decrease is larger than the model-simulated decrease
(Stone and Weaver, 2002, 2003; Braganza et al., 2004). This
discrepancy is associated with simulated increases in daily
maximum temperature being larger than observed, and could
be associated with simulated increases in cloud cover being
smaller than observed (Braganza et al., 2004; see Section
3.4.3.1 for observations), a result supported by other analyses
(Dai et al., 1999; Stone and Weaver, 2002, 2003).

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Understanding and Attributing Climate Change

Chapter 9

9.4.1.8 Remaining

Uncertainties

A much larger range of forcing combinations and climate

model simulations has been analysed in detection studies than
was available for the TAR (Supplementary Material, Table
S9.1). Detection and attribution analyses show robust evidence
for an anthropogenic in

uence on climate. However, some

forcings are still omitted by many models and uncertainties
remain in the treatment of those forcings that are included by
the majority of models.

Most studies omit two forcings that could have signi

cant

effects, particularly at regional scales, namely carbonaceous
aerosols and land use changes. However, detection and
attribution analyses based on climate simulations that include
these forcings, (e.g., Stott et al., 2006b), continue to detect a
signi

cant anthropogenic in

uence in 20th-century temperature

observations even though the near-surface patterns of response
to black carbon aerosols and sulphate aerosols could be so
similar at large spatial scales (although opposite in sign) that
detection analyses may be unable to distinguish between them
(Jones et al., 2005). Forcing from surface albedo changes due
to land use change is expected to be negative globally (Sections
2.5.3, 7.3.3 and 9.3.3.3) although tropical deforestation could
increase evaporation and warm the climate (Section 2.5.5),
counteracting cooling from albedo change. However, the
albedo-induced cooling effect is expected to be small and was
not detected in observed trends in the study by Matthews et al.
(2004).

For those forcings that have been included in attribution

analyses, uncertainties associated with the temporal and spatial
pattern of the forcing and the modelled response can affect the
results. Large uncertainties associated with estimates of past
solar forcing (Section 2.7.1) and omission of some chemical
and dynamical response mechanisms (Gray et al., 2005) make
it dif

cult to reliably estimate the contribution of solar forcing

to warming over the 20th century. Nevertheless, as discussed
above, results generally indicate that the contribution is small
even if allowance is made for ampli

cation of the response in

observations, and simulations used in attribution analyses use
several different estimates of solar forcing changes over the
20th century (Supplementary Material, Table S9.1). A number of
different volcanic reconstructions are included in the modelling
studies described in Section 9.4.1.2 (e.g., Sato et al., 1993;
Andronova et al., 1999; Ammann et al., 2003; Supplementary
Material, Table S9.1). Some models include volcanic effects by
simply perturbing the incoming shortwave radiation at the top
of the atmosphere, while others simulate explicitly the radiative
effects of the aerosols in the stratosphere. In addition, some
models include the indirect effects of tropospheric sulphate
aerosols on clouds (e.g., Tett et al., 2002), whereas others
consider only the direct radiative effect (e.g., Meehl et al., 2004).
In models that include indirect effects, different treatments of the
indirect effect are used, including changing the albedo of clouds
according to an off-line calculation (e.g., Tett et al., 2002) and
a fully interactive treatment of the effects of aerosols on clouds
(e.g., Stott et al., 2006b). The overall level of consistency between

attribution results derived from different models (as shown in
Figure 9.9), and the ability of climate models to simulate large-
scale temperature changes during the 20th century (Figures 9.5
and 9.6), indicate that such model differences are likely to have
a relatively small impact on attribution results of large-scale
temperature change at the surface.

There have also been methodological developments that

have resulted in attribution analyses taking uncertainties more
fully into account. Attribution analyses normally directly
account for errors in the magnitude of the model’s pattern of
response to different forcings by the inclusion of factors that
scale the model responses up or down to best match observed
climate changes. These scaling factors compensate for under-
or overestimates of the amplitude of the model response to
forcing that may result from factors such as errors in the model’s
climate sensitivity, ocean heat uptake ef

ciency or errors in the

imposed external forcing. Older analyses (e.g., Tett et al., 2002)
did not take account of uncertainty due to sampling signal
estimates from

nite-member ensembles. This can lead to a

low bias, particularly for weak forcings, in the scaling factor
estimates (Appendix 9.A.1; Allen and Stott, 2003; Stott et al.,
2003a). However, taking account of sampling uncertainty (as
most more recent detection and attribution studies do, including
those shown in Figure 9.9) makes relatively little difference to
estimates of attributable warming rates, particularly those due
to greenhouse gases; the largest differences occur in estimates
of upper bounds for small signals, such as the response to solar
forcing (Allen and Stott, 2003; Stott et al., 2003a). Studies
that compare results between models and analysis techniques
(e.g., Hegerl et al., 2000; Gillett et al., 2002a; Hegerl and
Allen, 2002), and more recently, that use multiple models to
determine

ngerprints of climate change (Gillett et al., 2002c;

Huntingford et al., 2006; Stott et al., 2006c; Zhang et al., 2006)

nd a robust detection of an anthropogenic signal in past

temperature change.

A common aspect of detection analyses is that they

assume the response in models to combinations of forcings
to be additive. This was shown to be the case for near-surface
temperatures in the PCM (Meehl et al., 2004), in the Hadley
Centre Climate Model version 2 (HadCM2; Gillett et al., 2004c)
and in the GFDL CM2.1 (see Table 8.1) model (Knutson et al.,
2006), although none of these studies considered the indirect
effects of sulphate aerosols. Sexton et al. (2003) did

nd some

evidence for a nonlinear interaction between the effects of
greenhouse gases and the indirect effect of sulphate aerosols in
the atmosphere-only version of HadCM3 forced by observed
SSTs; the additional effect of combining greenhouse gases and
indirect aerosol effects together was much smaller than each
term separately but was found to be comparable to the warming
due to increasing tropospheric ozone. In addition, Meehl et al.
(2003) found that additivity does not hold so well for regional
responses to solar and greenhouse forcing in the PCM. Linear
additivity was found to hold in the PCM model for changes
in tropopause height and synthetic satellite-borne Microwave
Sounding Unit (MSU) temperatures (Christy et al., 2000; Mears
et al., 2003; Santer et al., 2003b).

693

Chapter 9

Understanding and Attributing Climate Change

A further source of uncertainty derives from the estimates

of internal variability that are required for all detection
analyses. These estimates are generally model-based because
of dif

culties in obtaining reliable internal variability estimates

from the observational record on the spatial and temporal scales
considered in detection studies. However, models would need
to underestimate variability by factors of over two in their
standard deviation to nullify detection of greenhouse gases in
near-surface temperature data (Tett et al., 2002), which appears
unlikely given the quality of agreement between models and
observations at global and continental scales (Figures 9.7 and
9.8) and agreement with inferences on temperature variability
from NH temperature reconstructions of the last millennium.
The detection of the effects of other forcings, including aerosols,
is likely to be more sensitive (e.g., an increase of 40% in the
estimate of internal variability is enough to nullify detection of
aerosol and natural forcings in HadCM3; Tett et al., 2002)

Few detection studies have explicitly considered the in

uence

of observational uncertainty on near-surface temperature
changes. However, Hegerl et al. (2001) show that inclusion of
observational sampling uncertainty has relatively little effect on
detection results and that random instrumental error has even
less effect. Systematic instrumental errors, such as changes
in measurement practices or urbanisation, could be more
important, especially earlier in the record (Chapter 3), although
these errors are calculated to be relatively small at large spatial
scales. Urbanisation effects appear to have negligible effects
on continental and hemispheric average temperatures (Chapter
3). Observational uncertainties are likely to be more important
for surface temperature changes averaged over small regions
(Section 9.4.2) and for analyses of free atmosphere temperature
changes (Section 9.4.4).

9.4.2

Continental and Sub-continental Surface
Temperature Change

9.4.2.1 Observed

Changes

Over the 1901 to 2005 period there has been warming over

most of the Earth’s surface with the exception of an area south
of Greenland and parts of North and South America (Figure
3.9 and Section 3.2.2.7, see also Figure 9.6). Warming has
been strongest over the continental interiors of Asia and north-
western North America and some mid-latitude ocean regions
of the SH as well as south-eastern Brazil. Since 1979, almost
all land areas with observational data coverage show warming
(Figure 9.6). Warming is smaller in the SH than in the NH, with
cooling over parts of the mid-latitude oceans. There have been
widespread decreases in continental DTR since the 1950s which
coincide with increases in cloud amounts (Section 3.4.3.1).

9.4.2.2

Studies Based on Space-Time Patterns

Global-scale analyses using space-time detection techniques

(Section 9.4.1.4) have robustly identi

ed the in

uence of

anthropogenic forcing on the 20th-century global climate. A

number of studies have now extended these analyses to consider
sub-global scales. Two approaches have been used; one to assess
the extent to which global studies can provide information at
sub-global scales, the other to assess the in

uence of external

forcing on the climate in speci

c regions. Limitations and

problems in using smaller spatial scales are discussed at the end
of this section.

The approach taken by IDAG (2005) was to compare analyses

of full space-time

elds with results obtained after removing the

globally averaged warming trend, or after removing the annual
global mean from each year in the analysis. They

nd that the

detection of anthropogenic climate change is driven by the
pattern of the observed warming in space and time, not just by
consistent global mean temperature trends between models and
observations. These results suggest that greenhouse warming
should also be detectable at sub-global scales (see also Barnett et
al., 1999). It was also shown by IDAG (2005) that uncertainties
increase, as expected, when global mean information, which
has a high signal-to-noise ratio, is disregarded (see also North
et al., 1995).

Another approach for assessing the regional in

uence of

external forcing is to apply detection and attribution analyses
to observations in speci

c continental- or sub-continental

scale regions. A number of studies using a range of models
and examining various continental- or sub-continental scale
land areas

nd a detectable human in

uence on 20th-century

temperature changes, either by considering the 100-year period
from 1900 or the 50-year period from 1950. Stott (2003)
detects the warming effects of increasing greenhouse gas
concentrations in six continental-scale regions over the 1900
to 2000 period, using HadCM3 simulations. In most regions,
he

nds that cooling from sulphate aerosols counteracts some

of the greenhouse warming. However, the separate detection of
a sulphate aerosol signal in regional analyses remains dif

cult

because of lower signal-to-noise ratios, loss of large-scale
spatial features of response such as hemispheric asymmetry that
help to distinguish different signals, and greater modelling and
forcing uncertainty at smaller scales. Zwiers and Zhang (2003)
also detect human in

uence using two models (CGCM1 and

CGCM2; see Table 8.1, McAvaney et al., 2001) over the 1950 to
2000 period in a series of nested regions, beginning with the full
global domain and descending to separate continental domains
for North America and Eurasia. Zhang et al. (2006) update this
study using additional models (HadCM2 and HadCM3). They

nd evidence that climates in both continental domains have

been in

uenced by anthropogenic emissions during 1950 to

2000, and generally also in the sub-continental domains (Figure
9.11). This

nding is robust to the exclusion of NAO/Arctic

Oscillation (AO) related variability, which is associated with
part of the warming in Central Asia and could itself be related
to anthropogenic forcing (Section 9.5.3). As the spatial scales
considered become smaller, the uncertainty in estimated signal
amplitudes (as demonstrated by the size of the vertical bars
in Figure 9.11) becomes larger, reducing the signal-to-noise
ratio (see also Stott and Tett, 1998). The signal-to-noise ratio,
however, also depends on the strength of the climate change

694

Understanding and Attributing Climate Change

Chapter 9

and the local level of natural variability, and therefore differs
between regions. Most of the results noted above hold even
if the estimate of internal climate variability from the control
simulation is doubled.

The ability of models to simulate many features of the

observed temperature changes and variability at continental
and sub-continental scales and the detection of anthropogenic
effects on each of six continents provides stronger evidence
of human in

uence on climate than was available to the TAR.

A comparison between a large ensemble of 20th-century
simulations of regional temperature changes made with the
MMD at PCMDI (using the same simulations for which the
global mean temperatures are plotted in Figure 9.5) shows
that the spread of the multi-model ensembles encompasses the
observed changes in regional temperature changes in almost all
sub-continental regions (Figure 9.12; see also FAQ 9.2, Figure
1 and related

gures in Chapter 11). In many of the regions,

there is a clear separation between the ensembles of simulations
that include only natural forcings and those that contain both
anthropogenic and natural forcings. A more detailed analysis
of one particular model, HadCM3, shows that it reproduces
many features of the observed temperature changes and
variability in the different regions (IDAG, 2005). The GFDL-
CM2 model (see Table 8.1) is also able to reproduce many
features of the evolution of temperature change in a number
of regions of the globe (Knutson et al., 2006). Other studies
show success at simulating regional temperatures when models
include anthropogenic and natural forcings. Wang et al. (2007)
showed that all MMD 20C3M simulations replicated the late
20th-century arctic warming to various degrees, while both

forced and control simulations reproduce
multi-year arctic warm anomalies similar
in magnitude to the observed mid 20th-
century warming event.

There is some evidence that an

anthropogenic signal can now be detected
in some sub-continental scale areas using
formal detection methods (Appendix
9.A.1), although this evidence is weaker
than at continental scales. Zhang et al.
(2006) detect anthropogenic

ngerprints

in China and southern Canada. Spagnoli et
al. (2002)

nd some evidence for a human

uence on 30-year trends of summer

daily minimum temperatures in France,

but they use a

ngerprint estimated from

a simulation of future climate change and
do not detect an anthropogenic in

uence

on the other indices they consider,
including summer maximum daily
temperatures and winter temperatures.
Min et al. (2005)

nd an anthropogenic

uence on East Asian temperature

changes in a Bayesian framework, but
they do not consider anthropogenic
aerosols or natural forcings in their

analysis. Atmosphere-only general circulation model (AGCM)
simulations forced with observed SSTs can potentially detect
anthropogenic in

uence at smaller spatial and temporal scales

than coupled model analyses, but have the weakness that they
do not explain the observed SST changes (Sexton et al., 2003).
Two studies have applied attribution analysis to sub-continental
temperatures to make inferences about changes in related
variables. Stott et al. (2004) detect an anthropogenic in

uence

on southern European summer mean temperature changes of
the past 50 years and then infer the likelihood of exceeding an
extreme temperature threshold (Section 9.4.3.3). Gillett et al.
(2004a) detect an anthropogenic contribution to summer season
warming in Canada and demonstrate a statistical link with area
burned in forest

res. However, the robustness of these results

to factors such as the choice of model or analysis method
remains to be established given the limited number of studies at
sub-continental scales.

Knutson et al. (2006) assess temperature changes in regions

of the world covering between 0.3 and 7.4% of the area of
the globe and including tropical and extratropical land and
ocean regions. They

nd much better agreement between

climate simulations and observations when the models include
rather than exclude anthropogenic forcings, which suggests
a detectable anthropogenic warming signal over many of the
regions they examine. This would indicate the potential for
formal detection studies to detect anthropogenic warming in
many of these regions, although Knutson et al. (2006) also note
that in some regions the climate simulations they examined were
not very realistic and showed that some of these discrepancies
are associated with modes of variability such as the AO.

Figure 9.11.

Scaling factors indicating the match between observed and simulated decadal near-surface

air temperature change (1950–1999) when greenhouse gas and aerosol forcing responses (GS) are taken into
account in ‘optimal’ detection analyses (Appendix 9.A), at a range of spatial scales from global to sub-continental.
Thick bars indicate 90% confi dence intervals on the scaling factors, and the thin extensions indicate the increased
width of these confi dence intervals when estimates of the variance due to internal variability are doubled. Scaling
factors and uncertainties are provided for different spatial domains including Canada (Canadian land area south of
70°N), China, Southern Europe (European land area bounded by 10°W to 40°E, 35° to 50°N), North America (North
American land area between 30°N and 70°N), Eurasia (Eurasian land area between 30°N and 70°N), mid-latitude
land area between 30°N and 70°N (labelled NH-land), the NH mid-latitudes (30°N to 70°N including land and
ocean), the NH, and the globe. The GS signals are obtained from CGCM1 and CGCM2 combined (labelled CGCM,
see Table 8.1 of the TAR), HadCM2 (see Table 8.1 of the TAR), and HadCM3 (see Table 8.1, this report), and these
four models combined (‘ALL’). After Zhang et al. (2006) and Hegerl et al. (2006b).

695

Chapter 9

Understanding and Attributing Climate Change

Figur

e 9.12.

Comparison of multi-model da

ta set 20C3M model simula

tions containing all forcings (red shaded regions) and containing na

tural

forcings only (blue shaded regions) with obser

ved decadal mean tempera

ture

changes (°C) from 1906 to 2005 from the Hadley Centre/Clima

tic Research Unit gridded surface tempera

ture da

ta set (HadCR

UT3; Br

ohan et al.,

2006).

The panel labelled GLO sho

ws comparison for global mean; LAN,

global

land; and OCE,

global ocean da

ta.

Remaining panels display results for 22 sub-continental scale regions (see the Supplementar

terial,

ppendix 9.C for a description of the regions).

This fi

gure is produced identically to F

9.2,

igure 1 except sub-continental regions were used; a full description of the procedures for producing F

Q 9.2,

igure 1 is

given in the Supplementar

y Ma

terial,

ppendix 9.C.

Shaded bands represent the middle 90% range

estima

ted from the multi-model ensemble.

Note tha

t the model simula

tions ha

ve not been scaled in an

y way

. The same simula

tions

are used as in F

igure 9.5 (58 simula

tions using all forcings from 14 models,

and 19 simula

tions

using na

tural forcings only from 5 models).

Each simula

tion was sampled so tha

t covera

ge corresponds to tha

t of the obser

tion

and was centred rela

tive to the 1901 to 1950 mean obtained by tha

t simula

tion in the region of

interest.

Obser

tions in each region were centred rela

tive to the same period.

The obser

tions in each region are generally c

onsistent with model simula

tions tha

t inc

lude anthropogenic and na

tural forcings,

whereas in man

regions the obser

tions are inconsistent with model simula

tions tha

t inc

lude na

tural forcings only

. Lines are dashed where spa

tial covera

ge is less than 50%.

696

Understanding and Attributing Climate Change

Chapter 9

Frequently Asked Question 9.1

Can Individual Extreme Events
be Explained by Greenhouse Warming?

Changes in climate extremes are expected as the climate

warms in response to increasing atmospheric greenhouse gases
resulting from human activities, such as the use of fossil fuels.
However, determining whether a specifi c, single extreme event
is due to a specifi c cause, such as increasing greenhouse gases,
is diffi cult, if not impossible, for two reasons: 1) extreme events
are usually caused by a combination of factors and 2) a wide
range of extreme events is a normal occurrence even in an un-
changing climate. Nevertheless, analysis of the warming ob-
served over the past century suggests that the likelihood of some
extreme events, such as heat waves, has increased due to green-
house warming, and that the likelihood of others, such as frost
or extremely cold nights, has decreased. For example, a recent
study estimates that human infl uences have more than doubled
the risk of a very hot European summer like that of 2003.

People affected by an extreme weather event often ask

whether human influences on the climate could be held to
some extent responsible. Recent years have seen many ex-
treme events that some commentators have linked to increas-
ing greenhouse gases. These include the prolonged drought in
Australia, the extremely hot summer in Europe in 2003 (see
Figure 1), the intense North Atlantic hurricane seasons of 2004
and 2005 and the extreme rainfall events in Mumbai, India in
July 2005. Could a human influence such as increased concen-
trations of greenhouse gases in the atmosphere have ‘caused’
any of these events?

Extreme events usually result from a combination of fac-

tors. For example, several factors contributed to the extremely
hot European summer of 2003, including a persistent high-
pressure system that was associated with very clear skies and
dry soil, which left more solar energy available to heat the
land because less energy was consumed to evaporate moisture
from the soil. Similarly, the for-
mation of a hurricane requires
warm sea surface temperatures
and specific atmospheric circu-
lation conditions. Because some
factors may be strongly affected
by human activities, such as sea
surface temperatures, but oth-
ers may not, it is not simple to
detect a human influence on a
single, specific extreme event.

Nevertheless, it may be pos-

sible to use climate models to
determine whether human influ-
ences have changed the likeli-
hood of certain types of extreme

events. For example, in the case of the 2003 European heat
wave, a climate model was run including only historical changes
in natural factors that affect the climate, such as volcanic activ-
ity and changes in solar output. Next, the model was run again
including both human and natural factors, which produced a
simulation of the evolution of the European climate that was
much closer to that which had actually occurred. Based on these
experiments, it was estimated that over the 20th century, hu-
man influences more than doubled the risk of having a summer
in Europe as hot as that of 2003, and that in the absence of hu-
man influences, the risk would probably have been one in many
hundred years. More detailed modelling work will be required
to estimate the change in risk for specific high-impact events,
such as the occurrence of a series of very warm nights in an
urban area such as Paris.

The value of such a probability-based approach – ‘Does hu-

man influence change the likelihood of an event?’ – is that it
can be used to estimate the influence of external factors, such
as increases in greenhouse gases, on the frequency of specific
types of events, such as heat waves or frost. Nevertheless, care-
ful statistical analyses are required, since the likelihood of in-
dividual extremes, such as a late-spring frost, could change due
to changes in climate variability as well as changes in average
climate conditions. Such analyses rely on climate-model based
estimates of climate variability, and thus the climate models
used should adequately represent that variability.

The same likelihood-based approach can be used to examine

changes in the frequency of heavy rainfall or floods. Climate
models predict that human influences will cause an increase in
many types of extreme events, including extreme rainfall. There
is already evidence that, in recent decades, extreme rainfall has
increased in some regions, leading to an increase in flooding.

FAQ 9.1, Figure 1.

Summer temperatures in Switzerland from 1864 to 2003 are, on average, about 17°C, as shown by

the green curve. During the extremely hot summer of 2003, average temperatures exceeded 22°C, as indicated by the red bar
(a vertical line is shown for each year in the 137-year record). The fi tted Gaussian distribution is indicated in green. The years
1909, 1947 and 2003 are labelled because they represent extreme years in the record. The values in the lower left corner
indicate the standard deviation (

) and the 2003 anomaly normalised by the 1864 to 2000 standard deviation (T’/

). From

Schär et al. (2004).

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Chapter 9

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Karoly and Wu (2005) compare observed temperature

trends in 5° × 5° grid boxes globally over 30-, 50- and 100-
year periods ending in 2002 with 1) internal variability as
simulated by three models (GFDL R30, HadCM2, PCM) and 2)
the simulated response to greenhouse gas and sulphate aerosol
forcing in those models (see also Knutson et al., 1999). They

nd that a much higher percentage of grid boxes show trends

that are inconsistent with model-estimated internal variability
than would be expected by chance and that a large fraction of
grid boxes show changes that are consistent with the forced
simulations, particularly over the two shorter periods. This
assessment is essentially a global-scale detection result because
its interpretation relies upon a global composite of grid-box scale
statistics. As discussed in the paper, this result does not rule out
the possibility that individual grid box trends may be explained
by different external forcing combinations, particularly since
natural forcings and forcings that could be important at small
spatial scales, such as land use change or black carbon aerosols,
are missing from these models. The demonstration of local
consistency between models and observations in this study does
not necessarily imply that observed changes can be attributed
to anthropogenic forcing in a speci

c grid box, and it does not

allow con

dent estimates of the anthropogenic contribution to

change at those scales.

Models do not reproduce the observed temperature changes

equally well in all regions. Areas where temperature changes are
not particularly well simulated by some models include parts
of North America (Knutson et al., 2006) and mid-Asia (IDAG,
2005). This could be due to a regional trend or variation that
was caused by internal variability (a result that models would
not be expected to reproduce), uncertain forcings that are locally
important, or model errors. Examples of uncertain forcings
that play a small role globally, but could be more important
regionally, are the effects of land use changes (Sections 9.2 and
9.3) or atmospheric brown clouds. The latter could be important
in explaining observed temperature trends in South Asia and the
northern Indian Ocean (Ramanathan et al., 2005; see Chapter
2).

An analysis of the MMD 20C3M experiments indicates that

multi-decadal internal variability could be responsible for some
of the rapid warming seen in the central USA between 1901
and 1940 and rapid cooling between 1940 and 1979 (Kunkel
et al., 2006). Also, regional temperature is more strongly
in

uenced by variability and changes in climate dynamics,

such as temperature changes associated with the NAO, which
may itself show an anthropogenic in

uence (Section 9.5.3.2),

or the Atlantic Multi-decadal Oscillation (AMO), which could
in some regions and seasons be poorly simulated by models and
could be confounded with the expected temperature response
to external forcings. Thus the anthropogenic signal is likely to
be more easy to identify in some regions than in others, with
temperature changes in those regions most affected by multi-
decadal scale variability being the most dif

cult to attribute,

even if those changes are inconsistent with model estimated
internal variability and therefore detectable.

The extent to which temperature changes at sub-continental

scales can be attributed to anthropogenic forcings, and the extent
to which it is possible to estimate the contribution of greenhouse
gas forcing to regional temperature trends, remains a topic for
further research. Idealised studies (e.g., Stott and Tett, 1998)
suggest that surface temperature changes are detectable mainly
at large spatial scales of the order of several thousand kilometres
(although they also show that as the signal of climate change
strengthens in the 21st century, surface temperature changes are
expected to become detectable at increasingly smaller scales).
Robust detection and attribution are inhibited at the grid box scales
because it becomes dif

cult to separate the effects of the relatively

well understood large-scale external in

uences on climate, such

as greenhouse gas, aerosols, solar and volcanic forcing, from each
other and from local in

uences that may not be related to these

large-scale forcings. This occurs because the contribution from
internal climate variability increases at smaller scales, because
the spatial details that can help to distinguish between different
forcings at large scales are not available or unreliable at smaller
scales, and because forcings that could be important at small
spatial scales, such as land use change or black carbon aerosols,
are uncertain and may not have been included in the models used
for detection. Although models do not typically underestimate
natural internal variability of temperature at continental scales
over land (Figure 9.8), even at a grid box scale (Karoly and Wu,
2005), the credibility of small-scale details of climate simulated
by models is lower than for large-scale features. While the large-
scale coherence of temperatures means that temperatures at a
particular grid box should adequately represent a substantial part
of the variability of temperatures averaged over the area of that
grid box, the remaining variability from local-scale processes and
the upward cascades from smaller to larger scales via nonlinear
interactions may not be well represented in models at the grid
box scale. Similarly, the analysis of shorter temporal scales also
decreases the signal-to-noise ratio and the ability to use temporal
information to distinguish between different forcings. This is
why most detection and attribution studies use temporal scales
of 50 or more years.

9.4.2.3

Studies Based on Indices of Temperature Change
and Temperature-Precipitation Relationships

Studies based on indices of temperature change support the

robust detection of human in

uence on continental-scale land

areas. Observed trends in indices of North American continental-
scale temperature change, (including the regional mean, the
mean land-ocean temperature contrast and the annual cycle)
were found by Karoly et al. (2003) to be generally consistent with
simulated trends under historical forcing from greenhouse gases
and sulphate aerosols during the second half of the 20th century.
In contrast, they

nd only a small likelihood of agreement with

trends driven by natural forcing only during this period. An
analysis of changes in Australian mean, daily maximum and
daily minimum temperatures and diurnal temperature range
using six coupled climate models showed that it is likely that

698

Understanding and Attributing Climate Change

Chapter 9

there has been a signi

cant contribution to observed warming

in Australia from increasing greenhouse gases and sulphate
aerosols (Karoly and Braganza, 2005a). An anomalous
warming has been found over all Australia (Nicholls, 2003)
and in New South Wales (Nicholls et al., 2005) since the early
1970s, associated with a changed relationship between annual
mean maximum temperature and rainfall. Whereas interannual
rainfall and temperature variations in this region are strongly
inversely correlated, in recent decades temperatures have
tended to be higher for a given rainfall than in previous decades.
By removing the rainfall-related component of Australian
temperature variations, thereby enhancing the signal-to-noise
ratio, Karoly and Braganza (2005b) detect an anthropogenic
warming signal in south-eastern Australia, although their results
are affected by some uncertainty associated with their removal
of rainfall-related temperature variability. A similar technique
applied to the Sudan and Sahel region improved the agreement
between model simulations and observations of temperature
change over the last 60 years in this region (Douville, 2006)
and could improve the detectability of regional temperature
signals over other regions where precipitation is likely to affect
the surface energy budget (Trenberth and Shea, 2005).

9.4.3 Surface

Temperature

Extremes

9.4.3.1 Observed

Changes

Observed changes in temperature extremes are consistent with

the observed warming of the climate (Alexander et al., 2006) and
are summarised in Section 3.8.2.1. There has been a widespread
reduction in the number of frost days in mid-latitude regions in
recent decades, an increase in the number of warm extremes,
particularly warm nights, and a reduction in the number of cold
extremes, particularly cold nights. A number of regional studies
all show patterns of changes in extremes consistent with a
general warming, although the observed changes in the tails of
the temperature distributions are generally not consistent with a
simple shift of the entire distribution alone.

9.4.3.2 Global

Assessments

Evidence for observed changes in short-duration extremes

generally depends on the region considered and the analysis
method (IPCC, 2001). Global analyses have been restricted by
the limited availability of quality-controlled and homogenised
daily station data. Indices of temperature extremes have been
calculated from station data, including some indices from
regions where daily station data are not released (Frich et al.,
2002; Klein Tank and Können, 2003; Alexander et al., 2006).
Kiktev et al. (2003) analyse a subset of such indices by using

ngerprints from atmospheric model simulations driven by

prescribed SSTs. They

nd signi

cant decreases in the number

of frost days and increases in the number of very warm
nights over much of the NH. Comparisons of observed and
modelled trend estimates show that inclusion of anthropogenic
effects in the model integrations improves the simulation of

these changing temperature extremes, indicating that human
in

uences are probably an important contributor to changes in

the number of frost days and warm nights. Tebaldi et al. (2006)

nd that changes simulated by eight MMD models agreed well

with observed trends in heat waves, warm nights and frost days
over the last four decades.

Christidis et al. (2005) analyse a new gridded data set of

daily temperature data (Caesar et al., 2006) using the indices
shown by Hegerl et al. (2004) to have a potential for attribution,
namely the average temperature of the most extreme 1, 5, 10
and 30 days of the year. Christidis et al. (2005) detect robust
anthropogenic changes in indices of extremely warm nights
using signals estimated with the HadCM3 model, although with
some indications that the model overestimates the observed
warming of warm nights. They also detect human in

uence on

cold days and nights, but in this case the model underestimates
the observed changes, signi

cantly so in the case of the coldest

day of the year. Anthropogenic in

uence was not detected in

observed changes in extremely warm days.

9.4.3.3

Attributable Changes in the Risk of Extremes

Many important impacts of climate change may manifest

themselves through a change in the frequency or likelihood
of occurrence of extreme events. While individual extreme
events cannot be attributed to external in

uences, a change in

the probability of such events might be attributable to external
in

uences (Palmer, 1999; Palmer and Räisänen, 2002). One

study estimates that anthropogenic forcings have signi

cantly

increased the risk of extremely warm summer conditions over
southern Europe, as was observed during the 2003 European
heat wave. Stott et al. (2004) apply a methodology for making
quantitative statements about change in the likelihood of such
speci

c types of climatic events (Allen, 2003; Stone and Allen,

2005a), by expressing the contribution of external forcing to
the risk of an event exceeding a speci

c magnitude. If

is the

probability of a climatic event (such as a heat wave) occurring
in the presence of anthropogenic forcing of the climate system,
and

is the probability of it occurring if anthropogenic forcing

had not been present, then the fraction of the current risk that
is attributable to past greenhouse gas emissions (fraction of
attributable risk; FAR) is given by FAR = 1 –

/ P

(Allen,

2003). Stott et al. (2004) apply the FAR concept to mean summer
temperatures of a large part of continental Europe and the
Mediterranean. Using a detection and attribution analysis, they
determine that regional summer mean temperature has likely
increased due to anthropogenic forcing, and that the observed
change is inconsistent with natural forcing. They then use the
HadCM3 model to estimate the FAR associated with a particular
extreme threshold of regional summer mean temperature that
was exceeded in 2003, but in no other year since the beginning
of the record in 1851. Stott et al. (2004) estimate that it is very
likely that human in

uence has more than doubled the risk of

the regional summer mean temperature exceeding this threshold
(Figure 9.13).

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Chapter 9

Understanding and Attributing Climate Change

Figure 9.13.

Change in risk of mean European summer temperatures exceeding

a threshold of 1.6°C above 1961 to 1990 mean temperatures, a threshold that was
exceeded in 2003 but in no other year since the start of the instrumental record in
1851. (Top) Frequency histograms of the estimated likelihood of the risk (probability)
of exceeding a 1.6°C threshold (relative to the 1961–1990 mean) in the 1990s in the
presence (red curve) and absence (green curve) of anthropogenic change, expressed
as an occurrence rate. (Bottom) Fraction of attributable risk (FAR). The vertical line
indicates the ‘best estimate’ FAR, the mean risk attributable to anthropogenic factors
averaged over the distribution. The alternation between grey and white bands
indicates the deciles of the estimated FAR distribution. The shift from the green to
the red distribution in (a) implies a FAR distribution with mean 0.75, corresponding to
a four-fold increase in the risk of such an event (b). From Stott et al. (2004).

This study considered only continental mean seasonally

averaged temperatures. Consideration of shorter-term and
smaller-scale heat waves will require higher resolution
modelling and will need to take complexities such as land
surface processes into account (Schär and Jendritzky, 2004).
Also, Stott et al. (2004) assume no change in internal variability
in the region they consider (which was the case in HadCM3
21st-century climate projections for summer mean temperatures
in the region they consider), thereby ascribing the increase in
risk only to an increase in mean temperatures (i.e., as shown in
Box TS.5, Figure 1, which illustrates how a shift in the mean
of a distribution can cause a large increase in the frequency
of extremes). However, there is some evidence for a weak

increase in European temperature variability in summer (and a
decrease in winter) for the period 1961 to 2004 (Scherrer et al.,
2005), which could contribute to an increase in the likelihood
of extremes. Schär et al. (2004) show that the central European
heat wave of 2003 could also be consistent with model-predicted
increases in temperature variability due to soil moisture
and vegetation feedbacks. In addition, multi-decadal scale
variability, associated with basin-scale changes in the Atlantic
Ocean related to the Meridional Overturning Circulation
(MOC) could have contributed to changes in European summer
temperatures (Sutton and Hodson, 2005), although Klein Tank
et al. (2005) show evidence that patterns of change in European
temperature variance in spring and summer are not consistent
with patterns of change in temperature variance expected from
natural variability. Meteorological aspects of the summer 2003
European heat wave are discussed in Box 3.6.

9.4.4

Free Atmosphere Temperature

9.4.4.1 Observed

Changes

Observed free atmosphere temperature changes are discussed

in Section 3.4.1 and Karl et al. (2006) provide a comprehensive
review. Radiosonde-based observations (with near global coverage
since 1958) and satellite-based temperature measurements
(beginning in late 1978) show warming trends in the troposphere
and cooling trends in the stratosphere. All data sets show that the
global mean and tropical troposphere has warmed from 1958 to
the present, with the warming trend in the troposphere slightly
greater than at the surface. Since 1979, it is likely that there is
slightly greater warming in the troposphere than at the surface,
although uncertainties remain in observed tropospheric warming
trends and whether these are greater or less than the surface
trend. The range (due to different data sets) of the global mean
tropospheric temperature trend since 1979 is 0.12°C to 0.19°C per
decade based on satellite-based estimates (Chapter 3) compared
to a range of 0.16°C to 0.18°C per decade

for the global surface

warming. While all data sets show that the stratosphere has
cooled considerably from 1958 and from 1979 to present, there
are large differences in the linear trends estimated from different
data sets. However, a linear trend is a poor

t to the data in the

stratosphere and the tropics at all levels (Section 3.4.1). The
uncertainties in the observational records are discussed in detail
in Section 3.4.1 and by Karl et al. (2006). Uncertainties remain in
homogenised radiosonde data sets which could result in a spurious
inference of net cooling in the tropical troposphere. Differences
between temperature trends measured from different versions of
tropospheric satellite data result primarily from differences in how
data from different satellites are merged.

9.4.4.2

Changes in Tropopause Height

The height of the lapse rate tropopause (the boundary between

the stratosphere and the troposphere) is sensitive to bulk changes in
the thermal structure of the stratosphere and the troposphere, and
may also be affected by changes in surface temperature gradients

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Understanding and Attributing Climate Change

Chapter 9

Figure 9.14.

Comparison between reanalysis and climate-model simulated global monthly mean anomalies in tropopause height. Model results are from two different PCM

(Table 8.1) ensemble experiments using either natural forcings, or natural and anthropogenic forcings (ALL). There are four realisations of each experiment. Both the low-pass
fi ltered ensemble mean and the unfi ltered range between the highest and lowest values of the realisations are shown. All model anomalies are defi ned relative to climatological
monthly means computed over 1890 to 1999. Reanalysis-based tropopause height anomalies estimated from ERA-40 were fi ltered in the same way as model data. The ERA-40
record spans 1957 to 2002 and was forced to have the same mean as ALL over 1960 to 1999. After Santer et al. (2003a) and Santer et al. (2004).

(Schneider, 2004). Analyses of radiosonde data have documented
increases in tropopause height over the past 3 to 4 decades
(Highwood et al., 2000; Seidel et al., 2001). Similar increases
have been inferred from three different reanalysis products,
the European Centre for Medium Range Weather Forecasts
(ECMWF) 15- and 40-year reanalyses (ERA-15 and ERA-40)
and the NCAR- National Center for Environmental Prediction
(NCEP) reanalysis (Kalnay et al., 1996; Gibson et al., 1997;
Simmons and Gibson, 2000; Kistler et al., 2001), and from model
simulations with combined anthropogenic and natural forcing
(Santer et al., 2003a,b, 2004; see Figure 9.14). In both models
and reanalyses, changes in tropopause height over the satellite
and radiosonde eras are smallest in the tropics and largest over
Antarctica (Santer et al., 2003a,b, 2004). Model simulations with
individual forcings indicate that the major drivers of the model
tropopause height increases are ozone-induced stratospheric
cooling and the tropospheric warming caused by greenhouse gas
increases (Santer et al., 2003a). However, earlier model studies
have found that it is dif

cult to alter tropopause height through

stratospheric ozone changes alone (Thuburn and Craig, 2000).
Santer et al. (2003c) found that the model-simulated response to
combined anthropogenic and natural forcing is robustly detectable
in different reanalysis products, and that solar and volcanic
forcing alone could not explain the tropopause height increases
(Figure 9.14). Climate data from reanalyses, especially the ‘

rst

generation’ reanalysis analysed by Santer et al. (2003a), are subject
to some de

ciencies, notably inhomogeneities related to changes

over time in the availability and quality of input data, and are
subject to a number of speci

c technical choices in the reanalysis

scheme (see Santer et al., 2004, for a discussion). Also, the NCEP
reanalysis detection results could be due to compensating errors
because of excessive stratospheric cooling in the reanalysis
(Santer et al., 2004), since the stratosphere cools more relative to
the troposphere in the NCEP reanalysis while models warm the

troposphere. In contrast, the

nding of a signi

cant anthropogenic

uence on tropopause height in the ‘second generation’ ERA-40

reanalysis is driven by similar large-scale changes in both models
and the reanalysis. Detection results there are robust to removing
global mean tropopause height increases.

9.4.4.3

Overall Atmospheric Temperature Change

Anthropogenic in

uence on free atmosphere temperatures has

been detected in analyses of satellite data since 1979, although
this

nding has been found to be sensitive to which analysis of

satellite data is used. Satellite-borne MSUs, beginning in 1978,
estimate the temperature of thick layers of the atmosphere.
The main layers represent the lower troposphere (T2

), the

mid-troposphere (T2) and the lower stratosphere (T4) (Section
3.4.1.2.1). Santer et al. (2003c) compare T2 and T4 temperature
changes simulated by the PCM model including anthropogenic
and natural forcings with the University of Alabama in
Huntsville (UAH; Christy et al., 2000) and Remote Sensing
Systems (RSS; Mears and Wentz, 2005) satellite data sets
(Section 3.4.1.2.2). They

nd that the model

ngerprint of the

T4 response to combined anthropogenic and natural forcing is
consistently detected in both satellite data sets, whereas the T2
response is detected only in the RSS data set. However, when
the global mean changes are removed, the T2

ngerprint is

detected in both data sets, suggesting a common spatial pattern
of response overlain by a systematic global mean difference.

Anthropogenic in

uence on free atmosphere temperatures has

been robustly detected in a number of different studies analysing
various versions of the Hadley Centre Radiosonde Temperature
(HadRT2) data set (Parker et al., 1997) by means of a variety
of different diagnostics and

ngerprints estimated with the

HadCM2 and HadCM3 models (Tett et al., 2002; Thorne et al.,
2002, 2003; Jones et al., 2003). Whereas an analysis of spatial

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Chapter 9

Understanding and Attributing Climate Change

patterns of zonal mean free atmosphere temperature changes was
unable to detect the response to natural forcings (Tett et al., 2002),
an analysis of spatio-temporal patterns detected the in

uence

of volcanic aerosols and (less convincingly) solar irradiance
changes, in addition to detecting the effects of greenhouse gases
and sulphate aerosols (Jones et al., 2003). In addition, Crooks
(2004) detects a solar signal in atmospheric temperature changes
as seen in the HadRT2.1s radiosonde data set when a diagnostic
chosen to extract the solar signal from other signals is used. The
models used in these studies have poor vertical resolution in the
stratosphere and they signi

cantly underestimate stratospheric

variability, thus possibly overestimating the signi

cance of these

detected signals (Tett et al., 2002). However, a sensitivity study
(Thorne et al., 2002) showed that detection of human in

uence

on free atmosphere temperature changes does not depend on the
inclusion of stratospheric temperatures. An analysis of spatial
patterns of temperature change, represented by large-scale area
averages at the surface, in broad atmospheric layers and in lapse
rates between layers, showed robust detection of an anthropogenic
in

uence on climate when a range of uncertainties were explored

relating to the choice of

ngerprints and the radiosonde and

model data sets (Thorne et al., 2003). However, Thorne et
al. were not able to attribute recent observed tropospheric
temperature changes to any particular combination of external
forcing in

uences because the models analysed (HadCM2 and

HadCM3) overestimate free atmosphere warming as estimated
by the radiosonde data sets, an effect also seen by Douglass et
al. (2004) during the satellite era. However, there is evidence
that radiosonde data during the satellite era are contaminated by
spurious cooling trends (Sherwood et al., 2005; Randel and Wu,
2006; Section 3.4.1), and since structural uncertainty arising from
the choice of techniques used to analyse radiosonde data has not
yet been quanti

ed (Thorne et al., 2005), it is dif

cult to assess,

based on these analyses alone, whether model-data discrepancies
are due to model or observational de

ciencies. However further

information is provided by an analysis of modelled and observed
tropospheric lapse rates, discussed in Section 9.4.4.4.

A different approach is to assess detectability of observed

temperature changes through the depth of the atmosphere with
AGCM simulations forced with observed SSTs, although the
vertical pro

le of the atmospheric temperature change signal

estimated in this way can be quite different from the same signal
estimated by coupled models with the same external forcings
(Hansen et al., 2002; Sun and Hansen, 2003; Santer et al.,
2005). Sexton et al. (2001)

nd that inclusion of anthropogenic

effects improves the simulation of zonally averaged upper air
temperature changes from the HadRTt1.2 data set such that an
anthropogenic signal is detected at the 5% signi

cance level

in patterns of seasonal mean temperature change calculated as
overlapping eight-year means over the 1976 to 1994 period and
expressed as anomalies relative to the 1961 to 1975 base period.
In addition, analysing patterns of annual mean temperature
change for individual years shows that an anthropogenic signal
is also detected on interannual time scales for a number of years
towards the end of the analysis period.

9.4.4.4

Differential Temperature Trends

Subtracting temperature trends at the surface from those in

the free atmosphere removes much of the common variability
between these layers and tests whether the model-predicted
trends in tropospheric lapse rate are consistent with those
observed by radiosondes and satellites (Karl et al., 2006). Since
1979, globally averaged modelled trends in tropospheric lapse
rates are consistent with those observed. However, this is not the
case in the tropics, where most models have more warming aloft
than at the surface while most observational estimates show
more warming at the surface than in the troposphere (Karl et
al., 2006). Karl et al. (2006) carried out a systematic review of
this issue. There is greater consistency between simulated and
observed differential warming in the tropics in some satellite
measurements of tropospheric temperature change, particularly
when the effect of the cooling stratosphere on tropospheric
retrievals is taken into account (Karl et al., 2006). External
forcing other than greenhouse gas changes can also help to
reconcile some of the differential warming, since both volcanic
eruptions and stratospheric ozone depletion are expected to have
cooled the troposphere more than the surface over the last several
decades (Santer et al., 2000, 2001; IPCC, 2001; Free and Angell,
2002; Karl et al., 2006). There are, however, uncertainties in
quantifying the differential cooling caused by these forcings,
both in models and observations, arising from uncertainties in
the forcings and model response to the forcings. Differential
effects of natural modes of variability, such as ENSO and the
NAM, on observed surface and tropospheric temperatures,
which arise from differences in the amplitudes and spatial
expression of these modes at the surface and in the troposphere,
make only minor contributions to the overall differences in
observed surface and tropospheric warming rates (Santer et al.,
2001; Hegerl and Wallace, 2002; Karl et al., 2006).

A systematic intercomparison between radiosonde-based

(Radiosonde Atmospheric Temperature Products for Assessing
Climate (RATPAC); Free et al., 2005, and Hadley Centre
Atmospheric Temperature (HadAT), Thorne et al., 2005) and
satellite-based (RSS, UAH) observational estimates of tropical
lapse rate trends with those simulated by 19 MMD models
shows that on monthly and annual time scales, variations
in temperature at the surface are ampli

ed aloft in both

models and observations by consistent amounts (Santer et al.,
2005; Karl et al., 2006). It is only on longer time scales that
disagreement between modelled and observed lapse rates arises
(Hegerl and Wallace, 2002), that is, on the time scales over
which discrepancies would arise from inhomogeneities in the
observational record. Only one observational data set (RSS) was
found to be consistent with the models on both short and long
time scales. While Vinnikov et al. (2006) have not produced a
lower-tropospheric retrieval, their estimate of the T2 temperature
trend (Figure 3.18) is consistent with model simulations (Karl
et al., 2006). One possibility is that ampli

cation effects are

controlled by different physical mechanisms on short and long
time scales, although a more probable explanation is that some
observational records are contaminated by errors that affect
their long-term trends (Section 3.4.1; Karl et al., 2006).

702

Understanding and Attributing Climate Change

Chapter 9

Frequently Asked Question 9.2

Can the Warming of the 20th Century
be Explained by Natural Variability?

It is very unlikely that the 20th-century warming can be

explained by natural causes. The late 20th century has been
unusually warm. Palaeoclimatic reconstructions show that the
second half of the 20th century was likely the warmest 50-year
period in the Northern Hemisphere in the last 1300 years. This
rapid warming is consistent with the scientifi c understanding
of how the climate should respond to a rapid increase in green-
house gases like that which has occurred over the past century,
and the warming is inconsistent with the scientifi c understand-
ing of how the climate should respond to natural external fac-
tors such as variability in solar output and volcanic activity.
Climate models provide a suitable tool to study the various in-
fl uences on the Earth’s climate. When the effects of increasing
levels of greenhouse gases are included in the models, as well
as natural external factors, the models produce good simula-
tions of the warming that has occurred over the past century.
The models fail to reproduce the observed warming when run
using only natural factors. When human factors are included,
the models also simulate a geographic pattern of temperature
change around the globe similar to that which has occurred in
recent decades. This spatial pattern, which has features such as
a greater warming at high northern latitudes, differs from the
most important patterns of natural climate variability that are
associated with internal climate processes, such as El Niño.

Variations in the Earth’s climate over time are caused by

natural internal processes, such as El Niño, as well as changes
in external influences. These external influences can be natu-
ral in origin, such as volcanic activity and variations in so-
lar output, or caused by human activity, such as greenhouse
gas emissions, human-sourced aerosols, ozone depletion and
land use change. The role of natural internal processes can be
estimated by studying observed variations in climate and by
running climate models without changing any of the external
factors that affect climate. The effect of external influences can
be estimated with models by changing these factors, and by us-
ing physical understanding of the processes involved. The com-
bined effects of natural internal variability and natural external
factors can also be estimated from climate information recorded
in tree rings, ice cores and other types of natural ‘thermometers’
prior to the industrial age.

The natural external factors that affect climate include vol-

canic activity and variations in solar output. Explosive vol-
canic eruptions occasionally eject large amounts of dust and
sulphate aerosol high into the atmosphere, temporarily shield-
ing the Earth and reflecting sunlight back to space. Solar output
has an 11-year cycle and may also have longer-term varia-
tions. Human activities over the last 100 years, particularly the
burning of fossil fuels, have caused a rapid increase in carbon
dioxide and other greenhouse gases in the atmosphere. Before

the industrial age, these gases had remained at near stable con-
centrations for thousands of years. Human activities have also
caused increased concentrations of fine reflective particles, or
‘aerosols’, in the atmosphere, particularly during the 1950s and
1960s.

Although natural internal climate processes, such as El Niño,

can cause variations in global mean temperature for relatively
short periods, analysis indicates that a large portion is due to
external factors. Brief periods of global cooling have followed
major volcanic eruptions, such as Mt. Pinatubo in 1991. In the
early part of the 20th century, global average temperature rose,
during which time greenhouse gas concentrations started to
rise, solar output was probably increasing and there was little
volcanic activity. During the 1950s and 1960s, average global
temperatures levelled off, as increases in aerosols from fossil
fuels and other sources cooled the planet. The eruption of Mt.
Agung in 1963 also put large quantities of reflective dust into
the upper atmosphere. The rapid warming observed since the
1970s has occurred in a period when the increase in greenhouse
gases has dominated over all other factors.

Numerous experiments have been conducted using climate

models to determine the likely causes of the 20th-century cli-
mate change. These experiments indicate that models cannot
reproduce the rapid warming observed in recent decades when
they only take into account variations in solar output and vol-
canic activity. However, as shown in Figure 1, models are able
to simulate the observed 20th-century changes in temperature
when they include all of the most important external factors,
including human influences from sources such as greenhouse
gases and natural external factors. The model-estimated re-
sponses to these external factors are detectable in the 20th-cen-
tury climate globally and in each individual continent except
Antarctica, where there are insufficient observations. The hu-
man influence on climate very likely dominates over all other
causes of change in global average surface temperature during
the past half century.

An important source of uncertainty arises from the incom-

plete knowledge of some external factors, such as human-
sourced aerosols. In addition, the climate models themselves
are imperfect. Nevertheless, all models simulate a pattern of
response to greenhouse gas increases from human activities
that is similar to the observed pattern of change. This pattern
includes more warming over land than over the oceans. This
pattern of change, which differs from the principal patterns
of temperature change associated with natural internal vari-
ability, such as El Niño, helps to distinguish the response to
greenhouse gases from that of natural external factors. Models
and observations also both show warming in the lower part of

(continued)

704

Understanding and Attributing Climate Change

Chapter 9

9.4.5 Summary

Since the TAR, the evidence has strengthened that human

uence has increased global temperatures near the surface

of the Earth. Every year since the publication of the TAR has
been in the top ten warmest years in the instrumental global
record of near-surface temperatures. Many climate models
are now available which simulate global mean temperature
changes that are consistent with those observed over the last
century when they include the most important forcings of the
climate system. The fact that no coupled model simulation so
far has reproduced global temperature changes over the 20th
century without anthropogenic forcing is strong evidence for
the in

uence of humans on global climate. This conclusion is

robust to variations in model formulation and uncertainties in
forcings as far as they have been explored in the large multi-
model ensemble now available (Figure 9.5).

Many studies have detected a human in

uence on near-surface

temperature changes, applying a variety of statistical techniques
and using many different climate simulations. Comparison with
observations shows that the models used in these studies appear
to have an adequate representation of internal variability on the
decadal to inter-decadal time scales important for detection (Figure
9.7). When evaluated in a Bayesian framework, very strong
evidence is found for a human in

uence on global temperature

change regardless of the choice of prior distribution.

Since the TAR, there has been an increased emphasis on

partitioning the observed warming into contributions from
greenhouse gas increases and other anthropogenic and natural
factors. These studies lead to the conclusion that greenhouse
gas forcing has very likely been the dominant cause of the
observed global warming over the last 50 years, and account
for the possibility that the agreement between simulated and
observed temperature changes could be reproduced by different
combinations of external forcing. This is because, in addition
to detecting the presence of model-simulated spatio-temporal
response patterns in observations, such analyses also require
consistency between the model-simulated and observational
amplitudes of these patterns.

Detection and attribution analyses indicate that over the past

century there has likely been a cooling in

uence from aerosols

and natural forcings counteracting some of the warming
in

uence of the increasing concentrations of greenhouse

gases (Figure 9.9). Spatial information is required in addition
to temporal information to reliably detect the in

uence of

aerosols and distinguish them from the in

uence of increased

greenhouse gases. In particular, aerosols are expected to cause
differential warming and cooling rates between the NH and
SH that change with time depending on the evolution of the
aerosol forcing, and this spatio-temporal

ngerprint can help to

constrain the possible range of cooling from aerosols over the
century. Despite continuing uncertainties in aerosol forcing and
the climate response, it is likely that greenhouse gases alone
would have caused more warming than observed during the last
50 years, with some warming offset by cooling from aerosols

and other natural and anthropogenic factors. The overall
evidence from studies using instrumental surface temperature
and free atmospheric temperature data, along with evidence
from analysis of temperature over the last few hundred years
(Section 9.3.3.2), indicates that it is very unlikely that the
contribution from solar forcing to the warming of the last 50
years was larger than that from greenhouse gas forcing.

An important development since the TAR has been the

detection of an anthropogenic signal in surface temperature
changes since 1950 over continental and sub-continental scale
land areas. The ability of models to simulate many aspects of
the temperature evolution at these scales (Figure 9.12) and the
detection of signi

cant anthropogenic effects on each of six

continents provides stronger evidence of human in

uence on

the global climate than was available to the TAR. Dif

culties

remain in attributing temperature changes at smaller than
continental scales and over time scales of less than 50 years.
Attribution at these scales has, with limited exceptions, not yet
been established. Temperature changes associated with some
modes of variability, which could be wholly or partly naturally
caused, are poorly simulated by models in some regions and
seasons and could be confounded with the expected temperature
response to external forcings. Averaging over smaller regions
reduces the natural variability less than averaging over large
regions, making it more dif

cult to distinguish changes expected

from external forcing. In addition, the small-scale details of
external forcing and the response simulated by models are
less credible than large-scale features. Overall, uncertainties in
observed and model-simulated climate variability and change at
smaller spatial scales make it dif

cult at present to estimate the

contribution of anthropogenic forcing to temperature changes
at scales smaller than continental and on time scales shorter
than 50 years.

There is now some evidence that anthropogenic forcing has

affected extreme temperatures. There has been a signi

cant

decrease in the frequency of frost days and an increase in the
incidence of warm nights. A detection and attribution analysis
has shown a signi

cant human in

uence on patterns of changes

in extremely warm nights and evidence for a human-induced
warming of the coldest nights and days of the year. Many
important impacts of climate change are likely to manifest
themselves through an increase in the frequency of heat waves in
some regions and a decrease in the frequency of extremely cold
events in others. Based on a single study, and assuming a model-
based estimate of temperature variability, past human in

uence

may have more than doubled the risk of European mean summer
temperatures as high as those recorded in 2003 (Figure 9.13).

Since the TAR, further evidence has accumulated that

there has been a signi

cant anthropogenic in

uence on free

atmosphere temperature since widespread measurements
became available from radiosondes in the late 1950s. The
in

uence of greenhouse gases on tropospheric temperatures

has been detected, as has the in

uence of stratospheric ozone

depletion on stratospheric temperatures. The combination of
a warming troposphere and a cooling stratosphere has likely

705

Chapter 9

Understanding and Attributing Climate Change

led to an increase in the height of the tropopause and model-
data comparisons show that greenhouse gases and stratospheric
ozone changes are likely largely responsible (Figure 9.14).

Whereas, on monthly and annual time scales, variations

of temperature in the tropics at the surface are ampli

ed aloft

in both the MMD simulations and observations by consistent
amounts, on longer time scales, simulations of differential
tropical warming rates between the surface and the free
atmosphere are inconsistent with some observational records.
One possible explanation for the discrepancies on multi-
annual but not shorter time scales is that ampli

cation effects

are controlled by different physical mechanisms, but a more
probable explanation is that some observational records are
contaminated by errors that affect their long-term trends.

9.5 Understanding of Change in Other

Variables during the Industrial Era

The objective of this section is to assess large-scale climate

change in variables other than air temperature, including
changes in ocean climate, atmospheric circulation, precipitation,
the cryosphere and sea level. This section draws heavily on
Chapters 3, 4, 5 and 8. Where possible, it attempts to identify
links between changes in different variables, such as those
that associate some aspects of SST change with precipitation
change. It also discusses the role of external forcing, drawing
where possible on formal detection studies.

9.5.1 Ocean

Climate

Change

9.5.1.1

Ocean Heat Content Changes

Since the TAR, evidence of climate change has accumulated

within the ocean, both at regional and global scales (Chapter
5). The overall heat content in the World Ocean is estimated to
have increased by 14.2 × 10

J during the period 1961 to 2003

(Section 5.2.2). This overall increase has been superimposed on
strong interannual and inter-decadal variations. The fact that the
entire ocean, which is by far the system’s largest heat reservoir
(Levitus et al., 2005; see also Figure 5.4) gained heat during the
latter half of the 20th century is consistent with a net positive
radiative forcing of the climate system. Late 20th-century ocean
heat content changes were at least one order of magnitude larger
than the increase in energy content of any other component of
the Earth’s ocean-atmosphere-cryosphere system (Figure 5.4;
Levitus et al., 2005).

All analyses indicate a large anthropogenic component of the

positive trend in global ocean heat content. Levitus et al. (2001)
and Gregory et al (2004) analyse simulations from the GFDL
R30 and HadCM3 models respectively and show that climate
simulations agree best with observed changes when the models
include anthropogenic forcings from increasing greenhouse gas
concentrations and sulphate aerosols. Gent and Danabasoglu

(2004) show that the observed trend cannot be explained by
natural internal variability as simulated by a long control run of
the Community Climate System Model (CCSM2). Barnett et al.
(2001) and Reichert et al. (2002b) use detection analyses similar
to those described in Section 9.4 to detect model-simulated
ocean climate change signals in the observed spatio-temporal
patterns of ocean heat content across the ocean basins.

Barnett et al. (2005) extend previous detection and attribution

analyses of ocean heat content changes to a basin by basin
analysis of the temporal evolution of temperature changes in
the upper 700 m of the ocean (see also Pierce et al., 2006). They
report that whereas the observed change is not consistent with
internal variability and the response to natural external forcing
as simulated by two climate models (PCM and HadCM3),
the simulated ocean warming due to anthropogenic factors
(including well-mixed greenhouse gases and sulphate aerosols)
is consistent with the observed changes and reproduces many
of the different responses seen in the individual ocean basins
(Figure 9.15), indicating a human-induced warming of the
world’s oceans with a complex vertical and geographical
structure that is simulated quite well by the two AOGCMs.
Barnett et al. (2005)

nd that the earlier conclusions of Barnett

et al. (2001) were not affected by the Levitus et al. (2005)
revisions to the Levitus et al. (2000) ocean heat content data.

In contrast, changes in solar forcing can potentially explain

only a small fraction of the observationally based estimates of
the increase in ocean heat content (Crowley et al., 2003),

and

the cooling in

uence of natural (volcanic) and anthropogenic

aerosols would have slowed ocean warming over the last half
century. Delworth et al. (2005)

nd a delay of several decades and

a reduction in the magnitude of the warming of approximately
two-thirds in simulations with the GFDL-CM2 model that
included these forcings compared to the response to increasing
greenhouse gases alone, consistent with results based on an
upwelling diffusion EBM (Crowley et al., 2003). Reductions
in ocean heat content are found following volcanic eruptions in
climate simulations (Church et al., 2005), including a persistent
centennial time-scale signal of ocean cooling at depth following
the eruption of Krakatoa (Gleckler et al., 2006).

Although the heat uptake in the ocean cannot be explained

without invoking anthropogenic forcing, there is some evidence
that the models have overestimated how rapidly heat has penetrated
below the ocean’s mixed layer (Forest et al., 2006; see also Figure
9.15). In simulations that include natural forcings in addition to
anthropogenic forcings, eight coupled climate models simulate
heat uptake of 0.26 ± 0.06 W m

–2

(±1 standard deviation) for

1961 to 2003, whereas observations of ocean temperature changes
indicate a heat uptake of 0.21 ± 0.04 W m

–2

(Section 5.2.2.1). These

could be consistent within their uncertainties but might indicate a
tendency of climate models to overestimate ocean heat uptake.

In addition, the interannual to decadal variability seen in

Levitus et al. (2000, 2005) (Section 5.2.2) is underestimated
by models; Gregory et al. (2004) show signi

cant differences

between observed and modelled interannual deviations from
a linear trend in

ve-year running means of world ocean heat

content above 3,000 m for 1957 to 1994. While some studies

706

Understanding and Attributing Climate Change

Chapter 9

note the potential importance of the choice of in

lling method

in poorly sampled regions (Gregory et al., 2004; AchutaRao
et al., 2006), the consistency of the differently processed data
from the Levitus et al. (2005), Ishii et al. (2006) and Willis et
al. (2004) analyses adds con

dence to their use for analysing

trends in climate change studies (Chapter 5). Gregory et al.
(2004) show that agreement between models and observations
is better in the well-observed upper ocean (above 300 m) in the
NH and that there is large sensitivity to the method of in

lling

the observational data set outside this well-observed region. They

nd a strong maximum in variability in the Levitus data set at

around 500 m depth that is not seen in HadCM3 simulations,
a possible indication of model de

ciency or an artefact in the

Levitus data. AchutaRao et al. (2006) also

nd that observational

estimates of temperature variability over much of the oceans may
be substantially affected by sparse observational coverage and
the method of in

lling.

9.5.1.2 Water Mass Properties

Interior water masses, which are directly ventilated at the

ocean surface, act to integrate highly variable surface changes
in heat and freshwater, and could therefore provide indicators
of global change (Stark et al., 2006). Some studies have

attempted to investigate changes in three-dimensional water
mass properties (Section 5.3). Sub-Antarctic Mode Water
(SAMW) and the subtropical gyres have warmed in the Indian
and Paci

c basins since the 1960s, waters at high latitudes

have freshened in the upper 500 m and salinity has increased
in some of the subtropical gyres. These changes are consistent
with an increase in meridional moisture

ux over the oceans

over the last 50 years leading to increased precipitation at high
latitudes (Section 5.2.3; Wong et al., 1999) and a reduction in
the difference between precipitation and evaporation at mid-
latitudes (Section 5.6). This suggests that the ocean might
integrate rainfall changes to produce detectable salinity changes.
Boyer et al. (2005) estimated linear trends in salinity for the
global ocean from 1955 to 1998 that indicate salini

cation in the

Antarctic Polar Frontal Zone around 40°S and in the subtropical
North Atlantic, and freshening in the sub-polar Atlantic (Figures
5.5 and 5.7). However, variations in other terms (e.g., ocean
freshwater transport) may be contributing substantially to the
observed salinity changes and have not been quanti

ed.

An observed freshening of SAMW in the South Indian Ocean

between the 1960s and 1990s has been shown to be consistent
with anthropogenically forced simulations from HadCM3
(Banks et al., 2000) but care should be taken in interpreting
sparse hydrographic data, since apparent trends could re

ect

Figure 9.15.

Strength of observed and model-simulated warming signal by depth for the World Ocean and for each ocean basin individually (in

C, see Barnett et al., 2005

and Pierce et al., 2006 for calculation of signal strength). For ocean basins, the signal is estimated from PCM (Table 8.1) while for the World Ocean it is estimated from both PCM
and HadCM3 (Table 8.1). Red dots represent the projection of the observed temperature changes onto the normalised model-based pattern of warming. They show substantial
basin-to-basin differences in how the oceans have warmed over the past 40 years, although all oceans have experienced net warming over that interval. The red bars represent
the ±2 standard deviation limits associated with sampling uncertainty. The blue crosshatched swaths represent the 90% confi dence limits of the natural internal variability
strength. The green crosshatched swaths represent the range of the anthropogenically forced signal estimates from different realisations of identically forced simulations
with the PCM model for each ocean basin (the smaller dots within the green swaths are the individual realisations) and the green shaded regions represent the range of
anthropogenically forced signal estimates from different realisations of identically forced simulations with the PCM and HadCM3 models for the World Ocean (note that PCM and
HadCM3 use different representations of anthropogenic forcing). The ensemble-averaged strength of the warming signal in four PCM simulations with solar and volcanic forcing
is also shown (grey triangles). From Barnett et al. (2005) and Pierce et al. (2006).

707

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Understanding and Attributing Climate Change

natural variability or the aliased effect of changing observational
coverage. Although SAMW was fresher along isopycnals in 1987
than in the 1960s, in 2002 the salinity was again near the 1960s
values (Bindoff and McDougall, 2000; Bryden et al., 2003).
An analysis of an ocean model forced by observed atmospheric

uxes and SSTs indicates that this is likely associated with

natural variability (Murray et al., 2007), a result supported by
an analysis of 20th-century simulations with HadCM3 which
shows that it is not possible to reject the null hypothesis that the
observed differences are due to internal variability (Stark et al.,
2006), although this model does project a long-term freshening
trend in the 21st century due to the large-scale response to surface
heating and hydrological changes (Banks et al., 2000).

9.5.1.3

Changes in the Meridional Overturning
Circulation

It is possible that anthropogenic and natural forcing may

have in

uenced the MOC in the Atlantic (see also Box 5.1).

One possible oceanic consequence of climate change is a
slowing down or even halting of the MOC. An estimate of the
overturning circulation and associated heat transport based on
a trans-Atlantic section along latitude 25°N indicates that the
Atlantic MOC has slowed by about 30% over

ve samples taken

between 1957 and 2004 (Bryden et al., 2005), although given
the infrequent sampling and considerable variability it is not
clear whether the trend estimate is robust (Box 5.1). Freshening
of North East Atlantic Deep Water has been observed (Dickson
et al., 2002; Curry et al., 2003; Figure 5.6) and has been
interpreted as being consistent with an enhanced difference
between precipitation and evaporation at high latitudes and a
possible slowing down of the MOC. Wu et al. (2004) show that
the observed freshening trend is well reproduced by an ensemble
of HadCM3 simulations that includes both anthropogenic
and natural forcings, but this freshening coincides with a
strengthening rather than a weakening trend in the MOC.
Therefore, this analysis is not consistent with an interpretation
of the observed freshening trends in the North Atlantic as an
early signal of a slowdown of the thermohaline circulation.
Dickson et al. (2002) propose a possible role for the Arctic in

driving the observed freshening of the subpolar North Atlantic.
Wu et al. (2005) show that observed increases in arctic river

ow (Peterson et al., 2002) are well simulated by HadCM3

including anthropogenic and natural forcings and propose that
this increase is anthropogenic, since it is not seen in HadCM3
simulations including just natural forcing factors. However,
the relationship between this increased source of freshwater
and freshening in the Labrador Sea is not clear in the HadCM3
simulations, since Wu et al. (2007)

nd that recent freshening

in the Labrador Sea is simulated by the model when it is driven
by natural rather than anthropogenic forcings. Importantly,
freshening is also associated with decadal and multi-decadal
variability, with links to the NAO (Box 5.1) and the AMO (Box
5.1; Vellinga and Wu, 2004; Knight et al., 2005).

9.5.2 Sea

Level

A precondition for attributing changes in sea level rise

to anthropogenic forcing is that model-based estimates of
historical global mean sea level rise should be consistent with
observational estimates. Although AOGCM simulations of global
mean surface air temperature trends are generally consistent
with observations (Section 9.4.1, Figure 9.5), consistency with
surface air temperature alone does not guarantee a realistic
simulation of thermal expansion, as there may be compensating
errors among climate sensitivity, ocean heat uptake and radiative
forcing (see, e.g., Raper et al., 2002, see also Section 9.6).
Model simulations also offer the possibility of attributing past
sea level changes to particular forcing factors. The observational
budget for sea level (Section 5.5.6) assesses the periods 1961 to
2003 and 1993 to 2003. Table 9.2 evaluates the same terms from
20C3M simulations in the MMD at PCMDI, although most
20C3M simulations end earlier (between 1999 and 2002), so the
comparison is not quite exact.

Simulations including natural as well as anthropogenic

forcings (the ‘ALL’ models in Table 9.2) generally have smaller
ocean heat uptake during the period 1961 to 2003 than those
without volcanic forcing, since several large volcanic eruptions
cooled the climate during this period (Gleckler et al., 2006).
This leads to a better agreement of those simulations with

Table 9.2.

Components of the rate of global mean sea level rise (mm yr

–1

) from models and observations. All ranges are 5 to 95% confi dence intervals. The observational

components and the observed rate of sea level rise (‘Obs’ column) are repeated from Section 5.5.6 and Table 5.3. The ‘ALL’ column is computed (following the methods of
Gregory and Huybrechts, 2006 and Section 10.6.3.1) from eight 20C3M simulations that include both natural and anthropogenic forcings (models 3, 9, 11, 12, 14, 15, 19 and
21; see Table 8.1), and the ‘ALL/ANT’ column from 16 simulations: the eight ALL and eight others that have anthropogenic forcings only (models 4, 6, 7, 8, 13, 16, 20 and 22;
see Table 8.1).

1961–2003

1993–2003

Obs

ALL

ALL/ANT

Obs

ALL

ALL/ANT

Thermal expansion

0.42 ± 0.12

0.5 ± 0.2

0.7 ± 0.4

1.60 ± 0.50

1.5 ± 0.7

1.2 ± 0.9

Glaciers and ice caps

0.50 ± 0.18

0.5 ± 0.2

0.5 ± 0.3

0.77 ± 0.22

0.7 ± 0.3

0.8 ± 0.3

Ice sheets (observed)

0.19 ± 0.43

0.41 ± 0.35

Sum of components

1.1 ± 0.5

1.2 ± 0.5

1.4 ± 0.7

2.8 ± 0.7

2.6 ± 0.8

2.4 ± 1.0

Observed rate of rise

1.8 ± 0.5

3.1 ± 0.7

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Understanding and Attributing Climate Change

Chapter 9

thermal expansion estimates based on observed ocean warming
(Section 5.5.3) than for the complete set of model simulations
(‘ALL/ANT’ in Table 9.2). For 1993 to 2003, the models that
include natural forcings agree well with observations. Although
this result is somewhat uncertain because the simulations end
at various dates from 1999 onwards, it accords with results
obtained by Church et al. (2005) using the PCM and Gregory et
al. (2006) using HadCM3, which suggest that 0.5 mm yr

–1

of the

trend in the last decade may result from warming as a recovery
from the Mt. Pinatubo eruption of 1991. Comparison of the
results for 1961 to 2003 and 1993 to 2003 shows that volcanoes
in

uence the ocean differently over shorter and longer periods.

The rapid expansion of 1993 to 2003 was caused, in part, by
rapid warming of the upper ocean following the cooling due to
the Mt. Pinatubo eruption, whereas the multi-decadal response
is affected by the much longer persistence in the deep ocean of
cool anomalies caused by volcanic eruptions (Delworth et al.,
2005; Gleckler et al., 2006; Gregory et al., 2006).

Both observations and model results indicate that the global

average mass balance of glaciers and ice caps depends linearly
on global average temperature change, but observations of
accelerated mass loss in recent years suggest a greater sensitivity
than simulated by models. The global average temperature
change simulated by AOGCMs gives a good match to the
observational estimates of the contribution of glaciers and ice
caps to sea level change in 1961 to 2003 and 1993 to 2003
(Table 9.2) with the assumptions that the global average mass
balance sensitivity is 0.80 mm yr

–1

°C

–1

(sea level equivalent)

and that the climate of 1900 to 1929 was 0.16°C warmer than
the temperature required to maintain the steady state for glaciers
(see discussion in Section 10.6.3.1 and Appendix 10.A).

Calculations of ice sheet surface mass balance changes

due to climate change (following the methods of Gregory
and Huybrechts, 2006 and Section 10.6.3.1) indicate small
but uncertain contributions during 1993 to 2003 of 0.1 ±
0.1 mm yr

–1

(5 to 95% range) from Greenland and –0.2 ±

0.4 mm yr

–1

from Antarctica, the latter being negative because

rising temperature in AOGCM simulations leads to greater
snow accumulation (but negligible melting) at present. The
observational estimates (Sections 4.6.2 and 5.5.6) are 0.21 ± 0.07
mm yr

–1

for Greenland and 0.21 ± 0.35 mm yr

–1

for Antarctica.

For both ice sheets, there is a signi

cant contribution from recent

accelerations in ice

ow leading to greater discharge of ice into

the sea, an effect that is not included in the models because its
causes and mechanisms are not yet properly understood (see
Sections 4.6.2 and 10.6.4 for discussion). Hence, the surface
mass balance model underestimates the sea level contribution
from ice sheet melting. Model-based and observational estimates
may also differ because the model-based estimates are obtained
using estimates of the correlation between global mean climate
change and local climate change over the ice sheets in the
21st century under SRES scenarios. This relationship may not
represent recent changes over the ice sheets.

Summing the modelled thermal expansion, global glacier and

ice cap contributions and the observational estimates of the ice

sheet contributions results in totals that lie below the observed
rates of global mean sea level rise during 1961 to 2003 and
1993 to 2003. As shown by Table 9.2, the terms are reasonably
well reproduced by the models. Nevertheless, the discrepancy
in the total, especially for 1961 to 2003, indicates the lack of a
satisfactory explanation of sea level rise. This is also a dif

culty

for the observational budget (discussed in Section 5.5.6).

A discrepancy between model and observations could also

be partly explained by the internally generated variability of
the climate system, which control simulations suggest could
give a standard deviation in the thermal expansion component
of ~0.2 mm yr

–1

in 10-year trends. This variability may be

underestimated by models, since observations give a standard
deviation in 10-year trends of 0.7 mm yr

–1

in thermal expansion

(see Sections 5.5.3 and Section 9.5.1.1; Gregory et al., 2006).

Since recent warming and thermal expansion are likely

largely anthropogenic (Section 9.5.1.1), the model results
suggest that the greater rate of rise in 1993 to 2003 than in 1961
to 2003 could have been caused by rising anthropogenic forcing.
However, tide gauge estimates suggests larger variability than
models in 10-year trends, and that rates as large as that observed
during 1993 to 2003 occurred in previous decades (Section
5.5.2.4).

Overall, it is very likely that the response to anthropogenic

forcing contributed to sea level rise during the latter half of
the 20th century. Models including anthropogenic and natural
forcing simulate the observed thermal expansion since 1961
reasonably well. Anthropogenic forcing dominates the surface
temperature change simulated by models, and has likely
contributed to the observed warming of the upper ocean and
widespread glacier retreat. It is very unlikely that the warming
during the past half century is due only to known natural causes.
Lack of studies quantifying the contribution of anthropogenic
forcing to ocean heat content increase and glacier melting,
and the fact that the observational budget is not closed,
make it dif

cult to estimate the anthropogenic contribution.

Nevertheless, an expert assessment based on modelling and
ocean heat content studies suggests that anthropogenic forcing
has likely contributed at least one-quarter to one-half of the sea
level rise during the second half of the 20th century (see also
Woodworth et al., 2004).

Anthropogenic forcing is also expected to produce an

accelerating rate of sea level rise (Woodworth et al., 2004). On
the other hand, natural forcings could have increased the rate of
sea level rise in the early 20th century and decreased it later in
the 20th century, thus producing a steadier rate of rise during
the 20th century when combined with anthropogenic forcing
(Crowley et al., 2003; Gregory et al., 2006). Observational
evidence for acceleration during the 20th century is equivocal,
but the rate of sea level rise was greater in the 20th than in the
19th century (Section 5.5.2.4). An onset of higher rates of rise in
the early 19th century could have been caused by natural factors,
in particular the recovery from the Tambora eruption of 1815
(Crowley et al., 2003; Gregory et al., 2006), with anthropogenic
forcing becoming important later in the 19th century.

709

Chapter 9

Understanding and Attributing Climate Change

9.5.3 Atmospheric

Circulation

Changes

Natural low-frequency variability of the climate system is

dominated by a small number of large-scale circulation patterns
such as ENSO, the Paci

c Decadal Oscillation (PDO), and the

NAM and Southern Annular Mode (SAM) (Section 3.6 and
Box 3.4). The impact of these modes on terrestrial climate on
annual to decadal time scales can be profound, but the extent to
which they can be excited or altered by external forcing remains
uncertain. While some modes might be expected to change as a
result of anthropogenic effects such as the enhanced greenhouse
effect, there is little

a priori

expectation about the direction or

magnitude of such changes.

9.5.3.1

El Niño-Southern Oscillation/Paci

Decadal Oscillation

The El Niño-Southern Oscillation is the leading mode

of variability in the tropical Paci

c, and it has impacts on

climate around the globe (Section 3.6.2). There have been
multi-decadal oscillations in the ENSO index (conventionally
de

ned as a mean SST anomaly in the eastern equatorial

Paci

c) throughout the 20th century, with more intense El Niño

events since the late 1970s, which may re

ect in part a mean

warming of the eastern equatorial Paci

c (Mendelssohn et al.,

2005). Model projections of future climate change generally
show a mean state shift towards more El-Niño-like conditions,
with enhanced warming in the eastern tropical Paci

c and a

weakened Walker Circulation (Section 10.3.5.3); there is some
evidence that such a weakening has been observed over the past
140 years (Vecchi et al., 2006). While some simulations of the
response to anthropogenic in

uence have shown an increase

in ENSO variability in response to greenhouse gas increases
(Timmermann, 1999; Timmermann et al., 1999; Collins,
2000b), others have shown no change (e.g., Collins, 2000a)
or a decrease in variability (Knutson et al., 1997). A recent
survey of the simulated response to atmospheric CO

doubling

in 15 MMD AOGCMs (Merry

eld, 2006)

nds that three of

the models exhibited signi

cant increases in ENSO variability,

ve exhibited signi

cant decreases and seven exhibited no

signi

cant change. Thus, as yet there is no detectable change

in ENSO variability in the observations, and no consistent
picture of how it might be expected to change in response to
anthropogenic forcing (Section 10.3.5.3).

Decadal variability in the North Paci

c is characterised

by variations in the strength of the Aleutian Low coupled to
changes in North Paci

c SST (Sections 3.6.3 and 8.4.2). The

leading mode of decadal variability in the North Paci

c is

usually referred to as the PDO, and has a spatial structure in
the atmosphere and upper North Paci

c Ocean similar to the

pattern that is associated with ENSO. One recent study showed
a consistent tendency towards the positive phase of the PDO
in observations and simulations with the MIROC model that
included anthropogenic forcing (Shiogama et al., 2005),

although differences between the observed and simulated PDO
patterns, and the lack of additional studies, limit con

dence in

these

ndings.

9.5.3.2

North Atlantic Oscillation/Northern Annular
Mode

The NAM is an approximately zonally symmetric mode of

variability in the NH (Thompson and Wallace, 1998), and the
NAO (Hurrell, 1996) may be viewed as its Atlantic counterpart
(Section 3.6.4). The NAM index exhibited a pronounced trend
towards its positive phase between the 1960s and the 1990s,
corresponding to a decrease in surface pressure over the Arctic
and an increase over the subtropical North Atlantic (see Section
3.6.4; see also Hurrell, 1996; Thompson et al., 2000; Gillett
et al., 2003a). Several studies have shown this trend to be
inconsistent with simulated internal variability (Osborn et al.,
1999; Gillett et al., 2000, 2002b; Osborn, 2004; Gillett, 2005).
Although the NAM index has decreased somewhat since its
peak in the mid-1990s, the trend calculated over recent decades
remains signi

cant at the 5% signi

cance level compared to

simulated internal variability in most models (Osborn, 2004;
Gillett, 2005), although one study found that the NAO index
trend was marginally consistent with internal variability in one
model (Selten et al., 2004).

Most climate models simulate some increase in the NAM

index in response to increased concentrations of greenhouse
gases (Fyfe et al., 1999; Paeth et al., 1999; Shindell et al., 1999;
Gillett et al., 2003a,b; Osborn, 2004; Rauthe et al., 2004),
although the simulated trend is generally smaller than that
observed (Gillett et al., 2002b, 2003b; Osborn, 2004; Gillett,
2005; and see Figure 9.16). Simulated sea level pressure
changes are generally found to project more strongly onto the
hemispheric NAM index than onto a two-station NAO index
(Gillett et al., 2002b; Osborn, 2004; Rauthe et al., 2004).
Some studies have postulated an in

uence of ozone depletion

(Volodin and Galin, 1999; Shindell et al., 2001a), changes in
solar irradiance (Shindell et al., 2001a) and volcanic eruptions
(Kirchner et al., 1999; Shindell et al., 2001a; Stenchikov et al.,
2006) on the NAM. Stenchikov et al. (2006) examine changes
in sea level pressure following nine volcanic eruptions in the
MMD 20C3M ensemble of 20th-century simulations, and

nd that the models simulated a positive NAM response to

the volcanoes, albeit one that was smaller than that observed.
Nevertheless, ozone, solar and volcanic forcing changes are
generally not found to have made a large contribution to the
observed NAM trend over recent decades (Shindell et al.,
2001a; Gillett et al., 2003a). Simulations incorporating all
the major anthropogenic and natural forcings from the MMD
20C3M ensemble generally showed some increase in the NAM
over the latter part of the 20th century (Gillett, 2005; Miller et
al., 2006; and see Figure 9.16), although the simulated trend is
in all cases smaller than that observed, indicating inconsistency
between simulated and observed trends at the 5% signi

cance

level (Gillett, 2005).

710

Understanding and Attributing Climate Change

Chapter 9

The mechanisms underlying NH circulation changes remain

open to debate. Simulations in which observed SST changes,
which may in part be externally forced, were prescribed either
globally or in the tropics alone were able to capture around
half of the recent trend towards the positive phase of the NAO
(Hoerling et al., 2005; Hurrell et al., 2005), suggesting that the
trend may in part relate to SST changes, particularly over the
Indian Ocean (Hoerling et al., 2005). Another simulation in
which a realistic trend in stratospheric winds was prescribed
was able to reproduce the observed trend in the NAO (Scaife
et al., 2005). Rind et al. (2005a,b)

nd that both stratospheric

changes and changes in SST can force changes in the NAM
and NAO, with changes in SSTs being the dominant forcing
mechanism.

Over the period 1968 to 1997, the

trend in the NAM was associated with
approximately 50% of the winter surface
warming in Eurasia, due to increased
advection of maritime air onto the
continent, but only a small fraction (16%)
of the NH extratropical annual mean
warming trend (Thompson et al., 2000;
Section 3.6.4 and Figure 3.30). It was
also associated with a decrease in winter
precipitation over southern Europe and
an increase over northern Europe, due
the northward displacement of the storm
track (Thompson et al., 2000).

9.5.3.3 Southern

Annular

Mode

The SAM is more zonally symmetric

than its NH counterpart (Thompson and
Wallace, 2000; Section 3.6.5). It too has
exhibited a pronounced upward trend
over the past 30 years, corresponding
to a decrease in surface pressure over
the Antarctic and an increase over the
southern mid-latitudes (Figure 9.16),
although the mean SAM index since
2000 has been below the mean in the late
1990s, but above the long term mean
(Figure 3.32). An upward trend in the
SAM has occurred in all seasons, but the
largest trend has been observed during
the southern summer (Thompson et al.,
2000; Marshall, 2003). Marshall et al.
(2004) show that observed trends in the
SAM are not consistent with simulated
internal variability in HadCM3,
suggesting an external cause. On the
other hand, Jones and Widmann (2004)
develop a 95-year reconstruction of the
summer SAM index based largely on
mid-latitude pressure measurements, and

nd that their reconstructed SAM index

was as high in the early 1960s as in the late 1990s. However, a
more reliable reconstruction from 1958, using more Antarctic
data and a different method, indicates that the summer SAM
index was higher at the end of the 1990s than at any other time
in the observed record (Marshall et al., 2004).

Based on an analysis of the structure and seasonality

of the observed trends in SH circulation, Thompson and
Solomon (2002) suggest that they have been largely induced
by stratospheric ozone depletion. Several modelling studies
simulate an upward trend in the SAM in response to stratospheric
ozone depletion (Sexton, 2001; Gillett and Thompson, 2003;
Marshall et al., 2004; Shindell and Schmidt, 2004; Arblaster and
Meehl, 2006; Miller et al., 2006), particularly in the southern
summer. Stratospheric ozone depletion cools and strengthens

Figure 9.16.

Comparison between observed (top) and model-simulated (bottom) December to February sea-

level pressure trends (hPa per 50 years) in the NH (left panels) and SH (right panels) based on decadal means
for the period 1955 to 2005. Observed trends are based on the Hadley Centre Mean Sea Level Pressure data
set (HadSLP2r, an infi lled observational data set; Allan and Ansell, 2006). Model-simulated trends are the mean
simulated response to greenhouse gas, sulphate aerosol, stratospheric ozone, volcanic aerosol and solar irradiance
changes from eight coupled models (CCSM3, GFDL-CM2.0, GFDL-CM2.1, GISS-EH, GISS-ER, MIROC3.2(medres),
PCM, UKMO-HadCM3; see Table 8.1 for model descriptions). Streamlines indicate the direction of the trends
(m s

–1

per 50 years) in the geostrophic wind velocity derived from the trends in sea level pressure, and the shading

of the streamlines indicates the magnitude of the change, with darker streamlines corresponding to larger changes
in geostrophic wind. White areas in all panels indicate regions with insuffi cient station-based measurements to
constrain analysis. Further explanation of the construction of this fi gure is provided in the Supplementary Material,
Appendix 9.C. Updated after Gillett et al. (2005).

711

Chapter 9

Understanding and Attributing Climate Change

the antarctic stratospheric vortex in spring, and observations
and models indicate that this strengthening of the stratospheric
westerlies can be communicated downwards into the troposphere
(Thompson and Solomon, 2002; Gillett and Thompson, 2003).
While ozone depletion may be the dominant cause of the trends,
other studies have indicated that greenhouse gas increases
have also likely contributed (Fyfe et al., 1999; Kushner et al.,
2001; Stone et al., 2001; Cai et al., 2003; Marshall et al., 2004;
Shindell and Schmidt, 2004; Stone and Fyfe, 2005; Arblaster
and Meehl, 2006). During the southern summer, the trend in the
SAM has been associated with the observed increase of about
3 m s

–1

in the circumpolar westerly winds over the Southern

Ocean. This circulation change is estimated to explain most
of the summer surface cooling over the Antarctic Plateau, and
about one-third to one-half of the warming of the Antarctic
Peninsula (Thompson and Solomon, 2002; Carril et al., 2005;
Section 3.6.5), with the largest in

uence on the eastern side of

the Peninsula (Marshall et al., 2006), although other factors are
also likely to have contributed to this warming (Vaughan et al.,
2001).

9.5.3.4

Sea Level Pressure Detection and Attribution

Global December to February sea level pressure changes

observed over the past 50 years have been shown to be
inconsistent with simulated internal variability (Gillett et al.,
2003b, 2005), but are consistent with the simulated response
to greenhouse gas, stratospheric ozone, sulphate aerosol,
volcanic aerosol and solar irradiance changes based on 20C3M
simulations by eight MMD coupled models (Gillett et al., 2005;
Figure 9.16). This result is dominated by the SH, where the
inclusion of stratospheric ozone depletion leads to consistency
between simulated and observed sea level pressure changes. In
the NH, simulated sea level pressure trends are much smaller
than those observed (Gillett, 2005). Global mean sea level
pressure changes associated with increases in atmospheric
water vapour are small in comparison to the spatial variations
in the observed change in sea level pressure, and are hard to
detect because of large observational uncertainties (Trenberth
and Smith, 2005).

9.5.3.5 Monsoon

Circulation

The current understanding of climate change in the monsoon

regions remains one of considerable uncertainty with respect to
circulation and precipitation (Sections 3.7, 8.4.10 and 10.3.5.2).
The Asian monsoon circulation in the MMD models was found
to decrease by 15% by the late 21st century under the SRES
A1B scenario (Tanaka et al., 2005; Ueda et al., 2006), but
trends during the 20th century were not examined. Ramanathan
et al. (2005) simulate a pronounced weakening of the Asian
monsoon circulation between 1985 and 2000 in response to
black carbon aerosol increases. Chase et al. (2003) examine
changes in several indices of four major tropical monsoonal
circulations (Southeastern Asia, western Africa, eastern Africa
and the Australia/Maritime Continent) for the period 1950 to

1998. They

nd signi

cantly diminished monsoonal circulation

in each region, although this result is uncertain due to changes
in the observing system affecting the NCEP reanalysis (Section
3.7). These results are consistent with simulations (Ramanathan
et al., 2005; Tanaka et al., 2005) of weakening monsoons due to
anthropogenic factors, but further model and empirical studies
are required to con

rm this.

9.5.3.6 Tropical

Cyclones

Several recent events, including the active North Atlantic

hurricane seasons of 2004 and 2005, the unusual development
of a cyclonic system in the subtropical South Atlantic that hit
the coast of southern Brazil in March 2004 (e.g., Pezza and
Simmonds, 2005) and a hurricane close to the Iberian Peninsula
in October 2005, have raised public and media interest in the
possible effects of climate change on tropical cyclone activity.
The TAR concluded that there was ‘no compelling evidence
to indicate that the characteristics of tropical and extratropical
storms have changed’, but that an increase in tropical peak wind
intensities was likely to occur in some areas with an enhanced
greenhouse effect (see also Box 3.5 and Trenberth, 2005). The
spatial resolution of most climate models limits their ability to
realistically simulate tropical cyclones (Section 8.5.3), therefore,
most studies of projected changes in hurricanes have either
used time slice experiments with high-resolution atmosphere
models and prescribed SSTs, or embedded hurricane models
in lower-resolution General Circulation Models (GCMs)
(Section 10.3.6.3). While results vary somewhat, these studies
generally indicate a reduced frequency of tropical cyclones in
response to enhanced greenhouse gas forcing, but an increase
in the intensity of the most intense cyclones (Section 10.3.6.3).
It has been suggested that the simulated frequency reduction
may result from a decrease in radiative cooling associated
with increased CO

concentration (Sugi and Yoshimura, 2004;

Yoshimura and Sugi, 2005; Section 10.3.6.3; Box 3.5), while
the enhanced atmospheric water vapour concentration under
greenhouse warming increases available potential energy and
thus cyclone intensity (Trenberth, 2005).

There continues to be little evidence of any trend in the

observed total frequency of global tropical cyclones, at least
up until the late 1990s (e.g., Solow and Moore, 2002; Elsner et
al., 2004; Pielke et al., 2005; Webster et al., 2005). However,
there is some evidence that tropical cyclone intensity may have
increased. Globally, Webster et al. (2005)

nd a strong increase in

the number and proportion of the most intense tropical cyclones
over the past 35 years. Emanuel (2005) reports a marked increase
since the mid-1970s in the Power Dissipation Index (PDI), an
index of the destructiveness of tropical cyclones (essentially
an integral, over the lifetime of the cyclone, of the cube of the
maximum wind speed), in the western North Paci

c and North

Atlantic, re

ecting the apparent increases in both the duration

of cyclones and their peak intensity. Several studies have
shown that tropical cyclone activity was also high in the 1950
to 1970 period in the North Atlantic (Landsea, 2005) and North
Paci

c (Chan, 2006), although recent values of the PDI may be

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Chapter 9

Figure 9.17.

Global mean (ocean-only) anomalies relative to 1987 to 2000 in

column-integrated water vapour (%) from simulations with the GFDL AM2-LM2
AGCM forced with observed SSTs (red), and satellite observations from SSM/I (black,
Wentz and Schabel, 2000). From Soden et al. (2005).

higher than those recorded previously (Emanuel, 2005; Section
3.8.3). Emanuel (2005) and Elsner et al. (2006) report a strong
correlation between the PDI and tropical Atlantic SSTs, although
Chan and Liu (2004)

nd no analogous relationship in the western

North Paci

c. While changes in Atlantic SSTs have been linked

in part to the AMO, the recent warming appears to be mainly
associated with increasing global temperatures (Section 3.8.3.2;
Mann and Emanuel, 2006; Trenberth and Shea, 2006). Tropical
cyclone development is also strongly in

uenced by vertical wind

shear and static stability (Box 3.5). While increasing greenhouse
gas concentrations have likely contributed to a warming of
SSTs, effects on static stability and wind shear may have partly
opposed this in

uence on tropical cyclone formation (Box 3.5).

Thus, detection and attribution of observed changes in hurricane
intensity or frequency due to external in

uences remains dif

cult

because of de

ciencies in theoretical understanding of tropical

cyclones, their modelling and their long-term monitoring
(e.g., Emanuel, 2005; Landsea, 2005; Pielke, 2005). These
de

ciencies preclude a stronger conclusion than an assessment

that anthropogenic factors more likely than not have contributed
to an increase in tropical cyclone intensity.

9.5.3.7 Extratropical

Cyclones

Simulations of 21st-century climate change in the MMD

20C3M model ensemble generally exhibit a decrease in the total
number of extratropical cyclones in both hemispheres, but an
increase in the number of the most intense events (Lambert and
Fyfe, 2006), although this behaviour is not reproduced by all
models (Bengtsson et al., 2006; Section 10.3.6.4). Many 21st-
century simulations also show a poleward shift in the storm
tracks in both hemispheres (Bengtsson et al., 2006; Section
10.3.6.4). Recent observational studies of winter NH storms
have found a poleward shift in storm tracks and increased storm
intensity, but a decrease in total storm numbers, in the second
half of the 20th century (Section 3.5.3). Analysis of observed
wind and signi

cant wave height suggests an increase in storm

activity in the NH. In the SH, the storm track has also shifted
poleward, with increases in the radius and depth of storms,
but decreases in their frequency. These features appear to be
associated with the observed trends in the SAM and NAM. Thus,
simulated and observed changes in extratropical cyclones are
broadly consistent, but an anthropogenic in

uence has not yet

been detected, owing to large internal variability and problems
due to changes in observing systems (Section 3.5.3).

9.5.4 Precipitation

9.5.4.1

Changes in Atmospheric Water Vapour

The amount of moisture in the atmosphere is expected

to increase in a warming climate (Trenberth et al., 2005)
because saturation vapour pressure increases with temperature
according to the Clausius-Clapeyron equation. Satellite-borne
Special Sensor Microwave/Imager (SSM/I) measurements
of water vapour since 1988 are of higher quality than either

radiosonde or reanalysis data (Trenberth et al., 2005) and
show a statistically signi

cant upward trend in precipitable

(column-integrated) water of 1.2 ± 0.3 % per decade averaged
over the global oceans (Section 3.4.2.1). Soden et al. (2005)
demonstrate that the observed changes, including the upward
trend, are well simulated in the GFDL atmospheric model when
observed SSTs are prescribed (Figure 9.17). The simulation and
observations show common low-frequency variability, which
is largely associated with ENSO. Soden et al. (2005) also
demonstrate that upper-tropospheric changes in water vapour
are realistically simulated by the model. Observed warming
over the global oceans is likely largely anthropogenic (Figure
9.12), suggesting that anthropogenic in

uence has contributed

to the observed increase in atmospheric water vapour over the
oceans.

9.5.4.2

Global Precipitation Changes

The increased atmospheric moisture content associated

with warming might be expected to lead to increased global
mean precipitation (Section 9.5.4.1). Global annual land mean
precipitation showed a small, but uncertain, upward trend over
the 20th century of approximately 1.1 mm per decade

(Section

3.3.2.1 and Table 3.4). However, the record is characterised by
large inter-decadal variability, and global annual land mean
precipitation shows a non-signi

cant decrease since 1950

(Figure 9.18; see also Table 3.4).

9.5.4.2.1

Detection of external in

uence on precipitation

Mitchell et al. (1987) argue that global mean precipitation

changes should be controlled primarily by the energy budget
of the troposphere where the latent heat of condensation
is balanced by radiative cooling. Warming the troposphere
enhances the cooling rate, thereby increasing precipitation,
but this may be partly offset by a decrease in the ef

ciency of

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Chapter 9

Understanding and Attributing Climate Change

radiative cooling due to an increase in atmospheric CO

(Allen

and Ingram, 2002; Yang et al., 2003; Lambert et al., 2004;
Sugi and Yoshimura, 2004). This suggests that global mean
precipitation should respond more to changes in shortwave
forcing than CO

forcing, since shortwave forcings, such as

volcanic aerosol, alter the temperature of the troposphere
without affecting the ef

ciency of radiative cooling. This is

consistent with a simulated decrease in precipitation following
large volcanic eruptions (Robock and Liu, 1994; Broccoli et al.,
2003), and may explain why anthropogenic in

uence has not

been detected in measurements of global land mean precipitation

(Ziegler et al., 2003; Gillett et al., 2004b), although Lambert et
al. (2004) urge caution in applying the energy budget argument
to land-only data. Greenhouse-gas induced increases in global
precipitation may have also been offset by decreases due to
anthropogenic aerosols (Ramanathan et al., 2001).

Several studies have demonstrated that simulated land

mean precipitation in climate model integrations including
both natural and anthropogenic forcings is signi

cantly

correlated with that observed (Allen and Ingram, 2002; Gillett
et al., 2004b; Lambert et al., 2004), thereby detecting external
in

uence in observations of precipitation (see Section 8.3.1.2

for an evaluation of model-simulated precipitation). Lambert
et al. (2005) examine precipitation changes in simulations
of nine MMD 20C3M models including anthropogenic and
natural forcing (Figure 9.18a), and

nd that the responses to

combined anthropogenic and natural forcing simulated by

ve of the nine models are detectable in observed land mean

precipitation (Figure 9.18a). Lambert et al. (2004) detect the
response to shortwave forcing, but not longwave forcing,
in land mean precipitation using HadCM3, and Gillett et al.
(2004b) similarly detect the response to volcanic forcing using
the PCM. Climate models appear to underestimate the variance
of land mean precipitation compared to that observed (Gillett et
al., 2004b; Lambert et al., 2004, 2005), but it is unclear whether
this discrepancy results from an underestimated response
to shortwave forcing (Gillett et al., 2004b), underestimated
internal variability, errors in the observations, or a combination
of these.

Greenhouse gas increases are also expected to cause

enhanced horizontal transport of water vapour that is expected
to lead to a drying of the subtropics and parts of the tropics
(Kumar et al., 2004; Neelin et al., 2006), and a further increase
in precipitation in the equatorial region and at high latitudes
(Emori and Brown, 2005; Held and Soden, 2006). Simulations
of 20th-century zonal mean land precipitation generally
show an increase at high latitudes and near the equator, and
a decrease in the subtropics of the NH (Hulme et al., 1998;
Held and Soden, 2006; Figure 9.18b). Projections for the 21st
century show a similar effect (Figure 10.12). This simulated
drying of the northern subtropics and southward shift of the
Inter-Tropical Convergence Zone may relate in part to the
effects of sulphate aerosol (Rotstayn and Lohmann, 2002),
although simulations without aerosol effects also show drying
in the northern subtropics (Hulme et al., 1998). This pattern
of zonal mean precipitation changes is broadly consistent with
that observed over the 20th century (Figure 9.18b; Hulme et al.,
1998; Allen and Ingram, 2002; Rotstayn and Lohmann, 2002),
although the observed record is characterised by large inter-
decadal variability (Figure 3.15). The agreement between the
simulated and observed zonal mean precipitation trends is not
sensitive to the inclusion of forcing by volcanic eruptions in
the simulations, suggesting that anthropogenic in

uence may

be evident in this diagnostic.

Changes in runoff have been observed in many parts of the

world, with increases or decreases corresponding to changes in
precipitation (Section 3.3.4). Climate models suggest that runoff

Figure 9.18.

Simulated and observed anomalies (with respect to 1961-1990) in

terrestrial mean precipitation (a), and zonal mean precipitation trends 1901-1998
(b). Observations (thick black line) are based on a gridded data set of terrestrial
rain gauge measurements (Hulme et al., 1998). Model data are from 20th-century
MMD integrations with anthropogenic, solar and volcanic forcing from the following
coupled climate models (see Table 8.1 for model details): UKMO-HadCM3 (brown),
CCSM3 (dark blue), GFDL-CM2.0 (pale green), GFDL-CM2.1 (pale blue), GISS-EH
(red), GISS-ER (thin black), MIROC3.2(medres) (orange), MRI-CGCM2.3.2 (dashed
green) and PCM (pink). Coloured curves are ensemble means from individual
models. In (a), a fi ve-year running mean was applied to suppress other sources of
natural variability, such as ENSO. In (b), the grey band indicates the range of trends
simulated by individual ensemble members, and the thick dark blue line indicates
the multi-model ensemble mean. External infl uence in observations on global
terrestrial mean precipitation is detected with those precipitation simulations shown
by continuous lines in the top panel. Adapted from Lambert et al. (2005).

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Understanding and Attributing Climate Change

Chapter 9

will increase in regions where precipitation increases faster than
evaporation, such as at high northern latitudes (Section 10.3.2.3
and Figure 10.12; see also Milly et al., 2005; Wu et al., 2005).
Gedney et al. (2006) attribute increased continental runoff in
the latter decades of the 20th century in part to suppression of
transpiration due to CO

-induced stomatal closure. They

that observed climate changes (including precipitation changes)
alone are insuf

cient to explain the increased runoff, although

their result is subject to considerable uncertainty in the runoff
data. In addition, Qian et al. (2006) simulate observed runoff
changes in response to observed temperature and precipitation
alone, and Milly et al. (2005) demonstrate that 20th-century
runoff trends simulated by the MMD models are signi

cantly

correlated with observed runoff trends. Wu et al. (2005)
demonstrate that observed increases in arctic river discharge are
reproduced in coupled model simulations with anthropogenic
forcing, but not in simulations with natural forcings only.

Mid-latitude summer drying is another anticipated response

to greenhouse gas forcing (Section 10.3.6.1), and drying trends
have been observed in the both the NH and SH since the 1950s
(Section 3.3.4). Burke et al. (2006), using the HadCM3 model
with all natural and anthropogenic external forcings and a
global Palmer Drought Severity Index data set compiled from
observations by Dai et al. (2004), are able to formally detect
the observed global trend towards increased drought in the
second half of the 20th century, although the model trend is
weaker than observed and the relative contributions of natural
external forcings and anthropogenic forcings are not assessed.
The model also simulates some aspects of the spatial pattern
of observed drought trends, such as the trends across much of
Africa and southern Asia, but not others, such as the trend to
wetter conditions in Brazil and northwest Australia.

9.5.4.2.2

Changes in extreme precipitation

Allen and Ingram (2002) suggest that while global annual

mean precipitation is constrained by the energy budget of
the troposphere, extreme precipitation is constrained by the
atmospheric moisture content, as predicted by the Clausius-
Clapeyron equation. For a given change in temperature, they
therefore predict a larger change in extreme precipitation than
in mean precipitation, which is consistent with the HadCM3
response. Consistent with these

ndings, Emori and Brown

(2005) discuss physical mechanisms governing changes in the
dynamic and thermodynamic components of mean and extreme
precipitation and conclude that changes related to the dynamic
component (i.e., that due to circulation change) are secondary
factors in explaining the greater percentage increase in extreme
precipitation than in mean precipitation that is seen in models.
Meehl et al. (2005) demonstrate that tropical precipitation
intensity increases are related to water vapour increases,
while mid-latitude intensity increases are related to circulation
changes that affect the distribution of increased water vapour.

Climatological data show that the most intense precipitation

occurs in warm regions (Easterling et al., 2000) and diagnostic
analyses have shown that even without any change in total
precipitation, higher temperatures lead to a greater proportion of

total precipitation in heavy and very heavy precipitation events
(Karl and Trenberth, 2003). In addition, Groisman et al. (1999)
demonstrate empirically, and Katz (1999) theoretically, that as
total precipitation increases a greater proportion falls in heavy
and very heavy events if the frequency remains constant. Similar
characteristics are anticipated under global warming (Cubasch
et al., 2001; Semenov and Bengtsson, 2002; Trenberth et al.,
2003). Trenberth et al. (2005) point out that since the amount
of moisture in the atmosphere is likely to rise much faster as a
consequence of rising temperatures than the total precipitation,
this should lead to an increase in the intensity of storms, offset
by decreases in duration or frequency of events.

Model results also suggest that future changes in precipitation

extremes will likely be greater than changes in mean precipitation
(Section 10.3.6.1; see Section 8.5.2 for an evaluation of model-
simulated precipitation extremes). Simulated changes in
globally averaged annual mean and extreme precipitation appear
to be quite consistent between models. The greater and spatially
more uniform increases in heavy precipitation as compared to
mean precipitation may allow extreme precipitation change to
be more robustly detectable (Hegerl et al., 2004).

Evidence for changes in observations of short-duration

precipitation extremes varies with the region considered
(Alexander et al., 2006) and the analysis method

employed

(Folland et al., 2001; Section 3.8.2.2). Signi

cant increases in

observed extreme precipitation have been reported over some
parts of the world, for example over the USA, where the increase
is similar to changes expected under greenhouse warming
(e.g., Karl and Knight, 1998; Semenov and Bengtsson, 2002;
Groisman et al., 2005). However, a quantitative comparison
between area-based extreme events simulated in models and
station data remains dif

cult because of the different scales

involved (Osborn and Hulme, 1997). A

rst attempt based on

Frich et al. (2002) indices used

ngerprints from atmospheric

model simulations with prescribed SST (Kiktev et al., 2003)
and found little similarity between patterns of simulated
and observed rainfall extremes, in contrast to the qualitative
similarity found in other studies (Semenov and Bengtsson,
2002; Groisman et al., 2005). Tebaldi et al. (2006) report that
eight MMD 20C3M models show a general tendency towards
a greater frequency of heavy precipitation events over the past
four decades, most coherently at high latitudes of the NH,
broadly consistent with observed changes (Groisman et al.,
2005).

9.5.4.3

Regional Precipitation Changes

Observed trends in annual precipitation during the period

1901 to 2003 are shown in Figure 3.13 for regions in which
data is available. Responses to external forcing in regional
precipitation trends are expected to exhibit low signal-to-
noise ratios and are likely to exhibit strong spatial variations
because of the dependence of precipitation on atmospheric
circulation and on geographic factors such as orography. There
have been some suggestions, for speci

c regions, of a possible

anthropogenic in

uence on precipitation, which are discussed

below.

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Understanding and Attributing Climate Change

9.5.4.3.1 Sahel

drought

Rainfall decreased substantially across the Sahel from the

1950s until at least the late 1980s (Dai et al., 2004; Figure 9.19,
see also Figure 3.37). There has been a partial recovery since
about 1990, although rainfall has not returned to levels typical
of the period 1920 to 1965. Zeng (2003) note that two main
hypotheses have been proposed as a cause of the extended
drought: overgrazing and conversion of woodland to agriculture
increasing surface albedo and reducing moisture supply
to the atmosphere, and large-scale atmospheric circulation
changes related to decadal global SST changes that could be
of anthropogenic or natural origin (Nicholson, 2001). Black
carbon has also been suggested as a contributor (Menon et al.,
2002b). Taylor et al. (2002) examine the impact of land use
change with an atmospheric GCM forced only by estimates of
Sahelian land use change since 1961. They simulate a small
decrease in Sahel rainfall (around 5% by 1996) and conclude
that the impacts of recent land use changes are not large enough
to have been the principal cause of the drought.

Several recent studies have demonstrated that simulations

with a range of atmospheric models using prescribed observed
SSTs are able to reproduce observed decadal variations in
Sahel rainfall (Bader and Latif, 2003; Giannini et al., 2003;
Rowell, 2003; Haarsma et al., 2005; Held et al., 2005; Lu and
Delworth, 2005; see also Figure 9.19; Hoerling et al., 2006),
consistent with earlier

ndings (Folland, 1986; Rowell, 1996).

Hoerling et al. (2006) show that AGCMs with observed SST
changes typically underestimate the magnitude of the observed
precipitation changes, although the models and observations
are not inconsistent. These studies differ somewhat in terms of
which ocean SSTs they

nd to be most important: Giannini et

al. (2003) and Bader and Latif (2003) emphasize the role of
tropical Indian Ocean warming, Hoerling et al. (2006) attribute
the drying trend to a progressive warming of the South Atlantic
relative to the North Atlantic, and Rowell (2003)

nds that

Mediterranean SSTs are an additional important contributor
to decadal variations in Sahel rainfall. Based on a multi-model
ensemble of coupled model simulations Hoerling et al. (2006)
conclude that the observed drying trend in the Sahel is not
consistent with simulated internal variability alone.

Thus, recent research indicates that changes in SSTs are

probably the dominant in

uence on rainfall in the Sahel,

although land use changes possibly also contribute (Taylor et
al., 2002). But what has caused the differential SST changes?
Rotstayn and Lohmann (2002) propose that spatially varying,
anthropogenic sulphate aerosol forcing (both direct and indirect)
can alter low-latitude atmospheric circulation leading to a
decline in Sahel rainfall. They

nd a southward shift of tropical

rainfall due to a hemispheric asymmetry in the SST response
to changes in cloud albedo and lifetime in a climate simulation
forced with recent anthropogenic changes in sulphate aerosol.
Williams et al. (2001) also

nd a southward shift of tropical

rainfall as a response to the indirect effect of sulphate aerosol.
These results suggest that sulphate aerosol changes may have
led to reduced warming of the northern tropical oceans, which
in turn led to the decrease in Sahel rainfall, possibly enhanced

through land-atmosphere interaction, although a full attribution
analysis has yet to be conducted. Held et al. (2005) show that
historical climate simulations with the both the GFDL-CM2.0
and CM2.1 models (see Table 8.1 for details) exhibit drying
trends over the Sahel in the second half of the 20th century,
which they ascribe to a combination of greenhouse gas and
sulphate aerosol changes. The spatial pattern of the trends in
simulated rainfall also shows some agreement with observations.
However, Hoerling et al. (2006)

nd that eight other coupled

climate models with prescribed anthropogenic forcing do not
simulate signi

cant trends in Sahel rainfall over the 1950 to

1999 period.

9.5.4.3.2 Southwest

Australian

drought

Early winter (May–July) rainfall in the far southwest of

Australia declined by about 15% in the mid-1970s (IOCI, 2002)
and remained low subsequently. The rainfall decrease was
accompanied by a change in large-scale atmospheric circulation
in the surrounding region (Timbal, 2004). The circulation
and precipitation changes are somewhat consistent with, but
larger than, those simulated by climate models in response to
greenhouse gas increases. The Indian Ocean Climate Initiative
(IOCI, 2005) concludes that land cover change could not be
the primary cause of the rainfall decrease because of the
link between the rainfall decline and changes in large-scale
atmospheric circulation, and re-af

rms the conclusion of IOCI

Figure 9.19.

Observed (Climatic Research Unit TS 2.1; Mitchell and Jones, 2005)

Sahel July to September rainfall for each year (black), compared to an ensemble
mean of 10 simulations of the atmospheric/land component of the GFDL-CM2.0
model (see Table 8.1 for model details) forced with observed SSTs (red). Both model
and observations are normalized to unit mean over 1950-2000. The grey band
represents ±1 standard deviation of intra-ensemble variability. After Held et al.
(2005), based on results in Lu and Delworth (2005).

716

Understanding and Attributing Climate Change

Chapter 9

(2002) that both natural variability and greenhouse forcing
likely contributed. Timbal et al. (2005) demonstrate that climate
change signals downscaled from the PCM show some similarity
to observed trends, although the signi

cance of this

nding is

uncertain.

Some authors (e.g., Karoly, 2003) have suggested that the

decrease in rainfall is related to anthropogenic changes in the
SAM (see Section 9.5.3.3). However, the in

uence of changes

in circulation on southwest Australian drought remains unclear
as the largest SAM trend has occurred during the SH summer
(December–March; Thompson et al., 2000; Marshall et al.,
2004), while the largest rainfall decrease has occurred in early
winter (May–July).

9.5.4.3.3 Monsoon

precipitation

Decreasing trends in precipitation over the Indonesian

Maritime Continent, equatorial western and central Africa,
Central America, Southeast Asia and eastern Australia have
been observed over the period 1948 to 2003, while increasing
trends were found over the USA and north-western Australia
(Section 3.7). The TAR (IPCC, 2001, pp. 568) concluded that
an increase in Southeast Asian summer monsoon precipitation
is simulated in response to greenhouse gas increases in climate
models, but that this effect is reduced by an increase in sulphate
aerosols, which tend to decrease monsoon precipitation. Since
then, additional modelling studies have come to con

icting

conclusions regarding changes in monsoon precipitation (Lal
and Singh, 2001; Douville et al., 2002; Maynard et al., 2002;
May, 2004; Wardle and Smith, 2004; see also Section 9.5.3.5).
Ramanathan et al. (2005) were able to simulate realistic
changes in Indian monsoon rainfall, particularly a decrease that
occurred between 1950 and 1970, by including the effects of
black carbon aerosol. In both the observations and model, these
changes were associated with a decreased SST gradient over
the Indian Ocean and an increase in tropospheric stability, and
they were not reproduced in simulations with greenhouse gas
and sulphate aerosol changes only.

9.5.5 Cryosphere

Changes

9.5.5.1 Sea

Ice

Widespread warming would, in the absence of other

countervailing effects, lead to declines in sea ice, snow, and
glacier and ice sheet extent and thickness. The annual mean
area of arctic sea ice cover has decreased in recent decades,
with stronger declines in summer than in winter, and some
thinning (Section 4.4). Gregory et al. (2002b) show that a
four-member ensemble of HadCM3 integrations with all major
anthropogenic and natural forcing factors simulates a decline in
arctic sea ice extent of about 2.5% per decade over the period
1970 to 1999, which is close to the observed decline of 2.7%
per decade over the satellite period 1978 to 2004. This decline
is inconsistent with simulated internal climate variability and
the response to natural forcings alone (Vinnikov et al., 1999;
Gregory et al., 2002b; Johannssen et al., 2004), indicating that

anthropogenic forcing has likely contributed to the trend in NH
sea ice extent. Models such as those described by Rothrock
et al. (2003) and references therein are able to reproduce the
observed interannual variations in ice thickness, at least when
averaged over fairly large regions. Simulations of historical
arctic ice thickness or volume (Goeberle and Gerdes, 2003;
Rothrock et al., 2003) show a marked reduction in ice thickness
starting in the late 1980s, but disagree somewhat with respect
to trends and/or variations earlier in the century. Although some
of the dramatic change inferred may be a consequence of a
spatial redistribution of ice volume over time (e.g., Holloway
and Sou, 2002), thermodynamic changes are also believed to
be important. Low-frequency atmospheric variability (such
as interannual changes in circulation connected to the NAM)
appears to be important in

ushing ice out of the Arctic

Basin, thus increasing the amount of summer open water and
enhancing thermodynamic thinning through the ice-albedo
feedback (e.g., Lindsay and Zhang, 2005). Large-scale modes
of variability affect both wind driving and heat transport in the
atmosphere, and therefore contribute to interannual variations
in ice formation, growth and melt (e.g., Rigor et al., 2002;
Dumas et al., 2003). Thus, the decline in arctic sea ice extent
and its thinning appears to be largely, but not wholly, due to
greenhouse gas forcing.

Unlike in the Arctic, a strong decline in sea ice extent has

not been observed in the Antarctic during the period of satellite
observations (Section 4.4.2.2). Fichefet et al. (2003) conducted
a simulation of Antarctic ice thickness using observationally
based atmospheric forcing covering the period 1958 to 1999.
They note pronounced decadal variability, with area average ice
thickness varying by ±0.1 m (compared to a mean thickness
of roughly 0.9 m), but no long-term trend. However, Gregory
et al. (2002b)

nd a decline in antarctic sea ice extent in their

model, contrary to observations. They suggest that the lack of
consistency between the observed and modelled changes in sea
ice extent might re

ect an unrealistic simulation of regional

warming around Antarctica, rather than a de

ciency in the ice

model. Holland and Raphael (2006) examine sea ice variability
in six MMD 20C3M simulations that include stratospheric
ozone depletion. They conclude that the observed weak increase
in antarctic sea ice extent is not inconsistent with simulated
internal variability, with some simulations reproducing the
observed trend over 1979 to 2000, although the models exhibit
larger interannual variability in sea ice extent than satellite
observations.

9.5.5.2

Snow and Frozen Ground

Snow cover in the NH, as measured from satellites, has

declined substantially in the past 30 years, particularly from
early spring through summer (Section 4.2). Trends in snow
depth and cover can be driven by precipitation or temperature
trends. The trends in recent decades have generally been
driven by warming at lower and middle elevations. Evidence
for this includes: (a) interannual variations in NH April snow-
covered area are strongly correlated (r = –0.68) with April

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Chapter 9

Understanding and Attributing Climate Change

40°N to 60°N temperature; (b) interannual variations in snow
(water equivalent, depth or duration) are strongly correlated
with temperature at lower- and middle-elevation sites in North
America (Mote et al., 2005), Switzerland (Scherrer et al.,
2004) and Australia (Nicholls, 2005); (c) trends in snow water
equivalent or snow depth show strong dependence on elevation
or equivalently mean winter temperature, both in western North
America and Switzerland (with stronger decreases at lower,
warmer elevations where a warming is more likely to affect
snowfall and snowmelt); and (d) the trends in North America,
Switzerland and Australia have been shown to be well explained
by warming and cannot be explained by changes in precipitation.
In some very cold places, increases in snow depth have been
observed and have been linked to higher precipitation.

Widespread permafrost warming and degradation appear to

be the result of increased summer air temperatures and changes
in the depth and duration of snow cover (Section 4.7.2). The
thickness of seasonally frozen ground has decreased in response
to winter warming and increases in snow depth (Section
4.7.3).

9.5.5.3

Glaciers, Ice Sheets and Ice Shelves

During the 20th century, glaciers generally lost mass with

the strongest retreats in the 1930s and 1940s and after 1990
(Section 4.5). The widespread shrinkage appears to imply
widespread warming as the probable cause (Oerlemans, 2005),
although in the tropics changes in atmospheric moisture might
be contributing (Section 4.5.3). Over the last half century, both
global mean winter accumulation and summer melting have
increased steadily (Ohmura, 2004; Dyurgerov and Meier, 2005;
Greene, 2005), and at least in the NH, winter accumulation
and summer melting correlate positively with hemispheric
air temperature (Greene, 2005); the negative correlation of
net balance with temperature indicates the primary role of
temperature in forcing the respective glacier

uctuations.

There have been a few studies for glaciers in speci

c regions

examining likely causes of trends. Mass balances for glaciers
in western North America are strongly correlated with global
mean winter (October–April) temperatures and the decline in
glacier mass balance has paralleled the increase in temperature
since 1968 (Meier et al., 2003). Reichert et al. (2002a) forced
a glacier mass balance model for the Nigardsbreen and Rhône
glaciers with downscaled data from an AOGCM control
simulation and conclude that the rate of glacier advance during
the ‘Little Ice Age’ could be explained by internal climate
variability for both glaciers, but that the recent retreat cannot,
implying that the recent retreat of both glaciers is probably due
to externally forced climate change. As well, the thinning and
acceleration of some polar glaciers (e.g., Thomas et al., 2004)
appear to be the result of ice sheet calving driven by oceanic
and atmospheric warming (Section 4.6.3.4).

Taken together, the ice sheets of Greenland and Antarctica are

shrinking. Slight thickening in inland Greenland is more than
compensated for by thinning near the coast (Section 4.6.2.2).
Warming is expected to increase low-altitude melting and high-

altitude precipitation in Greenland; altimetry data suggest that
the former effect is dominant. However, because some portions
of ice sheets respond only slowly to climate changes, past
forcing may be in

uencing ongoing changes, complicating

attribution of recent trends (Section 4.6.3.2).

9.5.6 Summary

In the TAR, quantitative evidence for human in

uence on

climate was based almost exclusively on atmospheric and
surface temperature. Since then, anthropogenic in

uence has

also been identi

ed in a range of other climate variables, such

as ocean heat content, atmospheric pressure and sea ice extent,
thereby contributing further evidence of an anthropogenic
in

uence on climate, and improving con

dence in climate

models.

Observed changes in ocean heat content have now been shown

to be inconsistent with simulated natural climate variability, but
consistent with a combination of natural and anthropogenic
in

uences both on a global scale, and in individual ocean basins.

Models suggest a substantial anthropogenic contribution to sea
level rise, but underestimate the actual rise observed. While
some studies suggest that an anthropogenic increase in high-
latitude rainfall may have contributed to a freshening of the
Arctic Ocean and North Atlantic deep water, these results are
still uncertain.

There is no evidence that 20th-century ENSO behaviour is

distinguishable from natural variability. By contrast, there has
been a detectable human in

uence on global sea level pressure.

Both the NAM and SAM have shown signi

cant trends. Models

reproduce the sign but not magnitude of the NAM trend, and
models including both greenhouse gas and ozone simulate
a realistic trend in the SAM. Anthropogenic in

uence on

either tropical or extratropical cyclones has not been detected,
although the apparent increased frequency of intense tropical
cyclones, and its relationship to ocean warming, is suggestive
of an anthropogenic in

uence.

Simulations and observations of total atmospheric

water vapour averaged over oceans agree closely when the
simulations are constrained by observed SSTs, suggesting that
anthropogenic in

uence has contributed to an increase in total

atmospheric water vapour. However, global mean precipitation
is controlled not by the availability of water vapour, but by a
balance between the latent heat of condensation and radiative
cooling in the troposphere. This may explain why human
in

uence has not been detected in global precipitation, while

the in

uence of volcanic aerosols has been detected. However,

observed changes in the latitudinal distribution of land
precipitation are suggestive of a possible human in

uence as

is the observed increased incidence of drought as measured by
the Palmer Drought Severity Index. Observational evidence
indicates that the frequency of the heaviest rainfall events has
likely increased within many land regions in general agreement
with model simulations that indicate that rainfall in the heaviest
events is likely to increase in line with atmospheric water
vapour concentration. Many AGCMs capture the observed

718

Understanding and Attributing Climate Change

Chapter 9

decrease in Sahel rainfall when constrained by observed SSTs,
although this decrease is not simulated by most AOGCMs.
One study found that an observed decrease in Asian monsoon
rainfall could only be simulated in response to black carbon
aerosol, although conclusions regarding the monsoon response
to anthropogenic forcing differ.

Observed decreases in arctic sea ice extent have been

shown to be inconsistent with simulated internal variability,
and consistent with the simulated response to human in

uence,

but SH sea ice extent has not declined. The decreasing trend
in global snow cover and widespread melting of glaciers is
consistent with a widespread warming. Anthropogenic forcing
has likely contributed substantially to widespread glacier retreat
during the 20th century.

9.6

Observational Constraints on
Climate Sensitivity

This section assesses recent research that infers equilibrium

climate sensitivity and transient climate response from observed
changes in climate. ‘Equilibrium climate sensitivity’

(ECS) is

the equilibrium annual global mean temperature response to a
doubling of equivalent atmospheric CO

from pre-industrial

levels and is thus a measure of the strength of the climate
system’s eventual response to greenhouse gas forcing. ‘Transient
climate response’ (TCR) is the annual global mean temperature
change at the time of CO

doubling in a climate simulation

with a 1% yr

–1

compounded increase in CO

concentration (see

Glossary and Section 8.6.2.1 for detailed de

nitions). TCR is a

measure of the strength and rapidity of the climate response to
greenhouse gas forcing, and depends in part on the rate at which
the ocean takes up heat. While the direct temperature change
that results from greenhouse gas forcing can be calculated in
a relatively straightforward manner, uncertain atmospheric
feedbacks (Section 8.6) lead to uncertainties in estimates of
future climate change. The objective here is to assess estimates
of ECS and TCR that are based on observed climate changes,
while Chapter 8 assesses feedbacks individually. Inferences
about climate sensitivity from observed climate

changes

complement approaches in which uncertain parameters in
climate models are varied and assessed by evaluating the
resulting skill in reproducing observed

mean

climate (Section

10.5.4.4). While observed climate changes have the advantage
of being most clearly related to future climate change, the
constraints they provide on climate sensitivity are not yet very
strong, in part because of uncertainties in both climate forcing
and the estimated response (Section 9.2). An overall summary
assessment of ECS and TCR, based on the ability of models
to simulate climate change and mean climate and on other
approaches, is given in Box 10.2. Note also that this section
does not assess regional climate sensitivity or sensitivity to
forcings other than CO

9.6.1

Methods to Estimate Climate Sensitivity

The most straightforward approach to estimating climate

sensitivity would be to relate an observed climate change to a
known change in radiative forcing. Such an approach is strictly
correct only for changes between equilibrium climate states.
Climatic states that were reasonably close to equilibrium in the
past are often associated with substantially different climates
than the pre-industrial or present climate, which is probably
not in equilibrium (Hansen et al., 2005). An example is the
climate of the LGM (Chapter 6 and Section 9.3). However,
the climate’s sensitivity to external forcing will depend on the
mean climate state and the nature of the forcing, both of which
affect feedback mechanisms (Chapter 8). Thus, an estimate of
the sensitivity directly derived from the ratio of response to
forcing cannot be readily compared to the sensitivity of climate
to a doubling of CO

under idealised conditions. An alternative

approach, which has been pursued in most work reported here,
is based on varying parameters in climate models that in

uence

the ECS in those models, and then attaching probabilities to the
different ECS values based on the realism of the corresponding
climate change simulations. This ameliorates the problem of
feedbacks being dependent on the climatic state, but depends
on the assumption that feedbacks are realistically represented
in models and that uncertainties in all parameters relevant
for feedbacks are varied. Despite uncertainties, results from
simulations of climates of the past and recent climate change
(Sections 9.3 to 9.5) increase con

dence in this assumption.

The ECS and TCR estimates discussed here are generally

based on large ensembles of simulations using climate models
of varying complexity, where uncertain parameters in

uencing

the model’s sensitivity to forcing are varied. Studies vary key
climate and forcing parameters in those models, such as the
ECS, the rate of ocean heat uptake, and in some instances, the
strength of aerosol forcing, within plausible ranges. The ECS
can be varied directly in simple climate models and in some
EMICs (see Chapter 8), and indirectly in more complex EMICs
and AOGCMs by varying model parameters that in

uence

the strength of atmospheric feedbacks, for example, in cloud
parametrizations. Since studies estimating ECS and TCR
from observed climate changes require very large ensembles
of simulations of past climate change (ranging from several
hundreds to thousands of members), they are often, but not
always, performed with EMICs or EBMs.

The idea underlying this approach is that the plausibility of a

given combination of parameter settings can be determined from
the agreement of the resulting simulation of historical climate
with observations. This is typically evaluated by means of
Bayesian methods (see Supplementary Material, Appendix 9.B
for methods). Bayesian approaches constrain parameter values
by combining prior distributions that account for uncertainty
in the knowledge of parameter values with information about
the parameters estimated from data (Kennedy and O’Hagan,
2001). The uniform distribution has been used widely as a
prior distribution, which enables comparison of constraints
obtained from the data in different approaches. ECS ranges

719

Chapter 9

Understanding and Attributing Climate Change

encompassed by the uniform prior distribution must be limited
due to computer time limiting the size of model ensembles,
but generally cover the range considered possible by experts,
such as from 0°C to 10°C. Note that uniform prior distributions
for ECS, which only require an expert assessment of possible
range, generally assign a higher prior belief to high sensitivity
than, for example, non-uniform prior distributions that depend
more heavily on expert assessments (e.g., Forest et al., 2006). In
addition, Frame et al. (2005) point out that care must be taken
when specifying the uniform prior distribution. For example, a
uniform prior distribution for the climate feedback parameter
(see Glossary) implies a non-uniform prior distribution for ECS
due to the nonlinear relationship between the two parameters.

Since observational constraints on the upper bound of

ECS are still weak (as shown below), these prior assumptions
in

uence the resulting estimates. Frame et al. (2005) advocate

sampling a

at prior distribution in ECS if this is the target of

the estimate, or in TCR if future temperature trends are to be
constrained. In contrast, statistical research on the design and
interpretation of computer experiments suggests the use of prior
distributions for model input parameters (e.g., see Kennedy
and O’Hagan, 2001; Goldstein and Rougier, 2004). In such
Bayesian studies, it is generally good practice to explore the
sensitivity of results to different prior beliefs (see, for example,
Tol and Vos, 1998; O’Hagan and Forster, 2004). Furthermore,
as demonstrated by Annan and Hargreaves (2005) and Hegerl
et al. (2006a), multiple and independent lines of evidence about
climate sensitivity from, for example, analysis of climate change
at different times, can be combined by using information from
one line of evidence as prior information for the analysis of
another line of evidence. The extent to which the different lines
of evidence provide complete information on the underlying
physical mechanisms and feedbacks that determine the climate
sensitivity is still an area of active research. In the following,
uniform prior distributions for the target of the estimate are
used unless otherwise speci

ed.

Methods that incorporate a more comprehensive treatment

of uncertainty generally produce wider uncertainty ranges
for the inferred climate parameters. Methods that do not vary
uncertain parameters, such as ocean diffusivity, in the course
of the uncertainty analysis will yield probability distributions
for climate sensitivity that are conditional on these values,
and therefore are likely to underestimate the uncertainty in
climate sensitivity. On the other hand, approaches that do
not use all available evidence will produce wider uncertainty
ranges than estimates that are able to use observations more
comprehensively.

9.6.2

Estimates of Climate Sensitivity Based on
Instrumental Observations

9.6.2.1

Estimates of Climate Sensitivity Based on 20th-
Century Warming

A number of recent studies have used instrumental records

of surface, ocean and atmospheric temperature changes to

estimate climate sensitivity. Most studies use the observed
surface temperature changes over the 20th century or the last
150 years (Chapter 3). In addition, some studies also use the
estimated ocean heat uptake since 1955 based on Levitus et al.
(2000, 2005) (Chapter 5), and temperature changes in the free
atmosphere (Chapter 3; see also Table 9.3). For example, Frame
et al. (2005) and Andronova and Schlesinger (2000) use surface
air temperature alone, while Forest et al. (2002, 2006), Knutti et
al. (2002, 2003) and Gregory et al. (2002a) use both surface air
temperature and ocean temperature change to constrain climate
sensitivity. Forest et al. (2002, 2006) and Lindzen and Giannitsis
(2002) use free atmospheric temperature data from radiosondes
in addition to surface air temperature. Note that studies using
radiosonde data may be affected by recently discovered
inhomogeneities (Section 3.4.1.1), although Forest et al.
(2006) illustrate that the impact of the radiosonde atmospheric
temperature data on their climate sensitivity estimate is smaller
than that of surface and ocean warming data. A further recent
study uses Earth Radiation Budget Experiment (ERBE) data
(Forster and Gregory, 2006) in addition to surface temperature
changes to estimate climate feedbacks (and thus ECS) from
observed changes in forcing and climate.

Wigley et al. (1997) pointed out that uncertainties in

forcing and response made it impossible to use observed
global temperature changes to constrain ECS more tightly
than the range explored by climate models at the time (1.5°C
to 4.5°C), and particularly the upper end of the range, a
conclusion con

rmed by subsequent studies. A number of

subsequent publications qualitatively describe parameter
values that allow models to reproduce features of observed
changes, but without directly estimating a climate sensitivity
probability density function (PDF). For example, Harvey and
Kaufmann (2002)

nd a best-

t ECS of 2.0°C out of a range

of 1°C to 5°C, and constrain fossil fuel and biomass aerosol
forcing (Section 9.2.1.2). Lindzen and Giannitsis (2002) pose
the hypothesis that the rapid change in tropospheric (850–300
hPa) temperatures around 1976 triggered a delayed response
in surface temperature that is best modelled with a climate
sensitivity of less than 1°C. However, their estimate does not
account for substantial uncertainties in the analysis of such a
short time period, most notably those associated with the role of
internal climate variability in the rapid tropospheric warming of
1976. The 1976–1977 climate shift occurred along with a phase
shift of the PDO, and a concurrent change in the ocean (Section
3.6.3) that appears to contradict the Lindzen and Giannitsis
(2002) assumption that the change was initiated by tropospheric
forcing. In addition, the authors do not account for uncertainties
in the simple model whose sensitivity is

tted. The

nding of

Lindzen and Giannitsis is in contrast with that of Forest et al.
(2002, 2006) who consider the joint evolution of surface and
upper air temperatures on much longer time scales.

Several recent studies have derived probability estimates for

ECS using a range of models and diagnostics. The diagnostics,
which are used to compare model-simulated and observed
changes, are often simple temperature indices such as the
global mean surface temperature and ocean mean warming

720

Understanding and Attributing Climate Change

Chapter 9

(Knutti et al., 2002, 2003) or the differential warming between
the SH and NH (together with the global mean; Andronova and
Schlesinger, 2001). Results that use more detailed information
about the space-time evolution of climate may be able to
provide tighter constraints than those that use simpler indices.
Forest et al. (2002, 2006) use a so-called ‘optimal’ detection
method (Section 9.4.1.4 and Appendix 9.A.1) to diagnose the

t between model-simulated and observed patterns of zonal

mean temperature change. Frame et al. (2005) use detection
results from an analysis based on several multi-model AOGCM

ngerprints (Section 9.4.1.4) that separate the greenhouse gas

response from that to other anthropogenic and natural forcings
(Stott et al., 2006c). Similarly, Gregory et al. (2002a) apply
an inverse estimate of the range of aerosol forcing based on

ngerprint detection results. Note that while results from

ngerprint detection approaches will be affected by uncertainty

in separation between greenhouse gas and aerosol forcing, the
resulting uncertainty in estimates of the near-surface temperature
response to greenhouse gas forcing is relatively small (Sections
9.2.3 and 9.4.1.4).

al., 2005), which favour somewhat smaller ocean heat uptakes
than earlier data (Levitus et al., 2001; Forest et al., 2006). Only
a few estimates account for uncertainty in forcings other than
from aerosols (e.g., Gregory et al., 2002a; Knutti et al., 2002,
2003); some other studies perform some sensitivity testing to
assess the effect of forcing uncertainty not accounted for, for
example, in natural forcing (e.g., Forest et al., 2006; see Table
9.1 for an overview).

The treatment of uncertainty in the ocean’s uptake of

heat varies, from assuming a

xed value for a model’s ocean

diffusivity (Andronova and Schlesinger, 2001) to trying to allow
for a wide range of ocean mixing parameters (Knutti et al., 2002,
2003) or systematically varying the ocean’s effective diffusivity
(e.g., Forest et al., 2002, 2006; Frame et al., 2005). Furthermore,
all approaches that use the climate’s time evolution attempt to
account for uncertainty due to internal climate variability, either
by bootstrapping (Andronova and Schlesinger, 2001), by using
a noise model in

ngerprint studies whose results are used

(Frame et al., 2005) or directly (Forest et al., 2002, 2006).

Figure 9.20.

Comparison between different estimates of the PDF (or relative likelihood) for ECS (°C). All PDFs/

likelihoods have been scaled to integrate to unity between 0°C and 10°C ECS. The bars show the respective 5 to
95% ranges, dots the median estimate. The PDFs/likelihoods based on instrumental data are from Andronova and
Schlesinger (2001), Forest et al. (2002; dashed line, considering anthropogenic forcings only), Forest et al. (2006; solid,
anthropogenic and natural forcings), Gregory et al. (2002a), Knutti et al. (2002), Frame et al. (2005), and Forster and
Gregory (2006), transformed to a uniform prior distribution in ECS using the method after Frame et al. (2005). Hegerl
et al. (2006a) is based on multiple palaeoclimatic reconstructions of NH mean temperatures over the last 700 years.
Also shown are the 5 to 95% approximate ranges for two estimates from the LGM (dashed, Annan et al., 2005; solid,
Schneider von Deimling et al., 2006) which are based on models with different structural properties. Note that ranges
extending beyond the published range in Annan et al. (2005), and beyond that sampled by the climate model used
there, are indicated by dots and an arrow, since Annan et al. only provide an upper limit. For details of the likelihood
estimates, see Table 9.3. After Hegerl et al. (2006a).

A further consideration in

assessing these results is the
extent to which realistic forcing
estimates were used, and whether
forcing uncertainty was included.
Most studies consider a range of
anthropogenic forcing factors,
including greenhouse gases
and sulphate aerosol forcing,
sometimes directly including the
indirect forcing effect, such as
Knutti et al. (2002, 2003), and
sometimes indirectly accounting
for the indirect effect by using a
wide range of direct forcing (e.g.,
Andronova and Schlesinger, 2001;
Forest et al., 2002, 2006). Many
studies also consider tropospheric
ozone (e.g., Andronova and
Schlesinger, 2001; Knutti et al.,
2002, 2003). Forest et al. (2006)
demonstrate that the inclusion
of natural forcing affects the
estimated

PDF of climate

sensitivity since net negative
natural forcing in the second half
of the 20th century favours higher
sensitivities than earlier results that
disregarded natural forcing (Forest
et al., 2002; see Figure 9.20),
particularly if the same ocean
warming estimates were used.
Note that some of the changes due
to inclusion of natural forcing are
offset by using recently revised
ocean warming data (Levitus et

721

Chapter 9

Understanding and Attributing Climate Change

Table 9.3.

Results from key studies on observational estimates of ECS (in °C) from instrumental data, individual volcanic eruptions, data for the last millennium, and

simulations of the LGM . The fi nal three rows list some studies using non-uniform prior distributions, while the other studies use uniform prior distributions of ECS.

Study

Observational Data Used to
Constrain Study

Model

External Forcings
Included

Treatment of uncertainties

Estimated ECS
Range
5 to 95% (°C)

From Instrumental Data

Forest et al.
(2006)

Upper air, surface and deep
ocean space-time 20th-century
temperatures
Prior 0°C to 10°C

2-D
EMIC (~E6)

G, Sul, Sol, Vol,
OzS, land surface
changes
(2002: G, Sul, OzS)

obs

, noise,

aer

, sensitivity

tests for solar/volcanic.
forcing uncertainty

2.1 to 8.9
(1.4 to 7.7 without
natural forcings)

Andronova and
Schlesinger
(2001)

Global mean and hemispheric
difference in surface air
temperature 1856 to 1997

EBM

G, OzT, Sul, Sol,
Vol

Noise (bootstrap residual),
choice of radiative forcing
factors

1.0 to 9.3
prob ~ 54% that
ECS outside 1.5
to 4.5

Knutti et al.
(2002; 2003)

Global mean ocean heat uptake
1955 to 1995, mean surface air
temperature 1860 to 2000
Prior 0°C to 10°C

EMIC (~E1)
plus neural
net

G, OzT, OzS, fossil
fuel and biomass
burning BC+OM,
stratospheric water
vapour, Vol, Sol,
Sul, Suli

obs

forc

for multiple

forcings from IPCC (2001),

, different ocean mixing

schemes

2.2 to 9.2
prob ~ 50% that
ECS outside 1.5
to 4.5

Gregory et al.
(2002a)

Global mean change in surface
air temperature and ocean heat
change between 1861 to 1900
and 1957 to 1994

1-Box

G, Sul and Suli
(top down via
Stott et al., 2001),
Sol, Vol

obs

forc

1.1 to

∞

Frame et al.
(2005)

Global change in surface
temperature

EBM

G, accounted
for other
anthropogenic and
natural forcing by
fi ngerprints, Sul,
Nat

Noise, uncertainty in
amplitude but not pattern of
natural and anthropogenic.
forcings and response
(scaling factors),

(range

consistent with ocean
warming)

1.2 to 11.8

(continued)

Figure 9.20 compares results from many of these studies.

All PDFs shown are based on a uniform prior distribution of
ECS and have been rescaled to integrate to unity for all positive
sensitivities up to 10°C to enable comparisons of results using
different ranges of uniform prior distributions (this affects both
median and upper 95th percentiles if original estimates were
based on a wider uniform range). Thus, zero prior probability
is assumed for sensitivities exceeding 10°C, since many results
do not consider those, and for negative sensitivities. Negative
climate sensitivity would lead to cooling in response to a
positive forcing and is inconsistent with understanding of the
energy balance of the system (Stouffer et al., 2000; Gregory
et al., 2002a; Lindzen and Giannitsis, 2002). This

gure

shows that best estimates of the ECS (mode of the estimated
PDFs) typically range between 1.2°C and 4°C when inferred
from constraints provided by historical instrumental data, in
agreement with estimates derived from more comprehensive
climate models. Most studies suggest a 5th percentile for
climate sensitivity of 1°C or above. The upper 95th percentile
is not well constrained, particularly in studies that account
conservatively for uncertainty in, for example, 20th-century
radiative forcing and ocean heat uptake. The upper tail is
particularly long in studies using diagnostics based on large-

scale mean data because separation of the greenhouse gas
response from that to aerosols or climate variability is more
dif

cult with such diagnostics (Andronova and Schlesinger,

2001; Gregory et al., 2002a; Knutti et al., 2002, 2003). Forest
et al. (2006)

nd a 5 to 95% range of 2.1°C to 8.9°C for

climate sensitivity (Table 9.3), which is a wider range than
their earlier result based on anthropogenic forcing only (Forest
et al., 2002). Frame et al. (2005) infer a 5 to 95% uncertainty
range for the ECS of 1.2°C to 11.8°C, using a uniform prior
distribution that extends well beyond 10°C sensitivity. Studies
generally do not

nd meaningful constraints on the rate at

which the climate system mixes heat into the deep ocean
(e.g., Forest et al., 2002, 2006). However, Forest et al. (2006)

nd that many coupled AOGCMs mix heat too rapidly into

the deep ocean, which is broadly consistent with comparisons
based on heat uptake (Section 9.5.1.1,). However the relevance
of this

nding is unclear because most MMD AOGCMs were

not included in the Forest et al. comparison, and because they
used a relatively simple ocean model. Knutti et al. (2002) also
determine that strongly negative aerosol forcing, as has been
suggested by several observational studies (Anderson et al.,
2003), is incompatible with the observed warming trend over
the last century (Section 9.2.1.2 and Table 9.1).

722

Understanding and Attributing Climate Change

Chapter 9

Some studies have further attempted to use non-uniform

prior distributions. Forest et al. (2002, 2006) obtained narrower
uncertainty ranges when using expert prior distributions (Table
9.3). While they re

ect credible prior ranges of ECS, expert

priors may also be in

uenced by knowledge about observed

climate change, and thus may yield overly con

dent estimates

when combined with the same data (Supplementary Material,
Appendix 9.B). Frame et al. (2005)

nd that sampling uniformly

in TCR results in an estimated ECS of 1.2°C to 5.2°C with a

median value of 2.3°C. In addition, several approaches have
been based on a uniform prior distribution of climate feedback.
Translating these results into ECS estimates is equivalent to
using a prior distribution that favours smaller sensitivities,
and hence tends to result in narrower ECS ranges (Frame et
al., 2005). Forster and Gregory (2006) estimate ECS based on
radiation budget data from the ERBE combined with surface
temperature observations based on a regression approach, using
the observation that there was little change in aerosol forcing

Study

Observational Data Used to
Constrain Study

Model

External Forcings
Included

Treatment of uncertainties

Estimated ECS
Range
5 to 95% (°C)

Forster and
Gregory (2006)

1985 to 1996 ERBE data 60°N to
60°S, global surface temperature
Prior 0°C to 18.5°C, transformed
after Frame et al. (2005)

1-Box

G, Vol, Sol, Sul

obs

forc

1.2 to 14.2

From individual volcanic eruptions

Wigley et al.
(2005a)

Global mean surface temperature

EBM

From volcanic
forcing only

El Niño

Agung: 1.3 to 6.3;
El Chichon: 0.3 to
7.7; Mt. Pinatubo:
1.8 to 5.2

From last millenium

Hegerl et al.
(2006a)

NH mean surface air temperature
pre-industrial (1270/1505 to 1850)
from multiple reconstructions
Prior 0°C to 10°C

2D EBM

G, Sul, Sol, Vol

Noise (from residual),

uncertainty in magnitude of
reconstructions and solar
and volcanic forcing

1.2 to 8.6

From LGM

Schneider von
Deimling et al.
(2006)

LGM tropical SSTs and other
LGM data

EMIC (~E3)

LGM forcing:
greenhouse
gases, dust, ice
sheets, vegetation,
insolation

uncertainty of proxy-based
ice age SSTs (one type of
data); attempt to account
for structural uncertainty,
estimate of forcing
uncertainty

1.2 to 4.3 (based
on encompassing
several ranges
given)

Annan et al.
(2005)

LGM tropical SSTs, present-
day seasonal cycle of a number
of variables for sampling prior
distribution of model parameters

AGCM with
mixed-layer
ocean

PMIP2 LGM
forcing

Observational uncertainty in
tropical SST estimates (one
type of data)

<7% chance of
sensitivity >6

Using non-uniform prior distributions

Forest et al.
(2002, 2006)

Expert prior, 20th-century
temperature change (see above)

See Forest
et al.

see above

See individual estimates

1.9 to 4.7

Annan et al.
(2006)

Estimates from LGM, 20th-
century change, volcanism
combined

See Annan
et al.

see above

See individual estimates

> 1.7 to 4.5

Hegerl et al.
(2006a)

1950 to 2000 surface temperature
change (Frame et al., 2005), NH
mean pre-industrial surface air
temperature from last millennium

See Hegerl et
al. and Frame
et al. (2005)

see above

See individual estimates

1.5 to 6.2

Notes:

Range covered by uniform prior distribution if narrower than 0°C to 20°C.

Energy Balance Model (EBM), often with upwelling-diffusive ocean; 1-box energy balance models; EMIC (numbers refer to related EMICs described in Table 8.3).

G: greenhouse gases; Sul: direct sulphate aerosol effect; Suli: (fi rst) indirect sulphate effect; OzT: tropospheric ozone; OzS: stratospheric ozone; Vol: volcanism; Sol:

solar; BC+OM: black carbon and organic matter.).

Uncertainties taken into account (e.g., uncertainty in ocean diffusivity

, or total aerosol forcing

forc

). Ideally, studies account for model uncertainty, forcing

uncertainty (for example, in aerosol forcing

aer

or natural forcing

nat

), uncertainty in observations,

obs

, and internal climate variability (‘noise’).

Table 9.3 (continued)

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Understanding and Attributing Climate Change

over that time. They

nd a climate feedback parameter of 2.3

± 1.4 W m

– 2

°C

–1

, which corresponds to a 5 to 95% ECS range

of 1.0°C to 4.1°C if using a prior distribution that puts more
emphasis on lower sensitivities as discussed above, and a wider
range if the prior distribution is reformulated so that it is uniform
in sensitivity (Table 9.3). The climate feedback parameter
estimated from the MMD AOGCMs ranges from about 0.7 to
2.0 W m

–2

°C

–1

(Supplementary Material, Table S8.1).

9.6.2.2

Estimates Based on Individual Volcanic Eruptions

Some recent analyses have attempted to derive insights

into ECS from the well-observed forcing and response to the
eruption of Mt. Pinatubo, or from other major eruptions during
the 20th century. Such events allow for the study of physical
mechanisms and feedbacks and are discussed in detail in
Section 8.6. For example, Soden et al. (2002) demonstrate
agreement between observed and simulated responses based
on an AGCM with a climate sensitivity of 3.0°C coupled to a
mixed-layer ocean, and that the agreement breaks down if the
water vapour feedback in the model is switched off. Yokohata
et al. (2005)

nd that a version of the MIROC climate model

with a sensitivity of 4.0°C yields a much better simulation of
the Mt. Pinatubo eruption than a model version with sensitivity
of 6.3°C, concluding that the cloud feedback in the latter model
appears inconsistent with data. Note that both results may be
speci

c to the model analysed.

Constraining ECS from the observed responses to individual

volcanic eruptions is dif

cult because the response to short-term

volcanic forcing is strongly nonlinear in ECS, yielding only
slightly enhanced peak responses and substantially extended
response times for very high sensitivities (Frame et al., 2005;
Wigley et al., 2005a). The latter are dif

cult to distinguish from

a noisy background climate. A further dif

culty arises from

uncertainty in the rate of heat taken up by the ocean in response
to a short, strong forcing. Wigley et al. (2005a)

nd that the

lower boundary and best estimate obtained by comparing
observed and simulated responses to major eruptions in the 20th
century are consistent with the TAR range of 1.5°C to 4.5°C,
and that the response to the eruption of Mt. Pinatubo suggests
a best

t sensitivity of 3.0°C and an upper 95% limit of 5.2°C.

However, as pointed out by the authors, this estimate does not
account for forcing uncertainties. In contrast, an analysis by
Douglass and Knox (2005) based on a box model suggests a
very low climate sensitivity (under 1°C) and negative climate
feedbacks based on the eruption of Mt. Pinatubo. Wigley et
al. (2005b) demonstrate that the analysis method of Douglass
and Knox (2005) severely underestimates (by a factor of three)
climate sensitivity if applied to a model with known sensitivity.
Furthermore, as pointed out by Frame et al. (2005), the effect
of noise on the estimate of the climatic background level can
lead to a substantial underestimate of uncertainties if not taken
into account.

In summary, the responses to individual volcanic eruptions

provide a useful test for feedbacks in climate models (Section
8.6). However, due to the physics involved in the response,

such individual events cannot provide tight constraints on ECS.
Estimates of the most likely sensitivity from most such studies
are, however, consistent with those based on other analyses.

9.6.2.3

Constraints on Transient Climate Response

While ECS is the equilibrium global mean temperature

change that eventually results from atmospheric CO

doubling,

the smaller TCR refers to the global mean temperature change
that is realised at the time of CO

doubling under an idealised

scenario in which CO

concentrations increase by 1% yr

–1

(Cubasch et al., 2001; see also Section 8.6.2.1). The TCR is
therefore indicative of the temperature trend associated with
external forcing, and can be constrained by an observable
quantity, the observed warming trend that is attributable to
greenhouse gas forcing. Since external forcing is likely to
continue to increase through the coming century, TCR may be
more relevant to determining near-term climate change than
ECS.

Stott et al. (2006c) estimate TCR based on scaling factors for

the response to greenhouse gases only (separated from aerosol
and natural forcing in a three-pattern optimal detection analysis)
using

ngerprints from three different model simulations (Figure

9.21) and

nd a relatively tight constraint. Using three model

simulations together, their estimated median TCR is 2.1°C at the
time of CO

doubling (based on a 1% yr

–1

increase in CO

), with

a 5 to 95% range of 1.5°C to 2.8°C. Note that since TCR scales
linearly with the errors in the estimated scaling factors, estimates
do not show a tendency for a long upper tail, as is the case for ECS.
However, the separation of greenhouse gas response from the
responses to other external forcing in a multi-

ngerprint analysis

introduces a small uncertainty, illustrated by small differences in
results between three models (Figure 9.21). The TCR does not
scale linearly with ECS because the transient response is strongly
in

uenced by the speed with which the ocean transports heat

into its interior, while the equilibrium sensitivity is governed by
feedback strengths (discussion in Frame et al., 2005).

Estimates of a likely range for TCR can also be inferred

directly from estimates of attributable greenhouse warming
obtained in optimal detection analyses since there is a direct
linear relationship between the two (Frame et al., 2005). The
attributable greenhouse warming rates inferred from Figure 9.9
generally support the TCR range shown in Figure 9.21, although
the lowest 5th percentile (1.3°C) and the highest 95th percentile
(3.3°C) estimated in this way from detection and attribution
analyses based on individual models lie outside the 5% to 95%
range of 1.5°C to 2.8°C obtained from Figure 9.21.

Choosing lower and upper limits that encompass the range

of these results and de

ating signi

cance levels in order to

account for structural uncertainty in the estimate leads to the
conclusion that it is very unlikely that TCR is less than 1°C and
very unlikely that TCR is greater than 3.5°C. Information based
on the models discussed in Chapter 10 provides additional
information that can help constrain TCR further (Section
10.5.4.5).

724

Understanding and Attributing Climate Change

Chapter 9

9.6.3

Estimates of Climate Sensitivity Based on

Palaeoclimatic

Data

The palaeoclimate record offers a range of opportunities to

assess the response of climate models to changes in external
forcing. This section discusses estimates from both the
palaeoclimatic record of the last millennium, and from the
climate of the LGM. The latter gives a different perspective
on feedbacks than anticipated with greenhouse warming, and
thus provides a test bed for the physics in climate models.
There also appears to be a likely positive relationship between
temperature and CO

prior to the 650 kyr period covered by ice

core measurements of CO

(Section 6.3).

As with analyses of the instrumental record discussed in

Section 9.6.2, some studies using palaeoclimatic data have
also estimated PDFs for ECS by varying model parameters.
Inferences about ECS made through direct comparisons between
radiative forcing and climate response, without using climate
models, show large uncertainties since climate feedbacks,
and thus sensitivity, may be different for different climatic
background states and for different seasonal characteristics of
forcing (e.g., Montoya et al., 2000). Thus, sensitivity to forcing
during these periods cannot be directly compared to that for
atmospheric CO

doubling.

9.6.3.1

Estimates of Climate Sensitivity Based on Data for
the Last Millennium

The relationship between forcing and response based

on a long time horizon can be studied using palaeoclimatic
reconstructions  of temperature and
radiative forcing, particularly volcanism
and solar forcing, for the last millennium.
However, both forcing and temperature
reconstructions are subject to large
uncertainties (Chapter 6). To account
for the uncertainty in reconstructions,
Hegerl et al. (2006a) use several proxy
data reconstructions of NH extratropical
temperature for the past millennium
(Briffa et al., 2001; Esper et al., 2002;
Mann and Jones, 2003; Hegerl et al.,
2007)  to constrain ECS estimates for
the pre-industrial period up to 1850.
This  study used a large ensemble of
simulations  of the last millennium
performed with an energy balance model
forced with reconstructions of volcanic
(Crowley, 2000, updated), solar (Lean
et al., 2002) and greenhouse gas forcing
(see Section 9.3.3 for results on the
detection of these external in

uences).

Their estimated PDFs for ECS
incorporate an estimate of uncertainty
in the overall amplitude (including
an attempt to account for uncertainty

in ef

cacy), but not the time evolution, of volcanic and solar

forcing. They also attempt to account for uncertainty in the
amplitude of reconstructed temperatures in one reconstruction
(Hegerl et al., 2007), and assess the sensitivity of their results to
changes in amplitude for others. All reconstructions combined
yield a median climate sensitivity of 3.4°C and a 5 to 95% range
of 1.2°C to 8.6°C (Figure 9.20). Reconstructions with a higher
amplitude of past climate variations (e.g., Esper et al., 2002;
Hegerl et al., 2007) are found to support higher ECS estimates
than reconstructions with lower amplitude (e.g., Mann and
Jones, 2003). Note that the constraint on ECS originates mainly
from low-frequency temperature variations associated with
changes in the frequency and intensity of volcanism which lead
to a highly signi

cant detection of volcanic response (Section

9.3.3) in all records used in the study.

The results of Andronova et al. (2004) are broadly consistent

with these estimates. Andronova et al. (2004) demonstrate
that climate sensitivities in the range of 2.3°C to 3.4°C yield
reasonable simulations of both the NH mean temperature
from 1500 onward when compared to the Mann and Jones
(2003) reconstruction, and for the instrumental period. The
agreement is less good for reconstructed SH temperature, where
reconstructions are substantially more uncertain (Chapter 6).

Rind et al. (2004) studied the period from about 1675 to

1715 to attempt a direct estimate of climate sensitivity. This
period has reduced radiative forcing relative to the present due
to decreased solar radiation, decreased greenhouse gas and
possibly increased volcanic forcing (Section 9.2.1.3). Different
NH temperature reconstructions (Figure 6.10) have a wide
range of cooling estimates relative to the late 20th century that

Figure 9.21.

Probability distributions of TCR (expressed as warming at the time of CO

doubling), as constrained

by observed 20th-century temperature change, for the HadCM3 (Table 8.1, red), PCM (Table 8.1, green) and GFDL
R30 (Delworth et al., 2002, blue) models. The average of the PDFs derived from each model is shown in cyan.
Coloured circles show each model’s TCR. (After Stott et al., 2006c).

725

Chapter 9

Understanding and Attributing Climate Change

is broadly reproduced by climate model simulations. While
climate in this cold period may have been close to radiative
balance (Rind et al., 2004), some of the forcing during the
present period is not yet realised in the system (estimated as 0.85
W m

–2

; Hansen et al., 2005). Thus, ECS estimates based on a

comparison between radiative forcing and climate response are
subject to large uncertainties, but are broadly similar to estimates
discussed above. Again, reconstructions with stronger cooling
in this period imply higher climate sensitivities than those with
weaker cooling (results updated from Rind et al., 2004).

9.6.3.2

Inferences About Climate Sensitivity Based on the
Last Glacial Maximum

The LGM is one of the key periods used to estimate ECS

(Hansen et al., 1984; Lorius et al., 1990; Hoffert and Covey,
1992), since it represents a quasi-equilibrium climate response
to substantially altered boundary conditions. When forced
with changes in greenhouse gas concentrations and the extent
and height of ice sheet boundary conditions, AOGCMs or
EMICs identical or similar to those used for 20th- and 21st-
century simulations produce a 3.3°C to 5.1°C cooling for
this period in response to radiative perturbations of 4.6 to 7.2
W m

–2

(Sections 6.4.1.3; see also Section 9.3.2; see also

Masson-Delmotte et al., 2006). The simulated cooling in the
tropics ranges from 1.7°C to 2.4°C. The ECS of the models used
in PMIP2 ranges from 2.3°C to 3.7°C (Table 8.2), and there is
some tendency for models with larger sensitivity to produce
larger tropical cooling for the LGM, but this relationship is not
very tight. Comparison between simulated climate change and
reconstructed climate is affected by substantial uncertainties in
forcing and data (Chapter 6 and Section 9.2.1.3). For example,
the PMIP2 forcing does not account for changes in mineral
dust, since the level of scienti

c understanding for this forcing

is very low (Figure 6.5). The range of simulated temperature
changes is also affected by differences in the radiative in

uence

of the ice-covered regions in different models (Taylor et al.,
2000). Nevertheless, the PMIP2 models simulate LGM
climate changes that are approximately consistent with proxy
information (Chapter 6).

Recent studies (Annan et al., 2005; Schneider von Deimling

et al., 2006) attempt to estimate the PDF of ECS from
ensemble simulations of the LGM by systematically exploring
model uncertainty. Both studies investigate the relationship
between climate sensitivity and LGM tropical SSTs, which are
in

uenced strongly by CO

changes. In a perturbed physics

ensemble, Schneider von Deimling et al. (2006) vary 11 ocean
and atmospheric parameters in a 1,000-member ensemble
simulation of the LGM with the CLIMBER-2 EMIC (Table 8.3).
They

nd a close relationship between ECS and tropical SST

cooling in their model, implying a 5 to 95% range of ECS of
1.2°C to 4.3°C when attempting to account for model parameter,
forcing and palaeoclimate data uncertainties. Similar constraints
on climate sensitivity are found when proxy reconstructions of
LGM antarctic temperatures are used instead of tropical SSTs
(Schneider von Deimling et al., 2006). In contrast, Annan

et al. (2005) use a perturbed physics ensemble based on a
low-resolution version of the atmospheric component of the
MIROC3.2 model, perturbing a range of model parameters over
prior distributions determined from the ability of the model to
reproduce seasonal mean climate in a range of climate variables.
They

nd a best-

t sensitivity of about 4.5°C, and their results

suggest that sensitivities in excess of 6°C are unlikely given
observational estimates of LGM tropical cooling and the
relationship between tropical SST and sensitivity in their model.
Since the perturbed physics ensemble based on that atmospheric
model does not produce sensitivities less than 4°C, this result
cannot provide a lower limit or a PDF for ECS.

The discrepancy between the inferred upper limits in the two

studies probably arises from both different radiative forcing and
structural differences between the models used. Forcing from
changes in vegetation cover and dust is not included in the
simulations done by Annan et al. (2005), which according to
Schneider von Deimling et al. (2006) would reduce the Annan
et al. ECS estimates and yield better agreement between the
results of the two studies. However, the effect of these forcings
and their interaction with other LGM forcings is very uncertain,
limiting con

dence in such estimates of their effect (Figure 6.5).

Structural differences in models are also likely to play a role.
The Annan et al. (2005) estimate shows a weaker association
between simulated tropical SST changes and ECS than the
Schneider von Deimling et al. (2006) result. Since Annan et al.
use a mixed-layer ocean model, and Schneider von Deimling a
simpli

ed ocean model, both models may not capture the full

ocean response affecting tropical SSTs. The atmospheric model
used in Schneider von Deimling is substantially simpler than
that used in the Annan et al. (2005) study. Overall, estimates of
climate sensitivity from the LGM are broadly consistent with
other estimates of climate sensitivity derived, for example,
from the instrumental period.

9.6.4

Summary of Observational Constraints for
Climate Sensitivity

Any constraint of climate sensitivity obtained from

observations must be interpreted in light of the underlying
assumptions. These assumptions include (i) the choice of prior
distribution for each of the model parameters (Section 9.6.1
and Supplementary Material, Appendix 9.B), including the
parameter range explored, (ii) the treatment of other parameters
that in

uence the estimate, such as effective ocean diffusivity,

and (iii) the methods used to account for uncertainties, such
as structural and forcing uncertainties, that are not represented
by the prior distributions. Neglecting important sources of
uncertainty in these estimates will result in overly narrow
ranges that overstate the certainty with which the ECS or TCR is
known. Errors in assumptions about forcing or model response
will also result in unrealistic features of model simulations,
which can result in erroneous modes (peak probabilities)
and shapes of the PDF. On the other hand, using less than all
available information will yield results that are less constrained
than they could be under optimal use of available data.

726

Understanding and Attributing Climate Change

Chapter 9

While a variety of important uncertainties (e.g., radiative

forcing, mixing of heat into the ocean) have been taken into
account in most studies (Table 9.3), some caveats remain.
Some processes and feedbacks might be poorly represented
or missing, particularly in simple and many intermediate
complexity models. Structural uncertainties in the models, for
example, in the representation of cloud feedback processes
(Chapter 8) or the physics of ocean mixing, will affect results
for climate sensitivity and are very dif

cult to quantify. In

addition, differences in ef

cacy between forcings are not

directly represented in simple models, so they may affect the
estimate (e.g., Tett et al., 2007), although this uncertainty may be
folded into forcing uncertainty (e.g., Hegerl et al., 2003, 2007).
The use of a single value for the ECS further assumes that it
is constant in time. However, some authors (e.g., Senior and
Mitchell, 2000; Boer and Yu, 2003) have shown that ECS varies
in time in the climates simulated by their models. Since results
from instrumental data and the last millennium are dominated
primarily by decadal- to centennial-scale changes, they will
therefore only represent climate sensitivity at an equilibrium
that is not too far from the present climate. There is also a small
uncertainty in the radiative forcing due to atmospheric CO

doubling (<10%; see Chapter 2), which is not accounted for
in most studies that derive observational constraints on climate
sensitivity.

Despite these uncertainties, which are accounted for to

differing degrees in the various studies, con

dence is increased

by the similarities between individual ECS estimates (Figure
9.20). Most studies

nd a lower 5% limit of between 1°C and

2.2°C, and studies that use information in a relatively complete
manner generally

nd a most likely value between 2°C and

3°C (Figure 9.20). Constraints on the upper end of the likely
range of climate sensitivities are also important, particularly for
probabilistic forecasts of future climate with constant radiative
forcing. The upper 95% limit for ECS ranges from 5°C to 10°C,
or greater in different studies depending upon the approach
taken, the number of uncertainties included and speci

c details

of the prior distribution that was used. This wide range is
largely caused by uncertainties and nonlinearities in forcings
and response. For example, a high sensitivity is dif

cult to

rule out because a high aerosol forcing could nearly cancel
greenhouse gas forcing over the 20th century. This problem
can be addressed, at least to some extent, if the differences in
the spatial and temporal patterns of response between aerosol
and greenhouse gas forcing are used for separating these two
responses in observations (as, for example, in Gregory et al.,
2002a; Harvey and Kaufmann, 2002; Frame et al., 2005). In
addition, nonlinearities in the response to transient forcing
make it more dif

cult to constrain the upper limit on ECS

based on observed transient forcing responses (Frame et al.,
2005). The TCR, which may be more relevant for near-term
climate change, is easier to constrain since it relates more
linearly to observables. For the pre-instrumental part of the
last millennium, uncertainties in temperature and forcing
reconstructions, and the nonlinear connection between ECS
and the response to volcanism, prohibit tighter constraints.

Estimates of climate sensitivity based on the ability of climate
models to reproduce climatic conditions of the LGM broadly
support the ranges found from the instrumental period, although
a tight constraint is also dif

cult to obtain from this period alone

because of uncertainties in tropical temperature changes, forcing
uncertainties and the effect of structural model uncertainties. In
addition, the number of studies providing estimates of PDFs
from palaeoclimatic data, using independent approaches and
complementary sources of proxy data, are limited.

Thus, most studies that use a simple uniform prior distribution

of ECS are not able to exclude values beyond the traditional
IPCC First Assessment Report range of 1.5°C to 4.5°C (IPCC,
1990). However, considering all available evidence on ECS
together provides a stronger constraint than individual lines of
evidence. Bayesian methods can be used to incorporate multiple
lines of evidence to sharpen the posterior distribution of ECS,
as in Annan and Hargreaves (2006) and Hegerl et al. (2006a).
Annan and Hargreaves (2006) demonstrate that using three
lines of evidence, namely 20th-century warming, the response
to individual volcanic eruptions and the LGM response,
results in a tighter estimate of ECS, with a probability of less
than 5% that ECS exceeds 4.5°C. The authors

nd a similar

constraint using

ve lines of evidence under more conservative

assumptions about uncertainties (adding cooling during the
Little Ice Age and studies based on varying model parameters
to match climatological means, see Box 10.2). However, as
discussed in Annan and Hargreaves (2006), combining multiple
lines of evidence may produce overly con

dent estimates

unless every single line of evidence is entirely independent of
others, or dependence is explicitly taken into account. Hegerl et
al. (2006a) argue that instrumental temperature change during
the second half of the 20th century is essentially independent
of the palaeoclimate record of the last millennium and of the
instrumental data from the

rst half of the 20th century that is

used to calibrate the palaeoclimate records. Hegerl et al. (2006a)
therefore base their prior probability distribution for the climate
sensitivity on results from the late 20th century (Frame et al.,
2005), which reduces the 5 to 95% ECS range from all proxy
reconstructions analysed to 1.5°C to 6.2°C compared to the
previous range of 1.2°C to 8.6°C. Both results demonstrate that
independent estimates, when properly combined in a Bayesian
analysis, can provide a tighter constraint on climate sensitivity,
even if they individually provide only weak constraints. These
studies also

nd a 5% lower limit of 1.5°C or above, consistent

with several studies based on the 20th-century climate change
alone (Knutti et al., 2002; Forest et al., 2006) and estimates
that greenhouse warming contributes substantially to observed
temperature changes (Section 9.4.1.4).

Overall, several lines of evidence strengthen con

dence in

present estimates of ECS, and new results based on objective
analyses make it possible to assign probabilities to ranges of
climate sensitivity previously assessed from expert opinion
alone. This represents a signi

cant advance. Results from

studies of observed climate change and the consistency of
estimates from different time periods indicate that ECS is very
likely larger than 1.5°C with a most likely value between 2°C

727

Chapter 9

Understanding and Attributing Climate Change

and 3°C. The lower bound is consistent with the view that the
sum of all atmospheric feedbacks affecting climate sensitivity
is positive. Although upper limits can be obtained by combining
multiple lines of evidence, remaining uncertainties that are not
accounted for in individual estimates (such as structural model
uncertainties) and possible dependencies between individual
lines of evidence make the upper 95% limit of ECS uncertain
at present. Nevertheless, constraints from observed climate
change support the overall assessment that the ECS is likely
to lie between 2°C and 4.5°C with a most likely value of
approximately 3°C (Box 10.2).

9.7

Combining Evidence of
Anthropogenic Climate Change

The widespread change detected in temperature observations

of the surface (Sections 9.4.1, 9.4.2, 9.4.3), free atmosphere
(Section 9.4.4) and ocean (Section 9.5.1), together with
consistent evidence of change in other parts of the climate
system (Section 9.5), strengthens the conclusion that greenhouse
gas forcing is the dominant cause of warming during the past
several decades. This combined evidence, which is summarised
in Table 9.4, is substantially stronger than the evidence that is
available from observed changes in global surface temperature
alone (Figure 3.6).

The evidence from surface temperature observations is

strong: The observed warming is highly signi

cant relative to

estimates of internal climate variability which, while obtained
from models, are consistent with estimates obtained from
both instrumental data and palaeoclimate reconstructions. It
is extremely unlikely (<5%) that recent global warming is due
to internal variability alone such as might arise from El Niño
(Section 9.4.1). The widespread nature of the warming (Figures
3.9 and 9.6) reduces the possibility that the warming could have
resulted from internal variability. No known mode of internal
variability leads to such widespread, near universal warming
as has been observed in the past few decades. Although modes
of internal variability such as El Niño can lead to global
average warming for limited periods of time, such warming is
regionally variable, with some areas of cooling (Figures 3.27
and 3.28). In addition, palaeoclimatic evidence indicates that El
Niño variability during the 20th century is not unusual relative
to earlier periods (Section 9.3.3.2; Chapter 6). Palaeoclimatic
evidence suggests that such a widespread warming has not been
observed in the NH in at least the past 1.3 kyr (Osborn and
Briffa, 2006), further strengthening the evidence that the recent
warming is not due to natural internal variability. Moreover, the
response to anthropogenic forcing is detectable on all continents
individually except Antarctica, and in some sub-continental
regions. Climate models only reproduce the observed 20th-
century global mean surface warming when both anthropogenic
and natural forcings are included (Figure 9.5). No model that
has used natural forcing only has reproduced the observed

global mean warming trend or the continental mean warming
trends in all individual continents (except Antarctica) over
the second half of the 20th century. Detection and attribution
of external in

uences on 20th-century and palaeoclimatic

reconstructions, from both natural and anthropogenic sources
(Figure 9.4 and Table 9.4), further strengthens the conclusion
that the observed changes are very unusual relative to internal
climate variability.

The energy content change associated with the observed

widespread warming of the atmosphere is small relative to the
energy content change of the ocean, and also smaller than that
associated with other components such as the cryosphere. In
addition, the solid Earth also shows evidence for warming in
boreholes (Huang et al., 2000; Beltrami et al., 2002; Pollack and
Smerdon, 2004). It is theoretically feasible that the warming of
the near surface could have occurred due to a reduction in the
heat content of another component of the system. However,
all parts of the cryosphere (glaciers, small ice caps, ice sheets
and sea ice) have decreased in extent over the past half century,
consistent with anthropogenic forcing (Section 9.5.5, Table
9.4), implying that the cryosphere consumed heat and thus
indicating that it could not have provided heat for atmospheric
warming. More importantly, the heat content of the ocean (the
largest reservoir of heat in the climate system) also increased,
much more substantially than that of the other components of
the climate system (Figure 5.4; Hansen et al., 2005; Levitus
et al., 2005). The warming of the upper ocean during the
latter half of the 20th century was likely due to anthropogenic
forcing (Barnett et al., 2005; Section 9.5.1.1; Table 9.4). While
the statistical evidence in this research is very strong that the
warming cannot be explained by ocean internal variability as
estimated by two different climate models, uncertainty arises
since there are discrepancies between estimates of ocean heat
content variability from models and observations, although
poor sampling of parts of the World Ocean may explain this
discrepancy. However, the spatial pattern of ocean warming
with depth is very consistent with heating of the ocean resulting
from net positive radiative forcing, since the warming proceeds
downwards from the upper layers of the ocean and there
is deeper penetration of heat at middle to high latitudes and
shallower penetration at low latitudes (Barnett et al., 2005;
Hansen et al., 2005). This observed ocean warming pattern is
inconsistent with a redistribution of heat between the surface
and the deep ocean.

Thus, the evidence appears to be inconsistent with the

ocean or land being the source of the warming at the surface.
In addition, simulations forced with observed SST changes
cannot fully explain the warming in the troposphere without
increases in greenhouse gases (e.g., Sexton et al., 2001), further
strengthening the evidence that the warming does not originate
from the ocean. Further evidence for forced changes arises from
widespread melting of the cryosphere (Section 9.5.5), increases
in water vapour in the atmosphere (Section 9.5.4.1) and changes
in top-of-the atmosphere radiation that are consistent with
changes in forcing.

729

Chapter 9

Understanding and Attributing Climate Change

able 9.4.

A synthesis of c

lima

te change detection results:

(a) surface and a

tmospheric tempera

ture evidence and (b) evidence from other

variables.

Note tha

t our likelihood assessments are reduced compared to individual

detection studies in order to take into account remaining uncertainties (see Section 9.1.2),

such as forcing and model uncertai

nty not directly accounted for in the studies.

The likelihood assessment is indica

ted in percenta

ge terms,

in parentheses where the term is not from the standard IPCC likelihood levels.

Result

Region

Likelihood

Factors contributing to likelihood assessment

Surface temperature

arming during the past half century cannot be

explained without exter

nal radiative for

cing

Global

Extr

emely

likely (>95%)

Anthr

opogenic change has been detected in surface temperatur

e with very high signifi

cance

levels (less than 1% err

or pr

obability). This conclusion is str

engthened by detection of

anthr

opogenic change in the upper ocean with high signifi

cance level. Upper ocean warming

gues against the surface warming being due to natural inter

nal pr

ocesses. Observed change

is very lar

ge r

elative to climate-model simulated inter

nal variability

. Surface temperatur

variability simulated by models is consistent with variability estimated fr

om instrumental and

palaeor

ecor

ds. Main uncertainty fr

om for

cing and inter

nal variability estimates (Sections

9.4.1.2, 9.4.1.4, 9.5.1.1, 9.3.3.2, 9.7).

arming during the past half century is not solely due

to known natural causes

Global

ery Likely

This warming took place at a time when non-anthr

opogenic exter

nal factors would likely

have pr

oduced cooling. The combined ef

fect of known sour

ces of for

cing would have been

extr

emely likely to pr

oduce a warming. No climate model that has used natural for

cing only

has r

epr

oduced the observed global warming tr

end over the 2nd half of the 20th century

Main uncertainties arise fr

om for

cing, including solar

, model-simulated r

esponses and inter

nal

variability estimates (Sections 2.9.2, 9.2.1, 9.4.1.2, 9.4.1.4; Figur

es 9.5, 9.6, 9.9).

eenhouse gas for

cing has been the dominant cause

of the observed global warming over the last 50 years.

Global

ery likely

All multi-signal detection and attribution studies attribute mor

e warming to gr

eenhouse gas

for

cing than to a combination of all other sour

ces consider

ed, including inter

nal variability

with a very high signifi

cance. This conclusion accounts for observational, model and for

cing

uncertainty

, and the possibility that the r

esponse to solar for

cing could be under

estimated

by models. Main uncertainty fr

om for

cing and inter

nal variability estimates (Section 9.4.1.4;

Figur

e 9.9).

Incr

eases in gr

eenhouse gas concentrations alone

would have caused mor

e warming than observed over

the last 50 years because volcanic and anthr

opogenic

aer

osols have of

fset some warming that would

otherwise have taken place.

Global

Likely

Estimates fr

om dif

fer

ent analyses using dif

fer

ent models show consistently mor

e warming

than observed over the last 50 years at the 5% signifi

cance level. However

, separation of the

response to non-gr

eenhouse gas (particularly aer

osol) for

cing fr

om gr

eenhouse gas for

cing

varies between models (Section 9.4.1.4; Figur

e 9.9).

Ther

e has been a substantial anthr

opogenic

contribution to surface temperatur

e incr

eases in every

continent except Antar

ctica since the middle of the

20th century

Africa, Asia,

Australia, Eur

ope,

North America and

South America

Likely

Anthr

opogenic change has been estimated using detection and attribution methods on every

individual continent (except Antar

ctica). Gr

eater variability compar

ed to other continental

regions makes detection mor

e mar

ginal in Eur

ope. No climate model that used natural for

cing

only r

epr

oduced the observed continental mean warming tr

end over the second half of the

20th century

. Uncertainties arise because sampling ef

fects r

esult in lower signal-to-noise ratio

at continental than at global scales. Separation of the r

esponse to dif

fer

ent for

cings is mor

diffi

cult at these spatial scales (Section 9.4.2; F

AQ 9.2, Figur

e 1).

Early 20th-century warming is due in part to exter

nal

for

cing.

Global

ery Likely

A number of studies detect the infl

uence of exter

nal for

cings on early 20th-century warming,

including a warming fr

om anthr

opogenic for

cing. Both natural for

cing and r

esponse ar

uncertain, and dif

fer

ent studies fi

nd dif

fer

ent for

cings dominant. Some studies indicate that

inter

nal variability could have made a lar

ge contribution to early 20th-century warming. Some

observational uncertainty in early 20th-century tr

end (Sections 9.3.3.2, 9.4.1.4; Figur

es 9.4,

9.5).

(continued)

730

Understanding and Attributing Climate Change

Chapter 9

Result

Region

Likelihood

Factors contributing to likelihood assessment

Surface temperature

e-industrial temperatur

es wer

e infl

uenced by natural

exter

nal for

cing (period studied is past 7 centuries)

NH (mostly

extratr

opics)

ery Likely

Detection studies indicate that exter

nal for

cing explains a substantial fraction of inter

-decadal

variability in NH temperatur

e r

econstructions. Simulations in r

esponse to estimates of pr

industrial for

cing r

epr

oduce br

oad featur

es of r

econstructions. Substantial uncertainties

in r

econstructions and past for

cings ar

e unlikely to lead to a spurious agr

eement between

temperatur

e r

econstructions and for

cing r

econstructions as they ar

e derived fr

independent pr

oxies (Section 9.3.3; Figur

es 9.4, 6.13).

emperatur

e extr

emes have changed due to

anthr

opogenic for

cing

NH land ar

eas and

Australia combined.

Likely

A range of observational evidence indicates that temperatur

e extr

emes ar

e changing. An

anthr

opogenic infl

uence on the temperatur

es of the 1, 5, 10 and 30 warmest nights, coldest

days and coldest nights annually has been formally detected and attributed in one study

but observed change in the temperatur

e of the warmest day annually is inconsistent with

simulated change. The detection of changes in temperatur

e extr

emes is supported by

other comparisons between models and observations. Model uncertainties in changes

in temperatur

e extr

emes ar

e gr

eater than for mean temperatur

es and ther

e is limited

observational coverage and substantial observational uncertainty (Section 9.4.3).

Free atmosphere changes

ropopause height incr

eases ar

e detectable and

attributable to anthr

opogenic for

cing (latter half of the

20th century)

Global Likely

Ther

e has been r

obust detection of anthr

opogenic infl

uence on incr

easing tr

opopause height.

Simulated tr

opopause height incr

eases r

esult mainly fr

om gr

eenhouse gas incr

eases and

stratospheric ozone decr

eases. Detection and attribution studies r

ely on r

eanalysis data,

which ar

e subject to inhomogeneities r

elated to dif

fering availability and quality of input data,

although tr

opopause height incr

eases have also been identifi

ed in radiosonde observations.

Overall tr

opopause height incr

eases in r

ecent model and one r

eanalysis (ERA-40) appear

to be driven by similar lar

ge-scale changes in atmospheric temperatur

e, although err

ors in

opospheric warming and stratospheric cooling could lead to partly spurious agr

eement in

other data sets (Section 9.4.4.2; Figur

e 9.14).

ropospheric warming is detectable and attributable to

anthr

opogenic for

cing

(latter half of the 20th century)

Global

Likely

Ther

e has been r

obust detection and attribution of anthr

opogenic infl

uence on tr

opospheric

warming, which does not depend on including stratospheric cooling in the fi

ngerprint

patter

of r

esponse. Ther

e ar

e observational uncertainties in radiosonde and satellite r

ecor

ds. Models

generally pr

edict a r

elative warming of the fr

ee tr

opospher

e compar

ed to the surface in the

opics since 1979, which is not seen in the radiosonde r

ecor

d (possibly due to uncertainties

in the radiosonde r

ecor

d) but is seen in one version of the satellite r

ecor

d, although not others

(Section 9.4.4).

Simultaneous tr

opospheric warming and stratospheric

cooling due to the infl

uence of anthr

opogenic for

cing

has been observed (latter half of the 20th century)

Global

ery Likely

Simultaneous warming of the tr

opospher

e and cooling of the stratospher

e due to natural

factors is less likely than warming of the tr

opospher

e or cooling of the stratospher

e alone.

Cooling of the stratospher

e is in part r

elated to decr

eases in stratospheric ozone. Modelled

and observational uncertainties as discussed under entries for tr

opospheric warming with

additional uncertainties due to stratospheric observing systems and the r

elatively poor

repr

esentations of stratospheric pr

ocesses and variability in climate models (Section 9.4.4).

ble 9.4 (continued)

731

Chapter 9

Understanding and Attributing Climate Change

Result

Region

Likelihood

Factors contributing to likelihood assessment

Ocean changes

Anthr

opogenic for

cing has warmed the upper several

hundr

ed metr

es of the ocean during the latter half of

the 20th century

Global (but with

limited sampling in

some r

egions)

Likely

Robust detection and attribution of anthr

opogenic fi

ngerprint fr

om thr

ee dif

fer

ent models in

ocean temperatur

e changes, and in ocean heat content data, suggests high likelihood, but

observational and modelling uncertainty r

emains. 20th-century simulations with MMD models

simulate comparable ocean warming to observations only if anthr

opogenic for

cing is included.

Simulated and observed variability appear inconsistent, either due to sampling err

ors in

the observations or under

-simulated inter

nal variability in the models. Limited geographical

coverage in some ocean basins (Section 9.5.1.1; Figur

e 9.15).

Anthr

opogenic for

cing contributed to sea level rise

during the latter half 20th century

Global

ry likely

Natural factors alone do not satisfactorily explain either the observed thermal expansion of

the ocean or the observed sea level rise. Models including anthr

opogenic and natural for

cing

simulate the observed thermal expansion since 1961 r

easonably well. Anthr

opogenic for

cing

dominates the surface temperatur

e change simulated by models, and has likely contributed

to the observed warming of the upper ocean and widespr

ead glacier r

etr

eat. It is very

unlikely that the warming during the past half century is due only to known natural causes.

It is ther

efor

e very likely that anthr

opogenic for

cing contributed to sea level rise associated

with ocean thermal expansion and glacier r

etr

eat. However

, it r

emains diffi

cult to estimate

the anthr

opogenic contribution to sea level rise because suitable studies quantifying the

anthr

opogenic contribution to sea level rise and glacier r

etr

eat ar

e not available, and because

the observed sea level rise budget is not closed (T

able 9.2; Section 9.5.2).

Circulation

Sea level pr

essur

e shows a detectable anthr

opogenic

signatur

e during the latter half of the 20th century

Global Likely

Changes of similar natur

e ar

e observed in both hemispher

es and ar

e qualitatively

, but not

quantitatively consistent with model simulations. Uncertainty in models and observations.

Models under

estimate the observed NH changes for r

easons that ar

e not understood, based

on a small number of studies. Simulated r

esponse to 20th century for

cings is consistent with

observations in SH if ef

fect of stratospheric ozone depletion is included (Section 9.5.3.4;

Figur

e 9.16).

Anthr

opogenic for

cing contributed to the incr

ease in

equency of the most intense tr

opical cyclones since

the 1970s

ropical r

egions

Mor

e likely

than not

(>50%)

Recent observational evidence suggests an incr

ease in fr

equency of intense storms. Incr

ease

in intensity is consistent with theor

etical expectations. Lar

ge uncertainties due to models and

observations. Modelling studies generally indicate a r

educed fr

equency of tr

opical cyclones

in r

esponse to enhanced gr

eenhouse gas for

cing, but an incr

ease in the intensity of the most

intense cyclones. Observational evidence, which is af

fected by substantial inhomogeneities in

opical cyclone data sets for which corr

ections have been attempted, suggests that incr

eases

in cyclone intensity since the 1970s ar

e associated with SST and atmospheric water vapour

incr

eases (Section 3.8.3, Box 3.5 and Section 9.5.2.6).

Precipitation, Drought, Runoff

lcanic for

cing infl

uences total rainfall

Global land ar

eas

Mor

e likely

than not

(>50%)

Model r

esponse detectable in observations for some models and r

esult supported by

theor

etical understanding. However

, uncertainties in models, for

cings and observations.

Limited observational sampling, particularly in the SH (Section 9.5.4.2; Figur

e 9.18).

Incr

eases in heavy rainfall ar

e consistent with

anthr

opogenic for

cing during latter half 20th century

Global land ar

eas

(limited sampling)

Mor

e likely

than not

(>50%)

Observed incr

eases in heavy pr

ecipitation appear to be consistent with expectations of

sponse to anthr

opogenic for

cing. Models may not r

epr

esent heavy rainfall well; observations

suf

fer fr

om sampling inadequacies (Section 9.5.4.2).

(continued)

732

Understanding and Attributing Climate Change

Chapter 9

Result

Region

Likelihood

Factors contributing to likelihood assessment

Precipitation, Drought, Runoff

Incr

eased risk of dr

ought due to anthr

opogenic for

cing

during latter half 20th century

Global land ar

eas

Mor

e likely

than not

(>50%)

One detection study has identifi

ed an anthr

opogenic fi

ngerprint in a global Palmer Dr

ought

Severity Index data set with high signifi

cance, but the simulated r

esponse to anthr

opogenic

and natural for

cing combined is weaker than observed, and the model appears to have less

inter

-decadal variability than observed. Studies of some r

egions indicate that dr

oughts in

those r

egions ar

e linked either to SST changes that, in some instances, may be linked to

anthr

opogenic aer

osol for

cing (e.g., Sahel) or to a cir

culation r

esponse to anthr

opogenic

for

cing (e.g., southwest Australia). Models, observations and for

cing all contribute uncertainty

(Section 9.5.3.2).

yosphere

Anthr

opogenic for

cing has contributed to r

eductions

in NH sea ice extent during the latter half of the 20th

century

ctic

Likely

The observed change is qualitatively consistent with model-simulated changes for most

models and expectation of sea ice melting under ar

ctic warming. Sea ice extent change

detected in one study

. The model used has some defi

ciencies in ar

ctic sea ice annual cycle

and extent. The conclusion is supported by physical expectations and simulations with

another climate model. Change in SH sea ice pr

obably within range explained by inter

nal

variability (Section 9.5.5.1).

Anthr

opogenic for

cing has contributed to widespr

ead

glacier r

etr

eat during the 20th century

Global

Likely

Observed changes ar

e qualitatively consistent with theor

etical expectations and temperatur

detection. Anthr

opogenic contribution to volume change diffi

cult to estimate. Few detection

and attribution studies, but r

etr

eat in vast majority of glaciers consistent with expected

action to widespr

ead warming (Section 9.5.5.3).

ble 9.4 (continued)

733

Chapter 9

Understanding and Attributing Climate Change

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744

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Chapter 9

Appendix 9.A: Methods Used to Detect

Externally Forced Signals

This appendix very brie

y reviews the statistical methods

that have been used in most recent detection and attribution
work. Standard ‘frequentist’ methods (methods based on the
relative frequency concept of probability) are most often used,
but there is also increasing use of Bayesian methods of statistical
inference. The following sections brie

y describe the optimal

ngerprinting technique followed by a short discussion on the

differences between the standard and Bayesian approaches
to statistical inferences that are relevant to detection and
attribution.

9.A.1 Optimal

Fingerprinting

Optimal

ngerprinting is generalised multivariate

regression adapted to the detection of climate change and
the attribution of change to externally forced climate change
signals (Hasselmann, 1979, 1997; Allen and Tett, 1999). The
regression model has the form

y = Xa +u

, where vector

is a

ltered version of the observed record, matrix

contains the

estimated response patterns to the external forcings (signals)
that are under investigation,

is a vector of scaling factors

that adjusts the amplitudes of those patterns and

represents

internal climate variability. Vector

is usually assumed to be

a Gaussian random vector with covariance matrix

. Vector

is estimated with

â = (X

-1

, which is equivalent



)

-1



ỹ

, where matrix



and vector

ỹ

represent the

signal patterns and observations after normalisation by the
climate’s internal variability. This normalisation, standard
in linear regression, is used in most detection and attribution
approaches to improve the signal-to-noise ratio (see, e.g.,
Hasselmann, 1979; Allen and Tett, 1999; Mitchell et al.,
2001).

The matrix

typically contains signals that are estimated

with either an AOGCM, an AGCM (see Sexton et al., 2001,
2003) or a simpli

ed climate model such as an EBM. Because

AOGCMs simulate natural internal variability as well as the
response to speci

ed anomalous external forcing, AOGCM-

simulated climate signals are typically estimated by averaging
across an ensemble of simulations (for a discussion of optimal
ensemble size and composition, see Sexton et al., 2003). If
an observed response is to be attributed to anthropogenic
in

uence,

should at a minimum contain separate natural and

anthropogenic responses. In order to relax the assumption that
the relative magnitudes of the responses to individual forcings
are correctly simulated,

may contain separate responses to all

the main forcings, including greenhouse gases, sulphate aerosol,
solar irradiance changes and volcanic aerosol. The vector

accounts for possible errors in the amplitude of the external
forcing and the amplitude of the climate model’s response by
scaling the signal patterns to best match the observations.

Fitting the regression model requires an estimate of the

covariance matrix

(i.e., the internal variability), which

is usually obtained from unforced variation simulated by
AOGCMs (e.g., from long control simulations) because the
instrumental record is too short to provide a reliable estimate
and may be affected by external forcing. Atmosphere-Ocean
General Circulation Models may not simulate natural internal
climate variability accurately, particularly at small spatial scales,
and thus a residual consistency test (Allen and Tett, 1999) is
typically used to assess the model-simulated variability at the
scales that are retained in the analysis. To avoid bias (Hegerl
et al., 1996, 1997), uncertainty in the estimate of the vector of
scaling factors

is usually assessed with a second, statistically

independent estimate of the covariance matrix

which is

ordinarily obtained from an additional, independent sample of
simulated unforced variation.

Signal estimates are obtained by averaging across an

ensemble of forced climate change simulations, but contain
remnants of the climate’s natural internal variability because
the ensembles are

nite. When ensembles are small or signals

weak, these remnants may bias ordinary least-squares estimates
of

downward. This is avoided by estimating

with the total

least-squares algorithm (Allen and Stott 2003).

9.A.2 Methods of Inference

Detection and attribution questions are assessed through a

combination of physical reasoning (to determine, for example,
by assessing consistency of possible responses, whether other
mechanisms of change not included in the climate model could
plausibly explain the observed change) and by evaluating
speci

c hypotheses about the scaling factors contained in

. Most studies evaluate these hypotheses using standard

frequentist methods (Hasselmann, 1979, 1997; Hegerl et al.,
1997; Allen and Tett, 1999). Several recent studies have also
used Bayesian methods (Hasselmann, 1998; Leroy, 1998; Min et
al., 2004, 2005; Lee et al., 2005, 2006; Schnur and Hasselmann,
2005; Min and Hense, 2006a,b).

In the standard approach, detection of a postulated climate

change signal occurs when its amplitude in observations is
shown to be signi

cantly different from zero (i.e., when the null

hypothesis

a = 0

where

is a vector of zeros, is rejected)

with departure from zero in the physically plausible direction.
Subsequently, the second attribution requirement (consistency
with a combination of external forcings and natural internal
variability) is assessed with the ‘attribution consistency test’
(Hasselmann, 1997; see also Allen and Tett, 1999) that evaluates
the null hypothesis

a = 1

where

denotes a vector of units.

This test does not constitute a complete attribution assessment,
but contributes important evidence to such assessments (see
Mitchell et al., 2001). Attribution studies usually also test
whether the response to a key forcing, such as greenhouse gas
increases, is distinguishable from that to other forcings, usually
based on the results of multiple regression (see above) using the
most important forcings simultaneously in

. If the response to

a key forcing (e.g., due to greenhouse gas increases) is detected
by rejecting the hypothesis that its amplitude a

GHG

= 0 in such a

multiple regression, this provides strong attribution information