9

Understanding and Attributing

Climate Change

Coordinating Lead Authors:

Gabriele C. Hegerl (USA, Germany), Francis W. Zwiers (Canada)

Lead Authors:

Pascale Braconnot (France), Nathan P. Gillett (UK), Yong Luo (China), Jose A. Marengo Orsini (Brazil, Peru), Neville Nicholls (Australia),

Joyce E. Penner (USA), Peter A. Stott (UK) 

Contributing Authors:

M. Allen (UK), C. Ammann (USA), N. Andronova (USA), R.A. Betts (UK), A. Clement (USA), W.D. Collins (USA), S. Crooks (UK),

T.L. Delworth (USA), C. Forest (USA), P. Forster (UK), H. Goosse (Belgium), J.M. Gregory (UK), D. Harvey (Canada), G.S. Jones (UK),

F. Joos (Switzerland), J. Kenyon (USA), J. Kettleborough (UK), V. Kharin (Canada), R. Knutti (Switzerland), F.H. Lambert (UK),

M. Lavine (USA), T.C.K. Lee (Canada), D. Levinson (USA), V. Masson-Delmotte (France), T. Nozawa (Japan), B. Otto-Bliesner (USA),

D. Pierce (USA), S. Power (Australia), D. Rind (USA), L. Rotstayn (Australia), B. D. Santer (USA), C. Senior (UK), D. Sexton (UK),

S. Stark (UK), D.A. Stone (UK), S. Tett (UK), P. Thorne (UK), R. van Dorland (The Netherlands), M. Wang (USA), B. Wielicki (USA),

T. Wong (USA), L. Xu (USA, China), X. Zhang (Canada), E. Zorita (Germany, Spain)

Review Editors:

David J. Karoly (USA, Australia), Laban Ogallo (Kenya), Serge Planton (France)

This chapter should be cited as:

Hegerl, G.C., F. W. Zwiers, P. Braconnot, N.P. Gillett, Y. Luo, J.A. Marengo Orsini, N. Nicholls, J.E. Penner and P.A. Stott, 2007: Understanding and Attributing Climate Change. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the 

Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis,

K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
664

Understanding and Attributing Climate Change 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 of 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
665

Chapter 9 Understanding and Attributing Climate Change

Executive Summary

Evidence of the effect of external infl 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 

infl 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 infl 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 diffi 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 infl uence has been detected in 

every continent except Antarctica (which has insuffi 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 infl 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.

Diffi 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 diffi 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 smallscale 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 infl 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 infl 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 

infl 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 
666

Understanding and Attributing Climate Change 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 infl 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, insuffi cient understanding of natural variability, 

uncertainty in modelling intense cyclones and uncertainties in 

historical tropical cyclone data.

Analyses of palaeoclimate data have increased 

confi dence in the role of external infl 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 diffi 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 CO2 doubling in response to a 

1% yr–1 increase in CO2, 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 confi dence in the 

understanding of the infl 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 confi 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 confi 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 

confi 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 confi 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.
667

Chapter 9 Understanding and Attributing Climate Change

9.1 Introduction

The objective of this chapter is to assess scientifi c 

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 identifi 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 infl 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 infl 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 quantifi 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 multimodel 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 defi ned in the TAR 

(IPCC, 2001; Mitchell et al., 2001). ‘Detection’ is the process 

of demonstrating that climate has changed in some defi 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 identifi 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 
668

Understanding and Attributing Climate Change 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 infl 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 ‘fi 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 infl 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, fi lter out as much internal variability as 

possible (often by using a metric that reduces the infl 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 fi 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 defi ned level of confi 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 fi 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 suffi 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 infl 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). Confi 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). 
669

Chapter 9 Understanding and Attributing Climate Change

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 confi dence in the climate models is limited, 

such a result may simply refl ect weaknesses in models. On the 

other hand, if this occurs for variables where confi 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 

diffi 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 infl uence on temperature change during the latter half of 

the 20th century, are robust. 

Detection of anthropogenic infl 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 specifi 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 signifi 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 diffi 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 diffi cult to assess their statistical 

signifi 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 infl uence on the recent evolution of the 

climate has accumulated steadily during the past two decades. 

The fi rst IPCC Assessment Report (IPCC, 1990) contained little 

observational evidence of a detectable anthropogenic infl 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 infl uence on 

the climate of the 20th century. Considerably more evidence 

accumulated during the subsequent fi ve years, such that the TAR 

(IPCC, 2001) was able to draw a much stronger conclusion, 

not just on the detectability of a human infl 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 fi 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 fi 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 

infl uences were explicitly considered, and the amplitude of the 
670

Understanding and Attributing Climate Change 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 

infl ated (doubled or more) estimates of model-simulated 

internal variance were found unlikely to be large enough to 

nullify the detection of an anthropogenic infl 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 diffi cult to attribute the observed climate change 

to one specifi c combination of external infl 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 briefl 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 fi 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 (‘fi ngerprints’) for 

individual forcings or sets of forcings, which are then combined 

linearly to provide the best fi 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 fi 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 

defi 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 (CO2), 

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 infl 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 

infl 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 ‘effi cacy’ (i.e., 

effectiveness at changing the surface temperature compared to 

CO2; 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 

diffi 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 

fi 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 fi 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 fi 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 fi 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 fi ngerprints exploits the fact that the forcing from wellmixed 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 

considereda

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

Yearb 1980s 1990 2000 2000 2000 1990

Aerosol forcing 

(W m–2)c

–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 < –0.5d

Notes:

a 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.

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

c 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). 

d 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 spatiotemporal 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 fi 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 fi 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 diffi 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 fi 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 fi 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 confi 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 briefl y discuss the radiative forcing estimates used 

for understanding climate during the last millennium, the midHolocene 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 

identifi 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 CO2 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 CO2 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 signifi 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 fl 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 infl uence of volcanism on 

temperature reconstructions over longer periods, but is diffi 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 Scientifi c 

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 fi 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 infl 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 identifi ed 

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 

infl uence clouds through cosmic rays (Section 2.7.1.3). Note 

that there is substantial uncertainty in the identifi 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 diffi cult to separate from internal climate variations 

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

Volcanic sulphur dioxide (SO2) 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 
675

Chapter 9 Understanding and Attributing Climate Change

Figure 9.1. Zonal mean atmospheric temperature change from 1890 to 1999 (°C per century) as simulated by the PCM model from (a) solar forcing, (b) volcanoes, (c) wellmixed greenhouse gases, (d) tropospheric and stratospheric ozone changes, (e) direct sulphate aerosol forcing and (f) the sum of all forcings. Plot is from 1,000 hPa to 10 hPa 

(shown on left scale) and from 0 km to 30 km (shown on right). See Appendix 9.C for additional information. Based on Santer et al. (2003a).

Figure 9.2. The zonal mean equilibrium temperature change (°C) between a present day minus a pre-industrial simulation by the CSIRO atmospheric model coupled to a 

mixed-layer ocean model from (a) direct forcing from fossil fuel black carbon and organic matter (BC+OM) and (b) the sum of fossil fuel BC+OM and biomass burning. Plot is 

from 1,000 hPa to 10 hPa (shown on left scale) and from 0 km to 30 km (shown on right). Note the difference in colour scale from Figure 9.1. See Supplementary Material, 

Appendix 9.C for additional information. Based on Penner et al. (2007).
676

Understanding and Attributing Climate Change Chapter 9

tend to enhance the high-latitude response of both a positive 

forcing, such as that of CO2, 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 fi 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 fi 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 refl ective aerosol 

forcing has been changing over time comes from satellite 

observations of changes in top-of-atmosphere outgoing 

shortwave radiation fl ux. Increases in the outgoing shortwave 

radiation fl ux can be caused by increases in refl 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 fl 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 yr–1

on average (Pinker et al., 2005). Figure 9.3 shows the top-ofFigure 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, GFDLCM2.0, 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.
677

Chapter 9 Understanding and Attributing Climate Change

atmosphere outgoing shortwave radiation fl 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) fl ux data (FD) (Zhang et al., 2004). The downward 

trend in outgoing solar radiation is consistent with the longterm 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 

fl 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 fl 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% confi dence limits of ±0.11 W m–2 yr–1) and the 

ERBS data (–0.13 ± 0.08 W m–2 yr–1) from 1984 to 1999 are not 

signifi cantly different from each other at the 5% signifi 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 

‘fi ngerprints’) that provide the best fi t to the observations. 

The fi 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 SO2

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 confi 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 SO2 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 signifi cantly different from those reconstructed 

using the methodology of Novakov et al. (2003). For example, 

the emissions in Asia grow signifi 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 lowfrequency 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 

diffi 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 timeonly 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 misidentifi 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 CO2

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 diffi cult to accurately infer the climate sensitivity from 

observations (Section 9.6). It also increases uncertainties in 
679

Chapter 9 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 midHolocene (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 specifi 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 diffi 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 CO2

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 refl 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 amplifi 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 amplifi 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), infl uencing the 

Indian and African monsoons. Over West Africa, AOGCMsimulated 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 midcontinental drying in Eurasia, which is further amplifi 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 Pacifi 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 (OttoBliesner, 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 midHolocene and LGM. Nonetheless, there is evidence that climatic 

responses to forcing, together with natural internal variability 

of the climate system, produced several well-defi 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 Infl 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 
681

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 multidecadal period during the last millennium. In addition, there 

is signifi cant correlation between simulated and reconstructed 

variability (e.g., Yoshimori et al., 2005). By comparing 

simulated and observed atmospheric CO2 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 CO2. Since reconstructions of external forcing are virtually 

independent from the reconstructions of past temperatures, this 

broad consistency increases confi 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 diffi 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 fi 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 confi 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 fi 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 

signifi cance. They fi 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 infl 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 

refl 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 

confi 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 infl 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 fi 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 signifi cantly less 

ENSO variability, a period during which there were also cooler 

conditions in the northeast Pacifi c (MacDonald and Case, 2005) 

and evidence of droughts in central North America (Cook et al., 

2004). Cobb et al. (2003) fi nd that fl uctuations in reconstructed 

ENSO variability do not appear to be correlated in an obvious 

way with mean state changes in the tropical Pacifi c or global 

mean climate, while Adams et al. (2003) fi nd statistical evidence 

for an El Niño-like anomaly during the fi 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 preindustrial climate change. For example, land cover change may 

have infl 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 preagriculture 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 CO2 fl 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 

fi 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 infl uences explain 

a substantial fraction of inter-decadal temperature variability 

in the past. The available evidence also indicates that natural 

forcing may have infl 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 

confi 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 fi 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 infl 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 fi 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 multidecadal 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 multidecadal 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 fi 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 infl 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 multimodel 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 Pacifi c and South Pacifi c 

(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 20thcentury warming is much more likely to be anthropogenic than 

natural in origin, a fi nding which is confi 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 fi 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 fi nding that solar forcing is more important 

(Meehl et al., 2004) while other studies fi 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) fi 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 peakto-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 diffi 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% signifi 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 (°C2 yr–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.
687

Chapter 9 Understanding and Attributing Climate Change

9.4.1.4 The Infl uence of Greenhouse Gas and Total 

Anthropogenic Forcing on Global Surface 

Temperature

Since the TAR, a large number of studies based on the longer 

observational record, improved models and stronger signalto-noise ratio have increased confi dence in the detection of 

an anthropogenic signal in the instrumental record (see, e.g., 

the recent review by IDAG, 2005). Many more detection and 

attribution studies are now available than were available for the 

TAR, and these have used more recent climate data than previous 

studies and a much greater variety of climate simulations with 

more sophisticated treatments of a greater number of both 

anthropogenic and natural forcings. 

Fingerprint studies that use climate change signals estimated 

from an array of climate models indicate that detection of an 

anthropogenic contribution to the observed warming is a result 

that is robust to a wide range of model uncertainty, forcing 

uncertainties and analysis techniques (Hegerl et al., 2001; 

Gillett et al., 2002c; Tett et al., 2002; Zwiers and Zhang, 2003; 

IDAG, 2005; Stone and Allen, 2005b; Stone et al., 2007a,b; 

Stott et al., 2006b,c; Zhang et al., 2006). These studies account 

for the possibility that the agreement between simulated and 

observed global mean temperature changes could be fortuitous 

as a result of, for example, balancing too great (or too small) a 

model sensitivity with a too large (or too small) negative aerosol 

forcing (Schwartz, 2004; Hansen et al., 2005) or a too small (or 

too large) warming due to solar changes. Multi-signal detection 

and attribution analyses do not rely on such agreement because 

they seek to explain the observed temperature changes in terms 

of the responses to individual forcings, using model-derived 

patterns of response and a noise-reducing metric (Appendix 

9.A) but determining their amplitudes from observations. As 

discussed in Section 9.2.2.1, these approaches make use of 

differences in the temporal and spatial responses to forcings to 

separate their effect in observations. 

Since the TAR, there has also been an increased emphasis 

on quantifying the greenhouse gas contribution to observed 

warming, and distinguishing this contribution from other factors, 

both anthropogenic, such as the cooling effects of aerosols, and 

natural, such as from volcanic eruptions and changes in solar 

radiation. 

A comparison of results using four different models (Figure 

9.9) shows that there is a robust identifi cation of a signifi cant 

greenhouse warming contribution to observed warming that is 

likely greater than the observed warming over the last 50 years 

Figure 9.8. As Figure 9.7, except for continental 

mean temperature. Spectra are calculated in the 

same manner as Figure 9.7. See the Supplementary 

Material, Appendix 9.C for a description of the 

regions and for details of the method used. Models 

simulate variability on decadal time scales and 

longer that is consistent with observations in all 

cases except two models over South America, fi ve 

models over Asia and two models over Australia (at 

the 10% signifi cance level). 
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 signifi 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 fi nd consistent estimates for the greenhouse 

gas attributable warming over the century, expressed as the 

difference between temperatures in the last and fi 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) fi 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 fi 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 fi 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 

fi 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 fi 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) fi 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 spatiotemporal 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. 
689

Chapter 9 Understanding and Attributing Climate Change

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 

infl 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) fi nd decisive evidence 

for the infl 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 fi 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 defi 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 defi 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 confi 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 longterm 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, fi nd that the 

increase in global mean temperature over the 20th century is 

statistically signifi cant even if it is assumed that natural climate 

variability has strong serial correlation.

9.4.1.5 The Infl uence of Other Anthropogenic and 

Natural Forcings 

A signifi 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 wellmixed greenhouse gases (Schwartz, 1993). A key factor in 

identifying the aerosol fi 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 fi nd for an aerosol effect. 

Schnur and Hasselman (2005), for example, fail to fi nd decisive 

evidence for the infl 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 

fi 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 fi nds 

decisive evidence for an aerosol cooling effect (Smith et al., 

2003). Differences in the separate detection of sulphate aerosol 

infl uences in multi-signal approaches can also refl 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 diffi 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 fi 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 fi 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 nearsurface 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 
691

Chapter 9 Understanding and Attributing Climate Change

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 amplifi cation of the solar infl 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 20thcentury warming than the recent warming. A number of studies 

detect a signifi 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 fi nd a greater role 

for solar forcing than other forcings before 1950 (Stott et al., 

2003b), although one detection study fi nds a roughly equal 

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

others fi 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

Quantifi 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 

defi 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 suffi 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 CO2 following a 1% per year increase in CO2

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 fi ngerprints of climate 

change in the observational record is to use simple indices 

of surface air temperature patterns that refl 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).
692

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 infl 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 signifi 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 

signifi cant anthropogenic infl 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 diffi 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 amplifi 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 largescale 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 underor 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 effi 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 fi 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 fi ngerprints of climate change (Gillett et al., 2002c; 

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

fi 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 fi 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 diffi 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 infl 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 northwestern 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 identifi ed the infl 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 infl uence of external 

forcing on the climate in specifi 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 fi 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 fi 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 infl uence of 

external forcing is to apply detection and attribution analyses 

to observations in specifi 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 fi nd a detectable human infl 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 fi 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 diffi 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 infl 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 

fi nd evidence that climates in both continental domains have 

been infl uenced by anthropogenic emissions during 1950 to 

2000, and generally also in the sub-continental domains (Figure 

9.11). This fi 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 infl 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 fi 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 GFDLCM2 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 20thcentury 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 fi ngerprints 

in China and southern Canada. Spagnoli et 

al. (2002) fi nd some evidence for a human 

infl uence on 30-year trends of summer 

daily minimum temperatures in France, 

but they use a fi ngerprint estimated from 

a simulation of future climate change and 

do not detect an anthropogenic infl uence 

on the other indices they consider, 

including summer maximum daily 

temperatures and winter temperatures. 

Min et al. (2005) fi nd an anthropogenic 

infl 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 infl 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 infl 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 fi 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 fi 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 Figure 9.12. Comparison of multi-model data set 20C3M model simulations containing all forcings (red shaded regions) and containing natural forcings only (blue shaded regions) with observed decadal mean temperature changes (°C) from 1906 to 2005 from the Hadley Centre/Climatic Research Unit gridded surface temperature data set (HadCRUT3; Brohan et al., 2006). The panel labelled GLO shows comparison for global mean; LAN, global land; and OCE, global ocean data. Remaining panels display results for 22 sub-continental scale regions (see the Supplementary Material, Appendix 9.C for a description of the regions). This fi gure is produced identically to FAQ 9.2, Figure 1 except sub-continental regions were used; a full description of the procedures for producing FAQ 9.2, Figure 1 is given in the Supplementary Material, Appendix 9.C. Shaded bands represent the middle 90% range estimated from the multi-model ensemble. Note that the model simulations have not been scaled in any way. The same simulations are used as in Figure 9.5 (58 simulations using all forcings from 14 models, and 19 simulations using natural forcings only from 5 models). Each simulation was sampled so that coverage corresponds to that of the observations, and was centred relative to the 1901 to 1950 mean obtained by that simulation in the region of interest. Observations in each region were centred relative to the same period. The observations in each region are generally consistent with model simulations that include anthropogenic and natural forcings, whereas in many regions the observations are inconsistent with model simulations that include natural forcings only. Lines are dashed where spatial coverage 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 unchanging climate. Nevertheless, analysis of the warming observed over the past century suggests that the likelihood of some 

extreme events, such as heat waves, has increased due to greenhouse 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 extreme events that some commentators have linked to increasing 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 concentrations of greenhouse gases in the atmosphere have ‘caused’ 

any of these events?

Extreme events usually result from a combination of factors. For example, several factors contributed to the extremely 

hot European summer of 2003, including a persistent highpressure 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 formation of a hurricane requires 

warm sea surface temperatures 

and specific atmospheric circulation conditions. Because some 

factors may be strongly affected 

by human activities, such as sea 

surface temperatures, but others may not, it is not simple to 

detect a human influence on a 

single, specific extreme event. 

Nevertheless, it may be possible to use climate models to 

determine whether human influences have changed the likelihood 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 activity 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, human influences more than doubled the risk of having a summer 

in Europe as hot as that of 2003, and that in the absence of human 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 human 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, careful statistical analyses are required, since the likelihood of individual 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 (σ) and the 2003 anomaly normalised by the 1864 to 2000 standard deviation (T’/σ). From 

Schär et al. (2004).

o

C
697

Chapter 9 Understanding and Attributing Climate Change

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 

fi 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 specifi c grid box, and it does not 

allow confi 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 

infl uenced by variability and changes in climate dynamics, 

such as temperature changes associated with the NAO, which 

may itself show an anthropogenic infl 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 multidecadal scale variability being the most diffi 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 diffi cult to separate the effects of the relatively 

well understood large-scale external infl uences on climate, such 

as greenhouse gas, aerosols, solar and volcanic forcing, from each 

other and from local infl 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 largescale 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 infl uence on continental-scale land 

areas. Observed trends in indices of North American continentalscale 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 fi 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 signifi 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 

fi ngerprints from atmospheric model simulations driven by 

prescribed SSTs. They fi nd signifi 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 

infl uences are probably an important contributor to changes in 

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

fi 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 infl uence on 

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

the observed changes, signifi cantly so in the case of the coldest 

day of the year. Anthropogenic infl 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 infl uences, a change in 

the probability of such events might be attributable to external 

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

study estimates that anthropogenic forcings have signifi 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 

specifi 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 specifi c magnitude. If P1 is the 

probability of a climatic event (such as a heat wave) occurring 

in the presence of anthropogenic forcing of the climate system, 

and P0 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 – P0 / P1 (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 infl uence has more than doubled the risk of 

the regional summer mean temperature exceeding this threshold 

(Figure 9.13). 
699

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 fi 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 
700

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 diffi 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 ‘fi rst 

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

to some defi ciencies, notably inhomogeneities related to changes 

over time in the availability and quality of input data, and are 

subject to a number of specifi 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 fi nding of a signifi cant anthropogenic 

infl 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 infl uence on free atmosphere temperatures has 

been detected in analyses of satellite data since 1979, although 

this fi 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 (T2LT), 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 fi nd that the model fi 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 fi ngerprint is 

detected in both data sets, suggesting a common spatial pattern 

of response overlain by a systematic global mean difference. 

Anthropogenic infl 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 fi 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 
701

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 infl 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 signifi cantly underestimate stratospheric 

variability, thus possibly overestimating the signifi cance of these 

detected signals (Tett et al., 2002). However, a sensitivity study 

(Thorne et al., 2002) showed that detection of human infl 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 

infl uence on climate when a range of uncertainties were explored 

relating to the choice of fi 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 infl 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 quantifi ed (Thorne et al., 2005), it is diffi cult to assess, 

based on these analyses alone, whether model-data discrepancies 

are due to model or observational defi 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 profi 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) fi 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% signifi 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 amplifi 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 amplifi 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 greenhouse gases like that which has occurred over the past century, 

and the warming is inconsistent with the scientifi c understanding of how the climate should respond to natural external factors such as variability in solar output and volcanic activity. 

Climate models provide a suitable tool to study the various infl 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 simulations 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 natural in origin, such as volcanic activity and variations in solar 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 using physical understanding of the processes involved. The combined 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 volcanic activity and variations in solar output. Explosive volcanic eruptions occasionally eject large amounts of dust and 

sulphate aerosol high into the atmosphere, temporarily shielding the Earth and reflecting sunlight back to space. Solar output 

has an 11-year cycle and may also have longer-term variations. 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 concentrations 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 climate 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 volcanic 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 responses to these external factors are detectable in the 20th-century climate globally and in each individual continent except 

Antarctica, where there are insufficient observations. The human 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 incomplete knowledge of some external factors, such as humansourced 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 variability, 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)
703

Chapter 9 Understanding and Attributing Climate Change

FAQ 9.2, Figure 1. Temperature changes relative to the corresponding average for 1901-1950 (°C) from decade to decade from 1906 to 2005 over the Earth’s continents, 

as well as the entire globe, global land area and the global ocean (lower graphs). The black line indicates observed temperature change, while the coloured bands show the 

combined range covered by 90% of recent model simulations. Red indicates simulations that include natural and human factors, while blue indicates simulations that include 

only natural factors. Dashed black lines indicate decades and continental regions for which there are substantially fewer observations. Detailed descriptions of this fi gure and 

the methodology used in its production are given in the Supplementary Material, Appendix 9.C.

the atmosphere (the troposphere) and cooling higher up in the 

stratosphere. This is another ‘fingerprint’ of change that reveals 

the effect of human influence on the climate. If, for example, 

an increase in solar output had been responsible for the recent 

climate warming, both the troposphere and the stratosphere 

would have warmed. In addition, differences in the timing of 

the human and natural external influences help to distinguish 

the climate responses to these factors. Such considerations increase confidence that human rather than natural factors were 

the dominant cause of the global warming observed over the 

last 50 years.

Estimates of Northern Hemisphere temperatures over the last 

one to two millennia, based on natural ‘thermometers’ such as 

tree rings that vary in width or density as temperatures change, 

and historical weather records, provide additional evidence that 

the 20th-century warming cannot be explained by only natural internal variability and natural external forcing factors. 

Confidence in these estimates is increased because prior to the 

industrial era, much of the variation they show in Northern 

Hemisphere average temperatures can be explained by episodic 

cooling caused by large volcanic eruptions and by changes in 

the Sun’s output. The remaining variation is generally consistent with the variability simulated by climate models in the 

absence of natural and human-induced external factors. While 

there is uncertainty in the estimates of past temperatures, they 

show that it is likely that the second half of the 20th century 

was the warmest 50-year period in the last 1300 years. The 

estimated climate variability caused by natural factors is small 

compared to the strong 20th-century warming. 
704

Understanding and Attributing Climate Change Chapter 9

9.4.5 Summary

Since the TAR, the evidence has strengthened that human 

infl 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 infl 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 multimodel ensemble now available (Figure 9.5). 

Many studies have detected a human infl 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 infl 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 infl uence from aerosols 

and natural forcings counteracting some of the warming 

infl uence of the increasing concentrations of greenhouse 

gases (Figure 9.9). Spatial information is required in addition 

to temporal information to reliably detect the infl uence of 

aerosols and distinguish them from the infl 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 fi 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 signifi cant anthropogenic effects on each of six 

continents provides stronger evidence of human infl uence on 

the global climate than was available to the TAR. Diffi 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 diffi 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 diffi 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 signifi 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 signifi cant human infl 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 modelbased estimate of temperature variability, past human infl 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 signifi cant anthropogenic infl uence on free 

atmosphere temperature since widespread measurements 

became available from radiosondes in the late 1950s. The 

infl uence of greenhouse gases on tropospheric temperatures 

has been detected, as has the infl 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 modeldata 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 amplifi 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 multiannual but not shorter time scales is that amplifi 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 × 1022 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) fi 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 infl uence of natural (volcanic) and anthropogenic 

aerosols would have slowed ocean warming over the last half 

century. Delworth et al. (2005) fi 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 signifi cant differences 

between observed and modelled interannual deviations from 

a linear trend in fi 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 infi 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 confi 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 infi lling 

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

fi 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 defi ciency or an artefact in the 

Levitus data. AchutaRao et al. (2006) also fi nd that observational 

estimates of temperature variability over much of the oceans may 

be substantially affected by sparse observational coverage and 

the method of infi 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 Pacifi 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 fl 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 midlatitudes (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 salinifi 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 quantifi 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 refl 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 oC, 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

Chapter 9 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 

fl 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 infl 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 fi 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 

fl 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) fi 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
708

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 

infl 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 signifi cant contribution from recent 

accelerations in ice fl 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 diffi 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 diffi 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 Pacifi 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/Pacifi c 

Decadal Oscillation

The El Niño-Southern Oscillation is the leading mode 

of variability in the tropical Pacifi 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 

defi ned as a mean SST anomaly in the eastern equatorial 

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

events since the late 1970s, which may refl ect in part a mean 

warming of the eastern equatorial Pacifi 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 Pacifi 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 infl 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 CO2 doubling 

in 15 MMD AOGCMs (Merryfi eld, 2006) fi nds that three of 

the models exhibited signifi cant increases in ENSO variability, 

fi ve exhibited signifi cant decreases and seven exhibited no 

signifi 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 Pacifi c is characterised 

by variations in the strength of the Aleutian Low coupled to 

changes in North Pacifi c SST (Sections 3.6.3 and 8.4.2). The 

leading mode of decadal variability in the North Pacifi c is 

usually referred to as the PDO, and has a spatial structure in 

the atmosphere and upper North Pacifi 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 confi dence in 

these fi 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 signifi cant at the 5% signifi 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 infl 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 

fi 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% signifi 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) fi 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 

fi 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 sealevel 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 infl 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 fi nd signifi 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 confi 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 CO2 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) fi 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 Pacifi c and North 

Atlantic, refl 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 

Pacifi c (Chan, 2006), although recent values of the PDI may be 
712

Understanding and Attributing Climate Change 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) fi nd no analogous relationship in the western 

North Pacifi 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 infl 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 infl uence on tropical cyclone formation (Box 3.5). 

Thus, detection and attribution of observed changes in hurricane 

intensity or frequency due to external infl uences remains diffi cult 

because of defi ciencies in theoretical understanding of tropical 

cyclones, their modelling and their long-term monitoring 

(e.g., Emanuel, 2005; Landsea, 2005; Pielke, 2005). These 

defi 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 21stcentury 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 signifi 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 infl 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 signifi 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 infl 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-signifi cant decrease since 1950 

(Figure 9.18; see also Table 3.4). 

9.5.4.2.1 Detection of external infl 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 effi ciency of 
713

Chapter 9 Understanding and Attributing Climate Change

radiative cooling due to an increase in atmospheric CO2 (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 CO2 forcing, since shortwave forcings, such as 

volcanic aerosol, alter the temperature of the troposphere 

without affecting the effi 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 infl 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 signifi cantly 

correlated with that observed (Allen and Ingram, 2002; Gillett 

et al., 2004b; Lambert et al., 2004), thereby detecting external 

infl 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 fi nd that the responses to 

combined anthropogenic and natural forcing simulated by 

fi 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 interdecadal 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 infl 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).
714

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 CO2-induced stomatal closure. They fi nd 

that observed climate changes (including precipitation changes) 

alone are insuffi 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 signifi 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 ClausiusClapeyron 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 fi 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 modelsimulated 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). Signifi 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 diffi cult because of the different scales 

involved (Osborn and Hulme, 1997). A fi rst attempt based on 

Frich et al. (2002) indices used fi 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-tonoise 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 specifi c regions, of a possible 

anthropogenic infl uence on precipitation, which are discussed 

below.
715

Chapter 9 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 fi 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 fi 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) fi 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 infl 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 fi 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 fi 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) fi nd that eight other coupled 

climate models with prescribed anthropogenic forcing do not 

simulate signifi 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-affi 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 signifi cance of this fi 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 infl 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 confl 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 fl 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) fi 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 refl ect an unrealistic simulation of regional 

warming around Antarctica, rather than a defi 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 snowcovered area are strongly correlated (r = –0.68) with April 
717

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 fl uctuations. 

There have been a few studies for glaciers in specifi 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 highaltitude 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 infl uencing ongoing changes, complicating 

attribution of recent trends (Section 4.6.3.2). 

9.5.6 Summary

In the TAR, quantitative evidence for human infl uence on 

climate was based almost exclusively on atmospheric and 

surface temperature. Since then, anthropogenic infl uence has 

also been identifi 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 

infl uence on climate, and improving confi 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 

infl 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 highlatitude 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 infl uence on global sea level pressure. 

Both the NAM and SAM have shown signifi 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 infl 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 infl 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 infl 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 

infl uence has not been detected in global precipitation, while 

the infl uence of volcanic aerosols has been detected. However, 

observed changes in the latitudinal distribution of land 

precipitation are suggestive of a possible human infl 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 infl 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 CO2 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 CO2 doubling in a climate simulation 

with a 1% yr–1 compounded increase in CO2 concentration (see 

Glossary and Section 8.6.2.1 for detailed defi 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 CO2.

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 CO2 under idealised conditions. An alternative 

approach, which has been pursued in most work reported here, 

is based on varying parameters in climate models that infl 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 confi 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 infl 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 infl 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 

infl uence the resulting estimates. Frame et al. (2005) advocate 

sampling a fl 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 specifi 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 20thCentury 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 confi 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) fi nd a best-fi 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 fi tted. The fi 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 

fi 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 

fi 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 

fi ngerprint detection results. Note that while results from 

fi 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 fi 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 fi 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 Studya Modelb External Forcings Includedc Treatment of uncertaintiesd

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)

εobs, noise, κ, εaer, 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

εobs, εforc for multiple 

forcings from IPCC (2001), 

κ, 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

εobs, εforc 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), κ (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 fi 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 largescale mean data because separation of the greenhouse gas 

response from that to aerosols or climate variability is more 

diffi cult with such diagnostics (Andronova and Schlesinger, 

2001; Gregory et al., 2002a; Knutti et al., 2002, 2003). Forest 

et al. (2006) fi 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 fi 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) 

fi 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 fi 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 refl ect credible prior ranges of ECS, expert 

priors may also be infl uenced by knowledge about observed 

climate change, and thus may yield overly confi dent estimates 

when combined with the same data (Supplementary Material, 

Appendix 9.B). Frame et al. (2005) fi 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 Studya Modelb External Forcings Includedc Treatment of uncertaintiesd

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 εobs, εforc 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), κ, 

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, presentday 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, 20thcentury 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:

a Range covered by uniform prior distribution if narrower than 0°C to 20°C.

b 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).

c 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.).

d Uncertainties taken into account (e.g., uncertainty in ocean diffusivity K, or total aerosol forcing εforc). Ideally, studies account for model uncertainty, forcing 

uncertainty (for example, in aerosol forcing εaer or natural forcing εnat), uncertainty in observations, εobs, and internal climate variability (‘noise’). 

Table 9.3 (continued)
723

Chapter 9 Understanding and Attributing Climate Change

over that time. They fi 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) fi 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 

specifi c to the model analysed. 

Constraining ECS from the observed responses to individual 

volcanic eruptions is diffi 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 diffi cult to distinguish from 

a noisy background climate. A further diffi 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) fi 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 fi 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 CO2 doubling, 

the smaller TCR refers to the global mean temperature change 

that is realised at the time of CO2 doubling under an idealised 

scenario in which CO2 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 fi ngerprints from three different model simulations (Figure 

9.21) and fi nd a relatively tight constraint. Using three model 

simulations together, their estimated median TCR is 2.1°C at the 

time of CO2 doubling (based on a 1% yr–1 increase in CO2), 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-fi 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 

infl 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 defl ating signifi 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 CO2 prior to the 650 kyr period covered by ice 

core measurements of CO2 (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 CO2 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 infl uences). 

Their estimated PDFs for ECS 

incorporate an estimate of uncertainty 

in the overall amplitude (including 

an attempt to account for uncertainty 

in effi 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 signifi 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 CO2 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 21stcentury 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 scientifi 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 infl 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 

infl uenced strongly by CO2 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 fi 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 fi nd a best-fi 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 confi 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 

simplifi 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 infl 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 diffi cult to quantify. In 

addition, differences in effi 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 CO2

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, confi dence is increased 

by the similarities between individual ECS estimates (Figure 

9.20). Most studies fi nd a lower 5% limit of between 1°C and 

2.2°C, and studies that use information in a relatively complete 

manner generally fi 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 specifi 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 diffi 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 diffi 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 diffi 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 fi nd a similar 

constraint using fi 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 confi 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 fi 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 fi 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 confi 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 signifi 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 signifi 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 20thcentury 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 infl 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. 
728

Understanding and Attributing Climate Change Chapter 9

The simultaneous increase in energy content of all the 

major components of the climate system and the pattern and 

amplitude of warming in the different components, together 

with evidence that the second half of the 20th century was 

likely the warmest in 1.3 kyr (Chapter 6) indicate that the 

cause of the warming is extremely unlikely to be the result 

of internal processes alone. The consistency across different 

lines of evidence makes a strong case for a signifi cant human 

infl uence on observed warming at the surface. The observed 

rates of surface temperature and ocean heat content change are 

consistent with the understanding of the likely range of climate 

sensitivity and net climate forcings. Only with a net positive 

forcing, consistent with observational and model estimates of 

the likely net forcing of the climate system (as used in Figure 

9.5), is it possible to explain the large increase in heat content of 

the climate system that has been observed (Figure 5.4). 
729

Chapter 9 Understanding and Attributing Climate Change Table 9.4. A synthesis of climate change detection results: (a) surface and atmospheric temperature evidence and (b) evidence from other variables. Note that 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 uncertainty not directly accounted for in the studies. The likelihood assessment is indicated in percentage terms, in parentheses where the term is not from the standard IPCC likelihood levels. a)Result Region Likelihood Factors contributing to likelihood assessment Surface temperature Warming during the past half century cannot be explained without external radiative forcing Global Extremely likely (>95%) Anthropogenic change has been detected in surface temperature with very high signifi cance levels (less than 1% error probability). This conclusion is strengthened by detection of anthropogenic change in the upper ocean with high signifi cance level. Upper ocean warming argues against the surface warming being due to natural internal processes. Observed change is very large relative to climate-model simulated internal variability. Surface temperature variability simulated by models is consistent with variability estimated from instrumental and palaeorecords. Main uncertainty from forcing and internal variability estimates (Sections 9.4.1.2, 9.4.1.4, 9.5.1.1, 9.3.3.2, 9.7). Warming during the past half century is not solely due to known natural causes Global Very Likely This warming took place at a time when non-anthropogenic external factors would likely have produced cooling. The combined effect of known sources of forcing would have been extremely likely to produce a warming. No climate model that has used natural forcing only has reproduced the observed global warming trend over the 2nd half of the 20th century. Main uncertainties arise from forcing, including solar, model-simulated responses and internal variability estimates (Sections 2.9.2, 9.2.1, 9.4.1.2, 9.4.1.4; Figures 9.5, 9.6, 9.9). Greenhouse gas forcing has been the dominant cause of the observed global warming over the last 50 years. Global Very likely All multi-signal detection and attribution studies attribute more warming to greenhouse gas forcing than to a combination of all other sources considered, including internal variability, with a very high signifi cance. This conclusion accounts for observational, model and forcing uncertainty, and the possibility that the response to solar forcing could be underestimated by models. Main uncertainty from forcing and internal variability estimates (Section 9.4.1.4; Figure 9.9). Increases in greenhouse gas concentrations alone would have caused more warming than observed over the last 50 years because volcanic and anthropogenic aerosols have offset some warming that would otherwise have taken place. Global Likely Estimates from different analyses using different models show consistently more warming than observed over the last 50 years at the 5% signifi cance level. However, separation of the response to non-greenhouse gas (particularly aerosol) forcing from greenhouse gas forcing varies between models (Section 9.4.1.4; Figure 9.9). There has been a substantial anthropogenic contribution to surface temperature increases in every continent except Antarctica since the middle of the 20th century Africa, Asia, Australia, Europe, North America and South America Likely Anthropogenic change has been estimated using detection and attribution methods on every individual continent (except Antarctica). Greater variability compared to other continental regions makes detection more marginal in Europe. No climate model that used natural forcing only reproduced the observed continental mean warming trend over the second half of the 20th century. Uncertainties arise because sampling effects result in lower signal-to-noise ratio at continental than at global scales. Separation of the response to different forcings is more diffi cult at these spatial scales (Section 9.4.2; FAQ 9.2, Figure 1). Early 20th-century warming is due in part to external forcing. Global Very Likely A number of studies detect the infl uence of external forcings on early 20th-century warming, including a warming from anthropogenic forcing. Both natural forcing and response are uncertain, and different studies fi nd different forcings dominant. Some studies indicate that internal variability could have made a large contribution to early 20th-century warming. Some observational uncertainty in early 20th-century trend (Sections 9.3.3.2, 9.4.1.4; Figures 9.4, 9.5). (continued)
730

Understanding and Attributing Climate Change Chapter 9 Result Region Likelihood Factors contributing to likelihood assessment Surface temperature Pre-industrial temperatures were infl uenced by natural external forcing (period studied is past 7 centuries) NH (mostly extratropics) Very Likely Detection studies indicate that external forcing explains a substantial fraction of inter-decadal variability in NH temperature reconstructions. Simulations in response to estimates of pre- industrial forcing reproduce broad features of reconstructions. Substantial uncertainties in reconstructions and past forcings are unlikely to lead to a spurious agreement between temperature reconstructions and forcing reconstructions as they are derived from independent proxies (Section 9.3.3; Figures 9.4, 6.13). Temperature extremes have changed due to anthropogenic forcing NH land areas and Australia combined. Likely A range of observational evidence indicates that temperature extremes are changing. An anthropogenic infl uence on the temperatures 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 temperature of the warmest day annually is inconsistent with simulated change. The detection of changes in temperature extremes is supported by other comparisons between models and observations. Model uncertainties in changes in temperature extremes are greater than for mean temperatures and there is limited observational coverage and substantial observational uncertainty (Section 9.4.3). Free atmosphere changes Tropopause height increases are detectable and attributable to anthropogenic forcing (latter half of the 20th century) Global Likely There has been robust detection of anthropogenic infl uence on increasing tropopause height. Simulated tropopause height increases result mainly from greenhouse gas increases and stratospheric ozone decreases. Detection and attribution studies rely on reanalysis data, which are subject to inhomogeneities related to differing availability and quality of input data, although tropopause height increases have also been identifi ed in radiosonde observations. Overall tropopause height increases in recent model and one reanalysis (ERA-40) appear to be driven by similar large-scale changes in atmospheric temperature, although errors in tropospheric warming and stratospheric cooling could lead to partly spurious agreement in other data sets (Section 9.4.4.2; Figure 9.14). Tropospheric warming is detectable and attributable to anthropogenic forcing (latter half of the 20th century) Global Likely There has been robust detection and attribution of anthropogenic infl uence on tropospheric warming, which does not depend on including stratospheric cooling in the fi ngerprint pattern of response. There are observational uncertainties in radiosonde and satellite records. Models generally predict a relative warming of the free troposphere compared to the surface in the tropics since 1979, which is not seen in the radiosonde record (possibly due to uncertainties in the radiosonde record) but is seen in one version of the satellite record, although not others (Section 9.4.4). Simultaneous tropospheric warming and stratospheric cooling due to the infl uence of anthropogenic forcing has been observed (latter half of the 20th century) Global Very Likely Simultaneous warming of the troposphere and cooling of the stratosphere due to natural factors is less likely than warming of the troposphere or cooling of the stratosphere alone. Cooling of the stratosphere is in part related to decreases in stratospheric ozone. Modelled and observational uncertainties as discussed under entries for tropospheric warming with additional uncertainties due to stratospheric observing systems and the relatively poor representations of stratospheric processes and variability in climate models (Section 9.4.4). Table 9.4 (continued)
731

Chapter 9 Understanding and Attributing Climate Change b) Result Region Likelihood Factors contributing to likelihood assessment

Ocean changes Ocean changesAnthropogenic forcing has warmed the upper several hundred metres of the ocean during the latter half of the 20th century 

Global (but with limited sampling in some regions)

Likely 

Robust detection and attribution of anthropogenic fi ngerprint from three different models in ocean temperature changes, and in ocean heat content data, suggests high likelihood, but observational and modelling uncertainty remains. 20th-century simulations with MMD models simulate comparable ocean warming to observations only if anthropogenic forcing is included. Simulated and observed variability appear inconsistent, either due to sampling errors in the observations or under-simulated internal variability in the models. Limited geographical coverage in some ocean basins (Section 9.5.1.1; Figure 9.15).

Anthropogenic forcing contributed to sea level rise during the latter half 20th century Global Very likely

Natural factors alone do not satisfactorily explain either the observed thermal expansion of the ocean or the observed sea level rise. 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. It is therefore very likely that anthropogenic forcing contributed to sea level rise associated with ocean thermal expansion and glacier retreat. However, it remains diffi cult to estimate 

the anthropogenic contribution to sea level rise because suitable studies quantifying the anthropogenic contribution to sea level rise and glacier retreat are not available, and because the observed sea level rise budget is not closed (Table 9.2; Section 9.5.2).

Circulation CirculationSea level pressure shows a detectable anthropogenic signature during the latter half of the 20th century Global Likely 

Changes of similar nature are observed in both hemispheres and are qualitatively, but not quantitatively consistent with model simulations. Uncertainty in models and observations. Models underestimate the observed NH changes for reasons that are not understood, based on a small number of studies. Simulated response to 20th century forcings is consistent with observations in SH if effect of stratospheric ozone depletion is included (Section 9.5.3.4; Figure 9.16).

Anthropogenic forcing contributed to the increase in frequency of the most intense tropical cyclones since the 1970s 

Tropical regions 

More likely than not (>50%) 

Recent observational evidence suggests an increase in frequency of intense storms. Increase in intensity is consistent with theoretical expectations. Large uncertainties due to models and observations. Modelling 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. Observational evidence, which is affected by substantial inhomogeneities in tropical cyclone data sets for which corrections have been attempted, suggests that increases in cyclone intensity since the 1970s are associated with SST and atmospheric water vapour increases (Section 3.8.3, Box 3.5 and Section 9.5.2.6).

Precipitation, Drought, RunoffVolcanic forcing infl uences total rainfall Global land areas More likely than not 

(>50%)

Model response detectable in observations for some models and result supported by theoretical understanding. However, uncertainties in models, forcings and observations. Limited observational sampling, particularly in the SH (Section 9.5.4.2; Figure 9.18).

Increases in heavy rainfall are consistent with anthropogenic forcing during latter half 20th century

Global land areas (limited sampling)

More likely than not (>50%)

Observed increases in heavy precipitation appear to be consistent with expectations of response to anthropogenic forcing. Models may not represent heavy rainfall well; observations suffer from 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 Precipitation, Drought, Runoff Increased risk of drought due to anthropogenic forcing during latter half 20th century Global land areas More likely than not (>50%) One detection study has identifi ed an anthropogenic fi ngerprint in a global Palmer Drought Severity Index data set with high signifi cance, but the simulated response to anthropogenic and natural forcing combined is weaker than observed, and the model appears to have less inter-decadal variability than observed. Studies of some regions indicate that droughts in those regions are linked either to SST changes that, in some instances, may be linked to anthropogenic aerosol forcing (e.g., Sahel) or to a circulation response to anthropogenic forcing (e.g., southwest Australia). Models, observations and forcing all contribute uncertainty (Section 9.5.3.2). Cryosphere Cryosphere Anthropogenic forcing has contributed to reductions in NH sea ice extent during the latter half of the 20th century Arctic Likely The observed change is qualitatively consistent with model-simulated changes for most models and expectation of sea ice melting under arctic warming. Sea ice extent change detected in one study. The model used has some defi ciencies in arctic sea ice annual cycle and extent. The conclusion is supported by physical expectations and simulations with another climate model. Change in SH sea ice probably within range explained by internal variability (Section 9.5.5.1). Anthropogenic forcing has contributed to widespread glacier retreat during the 20th century Global Likely Observed changes are qualitatively consistent with theoretical expectations and temperature detection. Anthropogenic contribution to volume change diffi cult to estimate. Few detection and attribution studies, but retreat in vast majority of glaciers consistent with expected reaction to widespread warming (Section 9.5.5.3). Table 9.4 (continued)
733

Chapter 9 Understanding and Attributing Climate Change

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Appendix 9.A: Methods Used to Detect 

 Externally Forced Signals

This appendix very briefl 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 briefl y describe the optimal 

fi 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 fi 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 y is a 

fi ltered version of the observed record, matrix X contains the 

estimated response patterns to the external forcings (signals) 

that are under investigation, a is a vector of scaling factors 

that adjusts the amplitudes of those patterns and u represents 

internal climate variability. Vector u is usually assumed to be 

a Gaussian random vector with covariance matrix C. Vector a 

is estimated with â = (XTC-1X)-1XTC-1y, which is equivalent 

to (X

TX)-1XTỹ, where matrix X 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 X typically contains signals that are estimated 

with either an AOGCM, an AGCM (see Sexton et al., 2001, 

2003) or a simplifi ed climate model such as an EBM. Because 

AOGCMs simulate natural internal variability as well as the 

response to specifi ed anomalous external forcing, AOGCMsimulated 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 

infl uence, X 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, X may contain separate responses to all 

the main forcings, including greenhouse gases, sulphate aerosol, 

solar irradiance changes and volcanic aerosol. The vector a 

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 C (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 a is usually assessed with a second, statistically 

independent estimate of the covariance matrix C 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 fi nite. When ensembles are small or signals 

weak, these remnants may bias ordinary least-squares estimates 

of a downward. This is avoided by estimating a 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 

specifi c hypotheses about the scaling factors contained in 

a. 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 signifi cantly different from zero (i.e., when the null 

hypothesis HD : a = 0 where 0 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 HA : a = 1 where 1 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 X. If the response to 

a key forcing (e.g., due to greenhouse gas increases) is detected 

by rejecting the hypothesis that its amplitude aGHG = 0 in such a 

multiple regression, this provides strong attribution information 
745

Chapter 9 Understanding and Attributing Climate Change

because it demonstrates that the observed climate change is ‘not 

consistent with alternative, physically plausible explanations of 

recent climate change that exclude important elements of the 

given combination of forcings’ (Mitchell et al., 2001).

Bayesian approaches are of interest because they can be used 

to integrate information from multiple lines of evidence, and 

can incorporate independent prior information into the analysis. 

Essentially two approaches (described below) have been taken 

to date. In both cases, inferences are based on a posterior 

distribution that blends evidence from the observations with the 

independent prior information, which may include information 

on the uncertainty of external forcing estimates, climate models 

and their responses to forcing. In this way, all information that 

enters into the analysis is declared explicitly. 

Schnur and Hasselmann (2005) approach the problem by 

developing a fi ltering technique that optimises the impact 

of the data on the prior distribution in a manner similar to 

the way in which optimal fi ngerprints maximise the ratio of 

the anthropogenic signal to natural variability noise in the 

conventional approach. The optimal fi lter in the Bayesian 

approach depends on the properties of both the natural climate 

variability and model errors. Inferences are made by comparing 

evidence, as measured by Bayes Factors (Kass and Raftery, 

1995), for competing hypotheses. Other studies using similar 

approaches include Min et al. (2004) and Min and Hense 

(2006a,b). In contrast, Berliner et al. (2000) and Lee et al. 

(2005) use Bayesian methods only to make inferences about 

the estimate of a that is obtained from conventional optimal 

fi ngerprinting.