With the exception of Chapter and Section headings, all coloured text has been inserted by AccessIPCC. The non-coloured text is the IPCC original.

A number of emails from the Climate Research Unit (CRU) of the University of East Anglia were published on the Internet in November 2009. This has provided a window into the world of climate science.

We have identified a number of key individuals involved in the emails whom we have designated as Persons of Concern [PoC]; a Journal in which a PoC has published has been designated as a Journal of Concern [JoC].

This is not to suggest that we believe such papers are necessarily flawed, but rather that, as Joseph Alcamo noted at Bali in October 2009, "as policymakers and the public begin to grasp the multi-billion dollar price tag for mitigating and adapting to climate change, we should expect a sharper questioning of the science behind climate policy".

References occur in a list at the end of each chapter. Citations are within the normal text of sections and paragraphs.

Definition of Tags Used

Tag	Explanation	Where Used	References	Citations
PoC	Person of Concern Key individual involved in CRU emails as defined in this spreadsheet. NOTE: we anticipate that this definition will be expanded as more data is coded.	References, Citations, IPCC Roles	32	40
JoC	Journal of Concern A Journal which has published articles by one or more PoCs (Person of Concern)	References, Citations	539	669
MoS	Model or Simulation Reference appears to be a model or simulation, not observation or experiment	References, Citations	418	464
NPR	Non Peer Reviewed Reference has no Journal or no Volume or no Pages or it has Editors.	References, Citations	77	60
SRC	Self Reference Concern Author of a chapter containing references to own work.	References, Citations, IPCC Roles	198	297
ARC	Paper authored or co-authored by person who is also in list of Authors of another chapter.	References, Citations	168	164
2007	Paper dated 2007, when IPCC policy stated cutoff was December 2005 with mid-February 2006 for exceptions.	References, Citations	3	3
Ambiguous	The short inline citation matched with more than one reference; however, AccessIPCC will link to the first reference found.	Citations	-	20
NotFound	The short inline citation was not matched with any reference. Believed to be caused by typing errors.	Citations	-	1
Clean	The reference was probably peer reviewed. The reference was dated before the cutoff (2005-2006). Its authors were not members of the Team. It was not published in a Journal of Concern. Its authors were not AR4 Authors.	References, Citations	13	12

Chapter Table of Contents

Title	Clean	Tagged	NotFound	Ambiguous
Chapter 8: Climate Models and their Evaluation	-	-	-	-
8.1 Introduction and Overview	-	-	-	-
8.1.1 What is Meant by Evaluation?	-	-	-	-
8.1.2 Methods of Evaluation	-	1	-	-
8.1.2.1 Model Intercomparisons and Ensembles	-	1	-	-
8.1.2.2 Metrics of Model Reliability	-	5	-	-
8.1.2.3 Testing Models Against Past and Present Climate	-	2	-	-
8.1.2.4 Other Methods of Evaluation	-	-	-	-
8.1.3 How Are Models Constructed?	-	-	-	-
8.1.3.1 Parameter Choices and ‘Tuning’	-	8	-	-
8.1.3.2 Model Spectra or Hierarchies	-	-	-	-
8.2 Advances in Modelling	-	3	-	-
8.2.1 Atmospheric Processes	-	-	-	-
8.2.1.1 Numerics	-	-	-	-
8.2.1.2 Horizontal and Vertical Resolution	-	4	-	-
8.2.1.3 Parametrizations	-	17	-	-
8.2.2 Ocean Processes	-	-	-	-
8.2.2.1 Numerics	-	4	-	-
8.2.2.2 Horizontal and Vertical Resolution	-	5	-	2
8.2.2.3 Parametrizations	4	15	-	-
8.2.3 Terrestrial Processes	-	-	-	-
8.2.3.1 Surface Processes	-	31	-	-
8.2.3.2 Soil Moisture Feedbacks in Climate Models	-	9	-	-
8.2.4 Cryospheric Processes	-	-	-	-
8.2.4.1 Terrestrial Cryosphere	-	4	-	-
8.2.4.2 Sea Ice	-	19	-	-
8.2.5 Aerosol Modelling and Atmospheric Chemistry	-	7	-	-
8.2.6 Coupling Advances	-	4	-	-
8.2.7 Flux Adjustments and Initialisation	-	7	-	1
8.3 Evaluation of Contemporary Climate as Simulated by Coupled Global Models	-	3	-	-
8.3.1 Atmosphere	-	-	-	-
8.3.1.1 Surface Temperature and the Climate System’s Energy Budget	-	-	-	-
8.3.1.1.1 Temperature	-	2	-	-
8.3.1.1.2 The balance of radiation at the top of the atmosphere	-	2	-	-
8.3.1.2 Moisture and Precipitation	-	13	-	-
8.3.1.3 Extratropical Storms	-	11	-	-
8.3.2 Ocean	-	-	-	-
8.3.2.1 Simulation of Mean Temperature and Salinity Structure	-	8	-	-
8.3.2.2 Simulation of Circulation Features Important for Climate Response	-	-	-	-
8.3.2.2.1 Meridional overturning circulation	-	6	-	-
8.3.2.2.2 Southern Ocean circulation	-	2	-	-
8.3.2.3 Summary of Oceanic Component Simulation	-	1	-	-
8.3.3 Sea Ice	-	14	-	-
8.3.4 Land Surface	-	-	-	-
8.3.4.1 Snow Cover	-	8	-	-
8.3.4.2 Land Hydrology	-	7	-	-
8.3.4.3 Surface Fluxes	-	7	-	-
8.3.4.4 Carbon	-	2	-	-
8.3.5 Changes in Model Performance	-	3	-	-
8.4 Evaluation of Large-Scale Climate Variability as Simulated by Coupled Global Models	-	-	-	-
8.4.1 Northern and Southern Annular Modes	-	40	-	-
8.4.10 Monsoon Variability	-	3	1	-
8.4.11 Shorter-Term Predictions Using Climate Models	-	11	-	-
8.4.2 Pacific Decadal Variability	-	15	-	2
8.4.3 Pacific-North American Pattern	-	5	-	-
8.4.4 Cold Ocean-Warm Land Pattern	-	8	-	-
8.4.5 Atmospheric Regimes and Blocking	1	21	-	-
8.4.6 Atlantic Multi-decadal Variability	-	9	-	-
8.4.7 El Niño-Southern Oscillation	-	34	-	-
8.4.8 Madden-Julian Oscillation	-	19	-	5
8.4.9 Quasi-Biennial Oscillation	-	10	-	-
8.5 Model Simulations of Extremes	-	-	-	-
8.5.1 Extreme Temperature	1	6	-	2
8.5.2 Extreme Precipitation	-	8	-	-
8.5.3 Tropical Cyclones	-	13	-	-
8.5.4 Summary	-	1	-	-
8.6 Climate Sensitivity and Feedbacks	-	-	-	-
8.6.1 Introduction	-	-	-	-
8.6.2 Interpreting the Range of Climate Sensitivity Estimates Among General Circulation Models	-	-	-	-
8.6.2.1 Definition of Climate Sensitivity	-	9	-	-
8.6.2.2 Why Have the Model Estimates Changed Since the TAR?	-	5	-	-
8.6.2.3 What Explains the Current Spread in Models’ Climate Sensitivity Estimates?	-	26	-	-
8.6.3 Key Physical Processes Involved in Climate Sensitivity	-	-	-	-
8.6.3.1 Water Vapour and Lapse Rate	1	39	-	1
8.6.3.1.1 Evaluation of water vapour/lapse rate feedback processes in models	-	44	-	3
8.6.3.1.2 Summary of water vapour and lapse fate feedbacks	-	-	-	-
8.6.3.2 Clouds	-	14	-	-
8.6.3.2.1 Understanding of the physical processes involved in cloud feedbacks	4	36	-	-
8.6.3.2.2 Interpretation of the range of cloud feedbacks among climate models	-	21	-	1
8.6.3.2.3 Evaluation of cloud feedbacks produced by climate models	-	31	-	2
8.6.3.2.4 Conclusion on cloud feedbacks	-	-	-	-
8.6.3.3 Cryosphere Feedbacks	-	20	-	-
8.6.4 How to Assess Our Relative Confidence in Feedbacks Simulated by Different Models?	-	-	-	-
8.7 Mechanisms Producing	-	-	-	-
8.7.1 Introduction	-	4	-	-
8.7.2 Forced Abrupt Climate Change	-	-	-	-
8.7.2.1 Meridional Overturning Circulation Changes	1	36	-	1
8.7.2.2 Rapid West Antarctic and/or Greenland Ice Sheet Collapse and Meridional Overturning Circulation Changes	-	6	-	-
8.7.2.3 Volcanoes	-	2	-	-
8.7.2.4 Methane Hydrate Instability/Permafrost Methane	-	-	-	-
8.7.2.5 Biogeochemical	-	14	-	-
8.7.3 Unforced Abrupt Climate Change	-	3	-	-
8.8 Representing the Global System with Simpler Models	-	-	-	-
8.8.1 Why Lower Complexity?	-	6	-	-
8.8.2 Simple Climate Models	-	4	-	-
8.8.3 Earth System Models of Intermediate Complexity	-	18	-	-
Number of citations of various credibilities	12	776	1	20
Percentages of citations of various credibilities	1.5%	95.9%	0.1%	2.5%

Executive Summary

This chapter assesses the capacity of the global climate models used elsewhere in this report for projecting future climate change. Confidence in model estimates of future climate evolution has been enhanced via a range of advances since the IPCC Third Assessment Report (TAR).

Climate models are based on well-established physical principles and have been demonstrated to reproduce observed features of recent climate (see Chapters 8 and 9 )and past climate changes (see Chapter6 ). There is considerable confidence that Atmosphere-Ocean General Circulation Models (AOGCMs) provide credible quantitative estimates of future climate change, particularly at continental and larger scales. Confidence in these estimates is higher for some climate variables (e.g., temperature) than for others (e.g., precipitation). This summary highlights areas of progress since the TAR:

• Enhanced scrutiny of models and expanded diagnostic analysis of model behaviour have been increasingly facilitated by internationally coordinated efforts to collect and disseminate output from model experiments performed under common conditions. This has encouraged a more comprehensive and open evaluation of models. The expanded evaluation effort, encompassing a diversity of perspectives, makes it less likely that significant model errors are being overlooked.
• Climate models are being subjected to more comprehensive tests, including, for example, evaluations of forecasts on time scales from days to a year. This more diverse set of tests increases confidence in the fidelity with which models represent processes that affect climate projections.
• Substantial progress has been made in understanding the inter-model differences in equilibrium climate sensitivity. Cloud feedbacks have been confirmed as a primary source of these differences, with low clouds making the largest contribution. New observational and modelling evidence strongly supports a combined water vapour-lapse rate feedback of a strength comparable to that found in General Circulation Models (approximately 1 Wm^–2 °C^–1,corresponding to around a 50% amplification of global mean warming). The magnitude of cryospheric feedbacks remains uncertain, contributing to the range of model climate responses at mid- to high latitudes.
• There have been ongoing improvements to resolution, computational methods and parametrizations, and additional processes (e.g., interactive aerosols) have been included in more of the climate models.
• Most AOGCMs no longer use flux adjustments, which were previously required to maintain a stable climate. At the same time, there have been improvements in the simulation of many aspects of present climate. The uncertainty associated with the use of flux adjustments has therefore decreased, although biases and long-term trends remain in AOGCM control simulations.
• Progress in the simulation of important modes of climate variability has increased the overall confidence in the models’ representation of important climate processes. As a result of steady progress, some AOGCMs can now simulate important aspects of the El Niño-Southern Oscillation (ENSO). Simulation of the Madden-Julian Oscillation (MJO) remains unsatisfactory.
• The ability of AOGCMs to simulate extreme events, especially hot and cold spells, has improved. The frequency and amount of precipitation falling in intense events are underestimated.
• Simulation of extratropical cyclones has improved. Some models used for projections of tropical cyclone changes can simulate successfully the observed frequency and distribution of tropical cyclones.
• Systematic biases have been found in most models’ simulation of the Southern Ocean. Since the Southern Ocean is important for ocean heat uptake, this results in some uncertainty in transient climate response.
• The possibility that metrics based on observations might be used to constrain model projections of climate change has been explored for the first time, through the analysis of ensembles of model simulations. Nevertheless, a proven set of model metrics that might be used to narrow the range of plausible climate projections has yet to be developed.
• To explore the potential importance of carbon cycle feedbacks in the climate system, explicit treatment of the carbon cycle has been introduced in a few climate AOGCMs and some Earth System Models of Intermediate Complexity (EMICs).
• Earth System Models of Intermediate Complexity have been evaluated in greater depth than previously. Coordinated intercomparisons have demonstrated that these models are useful in addressing questions involving long time scales or requiring a large number of ensemble simulations or sensitivity experiments.

Developments in model formulation

Improvements in atmospheric models include reformulated dynamics and transport schemes, and increased horizontal and vertical resolution. Interactive aerosol modules have been incorporated into some models, and through these, the direct and the indirect effects of aerosols are now more widely included.

Significant developments have occurred in the representation of terrestrial processes. Individual components continue to be improved via systematic evaluation against observations and against more comprehensive models. The terrestrial processes that might significantly affect large-scale climate over the next few decades are included in current climate models. Some processes important on longer time scales are not yet included.

Development of the oceanic component of AOGCMs has continued. Resolution has increased and models have generally abandoned the ‘rigid lid’ treatment of the ocean surface. New physical parametrizations and numerics include true freshwater fluxes, improved river and estuary mixing schemes and the use of positive definite advection schemes. Adiabatic isopycnal mixing schemes are now widely used. Some of these improvements have led to a reduction in the uncertainty associated with the use of less sophisticated parametrizations (e.g., virtual salt flux).

Progress in developing AOGCM cryospheric components is clearest for sea ice. Almost all state-of-the-art AOGCMs now include more elaborate sea ice dynamics and some now include several sea ice thickness categories and relatively advanced thermodynamics. Parametrizations of terrestrial snow processes in AOGCMs vary considerably in formulation. Systematic evaluation of snow suggests that sub-grid scale heterogeneity is important for simulating observations of seasonal snow cover. Few AOGCMs include ice sheet dynamics; in all of the AOGCMs evaluated in this chapter and used in Chapter 10 for projecting climate change in the 21st century, the land ice cover is prescribed.

There is currently no consensus on the optimal way to divide computer resources among: finer numerical grids, which allow for better simulations; greater numbers of ensemble members, which allow for better statistical estimates of uncertainty; and inclusion of a more complete set of processes (e.g., carbon feedbacks, atmospheric chemistry interactions).

Developments in model climate simulation

The large-scale patterns of seasonal variation in several important atmospheric fields are now better simulated by AOGCMs than they were at the time of the TAR. Notably, errors in simulating the monthly mean, global distribution of precipitation, sea level pressure and surface air temperature have all decreased. In some models, simulation of marine low-level clouds, which are important for correctly simulating sea surface temperature and cloud feedback in a changing climate, has also improved. Nevertheless, important deficiencies remain in the simulation of clouds and tropical precipitation (with their important regional and global impacts).

Some common model biases in the Southern Ocean have been identified, resulting in some uncertainty in oceanic heat uptake and transient climate response. Simulations of the thermocline, which was too thick, and the Atlantic overturning and heat transport, which were both too weak, have been substantially improved in many models.

Despite notable progress in improving sea ice formulations, AOGCMs have typically achieved only modest progress in simulations of observed sea ice since the TAR. The relatively slow progress can partially be explained by the fact that improving sea ice simulation requires improvements in both the atmosphere and ocean components in addition to the sea ice component itself.

Since the TAR, developments in AOGCM formulation have improved the representation of large-scale variability over a wide range of time scales. The models capture the dominant extratropical patterns of variability including the Northern and Southern Annular Modes, the Pacific Decadal Oscillation, the Pacific-North American and Cold Ocean-Warm Land Patterns. AOGCMs simulate Atlantic multi-decadal variability, although the relative roles of high- and low-latitude processes appear to differ between models. In the tropics, there has been an overall improvement in the AOGCM simulation of the spatial pattern and frequency of ENSO, but problems remain in simulating its seasonal phase locking and the asymmetry between El Niño and La Niña episodes. Variability with some characteristics of the MJO is simulated by most AOGCMs, but the events are typically too infrequent and too weak.

Atmosphere-Ocean General Circulation Models are able to simulate extreme warm temperatures, cold air outbreaks and frost days reasonably well. Models used in this report for projecting tropical cyclone changes are able to simulate present-day frequency and distribution of cyclones, but intensity is less well simulated. Simulation of extreme precipitation is dependent on resolution, parametrization and the thresholds chosen. In general, models tend to produce too many days with weak precipitation (<10 mm day^–1)and too little precipitation overall in intense events (>10 mm day^–1).

Earth system Models of Intermediate Complexity have been developed to investigate issues in past and future climate change that cannot be addressed by comprehensive AOGCMs because of their large computational cost. Owing to the reduced resolution of EMICs and their simplified representation of some physical processes, these models only allow inferences about very large scales. Since the TAR, EMICs have been evaluated via several coordinated model intercomparisons which have revealed that, at large scales, EMIC results compare well with observational data and AOGCM results. This lends support to the view that EMICS can be used to gain understanding of processes and interactions within the climate system that evolve on time scales beyond those generally accessible to current AOGCMs. The uncertainties in long-term climate change projections can also be explored more comprehensively by using large ensembles of EMIC runs.

Developments in analysis methods

Since the TAR, an unprecedented effort has been initiated to make available new model results for scrutiny by scientists outside the modelling centres. Eighteen modelling groups performed a set of coordinated, standard experiments, and the resulting model output, analysed by hundreds of researchers worldwide, forms the basis for much of the current IPCC assessment of model results. The benefits of coordinated model intercomparison include increased communication among modelling groups, more rapid identification and correction of errors, the creation of standardised benchmark calculations and a more complete and systematic record of modelling progress.

Afew climate models have been tested for (and shown) capability in initial value predictions, on time scales from weather forecasting (a few days) to seasonal forecasting (annual). The capability demonstrated by models under these conditions increases confidence that they simulate some of the key processes and teleconnections in the climate system.

Developments in evaluation of climate feedbacks

Water vapour feedback is the most important feedback enhancing climate sensitivity. Although the strength of this feedback varies somewhat among models, its overall impact on the spread of model climate sensitivities is reduced by lapse rate feedback, which tends to be anti-correlated. Several new studies indicate that modelled lower- and upper-tropospheric humidity respond to seasonal and interannual variability, volcanically induced cooling and climate trends in a way that is consistent with observations. Recent observational and modelling evidence thus provides strong additional support for the combined water vapour-lapse rate feedback being around the strength found in AOGCMs.

Recent studies reaffirm that the spread of climate sensitivity estimates among models arises primarily from inter-model differences in cloud feedbacks. The shortwave impact of changes in boundary-layer clouds, and to a lesser extent mid-level clouds, constitutes the largest contributor to inter-model differences in global cloud feedbacks. The relatively poor simulation of these clouds in the present climate is a reason for some concern. The response to global warming of deep convective clouds is also a substantial source of uncertainty in projections since current models predict different responses of these clouds. Observationally based evaluation of cloud feedbacks indicates that climate models exhibit different strengths and weaknesses, and it is not yet possible to determine which estimates of the climate change cloud feedbacks are the most reliable.

Despite advances since the TAR, substantial uncertainty remains in the magnitude of cryospheric feedbacks within AOGCMs. This contributes to a spread of modelled climate response, particularly at high latitudes. At the global scale, the surface albedo feedback is positive in all the models, and varies between models much less than cloud feedbacks. Understanding and evaluating sea ice feedbacks is complicated by the strong coupling to polar cloud processes and ocean heat and freshwater transport. Scarcity of observations in polar regions also hampers evaluation. New techniques that evaluate surface albedo feedbacks have recently been developed. Model performance in reproducing the observed seasonal cycle of land snow cover may provide an indirect evaluation of the simulated snow-albedo feedback under climate change.

Systematic model comparisons have helped establish the key processes responsible for differences among models in the response of the ocean to climate change. The importance of feedbacks from surface flux changes to the meridional overturning circulation has been established in many models. At present, these feedbacks are not tightly constrained by available observations.

The analysis of processes contributing to climate feedbacks in models and recent studies based on large ensembles of models suggest that in the future it may be possible to use observations to narrow the current spread in model projections of climate change.

8.1 Introduction and Overview

The goal of this chapter is to evaluate the capabilities and limitations of the global climate models used elsewhere in this assessment. A number of model evaluation activities are described in various chapters of this report. This section provides a context for those studies and a guide to direct the reader to the appropriate chapters.

8.2 Advances in Modelling

Many modelling advances have occurred since the TAR. Space does not permit a comprehensive discussion of all major changes made over the past several years to the 23 AOGCMs used widely in this report (see Table 8.1 ). Model improvements can, however, be grouped into three categories. First, the dynamical cores (advection, etc.) have been improved, and the horizontal and vertical resolutions of many models have been increased. Second, more processes have been incorporated into the models, in particular in the modelling of aerosols, and of land surface and sea ice processes. Third, the parametrizations of physical processes have been improved. For example, as discussed further in Section 8.2.7 ,most of the models no longer use flux adjustments ( Manabe and Stouffer, 1988 [JoC, MoS, SRC] ; Sausen et al., 1988 [JoC, MoS, ARC] ) to reduce climate drift. These various improvements, developed across the broader modelling community, are well represented in the climate models used in this report.

Despite the many improvements, numerous issues remain. Many of the important processes that determine a model’s response to changes in radiative forcing are not resolved by the model’s grid. Instead, sub-grid scale parametrizations are used to parametrize the unresolved processes, such as cloud formation and the mixing due to oceanic eddies. It continues to be the case that multi-model ensemble simulations generally provide more robust information than runs of any single model. Table 8.1 summarises the formulations of each of the AOGCMs used in this report.

There is currently no consensus on the optimal way to divide computer resources among finer numerical grids, which allow for better simulations; greater numbers of ensemble members, which allow for better statistical estimates of uncertainty; and inclusion of a more complete set of processes (e.g., carbon feedbacks, atmospheric chemistry interactions).

8.3 Evaluation of Contemporary Climate as Simulated by Coupled Global Models

Due to nonlinearities in the processes governing climate, the climate system response to perturbations depends to some extent on its basic state ( Spelman and Manabe, 1984 [JoC, MoS] ). Consequently, for models to predict future climatic conditions reliably, they must simulate the current climatic state with some as yet unknown degree of fidelity. Poor model skill in simulating present climate could indicate that certain physical or dynamical processes have been misrepresented. The better a model simulates the complex spatial patterns and seasonal and diurnal cycles of present climate, the more confidence there is that all the important processes have been adequately represented. Thus, when new models are constructed, considerable effort is devoted to evaluating their ability to simulate today’s climate (e.g., Collins et al., 2006 [JoC, MoS, ARC] ; Delworth et al., 2006 [MoS, ARC] ).

Some of the assessment of model performance presented here is based on the 20th-century simulations that constitute a part of the MMD archived at PCMDI. In these simulations, modelling groups initiated the models from pre-industrial (circa 1860 ) ‘control’ simulations and then imposed the natural and anthropogenic forcing thought to be important for simulating the climate of the last 140 years or so. The 23 models considered here (see Table 8.1 )are those relied on in Chapters 9 and 10 to investigate historical and future climate changes. Some figures in this section are based on results from a subset of the models because the data set is incomplete.

In order to identify errors that are systematic across models, the mean of fields available in the MMD, referred to here as the ‘multi-model mean field’, will often be shown. The multi-model mean field results are augmented by results from individual models available as Supplementary Material (see Figures S8.1 to S8.15). The multi-model averaging serves to filter out biases of individual models and only retains errors that are generally pervasive. There is some evidence that the multi-model mean field is often in better agreement with observations than any of the fields simulated by the individual models (see Section 8.3.1.1.2 ), which supports continued reliance on a diversity of modelling approaches in projecting future climate change and provides some further interest in evaluating the multi-model mean results.

Faced with the rich variety of climate characteristics that could potentially be evaluated here, this section focuses on those elements that can critically affect societies and natural ecosystems and that are most likely to respond to changes in radiative forcing.

8.4 Evaluation of Large-Scale Climate Variability as Simulated by Coupled Global Models

The atmosphere-ocean coupled climate system shows various modes of variability that range widely from intra-seasonal to inter-decadal time scales. Successful simulation and prediction over a wide range of these phenomena increase confidence in the AOGCMs used for climate predictions of the future.

8.5 Model Simulations of Extremes

Society’s perception of climate variability and climate change is largely formed by the frequency and the severity of extremes. This is especially true if the extreme events have large and negative impacts on lives and property. As climate models’ resolution and the treatment of physical processes have improved, the simulation of extremes has also improved. Mainly because of increased data availability (e.g., daily data, various indices, etc.), the modelling community has now examined the model simulations in greater detail and presented a comprehensive description of extreme events in the coupled models used for climate change projections.

Some extreme events, by their very nature of being smaller in scale and shorter in duration, are manifestations of either a rapid amplification, or an equilibration at a higher amplitude, of naturally occurring local instabilities. Large-scale and long-duration extreme events are generally due to persistence of weather patterns associated with air-sea and air-land interactions. A reasonable hypothesis might be that the coarse-resolution AOGCMs might not be able to simulate local short-duration extreme events, but that is not the case. Our assessment of the recent scientific literature shows, perhaps surprisingly, that the global statistics of the extreme events in the current climate, especially temperature, are generally well simulated by the current models (see Section 8.5.1 ). These models have been more successful in simulating temperature extremes than precipitation extremes.

The assessment of extremes, especially for temperature, has been done by examining the amplitude, frequency and persistence of the following quantities: daily maximum and minimum temperature (e.g., hot days, cold days, frost days), daily precipitation intensity and frequency, seasonal mean temperature and precipitation and frequency and tracks of tropical cyclones. For precipitation, the assessment has been done either in terms of return values or extremely high rates of precipitation.

8.6 Climate Sensitivity and Feedbacks

8.7 Mechanisms Producing

8.8 Representing the Global System with Simpler Models

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