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Air quality research: perspective from climate change modelling research
Environment International 29 (2003) 253 – 261 www.elsevier.com/locate/envint
Air quality research: perspective from climate change modelling research Fredrick Semazzi * Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC 27695, USA
Abstract A major component of climate change is a manifestation of changes in air quality. This paper explores the question of air quality from the climate change modelling perspective. It reviews recent research advances on the cause – effect relationships between atmospheric air composition and climate change, primarily based on the Intergovernmental Panel on Climate Change (IPCC) assessment of climate change over the past decade. There is a growing degree of confidence that the warming world over the past century was caused by human-related changes in the composition of air. Reliability of projections of future climate change is highly dependent on future emission scenarios that have been identified that in turn depend on a multitude of complicated interacting social – economic factors. Anticipated improvements in the performance of climate models is a major source of optimism for better climate projections in the future, but the real benefits of its contribution will be closely coupled with other sources of uncertainty, and in particular emission projections. D 2002 Elsevier Science Ltd. All rights reserved. Keywords: Air quality; Climate change; Climate models
1. Introduction There is compelling evidence that the present-day composition and quality of the air is significantly different from the preindustrial period. This review focuses on recent research developments regarding the variability of climate because of anthropogenic and natural causes. This review is primarily based on previous Intergovernmental Panel on Climate Change (IPCC) community studies (IPCC, 1996a,b,c, and references therein), and work that has been carried out by the international IPCC research community during the past few years.
2. IPCC, climate change assessments This section summarises the findings of the IPCC reported through the Second Assessment Report (SAR; IPCC, 1996a) and Third Assessment Report (TAR; IPCC, 2001). Evaluation of complex climate models is not a clearcut issue. A large volume of studies have addressed this difficult problem (Shackley et al., 1998, 1999; HendersonSellers and McGuffie, 1999; Petersen, 2000); however,
* Tel.: +1-919-515-1434; fax: +1-919-515-1683. E-mail address: fred [email protected] (F. Semazzi).
many issues still remain unresolved. The philosophy adopted in the IPCC is based on the understanding that it will always be possible to find errors in simulations of particular variables or processes in climate models. What is important is to determine whether such errors make a given model ‘unusable’ in answering specific questions. Therefore, as models improve over time, coupled with acquisition of more comprehensive data sets and more powerful analysis methodologies, more previously untenable questions could be addressed. Evidence of acceptable model performance must be sought from multiple sources to minimise uncertainties associated with the evaluation of models. For instance, in addition to evaluation of models based on contemporary climatic conditions, models are also routinely assessed for their ability to simulate past climates. 2.1. Global model evaluation There are two fundamentally different sets of criteria used in evaluating models (IPCC, 2001). In the first, the important issues are the degree to which a model is physically based and the degree of complexity with which essential physical and dynamical processes and their interactions have been modelled. In the second, there are attempts to quantify model errors, to consider causes for those errors (where possible), and to understand the nature of interactions within the model. While this may seem a
0160-4120/02/$ - see front matter D 2002 Elsevier Science Ltd. All rights reserved. doi:10.1016/S0160-4120(02)00184-8
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more formal procedure, it must be recalled that the very complexity of most climate models means that there are severe limits placed on our ability to analyse and understand the model processes, interactions and uncertainties. The choice of model variables and model processes that are investigated are often based upon the judgement and experience of the modelling community. The rationale behind evaluation of the ability of climate models to simulate the climate of the present and the past is to build confidence and provide guidance regarding their capabilities to make projections of future climate. This is an important test of the models, but not a sufficient condition to demonstrate a correct climate change response. The importance of particular aspects of the present climate, in controlling the climate change response, is not well understood. Nevertheless, despite all the pending concerns, it is now considered that the current generation of coupled climate models can be used with some confidence in projections of future climate. The systematic evaluation of coupled climate models was only beginning to emerge at the time of the SAR. Suitable formalisms for evaluating fully coupled models were in the early stages of development, whereas considerable progress had been made in the evaluation of the performance of individual components (e.g., atmosphere, ocean, land-surface and sea-ice, and their interactions). Given present-day greenhouse gas concentrations, most coupled models at the time of the SAR had difficulty in obtaining a stable climate close to the present-day state. Therefore, ‘flux adjustment’ terms are often added to the surface fluxes of heat, water, and momentum that are passed from the atmosphere to the ocean model. Flux adjustments are nonphysical in that they cannot be related to any physical process in the climate system, and do not a priori conserve heat and water across the atmosphere– ocean interface. The need for flux adjustment, for those models that still use it, continues to be an area of concern. It appears that the success of the recent models that do not require heat flux adjustments is related to an improved ability to simulate the large-scale heat balances (Weaver and Hughes, 1996; Johns et al., 1997; Gordon et al., 2000). In 1995, a new feature of coupled model evaluation was an analysis of the variability of the coupled system over a range of time scales. 2.2. Regional model evaluation Five years ago, IPCC assessment (IPCC, 1996a) identified regional climate modelling as an important and valuable avenue to address the need to better understand and evaluate regional climate change information. Such information has been primarily obtained from coupled Atmosphere – Ocean General Circulation Model (AOGCM) simulations. However, resolution limitations pose severe constraints on the usefulness of regional AOGCM information, especially in regions characterised by complex topographic settings. For this reason, three categories of regionalisation techniques
have been developed with the aim of enhancing the regional information from AOGCMS.
High-resolution and variable-resolution ‘time slice’ Atmosphere GCM (AGCM) experiments. Nested limited area, or regional, climate models. Empirical/statistical and statistical/dynamical methods. These techniques exhibit different strengths and weaknesses, and their use depends on the needs of specific applications. However, all regionalisation techniques use coupled Atmospheric/Ocean GCM (AOGCM) information as input, and thus it is important for the AOGCM information to be of good quality. In the SAR, low confidence was placed in the simulation of regional climate change because of the complexity of the processes that regulate regional climates, and the poor performance of AOGCMs at the regional scale. Furthermore, at the time of the SAR, regionalisation techniques were at an early stage of development. Since then, a significant growth has been achieved in the development and understanding of these techniques. 2.3. Detection and attribution The SAR concluded that the balance of evidence suggests a discernible human influence on global climate. It noted that the anthropogenic signal was still emerging from the background of natural climate variability; hence we should not expect an extra few years of data to make a dramatic difference to this conclusion. The SAR also noted uncertainties in a number of factors, including internal variability and the magnitude and patterns of forcing and response, which prevented them from making a stronger conclusion. Much research has been carried out on these uncertainties, since 1995, in order to confirm and strengthen SAR conclusions. 2.3.1. A longer and more closely scrutinised observational record Three of the last five years have been the warmest in the instrumental record, consistent with the expectation that increases in greenhouse gases will lead to continued longterm warming. Confidence limits for observational sampling error have been estimated for the global and hemispheric mean temperature record. We also have a better understanding of the errors and uncertainties in the microwave sounder (MSU) satellite-based temperature record, and the global reanalysis data. Discrepancies between MSU and radiosonde data have largely been removed. New analyses of palaeodata over the last 1000 years indicate that the temperature changes over the last hundred years are unlikely to be natural in origin, even taking into account the large uncertainties in palaeo-reconstructions. Since the SAR, and in particular based on the recent TAR synthesis, more models have been used to estimate the magnitude of internal
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climate variability. Intercomparison of the models, generally used to assess significance levels in detection studies, suggests that the variance of the leading modes of global scale internal variability differs from that observed by no more than a factor of 3 on annual and decadal time scales. Some models show similar or larger variability than observed, although the observations have variance from both internal and external sources. Recent detection and attribution studies found no evidence that model-estimated internal variability was inconsistent with the residual variability that remained in the observations after removal of the estimated anthropogenic signals. No detection and attribution study to date has conclusively shown that the observed change in global surface temperature, in recent decades, is likely to be explained by internal variability.
3. Summary of recent developments 3.1. Global model evaluation 3.1.1. Reduced need for flux adjustment A growing number of coupled models can now be run without flux adjustment and are considered suitable for climate change prediction over century time scales. Improvements since the SAR have resulted from increased resolution (particularly in the ocean component), a wide range of model improvements, and greater attention to detail in the coupling of the atmosphere to the ocean. The general behaviour of flux-adjusted models compared to non-fluxadjusted models is now better understood. Comparison of flux-adjusted and non-flux-adjusted models shows no statistically significant difference in the variability between the two classes of model. While flux-adjusted models remain valid and useful for climate change projection and detection, particularly at longer time scales, the development of stable non-flux-adjusted models increases confidence in our ability to simulate future climates. 3.1.2. Climate of the 20th century Many coupled models have been run with various combinations of the radiative forcing applicable to the 20th century. SAR IPCC defined radiative forcing as: ‘‘the radiative forcing of the surface – troposphere system due to the perturbation in, or the introduction of, an agent (e.g., a change in greenhouse gas concentrations) as the change in net (down minus up) irradiance (solar plus longwave in watts per unit area) at the tropopause, after allowing for stratospheric temperatures to readjust to radiative equilibrium, but with surface and tropospheric temperatures and state held fixed at the unperturbed values.’’ Coupled model runs now reproduce the broad trends in surface air temperature, and some features of the variability. Models that use the most realistic forcing are producing encouraging simulations of the trends in surface air temperatures and precipitation over the past 100 years; however,
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not all aspects of the observed record have been successfully simulated. 3.1.3. Ongoing systematic model intercomparison The growth in systematic intercomparisons of models (e.g., Atmospheric Model Intercomparison Project [AMIP], the Coupled Model Intercomparison Project [CMIP], the Paleoclimate Model Intercomparison Project [PMIP]) provides the core evidence for our growing confidence in climate models. In particular, the CMIP is enabling a more comprehensive and systematic evaluation, and intercomparison of coupled models run in a standardised configuration. Some degree of quantification of improvements in coupled model performance has now been demonstrated. PMIP provides intercomparisons of models for the mid-Holocene (6000 years before present) and for the last glacial maximum (21,000 years before present). The ability of those models to simulate some aspects of palaeoclimates compared to a range of palaeoclimate proxy data gives us confidence in our models (at least the atmospheric component) over a range of different radiative forcing. 3.1.4. Importance of the land surface Recent results have demonstrated the relative importance of some components of land surface process schemes in controlling climate simulations, and in ‘time slice’ climate change experiments. The different sensitivity of surface variables (e.g., temperature and precipitation) to changes in specification of the land surface has been demonstrated, and shows that the magnitude of the response to climate forcing is dependent on the characterisation of the land surface (Sellers et al., 1996). 3.1.5. Analysis of individual phenomena Recent atmospheric models show improved performance in simulating many of the important phenomena, compared with those at the time of the SAR, by using better physical parameterisations and higher resolutions both in the horizontal and vertical domain. A systematic evaluation of the ability of coupled climate models to simulate a full range of the phenomena is yet to be undertaken. However, an intercomparison of El Nin˜o simulations, one of the most important phenomena, has revealed the ability of coupled climate models to simulate the El-Nin˜o-like SST variability in the tropical Pacific, and its associated changes in precipitation in the tropical monsoon regions (Yukimoto et al., 2000). However, the region of maximum SST variability is displaced further westward than in the observations. Other phenomena, for which advances have been achieved but further improvements are required, include the Pacific Decadal Oscillation (PDO), Monsoons (Sperber and Palmer, 1996; Sperber, 1999; Semazzi and Sun, 1997), the Madden and Julian Oscillation (MJO; Slingo et al., 1996), the North Atlantic Oscillation (NAO; Delworth, 1996), the Arctic Oscillation (AO), the Pacific-North American (PNA; Renshaw et al., 1998), Western and Pacific Patterns (WP;
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Kobayashi et al., 2000), and Blocking (D’Andrea et al., 1998). 3.1.6. Extreme event analysis Various forms of extreme event analysis have been performed but have mostly been restricted to atmospheric models. Analysis of extreme events in coupled models is underdeveloped. The most successful types of extreme event analyses have considered storm tracks and the frequency of storms. The more comprehensive analysis of extreme events in temperature and precipitation has moved towards analysis of return periods at various time scales, although characterisation of the precipitation frequency and intensity has continued. The limited amount of suitable gridded observational data is inhibiting comprehensive evaluation. Interest in the analysis of conditions under which tropical cyclones can form has continued, with the emergence of the concept of maximum potential intensity of a tropical storm offering great hope for the future. There have been claims that tropical cyclonelike vortices are being successfully simulated in climate model simulations. Although there continues to be debate over the interpretation of such vortices, useful information is being provided. However, further progress must wait for the next generation of high-resolution coupled models. 3.1.7. Overall assessment Much of the ongoing assessment of models is based on their ability to simulate aspects of the present climate. This is an important test of the models, but not a sufficient test to demonstrate a correct climate change response. The importance of particular aspects of the present climate in controlling the climate change response is not well understood. Nevertheless, despite all the pending concerns regarding the current generation of coupled climate models, confidence is emerging that they can be used with some confidence in projections of future climate. 3.2. Regional climate evaluation In recent years, work has been devoted, by the climate modelling community, to assessing confidence in the projection of regional climate change since the SAR, to evaluating the progress in regional climate research, and to providing guidelines for application of the broad range of different methods. Such efforts involve assessment of all methods for obtaining regional climate change information, including the use of AOGCMs and available regionalisation techniques. The models agree that all land regions undergo warming in all seasons, with the warming being generally more pronounced over cold climate regions and seasons. This warming is dependent upon the global climate sensitivity of the model and the forcing scenario. Average precipitation increases over most regions, especially in the cold season, because of an intensified hydrologic cycle. However, nota-
ble exceptions occur for broad regions of Central America, Australia and Southern Africa in DJF, and in the Mediterranean region in JJA (where most models concur in simulating decreases in precipitation). Underlying the specific regional changes is a general tendency for the models to display reduced synoptic variability, compared to observations, with a consequent reduction in extreme events. Also, model simulations show a prevailing tendency for interannual variability of precipitation to increase in future climate conditions. 3.2.1. High-resolution variable-resolution time slice AGCMs Since the SAR, variable and high-resolution AGCMs have been more widely used to provide high-resolution simulations of climate change for specific time periods (or ‘time slices’) using SST and sea ice forcing from AOGCM simulations. Nonetheless, the technique is clearly still in its infancy. Available studies show that many aspects of the models’ dynamics and large-scale flow are improved at higher resolution (Stratton, 1999), while in some cases systematic errors are worsened compared to coarser resolution models or are insensitive to resolution. In more recent developments, Pouliot (2000) have successfully developed and tested a semi-implicit semi-Lagrangian dynamic core for a global nonhydrostatic variable-resolution model. Other research investigators around the world are pursuing similar research initiatives in the development of prototypes for the next generation of global climate models. Some notable caveats have emerged from the few studies conducted with high-resolution and variable-resolution AGCMS. Firstly, the direct use of high-resolution versions of current AGCMs, without allowance for the dependence of models physical parameterisations on resolution, can lead to deterioration in model performance. Secondly, changes in the large-scale flow are noted with increased or variable resolution. This raises the question of consistency between high-resolution time slice simulations and the coarse resolution SST and sea-ice forcing used to drive them. Thirdly, the climate response, as indicated (e.g., by the temperature change), is sensitive to both the changing of resolution and the model biases, and systematic errors in the control run. Compared to this, the use of specific changes in SST for time slice simulations appears to have only a secondary effect. The above factors, coupled with the small number of studies carried out to date, imply that, at present, little confidence can be attached to any of the regional predictions provided by time slice simulations. The improvements seen with this technique are nonetheless encouraging, and they suggest that effort should be placed on improving the performance of current models at high resolution. 3.2.2. Regional climate models (RCMS) Nested regional modelling refers to the use of regional models to provide high-resolution climate simulations over given regions of interest, with initial and lateral driving
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meteorological conditions provided by corresponding GCM simulations. The nested regional modelling technique essentially originated from numerical weather prediction. The use of RCMs, for climate application, was pioneered by Dickinson et al. (1989) and Giorgi (1990). RCMs are now used in a wide range of climate applications, from paleoclimate (Hostetler et al., 1994) to anthropogenic climate change studies. They can provide high resolution (up to 10 – 20 km or less) and multidecadal simulations, and are capable of describing climate feedback mechanisms acting at the regional scale. A number of widely used, limited-area, modelling systems have been adapted to, or developed for, climate applications. More recently, RCMs have begun to couple atmospheric models with other climate process models (e.g., hydrology, ocean, sea ice, chemistry/aerosol, and land-biosphere models). These developments provide a link with air quality models that also treat atmospheric chemistry related to the generation of secondary air pollutants, such as tropospheric ozone and sulphate aerosols. Since the SAR, there have been significant advances in the development and understanding of nested regional climate models. These include new RCM systems, multiple nesting, coupling with different components of the climate system and the effects of domain size, resolution, boundary forcing and internal model variability. Furthermore, the application of RCMs has extended over a broader range of regions, time and spatial resolution, while the analysis has expanded beyond simple means to include higher-order climate statistics (Jones et al., 1997; Christensen, 1999; McGregor et al., 1999). In a recent study, Song and Semazzi (2002) demonstrated the benefits of more comprehensive coupling processes of RCMs with other components. More specifically, their results demonstrate that the hydrodynamics of Lake Victoria, in Eastern Africa, play an important role in determining the coupled variability of the lake and the regional climate. Their results show that adopting the traditional modelling approach, in which the lake hydrodynamics are neglected and the formulation is based on thermodynamics alone, is not satisfactory for Eastern Africa. Such a strategy precludes the ability of the coupled regional climate models to realistically transport heat within the lake, and thereby results in degraded simulation of the climate downstream over the rest of the lake and the surrounding land regions. As a consequence of the developments summarized above, nested RCMs have shown marked improvements in their ability to reproduce present-day climate. This includes skill in improving spatial climatic detail, especially in regions of complex topography, and in capturing a greater spectrum of weather events as seen in observations. The improved performance of present-day RCMs can be attributed to two factors: better representation of physical and dynamical processes, and better quality of driving GCM fields. The latter factor makes this approach dependent on further improvements in the quality of GCM simulation of large-scale fields. In addition, there is still a need for further
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advances in model performance in simulating climate variability at short time scales (daily to subdaily). Two main theoretical limitations of this technique are the effects of systematic errors in the driving fields provided by global models, and the lack of two-way interactions between regional and global climate. Overall, the evidence is strong that regional models consistently improve the spatial detail of simulated climate compared to GCMs because of their better representation of sub-GCM grid scale forcing. However, some aspects of the simulation can be degraded by the use of RCMs due to interactions between large-scale biases and fine-scale forcing. 3.2.3. Empirical downscaling Subsequent to SAR, an extensive range of statistical downscaling techniques has been developed. These approaches depend on the development of statistical relationships between suitable atmospheric predictors and regional climate predictors. In this manner, the equivalent predictors from GCMs are utilised to derive the regional climate response to the atmospheric forcing. A broad range of techniques is presently available, each with distinct characteristics making them suitable for different applications. They include regressions, neural networks and analogues. In one particular type of statistical downscaling, called statistical-dynamical downscaling, output of atmospheric mesoscale models is used in statistical relationships. Statistical downscaling techniques have their roots in synoptic climatology (Lamb, 1972). One of the primary advantages of these techniques is that they are computationally inexpensive, and thus can easily be applied to output from different GCM experiments. Another advantage is that they can be used to provide local information that is needed in many climate change impact applications. The major theoretical weakness of statistical downscaling methods is that their basic assumption is not verifiable (i.e., the statistical relationships developed for present-day climate also hold under the different forcing conditions of possible future climates). Many of these statistical methods hold significant advantages for scientists in developing nations, as they may be easily implemented, and are suitable for computing-limited environments. Furthermore, statistical techniques may directly relate GCM derived data to impact-relevant variables, like ecological variables or ocean wave heights, that are not simulated by contemporary climate models. However, there are some important caveats associated with this approach. Most notable is the concern that the predictors utilised may not represent the primary climate response to anthropogenic forcing. In this respect, many studies exclude atmospheric humidity and, as such, may only represent an incomplete climate response. It is apparent that the uncertainties associated with this approach are application dependent, although preliminary studies indicate that the uncertainties are of the same order of magnitude as found with dynamical downscaling through
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regional models. It is concluded that statistical downscaling techniques, in many cases, are a viable complement to process-based dynamical modelling and will remain so in the future. 3.2.4. Overall assessment Regionalisation techniques have different strengths and weaknesses, both from the theoretical and practical viewpoint, and the use of one technique vs. another is needdependent. High-resolution AGCMs offer the primary advantage of global coverage and two-way interactions between regional and global climate. Variable resolution and RCMs yield a greater increase in resolution, and can capture physical processes and feedbacks occurring at the regional scale, but are sensitive to errors of the AOGCM driving fields. Statistical downscaling techniques offer the advantages of being computationally inexpensive and allow significant tailoring to specific applications. In some instances, the joint use of different regionalisation methods may provide the best approach to address specific climate change problems. 3.3. Detection and attribution based on GCMs 3.3.1. New estimates of responses to natural forcing Fully coupled ocean –atmosphere models have been used to estimate the contribution of natural forcing agents to climate variability and change over the last one to three centuries. Including estimates of variations in solar irradiance over the last few centuries produces an increase in variance at longer (multidecadal) time scales, bringing the spectrum of variability closer to that deduced from palaeoreconstructions. Studies of the previous centuries also suggest that volcanism may induce climate variability on decadal time scales. Qualitative assessments, based on physical principles or model simulations, indicate that natural forcing alone is unlikely to explain both the recent global warming (natural effects produce a cooling over the last two decades) and the changes in vertical temperature structure (notably the cooling of the stratosphere). Statistical assessments confirm that simulated natural variability (internal and naturally forced) is unlikely to explain the recent warming, although there is evidence for a detectable volcanic influence on climate and a detectable solar influence, especially in the early part of the 20th century. The estimates of solar (Lockwood and Stamper, 1999) and volcanic (Free and Robock, 1999) forcing are based on proxy data for all but the two most recent decades. However, most detection methodologies allow for errors in the magnitude (but not in the structure) of simulated responses. 3.3.2. Improved anthropogenic forcing It is important to understand the underlying component of the ‘greenhouse’ forcing on which the anthropogenic component is superimposed. Investigation of these two components has been the subject of numerous publications.
Our synthesis is primarily based on the TAR, and the findings of the National Academy of Sciences (NAS, 2001). The natural ‘greenhouse’ effect is an essential component of the planet’s climate processes. A small percentage (roughly 2%) of the atmosphere is composed of greenhouse gases (water vapour, carbon dioxide, ozone and methane). These effectively prevent part of the heat radiated by the earth’s surface from otherwise escaping to space. The global system responds to this trapped heat with a climate that is warmer, on average, than it would be without the presence of these gases. In the absence of these greenhouse gases, the temperature would be too cold to support life, as we know it today. Water vapour is the most dominant greenhouse gas, but other gases are more effective in trapping heat energy from certain portions of the electromagnetic spectrum, where water vapour is semitransparent to heat escaping from the earth’s surface. However, greenhouse gases are increasing in the atmosphere because of human activities, and are trapping more heat. Recent measurements, based on air bubbles trapped within layers of accumulating snow, indicate that atmospheric carbon dioxide has increased by more than 30% since the beginning of the industrial age. Other greenhouse gases are also increasing. There are still uncertainties regarding the precise quantification of the relative sources of these gases but we are certain that, once in the atmosphere, these greenhouse gases have a relatively long lifetime, on the order of decades to centuries, and therefore become well mixed in the atmosphere. Particles (or aerosols) in the atmosphere, resulting from human activities, can also affect climate. Aerosols exhibit large spatial gradients in their distribution. Some aerosol types produce a cooling effect on the climate system (e.g., sulphate aerosol) while others cause warming (e.g., soot). There is a growing set of observations that yields a collective picture of a warming world over the past century. The global-average surface temperature has increased over the 20th century by 0.4– 0.8 jC. It is likely that the frequency of heavy and extreme precipitation events has increased as global temperatures have risen. There is also new evidence that most of the warming observed over the past 50 years is attributable to human activities. Scenarios of future human activities indicate changes in the atmospheric composition throughout the 21st century. A greenhouse gas warming could be reversed but only slowly. This quasi-irreversibility arises because of the slow rate of removal (centuries) of greenhouse gases from the atmosphere and the slow response of the oceans to thermal changes. The NAS (2001) investigation determined that it is not possible to define, in a precise manner, a safe level of greenhouse gases, in part, because there are still large uncertainties related to the projected rate and magnitude of climate change (also, testimony by Thomas Karl before the Committee on Governmental Affairs, United States Senate). Because there is considerable uncertainty in our current understanding of how the natural variability of the climate system reacts to emissions of greenhouse
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gases and aerosols, current estimates of the magnitude and impacts of future warming are subject to future adjustments (upward or downward). 3.3.3. Sensitivity to estimates of climate change signals Since the SAR, more simulations with increases in greenhouse gases and some representation of aerosol effects have become available. In some cases, ensembles of simulations have been run to reduce noise in the estimated climate response, particularly in the estimates of the timedependent response. Some studies have evaluated seasonal variation of the response. Uncertainties in the estimated climate signal have limited confidence in claims of attribution—there are differences in the patterns of response using different models. Studies have been performed using signals from several models. Despite the differences in patterns of response in those simulations that have been used in detection studies, they draw, almost without exception, consistent conclusions on the attribution of an anthropogenic influence on climate. 3.3.4. A wider range of detection techniques Another major advance since the SAR is the increase in the range of techniques used to detect atmospheric and climate change, and the evaluation of the degree to which the results are independent of the assumptions made in applying those techniques. There have been studies using pattern correlations, optimal detection studies using one or more fixed patterns and time-varying patterns, and a host of simpler techniques. Evidence of a human influence on climate is obtained over a wide range of techniques. The inclusion of the time dependence of signals helps to distinguish between natural and anthropogenic forcings. Results may be sensitive to the range of spatial and temporal scales that are considered. This issue has been addressed in the more recent studies by means of a simple test, and the danger of error was minimised by conservatively interpreting the test. Problems with increasing degeneracy between the different response patterns, as more patterns are included, have been taken into account. Idealised studies have demonstrated that surface temperature changes are detectable only on the largest space scales, and that the level of agreement found between simulations and observations in pattern correlation studies is close to what one would expect. Several decades of data are necessary to separate forced signals from internal variability. Studies conducted since the SAR have based their conclusions not just on statistical tests but also on estimates of the amplitude of anthropogenic signals in the observations and consideration of their consistency with model projections. Using the fact that optimal detection is a form of linear regression, studies have estimated the signal amplitude by finding the scaling that gives the best fit between models and observations. Scaling factors from studies reported to date suggest broad consistency between simulated anthropogenic climate change and observations on the scales considered.
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The increase in the number of studies, breadth of techniques, increased rigour in the assessment of the role of anthropogenic forcing in climate, and the robustness of results to the assumptions made using those techniques, has increased our confidence in these aspects of detection. 3.4. Overall assessment
Paleoclimatic reconstruction of the last 1000 years, and model estimates of natural climate variations, suggest that the observed warming over the last 100 years is exceptional and unlikely to be solely natural in origin. A wider range of models has been used to estimate natural internal variability, some of which show similar or larger variability than observed. In most cases, a substantial increase in the largest model estimate of variability would be required to nullify claims of attribution of a human influence on climate. New simulations of the response to natural forcing, including volcanic eruptions and changes in solar output, fail to provide a convincing explanation of late 20th century climate change. Estimation of anthropogenic signals has been improved through the use of newer models, ensemble simulations and the inclusion of additional anthropogenic and natural factors. Statistical techniques have been extended, in particular by applying optimal detection methods and estimating both natural and anthropogenic signals based on spatial and temporal information. The robustness of results to the use of different assumptions and different model data has been assessed. Most studies indicate that some human influence is needed to explain 20th century temperature changes. Regression techniques in a number of studies suggest that model estimates of anthropogenic temperature changes are broadly consistent with observed changes. Given the level of natural variability, we would not expect 5 years of additional data to make a substantial difference in the confidence of previous claims of attribution of a human effect on climate. Nevertheless, most research since the SAR strengthens the conclusion that the balance of evidence suggests a discernible human influence on climate. Attempts to quantify the anthropogenic influence indicate that it may account for a substantial fraction of the observed global temperature change over the 20th century. However, the accuracy of these estimates continues to be limited by uncertainties in the estimates of internal variability, of radiative forcing, and of the climate response to external forcing.
4. Future directions There remain discrepancies between the vertical profile of temperature change seen in global models and observations. There are large uncertainties in the simulation of
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internal climate variability by models, although as noted above, these are generally not large enough to nullify claims of detection and attribution. There is considerable uncertainty in the reconstruction of solar and volcanic forcing, which are based on proxy data for all but the last two decades. Possible ozone/solar interactions and (more speculatively) solar/cloud interactions are currently ignored. To date, the effects of many anthropogenic factors including soot, biogenic aerosols and changes in land use have been neglected, although their effect may be relatively small. The biggest uncertainty in anthropogenic forcing is associated with the indirect effect of aerosols (i.e., the influence of aerosols on the structure and radiative properties of clouds). Estimates of the size and geographic pattern of this effect considerably vary, and it has been represented in only two models used in detection studies. There are large differences in the response of different models to the same forcing. These differences, which are often greater than the difference in response in the same model with and without aerosol effects, highlight the large uncertainties in climate change prediction and the need for model improvement. Two factors that constrain current RCM development are identified. First, while some progress in developing highresolution climatologies for RCM validation has been achieved, more work is needed in this respect, especially for remote and physiographically complex regions. Second, a consistent set of RCM simulations of climate change for different regions is still not available. The specific need here is for a coordinated RCM simulation effort so that ensemble simulations with different models and scenarios for given regions may be developed to provide comprehensive information for impact assessments. Research in regionalisation methods is still a maturing process, and there remain significant uncertainties that are poorly understood. Most regionalisation research efforts have been carried out with specific objectives, and therefore a coherent picture of regional climate change via available regionalisation techniques cannot yet be drawn. More coordinated efforts are thus necessary to evaluate the different methodologies, to intercompare methods and models, and to apply these methods to climate change research in a comprehensive strategy. References Christensen OB. Relaxation of soil variables in a regional climate model. Tellus 1999;51A:674 – 85. D’Andrea F, Tibaldi S, Blackburn M, Boer G, Deque M, Dix MR, et al. Northern Hemisphere atmospheric blocking as simulated by 15 atmospheric general circulation models in the period 1979 – 1988. Clim Dyn 1998;14:385 – 407. Delworth TL. North Atlantic interannual variability in a coupled ocean – atmosphere model. J Climate 1996;9:2356 – 75. Dickinson RE, Errico RM, Giorgi F, Bates GT. A regional climate model for Western United States. Clim Change 1989;15:383 – 422. Free M, Robock A. Global warming in the context of the Little Ice Age. J Geophys Res 1999;104:19057 – 70.
Giorgi F. Simulation of regional climate using a limited area model nested in a general circulation model. J Climate 1990;3:941 – 63. Gordon C, Cooper C, Senior CA, Banks HT, Gregory JM, Johns TC, et al. The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Clim Dyn 2000;16:147 – 68. Henderson-Sellers A, McGuffie K. Concepts of good science in climate change modelling: comments on Shackley S, Young P, Parkinson S, Wynee B. Climatic Change 38; 1998: 159 – 205. Clim Change 1999;42: 597 – 610. Hostetler SW, Giorgi F, Bates GT, Bartlein PJ. Lake – atmosphere feedbacks associated with paleolakes Bonneville and Lahontan. Science 1994;263:665 – 8. IPCC. The science of climate change. In: Houghton JT, Meira-Filho LG, Callander BA, Harris N, Kattenberg A, Maskell K, editors. Contrib working group I, 2nd assessment report, Intergovernmental Panel on Climate Change (IPCC). Cambridge (UK): Cambridge Univ. Press; 1996a. 572 pp. IPCC. Impacts, adaptations and mitigation of climate change: scientific – technical analyses. In: Watson RT, Zinyowera MC, Moss RH, editors. Contrib working group II, 2nd assessment report, Intergovernmental Panel on Climate Change. Cambridge (UK): Cambridge Univ. Press; 1996b. 880 pp. IPCC. Climate change 1995: economic and social dimensions of climate change. In: Bruce JP, Lee H, Haites EF, editors. Contrib working group III, 2nd assessment report, Intergovernmental Panel on Climate Change. Cambridge (UK): Cambridge Univ. Press; 1996c. 448 pp. IPCC. Climate change 2001: the scientific basis. In: Houghton JT, Ding Y, Griggs DJ, Noguer M, van der Linden PJ, Dai X, Maskell K, Johnson CA, editors. Contrib working group I, 3rd assessment report, Intergovernmental Panel on Climate Change. Cambridge (UK): Cambridge Univ. Press; 2001. 881 pp. Johns TC, Carnell RE, Crossley JF, Gregory JM, Mitchell JFB, Senior CA, et al. The second Hadley Centre coupled atmosphere – ocean GCM: model description, spinup and validation. Clim Dyn 1997;13:103 – 34. Jones RG, Murphy JM, Noguer M, Keen AB. Simulation of climate change over Europe using a nested regional climate model: II. Comparison of driving and regional model responses to a doubling of carbon dioxide. Q J R Meteorol Soc 1997;123:265 – 92. Kobayashi C, Takano K, Kusunoki S, Sugi M, Kitoh A. Seasonal predictability in winter over Eastern Asia using the JMA global model. Q J R Meteorol Soc 2000;126:2111 – 23. Lamb HH. British isles weather types and a register of daily sequence of circulation patterns, 1861 – 1971. Geophysical memoir, vol. 116. London: Her Majesty Stationery Office (HMSO); 1972. 85 pp. Lockwood M, Stamper R. Long-term drift of the coronal soirse magnetic flux and total solar irradiance. Geophys Res Lett 1999;26:2461 – 4. McGregor JL, Katzfey JJ, Nguyen KC. Recent regional climate modeling experiments at CSIRO. In: Ritchie H, editor. Research activities in atmospheric and oceanic modeling. CAS/JSC working group on numerical experimentation, Report 28, World Meteorological Organization/ Technical Department (WMO/TD)-no 942. Geneva, Switzerland: WMO; 1999. p. 7.37 – 8. National Academy of Sciences (NAS). Climate change science: an analysis of some key questions. Washington (DC): National Academy Press; 2001. 28 pp. Petersen AC. Philosophy of climate science. Bull Am Meteorol Soc 2000;81:265 – 71. Pouliot G. A variable resolution nonhydrostatic global atmospheric semiimplicit semi-Lagrangian model. PhD Thesis, North Carolina State University; Raleigh, North Carolina, USA; 2000. p. 226. Renshaw AC, Rowell DP, Folland CK. Wintertime low-frequency weather variability in the North Pacific-American sector 1949 – 93. J Climate 1998;11:1073 – 93. Sellers PJ, Randall DA, Collatz CJ, Berry JA, Field CB, Dazlich DA, et al. A revised land – surface parameterization (SiB2) for atmospheric GCMs: Part 1. Model formulation. J Climate 1996;9:676 – 705.
F. Semazzi / Environment International 29 (2003) 253–261 Semazzi FHM, Sun L. The role of orography in determining the Sahelian climate. Int J Climatol 1997;17:581 – 96. Shackley S, Young P, Parkinson S, Wynne B. Uncertainty, complexity and concepts of good science in climate change modelling: are GCMs the best tools? Clim Change 1998;38:159 – 205. Shackley S, Young P, Parkinson S. Response to A. Henderson-Sellers and K. McGuffie. Clim Change 1999;42:611 – 7. Slingo JM, Sperber KR, Boyle JS, Ceron J-P, Dix M, Dugas B, et al. Intraseasonal oscillations in 15 atmospheric general circulation models: results from an AMIP diagnostic subproject. Clim Dyn 1996;12:325 – 57. Song Y, Semazzi HFM. Development of a coupled regional climate simulation model for the Lake Victoria basin. In: Odada EO, Olago DO, editors. The East African great lakes, limnology, paleolimnology and
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Air quality research: perspective from climate change modelling research Fredrick Semazzi * Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC 27695, USA
Abstract A major component of climate change is a manifestation of changes in air quality. This paper explores the question of air quality from the climate change modelling perspective. It reviews recent research advances on the cause – effect relationships between atmospheric air composition and climate change, primarily based on the Intergovernmental Panel on Climate Change (IPCC) assessment of climate change over the past decade. There is a growing degree of confidence that the warming world over the past century was caused by human-related changes in the composition of air. Reliability of projections of future climate change is highly dependent on future emission scenarios that have been identified that in turn depend on a multitude of complicated interacting social – economic factors. Anticipated improvements in the performance of climate models is a major source of optimism for better climate projections in the future, but the real benefits of its contribution will be closely coupled with other sources of uncertainty, and in particular emission projections. D 2002 Elsevier Science Ltd. All rights reserved. Keywords: Air quality; Climate change; Climate models
1. Introduction There is compelling evidence that the present-day composition and quality of the air is significantly different from the preindustrial period. This review focuses on recent research developments regarding the variability of climate because of anthropogenic and natural causes. This review is primarily based on previous Intergovernmental Panel on Climate Change (IPCC) community studies (IPCC, 1996a,b,c, and references therein), and work that has been carried out by the international IPCC research community during the past few years.
2. IPCC, climate change assessments This section summarises the findings of the IPCC reported through the Second Assessment Report (SAR; IPCC, 1996a) and Third Assessment Report (TAR; IPCC, 2001). Evaluation of complex climate models is not a clearcut issue. A large volume of studies have addressed this difficult problem (Shackley et al., 1998, 1999; HendersonSellers and McGuffie, 1999; Petersen, 2000); however,
* Tel.: +1-919-515-1434; fax: +1-919-515-1683. E-mail address: fred [email protected] (F. Semazzi).
many issues still remain unresolved. The philosophy adopted in the IPCC is based on the understanding that it will always be possible to find errors in simulations of particular variables or processes in climate models. What is important is to determine whether such errors make a given model ‘unusable’ in answering specific questions. Therefore, as models improve over time, coupled with acquisition of more comprehensive data sets and more powerful analysis methodologies, more previously untenable questions could be addressed. Evidence of acceptable model performance must be sought from multiple sources to minimise uncertainties associated with the evaluation of models. For instance, in addition to evaluation of models based on contemporary climatic conditions, models are also routinely assessed for their ability to simulate past climates. 2.1. Global model evaluation There are two fundamentally different sets of criteria used in evaluating models (IPCC, 2001). In the first, the important issues are the degree to which a model is physically based and the degree of complexity with which essential physical and dynamical processes and their interactions have been modelled. In the second, there are attempts to quantify model errors, to consider causes for those errors (where possible), and to understand the nature of interactions within the model. While this may seem a
0160-4120/02/$ - see front matter D 2002 Elsevier Science Ltd. All rights reserved. doi:10.1016/S0160-4120(02)00184-8
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more formal procedure, it must be recalled that the very complexity of most climate models means that there are severe limits placed on our ability to analyse and understand the model processes, interactions and uncertainties. The choice of model variables and model processes that are investigated are often based upon the judgement and experience of the modelling community. The rationale behind evaluation of the ability of climate models to simulate the climate of the present and the past is to build confidence and provide guidance regarding their capabilities to make projections of future climate. This is an important test of the models, but not a sufficient condition to demonstrate a correct climate change response. The importance of particular aspects of the present climate, in controlling the climate change response, is not well understood. Nevertheless, despite all the pending concerns, it is now considered that the current generation of coupled climate models can be used with some confidence in projections of future climate. The systematic evaluation of coupled climate models was only beginning to emerge at the time of the SAR. Suitable formalisms for evaluating fully coupled models were in the early stages of development, whereas considerable progress had been made in the evaluation of the performance of individual components (e.g., atmosphere, ocean, land-surface and sea-ice, and their interactions). Given present-day greenhouse gas concentrations, most coupled models at the time of the SAR had difficulty in obtaining a stable climate close to the present-day state. Therefore, ‘flux adjustment’ terms are often added to the surface fluxes of heat, water, and momentum that are passed from the atmosphere to the ocean model. Flux adjustments are nonphysical in that they cannot be related to any physical process in the climate system, and do not a priori conserve heat and water across the atmosphere– ocean interface. The need for flux adjustment, for those models that still use it, continues to be an area of concern. It appears that the success of the recent models that do not require heat flux adjustments is related to an improved ability to simulate the large-scale heat balances (Weaver and Hughes, 1996; Johns et al., 1997; Gordon et al., 2000). In 1995, a new feature of coupled model evaluation was an analysis of the variability of the coupled system over a range of time scales. 2.2. Regional model evaluation Five years ago, IPCC assessment (IPCC, 1996a) identified regional climate modelling as an important and valuable avenue to address the need to better understand and evaluate regional climate change information. Such information has been primarily obtained from coupled Atmosphere – Ocean General Circulation Model (AOGCM) simulations. However, resolution limitations pose severe constraints on the usefulness of regional AOGCM information, especially in regions characterised by complex topographic settings. For this reason, three categories of regionalisation techniques
have been developed with the aim of enhancing the regional information from AOGCMS.
High-resolution and variable-resolution ‘time slice’ Atmosphere GCM (AGCM) experiments. Nested limited area, or regional, climate models. Empirical/statistical and statistical/dynamical methods. These techniques exhibit different strengths and weaknesses, and their use depends on the needs of specific applications. However, all regionalisation techniques use coupled Atmospheric/Ocean GCM (AOGCM) information as input, and thus it is important for the AOGCM information to be of good quality. In the SAR, low confidence was placed in the simulation of regional climate change because of the complexity of the processes that regulate regional climates, and the poor performance of AOGCMs at the regional scale. Furthermore, at the time of the SAR, regionalisation techniques were at an early stage of development. Since then, a significant growth has been achieved in the development and understanding of these techniques. 2.3. Detection and attribution The SAR concluded that the balance of evidence suggests a discernible human influence on global climate. It noted that the anthropogenic signal was still emerging from the background of natural climate variability; hence we should not expect an extra few years of data to make a dramatic difference to this conclusion. The SAR also noted uncertainties in a number of factors, including internal variability and the magnitude and patterns of forcing and response, which prevented them from making a stronger conclusion. Much research has been carried out on these uncertainties, since 1995, in order to confirm and strengthen SAR conclusions. 2.3.1. A longer and more closely scrutinised observational record Three of the last five years have been the warmest in the instrumental record, consistent with the expectation that increases in greenhouse gases will lead to continued longterm warming. Confidence limits for observational sampling error have been estimated for the global and hemispheric mean temperature record. We also have a better understanding of the errors and uncertainties in the microwave sounder (MSU) satellite-based temperature record, and the global reanalysis data. Discrepancies between MSU and radiosonde data have largely been removed. New analyses of palaeodata over the last 1000 years indicate that the temperature changes over the last hundred years are unlikely to be natural in origin, even taking into account the large uncertainties in palaeo-reconstructions. Since the SAR, and in particular based on the recent TAR synthesis, more models have been used to estimate the magnitude of internal
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climate variability. Intercomparison of the models, generally used to assess significance levels in detection studies, suggests that the variance of the leading modes of global scale internal variability differs from that observed by no more than a factor of 3 on annual and decadal time scales. Some models show similar or larger variability than observed, although the observations have variance from both internal and external sources. Recent detection and attribution studies found no evidence that model-estimated internal variability was inconsistent with the residual variability that remained in the observations after removal of the estimated anthropogenic signals. No detection and attribution study to date has conclusively shown that the observed change in global surface temperature, in recent decades, is likely to be explained by internal variability.
3. Summary of recent developments 3.1. Global model evaluation 3.1.1. Reduced need for flux adjustment A growing number of coupled models can now be run without flux adjustment and are considered suitable for climate change prediction over century time scales. Improvements since the SAR have resulted from increased resolution (particularly in the ocean component), a wide range of model improvements, and greater attention to detail in the coupling of the atmosphere to the ocean. The general behaviour of flux-adjusted models compared to non-fluxadjusted models is now better understood. Comparison of flux-adjusted and non-flux-adjusted models shows no statistically significant difference in the variability between the two classes of model. While flux-adjusted models remain valid and useful for climate change projection and detection, particularly at longer time scales, the development of stable non-flux-adjusted models increases confidence in our ability to simulate future climates. 3.1.2. Climate of the 20th century Many coupled models have been run with various combinations of the radiative forcing applicable to the 20th century. SAR IPCC defined radiative forcing as: ‘‘the radiative forcing of the surface – troposphere system due to the perturbation in, or the introduction of, an agent (e.g., a change in greenhouse gas concentrations) as the change in net (down minus up) irradiance (solar plus longwave in watts per unit area) at the tropopause, after allowing for stratospheric temperatures to readjust to radiative equilibrium, but with surface and tropospheric temperatures and state held fixed at the unperturbed values.’’ Coupled model runs now reproduce the broad trends in surface air temperature, and some features of the variability. Models that use the most realistic forcing are producing encouraging simulations of the trends in surface air temperatures and precipitation over the past 100 years; however,
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not all aspects of the observed record have been successfully simulated. 3.1.3. Ongoing systematic model intercomparison The growth in systematic intercomparisons of models (e.g., Atmospheric Model Intercomparison Project [AMIP], the Coupled Model Intercomparison Project [CMIP], the Paleoclimate Model Intercomparison Project [PMIP]) provides the core evidence for our growing confidence in climate models. In particular, the CMIP is enabling a more comprehensive and systematic evaluation, and intercomparison of coupled models run in a standardised configuration. Some degree of quantification of improvements in coupled model performance has now been demonstrated. PMIP provides intercomparisons of models for the mid-Holocene (6000 years before present) and for the last glacial maximum (21,000 years before present). The ability of those models to simulate some aspects of palaeoclimates compared to a range of palaeoclimate proxy data gives us confidence in our models (at least the atmospheric component) over a range of different radiative forcing. 3.1.4. Importance of the land surface Recent results have demonstrated the relative importance of some components of land surface process schemes in controlling climate simulations, and in ‘time slice’ climate change experiments. The different sensitivity of surface variables (e.g., temperature and precipitation) to changes in specification of the land surface has been demonstrated, and shows that the magnitude of the response to climate forcing is dependent on the characterisation of the land surface (Sellers et al., 1996). 3.1.5. Analysis of individual phenomena Recent atmospheric models show improved performance in simulating many of the important phenomena, compared with those at the time of the SAR, by using better physical parameterisations and higher resolutions both in the horizontal and vertical domain. A systematic evaluation of the ability of coupled climate models to simulate a full range of the phenomena is yet to be undertaken. However, an intercomparison of El Nin˜o simulations, one of the most important phenomena, has revealed the ability of coupled climate models to simulate the El-Nin˜o-like SST variability in the tropical Pacific, and its associated changes in precipitation in the tropical monsoon regions (Yukimoto et al., 2000). However, the region of maximum SST variability is displaced further westward than in the observations. Other phenomena, for which advances have been achieved but further improvements are required, include the Pacific Decadal Oscillation (PDO), Monsoons (Sperber and Palmer, 1996; Sperber, 1999; Semazzi and Sun, 1997), the Madden and Julian Oscillation (MJO; Slingo et al., 1996), the North Atlantic Oscillation (NAO; Delworth, 1996), the Arctic Oscillation (AO), the Pacific-North American (PNA; Renshaw et al., 1998), Western and Pacific Patterns (WP;
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Kobayashi et al., 2000), and Blocking (D’Andrea et al., 1998). 3.1.6. Extreme event analysis Various forms of extreme event analysis have been performed but have mostly been restricted to atmospheric models. Analysis of extreme events in coupled models is underdeveloped. The most successful types of extreme event analyses have considered storm tracks and the frequency of storms. The more comprehensive analysis of extreme events in temperature and precipitation has moved towards analysis of return periods at various time scales, although characterisation of the precipitation frequency and intensity has continued. The limited amount of suitable gridded observational data is inhibiting comprehensive evaluation. Interest in the analysis of conditions under which tropical cyclones can form has continued, with the emergence of the concept of maximum potential intensity of a tropical storm offering great hope for the future. There have been claims that tropical cyclonelike vortices are being successfully simulated in climate model simulations. Although there continues to be debate over the interpretation of such vortices, useful information is being provided. However, further progress must wait for the next generation of high-resolution coupled models. 3.1.7. Overall assessment Much of the ongoing assessment of models is based on their ability to simulate aspects of the present climate. This is an important test of the models, but not a sufficient test to demonstrate a correct climate change response. The importance of particular aspects of the present climate in controlling the climate change response is not well understood. Nevertheless, despite all the pending concerns regarding the current generation of coupled climate models, confidence is emerging that they can be used with some confidence in projections of future climate. 3.2. Regional climate evaluation In recent years, work has been devoted, by the climate modelling community, to assessing confidence in the projection of regional climate change since the SAR, to evaluating the progress in regional climate research, and to providing guidelines for application of the broad range of different methods. Such efforts involve assessment of all methods for obtaining regional climate change information, including the use of AOGCMs and available regionalisation techniques. The models agree that all land regions undergo warming in all seasons, with the warming being generally more pronounced over cold climate regions and seasons. This warming is dependent upon the global climate sensitivity of the model and the forcing scenario. Average precipitation increases over most regions, especially in the cold season, because of an intensified hydrologic cycle. However, nota-
ble exceptions occur for broad regions of Central America, Australia and Southern Africa in DJF, and in the Mediterranean region in JJA (where most models concur in simulating decreases in precipitation). Underlying the specific regional changes is a general tendency for the models to display reduced synoptic variability, compared to observations, with a consequent reduction in extreme events. Also, model simulations show a prevailing tendency for interannual variability of precipitation to increase in future climate conditions. 3.2.1. High-resolution variable-resolution time slice AGCMs Since the SAR, variable and high-resolution AGCMs have been more widely used to provide high-resolution simulations of climate change for specific time periods (or ‘time slices’) using SST and sea ice forcing from AOGCM simulations. Nonetheless, the technique is clearly still in its infancy. Available studies show that many aspects of the models’ dynamics and large-scale flow are improved at higher resolution (Stratton, 1999), while in some cases systematic errors are worsened compared to coarser resolution models or are insensitive to resolution. In more recent developments, Pouliot (2000) have successfully developed and tested a semi-implicit semi-Lagrangian dynamic core for a global nonhydrostatic variable-resolution model. Other research investigators around the world are pursuing similar research initiatives in the development of prototypes for the next generation of global climate models. Some notable caveats have emerged from the few studies conducted with high-resolution and variable-resolution AGCMS. Firstly, the direct use of high-resolution versions of current AGCMs, without allowance for the dependence of models physical parameterisations on resolution, can lead to deterioration in model performance. Secondly, changes in the large-scale flow are noted with increased or variable resolution. This raises the question of consistency between high-resolution time slice simulations and the coarse resolution SST and sea-ice forcing used to drive them. Thirdly, the climate response, as indicated (e.g., by the temperature change), is sensitive to both the changing of resolution and the model biases, and systematic errors in the control run. Compared to this, the use of specific changes in SST for time slice simulations appears to have only a secondary effect. The above factors, coupled with the small number of studies carried out to date, imply that, at present, little confidence can be attached to any of the regional predictions provided by time slice simulations. The improvements seen with this technique are nonetheless encouraging, and they suggest that effort should be placed on improving the performance of current models at high resolution. 3.2.2. Regional climate models (RCMS) Nested regional modelling refers to the use of regional models to provide high-resolution climate simulations over given regions of interest, with initial and lateral driving
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meteorological conditions provided by corresponding GCM simulations. The nested regional modelling technique essentially originated from numerical weather prediction. The use of RCMs, for climate application, was pioneered by Dickinson et al. (1989) and Giorgi (1990). RCMs are now used in a wide range of climate applications, from paleoclimate (Hostetler et al., 1994) to anthropogenic climate change studies. They can provide high resolution (up to 10 – 20 km or less) and multidecadal simulations, and are capable of describing climate feedback mechanisms acting at the regional scale. A number of widely used, limited-area, modelling systems have been adapted to, or developed for, climate applications. More recently, RCMs have begun to couple atmospheric models with other climate process models (e.g., hydrology, ocean, sea ice, chemistry/aerosol, and land-biosphere models). These developments provide a link with air quality models that also treat atmospheric chemistry related to the generation of secondary air pollutants, such as tropospheric ozone and sulphate aerosols. Since the SAR, there have been significant advances in the development and understanding of nested regional climate models. These include new RCM systems, multiple nesting, coupling with different components of the climate system and the effects of domain size, resolution, boundary forcing and internal model variability. Furthermore, the application of RCMs has extended over a broader range of regions, time and spatial resolution, while the analysis has expanded beyond simple means to include higher-order climate statistics (Jones et al., 1997; Christensen, 1999; McGregor et al., 1999). In a recent study, Song and Semazzi (2002) demonstrated the benefits of more comprehensive coupling processes of RCMs with other components. More specifically, their results demonstrate that the hydrodynamics of Lake Victoria, in Eastern Africa, play an important role in determining the coupled variability of the lake and the regional climate. Their results show that adopting the traditional modelling approach, in which the lake hydrodynamics are neglected and the formulation is based on thermodynamics alone, is not satisfactory for Eastern Africa. Such a strategy precludes the ability of the coupled regional climate models to realistically transport heat within the lake, and thereby results in degraded simulation of the climate downstream over the rest of the lake and the surrounding land regions. As a consequence of the developments summarized above, nested RCMs have shown marked improvements in their ability to reproduce present-day climate. This includes skill in improving spatial climatic detail, especially in regions of complex topography, and in capturing a greater spectrum of weather events as seen in observations. The improved performance of present-day RCMs can be attributed to two factors: better representation of physical and dynamical processes, and better quality of driving GCM fields. The latter factor makes this approach dependent on further improvements in the quality of GCM simulation of large-scale fields. In addition, there is still a need for further
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advances in model performance in simulating climate variability at short time scales (daily to subdaily). Two main theoretical limitations of this technique are the effects of systematic errors in the driving fields provided by global models, and the lack of two-way interactions between regional and global climate. Overall, the evidence is strong that regional models consistently improve the spatial detail of simulated climate compared to GCMs because of their better representation of sub-GCM grid scale forcing. However, some aspects of the simulation can be degraded by the use of RCMs due to interactions between large-scale biases and fine-scale forcing. 3.2.3. Empirical downscaling Subsequent to SAR, an extensive range of statistical downscaling techniques has been developed. These approaches depend on the development of statistical relationships between suitable atmospheric predictors and regional climate predictors. In this manner, the equivalent predictors from GCMs are utilised to derive the regional climate response to the atmospheric forcing. A broad range of techniques is presently available, each with distinct characteristics making them suitable for different applications. They include regressions, neural networks and analogues. In one particular type of statistical downscaling, called statistical-dynamical downscaling, output of atmospheric mesoscale models is used in statistical relationships. Statistical downscaling techniques have their roots in synoptic climatology (Lamb, 1972). One of the primary advantages of these techniques is that they are computationally inexpensive, and thus can easily be applied to output from different GCM experiments. Another advantage is that they can be used to provide local information that is needed in many climate change impact applications. The major theoretical weakness of statistical downscaling methods is that their basic assumption is not verifiable (i.e., the statistical relationships developed for present-day climate also hold under the different forcing conditions of possible future climates). Many of these statistical methods hold significant advantages for scientists in developing nations, as they may be easily implemented, and are suitable for computing-limited environments. Furthermore, statistical techniques may directly relate GCM derived data to impact-relevant variables, like ecological variables or ocean wave heights, that are not simulated by contemporary climate models. However, there are some important caveats associated with this approach. Most notable is the concern that the predictors utilised may not represent the primary climate response to anthropogenic forcing. In this respect, many studies exclude atmospheric humidity and, as such, may only represent an incomplete climate response. It is apparent that the uncertainties associated with this approach are application dependent, although preliminary studies indicate that the uncertainties are of the same order of magnitude as found with dynamical downscaling through
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regional models. It is concluded that statistical downscaling techniques, in many cases, are a viable complement to process-based dynamical modelling and will remain so in the future. 3.2.4. Overall assessment Regionalisation techniques have different strengths and weaknesses, both from the theoretical and practical viewpoint, and the use of one technique vs. another is needdependent. High-resolution AGCMs offer the primary advantage of global coverage and two-way interactions between regional and global climate. Variable resolution and RCMs yield a greater increase in resolution, and can capture physical processes and feedbacks occurring at the regional scale, but are sensitive to errors of the AOGCM driving fields. Statistical downscaling techniques offer the advantages of being computationally inexpensive and allow significant tailoring to specific applications. In some instances, the joint use of different regionalisation methods may provide the best approach to address specific climate change problems. 3.3. Detection and attribution based on GCMs 3.3.1. New estimates of responses to natural forcing Fully coupled ocean –atmosphere models have been used to estimate the contribution of natural forcing agents to climate variability and change over the last one to three centuries. Including estimates of variations in solar irradiance over the last few centuries produces an increase in variance at longer (multidecadal) time scales, bringing the spectrum of variability closer to that deduced from palaeoreconstructions. Studies of the previous centuries also suggest that volcanism may induce climate variability on decadal time scales. Qualitative assessments, based on physical principles or model simulations, indicate that natural forcing alone is unlikely to explain both the recent global warming (natural effects produce a cooling over the last two decades) and the changes in vertical temperature structure (notably the cooling of the stratosphere). Statistical assessments confirm that simulated natural variability (internal and naturally forced) is unlikely to explain the recent warming, although there is evidence for a detectable volcanic influence on climate and a detectable solar influence, especially in the early part of the 20th century. The estimates of solar (Lockwood and Stamper, 1999) and volcanic (Free and Robock, 1999) forcing are based on proxy data for all but the two most recent decades. However, most detection methodologies allow for errors in the magnitude (but not in the structure) of simulated responses. 3.3.2. Improved anthropogenic forcing It is important to understand the underlying component of the ‘greenhouse’ forcing on which the anthropogenic component is superimposed. Investigation of these two components has been the subject of numerous publications.
Our synthesis is primarily based on the TAR, and the findings of the National Academy of Sciences (NAS, 2001). The natural ‘greenhouse’ effect is an essential component of the planet’s climate processes. A small percentage (roughly 2%) of the atmosphere is composed of greenhouse gases (water vapour, carbon dioxide, ozone and methane). These effectively prevent part of the heat radiated by the earth’s surface from otherwise escaping to space. The global system responds to this trapped heat with a climate that is warmer, on average, than it would be without the presence of these gases. In the absence of these greenhouse gases, the temperature would be too cold to support life, as we know it today. Water vapour is the most dominant greenhouse gas, but other gases are more effective in trapping heat energy from certain portions of the electromagnetic spectrum, where water vapour is semitransparent to heat escaping from the earth’s surface. However, greenhouse gases are increasing in the atmosphere because of human activities, and are trapping more heat. Recent measurements, based on air bubbles trapped within layers of accumulating snow, indicate that atmospheric carbon dioxide has increased by more than 30% since the beginning of the industrial age. Other greenhouse gases are also increasing. There are still uncertainties regarding the precise quantification of the relative sources of these gases but we are certain that, once in the atmosphere, these greenhouse gases have a relatively long lifetime, on the order of decades to centuries, and therefore become well mixed in the atmosphere. Particles (or aerosols) in the atmosphere, resulting from human activities, can also affect climate. Aerosols exhibit large spatial gradients in their distribution. Some aerosol types produce a cooling effect on the climate system (e.g., sulphate aerosol) while others cause warming (e.g., soot). There is a growing set of observations that yields a collective picture of a warming world over the past century. The global-average surface temperature has increased over the 20th century by 0.4– 0.8 jC. It is likely that the frequency of heavy and extreme precipitation events has increased as global temperatures have risen. There is also new evidence that most of the warming observed over the past 50 years is attributable to human activities. Scenarios of future human activities indicate changes in the atmospheric composition throughout the 21st century. A greenhouse gas warming could be reversed but only slowly. This quasi-irreversibility arises because of the slow rate of removal (centuries) of greenhouse gases from the atmosphere and the slow response of the oceans to thermal changes. The NAS (2001) investigation determined that it is not possible to define, in a precise manner, a safe level of greenhouse gases, in part, because there are still large uncertainties related to the projected rate and magnitude of climate change (also, testimony by Thomas Karl before the Committee on Governmental Affairs, United States Senate). Because there is considerable uncertainty in our current understanding of how the natural variability of the climate system reacts to emissions of greenhouse
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gases and aerosols, current estimates of the magnitude and impacts of future warming are subject to future adjustments (upward or downward). 3.3.3. Sensitivity to estimates of climate change signals Since the SAR, more simulations with increases in greenhouse gases and some representation of aerosol effects have become available. In some cases, ensembles of simulations have been run to reduce noise in the estimated climate response, particularly in the estimates of the timedependent response. Some studies have evaluated seasonal variation of the response. Uncertainties in the estimated climate signal have limited confidence in claims of attribution—there are differences in the patterns of response using different models. Studies have been performed using signals from several models. Despite the differences in patterns of response in those simulations that have been used in detection studies, they draw, almost without exception, consistent conclusions on the attribution of an anthropogenic influence on climate. 3.3.4. A wider range of detection techniques Another major advance since the SAR is the increase in the range of techniques used to detect atmospheric and climate change, and the evaluation of the degree to which the results are independent of the assumptions made in applying those techniques. There have been studies using pattern correlations, optimal detection studies using one or more fixed patterns and time-varying patterns, and a host of simpler techniques. Evidence of a human influence on climate is obtained over a wide range of techniques. The inclusion of the time dependence of signals helps to distinguish between natural and anthropogenic forcings. Results may be sensitive to the range of spatial and temporal scales that are considered. This issue has been addressed in the more recent studies by means of a simple test, and the danger of error was minimised by conservatively interpreting the test. Problems with increasing degeneracy between the different response patterns, as more patterns are included, have been taken into account. Idealised studies have demonstrated that surface temperature changes are detectable only on the largest space scales, and that the level of agreement found between simulations and observations in pattern correlation studies is close to what one would expect. Several decades of data are necessary to separate forced signals from internal variability. Studies conducted since the SAR have based their conclusions not just on statistical tests but also on estimates of the amplitude of anthropogenic signals in the observations and consideration of their consistency with model projections. Using the fact that optimal detection is a form of linear regression, studies have estimated the signal amplitude by finding the scaling that gives the best fit between models and observations. Scaling factors from studies reported to date suggest broad consistency between simulated anthropogenic climate change and observations on the scales considered.
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The increase in the number of studies, breadth of techniques, increased rigour in the assessment of the role of anthropogenic forcing in climate, and the robustness of results to the assumptions made using those techniques, has increased our confidence in these aspects of detection. 3.4. Overall assessment
Paleoclimatic reconstruction of the last 1000 years, and model estimates of natural climate variations, suggest that the observed warming over the last 100 years is exceptional and unlikely to be solely natural in origin. A wider range of models has been used to estimate natural internal variability, some of which show similar or larger variability than observed. In most cases, a substantial increase in the largest model estimate of variability would be required to nullify claims of attribution of a human influence on climate. New simulations of the response to natural forcing, including volcanic eruptions and changes in solar output, fail to provide a convincing explanation of late 20th century climate change. Estimation of anthropogenic signals has been improved through the use of newer models, ensemble simulations and the inclusion of additional anthropogenic and natural factors. Statistical techniques have been extended, in particular by applying optimal detection methods and estimating both natural and anthropogenic signals based on spatial and temporal information. The robustness of results to the use of different assumptions and different model data has been assessed. Most studies indicate that some human influence is needed to explain 20th century temperature changes. Regression techniques in a number of studies suggest that model estimates of anthropogenic temperature changes are broadly consistent with observed changes. Given the level of natural variability, we would not expect 5 years of additional data to make a substantial difference in the confidence of previous claims of attribution of a human effect on climate. Nevertheless, most research since the SAR strengthens the conclusion that the balance of evidence suggests a discernible human influence on climate. Attempts to quantify the anthropogenic influence indicate that it may account for a substantial fraction of the observed global temperature change over the 20th century. However, the accuracy of these estimates continues to be limited by uncertainties in the estimates of internal variability, of radiative forcing, and of the climate response to external forcing.
4. Future directions There remain discrepancies between the vertical profile of temperature change seen in global models and observations. There are large uncertainties in the simulation of
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internal climate variability by models, although as noted above, these are generally not large enough to nullify claims of detection and attribution. There is considerable uncertainty in the reconstruction of solar and volcanic forcing, which are based on proxy data for all but the last two decades. Possible ozone/solar interactions and (more speculatively) solar/cloud interactions are currently ignored. To date, the effects of many anthropogenic factors including soot, biogenic aerosols and changes in land use have been neglected, although their effect may be relatively small. The biggest uncertainty in anthropogenic forcing is associated with the indirect effect of aerosols (i.e., the influence of aerosols on the structure and radiative properties of clouds). Estimates of the size and geographic pattern of this effect considerably vary, and it has been represented in only two models used in detection studies. There are large differences in the response of different models to the same forcing. These differences, which are often greater than the difference in response in the same model with and without aerosol effects, highlight the large uncertainties in climate change prediction and the need for model improvement. Two factors that constrain current RCM development are identified. First, while some progress in developing highresolution climatologies for RCM validation has been achieved, more work is needed in this respect, especially for remote and physiographically complex regions. Second, a consistent set of RCM simulations of climate change for different regions is still not available. The specific need here is for a coordinated RCM simulation effort so that ensemble simulations with different models and scenarios for given regions may be developed to provide comprehensive information for impact assessments. Research in regionalisation methods is still a maturing process, and there remain significant uncertainties that are poorly understood. Most regionalisation research efforts have been carried out with specific objectives, and therefore a coherent picture of regional climate change via available regionalisation techniques cannot yet be drawn. More coordinated efforts are thus necessary to evaluate the different methodologies, to intercompare methods and models, and to apply these methods to climate change research in a comprehensive strategy. References Christensen OB. Relaxation of soil variables in a regional climate model. Tellus 1999;51A:674 – 85. D’Andrea F, Tibaldi S, Blackburn M, Boer G, Deque M, Dix MR, et al. Northern Hemisphere atmospheric blocking as simulated by 15 atmospheric general circulation models in the period 1979 – 1988. Clim Dyn 1998;14:385 – 407. Delworth TL. North Atlantic interannual variability in a coupled ocean – atmosphere model. J Climate 1996;9:2356 – 75. Dickinson RE, Errico RM, Giorgi F, Bates GT. A regional climate model for Western United States. Clim Change 1989;15:383 – 422. Free M, Robock A. Global warming in the context of the Little Ice Age. J Geophys Res 1999;104:19057 – 70.
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