Towards the Construction of Climate Change Scenarios

Climate impacts assessments need regional scenarios of climate change for a wide range of projected emissions. General circulation models (GCMs) are the most promising approach to providing such information, but as yet there is considerable uncertainty in their regional projections and they are still too costly to run for a large number of emission scenarios. Simpler models have been used to estimate global-mean temperature changes under a range of scenarios. In this paper we investigate whether a fixed pattern from a GCM experiment scaled by global-mean temperature changes from a simple model provides an acceptable estimate of the regional climate change over a range of scenarios. Changes estimated using this approximate approach are evaluated by comparing them with results from ensembles of a coupled ocean-atmosphere model. Five specific emissions scenarios are considered. For increases in greenhouse gases only, the 'error' in annual mean temperature for the cases considered is smaller than the sampling error due to the model's internal variability. The method may break down for scenarios of stabilisation of concentrations, because the patterns change as the model approaches equilibrium. The inclusion of large local perturbations due to sulphate aerosols can lead to significant deviations of the temperature pattern from that obtained using greenhouse gases alone. Combining separate patterns for the responses to greenhouse gases and aerosols may improve the accuracy of approximation. Finally, the accuracy of the scaling approach is more difficult to assess for deriving changes in regional precipitation because many of the regional changes are not statistically significant in the climate change projections considered here. If precipitation changes are only marginally significant in other models, the apparent disagreement between different models may be as much due to sampling error as to genuine differences in model response.     

Sources of systematic errors in the climatology of a regional climate model over Europe

 Two ten-year simulations made with a European regional climate model (RCM) are compared. They are driven by the same observed sea surface temperatures but use different lateral boundary forcing. For one simulation, RCM AMIP, this forcing is obtained from a standard integration of a global general circulation model (GCM AMIP), whereas for the other simulation, RCM ASSIM, it is derived from a time series of operational analyses. The archive of analysis fields (surface pressure plus winds and temperatures on various pressure levels) is not sufficiently comprehensive to provide directly the full set of driving fields required for the RCM (in particular, no moisture fields are present), so these are obtained via a GCM integration, GCM ASSIM, in which the model is continuously relaxed towards the analysis fields using a data assimilation technique. Errors in RCM AMIP can arise either from the internal RCM physics or from errors in the lateral boundary forcing inherited from GCM AMIP. Errors in RCM ASSIM can arise from the internal RCM physics or the boundary moisture forcing but not from the driving circulation. Although previous studies have considered RCM integrations driven either by output from standard GCM integrations or operational analyses, our study is the first to compare parallel integrations of each type. This allows the total systematic error in an RCM integration driven by standard GCM output to be partitioned into components arising from the driving circulation and the internal RCM physics. These components indicate the scope for reducing regional simulation biases by improving either the driving GCM or the RCM itself. The results relate mainly to elements of surface climate likely to be influenced by both the driving circulation and regional physical processes operating in the RCM. For cloud cover, errors are found to arise largely from the internal RCM physics. Values are too low despite a positive relative humidity bias, indicating shortcomings in the parametrisation scheme used to calculate cloud cover. In summer, surface temperature and precipitation errors are also explained principally by regional processes. For example excessive solar heating leads to anomalously high surface temperatures over southern Europe and excessive drying of the soil reduces precipitation in the south eastern sector of the domain. The lateral boundary forcing reduces precipitation in the south eastern sector of the domain. The lateral boundary forcing also exerts some influence, mainly via a tropospheric cold bias which partially offsets the warming over southern Europe and also increases precipitation. In other seasons the lateral boundary forcing and the regional physics both contribute significantly to the errors in surface temperature and precipitation. In winter the boundary forcing (apart from moisture) is responsible for about 60% of the total error variance for temperature and about 40% for precipitation, due to the cold bias and circulation errors such as a southward shift in the storm track. The remaining errors arise from the regional physics, although for precipitation an excessive supply of moisture from the lateral boundaries also contributes. The skill of the mesoscale component of the surface temperature and precipitation distributions exceeds previous estimates, due to more realistic observed climatology. The mesoscale patterns are very similar in the two RCM simulations indicating that errors in the simulation of fine scale detail arise mainly from inadequate representations of local forcings rather than errors in the large-scale circulation. Circulation errors in RCM AMIP (e.g. cold bias, southward shift of storm track) are also present in GCM AMIP, but are largely absent in RCM ASSIM except in summer. This confirms evidence from previous work that the key to reducing most circulation errors in the RCM lies in improving the driving GCM. Regional processes only make a major contribution to circulation errors in summer, when reduced advection from the boundaries allows errors in surface temperature to be transmitted more effectively into the troposphere. Finally, we find evidence of error balances in the GCM which act to minimise biases in important climatological variables. This reflects tuning of the model physics at GCM resolution. In order to achieve simultaneous optimisation of the RCM and GCM at widely differing resolutions it may be necessary to introduce explicit scale dependences into some aspects of the physics. Received: 17 September 1997/Accepted: 10 March 1998     

Creating regional climate change scenarios over southern South America for the 2020’s and 2050’s using the pattern scaling technique: validity and limitations

Dynamical downscaling of global climate simulations is the most adequate tool to generate regional projections of climate change. This technique involves at least a present climate simulation and a simulation of a future scenario, usually at the end of the twenty first century. However, regional projections for a variety of scenarios and periods, the 2020s or the 2050s, are often required by the impact community. The pattern scaling technique is used to estimate information on climate change for periods and scenarios not simulated by the regional model. We based our study on regional simulations performed over southern South America for present climate conditions and two emission scenarios at the end of the twenty first century. We used the pattern scaling technique to estimate mean seasonal changes of temperature and precipitation for the 2020s and the 2050s. The validity of the scalability assumptions underlying the pattern scaling technique for estimating near future regional climate change scenarios over southern South America is assessed. The results show that the pattern scaling works well for estimating mean temperature changes for which the regional changes are linearly related to the global mean temperature changes. For precipitation changes, the validity of the scalability assumption is weaker. The errors of estimating precipitation changes are comparable to those inherent to the regional model and to the projected changes themselves.     

RegCM3 regional climatologies for South America using reanalysis and ECHAM global model driving fields

To enable downscaling of seasonal prediction and climate change scenarios, long-term baseline regional climatologies which employ global model forcing are needed for South America. As a first step in this process, this work examines climatological integrations with a regional climate model using a continental scale domain nested in both reanalysis data and multiple realizations of an atmospheric general circulation model (GCM). The analysis presents an evaluation of the nested model simulated large scale circulation, mean annual cycle and interannual variability which is compared against observational estimates and also with the driving GCM for the Northeast, Amazon, Monsoon and Southeast regions of South America. Results indicate that the regional climate model simulates the annual cycle of precipitation well in the Northeast region and Monsoon regions; it exhibits a dry bias during winter (July–September) in the Southeast, and simulates a semi-annual cycle with a dry bias in summer (December–February) in the Amazon region. There is little difference in the annual cycle between the GCM and renalyses driven simulations, however, substantial differences are seen in the interannual variability. Despite the biases in the annual cycle, the regional model captures much of the interannual variability observed in the Northeast, Southeast and Amazon regions. In the Monsoon region, where remote influences are weak, the regional model improves upon the GCM, though neither show substantial predictability. We conclude that in regions where remote influences are strong and the global model performs well it is difficult for the regional model to improve the large scale climatological features, indeed the regional model may degrade the simulation. Where remote forcing is weak and local processes dominate, there is some potential for the regional model to add value. This, however, will require improvments in physical parameterizations for high resolution tropical simulations.     

A Climate Change Database for Biological Assessments in the Southeastern United States: Development and Case Study

A regional database containing historical time series and climate change scenarios for the Southeastern United States was developed for the U.S.D.A. Forest Service Southern Global Change Program (SGCP). Daily historical values of maximum temperature, minimum temperature and precipitation and empirically derived estimates of vapor pressure deficit and solar radiation across a uniform 1° latitude × 1° longitude grid were obtained. Climate change scenarios of temperature, precipitation, vapor pressure deficit and solar radiation were generated using semi-empirical techniques which combined historical time series and simulation field summaries from GISS, GFDL, OSU and UKMO General Circulation Model (GCM) experiments. An internally consistent 1° latitude × 1° longitude climate change scenario database was produced in which vapor pressure deficit and solar radiation conditions were driven by the GCM temperature projections, but were not constrained to agree with GCM calculated radiation and humidity fields. Some of the unique characteristics of the database were illustrated through a case study featuring growing season and annual potential evapotranspiration (ET_p) estimates. Overall, the unconstrained scenarios produced smaller median ET_p changes from historical baseline conditions, with a smaller range of outcomes than those driven by GCM-directed scenarios. Collectively, the range of annual and growing season ET changes from baseline estimates in response to the unconstrained climate scenarios was +10% to +40%. No outlier responses were identified. ET_p changes driven by GCM-directed (constrained) UKMO radiation and humidity scenarios were on the order of +100%, resulting in the identification of some ET_p responses as statistical outliers. These response differences were attributed to differences between the constrained and unconstrained humidity scenarios.     

A step-response approach for predicting and understanding non-linear precipitation changes

Future changes in precipitation represent one of the most important and uncertain possible effects of future climate change. We demonstrate a new approach based on idealised CO₂ step-change general circulation model (GCM) experiments, and test it using the HadCM3 GCM. The approach has two purposes: to help understand GCM projections, and to build and test a fast simple model for precipitation projections under a wide range of forcing scenarios. Overall, we find that the CO₂ step experiments contain much information that is relevant to transient projections, but that is more easily extracted due to the idealised experimental design. We find that the temporary acceleration of global-mean precipitation in this GCM following CO₂ ramp-down cannot be fully explained simply using linear responses to CO₂ and temperature. A more complete explanation can be achieved with an additional term representing interaction between CO₂ and temperature effects. Energy budget analysis of this term is dominated by clear-sky outgoing long-wave radiation (CSOLR) and sensible heating, but cloud and short-wave terms also contribute. The dominant CSOLR interaction is attributable to increased CO₂ raising the mean emission level to colder altitudes, which reduces the rate of increase of OLR with warming. This behaviour can be reproduced by our simple model. On regional scales, we compare our approach with linear ‘pattern-scaling’ (scaling regional responses by global-mean temperature change). In regions where our model predicts linear change, pattern-scaling works equally well. In some regions, however, substantial deviations from linear scaling with global-mean temperature are found, and our simple model provides more accurate projections. The idealised experiments reveal a complex pattern of non-linear behaviour. There are likely to be a range of controlling physical mechanisms, different from those dominating the global-mean response, requiring focussed investigation for individual regions, and in other GCMs.     

论区域气候模式与全球模式嵌套时边界区的选择

做了3个试验,第一个试验只用大气环流模式(GCM),主要考察GCM的性能并确定其误差的区域分布.后两个为对比试验,一个试验中,将区域气候模式(RCM)(NjU-RCM)的侧边界放在全球模式(L9R15)中我们感兴趣的区域,未考虑侧边界区GCM的误差大小,另一个试验中,RCM的侧边界位置根据GCM预报误差的空间分布选取,使其落在GCM预报误差较小的区域.3个试验都对1998年5、6、7月份中国区域的降水过程进行了模拟和比较.结果表明:单独使用GCM的效果最差;当用GCM-RCM嵌套模式对区域气候进行预测时,GCM侧边界值的误差对RCM的模拟结果有显著的影响,嵌套侧边界若选择在GCM系统性误差较小的地区,模拟或预测效果会有明显的改进.     

我国华东地区月尺度气候动力预测的研究:(Ⅱ) 嵌套模式的回报评估

采用数值模式方法对我国华东地区进行月尺度短期气候预测。预测框架由改进的低分辨率全球环流模式T63 L9嵌套并入了水文模型VXM的区域气候模式RegCM3构成,根据嵌套气候模式的积分结果,经剔除系统误差后制作短期气候预报。本文利用国家气候中心的评分方法对2003、2004两年的降水和地表气温回报结果作了评估;还将本系统的预报结果与CMAP降水资料、NMC温度资料及全国160站的观测资料进行了对比。结果表明,该系统可以比较稳定地对我国华东地区的降水和温度进行月尺度预测。     

嵌套域大小对区域气候模式模拟效果的影响

This paper presents a numerical study on the 1998 summer rainfall over the Yangtze River valley in central and eastern China, addressing effect of a nested area size on simulations in terms of the technique of nesting a regional climate model (RCM) upon a general circulation model (GCM). Evidence suggests that the size exerts greater impacts upon regional climate of the country, revealing that a larger nested size is su perior to a small one for simulation in mitigating errors of GCM-provided lateral boundary forcing. Also,simulations show that the RCM should incorporate regions of climate systems of great importance into study and a low-resolution GCM yields more pronounced errors as a rule when used in the research of the Tibetan Plateau, and, in contrast, our PσRCM can do a good job in describing the plateau′s role in a more realistic and accurate way. It is for this reason that the tableland should be included in the nested area when the RCM is employed to investigate the regional climate. Our PσRCM nesting upon a GCM reaches morerealistic results compared to a single GCM used.     

Temperature scaling pattern dependence on representative concentration pathway emission scenarios A letter

To preserve consistency among developed emission scenarios, the scenarios used in climate modeling, and the climate scenarios available for impact research, the pattern scaling technique is useful technique. The basic assumption of pattern scaling is that the spatial response pattern per 1 K increase in the global mean surface air temperature (SAT) (scaling pattern) is the same among emission scenarios, but this assumption requires further validation. We therefore investigated the dependence of the scaling pattern of the annual mean SAT on GHGs emission scenarios of representative concentration pathways (RCP) and the causes of that dependence using the Model for Interdisciplinary research on Climate 5 developed by Japanese research community. In particular, we focused on the relationships of the dependency with effects of aerosols and Atlantic meridional overturning circulation. We found significant dependencies of the scaling pattern on emission scenarios at middle and high latitudes of the Northern Hemisphere, with differences of >15 % over parts of East Asia, North America, and Europe. Impact researchers should take into account those dependencies that seriously affect their research. The mid-latitude dependence is caused by differences in sulfate aerosol emissions per 1 K increase in the global mean SAT, and the high-latitude dependence is mainly caused by nonlinear responses of sea ice and ocean heat transport to global warming. Long-term trends in land-use and land-cover changes did not significantly affect the scaling pattern of annual mean SAT, but they might have an effect at different timescales.     

初始条件和边界条件对区域模式模拟青藏高原区域气候的影响

用ＧＦＤＬ气候场及ＧＣＭ的模拟结果分别为区域模式（ＭＭ４）提供初始条件和边界条件,模拟了青藏高原地区夏季区域气候特征,通过比较不同初始条件和边界条件时区域模式的模拟结果,分析了初始条件和边界条件对模拟结果的影响,虽然这两种方法都模拟出青藏高原附近的主要区域气候特征,但ＧＦＤＬ提供初始条件和边界条件对ＭＭ４的模拟结果明显优于用ＧＣＭ提供初始条件和边界条件时的模拟结果,特别是高层,ＧＣＭ输出结果与实际     

Climate sensitivity and response

Results from climate change simulations indicate a reasonably robust proportionality between global mean radiative forcing and global mean surface air temperature response. The "constant" of proportionality is a measure of the overall strength of climate feedback processes and hence of global climate sensitivity. Geographically, however, temperature response patterns are generally not proportional to, nor do they resemble, their parent forcing patterns. Temperature response patterns, nevertheless, exhibit a remarkable additivity whereby the sum of response patterns for different forcings closely resembles the response pattern for the sum of the forcings. The geographical distribution of contributions to the climate sensitivity/feedback are obtained diagnostically from simulations with the Canadian Centre for Climate Modelling and Analysis (CCCma) coupled global climate model (GCM). There is positive feedback over high-latitude oceans, over northern land areas, and over the equatorial Pacific. The remaining regions over oceans and tropical land areas exhibit negative feedback. The feedback results are decomposed into components associated with short-and longwave radiative processes and in terms of cloud-free atmosphere/surface and cloud feedbacks. While the geographic pattern of the feedbacks may generally be linked to local processes, all feedback processes display regions of both positive and negative values (except for the solar atmosphere/surface feedback associated with the retreat of ice and snow which is positive) and all vary from place to place so that there is no simple physical picture that operates everywhere. The stable geographical pattern of the feedback is a consequence of the balance between local physical processes rather than the dominance of a particular process. The feedback results indicate that, to first order, temperature response patterns are determined by the geographical pattern of local feedback processes. The feedback processes act to localize forcing changes and to generate temperature response patterns which depend firstly on the pattern of feedbacks and only secondarily on the pattern of the forcing. The geographical distribution of feedback processes can be regarded as a feature of the climate model (and by inference of the climate system) and not (or only comparatively weak) functions of forcing and climate state. An illustrative model is able to reproduce qualitatively the kinds of forcing/temperature response behavior seen in the CCCma GCM including the quasi-independence of forcing and response patterns, the additivity of temperature response patterns, and the resulting "non-constancy" of the global climate sensitivity.     

Uncertainty in Regional Climate Model Mean Runoff Projections under Climate Change: Case Study of Labrador's Churchill River Basin

An ensemble of seven climate models from the North American Regional Climate Change Assessment Program (NARCCAP) was used to examine uncertainty in simulated runoff changes from a base period (1971–2000) to a future period (2041–2070) for the Churchill River basin, Labrador, Canada. Three approximations for mean annual runoff from each ensemble member were included in the analysis: (i) atmospheric moisture convergence, (ii) the balance between precipitation and evaporation, and (iii) instantaneous runoff output from respective land-surface schemes. Using data imputation (i.e., reconstruction) and variance decomposition it was found that choice of regional climate model (RCM) made the greatest contribution to uncertainty in the climate change signal, whereas the boundary forcing of a general circulation model (GCM) played a smaller, though non-negligible, role. It was also found that choice of runoff approximation made a substantial contribution to uncertainty, falling between the contribution from RCM and GCM choice. The NARCCAP output and imputed data were used to calculate mean and median annual changes and results were presented via probability distribution functions to facilitate decision making. Mean and median increases in runoff for the basin were found to be 11.2% and 8.9%, respectively.     

The contribution of anthropogenic forcings to regional changes in temperature during the last decade

Regional distributions of the mean annual temperature in the 2000s are computed with and without the effect of anthropogenic influences on the climate in several sub-continental regions. Simulated global patterns of the temperature response to external forcings are regressed against observations using optimal fingerprinting. The global analysis provides constraints which are then used to construct the regional temperature distributions. A similar approach was also employed in previous work, but here the methodology is extended to examine changes in any region, including areas with a poor observational coverage that were omitted in the earlier study. Two different General Circulation Models (GCMs) are used in the analysis. Anthropogenic forcings are found to have at least quadrupled the likelihood of occurrence of a year warmer than the warmest year since 1900 in 23 out of the 24 regions. The temperature distributions computed with the two models are very similar. While a more detailed assessment of model dependencies remains to be made once additional suitable GCM simulations become available, the present study introduces the statistical methodology and demonstrates its first application. The derived information concerning the effect of human influences on the regional climate is useful for adaptation planning. Moreover, by pre-computing the change in the likelihood of exceeding a temperature threshold over a range of thresholds, this kind of analysis enables a near real-time assessment of the anthropogenic impact on the observed regional temperatures.     

Extended regional climate model projections for Europe until the mid-twentyfirst century: combining ENSEMBLES and CMIP3

This study aims at sharpening the existing knowledge of expected seasonal mean climate change and its uncertainty over Europe for the two key climate variables air temperature and precipitation amount until the mid-twentyfirst century. For this purpose, we assess and compensate the global climate model (GCM) sampling bias of the ENSEMBLES regional climate model (RCM) projections by combining them with the full set of the CMIP3 GCM ensemble. We first apply a cross-validation in order to assess the skill of different statistical data reconstruction methods in reproducing ensemble mean and standard deviation. We then select the most appropriate reconstruction method in order to fill the missing values of the ENSEMBLES simulation matrix and further extend the matrix by all available CMIP3 GCM simulations forced by the A1B emission scenario. Cross-validation identifies a randomized scaling approach as superior in reconstructing the ensemble spread. Errors in ensemble mean and standard deviation are mostly less than 0.1 K and 1.0 % for air temperature and precipitation amount, respectively. Reconstruction of the missing values reveals that expected seasonal mean climate change of the ENSEMBLES RCM projections is not significantly biased and that the associated uncertainty is not underestimated due to sampling of only a few driving GCMs. In contrast, the spread of the extended simulation matrix is partly significantly lower, sharpening our knowledge about future climate change over Europe by reducing uncertainty in some regions. Furthermore, this study gives substantial weight to recent climate change impact studies based on the ENSEMBLES projections, since it confirms the robustness of the climate forcing of these studies concerning GCM sampling.     

Changes in frost, snow and Baltic sea ice by the end of the twenty-first century based on climate model projections for Europe

Changes in indices related to frost and snow in Europe by the end of the twenty-first century were analyzed based on experiments performed with seven regional climate models (RCMs). All the RCMs regionalized information from the same general circulation model (GCM), applying the IPCC-SRES A2 radiative forcing scenario. In addition, some simulations used SRES B2 radiative forcing and/or boundary conditions provided by an alternative GCM. Ice cover over the Baltic Sea was examined using a statistical model that related the annual maximum extent of ice to wintertime coastal temperatures. Fewer days with frost and snow, shorter frost seasons, a smaller liquid water equivalent of snow, and milder sea ice conditions were produced by all model simulations, irrespective of the forcing scenario and the driving GCM. The projected changes have implications across a diverse range of human activities. Details of the projections were subject to differences in RCM design, deviations between the boundary conditions of the driving GCMs, uncertainties in future emissions and random effects due to internal climate variability. A larger number of GCMs as drivers of the RCMs would most likely have resulted in somewhat wider ranges in the frost, snow and sea ice estimates than those presented in this paper.     

Evaluating GCM Output with Impact Models

This study uses empirical agricultural impact models to compare the U.S. climate change predictions of 16 General Circulation Models (GCMs). The impact analysis provides a policy-relevant index by which to judge complex climate predictions. National aggregate impacts vary widely across the 16 GCMs because of varying regional and seasonal patterns of predicted climate change. Examining the predicted impacts from the full set of GCMs reveals that the seasonal detail in the GCM predictions is so noisy that it is not significantly different from a constant annual change. However, a consistent regional pattern does emerge across the set of models. Nonetheless, aggregating climate change across seasons and regions within the United States, using a national-annual climate change provides a reasonable and efficient approximation to the expected impact predicted by the 16 GCM models.     

Use of general circulation model output in the creation of climate change scenarios for impact analysis

Many scientific studies warn of a rapid global climate change during the next century. These changes are understood with much less certainty on a regional scale than on a global scale, but effects on ecosystems and society will occur at local and regional scales. Consequently, in order to study the true impacts of climate change, regional scenarios of future climate are needed. One of the most important sources of information for creating scenarios is the output from general circulation models (GCMs) of the climate system. However, current state-of-the-art GCMs are unable to simulate accurately even the current seasonal cycle of climate on a regional basis. Thus the simple technique of adding the difference between 2 × CO₂ and 1 × CO₂ GCM simulations to current climatic time series cannot produce scenarios with appropriate spatial and temporal details without corrections for model deficiencies. In this study a technique is developed to allow the information from GCM simulations to be used, while accommodating for the deficiencies. GCM output is combined with knowledge of the regional climate to produce scenarios of the equilibrium climate response to a doubling of the atmospheric CO₂ concentration for three case study regions, China, Sub-Saharan Africa and Venezuela, for use in biological effects models. By combining the general climate change calculated with several GCMs with the observed patterns of interannual climate variability, reasonable scenarios of temperature and precipitation variations can be created. Generalizations of this procedure to other regions of the world are discussed.     

Analyse régionale des simulations climatiques du modèle canadien de circulation générale de l'atmosphère pour le territoire québécois

Abstract

Current understanding of the possible nature of climatic change at the regional scale is limited by the spatial resolution of General Circulation Models (GCM). The use of GCM outputs without correction linked to the spatial variability of the variables can bring significant errors in their utilization at the regional scale. The potential of the Canadian GCM for regional applications in Quebec has been analysed by comparison to the climatic normals of temperature and precipitation, measured over the Quebec climatological network, on an annual and seasonal basis. This analysis has been undertaken with the support of a geographical information system (GIS) (PAMAP). In summary, a difference between the climatic normal and the GCM output has been estimated at 20% for temperature and 30% for precipitation. We present an analysis of a corrected regionalized scenario for the province of Quebec of the possible climatic change simulated by the Canadian GCM under the hypothesis of a doubling of atmospheric CO₂. Results show an increase of the annual average temperature of 4° C for summer and 6°C for winter, associated with an average increase of 80 mm (10%) in annual precipitation, reaching 25% in some regions.