A multi-model ensemble of downscaled spatial climate change scenarios for the Dommel catchment, Western Europe

Regional or local scale hydrological impact studies require high resolution climate change scenarios which should incorporate some assessment of uncertainties in future climate projections. This paper describes a method used to produce a multi-model ensemble of multivariate weather simulations including spatial–temporal rainfall scenarios and single-site temperature and potential evapotranspiration scenarios for hydrological impact assessment in the Dommel catchment (1,350 km²) in The Netherlands and Belgium. A multi-site stochastic rainfall model combined with a rainfall conditioned weather generator have been used for the first time with the change factor approach to downscale projections of change derived from eight Regional Climate Model (RCM) experiments for the SRES A2 emission scenario for the period 2071–2100. For winter, all downscaled scenarios show an increase in mean daily precipitation (catchment average change of +9% to +40%) and typically an increase in the proportion of wet days, while for summer a decrease in mean daily precipitation (−16% to −57%) and proportion of wet days is projected. The range of projected mean temperature is 7.7°C to 9.1°C for winter and 19.9°C to 23.3°C for summer, relative to means for the control period (1961–1990) of 3.8°C and 16.8°C, respectively. Mean annual potential evapotranspiration is projected to increase by between +17% and +36%. The magnitude and seasonal distribution of changes in the downscaled climate change projections are strongly influenced by the General Circulation Model (GCM) providing boundary conditions for the RCM experiments. Therefore, a multi-model ensemble of climate change scenarios based on different RCMs and GCMs provides more robust estimates of precipitation, temperature and evapotranspiration for hydrological impact assessments, at both regional and local scale.     

GCM-based regional temperature and precipitation change estimates for Europe under four SRES scenarios applying a super-ensemble pattern-scaling method

Seasonal GCM-based temperature and precipitation projections for the end of the 21st century are presented for five European regions; projections are compared with corresponding estimates given by the PRUDENCE RCMs. For most of the six global GCMs studied, only responses to the SRES A2 and B2 forcing scenarios are available. To formulate projections for the A1FI and B1 forcing scenarios, a super-ensemble pattern-scaling technique has been developed. This method uses linear regression to represent the relationship between the local GCM-simulated response and the global mean temperature change simulated by a simple climate model. The method has several advantages: e.g., the noise caused by internal variability is reduced, and the information provided by GCM runs performed with various forcing scenarios is utilized effectively. The super-ensemble method proved especially useful when only one A2 and one B2 simulation is available for an individual GCM. Next, 95% probability intervals were constructed for regional temperature and precipitation change, separately for the four forcing scenarios, by fitting a normal distribution to the set of projections calculated by the GCMs. For the high-end of the A1FI uncertainty interval, temperature increases close to 10°C could be expected in the southern European summer and northern European winter. Conversely, the low-end warming estimates for the B1 scenario are ~ 1°C. The uncertainty intervals of precipitation change are quite broad, but the mean estimate is one of a marked increase in the north in winter and a drastic reduction in the south in summer. In the RCM simulations driven by a single global model, the spread of the temperature and precipitation projections tends to be smaller than that in the GCM simulations, but it is possible to reduce this disparity by employing several driving models for all RCMs. In the present suite of simulations, the difference between the mean GCM and RCM projections is fairly small, regardless of the number or driving models applied.     

Improved regional scale processes reflected in projected hydrological changes over large European catchments

For the fourth assessment report of the Intergovernmental Panel on Climate Change (IPCC), the recent version of the coupled atmosphere/ocean general circulation model (GCM) of the Max Planck Institute for Meteorology has been used to conduct an ensemble of transient climate simulations These simulations comprise three control simulations for the past century covering the period 1860–2000, and nine simulations for the future climate (2001–2100) using greenhouse gas (GHG) and aerosol concentrations according to the three IPCC scenarios B1, A1B and A2. For each scenario three simulations were performed. The global simulations were dynamically downscaled over Europe using the regional climate model (RCM) REMO at 0.44° horizontal resolution (about 50 km), whereas the physics packages of the GCM and RCM largely agree. The regional simulations comprise the three control simulations (1950–2000), the three A1B simulations and one simulation for B1 as well as for A2 (2001–2100). In our study we concentrate on the climate change signals in the hydrological cycle and the 2 m temperature by comparing the mean projected climate at the end of the twenty-first century (2071–2100) to a control period representing current climate (1961–1990). The robustness of the climate change signal projected by the GCM and RCM is analysed focussing on the large European catchments of Baltic Sea (land only), Danube and Rhine. In this respect, a robust climate change signal designates a projected change that sticks out of the noise of natural climate variability. Catchments and seasons are identified where the climate change signal in the components of the hydrological cycle is robust, and where this signal has a larger uncertainty. Notable differences in the robustness of the climate change signals between the GCM and RCM simulations are related to a stronger warming projected by the GCM in the winter over the Baltic Sea catchment and in the summer over the Danube and Rhine catchments. Our results indicate that the main explanation for these differences is that the finer resolution of the RCM leads to a better representation of local scale processes at the surface that feed back to the atmosphere, i.e. an improved representation of the land sea contrast and related moisture transport processes over the Baltic Sea catchment, and an improved representation of soil moisture feedbacks to the atmosphere over the Danube and Rhine catchments.     

High resolution regional climate model simulations for Germany: Part II—projected climate changes

The projected climate change signals of a five-member high resolution ensemble, based on two global climate models (GCMs: ECHAM5 and CCCma3) and two regional climate models (RCMs: CLM and WRF) are analysed in this paper (Part II of a two part paper). In Part I the performance of the models for the control period are presented. The RCMs use a two nest procedure over Europe and Germany with a final spatial resolution of 7 km to downscale the GCM simulations for the present (1971–2000) and future A1B scenario (2021–2050) time periods. The ensemble was extended by earlier simulations with the RCM REMO (driven by ECHAM5, two realisations) at a slightly coarser resolution. The climate change signals are evaluated and tested for significance for mean values and the seasonal cycles of temperature and precipitation, as well as for the intensity distribution of precipitation and the numbers of dry days and dry periods. All GCMs project a significant warming over Europe on seasonal and annual scales and the projected warming of the GCMs is retained in both nests of the RCMs, however, with added small variations. The mean warming over Germany of all ensemble members for the fine nest is in the range of 0.8 and 1.3 K with an average of 1.1 K. For mean annual precipitation the climate change signal varies in the range of ?2 to 9 % over Germany within the ensemble. Changes in the number of wet days are projected in the range of ±4 % on the annual scale for the future time period. For the probability distribution of precipitation intensity, a decrease of lower intensities and an increase of moderate and higher intensities is projected by most ensemble members. For the mean values, the results indicate that the projected temperature change signal is caused mainly by the GCM and its initial condition (realisation), with little impact from the RCM. For precipitation, in addition, the RCM affects the climate change signal significantly.     

区域和全球模式的嵌套技术 及其长期积分试验

将区域模式嵌入澳大利亚CSIRO (Commonwealth Scientific and Industrial Research Organization）的全球模式中,并将其应用于区域模式的长期气候积分试验。模拟结果表明,当区域与全球模式嵌套时,边界吸收问题十分重要,由区域模式得到的高分辨率大尺度环流形式在边界上必须与全球模式提供的强迫一致,同时区域模式必须给出基于模式内部物理过程产生的高分辨信息。因此,在嵌套过程中,必须仔细考虑缓冲区的设置,使大尺度强迫与中尺度特征充分混合,既保持区域模式内外的一致性,又使区域内部中尺度强迫物理过程得到充分发展。将区域模式与澳大利亚CSIRO的9层21波三角形截断谱模式嵌套后,完成了连续3年的区域气候模式积分。模拟结果表明,由于区域模式较好地刻划了区域尺度的地形、下垫面和海岸线分布等的细节特征,模拟的区域气候特征比全球模式有较大的改进,尤其是对季风降水的模拟,区域模式明显改进了全球模式的模拟结果。     

Gradient in the climate change signal of European discharge predicted by a multi-model ensemble

In order to perform hydrological studies on the PRUDENCE regional climate model (RCM) simulations, a special focus was put on the discharge from large river catchments located in northern and central Europe. The discharge was simulated with a simplified land surface (SL) scheme and the Hydrological Discharge (HD) model. The daily fields of precipitation, 2 m temperature and evapotranspiration from the RCM simulations were used as forcing. Therefore the total catchment water balances are constrained by the hydrological cycle of the different RCMs. The validation of the simulated hydrological cycle from the control simulations shows that the multi-model ensemble mean is closer to the observations than each of the models, especially if different catchments and hydrological variables are considered. Therefore, the multi-model ensemble mean can be used to largely reduce the uncertainty that is introduced by a single RCM. This also provides more confidence in the future projections for the multi-model ensemble means. The scenario simulations predict a gradient in the climate change signal over Northern and Central Europe. Common features are the overall warming and the general increase of evapotranspiration. But while in the northern parts the warming will enhance the hydrological cycle leading to an increased discharge, the large warming, especially in the summer, will slow down the hydrological cycle caused by a drying in the central parts of Europe which is accompanied by a reduction of discharge. The comparison of the changes predicted by the multi-model ensemble mean to the changes predicted by the driving GCM indicates that the RCMs can compensate problems that a driving GCM may have with local scale processes or parameterizations.     

A simple soil moisture scheme for regional climate simulations in the tropics

Summary A regional climate model (RCM) is described which incorporates an improved scheme for soil moisture availability (SMA) compared to an earlier version. The improvement introduces a sensitivity of SMA to soil type, vegetation cover and ground albedo, making the model more adaptable to divers regions. In addition, the interactive SMA depends on past precipitation, ground temperature and terrain relief. Six RCM simulations of the monthly mean climate over southern Africa are performed at 0.5° grid spacing. Improvements in the RCM climate simulations compared to control runs are attributed to the newer SMA scheme. Only a slight improvement in skill results from driving the RCM with observational analyses as opposed to GCM “predicted” lateral boundary conditions. The high spatial resolution of the RCM provides a distinct advantage in the simulated spatial distribution of precipitation compared with a global model run at an effective grid spacing of 2.8°. The mesoscale precipitation signal in the RCM simulations is more dominant during the rather dry December 1982 than during December 1988. The improved SMA scheme contributed to a realistic partition between latent and sensible heat fluxes at the ground-atmosphere boundary and consequently a realistic diurnal cycle of ground temperature. Simulated differences in the spatial distribution of rainfall between December 1982 and December 1988 are more realistic with the improved scheme. Received June 28, 2001 Revised August 27, 2001     

Climate simulation of the twenty-first century with interactive land-use changes

To include land-use dynamics in a general circulation model (GCM), the physical system has to be linked to a system that represents socio-economy. This issue is addressed by coupling an integrated assessment model, IMAGE2.2, to an ocean–atmosphere GCM, CNRM-CM3. In the new system, IMAGE2.2 provides CNRM-CM3 with all the external forcings that are scenario dependent: greenhouse gas (GHGs) concentrations, sulfate aerosols charge and land cover. Conversely, the GCM gives IMAGE changes in mean temperature and precipitation. With this new system, we have run an adapted scenario of the IPCC SRES scenario family. We have chosen a single scenario with maximum land-use changes (SRES A2), to illustrate some important feedback issues. Even in this two-way coupled model set-up, land use in this scenario is mainly driven by demographic and agricultural practices, which overpowers a potential influence of climate feedbacks on land-use patterns. This suggests that for scenarios in which socio-economically driven land-use change is very large, land-use changes can be incorporated in GCM simulations as a one-way driving force, without taking into account climate feedbacks. The dynamics of natural vegetation is more closely linked to climate but the time-scale of changes is of the order of a century. Thus, the coupling between natural vegetation and climate could generate important feedbacks but these effects are relevant mainly for multi-centennial simulations.     

Climate change in the northeastern US: regional climate model validation and climate change projections

A high resolution regional climate model (RCM) is used to simulate climate of the recent past and to project future climate change across the northeastern US. Different types of uncertainties in climate simulations are examined by driving the RCM with different boundary data, applying different emissions scenarios, and running an ensemble of simulations with different initial conditions. Empirical orthogonal functions analysis and K-means clustering analysis are applied to divide the northeastern US region into four climatologically different zones based on the surface air temperature (SAT) and precipitation variability. The RCM simulations tend to overestimate SAT, especially over the northern part of the domain in winter and over the western part in summer. Statistically significant increases in seasonal SAT under both higher and lower emissions scenarios over the whole RCM domain suggest the robustness of future warming. Most parts of the northeastern US region will experience increasing winter precipitation and decreasing summer precipitation, though the changes are not statistically significant. The greater magnitude of the projected temperature increase by the end of the twenty-first century under the higher emissions scenario emphasizes the essential role of emissions choices in determining the potential future climate change.     

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.     

Uncertainties in projected impacts of climate change on European agriculture and terrestrial ecosystems based on scenarios from regional climate models

The uncertainties and sources of variation in projected impacts of climate change on agriculture and terrestrial ecosystems depend not only on the emission scenarios and climate models used for projecting future climates, but also on the impact models used, and the local soil and climatic conditions of the managed or unmanaged ecosystems under study. We addressed these uncertainties by applying different impact models at site, regional and continental scales, and by separating the variation in simulated relative changes in ecosystem performance into the different sources of uncertainty and variation using analyses of variance. The crop and ecosystem models used output from a range of global and regional climate models (GCMs and RCMs) projecting climate change over Europe between 1961–1990 and 2071–2100 under the IPCC SRES scenarios. The projected impacts on productivity of crops and ecosystems included the direct effects of increased CO₂ concentration on photosynthesis. The variation in simulated results attributed to differences between the climate models were, in all cases, smaller than the variation attributed to either emission scenarios or local conditions. The methods used for applying the climate model outputs played a larger role than the choice of the GCM or RCM. The thermal suitability for grain maize cultivation in Europe was estimated to expand by 30–50% across all SRES emissions scenarios. Strong increases in net primary productivity (NPP) (35–54%) were projected in northern European ecosystems as a result of a longer growing season and higher CO₂ concentrations. Changing water balance dominated the projected responses of southern European ecosystems, with NPP declining or increasing only slightly relative to present-day conditions. Both site and continental scale models showed large increases in yield of rain-fed winter wheat for northern Europe, with smaller increases or even decreases in southern Europe. Site-based, regional and continental scale models showed large spatial variations in the response of nitrate leaching from winter wheat cultivation to projected climate change due to strong interactions with soils and climate. The variation in simulated impacts was smaller between scenarios based on RCMs nested within the same GCM than between scenarios based on different GCMs or between emission scenarios.     

Simulating future Caspian sea level changes using regional climate model outputs

We report on simulations of present-day climate (1961–1990) and future climate conditions (2071–2100, Special Report on Emissions Scenario A2) over the Caspian sea basin with a regional climate model (RCM) nested in time-slice general circulation model (GCM) simulations. We also calculate changes (A2 scenario minus present-day) in Caspian sea level (CSL) in response to changes in the simulated hydrologic budget of the basin. For the present-day run, both the GCM and RCM show a good performance in reproducing the water budget of the basin and the magnitude of multi-decadal changes in CSL. Compared to present-day climate, in the A2 scenario experiment we find an increase in cold season precipitation and an increase in temperature and evaporation, both over land and over the Caspian sea. We also find a large decrease of CSL in the A2 scenario run compared to the present-day run. This is due to increased evaporation loss from the basin (particularly over the sea) exceeding increased cold season precipitation over the basin. Our results suggest that the CSL might undergo large changes under future climate change, leading to potentially devastating consequences for the economy and environment of the region.     

Future evolution of extreme precipitation in the Mediterranean

Mediterranean basins can be impacted by severe floods caused by extreme rainfall, and there is a growing awareness about the possible increase in these heavy rainfall events due to climate change. In this study, the climate change impacts on extreme daily precipitation in 102 catchments covering the whole Mediterranean basin are investigated using nonstationary extreme value model applied to annual maximum precipitation in an ensemble of high-resolution regional climate model (RCM) simulations from the Euro-CORDEX experiment. Results indicate contrasted trends, with significant increasing trends in Northern catchments and conversely decreasing trends in Southern catchments. For most cases, the time of signal emergence for these trends is before the year 2000. The same spatial pattern is obtained under the two climate scenarios considered (RCP4.5 and RCP8.5) and in most RCM simulations, suggesting a robust climate change signal. The strongest multi-model agreement concerns the positive trends, which can exceed +?20% by the end of the twenty-first century in some simulations, impacting South France, North Italy, and the Balkans. For these areas, society-relevant strong impacts of such Mediterranean extreme precipitation changes could be expected in particular concerning flood-related damages.     

On interpreting hydrological change from regional climate models

Although representation of hydrology is included in all regional climate models (RCMs), the utility of hydrological results from RCMs varies considerably from model to model. Studies to evaluate and compare the hydrological components of a suite of RCMs and their use in assessing hydrological impacts from future climate change were carried out over Europe. This included using different methods to transfer RCM runoff directly to river discharge and coupling different RCMs to offline hydrological models using different methods to transfer the climate change signal between models. The work focused on drainage areas to the Baltic Basin, the Bothnian Bay Basin and the Rhine Basin. A total of 20 anthropogenic climate change scenario simulations from 11 different RCMs were used. One conclusion is that choice of GCM (global climate model) has a larger impact on projected hydrological change than either selection of emissions scenario or RCM used for downscaling.     

Climate change projections over India by a downscaling approach using PRECIS

This study presents a comprehensive assessment of the possible regional climate change over India by using Providing REgional Climates for Impacts Studies (PRECIS), a regional climate model (RCM) developed by Met Office Hadley Centre in the United Kingdom. The lateral boundary data for the simulations were taken from a sub-set of six members sampled from the Hadley Centre’s 17- member Quantified Uncertainty in Model Projections (QUMP) perturbed physics ensemble. The model was run with 25 km × 25 km resolution from the global climate model (GCM) - HadCM3Q at the emission rate of special report on emission scenarios (SRES) A1B scenarios. Based on the model performance, six member ensembles running over a period of 1970-2100 in each experiment were utilized to predict possible range of variations in the future projections for the periods 2020s (2005-2035), 2050s (2035-2065) and 2080s (2065-2095) with respect to the baseline period (1975-2005). The analyses concentrated on maximum temperature, minimum temperature and rainfall over the region. For the whole India, the projections of maximum temperature from all the six models showed an increase within the range 2.5°C to 4.4°C by end of the century with respect to the present day climate simulations. The annual rainfall projections from all the six models indicated a general increase in rainfall being within the range 15-24%. Mann-Kendall trend test was run on time series data of temperatures and rainfall for the whole India and the results from some of the ensemble members indicated significant increasing trends. Such high resolution climate change information may be useful for the researchers to study the future impacts of climate change in terms of extreme events like floods and droughts and formulate various adaptation strategies for the society to cope with future climate change.     

The WAMME regional model intercomparison study

Results from five regional climate models (RCMs) participating in the West African Monsoon Modeling and Evaluation (WAMME) initiative are analyzed. The RCMs were driven by boundary conditions from National Center for Environmental Prediction reanalysis II data sets and observed sea-surface temperatures (SST) over four May–October seasons, (2000 and 2003–2005). In addition, the simulations were repeated with two of the RCMs, except that lateral boundary conditions were derived from a continuous global climate model (GCM) simulation forced with observed SST data. RCM and GCM simulations of precipitation, surface air temperature and circulation are compared to each other and to observational evidence. Results demonstrate a range of RCM skill in representing the mean summer climate and the timing of monsoon onset. Four of the five models generate positive precipitation biases and all simulate negative surface air temperature biases over broad areas. RCM spatial patterns of June–September mean precipitation over the Sahel achieve spatial correlations with observational analyses of about 0.90, but within two areas south of 10°N the correlations average only about 0.44. The mean spatial correlation coefficient between RCM and observed surface air temperature over West Africa is 0.88. RCMs show a range of skill in simulating seasonal mean zonal wind and meridional moisture advection and two RCMs overestimate moisture convergence over West Africa. The 0.5° computing grid enables three RCMs to detect local minima related to high topography in seasonal mean meridional moisture advection. Sensitivity to lateral boundary conditions differs between the two RCMs for which this was assessed. The benefits of dynamic downscaling the GCM seasonal climate prediction are analyzed and discussed.     

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.     

The spread amongst ENSEMBLES regional scenarios: regional climate models, driving general circulation models and interannual variability

Various combinations of thirteen regional climate models (RCM) and six general circulation models (GCM) were used in FP6-ENSEMBLES. The response to the SRES-A1B greenhouse gas concentration scenario over Europe, calculated as the difference between the 2021–2050 and the 1961–1990 means can be viewed as an expected value about which various uncertainties exist. Uncertainties are measured here by variance explained for temperature and precipitation changes over eight European sub-areas. Three sources of uncertainty can be evaluated from the ENSEMBLES database. Sampling uncertainty is due to the fact that the model climate is estimated as an average over a finite number of years (30) despite a non-negligible interannual variability. Regional model uncertainty is due to the fact that the RCMs use different techniques to discretize the equations and to represent sub-grid effects. Global model uncertainty is due to the fact that the RCMs have been driven by different GCMs. Two methods are presented to fill the many empty cells of the ENSEMBLES RCM?×?GCM matrix. The first one is based on the same approach as in FP5-PRUDENCE. The second one uses the concept of weather regimes to attempt to separate the contribution of the GCM and the RCM. The variance of the climate response is analyzed with respect to the contribution of the GCM and the RCM. The two filling methods agree that the main contributor to the spread is the choice of the GCM, except for summer precipitation where the choice of the RCM dominates the uncertainty. Of course the implication of the GCM to the spread varies with the region, being maximum in the South-western part of Europe, whereas the continental parts are more sensitive to the choice of the RCM. The third cause of spread is systematically the interannual variability. The total uncertainty about temperature is not large enough to mask the 2021–2050 response which shows a similar pattern to the one obtained for 2071–2100 in PRUDENCE. The uncertainty about precipitation prevents any quantitative assessment on the response at grid point level for the 2021–2050 period. One can however see, as in PRUDENCE, a positive response in winter (more rain in the scenario than in the reference) in northern Europe and a negative summer response in southern Europe.     

SRES A2情景下未来30年我国东部夏季降水变化趋势

采用与全球海气耦合模式 (NCC/IAPT63) 嵌套的区域气候模式 (RegCM2_NCC), 对东亚区域进行了30年的气候积分 (1961—1990年), 作为控制试验的气候背景场, 在此基础上, 在IPCC第三次评估报告SRES排放情景A2下对我国未来30年 (2001—2030年) 的气候变化趋势进行了预估, 重点分析了我国东部季风区夏季降水的变化趋势及区域特征。结果显示:未来30年夏季平均降水量在北部地区呈现增加的趋势, 以降水量距平代表的夏季主要雨带转到长江以北地区, 且北方地区降水量增加主要以对流性降水量增加为主, 长江以南地区降水量有所减少, 特别是华南地区降水量减少较为明显, 据此预测结果, 未来30年华北地区夏季干旱可能有所缓解。未来30年夏季低层空气湿度也将发生明显变化, 主要表现为中高纬度地区湿度增大, 较低纬度地区湿度减小, 东亚夏季风有所增强, 特别是西南气流明显加强, 有利于暖湿空气向北方地区输送。由于预估结果的可信度取决于全球模式和区域模式的模拟性能以及温室气体排放浓度的准确性, 因此还需要更多的试验及进一步的综合比较, 以减少未来气候变化趋势预估的不确定性。     

Seasonal to yearly assessment of temperature and precipitation trends in the North Western Mediterranean Basin by dynamical downscaling of climate scenarios at high resolution (1971–2050)

The complex topography and high climatic variability of the North Western Mediterranean Basin (NWMB) require a detailed assessment of climate change projections at high resolution. ECHAM5/MPIOM global climate projections for mid-21st century and three different emission scenarios are downscaled at 10 km resolution over the NWMB, using the WRF-ARW regional model. High resolution improves the spatial distribution of temperature and precipitation climatologies, with Pearson's correlation against observation being higher for WRF-ARW (0.98 for temperature and 0.81 for precipitation) when compared to the ERA40 reanalysis (0.69 and 0.53, respectively). However, downscaled results slightly underestimate mean temperature (≈1.3 K) and overestimate the precipitation field (≈400 mm/year). Temperature is expected to raise in the NWMB in all considered scenarios (up to 1.4 K for the annual mean), and particularly during summertime and at high altitude areas. Annual mean precipitation is likely to decrease (around ?5 % to ?13 % for the most extreme scenarios). The climate signal for seasonal precipitation is not so clear, as it is highly influenced by the driving GCM simulation. All scenarios suggest statistically significant decreases of precipitation for mountain ranges in winter and autumn. High resolution simulations of regional climate are potentially useful to decision makers. Nevertheless, uncertainties related to seasonal precipitation projections still persist and have to be addressed.