High resolution regional climate model simulations for Germany: part I—validation

A five-member ensemble of regional climate model (RCM) simulations for Europe, with a high resolution nest over Germany, is analysed in a two-part paper: Part I (the current paper) presents the performance of the models for the control period, and Part II presents results for near future climate changes. Two different RCMs, CLM and WRF, were used to dynamically downscale simulations with the ECHAM5 and CCCma3 global climate models (GCMs), as well as the ERA40-reanalysis for validation purposes. Three realisations of ECHAM5 and one with CCCma3 were downscaled with CLM, and additionally one realisation of ECHAM5 with WRF. An approach of double nesting was used, first to an approximately 50 km resolution for entire Europe and then to a domain of approximately 7 km covering Germany and its near surroundings. Comparisons of the fine nest simulations are made to earlier high resolution simulations for the region with the RCM REMO for two ECHAM5 realisations. Biases from the GCMs are generally carried over to the RCMs, which can then reduce or worsen the biases. The bias of the coarse nest is carried over to the fine nest but does not change in amplitude, i.e. the fine nest does not add additional mean bias to the simulations. The spatial pattern of the wet bias over central Europe is similar for all CLM simulations, and leads to a stronger bias in the fine nest simulations compared to that of WRF and REMO. The wet bias in the CLM model is found to be due to a too frequent drizzle, but for higher intensities the distributions are well simulated with both CLM and WRF at the 50 and 7 km resolutions. Also the spatial distributions are close to high resolution gridded observations. The REMO model has low biases in the domain averages over Germany and no drizzle problem, but has a shift in the mean precipitation patterns and a strong overestimation of higher intensities. The GCMs perform well in simulating the intensity distribution of precipitation at their own resolution, but the RCMs add value to the distributions when compared to observations at the fine nest resolution.     

不同区域气候模式对中国地区温度和降水的长期模拟比较

利用亚洲区域模式比较计划RMIP第二阶段五个区域模式和一个变网格全球模式，对中国地区1988年12月～1998年11月十年模拟的平均温度和降水结果，分析比较了不同区域气候模式对中国地区温度和降水的模拟能力。研究结果表明:几乎所有模式都能模拟出中国地区多年平均温度和降水的基本空间分布形态，但模式模拟的温度普遍偏低，在大部分区域，大多数模式模拟的降水偏多，而且不同模式之间存在较大差别。模式能较好地反映出中国地区温度的年际变化，对夏季降水的年际变化模拟较差，对冬季模拟较好。     

Uncertainties in the regional climate models simulations of South-Asian summer monsoon and climate change

The uncertainties in the regional climate models (RCMs) are evaluated by analyzing the driving global data of ERA40 reanalysis and ECHAM5 general circulation models, and the downscaled data of two RCMs (RegCM4 and PRECIS) over South-Asia for the present day simulation (1971–2000) of South-Asian summer monsoon. The differences between the observational datasets over South-Asia are also analyzed. The spatial and the quantitative analysis over the selected climatic regions of South-Asia for the mean climate and the inter-annual variability of temperature, precipitation and circulation show that the RCMs have systematic biases which are independent from different driving datasets and seems to come from the physics parameterization of the RCMs. The spatial gradients and topographically-induced structure of climate are generally captured and simulated values are within a few degrees of the observed values. The biases in the RCMs are not consistent with the biases in the driving fields and the models show similar spatial patterns after downscaling different global datasets. The annual cycle of temperature and rainfall is well simulated by the RCMs, however the RCMs are not able to capture the inter-annual variability. ECHAM5 is also downscaled for the future (2071–2100) climate under A1B emission scenario. The climate change signal is consistent between ECHAM5 and RCMs. There is warming over all the regions of South-Asia associated with increasing greenhouse gas concentrations and the increase in summer mean surface air temperature by the end of the century ranges from 2.5 to 5 °C, with maximum warming over north western parts of the domain and 30 % increase in rainfall over north eastern India, Bangladesh and Myanmar.     

A regional climate model downscaling projection of China future climate change

Climate changes over China from the present (1990–1999) to future (2046–2055) under the A1FI (fossil fuel intensive) and A1B (balanced) emission scenarios are projected using the Regional Climate Model version 3 (RegCM3) nests with the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM). For the present climate, RegCM3 downscaling corrects several major deficiencies in the driving CCSM, especially the wet and cold biases over the Sichuan Basin. As compared with CCSM, RegCM3 produces systematic higher spatial pattern correlation coefficients with observations for precipitation and surface air temperature except during winter. The projected future precipitation changes differ largely between CCSM and RegCM3, with strong regional and seasonal dependence. The RegCM3 downscaling produces larger regional precipitation trends (both decreases and increases) than the driving CCSM. Contrast to substantial trend differences projected by CCSM, RegCM3 produces similar precipitation spatial patterns under different scenarios except autumn. Surface air temperature is projected to consistently increase by both CCSM and RegCM3, with greater warming under A1FI than A1B. The result demonstrates that different scenarios can induce large uncertainties even with the same RCM-GCM nesting system. Largest temperature increases are projected in the Tibetan Plateau during winter and high-latitude areas in the northern China during summer under both scenarios. This indicates that high elevation and northern regions are more vulnerable to climate change. Notable discrepancies for precipitation and surface air temperature simulated by RegCM3 with the driving conditions of CCSM versus the model for interdisciplinary research on climate under the same A1B scenario further complicated the uncertainty issue. The geographic distributions for precipitation difference among various simulations are very similar between the present and future climate with very high spatial pattern correlation coefficients. The result suggests that the model present climate biases are systematically propagate into the future climate projections. The impacts of the model present biases on projected future trends are, however, highly nonlinear and regional specific, and thus cannot be simply removed by a linear method. A model with more realistic present climate simulations is anticipated to yield future climate projections with higher credibility.     

Impacts of bias correction of lateral boundary conditions on regional climate projections in West Africa

Biases existing in the lateral boundary conditions (LBCs) influence climate simulations in regional climate models (RCMs). Correcting the biases in global climate model (GCM)-produced LBCs before running RCMs was proposed in previous studies as a possible way to reduce the GCM-related model dependence of future climate projections using RCMs. In this study the ICTP Regional Climate Model Version 4 (RegCM4) is used to investigate the impact of LBC bias correction on projected future changes of regional climate in West Africa. To accomplish this, two types of present versus future simulations are conducted using RegCM4: a control type where both the present and future LBCs are derived directly from the GCM output (as is done in most regional climate downscaling studies); an experiment type where the present-day LBCs are from reanalysis data and future LBCs are derived by combining the reanalysis data and the GCM-projected LBC changes. For each type of simulations, three different sets of LBCs are experimented on: 6-hourly synoptic forcing directly from the reanalysis or GCM, 6-hourly data interpolated from monthly climatology (without diurnal cycle), and 6-hourly data interpolated from the month-specific climatology of diurnal cycles. It is found that the simulations using different LBCs produce similar present-day summer rainfall patterns, but the predicted future changes differ significantly depending on how the LBC bias correction is treated. Specifically, both the bias correction applied at the synoptic scale and the bias correction applied to the monthly interpolated LBCs without diurnal cycle produce a spurious drying signal caused by physical inconsistency in the corrected future LBCs. Interpolated monthly LBCs with diurnal cycle alleviate the problem to a large extent. These results suggest that using bias-corrected LBCs to drive regional climate models may not guarantee reliable future projections although reasonable present climate can be simulated. Physical inconsistencies may be contained in the bias-corrected LBCs, increasing the uncertainties of RCM-produced future projections.     

Evaluation of the CORDEX regional climate models performance in simulating climate conditions of two catchments in Upper Blue Nile Basin

This study was targeted at evaluating the performance of six Regional Climate Models (RCMs) used in Coordinated Regional Climate Downscaling Experiment (CORDEX). The evaluation is on the bases of how well the RCMs simulate the seasonal mean climatology, interannual variability and annual cycles of rainfall, maximum and minimum temperature over two catchments in western Ethiopia during the period 1990–2008. Observed data obtained from the Ethiopian National Meteorological Agency was used for performance evaluation of the RCMs outputs. All Regional Climate Models (RCMs) have simulated seasonal mean annual cycles of precipitation with a significant bias shown on individual models; however, the ensemble mean exhibited better the magnitude and seasonal rainfall. Despite the highest biases of RCMs in the wet season, the annual cycle showed the prominent features of precipitation in the two catchments. In many aspects, CRCM5 and RACMO22 T simulate rainfall over most stations better than the other models. The highest biases are associated with the highest error in simulating maximum and minimum temperature with the highest biases in high elevation areas. The rainfall interannual variability is less evident in Finchaa with short rainy season experiencing a larger degree of interannual variability. The differences in performance of the Regional Climate Models in the two catchments show that all the available models are not equally good for particular locations and topographies. In this regard, the right regional climate models have to be used for any climate change impact study for local-scale climate projections.     

Introduction to the Regional Coupled Model WRF4-LICOM: Performance and Model Intercomparison over the Western North Pacific

Regional coupled modeling is one of the frontiers of regional climate modeling, but intercomparison has not been well coordinated. In this study, a community regional climate model, WRF4, with a resolution of 15 km, was coupled with a high-resolution(0.1°) North Pacific Ocean model(LICOM_np). The performance of the regional coupled model,WRF4_LICOM, was compared to that of another regional coupled model, RegCM4_LICOM, which was a coupling of version 4 of the Regional Climate Model(RegCM4) with LICOM_np. The analysis focused on the 2005 western North Pacific summer monsoon rainfall. The results showed that the regional coupled models with either RegCM4 or WRF4 as their atmospheric model component simulated the broad features over the WNP reasonably well. Quantitative intercomparison of the regional coupled simulations exhibited different biases for different climate variables.RegCM4_LICOM exhibited smaller biases in its simulation of the averaged June–July–August SST and rainfall, while WRF4_LICOM better captured the tropical cyclone(TC) intensity, the percentage contributions of rainfall induced by TCs to the total rainfall, and the diurnal cycle of rainfall and stratiform percentages, especially over land areas. The different behaviors in rainfall simulated by the two models were partly ascribed to the behaviors in the simulated western North Pacific subtropical high(WNPSH). The stronger(weaker) WNPSH in WRF4_LICOM(RegCM4_LICOM) was driven by overestimated(underestimated) diabatic heating, which peaked at approximately 450 hPa over the region around the Philippines in association with different condensation–radiation processes. Coupling of WRF4 with LIOCM is a crucial step towards the development of the next generation of regional earth system models at the Chinese Academy of Sciences.     

Global 60?km simulations with CCAM: evaluation over the tropics

A six-member ensemble of 60?km resolution global atmospheric simulations has been performed for studying future climate scenarios of Pacific island nations. The simulations were performed using the CSIRO Conformal Cubic Atmospheric Model (CCAM), driven by bias-corrected sea surface temperatures (SSTs) provided by six Coupled Model Intercomparison Project phase 3 global climate models (GCMs) from the Intergovernmental Panel on Climate Change Fourth Assessment Report for the period 1971–2100. This paper focuses on results for the representation of the current climate in the tropical region, a region where the “cold tongue” problem is apparent in all host GCMs. The SST bias-correction and the fine horizontal resolution employed in the CCAM simulations produce a significant improvement over the host GCMs in the rainfall patterns for the transient seasons March–April–May and September–October–November, and a moderate improvement for December–January–February and June–July–August. CCAM also simulates improved rainfall patterns over the South Pacific Convergence Zone. The performance of other tropical features, such as El Ni?o Southern Oscillation and the Walker circulation, is also evaluated.     

Climate change projections for the Texas High Plains and Rolling Plains

Potential changes in future climate in the Texas Plains region were investigated in the context of agriculture by analyzing three climate model projections under the A2 climate scenario (medium–high emission scenario). Spatially downscaled historic (1971–2000) and future (2041–2070) climate datasets (rainfall and temperature) were downloaded from the North American Regional Climate Change Assessment Program (NARCCAP). Climate variables predicted by three regional climate models (RCMs) namely the Regional Climate Model Version3–Geophysical Fluid Dynamics Laboratory (RCM3-GFDL), Regional Climate Model Version3–Third Generation Coupled Global Climate Model (RCM3-CGCM3), and Canadian Regional Climate Model–Community Climate System Model (CRCM-CCSM) were evaluated in this study. Gaussian and Gamma distribution mapping techniques were employed to remove the bias in temperature and rainfall data, respectively. Both the minimum and maximum temperatures across the study region in the future showed an upward trend, with the temperatures increasing in the range of 1.9 to 2.9 °C and 2.0 to 3.2 °C, respectively. All three climate models predicted a decline in rainfall within a range of 30 to 127 mm in majority of counties across the study region. In addition, they predicted an increase in the intensity of extreme rainfall events in the future. The frost-free season as predicted by the three models showed an increase by 2.6–3.4 weeks across the region, and the number of frost days declined by 17.9 to 30 %. Overall, these projections indicate considerable changes to the climate in the Texas Plains region in the future, and these changes could potentially impact agriculture in this region.

     

Bias Correction and Ensemble Projections of Temperature Changes over Ten Subregions in CORDEX East Asia

Regional climate models (RCMs) participating in the Coordinated Regional Downscaling Experiment (CORDEX) have been widely used for providing detailed climate change information for specific regions under different emissions scenarios. This study assesses the effects of three common bias correction methods and two multi-model averaging methods in calibrating historical (1980?2005) temperature simulations over East Asia. Future (2006?49) temperature trends under the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios are projected based on the optimal bias correction and ensemble averaging method. Results show the following: (1) The driving global climate model and RCMs can capture the spatial pattern of annual average temperature but with cold biases over most regions, especially in the Tibetan Plateau region. (2) All bias correction methods can significantly reduce the simulation biases. The quantile mapping method outperforms other bias correction methods in all RCMs, with a maximum relative decrease in root-mean-square error for five RCMs reaching 59.8% (HadGEM3-RA), 63.2% (MM5), 51.3% (RegCM), 80.7% (YSU-RCM) and 62.0% (WRF). (3) The Bayesian model averaging (BMA) method outperforms the simple multi-model averaging (SMA) method in narrowing the uncertainty of bias-corrected results. For the spatial correlation coefficient, the improvement rate of the BMA method ranges from 2% to 31% over the 10 subregions, when compared with individual RCMs. (4) For temperature projections, the warming is significant, ranging from 1.2°C to 3.5°C across the whole domain under the RCP8.5 scenario. (5) The quantile mapping method reduces the uncertainty over all subregions by between 66% and 94%.     

Downscaling over Vietnam using the stretched-grid CCAM: verification of the mean and interannual variability of rainfall

Rainfall over Vietnam is highly variable from north to south, due to the interaction of the monsoonal winds with the terrain. There is high rainfall from April to September, and little rainfall from October to March (except along the central Vietnam coast). In order to study the ability of the Commonwealth Scientific and Industrial Research Organisation stretched-grid Conformal Cubic Atmospheric Model (CCAM) to capture the climatic and interannual variability of rainfall, downscaled simulations at approximately 20 km horizontal resolution over the region were produced for the period 1979–2001. A scale-selective digital filter was used to force the winds, temperature and sea-level pressure from the ERA-Interim reanalysis for length scales greater than about 700 km. For wind and temperature, the forcing is applied for pressure-sigma levels above about 0.9. ERA-Interim sea surface temperatures were used over the oceans. The simulations were primarily validated against the gridded Asian Precipitation Highly Resolved Observational Data Integration Toward Evaluation of the Water Resources rainfall dataset and station observations using standard statistical methods. It was found that CCAM reproduces well the amount and spatial variability of rainfall, with an area-averaged bias for the entire study domain of less than 1 mm day^?1; CCAM is also able to capture the rainfall pattern under different El Niño Southern Oscillation phases reasonably well for the dry season. For interannual variability, the simulation generally performed better for North and Central Vietnam than for South Vietnam, where rainfall variability was overestimated.     

Validation of precipitation over Japan during 1985–2004 simulated by three regional climate models and two multi-model ensemble means

We dynamically downscaled Japanese reanalysis data (JRA-25) for 60 regions of Japan using three regional climate models (RCMs): the Non-Hydrostatic Regional Climate Model (NHRCM), modified RAMS version 4.3 (NRAMS), and modified Weather Research and Forecasting model (TWRF). We validated their simulations of the precipitation climatology and interannual variations of summer and winter precipitation. We also validated precipitation for two multi-model ensemble means: the arithmetic ensemble mean (AEM) and an ensemble mean weighted according to model reliability. In the 60 regions NRAMS simulated both the winter and summer climatological precipitation better than JRA-25, and NHRCM simulated the wintertime precipitation better than JRA-25. TWRF, however, overestimated precipitation in the 60 regions in both the winter and summer, and NHRCM overestimated precipitation in the summer. The three RCMs simulated interannual variations, particularly summer precipitation, better than JRA-25. AEM simulated both climatological precipitation and interannual variations during the two seasons more realistically than JRA-25 and the three RCMs overall, but the best RCM was often superior to the AEM result. In contrast, the weighted ensemble mean skills were usually superior to those of the best RCM. Thus, both RCMs and multi-model ensemble means, especially multi-model ensemble means weighted according to model reliability, are powerful tools for simulating seasonal and interannual variability of precipitation in Japan under the current climate.     

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.     

The Common Principal Component Analyses of Multi-RCMs

Based on a 10-year simulation of six Regional Climate Models(RCMs) in phase II of the Regional Climate Model Inter-Comparison Project(RMIP) for Asia,the multivariate statistical method of common principal components(CPCs) is used to analyze and compare the spatiotemporal characteristics of temperature and precipitation simulated by multi-RCMs over China,including the mean climate states and their seasonal transition,the spatial distribution of interannual variability,and the interannual variation.CPC is an effective statistical tool for analyzing the results of different models.Compared with traditional statistical methods,CPC analyses provide a more complete statistical picture for observation and simulation results.The results of CPC analyses show that the climatological means and the characteristics of seasonal transition over China can be accurately simulated by RCMs.However,large biases exist in the interannual variation in certain years or for individual models.     

Inter-Comparison of 10-year Precipitation Simulated by Several RCMs for Asia

In phase Ⅱ of the Regional Climate Model Inter-comparison Project (RMIP) for Asia, the regional climate has been simulated for July 1988 through December 1998 by five regional climate models and one global variable resolution model. Comparison of the 10-year simulated precipitation with the observations was carried out. The results show that most models have the capacity to reproduce the basic spatial pattern of precipitation for Asia, and the main rainbelt can be reproduced by most models, but there are distinctions in the location and the intensity. Most models overestimate the precipitation over most continental regions. Interannual variability of the precipitation can also be basically simulated, while differences exist between various models and the observations. The biases in the stream field are important reasons behind the simulation errors of the Regional Climate Models (RCMs). The cumulus scheme and land surface process have large influences on the precipitation simulation. Generally, the Grell cumulus scheme produces more precipitation than the Kuo scheme.     

An evaluation of RegCM3_CERES for regional climate modeling in China

A 20-year simulation of regional climate over East Asia by the regional climate model RegCM3_CERES (Regional Climate Model version 3 coupled with the Crop Estimation through Resource and Environment Synthesis) was carried out and compared with observations and the original RegCM3 model to comprehensively evaluate its performance in simulating the regional climate over continental China. The results showed that RegCM3_CERES reproduced the regional climate at a resolution of 60 km over China by using ERA40 data as the boundary conditions, albeit with some limitations. The model captured the basic characteristics of the East Asian circulation, the spatial distribution of mean precipitation and temperature, and the daily characteristics of precipitation and temperature. However, it underestimated both the intensity of the monsoon in the monsoonal area and precipitation in southern China, overestimated precipitation in northern China, and produced a systematic cold temperature bias over most of continental China. Despite these limitations, it was concluded that the RegCM3_CERES model is able to simulate the regional climate over continental China reasonably well.     

Assessment of Temperature Extremes in China Using RegCM4 and WRF

This study assesses the performance of temperature extremes over China in two regional climate models(RCMs),RegCM4 and WRF, driven by the ECMWF's 20 th century reanalysis. Based on the advice of the Expert Team on Climate Change Detection and Indices(ETCCDI), 12 extreme temperature indices(i.e., TXx, TXn, TNx, TNn, TX90 p, TN90 p,TX10 p, TN10 p WSDI, ID, FD, and CSDI) are derived from the simulations of two RCMs and compared with those from the daily station-based observational data for the period 1981–2010. Overall, the two RCMs demonstrate satisfactory capability in representing the spatiotemporal distribution of the extreme indices over most regions. RegCM performs better than WRF in reproducing the mean temperature extremes, especially over the Tibetan Plateau(TP). Moreover, both models capture well the decreasing trends in ID, FD, CSDI, TX10 p, and TN10 p, and the increasing trends in TXx, TXn, TNx, TNn, WSDI, TX90 p,and TN90 p, over China. Compared with observation, RegCM tends to underestimate the trends of temperature extremes,while WRF tends to overestimate them over the TP. For instance, the linear trends of TXx over the TP from observation,RegCM, and WRF are 0.53?C(10 yr)^-1, 0.44?C(10 yr)^-1, and 0.75?C(10 yr)^-1, respectively. However, WRF performs better than RegCM in reproducing the interannual variability of the extreme-temperature indices. Our findings are helpful towards improving our understanding of the physical realism of RCMs in terms of different time scales, thus enabling us in future work to address the sources of model biases.     

U.S. Climate Sensitivity Simulated with the NCEP Regional Spectral Model

10-year continuous U.S. climate simulations were conducted with the Regional Spectral Model (RSM) using boundary conditions from the National Centers for Environmental Prediction/Dept. of Energy reanalyses and the global PCM (Parallel Climate Model) simulations for present day (1986–1996) andfuture (2040–2050) CO₂ concentrations (about a 36% increasedCO₂). In order to examine the influence of physical parameterization differences as well as grid-resolution, fine resolution RSM simulations (50 km) were compared to coarse resolution (180 and 250 km) RSM simulations, which had resolutions comparable to the T62 reanalysis and PCM simulations. During the winter, the fine resolution RSM simulations provided more realistic detail over the western mountains. During the summer, large differences between the RSM and driving PCM simulations were found. Our results with presentCO₂ suggest that most of the differences between the regionalclimate model simulations and the climate simulations driven by the global model used to drive the regional climate model were not due to the finer resolution of the regional climate model but to the different treatment of the physical processes in the two models, especially when the subgrid scale physics was important, like during summer. Compared to the coarse resolution RSM simulation results, on the other hand, the fine resolution RSM simulations did show improved simulation skills especially when a good boundary condition such as the reanalysis was used to drive the RSM. Under increased CO₂, the driving PCM and downscaled RSM simulations exhibitedwarming over all vertical layers and all regions. Both the RSM and PCM had increased precipitation during the winter, but during the summer, the PCM simulation had an overall precipitation increase mainly due to increased subgrid scale convective activity, whereas the RSM simulations exhibited precipitation decreases and the resulting RSM soil moisture became dryer, especially in the U.S. Southwest. Most of differences in the simulated climate change signals were produced by the distinct model physics rather than by differences in grid resolution.     

CCSM4.0模式及其驱动下RegCM4.4模式对中国夏季气候的回报分析

使用区域气候模式Reg CM4.4(Regional Climate Model version 4.4)单向嵌套CCSM4.0(Community Climate System Model version 4.0)气候系统模式输出结果,进行了2001~2010年逐年2月1日至9月1日共10年长度的季节尺度气候预测回报试验,针对平均气温和降水,分析了两个模式对中国地区夏季(6~8月)气候的回报能力。首先对气候态的分析表明,Reg CM4.4对气温和降水的回报/模拟效果均较CCSM4.0有所改进,特别是在提供更详细可靠的局地信息方面,其中降水回报与观测的空间相关系数,由CCSM4.0的0.39提高到Reg CM4.4的0.53,但同时Reg CM4.4对中国东部季风降水的回报表现出类似CCSM4.0北方偏多的偏差。对两个模式2001~2010年逐年气温和降水距平的回报能力,通过回报与观测空间和时间距平相关系数(ACCs和ACCt)、回报与观测空间和时间距平符号一致率(PCs和PCt)以及趋势异常综合评分(PS)进行了考察,结果表明两个模式的表现在整体分布上有一定相似的同时,Reg CM4.4能够提供更多的空间分布细节,并对降水的回报结果有一定的改善,如CCSM4.0和Reg CM4.4回报降水的ACCs多年平均分别为0.03和0.10,PS分别为70.4和71.4。同时给出了两个具体年份(2003年和2009年)的个例分析。     

Comparisons of Low-Level Circulation Characteristics between ECHAM5/MPI-OM Results and NCEP/NCAR Re-Analysis Data in East Asia

Regional climate models(RCMs) can provide far more precise information than general circulation models(GCMs).However,RCMs depend on GCM results or re-analysis products providing boundary conditions,especially for future climate scenarios.Meanwhile,the capacity of RCMs to reproduce precipitation is strongly connected to its performance on circulation and moisture transport simulations in the low troposphere,which is the key problem with RCMs at present.In the Regional Climate Model Inter-comparison Project for East Asia(RMIP III),the results of ECHAM5/MPI-OM(the European Centre-Hamburg model version 5/Max Planck Institute Ocean Model,simplified as E5OM here) are used to drive RCMs for the past(1978?2000) climate simulation and future(2038?70) climate scenarios.Therefore,it is necessary to test E5OM’s ability to represent atmospheric circulation,which defines the large-scale circulation for RCMs.Here,comparisons between the E5OM results and NCEP/NCAR(simplified as NCEP) re-analysis data in the low troposphere for the years 1978 to 2000 are performed.The results show that E5OM results can generally reproduce atmospheric circulations in the low troposphere.However,differences can be detected in East Asian summer and winter monsoon simulations.For summer,there is an anti-cyclone circulation for the difference of wind vector at 850 hPa in Southeast China,the Indo-China Peninsula,the South China Sea,and the northwestern Pacific.For winter,due to the weaker northwesterly wind in Northeast Asia,the northeasterly wind from the Indo-China Peninsula to Taiwan in E5OM extends northward with greater intensity than that in NCEP.These differences will have a considerable influence on the low level atmospheric circulation and water vapor transport as well as the location and intensity of the precipitation.Therefore,when E5OM results are to be used as initial and boundary conditions to drive RCMs,the differences between NCEP and E5OM should be considered.