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。     

对流允许尺度区域气候模拟的研究进展

与以往的区域气候模式相比,对流允许区域气候模式不再依赖于对流参数化方案,其精细的分辨率可以显式表示深对流过程,在夏季对流降水的日变化和极端降水事件模拟等方面具有明显增值能力,是区域气候模拟的发展方向。对现有的对流允许尺度区域气候模拟研究进行了较为详细的回顾和介绍,简述了对流允许尺度区域气候模式中比较重要的物理过程及外部驱动条件的影响,总结了以往对流允许尺度区域气候模拟的研究成果以及当下所面临的挑战和对未来的展望,以期对中国及东亚区域对流允许区域气候模拟的研究提供有益参考。诸多研究表明,对流允许区域气候模拟作为一种有前景的气候模式,可提供更加可靠的区域尺度的气候信息。     

区域气候模式RegCM_NCC在东北地区的应用研究

引进国家气候中心业务化的区域气候模式RegCM_NCC,通过操作系统调试、模拟区域确定、模式分辨率调整等本地化工作,初步建立了针对东北地区的区域气候模式系统,并应用该模式以夏季为例,对东北地区的气候进行了15 a（1991—2005年）时间长度的数值积分试验。结果表明：模式对环流的特征和东北地区地面气温具有一定的模拟能力,对气温模拟存在系统性的暖偏差,对降水模拟能力较差。     

Performance of Regional Integrated Environment Modeling System (RIEMS) in precipitation simulations over East Asia

Regional climate models (RCMs) can provide much more precise information on surface characteristics and mesoscale circulation than general circulation models. This potential for obtaining more detailed model results has motivated to a significant focus on RCMs development in East Asia. The Regional Integrated Environment Modeling System, version 2.0 (RIEMS2.0) has been developed from an earlier RCM, RIEMS1.0, at the Key Laboratory of Regional Climate-Environment for East Asia and Nanjing University. To test the ability of RIEMS2.0 to simulate long-term climate and climate changes in East Asia and to provide a basis for further development and applications, we compare simulated precipitation from 1979 to 2008 (simulation duration from 1 January 1978 to 31 December 2008) to observed meteorological data. The results show that RIEMS2.0 reproduces the spatial distribution of precipitation in East Asia but that the simulation overestimates precipitation. The simulated 30-year precipitation average is 26 % greater than the observed precipitation. Simulated upper and root soil water correlate well with remote sensing derived soil moisture. Annual and interannual variation in the average precipitation and their anomalies are both well reproduced by the model. A further analysis of three subregions representing different latitude ranges shows that there is good correlation and consistency between the simulated results and the observed data. Annual variation, interannual variation of average precipitation, and the anomalies in the three sub-regions are also well captured by the model. The model’s performance on atmospheric circulation and moisture transport simulations is discussed to explore the bias between the simulation and observations. In summary, RIEMS2.0 shows stability and does well in both simulating long-term climate and climate changes in East Asia and in describing subregional characteristics.     

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.     

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.     

Comparison of four ensemble methods combining regional climate simulations over Asia

A number of uncertainties exist in climate simulation because the results of climate models are influenced by factors such as their dynamic framework, physical processes, initial and driving fields, and horizontal and vertical resolution. The uncertainties of the model results may be reduced, and the credibility can be improved by employing multi-model ensembles. In this paper, multi-model ensemble results using 10-year simulations of five regional climate models (RCMs) from December 1988 to November 1998 over Asia are presented and compared. The simulation results are derived from phase II of the Regional Climate Model Inter-comparison Project (RMIP) for Asia. Using the methods of the arithmetic mean, the weighted mean, multivariate linear regression, and singular value decomposition, the ensembles for temperature, precipitation, and sea level pressure are carried out. The results show that the multi-RCM ensembles outperform the single RCMs in many aspects. Among the four ensemble methods used, the multivariate linear regression, based on the minimization of the root mean square errors, significantly improved the ensemble results. With regard to the spatial distribution of the mean climate, the ensemble result for temperature was better than that for precipitation. With an increasing number of models used in the ensembles, the ensemble results were more accurate. Therefore, a multi-model ensemble is an efficient approach to improve the results of regional climate simulations.     

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.     

Multiyear hindcast simulations of summer monsoon over South Asia using a nested regional climate model��BCC_RegCM1.0

Multiyear (1983?C2006) hindcast simulation of summer monsoon over South Asia has been carried out using the regional climate model of the Beijing Climate Centre (BCC_RegCM1.0). The regional climate model (hereafter BCC RCM) is nested into the global climate model of the Beijing Climate Centre BCC_CGCM1.0 (here after CGCM). The regional climate model is initialized on 01 May and integrated up to the end of the September for 24?years. Compared to the driving CGCM the BCC RCM reproduces reasonably well the intensity and magnitude of the large-scale features associated with the South Asia summer monsoon such as the upper level anticyclone at 200?hPa, the mid-tropospheric warming over the Tibetan plateau, the surface heat low and the 850?hPa moisture transport from ocean to the land. Both models, i.e., BCC RCM and the driving CGCM overestimates (underestimates) the 850?hPa southwesterly flow over the northern (southern) Arabian Sea. Moreover, both models overestimate the seasonal mean precipitation over much of the South Asia region compared to the observations. However, the precipitation biases are significantly reduced in the BCC RCM simulations. Furthermore, both models simulate reasonably the interannual variability of the summer monsoon over India. The precipitation index simulated by BCC RCM shows significant correlation (0.62) with the observed one. The BCC RCM simulates reasonably well the spatial and temporal variation of the precipitation and surface air temperature compared to the driving CGCM. Further, the temperature biases are significantly reduced (1?C4°C) in the BCC RCM simulations. The simulated vertical structure of the atmosphere show biases above the four sub-regions, however, these biases are significantly reduced in the BCC RCM simulations compared to the driving CGCM. Compared to the driving CGCM, the evolution processes of the onset of summer monsoon, e.g., the meridional temperature gradient and the vertical wind shear are well simulated by the BCC RCM. The 24-year simulations also show that with a little exception the BCC RCM is capable to reproduce the monsoon active and break phases and the intraseasonal precipitation variation over the Indian subcontinent.     

Regional climate model projections for the State of Washington

Global climate models do not have sufficient spatial resolution to represent the atmospheric and land surface processes that determine the unique regional climate of the State of Washington. Regional climate models explicitly simulate the interactions between the large-scale weather patterns simulated by a global model and the local terrain. We have performed two 100-year regional climate simulations using the Weather Research and Forecasting (WRF) model developed at the National Center for Atmospheric Research (NCAR). One simulation is forced by the NCAR Community Climate System Model version 3 (CCSM3) and the second is forced by a simulation of the Max Plank Institute, Hamburg, global model (ECHAM5). The mesoscale simulations produce regional changes in snow cover, cloudiness, and circulation patterns associated with interactions between the large-scale climate change and the regional topography and land-water contrasts. These changes substantially alter the temperature and precipitation trends over the region relative to the global model result or statistical downscaling. To illustrate this effect, we analyze the changes from the current climate (1970–1999) to the mid twenty-first century (2030–2059). Changes in seasonal-mean temperature, precipitation, and snowpack are presented. Several climatological indices of extreme daily weather are also presented: precipitation intensity, fraction of precipitation occurring in extreme daily events, heat wave frequency, growing season length, and frequency of warm nights. Despite somewhat different changes in seasonal precipitation and temperature from the two regional simulations, consistent results for changes in snowpack and extreme precipitation are found in both simulations.     

Simulations of precipitation using NRCM and comparisons with satellite observations and CAM: annual cycle

The accurate representation of rainfall in models of global climate has been a challenging task for climate modelers owing to its small space and time scales. Quantifying this variability is important for comparing simulations of atmospheric behavior with real time observations. In this regard, this paper compares both the statistical and dynamically forced aspects of precipitation variability simulated by the high-resolution (36?km) Nested Regional Climate Model (NRCM), with satellite observations from the Tropical Rainfall Measuring Mission (TRMM) 3B42 dataset and simulations from the Community Atmosphere Model (CAM) at T85 spatial resolution. Six years of rainfall rate data (2000?C2005) from within the Tropics (30°S?C30°N) have been used in the analysis and results are presented in terms of long-term mean rain rates, amplitude and phase of the annual cycle and seasonal mean maps of precipitation. Our primary focus is on characterizing the annual cycle of rainfall over four land regions of the Tropics namely, the Indian Monsoon, the Amazon, Tropical Africa and the North American monsoon. The lower tropospheric circulation patterns are analyzed in both the observations and the models to identify possible causes for biases in the simulated precipitation. The 6-year mean precipitation simulated by both models show substantial biases throughout the global Tropics with NRCM/CAM systematically underestimating/overestimating rainfall almost everywhere. The seasonal march of rainfall across the equator, following the motion of the sun, is clearly seen in the harmonic vector maps. The timing of peak rainfall (phase) produced by NRCM is in closer agreement with the observations compared to CAM. However like the long-time mean, the magnitude of seasonal mean rainfall is greatly underestimated by NRCM throughout the Tropical land mass. Some of these regional biases can be attributed to erroneous circulation and moisture surpluses/deficits in the lower troposphere in both models. Overall, the results seem to indicate that employing a higher spatial resolution (36?km) does not significantly improve simulation of precipitation. We speculate that a combination of several physics parameterizations and lack of model tuning gives rise to the observed differences between NRCM and the observations.     

次网格坡地辐射参数化对RegCM4.1模式模拟东亚夏季气候的影响

本研究将次网格坡地辐射参数化方案引入到区域气候模式(RegCM4. 1)中,并研究次网格坡地辐射参数化对RegCM4. 1模拟东亚夏季气候性能的影响。结果表明:RegCM4. 1高估了夏季青藏高原的热源作用,模拟低层偏强的西南季风导致了模拟的中国夏季降水量总体偏大。引入次网格坡地辐射参数化方案后,模式模拟的青藏高原夏季地表太阳辐射通量和长波辐射通量减小分别可达5%和12%以上,夏季减弱的青藏高原热源作用使得低层西南季风减弱,从而改善了模式对中国夏季降水的模拟;而且改善程度自东南向西北递减,在东南地区模拟夏季降水的相对均方根误差减小9%,空间相关系数和Taylor评分分别提高0. 14和0. 08。     

EVALUATION OF CGCM AND SIMULATION OF REGIONAL CLIMATE CHANGE IN EAST ASIA

In this paper,experiment results about East Asia climate from five CGCMs are described.Theability of the models to simulate present climate and the simulated response to increased carbon dioxideare both covered.The results indicate that all models show substantial changes in climate whencarbon dioxide concentrations are doubled.In particular,the strong surface warming at high latitudesin winter and the significant increase of summer precipitation in the monsoon area are produced by allmodels.Regional evaluation results show that these five CGCMs are particularly good in simulatingspatial distribution of present climate.The main characteristics of the seasonal mean H500,SAT,MSLP field can be simulated by most CGCMs.But there are significant systematic errors in SAT,MSLP,HS00 fields in most models.On the whole,DKRZ OPYC is the best in simulating the presentclimate in East Asia.     

EVALUATION OF CGCM AND SIMULATION OF REGIONAL CLIMATE CHANGE IN EAST ASIA*

In this paper,experiment results about East Asia climate from five CGCMs are described.The ability of the models to simulate present climate and the simulated response to increased carbon dioxide are both covered.The results indicate that all models show substantial changes in climate when carbon dioxide concentrations are doubled.In particular,the strong surface warming at high latitudes in winter and the significant increase of summer precipitation in the monsoon area are produced by all models.Regional evaluation results show that these five CGCMs are particularly good in simulating spatial distribution of present climate.The main characteristics of the seasonal mean H500,SAT,MSLP field can be simulated by most CGCMs.But there are significant systematic errors in SAT,MSLP,HS00 fields in most models.On the whole,DKRZ OPYC is the best in simulating the present climate in East Asia.     

Climate Changes in the 21st Century over the Asia-Pacific Region Simulated by the NCAR CSM and PCM

The Climate System Model (CSM) and the Parallel Climate Model (PCM), two coupled global climate models without flux adjustments recently developed at NCAR, were used to simulate the 20th century climate using historical greenhouse gas and sulfate aerosol forcing. These simulations were extended through the 21st century under two newly developed scenarios, a business-as-usual case (BAU, CO2≈710 ppmv in 2100) and a CO2 stabilization case (STA550, CO2≈540 ppmv in 2100). The simulated changes in temperature, precipitation, and soil moisture over the Asia-Pacific region (10°-60°N, 55°-155°E) are analyzed, with a focus on the East Asian summer monsoon rainfall and climate changes over the upper reaches of the Yangtze River. Under the BAU scenario, both the models produce surface warming of about 3-5℃ in winter and 2-3℃ in summer over most Asia. Under the STA550 scenario, the warming is reduced by 0.5-1.0℃ in winter and by 0.5℃ in summer. The warming is fairly uniform at the low latitudes and does not induce significant changes in the zonal mean Hadley circulation over the Asia-Pacific do main. While the regional precipitation changes from single CSM integrations are noisy, the PCM ensemble mean precipitation shows 10%-30% increases north of ～ 30°N and ～ 10% decreases south of ～ 30°N over the Asia-Pacific region in winter and 10%-20% increases in summer precipitation over most of the region. Soil moisture changes are small over most Asia. The CSM single simulation suggests a 30% increase in river runoff into the Three Gorges Dam, but the PCM ensemble simulations show small changes in the runoff.     

CMIP5模式对中国东北气候模拟能力的评估

利用CN05观测资料和参与IPCC第五次评估报告的45个全球气候系统模式的模拟结果,分析了新一代全球气候模式对中国东北三省(1961~2005年)气温和降水的模拟能力。结果表明:1)绝大多数模式都能较好地模拟出研究区内显著增温的趋势,对气温的年际变化模拟能力则相对有限;2)所有模式均能很好地再现气温气候态的空间分布特征,且多模式集合模拟结果优于绝大多数单个模式,空间相关系数达到了0.96;3)对于降水的模拟结果,模式间差异较大,多模式集合能较好地再现其空间分布规律(空间相关系数为0.86),对降水年际变化及线性变化趋势的模拟能力则较差。总体来说,多模式集合对东北气候的时空变化特征具有一定的模拟能力,且对气温模拟效果优于降水,对空间分布的模拟能力优于时间变化。     

CSIRO Mk3.6.0模式及其驱动下RegCM4.4模式对中国气候变化的预估

使用区域气候模式RegCM4.4,对全球模式CSIRO-Mk3.6.0在RCP4.5情景下的气候变化试验结果（1950-2100年）在东亚地区进行25 km动力降尺度试验,比较了CSIRO-Mk3.6.0和RegCM4.4预估中国地区的21世纪气候变化。结果表明,两个模式预估未来中国地区气温持续升高,升温幅度具有区域性特征,RegCM4.4预估区域平均升温幅度低于CSIRO-Mk3.6.0,但二者年际波动基本一致。两个模式预估未来降水在中国西部以持续增加为主,东部则表现出较大的不一致性,预估区域平均年降水量变化不大,呈现冬季明显增加,夏季微弱减少的特点。此外,为了解区域气候模式对中国降水预估的不确定性,对本研究和以往RegCM3使用相同分辨率模拟得到的未来降水预估进行了对比,两个区域模式预估中国西部大部分地区未来降水一致性增加,东部存在明显不一致（冬季中、高纬除外）。     

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.     

基于高分辨率模拟数据RCP4.5情景下的华中区域气候变化预估

基于RegCM4.4高分辨率区域气候模式数据和华中区域1986—2005年逐日气象观测资料,在对模式模拟性能检验的基础上,对中国华中区域未来不同时期、1.5℃和2℃温升阈值下气候变化进行预估.结果表明:模拟结果能较准确反映出区域气温、降水年内变化特征及空间分布特征;与观测值相比,气温模拟值偏低、降水模拟值偏大;与198...     

Comparison of regional climate model and statistical downscaling simulations of different winter precipitation change scenarios over Romania

Summary Regional climate model and statistical downscaling procedures are used to generate winter precipitation changes over Romania for the period 2071–2100 (compared to 1961–1990), under the IPCC A2 and B2 emission scenarios. For this purpose, the ICTP regional climate model RegCM is nested within the Hadley Centre global atmospheric model HadAM3H. The statistical downscaling method is based on the use of canonical correlation analysis (CCA) to construct climate change scenarios for winter precipitation over Romania from two predictors, sea level pressure and specific humidity (either used individually or together). A technique to select the most skillful model separately for each station is proposed to optimise the statistical downscaling signal. Climate fields from the A2 and B2 scenario simulations with the HadAM3H and RegCM models are used as input to the statistical downscaling model. First, the capability of the climate models to reproduce the observed link between winter precipitation over Romania and atmospheric circulation at the European scale is analysed, showing that the RegCM is more accurate than HadAM3H in the simulation of Romanian precipitation variability and its connection with large-scale circulations. Both models overestimate winter precipitation in the eastern regions of Romania due to an overestimation of the intensity and frequency of cyclonic systems over Europe. Climate changes derived directly from the RegCM and HadAM3H show an increase of precipitation during the 2071–2100 period compared to 1961–1990, especially over northwest and northeast Romania. Similar climate change patterns are obtained through the statistical downscaling method when the technique of optimum model selected separately for each station is used. This adds confidence to the simulated climate change signal over this region. The uncertainty of results is higher for the eastern and southeastern regions of Romania due to the lower HadAM3H and RegCM performance in simulating winter precipitation variability there as well as the reduced skill of the statistical downscaling model.