末次冰期东亚区域气候变化的情景和机制研究

用一嵌套在全球大气环流模式中的区域气候模式,通过数值试验和对内外因作用的机制分析,探讨了以末次冰期为背景的大尺度强迫引起的大气环流和区域内下垫面条件异常等中尺度强迫影响区域气候变化的过程和机制。大尺度强迫和区域内局地的中尺度强迫通过不同的热力和动力学过程影响大气运动状况和区域气候的变化。末次冰期大尺度强迫引起的全球大气环流背景的变化是形成冰期和现代区域气候差异的主要原因。     

温室效应引起的中国区域气候变化的数值模拟Ⅰ:模式对中国气候模拟能力的检验

使用RegCM2区域气候模式单向嵌套澳大利亚CSIRO R21L9全球海-气耦合模式,进行了CO2加倍对中国区域气候变化影响的数值试验研究,分析了控制试验(1×CO2)即模式对中国当代气候的模拟情况.首先给出了全球模式控制试验在中国地区的结果,分析表明它对中国区域的地面气温和降水具有一定的模拟能力,其结果可以用来制作驱动区域气候模式的初始场和侧边界.对RegCM2 5 a时间长度控制试验积分结果的分析与检验表明,区域气候模式由于具有较高的分辨率和较完善的物理过程,它对中国区域地面气温和降水的模拟效果较全球模式有了较大提高,如它模拟的各月气温与实况的相关系数全年12个月的平均由全球模式的0.83提高到0.92,降水由0.48提高到0.65.     

月尺度区域气候数值预测试验

将９层全球气候谱模式与ＣＳＵ－ＲＡＭＳ中尺度数值式嵌套,进行了月尺度的短期区域气候预测试验。结果表明：ＧＣＭ模式的集合预报能够反映较大尺度的平均环流;在此基础上,被嵌套的ＣＳＵ－ＲＡＭＳ中尺度模式能够得到更为细致的区域环流特征以及它的短期气候尺度的演率。ＧＣＭ模式与中尺度模式相结合的“区域气候数据模式”是了解短期区域气候变化的有效方法之一。     

动力数值模式侧边界强迫的改进试验

通过修正嵌套模式侧边界缓冲区温度强迫项的计算方法,使区域气候模式所刻画的温度日变化信息与不同下垫面区域的天气变化规律相符合.进而利用数值模式ASRegCM研究了侧边界缓冲区温度强迫的改进处理对嵌套模式模拟效果的影响,并讨论了其对模拟结果改进的可能机制.数值试验结果表明,该侧边界缓冲区强迫的改进对地面气温和降水距平分布的...     

区域气候模式侧边界的处理对东亚夏季风降水模拟的影响

在区域气候模式模拟中,侧边界的作用是引入大尺度强迫场。如何处理好侧边界,即大尺度强迫场和区域气候模式本身之间的关系问题,对于区域气候模式模拟和预报东亚夏季风降水具有重要意义。本文利用美国纽约州立大学Albany分校的区域气候模式（SUNYA-ReCM）,设计了两种不同的侧边界处理方法,来探讨侧边界对东亚夏季风降水模拟的影响。驱动区域模式的大尺度强迫场来自欧洲中期天气预报中心（ECMWF）及热带海洋大气研究计划（TOGA）的分析资料场。试验结果表明:（1）当模式的区域较大时,采用较小的侧边界缓冲区会在缓冲区与模式内部的交界处产生不连续;扩大缓冲区并且考虑不同尺度强迫在垂直方向上的不同作用,可以避免这一缺陷。（2）更重要的是采用后一种方案,即减少了区域气候模式在模拟大尺度环流场方面所起的作用,使得模式更多地依赖侧边界来得到更真实的、对东亚夏季风降水起重大影响的一些气流,如副高、西南季风和南海季风,对东亚夏季风降水无论是在大小上还是在雨带位置的演变上都能进行更好的模拟。     

区域气候模式在东亚地区的应用研究：垂直分辨率与边界对夏季季风降水影响

将美国国家大气研究中心（ＮＣＡＲ）的区域气候模式（ＲｅｇＣＭ２－１９９６）设置在东亚－西太平洋区域。利用该模式研究东亚区域气候模式的几个重要问题,即：垂直分辨率的影响,侧边界条件的重要性等。数值试验结果表明：细垂直分辨率模拟的降水分布优于粗分辨率模式,但容易引起“数值点暴雨”;ＥｅｇＣＭ２／ＥＡ与不崃源的大尺度侧边界嵌套,模拟的降水会有明显的不同,当用ＲｅｇＣＭ２／ＥＡ与不同来源的大尺度侧边界嵌套     

CSU－RAMS模式在区域气侯模拟中的应用

将ＣＳＵ－ＲＡＭＳ（中尺度）数值模式改造成“区域气候数值模式”以及进行区域气候数值模拟的试验研究。说明将有限区域中尺度数值模式与ＧＣＭ模式嵌套应用到区域气候数值模拟研究上能够取得有意义的结果。它能在一定程度上改善ＧＣＭ模式的不足。可以更为细致地描述大气环流的变化特征,是了解区域气候变化的有效方法之一。     

CSU－RAMS模式在区域气侯模拟中的应用

将ＣＳＵ－ＲＡＭＳ（中尺度）数值模式改造成“区域气候数值模式”以及进行区域气候数值模拟的试验研究。说明将有限区域中尺度数值模式与ＧＣＭ模式嵌套区域气候数值模拟研究上能够取得有意义的结果,它能在一定程度上改善ＧＣＭ模式的不足,可以更为细致地描述大气环流的变化特征,是了解区域气候变化的有效方法之一。     

区域气候模式在东亚地区的应用研究——垂直分辨率与侧边界对夏季季风降水影响研究

将美国国家大气研究中心（NCAR）的区域气候模式（RegCM2-1996）设置在东亚-西太平洋区域（简称东亚区域气候模式RegCM2/EA）。利用该模式研究东亚区域气候模式的几个重要问题，即：垂直分辨率的影响，侧边界条件（如嵌套技术、缓冲区宽度、不同资料）的重要性等。数值试验结果表明：细垂直分辨率模拟的降水分布优于粗分辨率模式，但容易引起“数值点暴雨”；RegCM2/EA与不同来源的大尺度侧边界嵌套，模拟的降水会有明显的不同；当用RegCM2/EA模拟较大区域时，应该取较宽的缓冲区；在各种嵌套方案中，指数松弛嵌套方法最好。这些结果为进一步探讨东亚区域气候模式的特点以及发展与改造区域气候模式提供一定的依据。研究结果还需要用更多的数值试验来验证。     

东亚区域极端气候事件变化的数值模拟试验

使用ResCM2区域气候模式,嵌套澳大利亚CSIRO R21L9全球海气耦合模式,进行了温室效应（二氧化碳加倍）对东亚（主要是中国区域）极端气候事件影响的数值试验。控制试验的结果表明,区域模式能够较好地模拟中国区域的极端气候事件。对温室效应引起的它们的变化进行了信度检验,分析结果表明,温室效应将引起日最高和最低气温增加,日较差减小;使得高温天气增多,低温日数减少。降水日数和大雨日数在一些地区将增加。同时还会引起影响中国的台风活动的变化。     

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.     

Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs

Six approaches for downscaling climate model outputs for use in hydrologic simulation were evaluated, with particular emphasis on each method's ability to produce precipitation and other variables used to drive a macroscale hydrology model applied at much higher spatial resolution than the climate model. Comparisons were made on the basis of a twenty-year retrospective (1975–1995) climate simulation produced by the NCAR-DOE Parallel ClimateModel (PCM), and the implications of the comparison for a future(2040–2060) PCM climate scenario were also explored. The six approaches were made up of three relatively simple statistical downscaling methods – linear interpolation (LI), spatial disaggregation (SD), and bias-correction and spatial disaggregation (BCSD) – each applied to both PCM output directly(at T42 spatial resolution), and after dynamical downscaling via a Regional Climate Model (RCM – at 1/2-degree spatial resolution), for downscaling the climate model outputs to the 1/8-degree spatial resolution of the hydrological model. For the retrospective climate simulation, results were compared to an observed gridded climatology of temperature and precipitation, and gridded hydrologic variables resulting from forcing the hydrologic model with observations. The most significant findings are that the BCSD method was successful in reproducing the main features of the observed hydrometeorology from the retrospective climate simulation, when applied to both PCM and RCM outputs. Linear interpolation produced better results using RCM output than PCM output, but both methods (PCM-LI and RCM-LI) lead to unacceptably biased hydrologic simulations. Spatial disaggregation of the PCM output produced results similar to those achieved with the RCM interpolated output; nonetheless, neither PCM nor RCM output was useful for hydrologic simulation purposes without a bias-correction step. For the future climate scenario, only the BCSD-method (using PCM or RCM) was able to produce hydrologically plausible results. With the BCSD method, the RCM-derived hydrology was more sensitive to climate change than the PCM-derived hydrology.     

Comparison of a very-fine-resolution GCM with RCM dynamical downscaling in simulating climate in China

Regional climate simulation can generally be improved by using an RCM nested within a coarser-resolution GCM. However, whether or not it can also be improved by the direct use of a state-of-the-art GCM with very fine resolution, close to that of an RCM, and, if so, which is the better approach, are open questions. These questions are important for understanding and using these two kinds of simulation approaches, but have not yet been investigated. Accordingly, the present reported work compared simulation results over China from a very-fine-resolution GCM (VFRGCM) and from RCM dynamical downscaling. The results showed that: (1) The VFRGCM reproduces the climatologies and trends of both air temperature and precipitation, as well as inter-monthly variations of air temperature in terms of spatial pattern and amount, closer to observations than the coarse-resolution version of the GCM. This is not the case, however, for the inter-monthly variations of precipitation. (2) The VFRGCM captures the climatology, trend, and inter-monthly variation of air temperature, as well as the trend in precipitation, more reasonably than the RCM dynamical downscaling method. (3) The RCM dynamical downscaling method performs better than the VFRGCM in terms of the climatology and inter-monthly variation of precipitation. Overall, the results suggest that VFRGCMs possess great potential with regard to their application in climate simulation in the future, and the RCM dynamical downscaling method is still dominant in terms of regional precipitation simulation.     

Climate change scenarios for precipitation and potential evapotranspiration over central Belgium

In this article, we examine climate model estimations for the future climate over central Belgium. Our analysis is focused mainly on two variables: potential evapotranspiration (PET) and precipitation. PET is calculated using the Penman equation with parameters appropriately calibrated for Belgium, based on RCM data from the European project PRUDENCE database. Next, we proceed into estimating the model capacity to reproduce the reference climate for PET and precipitation. The same analysis for precipitation is also performed based on GCM data from the IPCC AR4 database. Then, the climate change signal is evaluated over central Belgium using RCM and GCM simulations based on several SRES scenarios. The RCM simulations show a clear shift in the precipitation pattern with an increase during winter and a decrease during summer. However, the inclusion of another set of SRES scenarios from the GCM simulations leads to a less clear climate change signal.     

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     

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.     

Testing the downscaling ability of a one-way nested regional climate model in regions of complex topography

The downscaling ability of a one-way nested regional climate model (RCM) is evaluated over a region subjected to strong surface forcing: the west of North America. The sensitivity of the results to the horizontal resolution jump and updating frequency of the lateral boundary conditions are also evaluated. In order to accomplish this, a perfect-model approach nicknamed the Big-Brother Experiment (BBE) was followed. The experimental protocol consists of first establishing a virtual-reality reference climate over a fairly large area by using the Canadian RCM with grid spacing of 45 km nested within NCEP analyses. The resolution of the simulated climate is then degraded to resemble that of operational general circulation models (GCM) or observation analyses by removing small scales; the filtered fields are then used to drive the same regional model, but over a smaller sub-area. This set-up permits a comparison between two simulations of the same RCM over a common region. The Big-Brother Experiment has been carried out for four winter months over the west coast of North America. The results show that complex topography and coastline have a strong positive impact on the downscaling ability of the one-way nesting technique. These surface forcings, found to be responsible for a large part of small-scale climate features, act primarily locally and yield good climate reproducibility. Precipitation over the Rocky Mountains region is a field in which such effect is found and for which the nesting technique displays significant downscaling ability. The best downscaling ability is obtained when the ratio of spatial resolution between the nested model and the nesting fields is less than 12, and when the update frequency is more than twice a day. Decreasing the spatial resolution jump from a ratio of 12 to six has more benefits on the climate reproducibility than a reduction of spatial resolution jump from two to one. Also, it is found that an update frequency of four times a day leads to a better downscaling than twice a day when a ratio of spatial resolution of one is used. On the other hand, no improvement was found by using high-temporal resolution when the driving fields were degraded in terms of spatial resolution.     

Testing the downscaling ability of a one-way nested regional climate model in regions of complex topography

The downscaling ability of a one-way nested regional climate model (RCM) is evaluated over a region subjected to strong surface forcing: the west of North America. The sensitivity of the results to the horizontal resolution jump and updating frequency of the lateral boundary conditions are also evaluated. In order to accomplish this, a perfect-model approach nicknamed the Big-Brother Experiment (BBE) was followed. The experimental protocol consists of first establishing a virtual-reality reference climate over a fairly large area by using the Canadian RCM with grid spacing of 45 km nested within NCEP analyses. The resolution of the simulated climate is then degraded to resemble that of operational general circulation models (GCM) or observation analyses by removing small scales; the filtered fields are then used to drive the same regional model, but over a smaller sub-area. This set-up permits a comparison between two simulations of the same RCM over a common region. The Big-Brother Experiment has been carried out for four winter months over the west coast of North America. The results show that complex topography and coastline have a strong positive impact on the downscaling ability of the one-way nesting technique. These surface forcings, found to be responsible for a large part of small-scale climate features, act primarily locally and yield good climate reproducibility. Precipitation over the Rocky Mountains region is a field in which such effect is found and for which the nesting technique displays significant downscaling ability. The best downscaling ability is obtained when the ratio of spatial resolution between the nested model and the nesting fields is less than 12, and when the update frequency is more than twice a day. Decreasing the spatial resolution jump from a ratio of 12 to six has more benefits on the climate reproducibility than a reduction of spatial resolution jump from two to one. Also, it is found that an update frequency of four times a day leads to a better downscaling than twice a day when a ratio of spatial resolution of one is used. On the other hand, no improvement was found by using high-temporal resolution when the driving fields were degraded in terms of spatial resolution. Figure legends were missing in original article. Climate Dynamics (2005) 23: 473-493. The complete article is given here. DOI: 10.1007/s00382-004-0438-5     

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.