多模式集合预估21世纪淮河流域气候变化情景

利用政府间气候变化委员会第四次评估报告(the Fourth Assessment Report of the Intergov-ernmental Panel on Climate Change,IPCC AR4)的14个全球气候耦合模式对中国淮河流域气温和降水的模拟能力进行了评估,预估了该地区21世纪的降水和气温变化。同时,还分析了14个气候模式对1961-1999年气温和降水的模拟能力,并且根据Taylor方法选取具有较好模拟能力的模式做集合分析。结果表明,不同的气候模式对淮河流域的气温和降水都具有一定的模拟能力,但大多数模式模拟的气温偏低、降水偏多;选取的模式集合可以明显改善模式的模拟能力,但是没有表现出明显的优势。对淮河流域降水和气温未来情景的预估表明,各模式给出的情景结果尽管存在一定的差异,但模拟的21世纪气候变化的趋势基本一致,即气温持续增加,降水出现区域性增加;还重点分析了14个模式集合的结果在2010-2039年、2040-2069年和2070-2099年3个时段的年平均、季节平均降水和气温变化及其时空变化特征,结果表明,3个时段的气温和降水在不同情景下都是逐渐增加的,A2情景下增幅最显著,B1情景下增幅最小。     

珠江流域1961-2007年气候变化及2011-2060年预估分析

 根据珠江流域1961-2007年气温、降水量观测资料和ECHAM5/MPI-OM模式2011-2060年预估结果,分析了流域过去47 a的气温和降水量变化,并预估未来50 a变化趋势。结果表明,在全球变暖的背景下,过去47 a温度呈上升趋势,约升高1.8℃。冬季增温最明显,夏季最弱。未来50 a流域温度仍呈上升趋势,A1B情景下升幅约1.9℃,并且年际变化增强。A2和B1两种排放情景下秋季升温最显著,冬季最弱,A1B排放情景与此相反。过去47 a秋季降水量呈减少趋势;春、夏、冬季和年降水量均呈增加趋势。未来50 a降水总体呈增加趋势,A1B排放情景降水增加最多,约为230 mm。A2、A1B和B1情景下降水季节分配未发生显著变化。年降水和冬季降水的年际变率增强,秋季减弱。     

5个IPCC AR4全球气候模式对东北三省降水模拟与预估

利用IPCC AR4中5个全球气候模式数据集和中国东北三省162个站降水实测资料,评估5个全球气候模式和多模式集合平均对中国东北三省降水的模拟能力,并对SRES B1、A1B和A2三种排放情景东北三省未来降水变化进行预估。结果表明：全球气候模式能较好再现东北三省降水的月变化,但存在系统性湿偏差;多模式集合平均能较好模拟东北三省年降水量的空间分布,但模拟中心偏北,强度略强,模式对东北三省夏季降水的模拟效果优于冬季降水;预估结果表明,三种排放情景下21世纪中前期和末期东北三省降水均将增多,21世纪末期增幅高于21世纪中前期,冬季增幅高于其他季节;就排放情景而言,SRES A1B和A2排放情景增幅相当,高于B1排放情景增幅;不同排放情景东北三省降水量增率分布呈较一致变化,A2排放情景下,增幅最显著的辽宁环渤海地区年降水量在21世纪中前期将增加7%以上,21世纪末期将增加16%。     

CMIP5气候模式对云南气候变化模拟评估及未来情景预估

基于8个气候模式和多模式集合数据(21个气候模式简单集合)和观测数据,评估了其在气候基准期内对云南气温、降水的模拟能力,在评估基础上应用多模式集合数据,预估了未来不同排放情景下云南气温、降水的空间变化情况。结果表明:①多模式集合和部分模式能较好的模拟出基准期内气温、降水的年际变化趋势;在空间分布特征上,气候模式(包括多模式集合)对降水的模拟偏差较差,对气温的模拟相对较好;但在月平均气温和月降水的年内分布模拟上,多模式集合数据的模拟效果明显优于8个气候模式数据;②预估结果表明,在未来3种排放情景下云南地区降水呈西增东减的空间部分特征,纵向岭谷地区降水增加幅度为1%～3%,而气温在3种排放情景下则表现为一致的增加,降水和气温均在RCP8.5情景下增幅最大。     

CMIP5多模式集合对南亚大河气候变化模拟评估及未来情景预估

利用英国东英格利亚大学CRU(Climatic Research Unit)逐月气温、日本高分辨率亚洲陆地降水数据集APHRODITE(Asian Precipitation-Highly-Resolved Observational Data IntegrationTowards Evaluation)逐日降水资料以及耦合模式比较计划CMIP5(Coupled Model Intercomparison Project phase 5)多模式集合逐月气温、降水格点数据,评估了CMIP5多模式集合对包括印度河、恒河、湄公河、萨尔温江、伊洛瓦底江和布拉马普特拉河全区域(简称南亚大河流域)气候变化的模拟能力,并对流域2016—2035、2046—2065和2081—2100年气候变化可能趋势进行了预估。结果表明:CMIP5多模式集合对流域年平均气温的时间变化和空间分布特征有较强的模拟能力,时间空间相关系数都达到0.01的显著性水平,尤其对夏季气温的模拟要优于其他季节;对降水而言,模式对其也有较好的模拟能力,尤其是降水的季节性波动。预估结果表明:RCP2.6、4.5、8.5情景下,相对于基准期(1986—2005年),21世纪前期(2016—2035年)、中期(2046—2065年)和末期(2081—2100年)全流域年平均气温都有上升,且上升增幅随排放情景增大而增大,流域高海拔地区增幅较大;降水除21世纪前期RCP4.5、8.5情景下的增长趋势较小外,全流域年降水量都将增大;未来上述三段时期夏季持续升温将引起北部高海拔地区冰川的进一步消融;春季降水未来将持续增加,对全区水资源的贡献将增加;流域冬季降水的少量增加有助冰川累积和高海拔地区水资源的增加;三段时期夏季降水都有增长,洪涝发生的风险加大,极端降水事件可能增多。     

基于CMIP5资料的云南及周边地区未来50年气候预估

利用CRU(Climatic Research Unit)高分辨率观测数据及云南省124站资料,检验了参与IPCC AR5(政府间气候变化专门委员会第5次评估报告)的7个全球海气耦合模式(Coupled Model Intercomparison Program 5,CMIP5)及模式集合平均对云南及周边地区气温和降水的模拟性能,同时进行该区域不同温室气体排放量情景下2006~2055年的气候预估。结果表明:全球海气耦合模式对该区域气温和降水气候场空间分布、气温的线性趋势和春、夏季降水的年代际振荡特征具有一定的模拟能力,且模式集合能力优于单一模式,气温模拟优于降水模拟,但春、夏季的降水好于其他季节,使得全年的总降水好于秋、冬两季。对未来情景预估表明,研究区域未来50年气温呈现显著的线性上升趋势,降水量保持年代际振荡特征并有所增加,2020年之前我国云南及其南部区域将经历相对的干旱时期。     

21世纪黄河流域上中游地区气候变化趋势分析

气候变化预估常用的全球气候模式（GCM）难以提供区域或更小尺度上可靠的逐日气候要素序列,针对这一问题,应用统计降尺度模型（statistical downscaling model,SDSM）将HadCM3的模拟数据（包括A2、B2两种情景）处理为具有较高可信度的逐日站点序列。以1961-1990年为基准期,分析了21世纪黄河流域上中游地区未来最高气温、最低气温与年降水量的变化。在A2、B2两种气候变化情景下,日最高气温、日最低气温均呈升高趋势;但A2的变化较显著,日最高气温的升高趋势在景泰站最明显,日最低气温的升高趋势在河曲站最显著。流域平均的年降水量变化范围为-18.2%～13.3%。A2情景下降水量增加和减少的面积基本相等,宝鸡站降水量增加最多;B2情景下大部分区域降水减少,西峰镇降水量减少最显著。     

21世纪黄河流域上中游地区气候变化趋势分析

 气候变化预估常用的全球气候模式（GCM）难以提供区域或更小尺度上可靠的逐日气候要素序列,针对这一问题,应用统计降尺度模型（statistical downscaling model,SDSM）将HadCM3的模拟数据（包括A2、B2两种情景）处理为具有较高可信度的逐日站点序列。以1961-1990年为基准期,分析了21世纪黄河流域上中游地区未来最高气温、最低气温与年降水量的变化。在A2、B2两种气候变化情景下,日最高气温、日最低气温均呈升高趋势;但A2的变化较显著,日最高气温的升高趋势在景泰站最明显,日最低气温的升高趋势在河曲站最显著。流域平均的年降水量变化范围为-18.2%～13.3%。A2情景下降水量增加和减少的面积基本相等,宝鸡站降水量增加最多;B2情景下大部分区域降水减少,西峰镇降水量减少最显著。     

澜沧江流域1951-2008年气候变化和2010-2099年不同情景下模式预估结果分析

利用澜沧江流域1951-2008年的降水和气温观测资料以及多模式集成的21世纪（2010-2099年）不同情景下（SRES A1B、SRES A2和SRES B1）气候变化模拟试验的预估结果,分析了该流域过去58年降水和气温的变化,并预估了未来90年的气候变化趋势。结果表明,在全球增暖的大背景下,过去58年澜沧江流域的年降水量下降了46.4 mm,气温有所上升,升温率达到了0.15℃/10a。在未来的90年,无论在哪种排放情景下,降水都表现为明显的上升趋势,而且相对于过去58年的结果,3种不同情景下降水的年代际变率都有所增加,其中A2情景值最大,B1情景值最小。年平均气温无论是在过去的58年还是在未来的90年都以明显的上升趋势为主,3种情景下气温的升温率远远超过过去58的结果。     

2011—2050年长江流域气候变化预估问题的探讨

利用长江流域1961—2008年观测气象资料,对IPCC 第四次评估报告中12个全球气候模式及所有模式集合平均进行比较验证,结果表明：MIUB_ECHO_G模式对该地区降水模拟能力较强,NCAR_CCSM3模式对温度模拟效果较好。进一步利用MIUB_ECHO_G模式和NCAR_CCSM3模式结果在SRES-A2、-A1B、-B1 3种排放情景下的降水和温度数据,分析2011—2050年3种排放情景下长江流域降水和温度变化特征。结果表明,2011—2050年长江流域降水变化趋势不明显,温度呈增加趋势,增幅在2℃内。     

2010—2100年淮河径流量变化情景预估

根据淮河流域14个气象站点1964—2007年观测降水量与温度数据和ECHAM5/MPI-OM模式在3种排放情景下对该流域2001—2100年的气候预估,利用人工神经网络模型预估淮河蚌埠站2010—2100年逐月径流量变化。计算结果表明:3种排放情景下2010—2100年淮河径流量年际变化幅度差异较大,SRES-A2情景总体处于波动上升趋势,其中2051—2085年上升趋势显著;SRES-A1B情景2024—2037年年平均流量显著降低;SRES-B1情景年平均流量的变率甚小。季节分析表明:春季径流量在2010—2100年变幅最小,距平百分率在-15.1%～18.6%之间小幅波动。夏季平均流量在2040年代前呈下降趋势,之后小幅波动上升。秋、冬季平均流量SRES-A2和SRES-A1B情景变幅显著,其中,秋季SRES-A2情景2060年代距平百分率下降达50.6%,为3种情景下各季节径流量降幅之最;冬季SRES-A1B情景2050年代其增幅达到54.7%,亦为上升幅度之最。     

基于ECHAM5模式预估2050年前中国旱涝格局趋势

 利用ECHAM5/MPI-OM气候模式输出的2001-2050年逐月降水量资料,考虑IPCC采用的3种排放情景（A2：温室气体高排放情景;A1B:温室气体中排放情景;B1：温室气体低排放情景）,计算其标准化降水指数,分析了中国2050年前3种排放情景下的旱涝格局。结果表明：3种情景下旱涝趋势空间分布不同,其中A2情景下旱涝格局同1961-2000年观测到的旱涝格局相似,均存在一条由东北向西南的干旱带;而A1B和B1情景下旱涝格局则发生了很大的变化,尤其B1情景下出现了"北涝南旱"的格局。未来50 a干旱面积在A2情景下呈略增加趋势;A1B和B1情景下为减少趋势。3种情景下干旱频率的空间分布也各不相同。     

Assessing future changes in seasonal climatic extremes in the Ganges river basin using an ensemble of regional climate models

Using an ensemble of four high resolution (~25 km) regional climate models, this study analyses the future (2021–2050) spatial distribution of seasonal temperature and precipitation extremes in the Ganges river basin based on the SRES A1B emissions scenario. The model validation results (1989–2008) show that the models simulate seasonality and spatial distribution of extreme temperature events better than precipitation. The models are able to capture fine topographical detail in the spatial distribution of indices based on their ability to resolve processes at a higher regional resolution. Future simulations of extreme temperature indices generally agree with expected warming in the Ganges basin, with considerable seasonal and spatial variation. Significantly warmer summers in the central part of the basin along with basin-wide increase in night temperature are expected during the summer and monsoon months. An increase in heavy precipitation indices during monsoon, coupled with extended periods without precipitation during the winter months; indicates an increase in the incidence of extreme events.     

Statistical downscaling of daily mean temperature, pan evaporation and precipitation for climate change scenarios in Haihe River, China

A statistical downscaling method (SDSM) was evaluated by simultaneously downscaling air temperature, evaporation, and precipitation in Haihe River basin, China. The data used for evaluation were large-scale atmospheric data encompassing daily NCEP/NCAR reanalysis data and the daily mean climate model results for scenarios A2 and B2 of the HadCM3 model. Selected as climate variables for downscaling were measured daily mean air temperature, pan evaporation, and precipitation data (1961–2000) from 11 weather stations in the Haihe River basin. The results obtained from SDSM showed that: (1) the pattern of change in and numerical values of the climate variables can be reasonably simulated, with the coefficients of determination between observed and downscaled mean temperature, pan evaporation, and precipitation being 99%, 93%, and 73%, respectively; (2) systematic errors existed in simulating extreme events, but the results were acceptable for practical applications; and (3) the mean air temperature would increase by about 0.7°C during 2011~2040; the total annual precipitation would decrease by about 7% in A2 scenario but increase by about 4% in B2 scenario; and there were no apparent changes in pan evaporation. It was concluded that in the next 30 years, climate would be warmer and drier, extreme events could be more intense, and autumn might be the most distinct season among all the changes.     

Climate Simulation and Future Projection of Precipitation and the Water Vapor Budget in the Haihe River Basin

The climatological characteristics of precipitation and the water vapor budget in the Haihe River basin (HRB) are analyzed using daily observations at 740 stations in China in 1951-2007 and the 4-time daily ERA40 reanalysis data in 1958-2001. The results show that precipitation and surface air temperature present significant interannual and interdecadal variability, with cold and wet conditions before the 1970s but warm and dry conditions after the 1980s. Precipitation has reduced substantially since the 1990s, with a continued increase of surface air temperature. The total column water vapor has also reduced remarkably since the late 1970s. The multi-model ensemble from the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC) has capably simulated the 20th century climate features and successfully reproduced the spatial patterns of precipitation and temperature. Unfortunately, the models do not reproduce the interdecadal changes. Based on these results, future projections of the climate in the HRB are discussed under the IPCC Special Report on Emissions Scenarios (SRES) B1, A1B, and A2. The results show that precipitation is expected to increase in the 21st century, with substantial interannual fluctuations relative to the models’ baseline climatology. A weak increasing trend in precipitation is projected before the 2040s, followed by an abrupt increase after the 2040s, especially in winter. Precipitation is projected to increase by 10%-18% by the end of the 21st century. Due to the persistent warming of surface air temperature, water vapor content in the lower troposphere is projected to increase. Relative humidity will decrease in the mid-lower troposphere but increase in the upper troposphere. On the other hand, precipitation minus evaporation remains positive throughout the 21st century. Based on these projection results, the HRB region is expected to get wetter in the 21st century due to global warming.     

Hydrological response to climate change in a glacierized catchment in the Himalayas

The analysis of climate change impact on the hydrology of high altitude glacierized catchments in the Himalayas is complex due to the high variability in climate, lack of data, large uncertainties in climate change projection and uncertainty about the response of glaciers. Therefore a high resolution combined cryospheric hydrological model was developed and calibrated that explicitly simulates glacier evolution and all major hydrological processes. The model was used to assess the future development of the glaciers and the runoff using an ensemble of downscaled climate model data in the Langtang catchment in Nepal. The analysis shows that both temperature and precipitation are projected to increase which results in a steady decline of the glacier area. The river flow is projected to increase significantly due to the increased precipitation and ice melt and the transition towards a rain river. Rain runoff and base flow will increase at the expense of glacier runoff. However, as the melt water peak coincides with the monsoon peak, no shifts in the hydrograph are expected.     

Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part 2: Precipitation mean state and seasonal cycle in South America

Evaluating the response of climate to greenhouse gas forcing is a major objective of the climate community, and the use of large ensemble of simulations is considered as a significant step toward that goal. The present paper thus discusses a new methodology based on neural network to mix ensemble of climate model simulations. Our analysis consists of one simulation of seven Atmosphere–Ocean Global Climate Models, which participated in the IPCC Project and provided at least one simulation for the twentieth century (20c3m) and one simulation for each of three SRES scenarios: A2, A1B and B1. Our statistical method based on neural networks and Bayesian statistics computes a transfer function between models and observations. Such a transfer function was then used to project future conditions and to derive what we would call the optimal ensemble combination for twenty-first century climate change projections. Our approach is therefore based on one statement and one hypothesis. The statement is that an optimal ensemble projection should be built by giving larger weights to models, which have more skill in representing present climate conditions. The hypothesis is that our method based on neural network is actually weighting the models that way. While the statement is actually an open question, which answer may vary according to the region or climate signal under study, our results demonstrate that the neural network approach indeed allows to weighting models according to their skills. As such, our method is an improvement of existing Bayesian methods developed to mix ensembles of simulations. However, the general low skill of climate models in simulating precipitation mean climatology implies that the final projection maps (whatever the method used to compute them) may significantly change in the future as models improve. Therefore, the projection results for late twenty-first century conditions are presented as possible projections based on the “state-of-the-art” of present climate modeling. First, various criteria were computed making it possible to evaluate the models’ skills in simulating late twentieth century precipitation over continental areas as well as their divergence in projecting climate change conditions. Despite the relatively poor skill of most of the climate models in simulating present-day large scale precipitation patterns, we identified two types of models: the climate models with moderate-to-normal (i.e., close to observations) precipitation amplitudes over the Amazonian basin; and the climate models with a low precipitation in that region and too high a precipitation on the equatorial Pacific coast. Under SRES A2 greenhouse gas forcing, the neural network simulates an increase in precipitation over the La Plata basin coherent with the mean model ensemble projection. Over the Amazonian basin, a decrease in precipitation is projected. However, the models strongly diverge, and the neural network was found to give more weight to models, which better simulate present-day climate conditions. In the southern tip of the continent, the models poorly simulate present-day climate. However, they display a fairly good convergence when simulating climate change response with a weak increase south of 45°S and a decrease in Chile between 30 and 45°S. Other scenarios (A1B and B1) strongly resemble the SRES A2 trends but with weaker amplitudes.     

5个全球气候模式对中国东北地区地面温度的模拟与预估

利用5个全球气候模式和中国东北地区162个站点地面温度实测资料,评估全球气候模式和多模式集合平均对中国东北地区地面温度的模拟能力,并对SRES B1、A1B和A2排放情景下,中国东北地区未来地面温度变化进行预估。结果表明：全球气候模式能够较好地再现了东北地区地面温度的年变化和空间分布特征,但存在系统性冷偏差,模式对夏季地面温度模拟偏低1.16 ℃,优于冬季。预估结果表明,3种排放情景下21世纪中期和末期东北地区地面温度均将升高,末期增幅高于中期,冬季增幅高于其他季节, SRES A2排放情景下增幅最大,B1排放情景下最小;增温幅度自南向北逐渐增大,增温最显著地区位于黑龙江小兴安岭;21世纪末期3种情景下中国东北地区年平均地面温度将分别升高2.39 ℃(SRES B1)、3.62 ℃(SRES A1B)和4.43 ℃(SRES A2)。