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1.
In this study, the applicability of the statistical downscaling model (SDSM) in modeling five extreme precipitation indices including R10 (no. of days with precipitation ≥10?mm?day?1), SDI (simple daily intensity), CDD (maximum number of consecutive dry days), R1d (maximum 1-day precipitation total) and R5d (maximum 5-day precipitation total) in the Yangtze River basin, China was investigated. The investigation mainly includes the calibration and validation of SDSM model on downscaling daily precipitation, the validation of modeling extreme precipitation indices using independent period of the NCEP reanalysis data, and the projection of future regional scenarios of extreme precipitation indices. The results showed that: (1) there existed good relationship between the observed and simulated extreme precipitation indices during validation period of 1991–2000, the amount and the change pattern of extreme precipitation indices could be reasonably simulated by SDSM. (2) Under both scenarios A2 and B2, during the projection period of 2010–2099, the changes of annual mean extreme precipitation indices in the Yangtze River basin would be not obvious in 2020s; while slightly increase in the 2050s; and significant increase in the 2080s as compared to the mean values of the base period. The summer might be the more distinct season with more projected increase of each extreme precipitation indices than in other seasons. And (3) there would be distinctive spatial distribution differences for the change of annual mean extreme precipitation indices in the river basin, but the most of Yangtze River basin would be dominated by the increasing trend.  相似文献   

2.
Regression-based statistical downscaling is a method broadly used to resolve the coarse spatial resolution of general circulation models. Nevertheless, the assessment of uncertainties linked with climatic variables is essential to climate impact studies. This study presents a procedure to characterize the uncertainty in regression-based statistical downscaling of daily precipitation and temperature over a highly vulnerable area (semiarid catchment) in the west of Iran, based on two downscaling models: a statistical downscaling model (SDSM) and an artificial neural network (ANN) model. Biases in mean, variance, and wet/dry spells are estimated for downscaled data using vigorous statistical tests for 30 years of observed and downscaled daily precipitation and temperature data taken from the National Center for Environmental Prediction reanalysis predictors for the years of 1961 to 1990. In the case of daily temperature, uncertainty is estimated by comparing monthly mean and variance of downscaled and observed daily data at a 95 % confidence level. In daily precipitation, downscaling uncertainties were evaluated from comparing monthly mean dry and wet spell lengths and their confidence intervals, cumulative frequency distributions of monthly mean of daily precipitation, and the distributions of monthly wet and dry days for observed and modeled daily precipitation. Results showed that uncertainty in downscaled precipitation is high, but simulation of daily temperature can reproduce extreme events accurately. Finally, this study shows that the SDSM is the most proficient model at reproducing various statistical characteristics of observed data at a 95 % confidence level, while the ANN model is the least capable in this respect. This study attempts to test uncertainties of regression-based statistical downscaling techniques in a semiarid area and therefore contributes to an improvement of the quality of predictions of climate change impact assessment in regions of this type.  相似文献   

3.
21世纪黄河流域上中游地区气候变化趋势分析   总被引：10，自引：0，他引：10  
 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。  相似文献   

4.
21世纪黄河流域上中游地区气候变化趋势分析   总被引：2，自引：0，他引：2  
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。  相似文献   

5.
Cambodia is one of the most vulnerable countries to climate change impacts such as floods and droughts. Study of future climate change and drought conditions in the upper Siem Reap River catchment is vital because this river plays a crucial role in maintaining the Angkor Temple Complex and livelihood of the local population since 12th century. The resolution of climate data from Global Circulation Models (GCM) is too coarse to employ effectively at the watershed scale, and therefore downscaling of the dataset is required. Artificial neural network (ANN) and Statistical Downscaling Model (SDSM) models were applied in this study to downscale precipitation and temperatures from three Representative Concentration Pathways (RCP 2.6, RCP 4.5 and RCP 8.5 scenarios) from Global Climate Model data of the Canadian Earth System Model (CanESM2) on a daily and monthly basis. The Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were adopted to develop criteria for dry and wet conditions in the catchment. Trend detection of climate parameters and drought indices were assessed using the Mann-Kendall test. It was observed that the ANN and SDSM models performed well in downscaling monthly precipitation and temperature, as well as daily temperature, but not daily precipitation. Every scenario indicated that there would be significant warming and decreasing precipitation which contribute to mild drought. The results of this study provide valuable information for decision makers since climate change may potentially impact future water supply of the Angkor Temple Complex (a World Heritage Site).  相似文献   

6.
基于统计降尺度模型的江淮流域极端气候的模拟与预估   总被引：4，自引：0，他引：4  
利用江淮流域29个代表站点1961--2000年逐日最高温度、最低温度和逐日降水资料,以及NCEP逐日大尺度环流场资料,引入基于多元线性回归与随机天气发生器相结合的统计降尺度模型SDSM（statistical downscalingmodel）,通过对每个站点建模,确立SDSM参数,并将该模型应用于SRESA2排放情景下HadCM3和cGcM3模式,得到了江淮流域各代表台站21世纪的逐日最高、最低温度和降水序列以及热浪、霜冻、强降水等极端气候指数。结果表明,当前气候下,统计降尺度方法模拟的极端温度指数与观测值有很好的一致性,能有效纠正耦合模式的“冷偏差”,如SDSM对江淮平均的冬季最高、最低温度的模拟偏差较CGCM3模式分别减少3℃和4．5℃。对于极端降水则能显著纠正耦合模式模拟的降水强度偏低的问题,如CGCM3对江淮流域夏季降水强度的模拟偏差为-60．6％,但降尺度后SDSM—CGCM3的偏差仅为-6％,说明降尺度模型SDSM的确有“增加值”的作用。21世纪末期在未来SRESA2情景下,对于极端温度,无论Had．CM3还是CGCM3模式驱动统计模型,江淮流域所有代表台站,各个季节的最高、最低温度都显著增加,且以夏季最为显著,增幅在2—4℃;与之相应霜冻天数将大幅减少,热浪天数大幅增多,各站点冬季霜冻天数减少幅度为5—25d,夏季热浪天数增加幅度为4～14d;对于极端降水指数,在两个不同耦合模式HadCM3和CGCM3驱动下的变化尤其是变化幅度的一致性比温度差,但大部分站点各个季节极端强降水事件将增多,强度增强,SDSM—HadCM3和SDSM-CGCM3预估的夏季极端降水贡献率将分别增加26％和27％。  相似文献   

7.
The study evaluates statistical downscaling model (SDSM) developed by annual and monthly sub-models for downscaling maximum temperature, minimum temperature, and precipitation, and assesses future changes in climate in the Jhelum River basin, Pakistan and India. Additionally, bias correction is applied on downscaled climate variables. The mean explained variances of 66, 76, and 11 % for max temperature, min temperature, and precipitation, respectively, are obtained during calibration of SDSM with NCEP predictors, which are selected through a quantitative procedure. During validation, average R 2 values by the annual sub-model (SDSM-A)—followed by bias correction using NCEP, H3A2, and H3B2—lie between 98.4 and 99.1 % for both max and min temperature, and 77 to 85 % for precipitation. As for the monthly sub-model (SDSM-M), followed by bias correction, average R 2 values lie between 98.5 and 99.5 % for both max and min temperature and 75 to 83 % for precipitation. These results indicate a good applicability of SDSM-A and SDSM-M for downscaling max temperature, min temperature, and precipitation under H3A2 and H3B2 scenarios for future periods of the 2020s, 2050s, and 2080s in this basin. Both sub-models show a mean annual increase in max temperature, min temperature, and precipitation. Under H3A2, and according to both sub-models, changes in max temperature, min temperature, and precipitation are projected as 0.91–3.15 °C, 0.93–2.63 °C, and 6–12 %, and under H3B2, the values of change are 0.69–1.92 °C, 0.56–1.63 °C, and 8–14 % in 2020s, 2050s, and 2080s. These results show that the climate of the basin will be warmer and wetter relative to the baseline period. SDSM-A, most of the time, projects higher changes in climate than SDSM-M. It can also be concluded that although SDSM-A performed well in predicting mean annual values, it cannot be used with regard to monthly and seasonal variations, especially in the case of precipitation unless correction is applied.  相似文献   

8.
Regression-based statistical downscaling model (SDSM) is an appropriate method which broadly uses to resolve the coarse spatial resolution of general circulation models (GCMs). Nevertheless, the assessment of uncertainty propagation linked with climatic variables is essential to any climate change impact study. This study presents a procedure to characterize uncertainty analysis of two GCM models link with Long Ashton Research Station Weather Generator (LARS-WG) and SDSM in one of the most vulnerable international wetland, namely “Shadegan” in an arid region of Southwest Iran. In the case of daily temperature, uncertainty is estimated by comparing monthly mean and variance of downscaled and observed daily data at a 95 % confidence level. Uncertainties were then evaluated from comparing monthly mean dry and wet spell lengths and their 95 % CI in daily precipitation downscaling using 1987–2005 interval. The uncertainty results indicated that the LARS-WG is the most proficient model at reproducing various statistical characteristics of observed data at a 95 % uncertainty bounds while the SDSM model is the least capable in this respect. The results indicated a sequences uncertainty analysis at three different climate stations and produce significantly different climate change responses at 95 % CI. Finally the range of plausible climate change projections suggested a need for the decision makers to augment their long-term wetland management plans to reduce its vulnerability to climate change impacts.  相似文献   

9.
根据海河流域1961-2010年气象观测资料,检验IPCC AR4中全球气候模式和多模式集合的模拟能力,并预估未来2011-2050年气候变化的可能趋势,结果表明:全球气候模式以及多模式集合对海河流域都具有一定的模拟能力,其中MIUB_ECHO_G模式和多模式集合具有相对较好的模拟能力.海河流域气温和降水未来情景预估表明:气温整体呈现增加趋势,尤其是A1B情景下各模式的年升温率均高于全国水平;未来降水也呈现增加趋势,在A1B和B1情景下,各模式都为夏季降水增加显著.A2情景下,春季时各模式降水均增加显著,A1B情景下,MIUB_ECHO_G模式模拟在2013年出现突变,降水量出现显著增长,A2情景下,MIUB_ECHO_G模式和多模式集合模拟的降水量则是在2031年和2001年出现突变,出现显著增长.  相似文献   

10.
Three statistical downscaling methods are compared with regard to their ability to downscale summer (June–September) daily precipitation at a network of 14 stations over the Yellow River source region from the NCEP/NCAR reanalysis data with the aim of constructing high-resolution regional precipitation scenarios for impact studies. The methods used are the Statistical Downscaling Model (SDSM), the Generalized LInear Model for daily CLIMate (GLIMCLIM), and the non-homogeneous Hidden Markov Model (NHMM). The methods are compared in terms of several statistics including spatial dependence, wet- and dry spell length distributions and inter-annual variability. In comparison with other two models, NHMM shows better performance in reproducing the spatial correlation structure, inter-annual variability and magnitude of the observed precipitation. However, it shows difficulty in reproducing observed wet- and dry spell length distributions at some stations. SDSM and GLIMCLIM showed better performance in reproducing the temporal dependence than NHMM. These models are also applied to derive future scenarios for six precipitation indices for the period 2046–2065 using the predictors from two global climate models (GCMs; CGCM3 and ECHAM5) under the IPCC SRES A2, A1B and B1scenarios. There is a strong consensus among two GCMs, three downscaling methods and three emission scenarios in the precipitation change signal. Under the future climate scenarios considered, all parts of the study region would experience increases in rainfall totals and extremes that are statistically significant at most stations. The magnitude of the projected changes is more intense for the SDSM than for other two models, which indicates that climate projection based on results from only one downscaling method should be interpreted with caution. The increase in the magnitude of rainfall totals and extremes is also accompanied by an increase in their inter-annual variability.  相似文献   

11.
利用区域气候模式RegCM3以及考虑作物生长过程的耦合模式RegCM3_CERES对东亚区域进行20年模拟,研究作物生长对流域水文过程与区域气候的影响。结果表明：考虑作物生长过程的耦合模式模拟海河流域、松花江流域、珠江流域多年平均降水效果明显改进,在除黑河流域外的各流域模拟的温度负偏差有所减小,其中在海河流域、淮河流域的夏季改进尤为明显。各流域夏季（6、7、8月）月蒸散量最高,其中长江流域、海河流域、淮河流域、珠江流域的夏季月蒸散量基本上在100 mm左右,并且七大流域蒸散发的季节变化趋势跟总降水基本一致。多数流域考虑作物生长过程的耦合模式模拟得出蒸散发减少且进入的水汽增加,导致局地水循环率减小;黑河流域与黄河流域降水有所增加,其他流域均有不同程度的减小。针对长江流域,比较耦合模式RegCM3_CERES与模式RegCM3模拟结果显示,叶面积指数减少1.20 m2/m2,根区土壤湿度增加0.01 m3/m3,进而导致潜热通量下降1.34 W/m2（其中在四川盆地地区减少16.00 W/m2左右）,感热通量增加2.04 W/m2,从而影响到降水和气温。  相似文献   

12.
Summary A pattern recognition methodology for estimating local climate variables such as regional precipitation and air temperature using local observation and scenario information provided by GCMs is presented. We have adopted a three step approach: (a) Feature information extraction of climate variables, where weather patterns are expanded by the Karhunen-Loeve (K-L) orthogonal functional series; (b) Grey associative clustering of the feature vectors; (3) Stochastic weather generation by a Monte Carlo simulation. The methods described in this paper were verified using the temperature and precipitation data set of Wuhan, Yangtze river basin and the Shun Tian catchment, Dongjiang River in China. The proposed method yields good stochastic simulations and also provides useful information on temporal or spatial downscaling and uncertainty.With 4 Figures  相似文献   

13.
基于永定河流域1958～2018年14个气象站的逐日观测数据,采用气候倾向率、Mann–Kendall趋势检验法分析蒸发皿蒸发量时空变化特征,并通过完全相关系数和多元回归分析识别气候因子与蒸发量的相关程度并定量计算其贡献率。结果表明:在全球气候变暖的背景下,60年来永定河流域气温以0.29°C/10 a的速率上升,而蒸发皿蒸发量不增反减,以－48.88 mm/10 a的速率显著下降（标准化统计量Z=－4.5）,该流域存在明显的“蒸发悖论”现象。流域蒸发量表现出显著的时空分布差异性,在季节上,春、夏季蒸发量分别占全年蒸发量的35%和37%,且春、夏两季蒸发量下降趋势较为显著;在空间上,永定河流域下游平原区下降趋势较上游山区（天镇、蔚县等地区）更为显著。完全相关分析表明,净辐射、平均风速和空气饱和差与蒸发量具有较强的相关性;贡献率分析表明,与基准期 1958～1979 年相比,1980～2018 年平均风速和净辐射减少对蒸发量减少的贡献率分别为77%和66%,空气饱和差的贡献率为－41%,净辐射和平均风速的减少是导致蒸发皿蒸发量下降的主要影响因素。  相似文献   

14.
澜沧江是我国为数不多的跨境河流,流域内多发暴雨、洪水灾害,因此定量、科学地评估澜沧江流域未来全球升温情景下极端降水的变化特征,能够为澜沧江-湄公河沿线国家共同管理流域水资源和抵御自然灾害提供一定的科学指导。文中基于部门间影响模式比较计划（ISI-MIP）下5个全球气候模式降水数据,通过偏差校正增强其在澜沧江流域极端降水的模拟能力,使用降水强度、日最大降水量和强降水量等9个指标评价未来全球升温1.5℃和2.0℃下澜沧江流域极端降水的变化情况,并对结果的不确定性和可信度进行研究,得出以下主要结论：随着全球温度的升高,澜沧江流域年降水和极端降水均呈现增大趋势,其中极强降水量（R99p）升幅最大,升温1.5℃和2.0℃下升幅分别为37%和75%;相对于基准期,全球升温2.0℃下各极端降水指数增幅明显大于升温1.5℃,前者升幅甚至超出后者一倍;未来全球升温情景下,澜沧江流域湿季会变得更湿润,而干季则会更干燥;澜沧江流域降水集中程度会增大,使得流域内洪涝灾害发生的风险增大;ISI-MIP气候模式对澜沧江流域未来极端降水模拟存在较大不确定性,升温2.0℃较升温1.5℃情景下不确定性更大,但相对于基准期,前者极端降水增大的可信度更高。  相似文献   

15.
利用1961~2002年ERA-40逐日再分析资料和江淮流域56个台站逐日观测降水量资料,引入基于自组织映射神经网络(Self-Organizing Maps,简称SOM)的统计降尺度方法,对江淮流域夏季(6~8月)逐日降水量进行统计建模与验证,以考察SOM对中国东部季风降水和极端降水的统计降尺度模拟能力。结果表明,SOM通过建立主要天气型与局地降水的条件转换关系,能够再现与观测一致的日降水量概率分布特征,所有台站基于概率分布函数的Brier评分(Brier Score)均近似为0,显著性评分(Significance Score)全部在0.8以上;模拟的多年平均降水日数、中雨日数、夏季总降水量、日降水强度、极端降水阈值和极端降水贡献率区域平均的偏差都低于11%;并且能够在一定程度上模拟出江淮流域夏季降水的时间变率。进一步将SOM降尺度模型应用到BCCCSM1.1(m)模式当前气候情景下,评估其对耦合模式模拟结果的改善能力。发现降尺度显著改善了模式对极端降水模拟偏弱的缺陷,对不同降水指数的模拟较BCC-CSM1.1(m)模式显著提高,降尺度后所有台站6个降水指数的相对误差百分率基本在20%以内,偏差比降尺度前减小了40%~60%;降尺度后6个降水指数气候场的空间相关系数提高到0.9,相对标准差均接近1.0,并且均方根误差在0.5以下。表明SOM降尺度方法显著提高日降水概率分布,特别是概率分布曲线尾部特征的模拟能力,极大改善了模式对极端降水场的模拟能力,为提高未来预估能力提供了基础。  相似文献   

16.
Physical scaling (SP) method downscales climate model data to local or regional scales taking into consideration physical characteristics of the area under analysis. In this study, multiple SP method based models are tested for their effectiveness towards downscaling North American regional reanalysis (NARR) daily precipitation data. Model performance is compared with two state-of-the-art downscaling methods: statistical downscaling model (SDSM) and generalized linear modeling (GLM). The downscaled precipitation is evaluated with reference to recorded precipitation at 57 gauging stations located within the study region. The spatial and temporal robustness of the downscaling methods is evaluated using seven precipitation based indices. Results indicate that SP method-based models perform best in downscaling precipitation followed by GLM, followed by the SDSM model. Best performing models are thereafter used to downscale future precipitations made by three global circulation models (GCMs) following two emission scenarios: representative concentration pathway (RCP) 2.6 and RCP 8.5 over the twenty-first century. The downscaled future precipitation projections indicate an increase in mean and maximum precipitation intensity as well as a decrease in the total number of dry days. Further an increase in the frequency of short (1-day), moderately long (2–4 day), and long (more than 5-day) precipitation events is projected.  相似文献   

17.
The main purpose of this study is to evaluate the impacts of climate change on Izmir-Tahtali freshwater basin, which is located in the Aegean Region of Turkey. For this purpose, a developed strategy involving statistical downscaling and hydrological modeling is illustrated through its application to the basin. Prior to statistical downscaling of precipitation and temperature, the explanatory variables are obtained from National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis data set. All possible regression approach is used to establish the most parsimonious relationship between precipitation, temperature, and climatic variables. Selected predictors have been used in training of artificial neural networks-based downscaling models and the trained models with the obtained relationships have been operated to produce scenario precipitation and temperature from the simulations of third Generation Coupled Climate Model. Biases from downscaled outputs have been reduced after downscaling process. Finally, the corrected downscaled outputs have been transformed to runoff by means of a monthly parametric hydrological model GR2M to assess the probable impacts of temperature and precipitation changes on runoff. According to the A1B climate scenario results, statistically significant trends are foreseen for precipitation, temperature, and runoff in the study basin.  相似文献   

18.
In this study, we focus on changes in three important components of the hydrological-cycle in the Haihe River basin (HRB) during 1957-2005: precipitation (Prep), actual evaportranspiration (ETa), and pan evaporation (PE)-a measure of potential evaporation. The changes in these components have been evaluated in relation to changes in the East Asian summer monsoon. Summer Prep for the whole basin has decreased significantly during 1957-2005. Recent weakening of the convergence of the integrated water vapor flux, in combination with a change from cyclonic-like large-scale circulation conditions to anti-cyclonic-like conditions, led to the decrease in the summer Prep in the HRB. ETa is positively correlated with Prep on the interannual timescale. On longer timescales, however, ETa is less dependent on Prep or the large-scale circulation. We found negative trends in ETa when the ERA40 reanalysis data were used, but positive trends in ETa when the NCEP/NCAR reanalysis data were used. PE declined during the period 1957-2001. The declining of PE could be explained by a combination of declining solar radiation and declining surface wind. However, the declining solar radiation may itself be related to the weakening winds, due to weaker dispersion of pollution. If so, the downward trend of PE may be mainly caused by weakening winds.  相似文献   

19.
近60年来中国主要流域极端降水演变特征   总被引：1，自引：0，他引：1  
江洁  周天军  张文霞 《大气科学》2022,46(3):707-724
在全球增暖背景下,中国极端降水事件及洪涝、干旱等次生灾害近年来频发,严重影响生态系统、人民的生产生活和社会经济发展。本文基于气候变化检测和指数专家组（ETCCDI）定义的10个降水指数,利用中国台站日降水资料,系统分析了1961～2017年中国及九大流域片降水变化情况,并利用空间场显著性检验考察不同降水指数的显著变化是否与外强迫作用有关。结果表明,各降水指数的变化具有区域性特征。整体而言,全国范围内平均降水、降水强度、极端强降水和连续性强降水呈增强趋势的台站数多于呈减弱趋势的台站数,呈显著增强趋势的台站占比不可能仅由气候系统内部变率引起,还受到外强迫的影响。此外,中国大部分站点连续干旱日数（CDD）减少,观测中CDD呈显著减弱趋势的台站占比也与外强迫作用有关。九大流域片中,内陆河片能够观测到平均降水、降水强度、极端强降水和连续性强降水的增多以及连续干旱日数的减少,有洪涝灾害增多的风险,且上述变化可归因为外强迫的作用。长江流域片、东南诸河片和珠海流域片平均降水、极端强降水和连续性强降水均增强,其中强降水的变化与外强迫作用有关。西南诸河片极端强降水增强,但大部分站点CDD呈增加趋势,有干旱增加的风险。黄河流域片、海河流域片、淮河流域片及松辽河流域片的大部分站点及区域平均结果中,降水指数多无显著变化趋势。增暖背景下,不同流域片呈现出不同的降水变化特征,将面临不同的气候灾害风险。  相似文献   

20.
利用1961—1990年江淮流域逐日降水资料、NCEP/NCAR再分析资料和HadCM3 SRES A1B情景下模式预估资料,采用典型相关分析统计降尺度方法,评估降尺度模型对当前极端降水指数的模拟能力,并对21世纪中期和末期的极端降水变化进行预估。结果表明：通过降尺度能够有效改善HadCM3对区域气候特征的模拟能力,极端降水指数气候平均态相对误差降低了30%～100%,但降尺度结果仍然在冬季存在湿偏差、夏季存在干偏差;在SRES A1B排放情景下,该区域大部分站点的极端强降水事件将增多,强度增大,极端强降水指数的变化幅度高于平均降水指数,且夏季增幅高于冬季;冬季极端降水贡献率（R95t）在21世纪中期和末期的平均增幅分别为14%和25%,夏季则分别增加24%和32%。  相似文献   

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