Simulation of extreme precipitation over the Yangtze River Basin using Wakeby distribution

Based on the daily observational precipitation data at 147 stations in the Yangtze River Basin during 1960–2005 and projected daily data of 79 grid cells from the ECHAM5/ MPI-OM model in the 20th and 21st century, time series of precipitation extremes which contain AM (Annual Maximum) and MI (Munger Index) are constructed. The distribution feature of precipitation extremes is analyzed based on the two index series. Three principal results were obtained, as stated in the sequel. (i) In the past half century, the intensity of extreme heavy precipitation and drought events was higher in the mid-lower Yangtze than in the upper Yangtze reaches. Although the ECHAM5 model still can’t capture the precipitation extremes over the Yangtze River Basin satisfactorily, spatial pattern of the observed and the simulated precipitation extremes are much similar to each other. (ii) For quantifying the characteristics of extremely high and extremely low precipitation over the Yangtze River Basin, four probability distributions are used, namely: General Extreme Value (GEV), General Pareto (GPA), General Logistic (GLO), and Wakeby (WAK). It was found that WAK can adequately describe the probability distribution of precipitation extremes calculated from both observational and projected data. (iii) Return period of precipitation extremes show spatially different changes under three greenhouse gas emission scenarios. The 50-year heavy precipitation and drought events from simulated data during 1951–2000 will become more frequent, with return period below 25 years, for the most mid-lower Yangtze region in 2001–2050. The changing character of return periods of precipitation extremes should be taken into account for the hydrological design and future water resources management.     

Application of statistical downscaling in GCMs at constructing the map of precipitation in the Mekong River basin

This study used the Statistical Downscaling Model (SDSM) to increase the resolution of the Global Circulation Model (GCM) at forecasting the amount of precipitation in the Mekong River basin. The model was initially calibrated using the reanalysis data by National Centers for Environmental Prediction (NCEP) and the data on observed precipitation. The results of comparison between the SDSM calculations and the observational data were used to generate the distribution of precipitation until 2099 using HadCM3, SRES A2 and B2 scenarios. After total annual precipitation had been downscaled, the percentage change in precipitation was interpolated among the selected stations in order to create precipitation maps. Both A2 and B2 scenario indicate the possibility of remarkable increase in annual precipitation in the Mekong basin, which may amount to 150 and 110%, respectively. The December–January–February precipitation is likely to increase significantly in the most part of the region, and in some areas, almost by three times. On the contrary, the June–July–August precipitation will remarkably decrease in the different parts of the territory under study. As the water resource sector is the backbone of the economics of this region including hydropower and agricultural sector, the changes in the amount of precipitation and its interannual variability can put the usual water business into stress. Thus, proper adaptive measures should be applied both at local and at regional levels for the benefit of all associated countries utilizing the resource of the Mekong River.     

高分辨率统计降尺度数据集NEX-GDDP对中国极端温度指数模拟能力的评估

利用1986—2005年中国地面气象台站观测的格点化逐日气温资料（CN05.1）评估了高分辨率统计降尺度数据集NASA Earth Exchange/Global Daily Downscaled Projections（NEX-GDDP）中21个全球气候模式对中国极端温度指数的模拟能力。在选用了日最低温度最大值（TNx）、日最高温度最大值（TXx）、暖夜指数（TN90p）和暖昼指数（TX90p）来研究极端温度事件的变化。结果显示:（1）除MRI-CGCM3模拟的日最高温度最大值外,其余模式对4个指数的模拟结果均表现出与观测一致的上升趋势,但模拟结果的平均值相对观测平均低0.26℃/（10 a）（日最低温度最大值）、0.19℃/（10 a）（日最高温度最大值）、2.21%/（10 a）（暖夜指数）、1.04%/（10 a）（暖昼指数）。（2）不同模式对各指数变化趋势空间分布特征的模拟存在较大差别,对日最低温度最大值、日最高温度最大值、暖夜指数和暖昼指数模拟能力最优模式分别为CCSM4、CESM1-BGC、MIROC-ESM-CHEM和bcc-csm1-1。模式模拟的日最低温度最大值和日最高温度最大值气候态平均值与观测值的相关系数在0.97以上。暖夜指数和暖昼指数模拟结果与观测值的标准差比值为0.34—1.58,均方根误差变化为1.6%—3.47%,对这两个指数模拟能力较优的模式分别为MIROC-ESM-CHEM（暖夜指数）和CESM1-BGC（暖昼指数）。（3）综合模式对4个指数在气候态平均值和变化趋势模拟能力的评估结果来看,CanESM2、CESM1-BGC和MIROC-ESM-CHEM显示了相对较高的模拟能力。因此,在利用GDDP-NEX研究未来极端温度事件时,建议将它们作为优选模式。      

长江中下游夏季极端降水指数的变化特征

利用长江中下游地区66个气象站逐日降水资料,通过经验正交函数分解分析中雨以上日数极端降水指数及形成的原因。结果表明:长江中下游中雨以上日数主要表现为全区一致型、南北反向型,且两种分布形势均存在准2 a周期的年际变化和年代际变化;中雨以上日数在1990s开始显著增长,2000s以来,长江以北地区偏多,长江以南地区偏少;2000—2011年,我国东部经向上仍旧存在"反气旋—气旋"水汽输送异常,蒙古高原反气旋型水汽输送加强,引起雨带停滞在长江以北,造成长江以南地区中雨以上日数偏少。     

长江流域年降水预测动力-统计降尺度方法及其应用

基于站点资料、再分析数据和动力气候模式回报数据,利用经验正交函数分解（EOF,Empirical Orthogonal Function）迭代和年际增量方法,探讨了长江流域年尺度降水异常的动力-统计降尺度预测方法及其应用效果。结果表明,基于再分析数据的年尺度环流场,建立的长江流域年尺度降水异常增量的统计降尺度预测方案,其26 a回报检验的距平相关系数（ACC）平均达0.6,证明该方案具有较高的可预报性。进一步利用模式预测的年尺度环流场,建立了年降水异常增量的动力-统计降尺度预测方案,其ACC平均为0.42,显示了较高的回报技巧,远优于模式直接输出的年降水动力预报结果。通过分析调制年降水预报技巧高低的因素发现,赤道中东太平洋年平均海温距平为负值时,预报技巧更高,ACC平均达0.5以上。在拉尼娜发展年或拉尼娜持续年的冷水背景下,利用EOF迭代选取的特征向量偏多时,多尺度的大气环流信息被纳入预测模型中作为预测信号,预测技巧得到了提高。     

CWRF模式对长江流域极端降水气候事件的模拟能力评估

基于1980—2016年长江流域站点观测降水,评估了CWRF区域气候模式对长江流域面雨量和极端降水气候事件的模拟能力.结果表明:CWRF模式能较好地再现1980—2016年长江流域及不同分区降水空间分布及月/季面雨量年际变率,且在冬、春季表现较好,夏、秋季次之.CWRF模式对长江流域面雨量存在系统性高估,对面雨量的模拟...     

NEX-GDDP降尺度数据对中国极端降水指数模拟能力的评估

利用1986—2005年中国地面气象台站观测的格点化逐日降水数据(CN05.1)评估了NASA高分辨率降尺度逐日数据集NEX-GDDP中21个全球气候模式在0.25?(约25 km×25 km)分辨率下对中国极端降水的模拟能力.选取年最大日降水量(RX1D)、年最大5 d降水量(RX5D)、湿日总降水量(PRCPTOT...     

1960-2005年长江中下游极端降水指数变化特征分析

本文利用长江中下游流域内的81个气象站,对长江中下游极端降水指数的时空变化特征进行分析.结果发现,在时间尺度上,长江中下游地区近46 a来极端降水指数呈上升趋势,其中降水强度上升趋势最明显,各个极端降水指数在年代际尺度上具有相同的变化特征,均存在着12 a左右的周期振荡,在年际尺度上,各极端降水指数变化周期并不一致.大雨日数与其他指数相比突变时间比较早,发生在1979年,其他几个指数突变时间比较接近,出现在1990年前后.在空间变化上,除极端湿天降水量在全区均为上升趋势外,其他几种极端降水指数在江苏东部地区、湖北西北部都存在着极端降水指数的负变化趋势,高值区主要分布在江西大部、湖北东南部、湖南东北部地区.     

Evaluation of SDSM developed by annual and monthly sub-models for downscaling temperature and precipitation in the Jhelum basin, Pakistan and India

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.     

SDSM统计降尺度方法对江淮地区地面气温模拟能力评估及其未来情景预估

运用SDSM统计降尺度对江淮地区五个代表站点进行统计降尺度研究,应用独立观测资料验证发现统计降尺度模式具有一定的可靠性.在A2排放情景下,对5个测站的未来情景研究发现,到21世纪中叶,各测站普遍增温1.5℃左右,到本世纪末,增温幅度在4℃左右.这一结论与LMDZ动力降尺度模式结果基本一致,但统计降尺度的模拟值要略高于动力降尺度.     

Factors favorable to frequent extreme precipitation in the upper Yangtze River Valley

Extreme precipitation events in the upper Yangtze River Valley (YRV) have recently become an increasingly important focus in China because they often cause droughts and floods. Unfortunately, little is known about the climate processes responsible for these events. This paper investigates factors favorable to frequent extreme precipitation events in the upper YRV. Our results reveal that a weakened South China Sea summer monsoon trough, intensified Eurasian-Pacific blocking highs, an intensified South Asian High, a southward subtropical westerly jet and an intensified Western North Pacific Subtropical High (WNPSH) increase atmospheric instability and enhance the convergence of moisture over the upper YRV, which result in more extreme precipitation events. The snow depth over the eastern Tibetan Plateau (TP) in winter and sea surface temperature anomalies (SSTAs) over three key regions in summer are important external forcing factors in the atmospheric circulation anomalies. Deep snow on the Tibetan Plateau in winter can weaken the subsequent East Asian summer monsoon circulation above by increasing the soil moisture content in summer and weakening the land–sea thermal contrast over East Asia. The positive SSTA in the western North Pacific may affect southwestward extension of the WNPSH and the blocking high over northeastern Asia by arousing the East Asian-Pacific pattern. The positive SSTA in the North Atlantic can affect extreme precipitation event frequency in the upper YRV via a wave train pattern along the westerly jet between the North Atlantic and East Asia. A tripolar pattern from west to east over the Indian Ocean can strengthen moisture transport by enhancing Somali cross-equatorial flow.     

Evaluation of statistical downscaling in short range precipitation forecasting

The objective of this study is to compare several statistical downscaling methods for the development of an operational short-term forecast of precipitation in the area of Bilbao (Spain). The ability of statistical downscaling methods nested inside numerical simulations run by both coarse and regional model simulations is tested with several selections of predictors and domain sizes. The selection of predictors is performed both in terms of sound physical mechanisms and also by means of “blind” criteria, such as “give the statistical downscaling methods all the information they can process”.Results show that the use of statistical downscaling methods improves the ability of the mesoscale and coarse resolution models to provide quantitative precipitation forecasts. The selection of predictors in terms of sound physical principles does not necessarily improve the ability of the statistical downscaling method to select the most relevant inputs to feed the precipitation forecasting model, due to the fact that the numerical models do not always fulfil conservation laws or because precipitation events do not reflect simple phenomenological laws. Coarse resolution models are able to provide information usable in combination with a statistical downscaling method to achieve a quantitative precipitation forecast skill comparable to that obtained by other systems currently in use.     

RCPs情景下汉江流域未来极端降水的模拟与预估

采用应用于跨行业影响模式比较计划(ISIMIP)的5个CMIP5全球气候模式模拟的历史和未来RCP排放情景下的逐日降水数据,在评估模式对汉江流域1961—2005年极端降水变化特征模拟能力的基础上,进一步计算了RCP2.6、RCP4.5和RCP8.5排放情景下汉江流域未来2016—2060年极端降水总量(R95p)、极端降水贡献率(PEP)、连续5 d最大降水(RX5d)和降水强度(SDII),结果表明:RCP4.5情景下的极端降水指数上升最明显,R95p和RX5d分别较基准期增加12.5%和8.2%,PEP增加3.2个百分点,SDII微弱上升。在不同排放情景下,PEP均有一定的增幅,以流域西北和东南部增幅较大;R95p在流域绝大部分区域表现出一定的增加,且流域东南部和北部是增幅高值区;RX5d在RCP2.6和RCP4.5情景下整体表现为增加的特征,但在RCP8.5情景下整体表现为减少的特征。对极端降水预估的不确定性中,SDII的不确定性最小,RX5d的不确定性最大;不确定性大值区主要位于流域东部、东南部和西北部部分区域。     

Effect of spring soil moisture over the Indo-China Peninsula on the following summer extreme precipitation events over the Yangtze River basin

Climate Dynamics - Extreme precipitation events (EPEs) over the Yangtze River basin (YRB) exert widespread impacts on regional ecological environment and people’s life. Using observed...     

Temporal statistical downscaling of precipitation and temperature forecasts using a stochastic weather generator

Statistical downscaling is based on the fact that the large-scale climatic state and regional/local physiographic features control the regional climate. In the present paper, a stochastic weather generator is applied to seasonal precipitation and temperature forecasts produced by the International Research Institute for Climate and Society(IRI). In conjunction with the GLM(generalized linear modeling) weather generator, a resampling scheme is used to translate the uncertainty in the seasonal forecasts(the IRI format only specifies probabilities for three categories: below normal, near normal, and above normal) into the corresponding uncertainty for the daily weather statistics. The method is able to generate potentially useful shifts in the probability distributions of seasonally aggregated precipitation and minimum and maximum temperature, as well as more meaningful daily weather statistics for crop yields, such as the number of dry days and the amount of precipitation on wet days. The approach is extended to the case of climate change scenarios, treating a hypothetical return to a previously observed drier regime in the Pampas.     

多模式动力降尺度对中国中东部地区极端气温指数的模拟评估

利用LMDZ4变网格大气环流模式分别嵌套于BCC-csm1.1-m、CNRM-CM5、FGOALS-g2、IPSL-CM5A-MR和MPI-ESM-MR等5个全球模式,进行中国中东部地区1961-2005年动力降尺度模拟试验,对比分析降尺度前后各模式对中国中东部极端气温指数的模拟能力。结果表明,相较全球模式,LMDZ4模式较好地刻画了青藏高原、四川盆地等复杂地形的变化,能更好地表现出中国中东部地区极端气温的空间分布。但降尺度改善效果具有明显的区域性差异,对于最高气温、最低气温和霜冻日数,降尺度之后主要在东北、西北、青藏高原以及西南地区改善明显,与观测场的空间相关系数提高至0.95以上,均方根误差低于0.5℃（0.5 d）,且降尺度后模式对最低气温和最高气温空间相关系数的改善程度随地形升高而增大;对于热浪指数,降尺度后在东北、华南以及西南地区热浪分布大值区改善效果明显,但模式间的一致性不高。降尺度在一定程度上模拟出与观测一致的最高、最低气温的线性趋势空间分布,在东北、华北、青藏高原和西南地区最低气温和霜冻日数趋势误差较全球模式小。降尺度模式集合（RMME）对极端气温气候平均场和线性趋势均有较高的模拟能力。多模式动力降尺度能够提高全球模式对中国区域极端气温的模拟能力,为提高未来预估能力提供了基础。