The Simulation of Extreme Precipitation over Hunan Province based on the Statistical Downscaling Method of Transform Cumulative Distribution Function (CDF-t)

  • ZHOU Li ,
  • LAN Mingcai ,
  • CAI Ronghui ,
  • HUANG Juan ,
  • JIANG Zhihong
Expand
  • Hunan Meteotological Observatory, Changsha 410118, Hunan, China;Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China

Received date: 2018-04-18

  Online published: 2019-08-28

Abstract

Intensify, frequency and duration of Extreme Precipitation would increase in the future under a warming climate. Especially for Hunan where is sensitive to the climate change. Based on the CMIP5 historical simulations datasets, the ability of the CMIP5 models in simulating the spatial pattern andinterannual variability of extreme precipitation over Hunan province are evaluated using the statistical downscaling method of transform cumulative distribution function (CDF-t) combined with four extreme precipitation indices. The results show that due to the low resolution of GCM models, characteristics of extreme precipitation related to the terrain and atmospheric circulation over Hunan were not exactly reproduced. There are great differences between patterns, and the model sets have relatively poor simulation results. CDF-t statistical downscaling can improve CMIP5 simulation of extreme precipitation in Hunan by establishing the functional relationship between large-scale variable CDF and the same regional variable CDF. As far as the spatial structure is concerned, this method can greatly improve the spatial structure ability of the model to simulate the heavy rain days (R10) and the continuous five-day maximum precipitation (R5d), and shows a high consistency between the models, especially the effect of R10 improvement is the most remarkable. Compared with the observation, the spatial average absolute error in Hunan area reaches 2.18 d, which is lower. The absolute error before scale is reduced by 45.46%. As far as time variability is concerned, this method can greatly improve the time variability ability of model simulation R90P and R5d. After scaling down, the IVS value decreases from 2.2 and 1.5 to 0.3 and 0.6 respectively.

Cite this article

ZHOU Li , LAN Mingcai , CAI Ronghui , HUANG Juan , JIANG Zhihong . The Simulation of Extreme Precipitation over Hunan Province based on the Statistical Downscaling Method of Transform Cumulative Distribution Function (CDF-t)[J]. Plateau Meteorology, 2019 , 38(4) : 734 -743 . DOI: 10.7522/j.issn.1000-0534.2018.00122

References

[1]Frich P, Alexander L V, Della-Marta P, et al, 2002. Observed coherent changes in climatic extremes during the second half of the twentieth century[J]. Climate Research, 19(3):193-212.
[2]Jiang Z, Song J, Li L, et al, 2012. Extreme climate events in China:IPCC-AR4 model evaluation and projection[J]. Climatic Change, 110(1/2):385-401.
[3]Michelangeli P A, Vrac M, Loukos H, 2009. Probabilistic downscaling approaches:Application to wind cumulative distribution functions[J]. Geophysical Research Letters, 36(11):163-182.
[4]Piani C, Weedon G P, Best M, et al, 2010. Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models[J]. Journal of Hydrology (Amsterdam), 395(3/4):199-215.
[5]Taylor K E, 2011. Summarizing multiple aspects of model performance in a single diagram[J]. Journal of Geophysical Research, 106:7183-7192.
[6]Xu Y, Gao X J, Giorgi F, 2010. Upgrades to the reliability ensemble averaging method for producing probabilistic climate-change projections[J]. Climate Research, 41(1):61-81.
[7]You Q, Kang S, Aguilar E, et al, 2011. Changes in daily climate extremes in China and their connection to the large scale atmospheric circulation during 1961-2003[J]. Climate Dynamics, 36(11/12):2399-2417.
[8]Zhou T, Yu R, 2006. Twentieth century surface air temperature over China and the globe simulated by coupled climate models[J]. Journal of Climate, 19 (22):5843-5858.
[9]曾小凡, 陈华, 张增信, 2009.长江中下游流域气候变化敏感区域的年降水量变化研究[J].科技创新导报, (8):237.
[10]曾小凡, 周建中, 翟建青, 等, 2011. 2011-2050年长江流域气候变化预估问题的探讨[J].气候变化研究进展, 7(2):116-122.
[11]初祁, 徐宗学, 刘文丰, 等, 2015. 24个CMIP5模式对长江中下游流域模拟能力评估[J].长江中下游流域资源与环境, 24(1):81-89.
[12]崔妍, 江志红, 陈威霖, 2010.典型相关分析方法对21世纪江淮流域极端降水的预估试验[J].气候变化研究进展, 6(6):405-410.
[13]高歌, 陈德亮, 徐影, 等, 2008.未来气候变化对淮河流域径流的可能影响[J].应用气象学报, 19(6):741-748.
[14]高涛, 谢立安, 2014.近50年来中国极端降水趋势与物理成因研究综述[J].地球科学进展, 29(5):577-589.
[15]胡芩, 姜大膀, 范广洲, 2015.青藏高原未来气候变化预估:CMIP5模式结果[J].大气科学, 39(2):260-270.
[16]桓玉, 李跃清, 2018.夏季东亚季风和南亚季风协同作用与我国南方夏季降水异常的关系[J].高原气象, 37(6):117-131. DOI:10.7522/j.issn.1000-0534.2018.00044.
[17]黄安宁, 张耀存, 2004.海温季节和年际变化对东亚区域气候变率模拟的影响[J].南京大学学报:自然科学版, 40(3):319-329.
[18]李博, 周天军, 2010.基于IPCC A1B情景的中国未来气候变化预估:多模式集合结果及其不确定性[J].气候变化研究进展, 6(4):270-276.
[19]李红梅, 周天军, 宇如聪, 2008.近40年我国东部盛夏日降水特征变化分析[J].大气科学32(2):358-370.
[20]廖玉芳, 彭嘉栋, 崔巍, 2012.湖南农业气候资源对全球气候变化的响应分析[J].中国农学通报, 28(8):287-293.
[21]刘吉峰, 李世杰, 丁裕国, 2008.基于气候模式统计降尺度技术的未来青海湖水位变化预估[J].水科学进展, 19(2):184-191.
[22]刘绿柳, 任国玉, 2012.百分位统计降尺度方法及在GCMs日降水订正中的应用[J].高原气象, 31(3):715-722.
[23]刘维成, 张强, 傅朝, 2017.近55年来中国西北地区降水变化特征及影响因素分析[J].高原气象, 36(6):1533-1545. DOI:10.7522/j.issn.1000-0534.2017.00081.
[24]刘向培, 王汉杰, 何明元, 2012.应用统计降尺度方法预估江淮流域未来降水[J].水科学进展, 23(1):29-37.
[25]陆魁东, 屈右铭, 张超, 等, 2007.湖南气候变化对农作物生产潜力的响应[J].湖南农业大学学报(自然科学版), 33(1):9-13.
[26]穆振侠, 姜卉芳, 2015.基于统计降尺度方法的高寒山区未来气候变化预估[J].干旱区研究, 32(2):290-296.
[27]潘留杰, 张宏芳, 陈小婷, 等, 2017. ECMWF集合预报在中国中部地区的降水概率预报性能评估[J].高原气象, 36(1):138-147. DOI:10.7522/j.issn.1000-0534.2016.00014.
[28]苏布达, 姜彤, 任国玉, 等, 2006.长江中下游流域1960-2004年极端强降水时空变化趋势[J].气候变化研究进展, 2(1):9-14.
[29]王爱珍, 陈江民, 李艳, 等, 2008.湖南冬季气温年代际变化与热岛效应和纬度的关系[J].安徽农业科学, 36(7):2874-2876.
[30]王小玲, 翟盘茂, 2008. 1957-2004年中国不同强度级别降水的变化趋势特征[J].热带气象学报, 24(5):459-466.
[31]伍清, 蒋兴文, 谢洁, 2017. CMIP5模式对西南地区气温的模拟能力评估[J].高原气象, 36 (2):358-370. DOI:10.7522/j.issn.1000-0534.2016.00046.
[32]熊伟, 居辉, 许吟隆, 等, 2006.气候变化对中国农业温度阈值影响研究及其不确定性分析[J].地球科学进展, 21(1):70-76.
[33]翟盘茂, 王萃萃, 李威, 2007.极端降水事件变化的观测研究[J].气候变化研究进展, 3(3):144-148.
[34]张蓓, 戴新刚, 2017.基于CMIP5的2006-2015年中国气温预估偏差分析及订正[J].高原气象, 36(6):1619-1629. DOI:10.7522/j.issn.1000-0534.2016.00136.
[35]张剑明, 黎祖贤, 章新平, 2008. 1960-2005年湖南省降水的变化[J].气候变化研究进展, 4(2):101-105.
[36]张莉, 丁一汇, 吴统文, 等, 2013. CMIP5模式对21世纪全球和中国年平均地表气温变化和2℃升温阈值的预估[J].气象学报, 71(6):1047-1060.
[37]张瑞, 彭月, 2011.1951-2010年湖南省降水气候特征分析[J].现代农业科技, (19):22-22.
[38]张武龙, 张井勇, 范广洲, 2015. CMIP5模式对我国西南地区干湿季降水的模拟和预估[J].大气科学, 39(3):559-570.
[39]张增信, Klaus F, 姜彤, 等, 2007. 2050年前长江中下游流域极端降水预估[J].气候变化研究进展, 3(6):340-344.
[40]赵芳芳, 徐宗学, 2007.统计降尺度方法和Delta方法建立黄河源区气候情景的比较分析[J].气象学报, 65(4):653-662.
[41]赵天保, 陈亮, 马柱国, 2014. CMIP5多模式对全球典型干旱半干旱区气候变化的模拟与预估[J].科学通报, 59(12):1148-1163.
[42]周莉, 胡瑞卿, 李伟, 等, 2019.湖南省夏季极端降水异常时空特征及其成因分析[J].气象科学, 38(6):135-145.
[43]周莉, 兰明才, 蔡荣辉, 等, 2018. 21世纪前期长江中下游流域极端降水预估及不确定性分析[J].气象学报, (1):47-61.
[44]周莉, 江志红, 2017.基于转移累计概率分布统计降尺度方法的未来降水预估研究——以湖南省为例[J].气象学报, 75(2):33-45.
[45]周秀华, 肖子牛, 2014.基于CMIP5资料的云南及周边地区未来50年气候预估[J].气候与环境研究, 19(5):601-613.
Outlines

/