高原气象

高原气象 >

2018 , Vol. 37 >Issue 3: 796 - 805

DOI: https://doi.org/10.7522/j.issn.1000-0534.2017.00064

论文

CDF-T方法在站点尺度日降水预估中的应用

  • 吴蔚 ,
  • 梁卓然 ,
  • 刘校辰
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  • 上海市气候中心/中国气象局上海城市气候变化应对重点开放实验室, 上海 200030;杭州市气象局, 浙江 杭州 310008

收稿日期: 2017-05-15

  网络出版日期: 2018-06-28

基金资助

国家重点研发计划项目（2016YFC0502700）；国家自然科学基金项目（41571044，41401661）

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Projection of the Daily Precipitation Using CDF-T Method at Meteorological Observation Site Scale

  • WU Wei ,
  • LIANG Zhuoran ,
  • LIU Xiaochen
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  • Shanghai Climate Center/Key Laboratory of Cities Mitigtion and Adaptation to Climate Change in Shanghai(CMACC), Shanghai 200030, China;Hangzhou Meteorological Bureau, Hangzhou 310008, Zhejiang, China

Received date: 2017-05-15

  Online published: 2018-06-28

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摘要

基于1961-2015年上海降水观测数据和8个全球气候模式GCMs模拟的日降水量数据，采用累计概率分布函数构建转换模型CDF-T建立了站点尺度日降水量的统计降尺度模型。结果表明，降尺度模型显著改善了GCMs对降水日数偏多、降水强度偏低和降水量偏少的模拟结果。与利用全年日降水序列建模结果相比，利用汛期日降水序列建模更好地刻画了汛期降水的累计概率分布曲线，同时提高了汛期总降水量、降水强度和年平均暴雨日数、暴雨量、暴雨强度的均值和变化趋势的降尺度效果。模型对较长年份的暴雨重现期订正效果更佳。与当代（2006-2015年）气候相比，2016-2095年上海降水呈现以下特征：全年和汛期总降水量和降水强度增加，降水日数减少，未来可能出现更多的旱涝年；汛期降水极端性增强，暴雨降水均值和极端值均增加；50年以上重现期的年最大日降水量未来呈前40年减少后40年增加的变化。CDF-T模型为站点尺度气候变化影响评估和未来预估提供降尺度技术和基础气候数据。

关键词： 累计概率分布函数; 统计降尺度; 日降水量; 全球气候模式

本文引用格式

吴蔚 , 梁卓然 , 刘校辰 . CDF-T方法在站点尺度日降水预估中的应用[J]. 高原气象, 2018 , 37(3) : 796 -805 . DOI: 10.7522/j.issn.1000-0534.2017.00064

Abstract

Based on climate change scenarios derived from 8 GCMs (Global Climate Models) and daily precipitation data during the period of 1961-2015 in Shanghai, a cumulative distribution function-transform (CDF-T) model was developed to downscale the daily precipitation on the meteorological observation site scale. The results showed that this downscaling method can improve the simulation results, which has more rain days, lower precipitation intensity and less precipitation. It shows that using the daily data in flood season to develop downscaling model can improve the CDF curve, the total amount and intensity of precipitation in flood season compared with that using whole-year daily data. Similarly, this method can improve the correlation of the observed and correct mean value of the days, amount and intensity of the rainstorm as well as the daily maximum precipitation in longer return periods. For the period of 2016-2095, it was found that the precipitation and its intensity will increase, while the rainy days both for the whole year and flood seasons will decrease in Shanghai, compared with the current stage (2006-2015). There is likely to have more drought and flood events and intensify extreme rainfall events with the increased average and extreme values of rainstorm. The daily maximum precipitation of the recurrence intervals over 50 years will decrease in the former 40 years and increase in the later 40 years in the future. Consequently, the downscaling model of CDF-T can be applied in meteorological observation site scale and provide the downscaling method and climate data for climate change projection and assessment.

Key words： Cumulative distribut; statistical downscal; daily precipitation; global climate model

参考文献

[1]Frich P, Alexander L V, Della-Marta P, et al, 2002.Observed coherent change in climatic extremes during the second half of the twentieth century[J].Climate Res, 19(3):193-212.
[2]Lang X M, Sui Y, 2013.Changes in mean and extreme climates over China with a 2℃ global warming[J].Chinese Sci Bull, 58 (12):1453-1461.
[3]Michelangeli P A, Vrac M, Loukos H, 2009.Probabilistic downscaling approaches:application to wind cumulative distribution functions[J].Geophys Res Lett, 36 (11):163-182.DOI:10.1029/2009GL038401.
[4]Taylor K E, Stouffer B J, Meehl G A, 2012.An overview of CMIP5 and the experiment design[J].Bull Amer Meteor Soc, 93(4):485-498.DOI:10.1175/BAMS-D-11-00094.1.
[5]Xu C H, Xu Y, 2012.The projection of temperature and precipitation over China under RCP scenarios using a CMIP5 multi-model ensemble[J].Atmos Ocean Sci Lett, 5(6):527-533.DOI:10.1080/16742834.2012.11447042.
[6]Xu Y, Xu C H, 2012.Preliminary assessment of simulations of climate changes over China by CMIP5 multi-models[J].Atmos Ocean Sci Lett, 5(6):489-494.DOI:10.1080/16742834.2012.11447041.
[7]Yao Y, Luo Y, Huang J B, et al, 2013.Comparison of monthly temperature extremes simulated by CMIP3 and CMIP5 models[J].J Climate, 26 (19):7692-7707.DOI:=10.1175/JCLI-D-12-00560.1.
[8]Chen X C, Xu Y, Xu C H, et al, 2014.Assessment of precipitation simulations in china by CMIP5 multi-models[J].Adv Climate Change Res, 10(3):217-225.<br/>陈晓晨, 徐影, 许崇海, 等, 2014.CMIP5全球气候模式对中国地区降水模拟能力的评估[J].气候变化研究进展, 10(3):217-225.
[9]Chu Q, Xu Z X, Liu W F, et al, 2015.Assessment on 24 global climate models in the CMIP5 over the Yangtze river[J].Res Environ Yangtze Basin, 24(1):81-89.<br/>初祁, 徐宗学, 刘文丰, 等, 2015.24个CMIP5模式对长江流域模拟能力评估[J].长江流域资源与环境, 24(1):81-89.
[10]Cui Y, Jiang Z H, Chen W L, 2010.Projection of extreme precipitation events in the 21st century in the Changjiang-Huaihe river valley based on canonical correlation analysis[J].Adv Climate Res, 6(6):405-410.<br/>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.
[11]Dong M, Wu T W, Zuo Q J, et al, 2018.A simulation comparison study on the climatic characteristics of the south Asia high by the BCC climate system model[J].Plateau Meteor, 37(2):455-468.DOI:10.7522/j.issn.1000-0534.2017.00051.<br/>董敏, 吴统文, 左群杰, 等, 2018.气候系统模式对南亚高压气候特征的模拟比较研究[J].高原气象, 37(2):455-468.
[12]Fan L J, Fu Z B, Chen D L, 2005.Review on creating future climate change scenarios by statistical downscaling techniques[J].Adv Earth Sci, 20(3):320-329.<br/>范丽军, 付淙斌, 陈德亮, 2005.统计降尺度法对未来区域气候变化情景预估的研究进展[J].地球科学进展, 20(3):320-329.
[13]Jiang Y M, Wu H M, 2013.Simulation capabilities of 20 CMIP5 models for annual mean air temperatures in central asia[J].Adv Climate Res, 9(2):110-116.<br/>姜燕敏, 吴昊旻, 2013.20个CMIP5模式对中亚地区年平均气温模拟能力评估[J].气候变化研究进展, 9(2):110-116.
[14]Kang H W, Zhu C W, Zuo Z Y, et al, 2012.Statistical downscaling of pattern projection using multi-model output variables as predictors[J].Acta Meteor Sinica, 70(2):192-201.<br/>康红文, 祝从文, 左志燕, 等, 2012.多模式集合预报及其降尺度技术在东亚夏季降水预测中的应用[J].气象学报, 70(2):192-201.
[15]Li P D, Si J H, Feng Q, et al, 2018.Temporal and spatial variation of extreme precipitation events in Dunhuang and surrounding areas from 1958 to 2015[J].Plateau Meteor, 37(2):535-544.DOI:10.7522/j.issn.1000-0534.2017.00055.<br/>李培都, 司建华, 冯起, 等, 2018.1958-2015年敦煌及周边地区极端降水事件的时空变化特征[J].高原气象, 37(2):535-544.
[16]Liu L L, Ren G Y, 2012.Percentile statistical downscaling method and its application in the correction of GCMs daily precipitation in China[J].Plateau Meteor, 31(3):715-722.<br/>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.
[17]Liu X H, Wu H B, 2006.Probability distribution of summer daily precipitation in China[J].Trans Atmos Sci, 29(2):173-180.<br/>刘学华, 吴洪宝, 2006.中国夏季雨日降水量的概率分布[J].大气科学学报, 29(2):173-180.
[18]Shan J K, Liang X Y, Sun L H, et al, 2016.Prediction of extreme winter cold days[J].Plateau Meteor, 35(6):1609-1614.DOI:10.7522/j.issn.1000-0534.2015.00112.<br/>单机坤, 梁潇云, 孙林海, 等, 2016.冬季极端低温日数预测方法研究[J].高原气象, 35(6):1609-1614.
[19]Sun J H, Zhao S X, Fu S M, et al, 2013.Multi-scale characteristics of record heavy rainfall over Beijing area on July 21, 2012[J].Chinese J Atmos Sci, 37 (3):705-718.<br/>孙建华, 赵思雄, 傅慎明, 等, 2013.2012年7月21日北京特大暴雨的多尺度特征[J].大气科学, 37(3):705-718.
[20]Wu Q, Jiang X W, Xie J, 2017.Evaluation of surface air temperature in southwestern China simulated by the CMIP5 models[J].Plateau Meteor, 36(2):358-370.DOI:10.7522/j.issn.1000-0534.2016.00046.<br/>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.
[21]Wu W, Mu H Z, Liang Z R, et al, 2016.Projected changes in extreme temperature and precipitation events in Shanghai based on CMIP5 simulations[J].Climat Environ Res, 21 (3):269-281.<br/>吴蔚, 穆海振, 梁卓然, 等, 2016.CMIP5全球气候模式对上海极端气温降水事件的情景预估[J].气候与环境研究, 21(3):269-281.
[22]Yang X, Li D L, Tang X, 2014.Probability assessment of temperature and precipitation over China by CMIP5 multi-model ensemble[J].J Desert Res, 34(3):795-804.<br/>杨绚, 李栋梁, 汤绪, 2014.基于CMIP5多模式集合资料的中国气温和降水预估及概率分析[J].中国沙漠, 34(3):795-804.
[23]Zhang M Y, Peng D Z, Hu L J, 2013.Research progress on statistical downscaling methods[J].South-to-North Water Transfers and Water Science &amp; Technology, 11(3):118-122.<br/>张明月, 彭定志, 胡林涓, 2013.统计降尺度方法研究进展综述[J].南北水调与水利科技, 11(3):118-122.
[24]Zhang Y F, Wang Y, Zhao C Y, et al, 2008.Climatic characters of precipitation days in Liaoning Province[J].J Nature Disaster, 17(5):75-81.<br/>张运福, 王颖, 赵春雨, 等, 2008.辽宁省降水日数的气候变化特征[J].自然灾害学报, 17(5):75-81.
[25]Zhou X H, Xiao Z N, 2014.Climate projection over Yunnan Province and the surrounding regions based on CMIP5 data[J].Climat Environ Res, 19(5):601-613.<br/>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.
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