Probability Assessment of Temperature and Precipitation over China by CMIP5 Multi-Model Ensemble

doi:10.7522/j.issn.1000-694X.2013.00381

JOURNAL OF DESERT RESEARCH

2014, Vol. 34

Issue (3): 795-804 DOI: 10.7522/j.issn.1000-694X.2013.00381

Probability Assessment of Temperature and Precipitation over China by CMIP5 Multi-Model Ensemble

Yang Xuan¹, Li Dongliang¹, Tang Xu²

1. College of Atmospheric Sciences/Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science & Technology, Nanjing 210044, China;
2. Shanghai Meteorological Bureau, Shanghai 200030, China

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Abstract By applying climate projections based on 30 Atmosphere-Ocean General Circulation Models (AOGCMs) under representative concentration pathway (RCP) scenarios in the Coupled Model Inter-comparison Project Phase 5 (CMIP5), we assess temperature and precipitation over China in terms of ensemble method. However, significant uncertainties exist among the AOGCM outputs. In this paper, a pseudo global warming (PGW) method is assumed to be a linear coupling of contemporary climatic fields and the difference component of climate perturbation signals by AOGCMs. Surface temperature rise of 1 ℃, 2 ℃, and 3 ℃ and precipitation increase 10%, 20% and 30% above the present level are assessed and a probabilistic approach is used to address the uncertainties. The results show that the ensembles of data are obtained through the PGW method, which efficiently reserves the contemporary climatic information. Warming is expected in all regions of China, with the northern China regions showing greater warming than the southern China regions, especially Tibet region. The increase linear trend of temperature is 0.28 ℃/10a in most parts of northern China. Projected temperature would raise 1 ℃ with above 50% in northern China at the start of 21 century. In the end of 21 century, surface temperature would exceed 2.0 ℃ with more than a probability of 60% in all regions of China. The northern Xinjiang and the southern Tibet would above 50% with a 3 ℃ warming. Projected precipitation would increase, and the increasing precipitation in the northern regions would more significant than in the southern China regions. The projected precipitation would increase more than 30% with a probability of 70% in China.

Key words: CMIP5 ensemble projection probability uncertainty climate change

Received: 19 November 2013 Published: 20 May 2014

ZTFLH:

P467

Corresponding Authors: 李栋梁（Email：lidl@nuist.edu.cn） E-mail: lidl@nuist.edu.cn



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	Yang Xuan
	Li Dongliang
	Tang Xu

Cite this article:

Yang Xuan, Li Dongliang, Tang Xu. Probability Assessment of Temperature and Precipitation over China by CMIP5 Multi-Model Ensemble. JOURNAL OF DESERT RESEARCH, 2014, 34(3): 795-804.

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http://www.desert.ac.cn/EN/10.7522/j.issn.1000-694X.2013.00381 OR http://www.desert.ac.cn/EN/Y2014/V34/I3/795

[1] 刘波,马柱国,冯锦明.1960-2004年新疆地区地表水热过程的数值模拟研究Ⅰ.以观测资料为基础的陆面过程模型大气驱动场的发展[J].中国沙漠,2012,32(2):491-502.
[2] 刘晓云,李栋梁,王劲松.1961-2009年中国区域干旱状况的时空变化特征[J].中国沙漠,2012,32(2):473-483.
[3] 贾丽红,李海燕,李如琦,等.南疆“3~12”强沙尘暴天气数值模拟与诊断分析[J].中国沙漠,2012,32(4):1135-1141.
[4] 李茜,魏凤英,李栋梁.近540 a东亚夏季海平面气压场的重建及其与数值模拟的比较[J].中国沙漠,2012,32(4):1017-1024.
[5] 石彦军,任余龙,王式功,等.BCC_CSM气候模式对中国区域气候变化模拟能力的检验[J].高原气象,2012,31(5):1257-1267.
[6] 李振朝,韦志刚,吕世华,等.CMIP5部分模式气温和降水模拟结果在北半球及青藏高原的检验[J].高原气象,2013,32(4):921-928.
[7] 王澄海,孙超.一个基于WRF+CLM区域气候模式(WRFC)的建立及初步试验[J].高原气象,2013,32(6):1626-1637.
[8] 黄乾,姚素香,张耀存.区域气候模式对中国沙尘天气气候特征的模拟研究[J].中国沙漠,2012,32(1):188-197.
[9] 李秀萍,徐宗学,程华琼.多模式集合预估21世纪淮河流域气候变化情景[J].高原气象,2012,31(6):1622-1635.
[10] Chen H P.Projected change in extreme rainfall events in China by the end of the 21st century using CMIP5 models[J]. Chinese Science Bulletin,2013,58:1-10.
[11] Xu C H,Xu Y.The projection of temperature and precipitation over China under RCP scenarios using a CMIP5 multi-model ensemble[J].Atmospheric and Oceanic Science Letters,2012,5:527-533.
[12] Xu Y,Xu C H.Preliminary assessment of simulations of climate changes over China by CMIP5 multi-models[J].Atmospheric and Oceanic Science Letters,2012,5:489-494.
[13] Zhang Y.Projections of 2~0 ℃ warming over the globe and China under RCP4~5[J].Atmospheric and Oceanic Science Letters,2012,5:514-520.
[14] Jiang D B,Zhang Y,Sun J Q.Ensemble projection of 1-3 ℃ warming in China[J].Chinese Science Bulletin,2009,54(24):3326-3334.
[15] 李博,周天军.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.2010,6(4):270-276.
[16] 姜大膀,富元海.2 ℃全球变暖背景下中国未来气候变化预估[J].大气科学,2012,36(2):234-246.
[17] 蒋冲,王文丽,陈爱芳,等.近52年渭河流域气候变化对植被净第一性生产力的影响[J].中国沙漠,2013,33(3):952-957.
[18] 裴亮,黄森旺,陈丽萍,等.京津沙源区植被的时空变化及其对气候因子的响应[J].中国沙漠,2013,33(5):1593-1597.
[19] 杨绚,汤绪,陈葆德,等.多模式气候预估对华北冬小麦产量模拟的不确定性分析[J].地理科学进展,2013,32(4):627-636.
[20] IPCC.Climate Change 2007:Impacts,Adaptation and Vulnerability [M].Cambridge,UK:Cambridge University Press,2007:779-810.
[21] Moss R H,Edmonds J A,Hibbard K A,et al.The next generation of scenarios for climate change research and assessment[J].Nature,2010,463:747-756.
[22] 王绍武,罗勇,赵宗慈,等.气候模式[J].气候变化研究进展,2013,9(2):150-154.
[23] Maslin M,Austin P.Uncertainty:climate models at their limit?[J].Nature,2012,486:183-184.
[24] Xu Y,Gao X J, Giorgi F.Upgrades to the reliability ensemble averaging method for producing probabilistic climate-change projections[J].Climate Research,2010,41(1):61-81.
[25] Semenov M A,Stratonovitch P.Use of multi-model ensembles from global climate models for assessment of climate change impacts[J].Climate Research,2010,41(1):1-14.
[26] Collins M.Ensembles and probabilities:a new era in the prediction of climate change[J].Philosophical Transactions of the Royal Society A,2007,365:1957-1970.
[27] Knutson T R,Zeng F R,Wittenberg A T.Multimodel assessment of regional surface temperature trends:CMIP3 and CMIP5 twentieth-century simulations[J].Journal of Climate,2013,26(22):8709-8743.
[28] Schär C,Frei C,L thi D,et al.Surrogate climate-change scenarios for regional climate models[J].Geophysical Research Letters,1996,23(6):669-672.
[29] Kimura F,Kitoh A.Downscaling by Pseudo Global Warming Method[R].The Final Report of ICCAP,2007,4346.
[30] Rasmussen R,Liu C,Ikeda K,et al.High-resolution coupled climate runoff simulations of seasonal snowfall over Colorado:a process study of current and warmer climate[J].Journal of Climate,2011,24(12):3015-3048.
[31] Yoshikane T,Kimura F,Kawase H,et al.Verification of the performance of the pseudo-global-warming method for future climate changes during June in East Asia[J].SOLA,2012,8:133-136.
[32] van Vuuren D P,Edmonds J,Kainuma M,et al.The representative concentration pathways:an overview[J].Climatic Change,2011,109(1):5-31.
[33] Taylor K E,Stouffer R J,Meehl G A.An overview of CMIP5 and the experiment design[J].Bulletin of the American Meteorological Society,2012,93(4):485-498.
[34] Meinshausen M,Meinshausen N,Hare W,et al.Greenhouse-gas emission targets for limiting global warming to 2 ℃[J].Nature,2009,458(7242):1158-1162.

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