极端温度指数在中国地区基于ETCCDI指数新结果的CMIP5模式输出（英文）
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摘要
使用新的数据集对由气候变化与监测指数专家团队(ETCCDI)定义的温度极端指数TX10p、TN10p、TX90p、TN90p、CSDI、WSDI的时空分布特点进行了研究.这6个指数都是基于百分位数的指数,通过半参数分位数回归的方法计算而得.我们在超出率序列中去除了自相关的影响,从而保证了计算分位数时的独立性条件.我们利用参与多模型比较计划第5阶段(CMIP5)的19种全球气候模型,在历史和3种典型浓度路径RCP 2.6、RCP 4.5、RCP 8.5模拟了6个指数的变化.根据新的结果,中国的高温事件在未来有持续上升趋势,而低温事件有减少趋势.到21世纪末,在RCP26、RCP4.5、RCP8.5情景下,TX90p、TN90p和WSDI会显著上升,而TX10p、TN10p和CSDI会下降.在中国,低温事件频率下降最大的区域是东北地区,而高温事件频率上升最显著的区域是华南和西南地区.
This study provides a view of temporal evolutions and spatial variations in temperature extremes indices(TX10 p,TN10 p,TX90 p,TN90 p,CSDI and WSDI)defined by the Expert Team of Climate Change and Detection Indices(ETCCDI)using a new dataset.All of the six indices are percentile-based and derived through semi-parametric quantile regression.The autocorrelation is removed from annual exceedance rate series,independence condition is guaranteed in estimation of percentiles.We use 19 global climate models,in the Couple Model Inter-comparison Project Phase 5,to simulate six indices for historical and three Representative Concentration Pathways(RCP2.6,RCP4.5 and RCP8.5)scenarios.The frequency of warm events in China shows an increasing trend in the future,while frequency of cold events tends to be lower in contrast.By the end of 21 st century,TX90 p,TN90 p and WSDI will significantly increase,but TX10 p,TN10 p and CSDI will go through a reduction under RCP2.6,RCP4.5,and RCP8.5,respectively.The largest slump of cold events is projected in northeast China,the strongest escalation of warm events is projected in south and southwest China.
This study provides a view of temporal evolutions and spatial variations in temperature extremes indices(TX10 p,TN10 p,TX90 p,TN90 p,CSDI and WSDI)defined by the Expert Team of Climate Change and Detection Indices(ETCCDI)using a new dataset.All of the six indices are percentile-based and derived through semi-parametric quantile regression.The autocorrelation is removed from annual exceedance rate series,independence condition is guaranteed in estimation of percentiles.We use 19 global climate models,in the Couple Model Inter-comparison Project Phase 5,to simulate six indices for historical and three Representative Concentration Pathways(RCP2.6,RCP4.5 and RCP8.5)scenarios.The frequency of warm events in China shows an increasing trend in the future,while frequency of cold events tends to be lower in contrast.By the end of 21 st century,TX90 p,TN90 p and WSDI will significantly increase,but TX10 p,TN10 p and CSDI will go through a reduction under RCP2.6,RCP4.5,and RCP8.5,respectively.The largest slump of cold events is projected in northeast China,the strongest escalation of warm events is projected in south and southwest China.
引文
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[2]YANG C,XU J.A semi-parametric regression approach to climatological quantile estimation for generating percentile-based temperature extremes indices[J].Atmospheric Science Letters,2017,18:60-6
[3]ZHANG X,HEGERL G,ZWIERS F W,et al.Avoiding inhomogeneity in percentile-based indices of temperature extremes[J].Journal of Climate,2005,18(11):1641
[4]SILLMANN J,KHARIN V V,ZWIERS F W,et al.Climate extremes indices in the CMIP5 multimodel ensemble:part 2.future climate projections[J].Journal of Geophysical Research Atmospheres,2013,118(6):2473
[5]ZHOU B,XU Y,WU J,et al.Changes in temperature and precipitation extreme indices over China:analysis of a high-resolution grid dataset[J].International Journal of Climatology,2016,36(3):1051
[6]YANG C.Dataset of percentile-based ETCCDItemperature extremes indices for CMIP5 model output[J/OL].Global Change Research Data Publishing&Repository,2017,4(2).http:∥www.geodoi.ac.cn/WebEn/doi.aspx?Id=683
[7]YANG C,LI L,XU J.Changing temperature extremes based on CMIP5 output via semiparametric quantile regression approach[J].International Journal of Climatology,2018,38:3736
[8]SIPPEL S,OTTO F E L,FORKEL M,et al.A novel bias correction methodology for climate impact simulations[J].Earth System Dynamics,7,1(2016-02-02),2016,6(2):1999
[9]SILLMANN J,THORARINSDOTTIR T,KEENLYSIDE N,et al.Understanding,modeling and predicting weather and climate extremes:challenges and opportunities[J].Weather&Climate Extremes,2017,18:65
[10]CAESAR J,ALEXANDER L,VOSE R.Large-scale changes in observed daily maximum and minimum temperatures:creation and analysis of a new gridded data set[J].Journal of Geophysical Research Atmospheres,2006,111:D5
[11]MA T,WU Z,JIANG Z.How does coldwave frequency in china respond to a warming climate?[J].Climate Dynamics,2012,39(9/10):2487
[12]JIANG Z,CHEN W,LI L.Probabilistic projections of climate change over China under the SRES A1Bscenario using 28 AOGCMs[J].Journal of Climate,2011,24(17):4741
[13]GAO X,ZHAO Z,GIORGI F.Changes of extreme events in regional climate simulations over East Asia[J].Advances in Atmospheric Sciences,2002,19(5):927
[14]JIANG D B,WANG H J,LANG X M.Multimodel ensemble prediction for climate change trend of China under sres A2 scenario[J].Chinese Journal of Geophysics,2004,47(5):878
[15]DING Y,REN G,ZHAO Z,et al.Detection,causes and projection of climate change over China:an overview of recent progress[J].Advances in Atmospheric Sciences,2007,24(6):954
[16]ZHAI P,PAN X.Trends in temperature extremes during 1951&ndash:1999in China[J].Geophysical Research Letters,2003,30(17):169
[17]CHEN H,SUN J.Changes in climate extreme events in China associated with warming[J].International Journal of Climatology,2015,35(10):2735
[18]YANG X,LIN E,MA S,et al.Adaptation of agriculture to warming in Northeast China[J].Climatic Change,2007,84(1):45
[19]X U X,DU Y,TANG J,et al.Variations of temperature and precipitation extremes in recent two decades over China[J].Atmospheric Research,2011,101(1/2):143
[20]DING Y,REN G,SHI G,et al.China's national assessment report on climate change(summary)[J].World Environment,2007,3(12):1
[21]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
[22]ZHOU B,WEN Q H,XU Y,et al.Projected changes in temperature and precipitation extremes in China by the CMIP5 multimodel ensembles[J].Journal of Climate,2014,27(17):6591
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