Statistical Modeling of CMIP5 Projected Changes in Extreme Wet Spells over China in the Late 21st Century
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摘要
The observed intensity, frequency, and duration(IFD) of summer wet spells, defined here as extreme events with one or more consecutive days in which daily precipitation exceeds a given threshold(the 95th percentile), and their future changes in RCP4.5 and RCP8.5 in the late 21st century over China, are investigated by using the wet spell model(WSM) and by extending the point process approach to extreme value analysis. Wet spell intensity is modeled by a conditional generalized Pareto distribution, frequency by a Poisson distribution, and duration by a geometric distribution, respectively. The WSM is able to realistically model summer extreme rainfall spells during 1961–2005, as verified with observations at 553 stations throughout China. To minimize the impact of systematic biases over China in the global climate models(GCMs) of the Coupled Model Intercomparison Project Phase 5(CMIP5), five best GCMs are selected based on their performance to reproduce observed wet spell IFD and average precipitation during the historical period. Furthermore, a quantile–quantile scaling correction procedure is proposed and applied to produce ensemble projections of wet spell IFD and corresponding probability distributions. The results show that in the late 21st century, most of China will experience more extreme rainfall and less low-intensity rainfall. The intensity and frequency of wet spells are projected to increase considerably, while the duration of wet spells will increase but to a much less extent. The IFD changes in RCP8.5 are in general much larger than those in RCP4.5.
The observed intensity, frequency, and duration(IFD) of summer wet spells, defined here as extreme events with one or more consecutive days in which daily precipitation exceeds a given threshold(the 95th percentile), and their future changes in RCP4.5 and RCP8.5 in the late 21st century over China, are investigated by using the wet spell model(WSM) and by extending the point process approach to extreme value analysis. Wet spell intensity is modeled by a conditional generalized Pareto distribution, frequency by a Poisson distribution, and duration by a geometric distribution, respectively. The WSM is able to realistically model summer extreme rainfall spells during 1961–2005, as verified with observations at 553 stations throughout China. To minimize the impact of systematic biases over China in the global climate models(GCMs) of the Coupled Model Intercomparison Project Phase 5(CMIP5), five best GCMs are selected based on their performance to reproduce observed wet spell IFD and average precipitation during the historical period. Furthermore, a quantile–quantile scaling correction procedure is proposed and applied to produce ensemble projections of wet spell IFD and corresponding probability distributions. The results show that in the late 21st century, most of China will experience more extreme rainfall and less low-intensity rainfall. The intensity and frequency of wet spells are projected to increase considerably, while the duration of wet spells will increase but to a much less extent. The IFD changes in RCP8.5 are in general much larger than those in RCP4.5.
The observed intensity, frequency, and duration(IFD) of summer wet spells, defined here as extreme events with one or more consecutive days in which daily precipitation exceeds a given threshold(the 95th percentile), and their future changes in RCP4.5 and RCP8.5 in the late 21st century over China, are investigated by using the wet spell model(WSM) and by extending the point process approach to extreme value analysis. Wet spell intensity is modeled by a conditional generalized Pareto distribution, frequency by a Poisson distribution, and duration by a geometric distribution, respectively. The WSM is able to realistically model summer extreme rainfall spells during 1961–2005, as verified with observations at 553 stations throughout China. To minimize the impact of systematic biases over China in the global climate models(GCMs) of the Coupled Model Intercomparison Project Phase 5(CMIP5), five best GCMs are selected based on their performance to reproduce observed wet spell IFD and average precipitation during the historical period. Furthermore, a quantile–quantile scaling correction procedure is proposed and applied to produce ensemble projections of wet spell IFD and corresponding probability distributions. The results show that in the late 21st century, most of China will experience more extreme rainfall and less low-intensity rainfall. The intensity and frequency of wet spells are projected to increase considerably, while the duration of wet spells will increase but to a much less extent. The IFD changes in RCP8.5 are in general much larger than those in RCP4.5.
引文
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Bai,A.J.,P.M.Zhai,and X.D.Liu,2007:Climatology and trends of wet spells in China.Theor.Appl.Climatol.,88,139-148,doi:10.1007/s00704-006-0235-7.
Chen,H.P.,2013:Projected change in extreme rainfall events in China by the end of the 21st century using CMIP5 models.Chinese Sci.Bull.,58,1462-1472,doi:10.1007/s11434-012-5612-2.
Cheng,L.Y.,and A.Agha Kouchak,2014:Nonstationary precipitation intensity-duration-frequency curves for infrastructure design in a changing climate.Sci.Rep.,4,7093,doi:10.1038/srep07093.
Chen,W.L.,Z.H.Jiang,and L.Li,2011:Probabilistic projections of climate change over China under the SRES A1Bscenario using 28 AOGCMs.J.Climate,24,4741-4756,doi:10.1175/2011JCLI4102.1.
Chen,H.P.,and J.Q.Sun,2015:Changes in climate extreme events in China associated with warming.Int.J.Climatol.,35,2735-2751,doi:10.1002/joc.4168.
Coles,S.,2001:An Introduction to Statistical Modeling of Extreme Values:Springer Series in Statistics.Springer,London,209 pp.
Ding,Y.G.,B.Y.Cheng,and Z.H.Jiang,2008:A newlydiscovered GPD-GEV relationship together with comparing their models of extreme precipitation in summer.Adv.Atmos.Sci.,25,507-516,doi:10.1007/s00376-008-0507-5.
Fan,L.J.,and D.L.Chen,2016:Trends in extreme precipitation indices across China detected using quantile regression.Atmos.Sci.Lett.,17,400-406,doi:10.1002/asl.671.
Fan,L.J.,Z.W.Yan,D.L.Chen,et al.,2015:Comparison between two statistical downscaling methods for summer daily rainfall in Chongqing,China.Int.J.Climatol.,35,3781-3797,doi:10.1002/joc.4246.
Fang,G.H.,J.Yang,Y.N.Chen,et al.,2015:Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China.Hydrol.Earth Syst.Sci.,19,2547-2559,doi:10.5194/hess-19-2547-2015.
Furrer,E.M.,R.W.Katz,M.D.Walter,et al.,2010:Statistical modeling of hot spells and heat waves.Climate Res.,43,191-205,doi:10.3354/cr00924.
Guo,P.W.,X.K.Zhang,S.Y.Zhang,et al.,2014:Decadal variability of extreme precipitation days over Northwest China from 1963 to 2012.J.Meteor.Res.,28,1099-1113,doi:10.1007/s13351-014-4022-6.
IPCC,2013:Climate Change 2013:The Physical Science Basis.Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.Stocker,T.F.,et al.,Eds.,Cambridge University Press,Cambridge,United Kingdom and New York,NY,USA,1535 pp.
Jiang,Z.H.,W.Li,J.J.Xu,et al.,2015:Extreme precipitation indices over China in CMIP5 models.Part I:Model evaluation.J.Climate,28,8603-8619,doi:10.1175/JCLI-D-15-0099.1.
Kallache,M.,M.Vrac,P.Naveau,et al.,2011:Nonstationary probabilistic downscaling of extreme precipitation.J.Geophys.Res.,116,doi:10.1029/2010JD014892.
Karl,T.R.,N.Nicholls,and A.Ghazi,1999:Clivar/GCOS/WMOworkshop on indices and indicators for climate extremes workshop summary.Climatic Change,42,3-7,doi:10.1023/A:1005491526870.
Katz,R.W.,2013:Statistical methods for nonstationary extremes.Extremes in a Changing Climate:Detection,Analysis and Uncertainty.Agha Kouchak,A.,D.Easterling,K.Hsu,et al.,Eds.,Springer,Netherlands,15-37.
Ke,D.,and Z.Y.Guan,2014:Variations in regional mean daily precipitation extremes and related circulation anomalies over central China during boreal summer.J.Meteor.Res.,28,524-539,doi:10.1007/s13351-014-3246-9.
Lau,W.K.M.,H.T.Wu,and K.M.Kim,2013:A canonical response of precipitation characteristics to global warming from CMIP5 models.Geophys.Res.Lett.,40,3163-3169,doi:10.1002/grl.50420.
Li,M.G.,Z.Y.Guan,D.C.Jin,et al.,2016:Anomalous circulation patterns in association with two types of daily precipitation extremes over southeastern China during boreal summer.J.Meteor.Res.,30,183-202,doi:10.1007/s13351-016-5070-x.
Li,X.,A.Meshgi,and V.Babovic,2016:Spatio-temporal variation of wet and dry spell characteristics of tropical precipitation in Singapore and its association with ENSO.Int.J.Climatol.,36,4831-4846,doi:10.1002/joc.4672.
Li,Y.,W.Cai,and E.P.Campbell,2005:Statistical modeling of extreme rainfall in southwest western Australia.J.Climate,18,852-863,doi:10.1175/JCLI-3296.1.
Liang,K.,S.Liu,P.Bai,et al.,2015:The Yellow River basin becomes wetter or drier?The case as indicated by mean precipitation and extremes during 1961-2012.Theor.Appl.Climatol.,119,701-722,doi:10.1007/s00704-014-1138-7.
Ma,S.M.,and T.J.Zhou,2015:Observed trends in the timing of wet and dry season in China and the associated changes in frequency and duration of daily precipitation.Int.J.Climatol.,35,4631-4641,doi:10.1002/joc.4312.
Mondal,A.,and P.P.Mujumdar,2015:Modeling non-stationarity in intensity,duration and frequency of extreme rainfall over India.J.Hydrol.,521,217-231,doi:10.1016/j.jhydrol.2014.11.071.
Moss,R.H.,J.A.Edmonds,K.A.Hibbard,et al.,2010:The next generation of scenarios for climate change research and assessment.Nature,463,747-756,doi:10.1038/nature08823.
Ou,T.H.,D.L.Chen,H.W.Linderholm,et al.,2013:Evaluation of global climate models in simulating extreme precipitation in China.Tellus A,65,19799,doi:10.3402/tellusa.v65i0.19799.
Qian,X.,Q.L.Miao,P.M.Zhai,et al.,2014:Cold-wet spells in mainland China during 1951-2011.Nat.Hazards,74,931-946,doi:10.1007/s11069-014-1227-z.
R Core Team,2016:R:A language and environment for statistical computing.R Foundation for Statistical Computing,Vienna,Austria.URL https://www.R-project.org/.
She,D.X.,J.Xia,J.Y.Song,et al.,2013:Spatio-temporal variation and statistical characteristic of extreme dry spell in Yellow River basin,China.Theor.Appl.Climatol.,112,201-213,doi:10.1007/s00704-012-0731-x.
Su,B.D.,B.Xiao,D.M.Zhu,et al.,2005:Trends in frequency of precipitation extremes in the Yangtze River basin,China:1960-2003.Hydrological Sciences Journal,50,479-492,doi:10.1623/hysj.50.3.479.65022.
Sugahara,S.,R.P.Da Rocha,and R.Silveira,2009:Non-stationary frequency analysis of extreme daily rainfall in Sao Paulo,Brazil.Int.J.Climatol.,29,1339-1349,doi:10.1002/joc.1760.
Sun,J.Q.,and J.Ao,2013:Changes in precipitation and extreme precipitation in a warming environment in China.Chinese Sci.Bull.,58,1395-1401,doi:10.1007/s11434-012-5542-z.
Sun,Y.,S.Solomon,A.G.Dai,et al.,2007:How often will it rain?J.Climate,20,4801-4818,doi:10.1175/JCLI4263.1.
Taylor,K.E.,R.J.Stouffer,and G.A.Meehl,2012:An overview of CMIP5 and the experiment design.Bull.Amer.Meteor.Soc.,93,485-498,doi:10.1175/BAMS-D-11-00094.1.
Teutschbein,C.,and J.Seibert,2012:Bias correction of regional climate model simulations for hydrological climate-change impact studies:Review and evaluation of different methods.J.Hydrol.,456-457,12-29,doi:10.1016/j.jhydrol.2012.05.052.
Theme?l,M.J.,A.Gobiet,and G.Heinrich,2012:Empiricalstatistical downscaling and error correction of regional climate models and its impact on the climate change signal.Climatic Change,112,449-468,doi:10.1007/s10584-011-0224-4.
Tolika,K.,and P.Maheras,2005:Spatial and temporal characteristics of wet spells in Greece.Theor.Appl.Climatol.,81,71-85,doi:10.1007/s00704-004-0089-9.
van der Schrier,G.,J.Barichivich,K.R.Briffa,et al.,2013:Asc PDSI-based global data set of dry and wet spells for1901-2009.J.Geophys.Res.,118,4025-4048,doi:10.1002/jgrd.50355.
Wan,S.Q.,Y.L.Hu,Z.Y.You,et al.,2013:Extreme monthly precipitation pattern in China and its dependence on Southern Oscillation.Int.J.Climatol.,33,806-814,doi:10.1002/joc.3466.
Wang,K.,L.Wang,Y.M.Wei,et al.,2013:Beijing storm of July21,2012:Observations and reflections.Nat.Hazards,67,969-974,doi:10.1007/s11069-013-0601-6.
Wang,W.W.,W.Zhou,Y.Li,et al.,2015:Statistical modeling and CMIP5 simulations of hot spell changes in China.Climate Dyn.,44,2859-2872,doi:10.1007/s00382-014-2287-1.
Wen,Y.R.,L.Xue,Y.Li,et al.,2015:Interaction between Typhoon Vicente(1208)and the western Pacific subtropical high during the Beijing extreme rainfall of 21 July 2012.J.Meteor.Res.,29,293-304,doi:10.1007/s13351-015-4097-8.
Wu,H.,P.M.Zhai,and Y.Chen,2016:A comprehensive classification of anomalous circulation patterns responsible for persistent precipitation extremes in South China.J.Meteor.Res.,30,483-495,doi:10.1007/s13351-016-6008-z.
Wu,J.,and X.J.Gao,2013:A gridded daily observation dataset over China region and comparison with the other datasets.Chinese J.Geophys.,56,1102-1111.(in Chinese)
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