Assessment of Temperature Extremes in China Using RegCM4 and WRF
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摘要
This study assesses the performance of temperature extremes over China in two regional climate models(RCMs),RegCM4 and WRF, driven by the ECMWF's 20 th century reanalysis. Based on the advice of the Expert Team on Climate Change Detection and Indices(ETCCDI), 12 extreme temperature indices(i.e., TXx, TXn, TNx, TNn, TX90 p, TN90 p,TX10 p, TN10 p WSDI, ID, FD, and CSDI) are derived from the simulations of two RCMs and compared with those from the daily station-based observational data for the period 1981–2010. Overall, the two RCMs demonstrate satisfactory capability in representing the spatiotemporal distribution of the extreme indices over most regions. RegCM performs better than WRF in reproducing the mean temperature extremes, especially over the Tibetan Plateau(TP). Moreover, both models capture well the decreasing trends in ID, FD, CSDI, TX10 p, and TN10 p, and the increasing trends in TXx, TXn, TNx, TNn, WSDI, TX90 p,and TN90 p, over China. Compared with observation, RegCM tends to underestimate the trends of temperature extremes,while WRF tends to overestimate them over the TP. For instance, the linear trends of TXx over the TP from observation,RegCM, and WRF are 0.53?C(10 yr)~(-1), 0.44?C(10 yr)~(-1), and 0.75?C(10 yr)~(-1), respectively. However, WRF performs better than RegCM in reproducing the interannual variability of the extreme-temperature indices. Our findings are helpful towards improving our understanding of the physical realism of RCMs in terms of different time scales, thus enabling us in future work to address the sources of model biases.
This study assesses the performance of temperature extremes over China in two regional climate models(RCMs),RegCM4 and WRF, driven by the ECMWF's 20 th century reanalysis. Based on the advice of the Expert Team on Climate Change Detection and Indices(ETCCDI), 12 extreme temperature indices(i.e., TXx, TXn, TNx, TNn, TX90 p, TN90 p,TX10 p, TN10 p WSDI, ID, FD, and CSDI) are derived from the simulations of two RCMs and compared with those from the daily station-based observational data for the period 1981–2010. Overall, the two RCMs demonstrate satisfactory capability in representing the spatiotemporal distribution of the extreme indices over most regions. RegCM performs better than WRF in reproducing the mean temperature extremes, especially over the Tibetan Plateau(TP). Moreover, both models capture well the decreasing trends in ID, FD, CSDI, TX10 p, and TN10 p, and the increasing trends in TXx, TXn, TNx, TNn, WSDI, TX90 p,and TN90 p, over China. Compared with observation, RegCM tends to underestimate the trends of temperature extremes,while WRF tends to overestimate them over the TP. For instance, the linear trends of TXx over the TP from observation,RegCM, and WRF are 0.53?C(10 yr)~(-1), 0.44?C(10 yr)~(-1), and 0.75?C(10 yr)~(-1), respectively. However, WRF performs better than RegCM in reproducing the interannual variability of the extreme-temperature indices. Our findings are helpful towards improving our understanding of the physical realism of RCMs in terms of different time scales, thus enabling us in future work to address the sources of model biases.
This study assesses the performance of temperature extremes over China in two regional climate models(RCMs),RegCM4 and WRF, driven by the ECMWF's 20 th century reanalysis. Based on the advice of the Expert Team on Climate Change Detection and Indices(ETCCDI), 12 extreme temperature indices(i.e., TXx, TXn, TNx, TNn, TX90 p, TN90 p,TX10 p, TN10 p WSDI, ID, FD, and CSDI) are derived from the simulations of two RCMs and compared with those from the daily station-based observational data for the period 1981–2010. Overall, the two RCMs demonstrate satisfactory capability in representing the spatiotemporal distribution of the extreme indices over most regions. RegCM performs better than WRF in reproducing the mean temperature extremes, especially over the Tibetan Plateau(TP). Moreover, both models capture well the decreasing trends in ID, FD, CSDI, TX10 p, and TN10 p, and the increasing trends in TXx, TXn, TNx, TNn, WSDI, TX90 p,and TN90 p, over China. Compared with observation, RegCM tends to underestimate the trends of temperature extremes,while WRF tends to overestimate them over the TP. For instance, the linear trends of TXx over the TP from observation,RegCM, and WRF are 0.53?C(10 yr)~(-1), 0.44?C(10 yr)~(-1), and 0.75?C(10 yr)~(-1), respectively. However, WRF performs better than RegCM in reproducing the interannual variability of the extreme-temperature indices. Our findings are helpful towards improving our understanding of the physical realism of RCMs in terms of different time scales, thus enabling us in future work to address the sources of model biases.
引文
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Gao,X.J.,Y.Shi,Z.Y.Han,M.L.Wang,J.Wu,D.F.Zhang,Y.Xu,and F.Giorgi,2017:Performance of RegCM4 over major river basins in China.Adv.Atmos.Sci.,34(4),441-455,https://doi.org/10.1007/s00376-016-6179-7.
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Giorgi,F.,C.Jones,and G.R.Asrar,2009:Addressing climate information needs at the regional level:The CORDEX framework.WMO Bulletin,58,175-183.
Giorgi,F,and Coauthors,2012:RegCM4:Model description and preliminary tests over multiple CORDEX domains.Climate Research,52,7-29,https://doi.org/10.3354/cr01018.
Girogi,F.,and W.G.Gutowski,2015:Regional dynamical downscaling and the CORDEX initiative.Annual Review of Environment and Resources,40,467-490,https://doi.org/10.1146/annurev-environ-102014-021217.
Guo,D.L.,E.T.Yu,and H.J.Wang,2016:Will the Tibetan Plateau warming depend on elevation in the future?J.Geophys.Res.,121,3639-3978,https://doi.org/10.1002/2016JD024871.
Han,J.,and H.L.Pan,2011:Revision of convection and vertical diffusion schemes in the NCEP Global Forecast System.Wea.Forecasting,2,520-533,https://doi.org/10.1175/WAF-D-10-05038.1.
Hersbach,H.C.,C.Peubey,A.Simmons,P.Berrisford,P,Poli,and D.Dee,2015:ERA-20CM:A twentieth-century atmospheric model ensemble.Quart.J.Roy.Meteor.Soc.,141,2350-2375,https://doi.org/10.1002/qj.2528.
Holtslag,A.A.M.,de Bruijn,E.I.F.,and H.L.Pan,1990:A high resolution air mass transformation model for short-range weather forecasting.Mon.Wea.Rev.,118,1561-1575,https://doi.org/10.1175/1520-0493(1990)118<1561:AHRAMT>2.0.CO;2.
Hong,S.Y.,and J.O.J.Lim,2006:The WRF single-moment 6-class microphysics scheme(WSM6).Journal of the Korean Meteorological Society,42,129-151.
Hong,Y.,M.G.Donat,L.V.Alexander,and Y.Sun,2015:Multidataset comparison of gridded observed temperature and precipitation extremes over China.International Journal of Climatology,35,2809-2827,https://doi.org/10.1002/joc.4174.
Hui,P.H.,J.P.Tang,S.Y.Wang,X.R.Niu,P.S.Zong,and X.N.Dong,2018:Climate change projections over China using regional climate models forced by two CMIP5 global models.Part I:Evaluation of historical simulations.International Journal of Climatology,38,e57-e77,https://doi.org/10.1002/joc.5351.
IPCC,2012:Managing the risks of extreme events and disasters to advance climate change adaptation.A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change,C.B.Field,et al.,Eds.,Cambridge University Press,Cambridge,United Kingdom and New York,NY,USA,582 pp.
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Jacob,D.,and Coauthors,2014:EURO-CORDEX:New highresolution climate change projections for European impact research.Regional Environmental Change,14,563-578,https://doi.org/10.1007/s10113-013-0499-2.
Ji,Z.M.,and S.C.Kang,2015:Evaluation of extreme climate events using a regional climate model for China.International Journal of Climatology,35,888-902,https://doi.org/10.1002/joc.4024.
Karl,T.R.,N.Nicholls,and A.Ghazi,1999:Clivar/GCOS/WMOworkshop on indices and indicators for climate extremes workshop summary.Climate Change,42,3-7,https://doi.org/10.1023/A:1005491526870.
Kiehl,J.T.,J.J.Hack,G.B.Bonan,B.A.Boville,B.P.Briegleb,D.L.Williamson,and P.J.Rasch,1996:Description of the NCAR Community Climate Model(CCM3).NCAR Technical Note,NCAR/TN-420+STR,https://doi.org/10.5065/D6FF3Q99.
Liang,X.Z.,and Coauthors,2018:CWRF performance at downscaling China climate characteristics.Climate Dyn.,https://doi.org/10.1007/s00382-018-4257-5.
Liu,B.H.,M.Henderson,and M.Xu,2008:Spatiotemporal change in China’s frost days and frost-free season,1955-2000.J.Geophys.Res.,113,D12104,https://doi.org/10.1029/2007JD009259.
Liu,X.D.,and B.D.Chen,2000:Climatic warming in the Tibetan Plateau during recent decades.International Journal of Climatology,20,1729-1742,https://doi.org/10.1002/1097-0088(20001130)20:14<1729::AID-JOC556>3.0.CO;2-Y.
Mass,C.F.,D.Ovens,K.Westrick,and B.A.Colle,2002:Does increasing horizontal resolution produce more skillful forecasts:The results of two years of real-time numerical weather prediction over the Pacific Northwest.Bull.Amer.Meteor.Soc.,83,407-430,https://doi.org/10.1175/1520-0477(2002)083<0407:DIHRPM>2.3.CO;2.
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