Assessment of Temperature Extremes in China Using RegCM4 and WRF  

作　　者：Xianghui KONG Aihui WANG Xunqiang BI Dan WANG 

机构地区：[1]Nansen-Zhu International Research Centre,Institute of Atmospheric Physics,Chinese Academy of Sciences [2]Climate Change Research Center,Institute of Atmospheric Physics,Chinese Academy of Sciences

出　　处：《Advances in Atmospheric Sciences》2019年第4期363-377,共15页大气科学进展（英文版）

基　　金：supported by the Key Project of the Ministry of Science and Technology of China(Grant No.2016YFA0602401);National Natural Science Foundation of China(Grant No.41575089)

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.

关 键 词：DYNAMICAL DOWNSCALING extreme-temperature index OBSERVATION REGCM WRF 

分 类 号：P412[天文地球—大气科学及气象学] P423