Bias Correction and Ensemble Projections of Temperature Changes over Ten Subregions in CORDEX East Asia

作     者：Chenwei SHEN Qingyun DUAN Chiyuan MIAO Chang XING Xuewei FAN Yi WU Jingya HAN Chenwei SHEN;Qingyun DUAN;Chiyuan MIAO;Chang XING;Xuewei FAN;Yi WU;Jingya HAN

作者机构：State Key Laboratory of Earth Surface Processes and Resource EcologyFaculty of Geographical ScienceBeijing Normal UniversityBeijing 100875China 

出 版 物：《Advances in Atmospheric Sciences》 (大气科学进展（英文版）)

年 卷 期：2020年第37卷第11期

页      面：1191-1210页

核心收录：

学科分类：07[理学] 070601[理学-气象学] 0706[理学-大气科学] 

基　　金：supported by the Strategic Priority Research Program of the Chinese Academy of Sciences 国家自然科学基金 

主　　题：CORDEX-EA bias correction BMA temperature projection 

摘      要：Regional climate models(RCMs)participating in the Coordinated Regional Downscaling Experiment(CORDEX)have been widely used for providing detailed climate change information for specific regions under different emissions *** study assesses the effects of three common bias correction methods and two multi-model averaging methods in calibrating historical(1980−2005)temperature simulations over East ***(2006−49)temperature trends under the Representative Concentration Pathway(RCP)4.5 and 8.5 scenarios are projected based on the optimal bias correction and ensemble averaging *** show the following:(1)The driving global climate model and RCMs can capture the spatial pattern of annual average temperature but with cold biases over most regions,especially in the Tibetan Plateau region.(2)All bias correction methods can significantly reduce the simulation *** quantile mapping method outperforms other bias correction methods in all RCMs,with a maximum relative decrease in root-mean-square error for five RCMs reaching 59.8%(HadGEM3-RA),63.2%(MM5),51.3%(RegCM),80.7%(YSU-RCM)and 62.0%(WRF).(3)The Bayesian model averaging(BMA)method outperforms the simple multi-model averaging(SMA)method in narrowing the uncertainty of bias-corrected *** the spatial correlation coefficient,the improvement rate of the BMA method ranges from 2%to 31%over the 10 subregions,when compared with individual RCMs.(4)For temperature projections,the warming is significant,ranging from 1.2°C to 3.5°C across the whole domain under the RCP8.5 scenario.(5)The quantile mapping method reduces the uncertainty over all subregions by between 66%and 94%.