The Common Principal Component Analyses of Multi-RCMs  

作　　者：FENG Jin-Ming WANG Yong-Li FU Cong-Bin 

机构地区：[1]Key Laboratory of Regional Climate-Environment for Temperate East Asia,Institute of Atmospheric Physics,Chinese Academy of Sciences [2]Graduate University of Chinese Academy of Sciences

出　　处：《Atmospheric and Oceanic Science Letters》2013年第1期14-20,共7页大气和海洋科学快报（英文版）

基　　金：supported by the National Natural Science Foundation of China (General Program,Grant No.40975048);the Strategic Priority Research Program-Climate Change:Carbon Budget and Relevant Issues of the Chinese Academy of Sciences (Grant No. XDA05090207);the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No.KZCX2-EW-202)

摘　　要：Based on a 10-year simulation of six Regional Climate Models(RCMs) in phase II of the Regional Climate Model Inter-Comparison Project(RMIP) for Asia,the multivariate statistical method of common principal components(CPCs) is used to analyze and compare the spatiotemporal characteristics of temperature and precipitation simulated by multi-RCMs over China,including the mean climate states and their seasonal transition,the spatial distribution of interannual variability,and the interannual variation.CPC is an effective statistical tool for analyzing the results of different models.Compared with traditional statistical methods,CPC analyses provide a more complete statistical picture for observation and simulation results.The results of CPC analyses show that the climatological means and the characteristics of seasonal transition over China can be accurately simulated by RCMs.However,large biases exist in the interannual variation in certain years or for individual models.Based on a 10-year simulation of six Regional Climate Models （RCMs） in phase II of the Regional Cli- mate Model Inter-Comparison Project （RMIP） for Asia, the multivariate statistical method of common principal components （CPCs） is used to analyze and compare the spatiotemporal characteristics of temperature and precipi- tation simulated by multi-RCMs over China, including the mean climate states and their seasonal transition, the spatial distribution of interannual variability, and the in- terannual variation. CPC is an effective statistical tool for analyzing the results of different models. Compared with traditional statistical methods, CPC analyses provide a more complete statistical picture for observation and si- mulation results. The results of CPC analyses show that the climatological means and the characteristics of sea- sonal transition over China can be accurately simulated by RCMs. However, large biases exist in the interannual variation in certain years or for individual models.

关 键 词：RMIP for Asia common principal compo- nents spatiotemporal characteristic interannual variation 

分 类 号：P463[天文地球—大气科学及气象学]