Statistical downscaling model for late-winter rainfall over Southwest China

作     者：RUAN ChengQing LI JianPing FENG Juan Ruan C Q;Li J P;Feng J

作者机构：State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of Sciences College of Earth Sciences University of Chinese Academy of Sciences College of Global Change and Earth System Science Beijing Normal University Joint Center for Global Change Studies 

出 版 物：《Science China Earth Sciences》 (中国科学（地球科学英文版）)

年 卷 期：2015年第58卷第7期

页      面：1827-1839页

核心收录：

学科分类：07[理学] 070601[理学-气象学] 0708[理学-地球物理学] 0706[理学-大气科学] 0704[理学-天文学] 

基　　金：jointly supported by the"Strategic Priority Research Program-Climate Change:Carbon Budget and Relevant Issues"of the Chinese Academy of Sciences(Grant No.XDA05090403) the National Key Program for Developing Basic Sciences(Grant No.2013CB430200) the National Natural Science Foundation of China(Grant No.41205046) 

主　　题：statistical downscaling Southwest China late-winter rainfall partial correlation 

摘      要：A statistical downscaling model is built for the late-winter rainfall over Southwest China(SWC).A partial-correlation method is used for selecting *** results show that the selected factors for late-winter rainfall in SWC are sea level pressure in Western Europe(SNAO)and sea surface temperature in Western Pacific(WPT).SNAO is related to the southern pole of North Atlantic Oscillation(NAO)and excites Southern Eurasian teleconnection,which influences the development of the southern branch trough and the water vapor transport to *** indicates the variability of ENSO in the tropical Western *** excites Pacific-East Asia teleconnection and an anticyclone(cyclone)is formed in the southern part of China and suppresses(enhances)rainfall over SWC.A regression statistical downscaling model using SNAO and WPT shows good performance in fitting the variability of late-winter rainfall in the whole SWC region and every observation station,and the model also shows strong robustness in the independent *** statistical model can be used for downscaling output from seasonal forecast numerical models and improve the SWC late winter rainfall prediction in the future.