IMPROVEMENT OF OCEAN DATA ASSIMILATION SYSTEM AND CLIMATE PREDICTION BY ASSIMILATING ARGO DATA  

作　　者：李清泉 张人禾 刘益民 

机构地区：[1]Laboratory for Climate Studies, National Climate Center, China Meteorological Administration [2]Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters , Nanjing University of Information Science & Technology [3]State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences

出　　处：《Journal of Tropical Meteorology》2015年第2期171-184,共14页热带气象学报（英文版）

基　　金：National Program on Key Basic Research Project of China(2012CB955203,2013CB430202);National Natural Science Foundation of China(40231014,41175065);China Meteorological Administration R&D Special Fund for Public Welfare(meteorology)(GYHY201306021);National High Technology Research and Development Program of China(2010AA012404)

摘　　要：The Argo(Array for Real-time Geostrophic Oceanography) data from 1998 to 2003 were used in the Beijing Climate Center-Global Ocean Data Assimilation System(BCC-GODAS). The results show that the utilization of Argo global ocean data in BCC-GODAS brings about remarkable improvements in assimilation effects. The assimilated sea surface temperature(SST) of BCC-GODAS can well represent the climatological states of observational data. Comparison experiments based on a global coupled atmosphere-ocean general circulation model(AOCGM) were conducted for exploring the roles of ocean data assimilation system with or without Argo data in improving the climate predictability of rainfall in boreal summer. Firstly, the global ocean data assimilation system BCC-GODAS was used to obtain ocean assimilation data under the conditions with or without Argo data. Then, the global coupled atmosphere-ocean general circulation model(AOCGM) was utilized to do hindcast experiments with the two sets of the assimilation data as initial oceanic fields. The simulated results demonstrate that the seasonal predictability of rainfall in boreal summer, particularly in China, increases greatly when initial oceanic conditions with Argo data are utilized. The distribution of summer rainfall in China hindcast by the AOGCM under the condition when Argo data are used is more in accordance with observation than that when no Agro data are used. The area of positive correlation between hindcast and observation enlarges and the hindcast skill of rainfall over China in summer improves significantly when Argo data are used.The Argo（Array for Real-time Geostrophic Oceanography） data from 1998 to 2003 were used in the Beijing Climate Center-Global Ocean Data Assimilation System（BCC-GODAS）. The results show that the utilization of Argo global ocean data in BCC-GODAS brings about remarkable improvements in assimilation effects. The assimilated sea surface temperature（SST） of BCC-GODAS can well represent the climatological states of observational data. Comparison experiments based on a global coupled atmosphere-ocean general circulation model（AOCGM） were conducted for exploring the roles of ocean data assimilation system with or without Argo data in improving the climate predictability of rainfall in boreal summer. Firstly, the global ocean data assimilation system BCC-GODAS was used to obtain ocean assimilation data under the conditions with or without Argo data. Then, the global coupled atmosphere-ocean general circulation model（AOCGM） was utilized to do hindcast experiments with the two sets of the assimilation data as initial oceanic fields. The simulated results demonstrate that the seasonal predictability of rainfall in boreal summer, particularly in China, increases greatly when initial oceanic conditions with Argo data are utilized. The distribution of summer rainfall in China hindcast by the AOGCM under the condition when Argo data are used is more in accordance with observation than that when no Agro data are used. The area of positive correlation between hindcast and observation enlarges and the hindcast skill of rainfall over China in summer improves significantly when Argo data are used.

关 键 词：Argo data ocean data assimilation climate prediction AOGCM 

分 类 号：P731.11[天文地球—海洋科学]