ENSO ensemble prediction:Initial error perturbations vs. model error perturbations  

作　　者：ZHENG Fei WANG Hui ZHU Jiang 

机构地区：[1]International Center for Climate and Environment Science (ICCES), Institute of Atmospheric Physics, Chinese Academy of Sci ences, Beijing 100029, China [2]National Meteorological Center, Beijing 100081, China [3]State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China

出　　处：《Chinese Science Bulletin》2009年第14期2516-2523,共8页

基　　金：Supported by the Knowledge Innovation Program of Chinese Academy of Sciences (Grant Nos. KZCX2-YW-202 and KZCX1-YW-12-03);National Basic Research Program of China (Grant No. 2006CB403600);National Natural Science Foun-dation of China (Grant Nos. 40805033 and 40221503)

摘　　要：Based on our developed ENSO (El Nio-Southern Oscillation) ensemble prediction system (EPS), the impacts of stochastic initial-error and model-error perturbations on ENSO ensemble predictions are examined and discussed by performing four sets of 14-a retrospective forecast experiments in both a deterministic and probabilistic sense. These forecast schemes are differentiated by whether they considered the initial or model stochastic perturbations. The comparison results suggest that the stochastic model-error perturbations, which are added into the modeled physical fields to mainly represent the uncertainties of the physical model, have significant, positive impacts on improving the ensemble prediction skills during the entire 12-month forecast process. However, the stochastic initial-error perturbations have relatively small impacts on the ensemble prediction system, and its impacts are mainly focusing on the first 3-month predictions.Based on our developed ENSO （El Nino-Southern Oscillation） ensemble prediction system （EPS）, the impacts of stochastic initial-error and model-error perturbations on ENSO ensemble predictions are examined and discussed by performing four sets of 14-a retrospective forecast experiments in both a deterministic and probabilistic sense, These forecast schemes are differentiated by whether they con- sidered the initial or model stochastic perturbations, The comparison results suggest that the sto- chastic model-error perturbations, which are added into the modeled physical fields to mainly represent the uncertainties of the physical model, have significant, positive impacts on improving the ensemble prediction skills during the entire 12-month forecast process, However, the stochastic initial-error perturbations have relatively small impacts on the ensemble prediction system, and its impacts are mainly focusing on the first 3-month predictions.

关 键 词：集合预报系统 模型误差 随机扰动 ENSO 集合预测 物理模型 每股收益 南方涛动 

分 类 号：P456[天文地球—大气科学及气象学] P207