Contrasting the Skills and Biases of Deterministic Predictions for the Two Types of El Nio

作     者：Fei ZHENG Jin-Yi YU 

作者机构：International Center for Climate and Environment Science Institute of Atmospheric PhysicsChinese Academy of Sciences Collaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersNanjing University of Information Science and Technology Department of Earth System Science University of California 

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

年 卷 期：2017年第34卷第12期

页      面：1395-1403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 National Program for Support of Top-notch Young Professionals the National Natural Science Foundation of China (Grant No. 41576019) J.-Y. YU was supported by the US National Science Foundation (Grant No. AGS-150514) 

主　　题：ENSO EP El Nio CP El Nio prediction skill systematic bias spring prediction barrier 

摘      要：The tropical Pacific has begun to experience a new type of El Nio, which has occurred particularly frequently during the last decade, referred to as the central Pacific(CP) El Nio. Various coupled models with different degrees of complexity have been used to make real-time El Nio predictions, but high uncertainty still exists in their forecasts. It remains unknown as to how much of this uncertainty is specifically related to the new CP-type El Nio and how much is common to both this type and the conventional Eastern Pacific(EP)-type El Nio. In this study, the deterministic performance of an El Nio–Southern Oscillation(ENSO) ensemble prediction system is examined for the two types of El Nio. Ensemble hindcasts are run for the nine EP El Nio events and twelve CP El Nio events that have occurred since 1950. The results show that(1) the skill scores for the EP events are significantly better than those for the CP events, at all lead times;(2) the systematic forecast biases come mostly from the prediction of the CP events; and(3) the systematic error is characterized by an overly warm eastern Pacific during the spring season, indicating a stronger spring prediction barrier for the CP El Nio. Further improvements to coupled atmosphere–ocean models in terms of CP El Nio prediction should be recognized as a key and high-priority task for the climate prediction community.