Initial Error-induced Optimal Perturbations in ENSO Predictions, as Derived from an Intermediate Coupled Model

作     者：Ling-Jiang TAO Rong-Hua ZHANG Chuan GAO 

作者机构：Key Laboratory of Ocean Circulation and Waves Institute of Oceanology Chinese Academy of Sciences Qingdao 266071 China University of Chinese Academy of Sciences Beijing 10029 China Laboratory for Ocean and Climate Dynamics Qingdao National Laboratory for Marine Science and Technology Qingdao 266237 China 

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6期

页      面：791-803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 Natural Science Foundation of China (NFSC Grant Nos. 41690122, 41690120, 41490644, 41490640 and 41475101) the Ao Shan Talents Program supported by Qingdao National Laboratory for Marine Science and Technology (Grant No. 2015ASTP) a Chinese Academy of Sciences Strategic Priority Project the Western Pacific Ocean System (Grant Nos. XDA11010105, XDA11020306) the NSFC–Shandong Joint Fund for Marine Science Research Centers (Grant No. U1406401) the National Natural Science Foundation of China Innovative Group Grant (Grant No. 41421005) the Taishan Scholarship and Qingdao Innovative Program (Grant No. 2014GJJS0101) 

主　　题：E1 Nino predictability initial errors intermediate coupled model spring predictability barrier 

摘      要：The initial errors constitute one of the main limiting factors in the ability to predict the E1 Nino-Southem Oscillation （ENSO） in ocean-atmosphere coupled models. The conditional nonlinear optimal perturbation （CNOP） approach was em- ployed to study the largest initial error growth in the E1 Nino predictions of an intermediate coupled model （ICM）. The optimal initial errors （as represented by CNOPs） in sea surface temperature anomalies （SSTAs） and sea level anomalies （SLAs） were obtained with seasonal variation. The CNOP-induced perturbations, which tend to evolve into the La Nifia mode, were found to have the same dynamics as ENSO itself. This indicates that, if CNOP-type errors are present in the initial conditions used to make a prediction of E1 Nino, the E1 Nino event tends to be under-predicted. In particular, compared with other seasonal CNOPs, the CNOPs in winter can induce the largest error growth, which gives rise to an ENSO amplitude that is hardly ever predicted accurately. Additionally, it was found that the CNOP-induced perturbations exhibit a strong spring predictability barrier （SPB） phenomenon for ENSO prediction. These results offer a way to enhance ICM prediction skill and, particularly, weaken the SPB phenomenon by filtering the CNOP-type errors in the initial state. The characteristic distributions of the CNOPs derived from the ICM also provide useful information for targeted observations through data assimilation. Given the fact that the derived CNOPs are season-dependent, it is suggested that seasonally varying targeted observations should be implemented to accurately predict ENSO events.