中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Season-dependent predictability and error growth dynamics for La Nina predictions

文献类型:期刊论文

作者Hu, Junya1; Duan, Wansuo2,3; Zhou, Qian4
刊名CLIMATE DYNAMICS
出版日期2019-07-01
卷号53期号:1-2页码:1063-1076
关键词La Nina events Spring predictability barrier Initial errors
ISSN号0930-7575
DOI10.1007/s00382-019-04631-5
通讯作者Duan, Wansuo(duanws@lasg.iap.ac.cn)
英文摘要The spring predictability barrier (SPB) is a well-known characteristic of ENSO prediction, which has been widely studied for El Nino events. However, due to the nonlinearity of the coupled ocean-atmosphere system and the asymmetries between El Nino and La Nina, it is worthy to investigate the SPB for La Nina events and reveal their differences with El Nino. This study investigates the season-dependent predictability of sea surface temperature (SST) for La Nina events by exploring initial error growth in a perfect model scenario within the Community Earth System Model. The results show that for the prediction through the spring season, the prediction errors caused by initial errors have a season-dependent evolution and induce an SPB for La Nina events. Two types of initial errors that often yield the SPB phenomenon are identified: the first are type-1 initial errors showing positive SST errors in the central-eastern equatorial Pacific accompanied by a large positive error in the upper layers of the eastern equatorial Pacific. The second are type-2 errors presenting an SST pattern with positive errors in the southeastern equatorial Pacific and a west-east dipole pattern in the subsurface ocean. The type-1 errors exhibit an evolving mode similar to the growth phase of an El Nino-like event, while the type-2 initially experience a La Nina-like decay and then a transition to the growth phase of an El Nino-like event. Both types of initial errors cause positive prediction errors for Nino3 SST and under-predict the corresponding La Nina events. The resultant prediction errors of type-1 errors are owing to the growth of the initial errors in the upper layers of the eastern equatorial Pacific. For the type-2 errors, the prediction errors originate from the initial errors in the subsurface layers of the western equatorial Pacific. These two regions may represent the sensitive areas of targeted observation for La Nina prediction. In addition, the type-2 errors in the equatorial regions are enlarged by the recharge process from 10 degrees N in the central Pacific during the eastward propagation. Therefore, the off-equatorial regions around 10 degrees N in the central Pacific may represent another sensitive area of La Nina prediction. Additional observations may be prioritized in these identified sensitive areas to better predict La Nina events.
资助项目National Natural Science Foundation of China[41690124] ; National Natural Science Foundation of China[41706016] ; National Natural Science Foundation of China[41525017] ; National Natural Science Foundation of China[41606019] ; National Programme on Global Change and Air-Sea Interaction[GASI-IPOVAI-06]
WOS研究方向Meteorology & Atmospheric Sciences
语种英语
WOS记录号WOS:000471722400061
出版者SPRINGER
源URL[http://ir.qdio.ac.cn/handle/337002/161761]  
专题海洋研究所_海洋环流与波动重点实验室
通讯作者Duan, Wansuo
作者单位1.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao 266071, Shandong, Peoples R China
2.Chinese Acad Sci, Inst Atmospher Phys, State Key Lab Numer Modeling Atmospher Sci & Geop, Beijing 100029, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Minist Nat Resources, Natl Marine Environm Forecasting Ctr, Beijing 100081, Peoples R China
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Hu, Junya,Duan, Wansuo,Zhou, Qian. Season-dependent predictability and error growth dynamics for La Nina predictions[J]. CLIMATE DYNAMICS,2019,53(1-2):1063-1076.
APA Hu, Junya,Duan, Wansuo,&Zhou, Qian.(2019).Season-dependent predictability and error growth dynamics for La Nina predictions.CLIMATE DYNAMICS,53(1-2),1063-1076.
MLA Hu, Junya,et al."Season-dependent predictability and error growth dynamics for La Nina predictions".CLIMATE DYNAMICS 53.1-2(2019):1063-1076.

入库方式: OAI收割

来源:海洋研究所

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