中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Progress in ENSO prediction and predictability study

文献类型:期刊论文

作者Tang, Youmin1,2; Zhang, Rong-Hua3,4; Liu, Ting1,2; Duan, Wansuo5; Yang, Dejian6; Zheng, Fei7; Ren, Hongli8,9; Lian, Tao1; Gao, Chuan3,4; Chen, Dake1
刊名NATIONAL SCIENCE REVIEW
出版日期2018-11-01
卷号5期号:6页码:826-839
ISSN号2095-5138
关键词ENSO prediction and predictability coupled model ensemble prediction optimal error growth probabilistic prediction
DOI10.1093/nsr/nwy105
通讯作者Tang, Youmin(ytang@unbc.ca) ; Liu, Ting(liut@sio.org.cn)
英文摘要ENSO is the strongest interannual signal in the global climate system with worldwide climatic, ecological and societal impacts. Over the past decades, the research about ENSO prediction and predictability has attracted broad attention. With the development of coupled models, the improvement in initialization schemes and the progress in theoretical studies, ENSO has become the most predictable climate mode at the time scales from months to seasons. This paper reviews in detail the progress in ENSO predictions and predictability studies achieved in recent years. An emphasis is placed on two fundamental issues: the improvement in practical prediction skills and progress in the theoretical study of the intrinsic predictability limit. The former includes progress in the couple models, data assimilations, ensemble predictions and so on, and the latter focuses on efforts in the study of the optimal error growth and in the estimate of the intrinsic predictability limit.
资助项目National Natural Science Foundation of China[41690124] ; National Natural Science Foundation of China[41690121] ; National Natural Science Foundation of China[41690120] ; National Natural Science Foundation of China[41705049] ; National Natural Science Foundation of China[41621064] ; National Natural Science Foundation of China[41530961] ; National Key Research and Development Program[2017YFA0604202] ; National Programe on Global Change and Air-Sea Interaction[GASI-IPOVAI-06] ; Scientific Research Fund of the Second Institute of Oceanography[JG1810]
WOS研究方向Science & Technology - Other Topics
语种英语
出版者OXFORD UNIV PRESS
WOS记录号WOS:000456418700014
源URL[http://ir.qdio.ac.cn/handle/337002/160667]  
专题海洋研究所_海洋环流与波动重点实验室
通讯作者Tang, Youmin; Liu, Ting
作者单位1.Second Inst Oceanog, State Key Lab Satellite Ocean Environm Dynam, Hangzhou 310012, Zhejiang, Peoples R China
2.Univ Northern British Columbia, Environm Sci & Engn, Prince George, BC V2N 4Z9, Canada
3.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China
4.Qingdao Natl Lab Marine Sci & Technol, Qingdao 266237, Peoples R China
5.Chinese Acad Sci, Inst Atmospher Phys, State Key Lab Numer Modeling Atmospher Sci & Geop, Beijing 100029, Peoples R China
6.Hohai Univ, Coll Oceanog, Nanjing 210098, Jiangsu, Peoples R China
7.Chinese Acad Sci, Inst Atmospher Phys, Int Ctr Climate & Environm Sci, Beijing 100029, Peoples R China
8.China Meteorol Adm, Natl Climate Ctr, Lab Climate Studies, Beijing 100081, Peoples R China
9.China Meteorol Adm, Natl Climate Ctr, CMA NJU Joint Lab Climate Predict Studies, Beijing 100081, Peoples R China
10.Fudan Univ, Coll Atmospher & Ocean Sci, Shanghai 200438, Peoples R China
推荐引用方式
GB/T 7714
Tang, Youmin,Zhang, Rong-Hua,Liu, Ting,et al. Progress in ENSO prediction and predictability study[J]. NATIONAL SCIENCE REVIEW,2018,5(6):826-839.
APA Tang, Youmin.,Zhang, Rong-Hua.,Liu, Ting.,Duan, Wansuo.,Yang, Dejian.,...&Mu, Mu.(2018).Progress in ENSO prediction and predictability study.NATIONAL SCIENCE REVIEW,5(6),826-839.
MLA Tang, Youmin,et al."Progress in ENSO prediction and predictability study".NATIONAL SCIENCE REVIEW 5.6(2018):826-839.

入库方式: OAI收割

来源:海洋研究所

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