IOCAS-IR  > 海洋环流与波动重点实验室
Progress in ENSO prediction and predictability study
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; Mu, Mu10
2018-11-01
Source PublicationNATIONAL SCIENCE REVIEW
ISSN2095-5138
Volume5Issue:6Pages:826-839
Corresponding AuthorTang, Youmin(ytang@unbc.ca) ; Liu, Ting(liut@sio.org.cn)
AbstractENSO 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.
KeywordENSO prediction and predictability coupled model ensemble prediction optimal error growth probabilistic prediction
DOI10.1093/nsr/nwy105
Indexed BySCI
Language英语
Funding ProjectNational 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 Research AreaScience & Technology - Other Topics
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000456418700014
PublisherOXFORD UNIV PRESS
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.qdio.ac.cn/handle/337002/160668
Collection海洋环流与波动重点实验室
Corresponding AuthorTang, Youmin; Liu, Ting
Affiliation1.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
Recommended Citation
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.
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