A multi-model prediction system for ENSO

doi:10.1007/s11430-022-1094-0

IOCAS-IR > 海洋环流与波动重点实验室

	A multi-model prediction system for ENSO
	Liu, Ting 1,2,3; Gao, Yanqiu 1,2,3; Song, Xunshu 1,2,3; Gao, Chuan4,11 ; Tao, Lingjiang5,6 ; Tang, Youmin 7,8; Duan, Wansuo 9; Zhang, Rong-Hua10,11 ; Chen, Dake 1,2,3
	2023-05-15
发表期刊	SCIENCE CHINA-EARTH SCIENCES
ISSN	1674-7313
页码	10
通讯作者	Tang, Youmin(ytang@unbc.ca) ; Chen, Dake(dchen@sio.org.cn)
摘要	The El Nino and Southern Oscillation (ENSO) is the primary source of predictability for seasonal climate prediction. To improve the ENSO prediction skill, we established a multi-model ensemble (MME) prediction system, which consists of 5 dynamical coupled models with various complexities, parameterizations, resolutions, initializations and ensemble strategies, to account for the uncertainties as sufficiently as possible. Our results demonstrated the superiority of the MME over individual models, with dramatically reduced the root mean square error and improved the anomaly correlation skill, which can compete with, or even exceed the skill of the North American Multi-Model Ensemble. In addition, the MME suffered less from the spring predictability barrier and offered more reliable probabilistic prediction. The real-time MME prediction adequately captured the latest successive La Nina events and the secondary cooling trend six months ahead. Our MME prediction has, since April 2022, forecasted the possible occurrence of a third-year La Nina event. Overall, our MME prediction system offers better skill for both deterministic and probabilistic ENSO prediction than all participating models. These improvements are probably due to the complementary contributions of multiple models to provide additive predictive information, as well as the large ensemble size that covers a more reasonable uncertainty distribution.
关键词	MME ENSO Prediction
DOI	10.1007/s11430-022-1094-0
收录类别	SCI
语种	英语
资助项目	Scientific Research Fund of the Second Institute of Oceanography, MNR[QNYC2101] ; Scientific Research Fund of the Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)[SML2021SP310] ; National Natural Science Foundation of China[42030410] ; National Natural Science Foundation of China[41690124] ; National Natural Science Foundation of China[4160120] ; National Key Research and Development Program[2017YFA0604202] ; Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)[311021001] ; Laoshan Laboratory Programe[LSL202202402] ; Startup Foundation for Introducing Talent of NUIST
WOS研究方向	Geology
WOS类目	Geosciences, Multidisciplinary
WOS记录号	WOS:000991650900002
出版者	SCIENCE PRESS
WOS关键词	INTERMEDIATE COUPLED MODEL ; TO-INTERANNUAL PREDICTION ; EL-NINO ; SEASONAL CLIMATE ; SKILL ; PREDICTABILITY ; FORECASTS ; WEATHER ; MONSOON
引用统计
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/183175
专题	海洋环流与波动重点实验室
通讯作者	Tang, Youmin; Chen, Dake
作者单位	1.Minist Nat Resources, Inst Oceanography 2, State Key Lab Satellite Ocean Environm Dynam, Hangzhou 300012, Peoples R China 2.Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai 519000, Peoples R China 3.Chinese Acad Sci, Ctr Ocean Mega Sci, Qingdao 266071, Peoples R China 4.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 5.Fudan Univ, Dept Atmospher & Ocean Sci, Shanghai 200438, Peoples R China 6.Fudan Univ, Inst Atmospher Sci, Shanghai 200438, Peoples R China 7.Hohai Univ, Coll Oceanography, Nanjing 210098, Peoples R China 8.Univ Northern British Columbia, Environm Sci & Engn, Prince George, BC V2N 4Z9, Canada 9.Univ Chinese Acad Sci, Chinese Acad Sci, Inst Atmospher Phys, State Key Lab Numer Modeling Atmospher Sci & Geoph, Beijing 100029, Peoples R China 10.Nanjing Univ Informat Sci & Technol, Sch Marine Sci, Nanjing 210044, Peoples R China 11.Laoshan Lab, Qingdao 266237, Peoples R China
推荐引用方式 GB/T 7714	Liu, Ting,Gao, Yanqiu,Song, Xunshu,et al. A multi-model prediction system for ENSO[J]. SCIENCE CHINA-EARTH SCIENCES,2023:10.
APA	Liu, Ting.,Gao, Yanqiu.,Song, Xunshu.,Gao, Chuan.,Tao, Lingjiang.,...&Chen, Dake.(2023).A multi-model prediction system for ENSO.SCIENCE CHINA-EARTH SCIENCES,10.
MLA	Liu, Ting,et al."A multi-model prediction system for ENSO".SCIENCE CHINA-EARTH SCIENCES (2023):10.