A multi-model prediction system for ENSO

doi:10.1007/s11430-022-1094-0

CORC > 海洋研究所 > 中国科学院海洋研究所 > 海洋环流与波动重点实验室

	A multi-model prediction system for ENSO
	Liu, Ting 1,2,10; Gao, Yanqiu 1,2,10; Song, Xunshu 1,2,10; Gao, Chuan 9,11; Tao, Lingjiang 3,4; Tang, Youmin 5,6; Duan, Wansuo 7; Zhang, Rong-Hua 8,9; Chen, Dake 1,2,10
刊名	SCIENCE CHINA-EARTH SCIENCES
	2023-05-15
页码	10
关键词	MME ENSO Prediction
ISSN号	1674-7313
DOI	10.1007/s11430-022-1094-0
通讯作者	Tang, Youmin(ytang@unbc.ca) ; Chen, Dake(dchen@sio.org.cn)
英文摘要	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.
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关键词	INTERMEDIATE COUPLED MODEL ; TO-INTERANNUAL PREDICTION ; EL-NINO ; SEASONAL CLIMATE ; SKILL ; PREDICTABILITY ; FORECASTS ; WEATHER ; MONSOON
WOS研究方向	Geology
语种	英语
出版者	SCIENCE PRESS
WOS记录号	WOS:000991650900002
内容类型	期刊论文
源URL	[http://ir.qdio.ac.cn/handle/337002/183174]
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; Chen, Dake
作者单位	1.Chinese Acad Sci, Ctr Ocean Mega Sci, Qingdao 266071, Peoples R China 2.Minist Nat Resources, Inst Oceanography 2, State Key Lab Satellite Ocean Environm Dynam, Hangzhou 300012, Peoples R China 3.Fudan Univ, Dept Atmospher & Ocean Sci, Shanghai 200438, Peoples R China 4.Fudan Univ, Inst Atmospher Sci, Shanghai 200438, Peoples R China 5.Hohai Univ, Coll Oceanography, Nanjing 210098, Peoples R China 6.Univ Northern British Columbia, Environm Sci & Engn, Prince George, BC V2N 4Z9, Canada 7.Univ Chinese Acad Sci, Chinese Acad Sci, Inst Atmospher Phys, State Key Lab Numer Modeling Atmospher Sci & Geoph, Beijing 100029, Peoples R China 8.Nanjing Univ Informat Sci & Technol, Sch Marine Sci, Nanjing 210044, Peoples R China 9.Laoshan Lab, Qingdao 266237, Peoples R China 10.Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai 519000, 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.