The Optimal Precursor of El Nino in the GFDL CM2p1 Model

doi:10.1029/2019JC015797

IOCAS-IR > 海洋环流与波动重点实验室

	The Optimal Precursor of El Nino in the GFDL CM2p1 Model
	Yang, Zeyun1,4 ; Fang, Xianghui 2,3,5; Mu, Mu 2,3,5
	2020-03-01
发表期刊	JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS
ISSN	2169-9275
卷号	125 期号:3 页码:14
通讯作者	Fang, Xianghui([email protected])
摘要	By applying the principal component analysis-based particle swarm optimization algorithm, the conditional nonlinear optimal perturbation is firstly calculated in the Geophysical Fluid Dynamics Laboratory Climate Model version 2p1 (GFDL CM2p1) to identify the optimal precursor (OPR) of El Nino. Specifically, through optimizing the initial perturbation, the OPRs that have the largest nonlinear evolution (i.e., mature state of El Nino) for two reference states are obtained, which are then confirmed according to the validation test. The results indicate that both OPRs show positive sea surface temperature perturbation in the west (2 degrees N-2 degrees S, 135.5-165.5 degrees E). For the subsurface component, they exhibit positive subsurface temperature perturbation (STP) in the whole mixed layer of the west and negative STP in the upper layer of the east (i.e., 0- to 85-m depth, 2 degrees N-2 degrees S, 79.5-109.5 degrees W). Further analyses of the evolution of the sea surface temperature perturbation, STP, and surface wind perturbation suggest that the development of the OPRs in the model is consistent with the recognized mechanism for El Nino-Southern Oscillation development, that is, through the Bjerknes positive feedback. The results indicate that the model can realistically capture the dominant processes for El Nino development, and the principal component analysis-based particle swarm optimization algorithm is a practical solution for calculating the conditional nonlinear optimal perturbation in a complicated numerical model such as the GFDL CM2p1. They both shed a light on guiding the realistic observing systems. Key Points < id="jgrc23894-li-0001">CNOP approach is applied to identify the optimal precursors of El Nino in the GFDL CM2p1 model < id="jgrc23894-li-0002">The PPSO algorithm is firstly and successfully adopted in the GFDL CM2p1 model to calculate the CNOP < id="jgrc23894-li-0003">Results suggest that the surface and subsurface temperature perturbations with specific patterns are crucial for El Nino development
关键词	optimal precursor ENSO CNOP GFDL CM2p1 PPSO
DOI	10.1029/2019JC015797
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[41805045]
WOS研究方向	Oceanography
WOS类目	Oceanography
WOS记录号	WOS:000534229400029
出版者	AMER GEOPHYSICAL UNION
引用统计	被引频次：20[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/167552
专题	海洋环流与波动重点实验室
通讯作者	Fang, Xianghui
作者单位	1.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China 2.Fudan Univ, Dept Atmospher & Ocean Sci, Shanghai, Peoples R China 3.Fudan Univ, Inst Atmospher Sci, Shanghai, Peoples R China 4.Univ Chinese Acad Sci, Beijing, Peoples R China 5.Zhuhai Fudan Innovat Res Inst, Innovat Ctr Ocean & Atmosphere Syst, Zhuhai, Peoples R China
第一作者单位	海洋环流与波动重点实验室
推荐引用方式 GB/T 7714	Yang, Zeyun,Fang, Xianghui,Mu, Mu. The Optimal Precursor of El Nino in the GFDL CM2p1 Model[J]. JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS,2020,125(3):14.
APA	Yang, Zeyun,Fang, Xianghui,&Mu, Mu.(2020).The Optimal Precursor of El Nino in the GFDL CM2p1 Model.JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS,125(3),14.
MLA	Yang, Zeyun,et al."The Optimal Precursor of El Nino in the GFDL CM2p1 Model".JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS 125.3(2020):14.

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