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, Xianghui2,3,5; Mu, Mu2,3,5
2020-03-01
Source PublicationJOURNAL OF GEOPHYSICAL RESEARCH-OCEANS
ISSN2169-9275
Volume125Issue:3Pages:14
Corresponding AuthorFang, Xianghui(fangxh@fudan.edu.cn)
AbstractBy 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
Keywordoptimal precursor ENSO CNOP GFDL CM2p1 PPSO
DOI10.1029/2019JC015797
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41805045]
WOS Research AreaOceanography
WOS SubjectOceanography
WOS IDWOS:000534229400029
PublisherAMER GEOPHYSICAL UNION
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.qdio.ac.cn/handle/337002/167552
Collection海洋环流与波动重点实验室
Corresponding AuthorFang, Xianghui
Affiliation1.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
First Author AffilicationKey Laboratory of Ocean Circulation and Wave Studies, Institute of Oceanology, Chinese Academy of Sciences
Recommended Citation
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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