首页|Optimal Precursors Triggering the Kuroshio Extension State Transition Obtained by the Conditional Nonlinear Optimal Perturbation Approach

Optimal Precursors Triggering the Kuroshio Extension State Transition Obtained by the Conditional Nonlinear Optimal Perturbation Approach

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In this study,the initial perturbations that are the easiest to trigger the Kuroshio Extension (KE) transition connecting a basic weak jet state and a strong,fairly stable meandering state,are investigated using a reduced-gravity shallow water ocean model and the CNOP (Conditional Nonlinear Optimal Perturbation) approach.This kind of initial perturbation is called an optimal precursor (OPR).The spatial structures and evolutionary processes of the OPRs are analyzed in detail.The results show that most of the OPRs are in the form of negative sea surface height (SSH) anomalies mainly located in a narrow band region south of the KE jet,in basic agreement with altimetric observations.These negative SSH anomalies reduce the meridional SSH gradient within the KE,thus weakening the strength of the jet.The KE jet then becomes more convoluted,with a high-frequency and large-amplitude variability corresponding to a high eddy kinetic energy level;this gradually strengthens the KE jet through an inverse energy cascade.Eventually,the KE reaches a high-energy state characterized by two well defined and fairly stable anticyclonic meanders.Moreover,sensitivity experiments indicate that the spatial structures of the OPRs are not sensitive to the model parameters and to the optimization times used in the analysis.

Kuroshio Extensionstates transitionCNOP approachoptimal precursorocean modeling

Xing ZHANG、Mu MU、Qiang WANG、Stefano PIERINI

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Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China

University of Chinese Academy of Sciences, Beijing 100049, China

Institute of Atmospheric Sciences, Fudan University, Shanghai 200433, China

Function Laboratory for Ocean Dynamics and Climate, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China

Department of Science and Technology, Parthenope University of Naples, Naples, 80143, Italy

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This study was supported by the National Natural Science Foundation of ChinaThis study was supported by the National Natural Science Foundation of ChinaThis study was supported by the National Natural Science Foundation of ChinaThis study was supported by the National Natural Science Foundation of ChinaNatural Science Foundation Of China (NSFC) Innovative GroupNSFC-Shandong Joint Fund for Marine Science Research CentersS.PIERINI acknowledges support from the University of Naples Parthenope

4157601541306023414906444149064041421005U1406401DSTE315

2017

10.1007/s00376-017-6263-7

大气科学进展(英文版)
中国科学院大气物理研究所

大气科学进展(英文版)

CSTPCDCSCDSCI
影响因子：0.741
ISSN：0256-1530
年,卷(期)：2017.34(6)
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