U-Net Models for Representing Wind Stress Anomalies over the Tropical Pacific and Their Integrations with an Intermediate Coupled Model for ENSO Studies

作     者：Shuangying Du Rong-Hua Zhang Shuangying DU;Rong-Hua ZHANG

作者机构：Key Laboratory of Ocean Circulation and WavesInstitute of OceanologyChinese Academy of SciencesQingdao266071China School of Marine SciencesNanjing University of Information Science and TechnologyNanjing210044China Laosan LaboratoryQingdao266237China University of Chinese Academy of SciencesBeijing100049China 

出 版 物：《Advances in Atmospheric Sciences》 (大气科学进展（英文版）)

年 卷 期：2024年第41卷第7期

页      面：1403-1416页

核心收录：

学科分类：07[理学] 070601[理学-气象学] 0706[理学-大气科学] 

基　　金：supported by the National Natural Science Foundation of China(NFSC Grant No.42030410) Laoshan Laboratory(No.LSKJ202202402) the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB40000000) the Startup Foundation for Introducing Talent of NUIST 

主　　题：U-Net models wind stress anomalies ICM integration of AI and physical components 

摘      要：El Niño-Southern Oscillation(ENSO)is the strongest interannual climate mode influencing the coupled ocean-atmosphere system in the tropical Pacific,and numerous dynamical and statistical models have been developed to simulate and predict *** some simplified coupled ocean-atmosphere models,the relationship between sea surface temperature(SST)anomalies and wind stress(τ)anomalies can be constructed by statistical methods,such as singular value decomposition(SVD).In recent years,the applications of artificial intelligence(AI)to climate modeling have shown promising prospects,and the integrations of AI-based models with dynamical models are active areas of *** study constructs U-Net models for representing the relationship between SSTAs andτanomalies in the tropical Pacific;the UNet-derivedτmodel,denoted asτUNet,is then used to replace the original SVD-basedτmodel of an intermediate coupled model(ICM),forming a newly AI-integrated ICM,referred to as *** simulation results obtained from ICM-UNet demonstrate their ability to represent the spatiotemporal variability of oceanic and atmospheric anomaly fields in the equatorial *** the ocean-only case study,theτUNet-derived wind stress anomaly fields are used to force the ocean component of the ICM,the results of which also indicate reasonable simulations of typical ENSO *** results demonstrate the feasibility of integrating an AI-derived model with a physics-based dynamical model for ENSO modeling ***,the successful integration of the dynamical ocean models with the AI-based atmospheric wind model provides a novel approach to ocean-atmosphere interaction modeling studies.