大气科学学报2026，Vol.49Issue(1)：1-19,19.DOI:10.13878/j.cnki.dqkxxb.20251127007
厄尔尼诺-南方涛动研究的海气耦合模式:物理驱动与数据驱动模型的融合建模及示范案例
Ocean-atmosphere coupled models for El Niño-Southern Oscillation(EN-SO)research:a fusion of physics-and data-driven approaches with illus-trative examples
摘要
Abstract
Current approaches to simulating and predicting the El Niño-Southern Oscillation(ENSO)rely pri-marily on two classes of models:physics-based dynamical models and data-driven statistical or machine learning models.Physics-based models explicitly represent physical processes and provide mechanistic insight,but their performance is often constrained by model resolution,parameterizations,and limited predictive skill.In contrast,data-driven models excel at capturing complex nonlinear spatiotemporal patterns and achieving high short-term prediction accuracy;however,they frequently lack physical constraints and robust generalization capability.Fusion modeling approaches that combine physical knowledge with artificial intelligence(AI)have therefore emerged as a promising avenue for overcoming these limitations.By embedding AI techniques into dynamical models or in-corporating physical constraints into data-driven frameworks,fusion approaches can simultaneously enhance pre-dictive accuracy,physical consistency,and interpretability.Recent studies demonstrate that such integrations im-prove model parameterizations,strengthen model robustness,and advance ENSO predictability.This paper presents three representative cases illustrating fusion modeling applications in ocean-atmosphere coupled studies. 1)Physics-informed neural-network parameterization of ocean vertical mixing.Uncertainties and biases in o-cean vertical-mixing parameterizations are among the primary sources of error in oceanic and climate simulations.Owing to limited understanding of the underlying processes,traditional physics-based parameterization schemes often perform unsatisfactorily in the tropical Pacific,leading to systematic biases in simulations of climatological mean state and ENSO variability.Recent advances in deep-learning methodologies,together with the increasing a-vailability of long-term turbulence observations,provide new opportunities to develop data-driven approaches for parameterizing oceanic vertical-mixing processes.Zhu et al.(2022)introduced a novel parameterization based on an artificial neural network trained using a decadal-long record of hydrographic and turbulence observations in the tropical Pacific.This data-driven parameterization achieves higher accuracy than existing schemes while exhibiting good generalization ability under imposed physical constraints.When integrated into an ocean general circulation model(OCM),the physics-informed neural network(PINN)-based parameterization substantially improves sim-ulations in both ocean-only and coupled modeling configurations.As a novel application of machine learning in physical oceanography and climate science,these activities demonstrate the feasibility of constructing physically consistent,data-driven parametrizations using limited observations and well-established physical constraints to im-prove climate simulations. 2)U-Net-based representation of surface wind stress anomalies and its integration with an intermediate cou-pled model for ENSO studies.Numerous dynamical and statistical models have been developed to simulate and predict ENSO.In simplified coupled ocean-atmosphere models,the relationship between sea surface temperature a-nomalies(SSTAs)and surface wind stress(τ)anomalies is often represented using statistical methods such as singular value decomposition(SVD),which captures only linear responses of wind stress to SSTAs.In recent years,the application of artificial intelligence(AI)to climate modeling has shown considerable promise,and the integration of AI-based models with dynamical models has become an active area of research.Previous studies have demonstrated that AI-based τ models,when trained with extensive datasets,can effectively represent nonlin-ear relationships among climate variables. Du and Zhang(2024)developed U-Net-based models to represent the relationship between τ anomalies and SSTAs over the tropical Pacific.The resulting U-Net derived τ model,denoted as τUNet,was used to replace the o-riginal SVD-based τ model in an intermediate coupled model(ICM),thereby forming a new AI-integrated cou-pled system,referred to as ICM-UNet.Simulation results from the ICM-UNet indicate that the model can reasona-bly represent the spatiotemporal variability of oceanic and atmospheric anomaly fields in the equatorial Pacific.A series of experiments were conducted to evaluate model performance.In an ocean-only configuration,the U-Net-derived wind stress anomaly fields were used to force the ocean component of the ICM,yielding realistic simula-tions of typical ENSO events.These results demonstrate the feasibility of integrating AI-derived model atmospheric components with physics-based dynamical model for ENSO studies.Moreover,the successful coupling of a dynamical ocean model with an AI-based wind stress model provides a novel framework for investi-gating ocean-atmosphere interactions. The ICM-UNet simulations reproduce quasi-periodic variations in both atmospheric and oceanic anomaly fields,with the spatiotemporal evolution of SSTAs exhibiting physically consistent patterns.These findings suggest that AI-derived models can serve as effective components within dynamical frameworks for representing ENSO variability.In addition,the ocean component of the IOCAS(Institute of Oceanology,Chinese Academy of Sci-ences)ICM,when forced by the U-Net-derived wind stress anomalies,is able to reasonably capture typical El Niño events.This case study further confirms the potential of integrating AI-based wind models with dynamical o-cean models as a promising approach for hybrid climate modeling. Nevertheless,the present study represents an initial attempt at such integrations,and several limitations re-main.For example,the simulated SSTAs exhibit relatively regular temporal evolution,which does not fully capture the diversity and irregularity of observed ENSO events.As neural networks are inherently data-driven,they can learn nonlinear relationships without physical constraints,whereas the original ICM represents linear relationships based on SVD analysis.Modifying individual components within a coupled system may therefore introduce unin-tended effects,such as reduced variability in other regions of the Pacific.Furthermore,the potential impacts of the AI-based τ model on other components of the coupled system were not fully assessed.Additional validation is re-quired to ensure that the integrated model adheres to fundamental physical laws.Computational efficiency is anoth-er concern,as the AI-integrated model currently incurs higher computational costs. Future work should focus on several key directions.First,the adaptability of the ICM-UNet should be im-proved to better capture the diversity of ENSO events,potentially through parameter optimization or the develop-ment of more advanced AI-based τ models.Second,comprehensive validation experiments are needed to assess the impacts of the AI-based wind stress model on other components of the coupled system,with adjustments made as necessary to preserve essential ocean-atmosphere dynamical processes.Third,improvements in data exchange effi-ciency and computational resource utilization are required to enable longer simulations at reduced computational cost.Finally,future studies may explore the construction of alternative AI-based τ models to represent nonlinear interactions among key physical variables and integrate them into other coupled modeling frameworks.This ap-proach holds significant promise as an effective interface between AI techniques and physics-based models in physical oceanography and atmospheric sciences.Ultimately,physics-informed and data-driven integration is ex-pected to establish a new paradigm for ENSO simulation and prediction,with broader implications for climate modeling and sustainable decision-making. 3)A hybrid coupled model for the tropical Pacific based on ROMS and statistical atmospheric models.Cou-pled climate models with varying levels of complexity often exhibit substantial biases and inter-model differences in simulating ENSO,highlighting the need for alternative strategies,including ICMs,HCMs,and OGCMs.The Re-gional Ocean Modeling System(ROMS)is a state-of the-art ocean model widely used in regional studies and has been coupled with various atmospheric models.However,its application to basin-scale ENSO simulations in the tropical Pacific has remained largely unexplored. This study presents,for the first time,the development of a basin-scale hybrid coupled model for the tropical Pacific that integrates ROMS with a statistical atmospheric model representing interannual relationships between SST and surface wind stress anomalies.Two atmospheric wind stress models are implemented:one based on an SVD-derived statistical formulation(denoted as HCMSVDOGCM)and the other based on a U-Net-derived AI formula-tion(denoted as HCM AIOGCM).The performance of these two HCM configurations is evaluated in terms of their a-bility to simulate the annual mean state,seasonal variability,and interannual variations of the tropical Pacific O-cean. The results demonstrate that both HCMs are capable of reproducing the ENSO cycle,with a dominant oscil-lation period of approximately two years.Notably,the AI-based configuration,HCMAIOGCM,more effectively captures the irregularity and diversity of ENSO events compared with SVD-based configuration,HCMSVDOGCM.The ROMS-based HCM developed in this study thus provides an efficient and robust framework for investigating cli-mate variability and ocean-atmosphere interactions in the tropical Pacific.关键词
海气耦合/ENSO/物理驱动模式/数据驱动模型/融合建模/示范案例Key words
ocean-atmosphere coupling/ENSO/physics-driven model/data-driven model/a fusion of modeling/illustrative examples引用本文复制引用
张荣华,冯立成,陈林,徐邦琪,陆波,李殷楠,杜双盈,高川,周路,朱聿超,于洋,陶灵江,智海..厄尔尼诺-南方涛动研究的海气耦合模式:物理驱动与数据驱动模型的融合建模及示范案例[J].大气科学学报,2026,49(1):1-19,19.基金项目
国家自然科学基金项目(42176032 （）
42030410) （）
崂山实验室科技创新项目(LSKJ202202402) （LSKJ202202402）
江苏双创团队项目(JSSCTD202346) （JSSCTD202346）
气候系统预测与变化应对全国重点实验室项目(CPRM202607) （CPRM202607）
博士后创新人才支持计划资助项目(BX20240169) （BX20240169）
中国博士后科学基金科研资助项目(2141062400101) （2141062400101）
国家重点研发计划项目(2024YFC2815702) （2024YFC2815702）
