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

Abstract

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 it. In 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 research. This study constructs U-Net models for representing the relationship between SSTAs and τ anomalies in the tropical Pacific; the UNet-derived τ model, denoted as τ_UNet, is then used to replace the original SVD-based τ model of an intermediate coupled model (ICM), forming a newly AI-integrated ICM, referred to as ICM-UNet. The simulation results obtained from ICM-UNet demonstrate their ability to represent the spatiotemporal variability of oceanic and atmospheric anomaly fields in the equatorial Pacific. In the ocean-only case study, the τ_UNet-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 events. These results demonstrate the feasibility of integrating an AI-derived model with a physics-based dynamical model for ENSO modeling studies. Furthermore, 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.