OA-Bridge: One-Step Coupled Ocean-Atmosphere Forecasting with a Vision–Language Model

Atmosphere–ocean coupling is a fundamental physical process of the Earth system, yet most AI-based forecasting models treat the atmosphere and ocean as independent components. Learning a unified forecasting model is challenging because atmospheric and oceanic variables evolve on substantially different timescales, while straightforward joint training can cause negative transfer and degrade the predictive skill of otherwise strong component models. We propose OA-Bridge, the first VLM-based framework for explicitly coupled global atmosphere–ocean forecasting at daily resolution. Given the current atmospheric and oceanic states and a specified lead time, OA-Bridge directly predicts their joint future states without recursively applying a fixed-step transition model. A shared frozen Qwen2.5-VL backbone jointly processes the two representations conditioned on the requested lead time. Component-specific encoders, output adapters, and decoders preserve specialized representations, while a coupling adapter uses bidirectional cross-attention to exchange information between the two systems. Experiments from 1 to 60 days against a jointly trained FuXi baseline show that OA-Bridge substantially improves long-range ocean RMSE and ACC across depths, extreme-event skill, and SSH-derived geostrophic currents, while also improving atmospheric skill at extended lead times.