Improving dynamical climate predictions with machine learning: insights from a twin experiment framework

Abstract. Systematic errors in dynamical climate models remain a significant challenge to accurate climate predictions, particularly when modeling the nonlinear coupling between the atmosphere and ocean. Despite notable advances in dynamical climate modeling that have improved our understanding of climate variability, these systematic errors can still degrade prediction skills. In this study, we adopt a twin experiment framework with a reduced-order coupled atmosphere-ocean model to explore the utility of machine learning in mitigating these errors. Specifically, we train a data-driven model on data assimilation increments to learn and emulate the underlying dynamical climate model error, which is then integrated with the dynamical climate model to form a hybrid model. Comparison experiments show that the hybrid model consistently outperforms the standalone dynamical climate model in predicting atmospheric and oceanic variables. Further investigation using hybrid models that correct only atmospheric or only oceanic errors reveals that atmospheric corrections are essential for improving short-term predictions, while concurrently addressing both atmospheric and oceanic errors yields superior performance in long-term climate prediction.