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 oceans. Despite notable advances in dynamical climate modeling that have improved our understanding of climate variability, these systematic errors can still degrade predictive 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 model error, which is then integrated with the dynamical model to form a hybrid system. Comparison experiments show that the hybrid model consistently outperforms the standalone dynamical 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 forecasts, while concurrently addressing both atmospheric and oceanic errors yields superior performance in long-term climate prediction.