Dynamical models used in climate prediction often have systematic errors
that can deteriorate predictions. In this study, we work in a twin
experiment framework with a reduced-order coupled ocean-atmosphere model
and aim to demonstrate the benefit of machine learning for climate
prediction. Machine learning is applied to learn the model error and
thus build a data-driven model to emulate the dynamical model error.
Then we build a hybrid model by combining the data-driven and dynamical
models. The prediction skill of the hybrid model is compared to that of
the standalone dynamical model. We applied this approach to the
ocean-atmosphere coupled model. The results show that the hybrid model
outperforms the dynamical model alone for both atmospheric and oceanic
variables. Also, we build two other hybrid models only correcting either
atmospheric errors or oceanic errors. It was found that correcting both
atmospheric and oceanic errors leads to the best performance.