Machine-Learning-Based History Matching for Parameter Tuning of an Atmosphere-Ocean Coupled Model: Reducing the Double ITCZ Bias

Earth system models are essential tools for projecting future climate change, yet their performance is limited by uncertainties in the parameterization. One of the most persistent biases is the double Intertropical Convergence Zone (ITCZ) problem. Here, we apply a machine-learning-based history matching approach to an atmosphere–ocean coupled model (GRIMs-NEMO) to reduce ITCZ-related biases while maintaining the global radiative balance. Radiative fluxes, precipitation, sea surface temperature (SST), and cloud fraction are selected as target variables, and a Gaussian Process emulator is used to efficiently explore the parameter space. The optimized parameter set reduces global-mean biases in outgoing shortwave and longwave radiation and alleviates the double ITCZ bias in the model. However, SST and cloud biases increase in parts of the tropical Pacific, which is interpreted as a consequence of enhanced cloud formation that reduces shortwave radiation and amplifies surface cooling. This limitation suggests that future tuning should include parameters related to ocean vertical mixing and cloud convection to better represent atmosphere-ocean interactions. This study demonstrates that ML-based history matching is an effective tool for reducing persistent biases in complex Earth system models and can contribute to improving the reliability of future climate projections.

※ This work was supported by the Korea Environment Industry & Technology Institute (KEITI) through the “Climate Change R&D Project for New Climate Regime” funded by the Korea Ministry of Environment (MOE) (2022003560001)