Unravelling the role of increased model resolution on surface temperature fields using explainable AI

Reducing climate model biases is crucial for decreasing uncertainties in future climate projections. Despite recent efforts, improvements between the latest generations of Earth System Models (ESMs) have been modest, primarily due to the continued reliance on subgrid-scale parametrizations. These parametrizations are necessary because the model resolutions in CMIP6 are too coarse to explicitly simulate too small-scale processes such as ocean mesoscale eddies and deep atmospheric convection, which significantly influence regional and global climate patterns. Recent advances in computational power have enabled higher-resolution models, allowing for some of these processes to be simulated explicitly, reducing the need for parametrization. Here, we combine a convolutional neural network (CNN) classifier and explainable AI (XAI) to investigate the role of increased resolution in simulating winter surface temperature fields. The CNN is used to classify ESMs with varying resolutions based on snapshots of their surface temperature fields, while the XAI approach explains which regions and features the CNN relies on to make these distinctions, providing deeper insights into ESM performance. Results indicate that models with similar ocean grids are more frequently confused by the CNN than those from similar modeling centers, emphasizing the crucial role of ocean resolution, particularly the presence of mesoscale eddies, in shaping climate simulations. Although the analysis is restricted to surface air temperature, the XAI approach offers a more nuanced understanding of model differences compared to traditional bias analyses. This methodology can be extended to other climate variables and ESM features, offering a powerful tool for enhancing model intercomparison and evaluating ESM performance.