Towards Generative Machine Learning-based Downscaling for Atmosphere-Surface Coupling in the Bern3D EMIC

Earth System Models of Intermediate Complexity (EMICs) are essential tools for investigating climate dynamics on millennial to orbital time scales, which are computationally prohibitive for high-resolution CMIP-class models. The computational efficiency of EMICs is primarily achieved by reduced spatial resolution of the atmosphere and ocean components. However, EMICs often couple ice-sheet and terrestrial vegetation components, which require much higher spatial resolution. The coupling of these components therefore remains a major challenge and often results in inadequate climatic forcing for these sub-modules, particularly regarding precipitation patterns. Generative machine learning, specifically diffusion models and their variants, has emerged as a powerful technique to bridge this resolution gap. Here, we present the integration of a consistency model-based approach to facilitate efficient, online downscaling of temperature and precipitation within the Bern3D EMIC with negligible computational overhead.

To achieve this, the consistency model was trained on monthly ERA5 ensemble output to learn the mapping from the coarse Bern3D grid to high-resolution fields.  This approach successfully reconstructs high-resolution spatial variability while maintaining inference speeds compatible with long model integration times, effectively avoiding additional runtime costs. This framework therefore allows for the representation of small-scale heterogeneity in surface boundary conditions which is critical for realistic ice sheet and vegetation dynamics. 

Ultimately, this approach opens new avenues to investigate complex climate-ice-vegetation feedback on orbital time scales, such as during the Last Glacial Cycle or the Mid-Pleistocene Transition.