Physics-constrained generative machine learning-based high-resolution downscaling of Greenland's surface mass balance and surface temperature
This work addresses the need for accurate, high-resolution climate data for ice-sheet simulations to improve sea-level rise projections, representing an incremental advance by applying a novel method to a known bottleneck in climate modeling.
The paper tackles the problem of generating high-resolution projections of Greenland's surface mass balance and surface temperature by introducing a physics-constrained generative modeling framework that downscales low-resolution fields by up to a factor of 32, achieving a continued ranked probability score of 6.31 mmWE for SMB and 0.1 K for temperature, outperforming interpolation-based methods.
Accurate, high-resolution projections of the Greenland ice sheet's surface mass balance (SMB) and surface temperature are essential for understanding future sea-level rise, yet current approaches are either computationally demanding or limited to coarse spatial scales. Here, we introduce a novel physics-constrained generative modeling framework based on a consistency model (CM) to downscale low-resolution SMB and surface temperature fields by a factor of up to 32 (from 160 km to 5 km grid spacing) in a few sampling steps. The CM is trained on monthly outputs of the regional climate model MARv3.12 and conditioned on ice-sheet topography and insolation. By enforcing a hard conservation constraint during inference, we ensure approximate preservation of SMB and temperature sums on the coarse spatial scale as well as robust generalization to extreme climate states without retraining. On the test set, our constrained CM achieves a continued ranked probability score of 6.31 mmWE for the SMB and 0.1 K for the surface temperature, outperforming interpolation-based downscaling. Together with spatial power-spectral analysis, we demonstrate that the CM faithfully reproduces variability across spatial scales. We further apply bias-corrected outputs of the NorESM2 Earth System Model as inputs to our CM, to demonstrate the potential of our model to directly downscale ESM fields. Our approach delivers realistic, high-resolution climate forcing for ice-sheet simulations with fast inference and can be readily integrated into Earth-system and ice-sheet model workflows to improve projections of the future contribution to sea-level rise from Greenland and potentially other ice sheets and glaciers too.