A machine-learning-based super-resolution approach for dynamical ice-sheet modeling

Dynamical ice-sheet models are among the primary tools used to investigate the evolution of ice sheets. However, their computational cost increases rapidly with spatial resolution, often making long-term or ensemble simulations prohibitively expensive. Here, we investigate whether recent advances in machine-learning-based super-resolution techniques for spatiotemporal data can be leveraged to reduce these computational costs while retaining high-resolution information.

Using one pair of low- and high-resolution simulations of the Greenland ice sheet for the 20^th century, generated with the PISM dynamical ice-sheet model, we train a machine-learning-based super-resolution model to learn the mapping from low- to high-resolution states. For subsequent simulations, computationally inexpensive low-resolution model runs are combined with the trained super-resolution model to reconstruct high-resolution fields. We evaluate this hybrid framework by assessing (1) whether the super-resolution model can accurately reproduce the spatial details of high-resolution simulations, and (2) whether it can mitigate deficiencies in the long-term trends produced by low-resolution models. Our results provide insight into the potential of machine-learning-based super-resolution as a cost-effective tool for high-resolution dynamical ice-sheet modeling.