Learning to melt: Emulating Greenland surface melt from a polar RCM with machine learning

Abstract

Predicting surface melt on the Greenland ice sheet is critical for understanding surface mass balance (SMB) and its sensitivity to a changing climate. Polar regional climate models (RCMs) are the primary tools for simulating melt and projecting future SMB, but different models produce significantly different results. However, they are too computationally expensive to create the large ensembles needed to quantify this uncertainty. We develop a neural network based emulator that predicts daily surface melt from atmospheric variables, trained on output from the polar RCM HIRHAM5 and its firn model DMIHH forced by ERA-Interim. The emulator uses a physics-informed design combining short-term weather with long-term climate memory, capturing both immediate atmospheric forcing and accumulated firn characteristics. Input selection study shows that turbulent heat fluxes, downwelling radiation, and precipitation together with seasonal encoding suffice to reproduce surface melt. The emulator achieves mean absolute error below 0.21 mm w.e. per day relative to the surface melt produced by DMIHH across all six Greenland drainage basins, with the errors primarily attributable to spatial over-smoothing. Our work demonstrates that machine learning can successfully emulate firn model behavior from climate forcing alone with computational costs orders of magnitude lower than traditional simulations. Once retrained for specific climate forcings, the emulator thus enables extensive ensemble projections. Furthermore, the modular architecture can be readily adapted to emulate other SMB quantities such as runoff. This represents a crucial first step toward computationally efficient emulation of polar regional climate models and surrogate modeling of SMB components in Earth system modeling.