Emulating Greenland Ice Sheet melt and runoff from polar RCMs with machine learning

Accurate projections of ice sheet surface mass balance (SMB) are critical for sea-level rise estimates. Polar regional climate models (RCMs) coupled with firn models are the primary tools for simulating melt and projecting future SMB. However, the projections from different RCMs significantly deviate from each other. Additionally, the computational costs of polar RCMs and their firn models limit the creation of large ensembles needed to statistically assess the likely range in future melt and runoff.

Machine learning emulators offer a promising solution by enabling rapid predictions at a fraction of the computational cost. We therefore present machine learning emulators that predict daily surface melt and runoff from atmospheric variables from their associated polar RCM over Greenland. The emulators use a novel physics-informed, modular architecture that combines short-term weather patterns with long-term climate memory, capturing both immediate atmospheric forcing and accumulated firn characteristics.

Our work demonstrates that machine learning can successfully emulate firn model behavior from climate forcing alone. This represents a crucial first step toward computationally efficient emulation of polar RCMs, facilitating generation of large ensembles, sensitivity analysis, and potential integration as surrogate models within Earth system models.