Abstract :
[en] Abstract. Surface melting over the Greenland Ice Sheet has become one of the dominant sources of contemporary and projected global sea-level rise, with melt rates accelerating over recent decades. Understanding those processes and feedbacks that control Greenland's surface melt is central to improving projections of future mass loss and to clarifying how changes in surface energy balance components shape ice-sheet stability. To this aim, we developed MAR-IA - a machine-learning emulator of the MAR regional climate model – designed to emulate daily surface meltwater production over Greenland and to enable attribution of melt drivers. We implement two complementary emulators: a high-fidelity MAR-IA trained on full MAR surface energy balance fields and a reanalysis-compatible MAR-IA-ERA trained on predictors available from products such as ERA5, thereby extending applicability beyond MAR-specific outputs. Both emulators employ gradient-boosted trees optimized via Bayesian hyperparameter search, achieving high test-set skill, with the best-performing final configuration reaching R2=0.987, low root mean squared error (<10 mm w.e. d−1), and negligible bias relative to MAR meltwater outputs. We apply an explainable artificial intelligence (AI) analysis based on Shapley Additive Explanations (SHAP) to quantify how the importance of surface energy balance components,e.g., albedo, shortwave and longwave radiation, etc.,evolves across space and time over Greenland. Our results reveal robust spatial and temporal patterns in the dominance of radiative versus non-radiative drivers and demonstrate long-term trends in the relative contribution of temperature, shortwave radiation, and albedo to melt variability. These findings show that emulators can be used as powerful tools to complement regional climate models by enabling computationally efficient ensemble simulations and physically interpretable attribution of past and future Greenland surface melt.
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