Regional glacier melt modeling: insights from surface energy balance and positive degree-day comparisons

Abstract Large-scale glacier mass-balance models often rely on positive degree-day (PDD) melt models, which have known limitations. This study evaluates a relatively simple, elevation-dependent surface energy balance (SEB) model that requires minimal downscaling of climate input data to simulate glacier melt. Using ECMWF Reanalysis v5 (ERA5) reanalysis data and multi-year mass-balance observations from 23 glaciers across Canada, we compare mass-balance models incorporating SEB and PDD components under various calibration scenarios. Initial tests with the uncalibrated SEB model highlight the importance of accurate ERA5 inputs, particularly lapse-rate corrections for 2 m air temperature. Mass-balance simulations with the SEB model that includes calibrated corrections for precipitation and albedo match or outperform those with the PDD model, especially when using a machine learning-derived albedo trained on remote sensing data, which tends to underestimate summer albedo in accumulation zones. Seasonal calibration further improves accuracy of the mass-balance simulations by addressing biases in summer melt and winter accumulation. Despite its simplicity, the SEB model provides a good balance of performance and computational efficiency, emphasizing its utility for regional-scale applications when calibrated appropriately.