Abstract ID: 3.13310 | Accepted as Talk | Talk/Oral | TBA | TBA

Glacier mass loss in Alaska is accelerating in part due to albedo feedbacks which have not yet been methodically incorporated into glacier models. For example, wildfires deposit black carbon which darkens snow and accelerates melt rates. We present a new glacier energy balance model with a 1D snow layer scheme that allows accumulation and percolation of black carbon and dust and calculates albedo using a fully coupled aerosol radiative transfer model, SNICAR. The model is first applied to Gulkana Glacier, Alaska where a robust in situ dataset exists for forcing, calibration and validation. To enable regional scale-up, the model’s performance is compared under two forcing scenarios: first, using in situ meteorological measurements from an on-ice automatic weather station, and second, using statistically downscaled climate reanalysis data. The model is then assessed against seasonal and annual point mass balance, end-of-winter snow depth and density, and daily surface height change over the 2024 melt season. By performing a grid search on two parameters, we assess tradeoffs between error metrics and validate the model on the 2024 melt season observations. The model framework allows for analysis of albedo feedbacks and their impact on glacier mass loss in Alaska.