Data and code for "Projections of 21st century glacier change in Alaska’s national parks calibrated against spatially-distributed elevation change observations"

Despite advances in global glacier modeling, notable uncertainties remain in part due to model overparameterization. We develop a novel Bayesian inference framework to calibrate the Python Glacier Evolution Model (PyGEM) against spatially-distributed surface elevation change data. The utility of this framework is demonstrated using a 1994–2021 airborne laser altimetry record for 185 glaciers in Alaska, which altogether represent over half of the region's glacier area. Calibration against these data reduces parameter uncertainty by 5–18% and lowers the mean absolute error of hindcast surface elevation changes by 0.18 m a^-1 (15% compared to prior models). Model projections for these 185 glaciers estimate mass loss ranging from 43 ± 17 to 67 ± 27% depending on the emissions scenario. The projections highlight considerable subregional variability ranging from a median glacier mass loss of 57–99% by the end of the century for Lake Clark, Denali, Kenai Fjords, and Glacier Bay national parks, compared to 28–51% lost for Wrangell–St. Elias National Park and Preserve. As additional spatially-distributed elevation change datasets become available in Alaska and elsewhere, the framework presented here is well suited to further refine model calibration and improve projections, while remaining flexible to incorporate additional glacier change observations as they become available.