Global glacier mass is changing rapidly, and projections of global glacier mass balance under changing climatic conditions are crucial for informed decision-making. Existing glacier projections use relatively simple glacier models constrained by sparse observations. In these projections, model calibration plays a key role in constraining uncertainty. The use of Bayesian data assimilation methods for calibration by integrating multiple emerging observational data sets (in-situ mass balance measurements, climate reanalyses, and satellite remote sensing) to constrain model parameters and their dynamics remains relatively unexplored. Such a probabilistic calibration strategy could enable us to quantify and disentangle uncertainties related to the glacier model, the selected climate model forcing, and internal climate variability.

The Python Glacier Evolution Model (PyGEM) is one of a handful of global glacier models that allow us to simulate the evolution of the mass balance of all glaciers in the world. In this work, we adopt ensemble-based data assimilation methods to calibrate PyGEM model parameters and thus constrain future projections of glacier mass balance across Scandinavia. We compare our results with traditional glacier model calibration algorithms and the Bayesian gold standard Markov Chain Monte Carlo (MCMC) method in PyGEM for glacio-hydrological indicators (surface mass balance and runoff projections) between 2015 and 2100 with four SSP scenarios. This work serves as the kernel for a scalable glacier data assimilation framework to produce policy relevant global glacier projections and scenarios within the recently funded ERC-AdG GLACMASS project. The probabilistic calibration framework developed in this study can in principle be adapted for a wide range of cryospheric applications.