Bayesian data assimilation on an Arctic glacier: learning from large ensemble twin experiments

Abstract Numerical modeling is crucial for quantifying the evolution of cryospheric processes. At the same time, uncertainties hamper process understanding and predictive accuracy. Here, we suggest improving glacier surface mass balance simulations for the Kongsvegen glacier in Svalbard through the application of Bayesian data assimilation techniques in a set of large ensemble twin experiments. Noisy synthetic observations of albedo and snow depth, generated using the multilayer CryoGrid community model with a full energy balance, are assimilated using two ensemble-based data assimilation schemes: the particle batch smoother and the ensemble smoother. A comprehensive evaluation exercise demonstrates that the joint assimilation of albedo and snow depth improves the simulation skill by up to 86% relative to the prior in specific glacier regions. The particle batch smoother excels in representing albedo dynamics, while the ensemble smoother is marginally more effective for snow depth under low snowfall conditions in the ablation area. By combining the strengths of both observations, the joint assimilation achieves improved surface mass balance simulations across different glacier zones using either assimilation scheme. This work underscores the potential of ensemble-based data assimilation methods for refining glacier models by offering a robust framework to enhance predictive accuracy and reduce uncertainties in cryospheric simulations. Further advances in glacier data assimilation research with both synthetic and real observations will be critical to better understanding the fate and role of Arctic glaciers in a changing climate