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The future behavior of the West Antarctic Ice Sheet (WAIS) may have a majorimpact on future climate. For instance, ice sheet melt may contributesignificantly to global sea level rise. Understanding the current state of WAISis therefore of great interest. WAIS is drained by fast-flowing glaciers whichare major contributors to ice loss. Hence, understanding the stability anddynamics of glaciers is critical for predicting the future of the ice sheet.Glacier dynamics are driven by the interplay between the topography,temperature and basal conditions beneath the ice. A glacier dynamics modeldescribes the interactions between these processes. We develop a hierarchicalBayesian model that integrates multiple ice sheet surface data sets with aglacier dynamics model. Our approach allows us to (1) infer importantparameters describing the glacier dynamics, (2) learn about ice sheetthickness, and (3) account for errors in the observations and the model.Because we have relatively dense and accurate ice thickness data from theThwaites Glacier in West Antarctica, we use these data to validate the proposedapproach. The long-term goal of this work is to have a general model that maybe used to study multiple glaciers in the Antarctic.  Keywords: ice sheet, glacier dynamics, hierarchical Bayes, Gaussian process,Markov chain Monte Carlo, West Antarctic ice sheet.

Inferring Ice Thickness from a Glacier Dynamics Model and Multiple Surface Datasets