  The future behavior of the West Antarctic Ice Sheet (WAIS) may have a major
impact on future climate. For instance, ice sheet melt may contribute
significantly to global sea level rise. Understanding the current state of WAIS
is therefore of great interest. WAIS is drained by fast-flowing glaciers which
are major contributors to ice loss. Hence, understanding the stability and
dynamics 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 model
describes the interactions between these processes. We develop a hierarchical
Bayesian model that integrates multiple ice sheet surface data sets with a
glacier dynamics model. Our approach allows us to (1) infer important
parameters describing the glacier dynamics, (2) learn about ice sheet
thickness, and (3) account for errors in the observations and the model.
Because we have relatively dense and accurate ice thickness data from the
Thwaites Glacier in West Antarctica, we use these data to validate the proposed
approach. The long-term goal of this work is to have a general model that may
be 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.
