"Bayesian Inversion for Large Scale Antarctic Ice Sheet Flow" by Omar Ghattas (ICES, University of Texas at Austin)
The flow of ice from the interior of polar ice sheets is the primary contributor to projected sea level rise. One of the main difficulties faced in modeling ice sheet flow is the uncertain spatially-varying Robin boundary condition that describes the resistance to sliding at the base of the ice. Satellite observations of the surface ice flow velocity, along with a model of ice as a creeping incompressible shear-thinning fluid, can be used to infer this uncertain basal boundary condition. We cast this ill-posed inverse problem in the framework of Bayesian inference, which allows us to infer not only the basal sliding parameters, but also the associated uncertainty. To overcome the prohibitive nature of Bayesian methods for large-scale inverse problems, we exploit the fact that, despite the large size of observational data, they typically provide only sparse information on model parameters. We give results for Bayesian inversion of the basal
sliding parameter field for the full Antarctic continent.
This work is joint with Tobin Isaac (U. Chicago), Noemi Petra (UC Merced), and Georg Stadler (NYU)