Collaborative Research: The Land Unknown: Assessing Data Requirements for Modeling Change in the Antarctic Ice Sheet with an Emphasis on the Subglacial Bed

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Among the most dramatic changes now underway in the polar cryosphere is the several-fold speedup of Greenland and West Antarctic outlet glaciers. Computational models of ice sheets face considerable challenges in correctly simulating these events, which in turn, limits glaciologists' and climatologists' ability to predict future change. The challenge arises from limited knowledge of key variables, for example, the shape of the land surface beneath the ice. Data limitations present two problems; first, important details of ice behavior may be missed and second, models may produce results that compare well with observations but do so for the wrong reasons. The goal of this project is to use these uncertainties themselves as a guide to identifying which types of data are most important to producing useful projection of future ice sheet change. The work is focused on two regions, the Aurora basin in East Antarctica, where new high resolution data have recently become available, and the catchment surrounding South Pole station deep in the interior or East Antarctica, where ice flow is currently sluggish may have been faster in the past. Bayesian inference may be used to investigate formally the contributions of various model attributes to uncertainty in model projections by synthesizing information from different sources of data, modeling results, and corresponding error information. Here, an ice sheet model will be asked to identify the regions and processes that have the largest influence on ice flow and thickness change, according to an appropriate range of boundary conditions and initial model states. The approach will allow us to create and analyze correlation maps showing relationships between various boundary conditions and model outcome in selected study areas. Those parameters or regions that are most important should dominate the scatter in model projections of change. The associated posterior probability density will provide guidance on how to design observational strategies to meet specified scientific criteria.