Dynamics of Ice Streams: A Physical Statistical Approach
Ice streams are believed to play a major role in determining the response of their parent ice sheet to climate change, and in determining global sea level by serving as regulators on the fresh water stored in the ice sheets. Ice streams are characterized by rapid, laterally confined flow which makes them uniquely identifiable within the body of the more slowly and more homogeneously flowing ice sheet. But while these characteristics enable the identification of ice streams, the processes which control ice-stream motion and evolution, and differences among ice streams in the polar regions, are only partially understood. Understanding the relative importance of lateral and basal drags, as well as the role of gradients in longitudinal stress, is essential for developing models for future evolution of the polar ice
sheets. In this project, physical statistical models will be used to explore the processes that control ice-stream flow, and to compare these processes between seemingly different ice-stream systems. In particular, Whillans Ice Stream draining into the Ross Ice Shelf, will be compared with Recovery and RAMP glaciers draining into the Ronne-Filchner Ice Shelf, and the Northeast Ice Stream in Greenland. Geophysical models lie at the core of the approach, but are embellished by modeling various components of variability statistically. One important component comes from the uncertainty in observations on basal elevation, surface elevation, and surface velocity. In this project new observational data collected using remote-sensing techniques will be used. The various components, some of which are spatial, are combined hierarchically using Bayesian statistical methodology. All these components will be combined mathematically into a physical statistical model that yields the posterior distribution for basal, longitudinal, and lateral stress fields, and velocity fields, conditional on the data. Inference based on this distribution will be carried out via Markov chain Monte Carlo techniques, to obtain estimates of these unknown fields along with uncertainty measures associated with them.
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