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 that 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 are used to explore the processes that control ice-stream flow, and to compare these processes between seemingly different ice-stream systems. 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 are used. The various components, some of which are spatial, are combined hierarchically using Bayesian statistical methodology, yielding the posterior distribution for stress fields and velocity fields, conditional on the data. Inference based on this distribution is carried out using Markov chain Monte Carlo techniques, to obtain estimates of these unknown fields along with uncertainty measures associated with them.

• SSES Web-Project: Ice Streams
• Tutorial on Bayesian Statistics for Glaciologists
• Byrd Polar Research Center
• NSF Office of Polar Programs
• NSF Division of Mathematical Sciences