Ice streams are believed to play a major role in determining the response of ice sheets to climate change and in determining global sea level. In general the motion of ice streams is a response to gravity, moderated by drag from the sides and base of the stream. Understanding the relative importance of these two components is essential for developing models for future evolution of the polar ice sheets. In this project* we develop hierarchical Bayesian statistical models which rely heavily on physical models for ice-stream flow. The intent is that the output of these models combine physical theory and statistical information contained in observational data, while managing the uncertainty in both information sources.
I will review the primary datasets used in our first analysis: these include data on the surface and basal topography as well as estimated surface velocities for a portion of the North-East Ice Steam in Greenland. I will also review the simple physics relations incorporated into the Bayesian model, and then report on the preliminary data analyses and our experiences in model development. Of course, an overview of the full Bayesian model, including our plans for a Markov Chain Monte Carlo analysis, is presented. A summary of where we are and what's next concludes the talk.
*This talk is a progress report on an NSF-funded collaborative project featuring researchers from the Byrd Polar Research Center and the Department of Statistics at Ohio State.