The goal of this project is to study the past, current, and future behavior of the Antarctic ice sheet by using novel statistical methods for combining information from both state-of-the-art ice sheet models and observational data. Ice sheet models such as the PSU 3D ice sheet model may be used to understand the interplay between the processes that drive the behavior of the Antarctic ice sheet. They can also be used to project the future behavior of the ice sheet, which is of great interest in projections of climate change, particularly sea level rise. Learning about future sea-level rise is important not only to scientists but also policymakers. This interdisciplinary research project combines expertise in statistical methodology, ice sheet modeling, computing, and forcing scenarios.
Because ice sheet model outputs are high-dimensional, non-Gaussian, and spatio-temporal, existing approaches for computer model emulation and calibration either do not apply or are computationally infeasible. It is challenging to build computationally expedient methods that are flexible enough to utilize all the information in large data sets and account for uncertainties, complicated errors, and dependencies. We will develop new statistical models and algorithms to address this challenge. Our statistical methodology will build upon Markov random field models (Gaussian and non-Gaussian), composite likelihood and related likelihood approximation methods, and principal components methods generalized to exponential families. The objectives are twofold: (i) developing new statistical and computational methods for emulation (stochastic approximation) of and calibration (input parameter learning) for complex computer models with high-dimensional non-Gaussian space-time data, and (ii) learning the major scientific processes driving the behavior of the Antarctic ice sheet and making projections for ice retreat in this region in the future using, for the first time in this context, information from geologic data over the past 20,000 years. We will focus on the Amundsen Sea Sector of West Antarctica, which contains the rapidly retreating and thinning Pine Island and Thwaites Glaciers; this sector is currently the largest Antarctic contributor to sea-level rise and is considered particularly vulnerable to drastic future retreat. Rigorous statistical comparisons of our ice-sheet model versus geologic data in this sector during the deglacial recession of the last 20,000 years will yield more robust and confident predictions of further future retreat. We will also explore sensitivity of Antarctic ice sheet response to atmosphere-ocean processes as driven by scenarios estimated from coupled earth system models. The processes driving the regional ice sheet will be investigated within the components of the Community Earth System Model (CESM). Ice sheet melting influences sea level rise and can have major impacts on human and ecological systems. Hence, understanding them is paramount to scientists and policy makers. The statistical methodology and computational tools developed here will be widely applicable across a range of disciplines where computer models with high-dimensional output are common; these fields include ecology, hydrology, mechanical engineering, and astronomy. Our efficient methods and software will allow us (and others) to routinely fit more sophisticated models to larger data sets than currently feasible, thus using as much information as possible when drawing scientific conclusions. This will allow for reduced uncertainties, thereby turning the size of the data sets into an asset.
