CAREER: What's Past is Prologue: Seamless Assimilation of Past Observations into Simulations of Future Ice Sheets

Awareness of sea level rise and its potential to impact coastal communities is a pre-requisite for informed decision-making at all levels of government and private industry. However, most of the uncertainty in future ice sheet contribution to sea level rise under particular emissions pathways comes from incompletely constrained ice sheet models. In practice, differences in calibration data and procedures between models lead to large uncertainties in sea level projections at 2100 and beyond. To make optimal use of the benefits of both modern and geological observations of ice sheet change, the proposed project will adapt state-of-the-art computational methods from applied mathematics and computer science to assimilate many different types of observations into ice sheet models. Ultimately, these methods will be used to produce gapless estimates of past glacier state over multimillennial time scales and improved projections of future ice sheet change. The educational component of this project will extend an existing standards-based curriculum on sea level rise through high school computer science courses. The main focus of the research component of this project is the development of an open-source software package for applying transient ensemble Kalman filtering techniques to a wide range of ice sheet models, enabling the seamless integration of glaciological and geological observations with physical models. Two initial case studies will focus on reconstructing past glacier changes and using these reconstructions to initialize future predictions of change at Sermeq Kujalleq (aka Jakobshavn Isbrae) in Greenland and Thwaites Glacier in West Antarctica. The final part of this project will add these data assimilation capabilities to a cloud-based ice sheet model with a browser-based interface so that it can be used to directly assimilate data from a community repository of ice sheet observations during simulations of past and future glacier behavior. The educational component will develop a new high school curriculum focused on data-based exploration of past and future sea level rise and its driving processes, in alignment with existing Georgia (and national) standards in Computer Science and Statistics courses. This curriculum will be developed in collaboration with an Atlanta-area teacher and an experienced education researcher at Georgia Tech, and piloted at a local high school. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.