NSF Award
2319109
Predicting future sea level relies on improved modeling of how the climate forces the ice sheet to change. This is particularly challenging at the ice-ocean boundary where there are multiple processes occurring simultaneously. At present, no single equation adequately describes the changing ice-ocean boundary, which poses problems for coupling ice sheet models to climate models. This project will improve understanding of how the variables that influence the ice-ocean boundary may change over time and space using machine learning to search for relationships amidst available data. This technique will allow the research team to categorize glacier terminus behavior and identify the relevant parameters forcing change for a particular glacier around the Greenland Ice Sheet. Results of the machine learning exercise will be used to develop an equation to represent ice-ocean interactions in an ice sheet model which will be used to determine future changes to the ice sheet forced by the ocean into the future. <br/> <br/>Models of future ice sheet change yield reliable forecasts of sea level rise only when all the critical processes controlling ice sheet evolution are appropriately accounted for. However, many physical processes are currently poorly understood. One such process is ablation (iceberg calving and submarine melt) at the terminus of outlet glaciers, which has been shown to be the dominant control on mass change at particular glaciers. The goal of this project is to improve model forecasts of sea-level from Greenland by using machine learning analyses of glaciological observations to inform physics-based modeling of outlet glaciers, with a focus on the ice-ocean boundary. Machine learning tools will be used to determine what controls changes in terminus position over a range of time scales for all glaciers in Greenland over a period of pronounced historical change (the satellite era). Analysis of the model performance will enable the research team to determine the dominant controls on terminus position for individual and groups of glaciers and to test how well the model performs as new glaciological and environmental data become available. The machine learning model of terminus positions will be used to improve projections of outlet glacier mass change using a physically-based numerical ice flow model. The team will examine how robust model prediction is on various time-scales as more and more data become available over the course of this project. The project will result in refined projections of dynamic loss from the Greenland Ice Sheet, which is important for policy makers needing to make critical infrastructure and resource decisions globally. This goal is a central focus for research within NSF's Office of Polar Programs, NSF's Navigating the New Arctic, and other national (e.g., NASA, NOAA) and international priorities. The project integrates researchers across disciplines, genders, and career stages. Data products and methods produced through this project will be make publicly available and will be useful to the broader scientific community. <br/><br/>This project is co-funded by a collaboration between the Directorate for Geosciences and Office of Advanced Cyberinfrastructure to support AI/ML and open science activities in the geosciences.<br/><br/>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.
