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. 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 f