Outlet glacier seasonal terminus prediction using interpretable machine learning

Abstract

Glacier terminus retreat involves complex processes superimposed at the interface between the ice sheet, the ocean, and the subglacial substrate, posing challenges for accurate physical modeling of terminus change. To enhance our understanding of outlet glacier ablation, numerous studies have focused on investigating terminus position changes on a seasonal scale with no clear control on seasonal terminus change that has been identified across all glaciers. Here, we explore the potential of machine learning to analyze glaciological time series data to gain insight into the seasonal changes of outlet glacier termini. Using XGBoost machine learning models, we forecast seasonal changes in terminus positions for 46 outlet glaciers in Greenland. Through the SHapley Additive exPlanations (SHAP) feature importance analysis, we identify the dominant predictors of seasonal terminus position change for each glacier. We find that glacier geometry is important for accurate predictions of the magnitude of terminus seasonality and that environmental variables (mélange, ocean thermal forcing, runoff, and air temperature) are important for determining the onset of seasonal terminus change. Our work highlights the utility of machine learning in understanding and forecasting glacier behavior.