The satellite era has brought a wealth of knowledge and complexity to the field of ice-sheet modeling.  Yet, these data have also brought acceptance of looming uncertainties in model-based projections of future sea-level change. 

In order to improve current ice-sheet model projections, it is crucial to take advantage of the symbiotic relationship between data and models to identify which aspects of the climate contribute the most to model uncertainty.  

The Jet Propulsion Laboratory-University of California at Irvine Ice-sheet and Sea-level System Model (ISSM) is a thermo-mechanical 2D/3D parallelized finite element software used to physically model the continental-scale flow of ice at high resolutions. 

Here, we discuss the roles that satellite data plays in informing ice-sheet model projections and the uncertainty quantification (UQ) tools embedded in the ISSM framework. ISSM offers various UQ methods for the investigation of how errors in model input impact uncertainty in simulation results, and here we describe the design of experiments to utilize these tools.  

For example, we regionally sample model input and key variables, based on specified bounds of uncertainty, to run a suite of continental-scale 100-year ISSM forward simulations of the Antarctic Ice Sheet (AIS). Resulting diagnostics (e.g., regional mass balance) inform our conclusion about which boundary conditions and/or forcing have the greatest impact on century-scale model simulations of ice sheet evolution. 

The results allow us to prioritize the key datasets, measurements, and target regions in Antarctica that are critical for the minimization of ice-sheet model uncertainty.