Random forest parameterization of Antarctic subglacial hydrology for coupled ice-flow modelling

Abstract. Antarctic ice-sheet flow is sensitive to changes in basal friction. These frictional changes are modulated by the effective pressure in the subglacial drainage system in response to changes in ice thickness, basal melt, and slip rates. To overcome the computational burden of coupled modelling of the ice sheet and the subglacial drainage system, we develop and evaluate a machine-learning parameterization of basal effective pressure. The parameterization, consisting of a random forest regression model, is trained to predict continent-wide effective pressure based on ensembles of simulations with the physics-based Glacier Drainage System (GlaDS) model in seven major ice-flow basins. The ensembles vary the values of five subglacial drainage model parameters, allowing the parameterization to predict how effective pressure varies across parameter space. The random forest parameterization explains 65 % of the variance of the effective pressure predicted by the numerical model, but 99 % of the variance in ice speed when coupled to the ice-flow solver in the Ice-Sheet and Sea-level System (ISSM) model. We assess the influence of effective pressure on future flow speeds by imposing plausible ice-sheet thickness changes drawn from ice-sheet model projections. Using the random forest parameterization instead of the numerical subglacial drainage model results in differences in grounding line speed of 127–311 m a^-1 (2.1–10 %). Other approaches, such as holding effective pressure constant in time or assuming ocean connectivity, result in larger grounding-line speed differences of 441–2199 m a^-1 (8.5–74 %). These results suggest that the random forest parameterization for effective pressure can be used to add active subglacial hydrology to ice-sheet modelling with higher fidelity than other effective-pressure parameterizations while reducing computation time from as long as 16 days to 0.5 seconds.