An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass-change

Abstract. This study investigated the computational benefits of using multi-fidelity uncertainty quantification (MFUQ) algorithms to quantify uncertainty in the mass change of Humboldt Glacier, Greenland, between 2007 and 2100 using a single climate change scenario. The goal of this study was to determine whether MFUQ can use multiple models of varying cost and accuracy to reduce the computational cost of estimating the mean and variance of the projected mass change of an ice sheet. The problem size and complexity were chosen to be representative of future continental scale studies while still facilitating a computationally feasible investigation of MFUQ methods. When quantifying uncertainty introduced by a high-dimensional parameterization of basal friction field, MFUQ was able to reduce the mean-squared error in the estimates of the statistics by well over an order of magnitude when compared to a single fidelity approach that only used the highest fidelity model. This significant reduction in computational cost was achieved despite the low-fidelity models used being incapable of capturing the local features of the ice flow fields predicted by the high-fidelity model. The MFUQ algorithms were able to effectively leverage the high correlation between each model's prediction of mass change, which all responded similarly to perturbations in the model inputs. Consequently, our results suggest that MFUQ could be highly useful for reducing the cost of computing continental scale probabilistic projections of sea-level rise due to ice-sheet mass change.