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We consider the scientifically challenging and policy-relevant task ofunderstanding the past and projecting the future dynamics of the Antarctic icesheet. The Antarctic ice sheet has shown a highly nonlinear threshold responseto past climate forcings. Triggering such a threshold response throughanthropogenic greenhouse gas emissions would drive drastic and potentially fastsea level rise with important implications for coastal flood risks. Previousstudies have combined information from ice sheet models and observations tocalibrate model parameters. These studies have broken important new ground buthave either adopted simple ice sheet models or have limited the number ofparameters to allow for the use of more complex models. These limitations arelargely due to the computational challenges posed by calibration as modelsbecome more computationally intensive or when the number of parametersincreases. Here we propose a method to alleviate this problem: a fastsequential Monte Carlo method that takes advantage of the massiveparallelization afforded by modern high performance computing systems. We usesimulated examples to demonstrate how our sample-based approach providesaccurate approximations to the posterior distributions of the calibratedparameters. The drastic reduction in computational times enables us to providenew insights into important scientific questions, for example, the impact ofPliocene era data and prior parameter information on sea level projections.These studies would be computationally prohibitive with other computationalapproaches for calibration such as Markov chain Monte Carlo or emulation-basedmethods. We also find considerable differences in the distributions of sealevel projections when we account for a larger number of uncertain parameters.

A Fast Particle-Based Approach for Calibrating a 3-D Model of the Antarctic Ice Sheet