  We consider the scientifically challenging and policy-relevant task of
understanding the past and projecting the future dynamics of the Antarctic ice
sheet. The Antarctic ice sheet has shown a highly nonlinear threshold response
to past climate forcings. Triggering such a threshold response through
anthropogenic greenhouse gas emissions would drive drastic and potentially fast
sea level rise with important implications for coastal flood risks. Previous
studies have combined information from ice sheet models and observations to
calibrate model parameters. These studies have broken important new ground but
have either adopted simple ice sheet models or have limited the number of
parameters to allow for the use of more complex models. These limitations are
largely due to the computational challenges posed by calibration as models
become more computationally intensive or when the number of parameters
increases. Here we propose a method to alleviate this problem: a fast
sequential Monte Carlo method that takes advantage of the massive
parallelization afforded by modern high performance computing systems. We use
simulated examples to demonstrate how our sample-based approach provides
accurate approximations to the posterior distributions of the calibrated
parameters. The drastic reduction in computational times enables us to provide
new insights into important scientific questions, for example, the impact of
Pliocene era data and prior parameter information on sea level projections.
These studies would be computationally prohibitive with other computational
approaches for calibration such as Markov chain Monte Carlo or emulation-based
methods. We also find considerable differences in the distributions of sea
level projections when we account for a larger number of uncertain parameters.
