  Human-induced climate change may cause significant ice volume loss from the
West Antarctic Ice Sheet (WAIS). Projections of ice volume change from
ice-sheet models and corresponding future sea-level rise have large
uncertainties due to poorly constrained input parameters. In most future
applications to date, model calibration has utilized only modern or recent
(decadal) observations, leaving input parameters that control the long-term
behavior of WAIS largely unconstrained. Many paleo-observations are in the form
of localized time series, while modern observations are non-Gaussian spatial
data; combining information across these types poses non-trivial statistical
challenges. Here we introduce a computationally efficient calibration approach
that utilizes both modern and paleo-observations to generate better-constrained
ice volume projections. Using fast emulators built upon principal component
analysis and a reduced dimension calibration model, we can efficiently handle
high-dimensional and non-Gaussian data. We apply our calibration approach to
the PSU3D-ICE model which can realistically simulate long-term behavior of
WAIS. Our results show that using paleo observations in calibration
significantly reduces parametric uncertainty, resulting in sharper projections
about the future state of WAIS. One benefit of using paleo observations is
found to be that unrealistic simulations with overshoots in past ice retreat
and projected future regrowth are eliminated.
