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Human-induced climate change may cause significant ice volume loss from theWest Antarctic Ice Sheet (WAIS). Projections of ice volume change fromice-sheet models and corresponding future sea-level rise have largeuncertainties due to poorly constrained input parameters. In most futureapplications to date, model calibration has utilized only modern or recent(decadal) observations, leaving input parameters that control the long-termbehavior of WAIS largely unconstrained. Many paleo-observations are in the formof localized time series, while modern observations are non-Gaussian spatialdata; combining information across these types poses non-trivial statisticalchallenges. Here we introduce a computationally efficient calibration approachthat utilizes both modern and paleo-observations to generate better-constrainedice volume projections. Using fast emulators built upon principal componentanalysis and a reduced dimension calibration model, we can efficiently handlehigh-dimensional and non-Gaussian data. We apply our calibration approach tothe PSU3D-ICE model which can realistically simulate long-term behavior ofWAIS. Our results show that using paleo observations in calibrationsignificantly reduces parametric uncertainty, resulting in sharper projectionsabout the future state of WAIS. One benefit of using paleo observations isfound to be that unrealistic simulations with overshoots in past ice retreatand projected future regrowth are eliminated.

Improving Ice Sheet Model Calibration Using Paleoclimate and Modern Data