Improving ice sheet model calibration using paleoclimate and modern data

Won Chang, Murali Haran, Patrick Applegate, David Pollard

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

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 nontrivial 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 paleoobservations 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.

Original languageEnglish (US)
Pages (from-to)2274-2302
Number of pages29
JournalAnnals of Applied Statistics
Volume10
Issue number4
DOIs
StatePublished - Dec 2016

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty

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