Improving Ice Sheet Model Calibration Using Paleoclimate and Modern Data
Won Chang, Murali Haran, Patrick Applegate, David Pollard

TL;DR
This paper presents a new calibration method combining paleo and modern data to improve ice sheet models, reducing uncertainty and eliminating unrealistic projections of West Antarctic Ice Sheet behavior.
Contribution
It introduces an efficient calibration approach that integrates diverse data types using emulators and reduced dimension models, enhancing long-term ice volume projections.
Findings
Paleo data significantly reduce model parameter uncertainty.
Calibration with paleo data eliminates unrealistic ice retreat simulations.
The method improves the realism and precision of ice sheet projections.
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 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…
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