Quantifying Spatio-Temporal Boundary Condition Uncertainty for the North American Deglaciation

• Published in: SIAM/ASA Journal on Uncertainty Quantification
• Main Authors: Salter, James M., Williamson, Daniel B., Gregoire, Lauren J., Edwards, Tamsin L.
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2022
• Subjects: calibration; dimension reduction; emulation; history matching; ice sheet model; uncertainty quantification; Ice Sheet
• Online Access: https://kclpure.kcl.ac.uk/portal/en/publications/quantifying-spatiotemporal-boundary-condition-uncertainty-for-the-north-american-deglaciation(315a9fa3-f95c-4122-9700-a2fd7beeaa65).html; https://doi.org/10.1137/21M1409135; http://www.scopus.com/inward/record.url?scp=85134819003&partnerID=8YFLogxK

Ice sheet models are used to study the deglaciation of North America at the end of the last ice age (past 21,000 years), so that we might understand whether and how existing ice sheets may reduce or disappear under climate change. Though ice sheet models have a few parameters controlling physical behavior of the ice mass, they also require boundary conditions for climate (spatio-temporal fields of temperature and precipitation, typically on regular grids and at monthly intervals). The behavior of the ice sheet is highly sensitive to these fields, and there is relatively little data from geological records to constrain them as the land was covered with ice. We develop a methodology for generating a range of plausible boundary conditions, using a low-dimensional basis representation of the spatio-temporal input. We derive this basis by combining key patterns, extracted from a small ensemble of climate model simulations of the deglaciation, with sparse spatio-temporal observations. By jointly varying the ice sheet parameters and basis vector coefficients, we run ensembles of the Glimmer ice sheet model that simultaneously explore both climate and ice sheet model uncertainties. We use these to calibrate the ice sheet physics and boundary conditions for Glimmer by ruling out regions of the joint coefficient and parameter space via history matching. We use binary ice/no ice observations from reconstructions of past ice sheet margin position to constrain this space by introducing a novel metric for history matching to binary data.