An ensemble of Antarctic deglacial simulations constrained by geological observations

Pittard, M.; Whitehouse, P.L.; Bentley, M.; Small, D.

Abstract

The Antarctic ice sheet has the potential to make a significant contribution to future sea-level rise. Understanding this potential and making projections of future ice sheet mass change requires use of numerical models. Confidence in model projections can be improved by hindcasting: testing the model against past ice sheet changes. Robust deglacial model reconstructions are also used to correct satellite gravimetric measurements of current change for glacial isostatic adjustment processes. Here we present a new model ensemble of post-Last Glacial Maximum Antarctic deglaciation reconstructions. The ensemble is generated using the Parallel Ice Sheet Model (PISM) in which we vary a range of parameters including key glaciological controls, basal sediment strength, mantle viscosity, and climate forcing. We test the ensemble results against a database of geological constraints, and develop a new scoring scheme that allows us to determine metrics of model performance against past ice sheet grounding line extent, ice sheet thickness, and thinning rates, as well as present-day ice sheet configuration. We discuss the parameter combinations that lead to the highest-scoring simulations and we also compare our ensemble performance with existing published deglacial models, using the same scoring scheme. Exploring the characteristics of the highest-scoring ensemble members highlights some key features of deglacial behaviour including a relatively narrow range of past excess ice volumes at the LGM, Holocene retreat behind present-day grounding lines with commensurate volume minima, and readvance behaviours. The comparison also allows us to identify areas where more geological data would have high constraining power for ice sheet models.

Citation

Pittard, M., Whitehouse, P., Bentley, M., & Small, D. (2022). An ensemble of Antarctic deglacial simulations constrained by geological observations. Quaternary Science Reviews, 298, Article 107800. https://doi.org/10.1016/j.quascirev.2022.107800

• Journal Article Type: Article
• Acceptance Date: Sep 24, 2022
• Online Publication Date: Nov 9, 2022
• Publication Date: 2022
• Deposit Date: Oct 21, 2022
• Publicly Available Date: Nov 21, 2022
• Journal: Quaternary Science Reviews
• Print ISSN: 0277-3791
• Electronic ISSN: 1873-457X
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 298
• Article Number: 107800
• DOI: https://doi.org/10.1016/j.quascirev.2022.107800
• Public URL: https://durham-repository.worktribe.com/output/1188679