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De-tuning a coupled Climate Ice Sheet Model to simulate the North American Ice Sheet at the Last Glacial Maximum
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  • Niall Gandy,
  • Lachlan C Astfalck,
  • Lauren J Gregoire,
  • Ivanovic Ruza F,
  • Violet L Patterson,
  • Sherriff-Tadano Sam,
  • Robin Stuart Smith,
  • Williamson Danny,
  • Richard Rigby
Niall Gandy
Sheffield Hallam University

Corresponding Author:[email protected]

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Lachlan C Astfalck
School of Physics, Mathematics and Computing, The University of Western Australia
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Lauren J Gregoire
University of Leeds
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Ivanovic Ruza F
University of Leeds
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Violet L Patterson
University of Leeds
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Sherriff-Tadano Sam
University of Leeds
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Robin Stuart Smith
University of Reading
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Williamson Danny
University of Exeter
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Richard Rigby
University of Leeds
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Abstract

The maximum extent of the last North American ice sheet is well constrained empirically, but it has proven to be challenging to simulate with coupled Climate-Ice Sheet models. Coupled Climate-Ice Sheet models are often too computationally expensive to sufficiently explore uncertainty in input parameters, and it is unlikely values calibrated to reproduce modern ice sheets will reproduce the known extent of the ice at the Last Glacial Maximum. To address this, we run a series of ensembles with a coupled Climate-Ice Sheet model (FAMOUS-ice), simulating the final stages of growth of the last North American Ice Sheets’ maximum extent. Using this large ensemble approach, we explore the influence of uncertain ice sheet, albedo, atmospheric, and oceanic parameters on the ice sheet extent. We find that albedo parameters determine the majority of uncertainty when simulating the Last Glacial Maximum North American Ice Sheets. Importantly, different albedo parameters are needed to produce a good match to the Last Glacial Maximum North American Ice Sheets than have previously been used to model the contemporary Greenland Ice Sheet, due to differences in cloud cover over ablation zones. Thus calibrating coupled climate-ice sheet models solely for present day strongly biases simulations of past and future climates different from today.