Articles | Volume 9, issue 5
https://doi.org/10.5194/gmd-9-1697-2016
https://doi.org/10.5194/gmd-9-1697-2016
Methods for assessment of models
 | 
04 May 2016
Methods for assessment of models |  | 04 May 2016

Large ensemble modeling of the last deglacial retreat of the West Antarctic Ice Sheet: comparison of simple and advanced statistical techniques

David Pollard, Won Chang, Murali Haran, Patrick Applegate, and Robert DeConto

Abstract. A 3-D hybrid ice-sheet model is applied to the last deglacial retreat of the West Antarctic Ice Sheet over the last  ∼  20 000 yr. A large ensemble of 625 model runs is used to calibrate the model to modern and geologic data, including reconstructed grounding lines, relative sea-level records, elevation–age data and uplift rates, with an aggregate score computed for each run that measures overall model–data misfit. Two types of statistical methods are used to analyze the large-ensemble results: simple averaging weighted by the aggregate score, and more advanced Bayesian techniques involving Gaussian process-based emulation and calibration, and Markov chain Monte Carlo. The analyses provide sea-level-rise envelopes with well-defined parametric uncertainty bounds, but the simple averaging method only provides robust results with full-factorial parameter sampling in the large ensemble. Results for best-fit parameter ranges and envelopes of equivalent sea-level rise with the simple averaging method agree well with the more advanced techniques. Best-fit parameter ranges confirm earlier values expected from prior model tuning, including large basal sliding coefficients on modern ocean beds.

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Short summary
Computer modeling of variations of the Antarctic Ice Sheet help to understand the ice sheet's sensitivity to climate change. We apply a numerical model to its retreat over the last 20 000 years, from its maximum glacial extent to modern. An ensemble of 625 simulations is performed with systematic combinations of uncertain model parameter values. Results are analyzed using (1) simple averaging, and (2) advanced statistical techniques, and reasonable agreement is found between the two.