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The Cryosphere An interactive open-access journal of the European Geosciences Union
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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 03 Jan 2020

Submitted as: research article | 03 Jan 2020

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A revised version of this preprint is currently under review for the journal TC.

Bayesian calibration of firn densification models

Vincent Verjans1, Amber Alexandra Leeson1, Christopher Nemeth2, C. Max Stevens3, Peter Kuipers Munneke4, Brice Noël4, and Jan Melchior van Wessem4 Vincent Verjans et al.
  • 1Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YW, UK
  • 2Department of Mathematics and Statistics, Lancaster University, Lancaster LA1 4YF, UK
  • 3Department of Earth and Space Sciences, University of Washington, Seattle, WA, USA
  • 4Institute for Marine and Atmospheric research Utrecht, Utrecht University, Utrecht, the Netherlands

Abstract. Firn densification modelling is key to understanding ice sheet mass balance, ice sheet surface elevation change, and the age difference between ice and the air in enclosed air bubbles. This has resulted in the development of many firn models, all relying to a certain degree on parameter calibration against observed data. We present a novel Bayesian calibration method for these parameters, and apply it to three existing firn models. Using an extensive dataset of firn cores from Greenland and Antarctica, we reach optimal parameter estimates applicable to both ice sheets. We then use these to simulate firn density and evaluate against independent observations. Our simulations show a significant decrease (25 and 55 %) in observation-model discrepancy for two models and a small increase (11 %) for the third. As opposed to current methods, the Bayesian framework allows for robust uncertainty analysis related to parameter values. Based on our results, we review some inherent model assumptions and demonstrate how model- and parameter-related uncertainties potentially affect ice sheet mass balance assessments.

Vincent Verjans et al.

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Vincent Verjans et al.

Vincent Verjans et al.


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Latest update: 13 Jul 2020
Publications Copernicus
Short summary
Ice sheets are covered by a firn layer, which is the transition stage between fresh snow and ice. Accurate modelling of firn density properties is important in many glaciological aspects. Current models show disagreements, are mostly calibrated to match specific observations of firn density and lack of thorough uncertainty analysis. We use a novel calibration method for firn models based on a Bayesian statistical framework, which results in improved model accuracy and in uncertainty evaluation.
Ice sheets are covered by a firn layer, which is the transition stage between fresh snow and...