Articles | Volume 13, issue 9
https://doi.org/10.5194/gmd-13-4355-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-4355-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
17 Sep 2020
Model description paper |
| 17 Sep 2020
| 17 Sep 2020
The Community Firn Model (CFM) v1.0
Department of Earth and Space Sciences, University of Washington, Seattle, WA, USA
Vincent Verjans
Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UK
Jessica M. D. Lundin
Department of Earth and Space Sciences, University of Washington, Seattle, WA, USA
Salesforce, San Francisco, CA, USA
Emma C. Kahle
Department of Earth and Space Sciences, University of Washington, Seattle, WA, USA
Annika N. Horlings
Department of Earth and Space Sciences, University of Washington, Seattle, WA, USA
Brita I. Horlings
Department of Earth and Space Sciences, University of Washington, Seattle, WA, USA
now at: Thayer School of Engineering, Dartmouth College, Hanover, NH, USA
Edwin D. Waddington
Department of Earth and Space Sciences, University of Washington, Seattle, WA, USA
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In the vast interior of the Greenland ice sheet, snow accumulates into a thick and porous layer called firn. Each summer, the firn retains part of the meltwater generated at the surface and buffers sea-level rise. In this study, we compare nine firn models traditionally used to quantify this retention at four sites and evaluate their performance against a set of in situ observations. We highlight limitations of certain model designs and give perspectives for future model development.
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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 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.
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Short summary
Understanding processes in snow (firn), including compaction and airflow, is important for calculating how much mass the ice sheets are losing and for interpreting climate records from ice cores. We have developed the open-source Community Firn Model to simulate these processes. We used it to compare 13 different firn compaction equations and found that they do not agree within 10 %. We also show that including firn compaction in a firn-air model improves the match with data from ice cores.
Understanding processes in snow (firn), including compaction and airflow, is important for...
