Articles | Volume 14, issue 3
https://doi.org/10.5194/tc-14-811-2020
© 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/tc-14-811-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
05 Mar 2020
Research article | Highlight paper |
| 05 Mar 2020
| 05 Mar 2020
Assimilation of surface observations in a transient marine ice sheet model using an ensemble Kalman filter
Fabien Gillet-Chaulet
CORRESPONDING AUTHOR
Univ. Grenoble Alpes, CNRS, IRD, IGE, 38000 Grenoble, France
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Total article views: 5,886 (including HTML, PDF, and XML)
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Total article views: 5,156 (including HTML, PDF, and XML)
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- Increased sea-level contribution from northwestern Greenland for models that reproduce observations J. Badgeley et al. 10.1073/pnas.2411904122
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- Data‐Driven Inference of the Mechanics of Slip Along Glacier Beds Using Physics‐Informed Neural Networks: Case Study on Rutford Ice Stream, Antarctica B. Riel et al. 10.1029/2021MS002621
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Latest update: 03 Jul 2025
Short summary
Marine-based sectors of the Antarctic Ice Sheet are increasingly contributing to sea-level rise. The basal conditions exert an important control on the ice dynamics. For obvious reasons of inaccessibility, they are an important source of uncertainties in numerical ice flow models used for sea-level projections. Here we assess the performance of an ensemble Kalman filter for the assimilation of transient observations of surface elevation and velocities in a marine ice sheet model.
Marine-based sectors of the Antarctic Ice Sheet are increasingly contributing to sea-level rise....
