Articles | Volume 14, issue 3
The Cryosphere, 14, 811–832, 2020
https://doi.org/10.5194/tc-14-811-2020
The Cryosphere, 14, 811–832, 2020
https://doi.org/10.5194/tc-14-811-2020
Research article
 | Highlight paper
05 Mar 2020
Research article  | Highlight paper | 05 Mar 2020

Assimilation of surface observations in a transient marine ice sheet model using an ensemble Kalman filter

Fabien Gillet-Chaulet

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Cited articles

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Bannister, R. N.: A review of operational methods of variational and ensemble-variational data assimilation: Ensemble-variational Data Assimilation, Q. J. Roy. Meteorol. Soc., 143, 607–633, https://doi.org/10.1002/qj.2982, 2017. a, b, c
Bishop, C. H., Etherton, B. J., and Majumdar, S. J.: Adaptive Sampling with the Ensemble Transform Kalman Filter. Part I: Theoretical Aspects, Mon. Weather Rev., 129, 420–436, https://doi.org/10.1175/1520-0493(2001)129<0420:ASWTET>2.0.CO;2, 2001. a, b
Bonan, B., Nodet, M., Ritz, C., and Peyaud, V.: An ETKF approach for initial state and parameter estimation in ice sheet modelling, Nonlin. Processes Geophys., 21, 569–582, https://doi.org/10.5194/npg-21-569-2014, 2014. a, b, c, d, e, f, g, h, i, j, k
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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.