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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 21, issue 2
Nonlin. Processes Geophys., 21, 569–582, 2014
https://doi.org/10.5194/npg-21-569-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Ensemble methods in geophysical sciences

Nonlin. Processes Geophys., 21, 569–582, 2014
https://doi.org/10.5194/npg-21-569-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 25 Apr 2014

Research article | 25 Apr 2014

An ETKF approach for initial state and parameter estimation in ice sheet modelling

B. Bonan1, M. Nodet1,2, C. Ritz3, and V. Peyaud3 B. Bonan et al.
  • 1INRIA, Laboratoire Jean Kuntzmann (LJK), Grenoble, France
  • 2Université Joseph Fourier Grenoble I (UJF), Laboratoire Jean Kuntzmann (LJK), Grenoble, France
  • 3UJF – Grenoble 1/CNRS, Laboratoire de Glaciologie et Géophysique de l'Environnement (LGGE) UMR5183, Grenoble, 38041, France

Abstract. Estimating the contribution of Antarctica and Greenland to sea-level rise is a hot topic in glaciology. Good estimates rely on our ability to run a precisely calibrated ice sheet evolution model starting from a reliable initial state. Data assimilation aims to provide an answer to this problem by combining the model equations with observations. In this paper we aim to study a state-of-the-art ensemble Kalman filter (ETKF) to address this problem. This method is implemented and validated in the twin experiments framework for a shallow ice flowline model of ice dynamics. The results are very encouraging, as they show a good convergence of the ETKF (with localisation and inflation), even for small-sized ensembles.

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