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