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

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 co...

Full description

Bibliographic Details
Published in:Nonlinear Processes in Geophysics
Main Authors: B. Bonan, M. Nodet, C. Ritz, V. Peyaud
Format: Article in Journal/Newspaper
Language:English
Published: Copernicus Publications 2014
Subjects:
Science
Q
Physics
QC1-999
Geophysics. Cosmic physics
QC801-809
Greenland
Antarc*
Antarctica
Ice Sheet
Online Access:https://doi.org/10.5194/npg-21-569-2014
https://doaj.org/article/a32ba3a9af26417996c0b5215f48caaf
Description
Summary: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.

Similar Items