Ensemble methods for ice sheet model initialisation
International audience A hot topic in ice sheet modelling is to run prognostic simulations over the next 100 years to investigate the impact of Antarctica and Greenland ice sheets on sea level change. Such simulations require an initial state of ice sheets which must be as close as possible to what...
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ftunivsavoie:oai:HAL:hal-00763106v1 2024-05-19T07:30:36+00:00 Ensemble methods for ice sheet model initialisation Bonan, Bertrand Nodet, Maëlle Ritz, Catherine Modelling, Observations, Identification for Environmental Sciences (MOISE) Inria Grenoble - Rhône-Alpes Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK) Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS) Laboratoire de glaciologie et géophysique de l'environnement (LGGE) Observatoire des Sciences de l'Univers de Grenoble (OSUG) Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB Université de Savoie Université de Chambéry )-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB Université de Savoie Université de Chambéry )-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS) Toulouse, France 2012-11-12 https://inria.hal.science/hal-00763106 en eng HAL CCSD hal-00763106 https://inria.hal.science/hal-00763106 International Conference on Ensemble Methods in Geophysical Sciences https://inria.hal.science/hal-00763106 International Conference on Ensemble Methods in Geophysical Sciences, Nov 2012, Toulouse, France [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC] [SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology info:eu-repo/semantics/conferenceObject Conference papers 2012 ftunivsavoie 2024-05-02T00:07:02Z International audience A hot topic in ice sheet modelling is to run prognostic simulations over the next 100 years to investigate the impact of Antarctica and Greenland ice sheets on sea level change. Such simulations require an initial state of ice sheets which must be as close as possible to what is currently observed. Large scale ice sheet dynamical models are mostly governed by the following input parameters and variables: basal dragging coefficient, bedrock topography, surface elevation, temperature field. But we do not have satisfying initial states for simulations. Fortunately, some observations are available such as surface and (sparse) bedrock topography, surface velocities, surface elevation trend. The use of advanced inverse methods appears to be the adequate tool to produce satisfying initial states. We develop ensemble methods based on Ensemble Kalman filter to infer optimal initial states for ice sheet model initialisation thanks to available observations. As we first want to assess the validity of the method we begin with twin experiments with a simple flow-line large scale model, Winnie, as a first step toward data assimilation for a full 3D ice sheet model, GRISLI. Despite its simplicity, Winnie flow line model is strongly non-linear and is a good prototype to validate our methods. We also run several diagnostics to assess the quality of the recovered parameters. Conference Object Antarc* Antarctica Greenland Ice Sheet Université Savoie Mont Blanc: HAL |
institution |
Open Polar |
collection |
Université Savoie Mont Blanc: HAL |
op_collection_id |
ftunivsavoie |
language |
English |
topic |
[MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC] [SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology |
spellingShingle |
[MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC] [SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology Bonan, Bertrand Nodet, Maëlle Ritz, Catherine Ensemble methods for ice sheet model initialisation |
topic_facet |
[MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC] [SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology |
description |
International audience A hot topic in ice sheet modelling is to run prognostic simulations over the next 100 years to investigate the impact of Antarctica and Greenland ice sheets on sea level change. Such simulations require an initial state of ice sheets which must be as close as possible to what is currently observed. Large scale ice sheet dynamical models are mostly governed by the following input parameters and variables: basal dragging coefficient, bedrock topography, surface elevation, temperature field. But we do not have satisfying initial states for simulations. Fortunately, some observations are available such as surface and (sparse) bedrock topography, surface velocities, surface elevation trend. The use of advanced inverse methods appears to be the adequate tool to produce satisfying initial states. We develop ensemble methods based on Ensemble Kalman filter to infer optimal initial states for ice sheet model initialisation thanks to available observations. As we first want to assess the validity of the method we begin with twin experiments with a simple flow-line large scale model, Winnie, as a first step toward data assimilation for a full 3D ice sheet model, GRISLI. Despite its simplicity, Winnie flow line model is strongly non-linear and is a good prototype to validate our methods. We also run several diagnostics to assess the quality of the recovered parameters. |
author2 |
Modelling, Observations, Identification for Environmental Sciences (MOISE) Inria Grenoble - Rhône-Alpes Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK) Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS) Laboratoire de glaciologie et géophysique de l'environnement (LGGE) Observatoire des Sciences de l'Univers de Grenoble (OSUG) Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB Université de Savoie Université de Chambéry )-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB Université de Savoie Université de Chambéry )-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS) |
format |
Conference Object |
author |
Bonan, Bertrand Nodet, Maëlle Ritz, Catherine |
author_facet |
Bonan, Bertrand Nodet, Maëlle Ritz, Catherine |
author_sort |
Bonan, Bertrand |
title |
Ensemble methods for ice sheet model initialisation |
title_short |
Ensemble methods for ice sheet model initialisation |
title_full |
Ensemble methods for ice sheet model initialisation |
title_fullStr |
Ensemble methods for ice sheet model initialisation |
title_full_unstemmed |
Ensemble methods for ice sheet model initialisation |
title_sort |
ensemble methods for ice sheet model initialisation |
publisher |
HAL CCSD |
publishDate |
2012 |
url |
https://inria.hal.science/hal-00763106 |
op_coverage |
Toulouse, France |
genre |
Antarc* Antarctica Greenland Ice Sheet |
genre_facet |
Antarc* Antarctica Greenland Ice Sheet |
op_source |
International Conference on Ensemble Methods in Geophysical Sciences https://inria.hal.science/hal-00763106 International Conference on Ensemble Methods in Geophysical Sciences, Nov 2012, Toulouse, France |
op_relation |
hal-00763106 https://inria.hal.science/hal-00763106 |
_version_ |
1799487870837194752 |