To which extent can global ice volume records provide information on past ice sheets evolution and the climate that drove them?
International audience We use advanced data assimilation methods to assess the feasability of retrieving climate scenarios from past ice sheets data such as global ice volume and/or extent. Some of the questions we would like to answer are: Is the knowledge of ice-sheet volume evolution sufficient t...
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HAL CCSD
2011
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Online Access: | https://hal.inria.fr/inria-00587253 |
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Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) |
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ftccsdartic |
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 To which extent can global ice volume records provide information on past ice sheets evolution and the climate that drove them? |
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 We use advanced data assimilation methods to assess the feasability of retrieving climate scenarios from past ice sheets data such as global ice volume and/or extent. Some of the questions we would like to answer are: Is the knowledge of ice-sheet volume evolution sufficient to infer a unique climate scenario? If not, are ice-sheet extent observations a sufficient complement to ensure uniqueness? And subsequent question: how to extract relevant information from extent observations within an ice-sheet model where the extent is not a state variable? Data assimilation covers all mathematical methods that allow us to blend, as optimally as possible, information included in numerical models and observations, in order to identify poorly known parameters. In the study presented here, we first try to infer climate scenario from volume observations. To do so, a so-called cost function is defined, measuring the difference between the observed volume and the model-computed volume, as a function of the climate scenario parameters. We then want to find the minimizer of the cost function, that is the climate scenario which would cause the best fit between the observed volume evolution and the one produced by the model. The implementation of a variational data assimilation system is a very heavy task, as it requires the derivation of the adjoint model, and a very fine tuning of the optimization procedure. As we first want to assess the validity of the method we begin with a simple flow-line model, Winnie, as a first step toward adjoint data assimilation for a full 3D ice sheet model, GRISLI. Despite its simplicity, Winnie flow line model is strongly non-linear and not auto-adjoint, and is a good prototype to validate the methods. In particular, we will closely study the question of uniqueness: can two different climate scenarios produce similar observed volume evolution? And we will also examine the question of data assimilation of ice-sheet extent observations, as extent is not a state variable. |
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) Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF) Laboratoire de glaciologie et géophysique de l'environnement (LGGE) Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-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 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) |
format |
Conference Object |
author |
Bonan, Bertrand Nodet, Maëlle Ritz, Catherine |
author_facet |
Bonan, Bertrand Nodet, Maëlle Ritz, Catherine |
author_sort |
Bonan, Bertrand |
title |
To which extent can global ice volume records provide information on past ice sheets evolution and the climate that drove them? |
title_short |
To which extent can global ice volume records provide information on past ice sheets evolution and the climate that drove them? |
title_full |
To which extent can global ice volume records provide information on past ice sheets evolution and the climate that drove them? |
title_fullStr |
To which extent can global ice volume records provide information on past ice sheets evolution and the climate that drove them? |
title_full_unstemmed |
To which extent can global ice volume records provide information on past ice sheets evolution and the climate that drove them? |
title_sort |
to which extent can global ice volume records provide information on past ice sheets evolution and the climate that drove them? |
publisher |
HAL CCSD |
publishDate |
2011 |
url |
https://hal.inria.fr/inria-00587253 |
op_coverage |
Vienne, Austria |
genre |
Ice Sheet |
genre_facet |
Ice Sheet |
op_source |
EGU General Assembly https://hal.inria.fr/inria-00587253 EGU General Assembly, Apr 2011, Vienne, Austria |
op_relation |
inria-00587253 https://hal.inria.fr/inria-00587253 |
_version_ |
1766030607759966208 |
spelling |
ftccsdartic:oai:HAL:inria-00587253v1 2023-05-15T16:40:14+02:00 To which extent can global ice volume records provide information on past ice sheets evolution and the climate that drove them? 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) Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF) Laboratoire de glaciologie et géophysique de l'environnement (LGGE) Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-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 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) Vienne, Austria 2011-04-03 https://hal.inria.fr/inria-00587253 en eng HAL CCSD inria-00587253 https://hal.inria.fr/inria-00587253 EGU General Assembly https://hal.inria.fr/inria-00587253 EGU General Assembly, Apr 2011, Vienne, Austria [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 2011 ftccsdartic 2021-10-24T17:52:34Z International audience We use advanced data assimilation methods to assess the feasability of retrieving climate scenarios from past ice sheets data such as global ice volume and/or extent. Some of the questions we would like to answer are: Is the knowledge of ice-sheet volume evolution sufficient to infer a unique climate scenario? If not, are ice-sheet extent observations a sufficient complement to ensure uniqueness? And subsequent question: how to extract relevant information from extent observations within an ice-sheet model where the extent is not a state variable? Data assimilation covers all mathematical methods that allow us to blend, as optimally as possible, information included in numerical models and observations, in order to identify poorly known parameters. In the study presented here, we first try to infer climate scenario from volume observations. To do so, a so-called cost function is defined, measuring the difference between the observed volume and the model-computed volume, as a function of the climate scenario parameters. We then want to find the minimizer of the cost function, that is the climate scenario which would cause the best fit between the observed volume evolution and the one produced by the model. The implementation of a variational data assimilation system is a very heavy task, as it requires the derivation of the adjoint model, and a very fine tuning of the optimization procedure. As we first want to assess the validity of the method we begin with a simple flow-line model, Winnie, as a first step toward adjoint data assimilation for a full 3D ice sheet model, GRISLI. Despite its simplicity, Winnie flow line model is strongly non-linear and not auto-adjoint, and is a good prototype to validate the methods. In particular, we will closely study the question of uniqueness: can two different climate scenarios produce similar observed volume evolution? And we will also examine the question of data assimilation of ice-sheet extent observations, as extent is not a state variable. Conference Object Ice Sheet Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) |