Large ensemble particle filter for spatial climate reconstructions using a Linear inverse model
International audience Proxy records that document the last 2000 years climate provide evidences for the wide range of the natural climate variability from inter-annual to secular timescales not captured by the short window of recent direct observations. Assessing climate models ability to reproduce...
Published in: | Journal of Advances in Modeling Earth Systems |
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Main Authors: | , |
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Format: | Article in Journal/Newspaper |
Language: | English |
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HAL CCSD
2023
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Online Access: | https://hal.science/hal-03993775 https://hal.science/hal-03993775/document https://hal.science/hal-03993775/file/J%20Adv%20Model%20Earth%20Syst%20-%202023%20-%20Jebri%20-%20Large%20Ensemble%20Particle%20Filter%20for%20Spatial%20Climate%20Reconstructions%20Using%20a%20Linear.pdf https://doi.org/10.1029/2022MS003094 |
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Muséum National d'Histoire Naturelle (MNHM): HAL |
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ftmuseumnhn |
language |
English |
topic |
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] |
spellingShingle |
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] Jebri, Beyrem Khodri, Myriam Large ensemble particle filter for spatial climate reconstructions using a Linear inverse model |
topic_facet |
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] |
description |
International audience Proxy records that document the last 2000 years climate provide evidences for the wide range of the natural climate variability from inter-annual to secular timescales not captured by the short window of recent direct observations. Assessing climate models ability to reproduce such natural variations is crucial to understand climate sensitivity and impacts of future climate change. Paleoclimate data assimilation (PDA) offers a powerful way to extend the short instrumental period by optimally combining the physics described by General Circulation Climate Models (GCMs) with information from available proxy records while taking into account their uncertainties. Here we present a new PDA approach based on a sequential importance resampling (SIR) particle filter that uses Linear Inverse Modeling (LIM) as an emulator of several CMIP-class GCMs. We examine in a perfect-model framework the skill of the various LIMs to forecast the dynamic of the surface temperatures and provide spatial field reconstructions over the last millennium in a SIR particle filter. Our results show that the LIMs allow for skilful ensemble forecasts at one-year lead-time based on GCMs dynamical knowledge with best prediction in the tropics and the North Atlantic. The PDA further provides a set of physically consistent spatial fields allowing robust uncertainty quantification related to climate models biases and proxy spatial sampling. Our results indicate that the LIM yields dynamical memory improving climate variability reconstructions and support the use of the LIM as a GCM44emulator in real reconstruction to propagate large ensembles of particles at low cost in SIR particle filter. |
author2 |
Océan et variabilité du climat (VARCLIM) Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques (LOCEAN) Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)) École normale supérieure - Paris (ENS-PSL) Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales Toulouse (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-École normale supérieure - Paris (ENS-PSL) Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-École polytechnique (X)-Centre National d'Études Spatiales Toulouse (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)) Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-École polytechnique (X)-Centre National d'Études Spatiales Toulouse (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité) |
format |
Article in Journal/Newspaper |
author |
Jebri, Beyrem Khodri, Myriam |
author_facet |
Jebri, Beyrem Khodri, Myriam |
author_sort |
Jebri, Beyrem |
title |
Large ensemble particle filter for spatial climate reconstructions using a Linear inverse model |
title_short |
Large ensemble particle filter for spatial climate reconstructions using a Linear inverse model |
title_full |
Large ensemble particle filter for spatial climate reconstructions using a Linear inverse model |
title_fullStr |
Large ensemble particle filter for spatial climate reconstructions using a Linear inverse model |
title_full_unstemmed |
Large ensemble particle filter for spatial climate reconstructions using a Linear inverse model |
title_sort |
large ensemble particle filter for spatial climate reconstructions using a linear inverse model |
publisher |
HAL CCSD |
publishDate |
2023 |
url |
https://hal.science/hal-03993775 https://hal.science/hal-03993775/document https://hal.science/hal-03993775/file/J%20Adv%20Model%20Earth%20Syst%20-%202023%20-%20Jebri%20-%20Large%20Ensemble%20Particle%20Filter%20for%20Spatial%20Climate%20Reconstructions%20Using%20a%20Linear.pdf https://doi.org/10.1029/2022MS003094 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
ISSN: 1942-2466 Journal of Advances in Modeling Earth Systems https://hal.science/hal-03993775 Journal of Advances in Modeling Earth Systems, 2023, ⟨10.1029/2022MS003094⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1029/2022MS003094 hal-03993775 https://hal.science/hal-03993775 https://hal.science/hal-03993775/document https://hal.science/hal-03993775/file/J%20Adv%20Model%20Earth%20Syst%20-%202023%20-%20Jebri%20-%20Large%20Ensemble%20Particle%20Filter%20for%20Spatial%20Climate%20Reconstructions%20Using%20a%20Linear.pdf doi:10.1029/2022MS003094 IRD: fdi:010087449 WOS: 000940633800001 |
op_rights |
http://creativecommons.org/licenses/by-nc-nd/ info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.1029/2022MS003094 |
container_title |
Journal of Advances in Modeling Earth Systems |
container_volume |
15 |
container_issue |
3 |
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1799485357524254720 |
spelling |
ftmuseumnhn:oai:HAL:hal-03993775v1 2024-05-19T07:45:20+00:00 Large ensemble particle filter for spatial climate reconstructions using a Linear inverse model Jebri, Beyrem Khodri, Myriam Océan et variabilité du climat (VARCLIM) Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques (LOCEAN) Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)) École normale supérieure - Paris (ENS-PSL) Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales Toulouse (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-École normale supérieure - Paris (ENS-PSL) Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-École polytechnique (X)-Centre National d'Études Spatiales Toulouse (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)) Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-École polytechnique (X)-Centre National d'Études Spatiales Toulouse (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité) 2023-02-27 https://hal.science/hal-03993775 https://hal.science/hal-03993775/document https://hal.science/hal-03993775/file/J%20Adv%20Model%20Earth%20Syst%20-%202023%20-%20Jebri%20-%20Large%20Ensemble%20Particle%20Filter%20for%20Spatial%20Climate%20Reconstructions%20Using%20a%20Linear.pdf https://doi.org/10.1029/2022MS003094 en eng HAL CCSD American Geophysical Union info:eu-repo/semantics/altIdentifier/doi/10.1029/2022MS003094 hal-03993775 https://hal.science/hal-03993775 https://hal.science/hal-03993775/document https://hal.science/hal-03993775/file/J%20Adv%20Model%20Earth%20Syst%20-%202023%20-%20Jebri%20-%20Large%20Ensemble%20Particle%20Filter%20for%20Spatial%20Climate%20Reconstructions%20Using%20a%20Linear.pdf doi:10.1029/2022MS003094 IRD: fdi:010087449 WOS: 000940633800001 http://creativecommons.org/licenses/by-nc-nd/ info:eu-repo/semantics/OpenAccess ISSN: 1942-2466 Journal of Advances in Modeling Earth Systems https://hal.science/hal-03993775 Journal of Advances in Modeling Earth Systems, 2023, ⟨10.1029/2022MS003094⟩ [SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] info:eu-repo/semantics/article Journal articles 2023 ftmuseumnhn https://doi.org/10.1029/2022MS003094 2024-04-25T00:40:20Z International audience Proxy records that document the last 2000 years climate provide evidences for the wide range of the natural climate variability from inter-annual to secular timescales not captured by the short window of recent direct observations. Assessing climate models ability to reproduce such natural variations is crucial to understand climate sensitivity and impacts of future climate change. Paleoclimate data assimilation (PDA) offers a powerful way to extend the short instrumental period by optimally combining the physics described by General Circulation Climate Models (GCMs) with information from available proxy records while taking into account their uncertainties. Here we present a new PDA approach based on a sequential importance resampling (SIR) particle filter that uses Linear Inverse Modeling (LIM) as an emulator of several CMIP-class GCMs. We examine in a perfect-model framework the skill of the various LIMs to forecast the dynamic of the surface temperatures and provide spatial field reconstructions over the last millennium in a SIR particle filter. Our results show that the LIMs allow for skilful ensemble forecasts at one-year lead-time based on GCMs dynamical knowledge with best prediction in the tropics and the North Atlantic. The PDA further provides a set of physically consistent spatial fields allowing robust uncertainty quantification related to climate models biases and proxy spatial sampling. Our results indicate that the LIM yields dynamical memory improving climate variability reconstructions and support the use of the LIM as a GCM44emulator in real reconstruction to propagate large ensembles of particles at low cost in SIR particle filter. Article in Journal/Newspaper North Atlantic Muséum National d'Histoire Naturelle (MNHM): HAL Journal of Advances in Modeling Earth Systems 15 3 |