Reconstructing climatic modes of variability from proxy records using ClimIndRec version 1.0
International audience Modes of climate variability strongly impact our climate and thus human society. Nevertheless, the statistical properties of these modes remain poorly known due to the short time frame of instrumental measurements. Reconstructing these modes further back in time using statisti...
Published in: | Geoscientific Model Development |
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Main Authors: | , , , , , |
Other Authors: | , , , , , , , , , , , , , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
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HAL CCSD
2020
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Online Access: | https://hal.sorbonne-universite.fr/hal-02556996 https://hal.sorbonne-universite.fr/hal-02556996/document https://hal.sorbonne-universite.fr/hal-02556996/file/gmd-13-841-2020.pdf https://doi.org/10.5194/gmd-13-841-2020 |
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Muséum National d'Histoire Naturelle (MNHM): HAL |
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English |
topic |
[SDU.STU]Sciences of the Universe [physics]/Earth Sciences |
spellingShingle |
[SDU.STU]Sciences of the Universe [physics]/Earth Sciences Michel, Simon Swingedouw, Didier Chavent, Marie Ortega, Pablo Mignot, Juliette Khodri, Myriam Reconstructing climatic modes of variability from proxy records using ClimIndRec version 1.0 |
topic_facet |
[SDU.STU]Sciences of the Universe [physics]/Earth Sciences |
description |
International audience Modes of climate variability strongly impact our climate and thus human society. Nevertheless, the statistical properties of these modes remain poorly known due to the short time frame of instrumental measurements. Reconstructing these modes further back in time using statistical learning methods applied to proxy records is useful for improving our understanding of their behaviour. For doing so, several statistical methods exist, among which principal component regression is one of the most widely used in paleoclimatology. Here, we provide the software ClimIndRec to the climate community; it is based on four regression methods (principal component regression, PCR; partial least squares, PLS; elastic net, Enet; random forest, RF) and cross-validation (CV) algorithms, and enables the systematic reconstruction of a given climate index. A prerequisite is that there are proxy records in the database that overlap in time with its observed variations. The relative efficiency of the methods can vary, according to the statistical properties of the mode and the proxy records used. Here, we assess the sensitivity to the reconstruction technique. ClimIndRec is modular as it allows different inputs like the proxy database or the regression method. As an example, it is here applied to the reconstruction of the North Atlantic Oscillation by using the PAGES 2k database. In order to identify the most reliable reconstruction among those given by the different methods, we use the modularity of ClimIndRec to investigate the sensitivity of the methodological setup to other properties such as the number and the nature of the proxy records used as predictors or the targeted reconstruction period. We obtain the best reconstruction of the North Atlantic Oscillation (NAO) using the random forest approach. It shows significant correlation with former reconstructions, but exhibits higher validation scores. |
author2 |
Environnements et Paléoenvironnements OCéaniques (EPOC) Observatoire aquitain des sciences de l'univers (OASU) Université Sciences et Technologies - Bordeaux 1 (UB)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Sciences et Technologies - Bordeaux 1 (UB)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-École Pratique des Hautes Études (EPHE) Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Centre National de la Recherche Scientifique (CNRS) Quality control and dynamic reliability (CQFD) Institut de Mathématiques de Bordeaux (IMB) Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria) NCAS-Climate Reading Department of Meteorology Reading University of Reading (UOR)-University of Reading (UOR) 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) LEFE-IMAGO project VADEMECUM European Project: 727852,Blue-Action(2016) |
format |
Article in Journal/Newspaper |
author |
Michel, Simon Swingedouw, Didier Chavent, Marie Ortega, Pablo Mignot, Juliette Khodri, Myriam |
author_facet |
Michel, Simon Swingedouw, Didier Chavent, Marie Ortega, Pablo Mignot, Juliette Khodri, Myriam |
author_sort |
Michel, Simon |
title |
Reconstructing climatic modes of variability from proxy records using ClimIndRec version 1.0 |
title_short |
Reconstructing climatic modes of variability from proxy records using ClimIndRec version 1.0 |
title_full |
Reconstructing climatic modes of variability from proxy records using ClimIndRec version 1.0 |
title_fullStr |
Reconstructing climatic modes of variability from proxy records using ClimIndRec version 1.0 |
title_full_unstemmed |
Reconstructing climatic modes of variability from proxy records using ClimIndRec version 1.0 |
title_sort |
reconstructing climatic modes of variability from proxy records using climindrec version 1.0 |
publisher |
HAL CCSD |
publishDate |
2020 |
url |
https://hal.sorbonne-universite.fr/hal-02556996 https://hal.sorbonne-universite.fr/hal-02556996/document https://hal.sorbonne-universite.fr/hal-02556996/file/gmd-13-841-2020.pdf https://doi.org/10.5194/gmd-13-841-2020 |
genre |
Arctic North Atlantic North Atlantic oscillation |
genre_facet |
Arctic North Atlantic North Atlantic oscillation |
op_source |
ISSN: 1991-9603 EISSN: 1991-959X Geoscientific Model Development https://hal.sorbonne-universite.fr/hal-02556996 Geoscientific Model Development, 2020, 13 (2), pp.841 - 858. ⟨10.5194/gmd-13-841-2020⟩ |
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info:eu-repo/semantics/altIdentifier/doi/10.5194/gmd-13-841-2020 info:eu-repo/grantAgreement//727852/EU/Blue-Action: Arctic impact on weather and climat/Blue-Action hal-02556996 https://hal.sorbonne-universite.fr/hal-02556996 https://hal.sorbonne-universite.fr/hal-02556996/document https://hal.sorbonne-universite.fr/hal-02556996/file/gmd-13-841-2020.pdf doi:10.5194/gmd-13-841-2020 IRD: fdi:010078029 WOS: 000518794000001 |
op_rights |
info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.5194/gmd-13-841-2020 |
container_title |
Geoscientific Model Development |
container_volume |
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ftmuseumnhn:oai:HAL:hal-02556996v1 2024-05-19T07:33:27+00:00 Reconstructing climatic modes of variability from proxy records using ClimIndRec version 1.0 Michel, Simon Swingedouw, Didier Chavent, Marie Ortega, Pablo Mignot, Juliette Khodri, Myriam Environnements et Paléoenvironnements OCéaniques (EPOC) Observatoire aquitain des sciences de l'univers (OASU) Université Sciences et Technologies - Bordeaux 1 (UB)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Sciences et Technologies - Bordeaux 1 (UB)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-École Pratique des Hautes Études (EPHE) Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Centre National de la Recherche Scientifique (CNRS) Quality control and dynamic reliability (CQFD) Institut de Mathématiques de Bordeaux (IMB) Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria) NCAS-Climate Reading Department of Meteorology Reading University of Reading (UOR)-University of Reading (UOR) 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) LEFE-IMAGO project VADEMECUM European Project: 727852,Blue-Action(2016) 2020 https://hal.sorbonne-universite.fr/hal-02556996 https://hal.sorbonne-universite.fr/hal-02556996/document https://hal.sorbonne-universite.fr/hal-02556996/file/gmd-13-841-2020.pdf https://doi.org/10.5194/gmd-13-841-2020 en eng HAL CCSD European Geosciences Union info:eu-repo/semantics/altIdentifier/doi/10.5194/gmd-13-841-2020 info:eu-repo/grantAgreement//727852/EU/Blue-Action: Arctic impact on weather and climat/Blue-Action hal-02556996 https://hal.sorbonne-universite.fr/hal-02556996 https://hal.sorbonne-universite.fr/hal-02556996/document https://hal.sorbonne-universite.fr/hal-02556996/file/gmd-13-841-2020.pdf doi:10.5194/gmd-13-841-2020 IRD: fdi:010078029 WOS: 000518794000001 info:eu-repo/semantics/OpenAccess ISSN: 1991-9603 EISSN: 1991-959X Geoscientific Model Development https://hal.sorbonne-universite.fr/hal-02556996 Geoscientific Model Development, 2020, 13 (2), pp.841 - 858. ⟨10.5194/gmd-13-841-2020⟩ [SDU.STU]Sciences of the Universe [physics]/Earth Sciences info:eu-repo/semantics/article Journal articles 2020 ftmuseumnhn https://doi.org/10.5194/gmd-13-841-2020 2024-05-01T23:51:54Z International audience Modes of climate variability strongly impact our climate and thus human society. Nevertheless, the statistical properties of these modes remain poorly known due to the short time frame of instrumental measurements. Reconstructing these modes further back in time using statistical learning methods applied to proxy records is useful for improving our understanding of their behaviour. For doing so, several statistical methods exist, among which principal component regression is one of the most widely used in paleoclimatology. Here, we provide the software ClimIndRec to the climate community; it is based on four regression methods (principal component regression, PCR; partial least squares, PLS; elastic net, Enet; random forest, RF) and cross-validation (CV) algorithms, and enables the systematic reconstruction of a given climate index. A prerequisite is that there are proxy records in the database that overlap in time with its observed variations. The relative efficiency of the methods can vary, according to the statistical properties of the mode and the proxy records used. Here, we assess the sensitivity to the reconstruction technique. ClimIndRec is modular as it allows different inputs like the proxy database or the regression method. As an example, it is here applied to the reconstruction of the North Atlantic Oscillation by using the PAGES 2k database. In order to identify the most reliable reconstruction among those given by the different methods, we use the modularity of ClimIndRec to investigate the sensitivity of the methodological setup to other properties such as the number and the nature of the proxy records used as predictors or the targeted reconstruction period. We obtain the best reconstruction of the North Atlantic Oscillation (NAO) using the random forest approach. It shows significant correlation with former reconstructions, but exhibits higher validation scores. Article in Journal/Newspaper Arctic North Atlantic North Atlantic oscillation Muséum National d'Histoire Naturelle (MNHM): HAL Geoscientific Model Development 13 2 841 858 |