Reconstructing climatic modes of variability from proxy records using ClimIndRec version 1.0
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 ap...
Published in: | Geoscientific Model Development |
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Language: | English |
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European Geosciences Union
2020
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Online Access: | https://doi.org/10.5194/gmd-13-841-2020 |
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ftoskarbordeaux:oai:oskar-bordeaux.fr:20.500.12278/8755 2023-05-15T15:16:04+02:00 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 2020 https://doi.org/10.5194/gmd-13-841-2020 en eng European Geosciences Union 1991-959X doi:10.5194/gmd-13-841-2020 Planète et Univers [physics]/Sciences de la Terre Article de revue 2020 ftoskarbordeaux https://doi.org/10.5194/gmd-13-841-2020 2021-10-26T22:29:49Z 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. Blue-Action: Arctic impact on weather and climat Other/Unknown Material Arctic North Atlantic North Atlantic oscillation OSKAR Bordeaux (Open Science Knowledge ARchive) Arctic Geoscientific Model Development 13 2 841 858 |
institution |
Open Polar |
collection |
OSKAR Bordeaux (Open Science Knowledge ARchive) |
op_collection_id |
ftoskarbordeaux |
language |
English |
topic |
Planète et Univers [physics]/Sciences de la Terre |
spellingShingle |
Planète et Univers [physics]/Sciences de la Terre 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 |
Planète et Univers [physics]/Sciences de la Terre |
description |
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. Blue-Action: Arctic impact on weather and climat |
format |
Other/Unknown Material |
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 |
European Geosciences Union |
publishDate |
2020 |
url |
https://doi.org/10.5194/gmd-13-841-2020 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic North Atlantic North Atlantic oscillation |
genre_facet |
Arctic North Atlantic North Atlantic oscillation |
op_relation |
1991-959X doi:10.5194/gmd-13-841-2020 |
op_doi |
https://doi.org/10.5194/gmd-13-841-2020 |
container_title |
Geoscientific Model Development |
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13 |
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841 |
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858 |
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