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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2018-211
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2018-211
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Model description paper 19 Nov 2018

Model description paper | 19 Nov 2018

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This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

Reconstructing climatic modes of variability from proxy records: sensitivity to the methodological approach

Simon Michel1, Didier Swingedouw1, Marie Chavent2, Pablo Ortega3, Juliette Mignot4, and Myriam Khodri4 Simon Michel et al.
  • 1Environnements et Paleoenvironnements Oceaniques et Continentaux (EPOC), UMR CNRS 5805 EPOC-OASU-Universite de Bordeaux, Allee Geoffroy Saint-Hilaire, Pessac 33615, France
  • 2Institut National de la Recherche en Informatique et Automatique (INRIA), CQFD, 33400 Talence, France
  • 3BSC, Barcelona, Spain
  • 4Sorbonne Universites (UPMC, Univ. Paris 06)-CNRS-IRD-MNHN, LOCEAN Laboratory, 4 place Jussieu, 75005 Paris, France

Abstract. Modes of climate variability strongly impact our climate and thus human society. Nevertheless, their statistical properties 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 a useful way to improve our understanding of their behaviours and meteorological impacts. For doing so, several statistical reconstruction methods exist, among which the Principal Component Regression is one of the most widely used. Additional predictive, and then reconstructive, statistical methods have been developed recently, following the advent of big data. Here, we provide to the climate community a multi-statistical toolbox, based on four statistical learning methods and cross validation algorithms, that enables systematic reconstruction of any climate mode of variability as long as there are proxy records that overlap in time with the observed variations of the considered mode. The efficiency of the methods can vary, depending on the statistical properties of the mode and the learning set, thereby allowing to assess sensitivity related to the reconstruction techniques. This toolbox is modular in the sense that it allows different inputs like the proxy database or the chosen variability mode. As an example, the toolbox is here applied to the reconstruction of the North Atlantic Oscillation (NAO) by using Pages 2K database. In order to identify the most reliable reconstruction among those given by the different methods, we also investigate the sensitivity to the methodological setup to other properties such as the number and the nature of the proxy records used as predictors or the reconstruction period targeted. The best reconstruction of the NAO that we thus obtain shows significant correlation with former reconstructions, but exhibits better validation scores.

Simon Michel et al.
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Simon Michel et al.
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Pages 2K database 2014 version Pages 2K Network https://doi.org/10.1038/NGEO1797

Simon Michel et al.
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Latest update: 10 Dec 2018
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Short summary
Natural archives such as sediments, ice, tree rings or speleothems provide indirect observations of past climate at local and regional scales. In this paper, we provide a computational device to combine those paleo-climate observations in order to make reconstructions of global climate indices. We then identify the different parameters associated to the methodological setup that can affect the final reconstruction. Hence, we provide different techniques to overcome those limitations.
Natural archives such as sediments, ice, tree rings or speleothems provide indirect observations...
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