Integrating palaeoclimate time series with rich metadata for uncertainty modelling: strategy and documentation of the PalMod 130k marine palaeoclimate data synthesis
International audience Abstract. Palaeoclimate data hold the unique promise of providing a long-term perspective on climate change and as such can serve as an important benchmark for climate models. However, palaeoclimate data have generally been archived with insufficient standardisation and metada...
Published in: | Earth System Science Data |
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Main Authors: | , , , , , , |
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Format: | Article in Journal/Newspaper |
Language: | English |
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HAL CCSD
2020
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Online Access: | https://hal.inrae.fr/hal-03311934 https://hal.inrae.fr/hal-03311934/document https://hal.inrae.fr/hal-03311934/file/essd-12-1053-2020.pdf https://doi.org/10.5194/essd-12-1053-2020 |
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Open Polar |
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Institut national des sciences de l'Univers: HAL-INSU |
op_collection_id |
ftinsu |
language |
English |
topic |
Sea-surface Temperature Atlantic Deep-water Eastern Equatorial Pacific South China Sea Northern-hemisphere Climate Millennial-scale Changes Southeastern New-zealand Western Iberian Margin Intermediate Water Llast Deglaciation [SDU.STU.AG]Sciences of the Universe [physics]/Earth Sciences/Applied geology [SDU]Sciences of the Universe [physics] |
spellingShingle |
Sea-surface Temperature Atlantic Deep-water Eastern Equatorial Pacific South China Sea Northern-hemisphere Climate Millennial-scale Changes Southeastern New-zealand Western Iberian Margin Intermediate Water Llast Deglaciation [SDU.STU.AG]Sciences of the Universe [physics]/Earth Sciences/Applied geology [SDU]Sciences of the Universe [physics] Jonkers, Lukas Cartapanis, Olivier Langner, Michael Mckay, Nick Mulitza, Stefan Strack, Anne Kucera, Michal Integrating palaeoclimate time series with rich metadata for uncertainty modelling: strategy and documentation of the PalMod 130k marine palaeoclimate data synthesis |
topic_facet |
Sea-surface Temperature Atlantic Deep-water Eastern Equatorial Pacific South China Sea Northern-hemisphere Climate Millennial-scale Changes Southeastern New-zealand Western Iberian Margin Intermediate Water Llast Deglaciation [SDU.STU.AG]Sciences of the Universe [physics]/Earth Sciences/Applied geology [SDU]Sciences of the Universe [physics] |
description |
International audience Abstract. Palaeoclimate data hold the unique promise of providing a long-term perspective on climate change and as such can serve as an important benchmark for climate models. However, palaeoclimate data have generally been archived with insufficient standardisation and metadata to allow for transparent and consistent uncertainty assessment in an automated way. Thanks to improved computation capacity, transient palaeoclimate simulations are now possible, calling for data products containing multi-parameter time series rather than information on a single parameter for a single time slice. Efforts are underway to simulate a complete glacial–interglacial cycle using general circulation models (https://www.palmod.de/, last access: 6 May 2020), and to confront these simulations with palaeoclimate data, we have compiled a multi-parameter marine palaeoclimate data synthesis that contains time series spanning 0 to 130 000 years ago. We present the first version of the data product that focuses exclusively on time series for which a robust chronology based on benthic foraminifera δ18O and radiocarbon dating is available. The product contains 896 time series of eight palaeoclimate parameters from 143 individual sites, each associated with rich metadata, age–depth model ensembles, and information to refine and update the chronologies. This version contains 205 time series of benthic foraminifera δ18O; 169 of benthic foraminifera δ13C; 131 of seawater temperature; 174 and 119 of planktonic foraminifera δ18O and δ13C; and 44, 38 and 16 of carbonate, organic carbon and biogenic silica content, respectively. The data product is available in three formats (R, LiPD and netCDF) facilitating use across different software and operating systems and can be downloaded at https://doi.org/10.1594/PANGAEA.908831 (Jonkers et al., 2019). This data descriptor presents our data synthesis strategy and describes the contents and format of the data product in detail. It ends with a set of recommendations for data ... |
author2 |
Oeschger Centre for Climate Change Research (OCCR) Universität Bern / University of Bern (UNIBE) Centre européen de recherche et d'enseignement des géosciences de l'environnement (CEREGE) Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Collège de France (CdF (institution))-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) Center for Marine Environmental Sciences Bremen (MARUM) Universität Bremen School of Earth and Sustainability Northern Arizona University Flagstaff Bundesministerium fur Bildung und Forschung (PalMod grant) - Swiss National Science FoundationPP00P2-144811 |
format |
Article in Journal/Newspaper |
author |
Jonkers, Lukas Cartapanis, Olivier Langner, Michael Mckay, Nick Mulitza, Stefan Strack, Anne Kucera, Michal |
author_facet |
Jonkers, Lukas Cartapanis, Olivier Langner, Michael Mckay, Nick Mulitza, Stefan Strack, Anne Kucera, Michal |
author_sort |
Jonkers, Lukas |
title |
Integrating palaeoclimate time series with rich metadata for uncertainty modelling: strategy and documentation of the PalMod 130k marine palaeoclimate data synthesis |
title_short |
Integrating palaeoclimate time series with rich metadata for uncertainty modelling: strategy and documentation of the PalMod 130k marine palaeoclimate data synthesis |
title_full |
Integrating palaeoclimate time series with rich metadata for uncertainty modelling: strategy and documentation of the PalMod 130k marine palaeoclimate data synthesis |
title_fullStr |
Integrating palaeoclimate time series with rich metadata for uncertainty modelling: strategy and documentation of the PalMod 130k marine palaeoclimate data synthesis |
title_full_unstemmed |
Integrating palaeoclimate time series with rich metadata for uncertainty modelling: strategy and documentation of the PalMod 130k marine palaeoclimate data synthesis |
title_sort |
integrating palaeoclimate time series with rich metadata for uncertainty modelling: strategy and documentation of the palmod 130k marine palaeoclimate data synthesis |
publisher |
HAL CCSD |
publishDate |
2020 |
url |
https://hal.inrae.fr/hal-03311934 https://hal.inrae.fr/hal-03311934/document https://hal.inrae.fr/hal-03311934/file/essd-12-1053-2020.pdf https://doi.org/10.5194/essd-12-1053-2020 |
geographic |
New Zealand Pacific |
geographic_facet |
New Zealand Pacific |
genre |
Planktonic foraminifera |
genre_facet |
Planktonic foraminifera |
op_source |
ISSN: 1866-3508 Earth System Science Data https://hal.inrae.fr/hal-03311934 Earth System Science Data, 2020, 12 (2), pp.1053-1081. ⟨10.5194/essd-12-1053-2020⟩ |
op_relation |
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op_rights |
http://creativecommons.org/licenses/by/ info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.5194/essd-12-1053-2020 |
container_title |
Earth System Science Data |
container_volume |
12 |
container_issue |
2 |
container_start_page |
1053 |
op_container_end_page |
1081 |
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spelling |
ftinsu:oai:HAL:hal-03311934v1 2024-02-04T10:04:04+01:00 Integrating palaeoclimate time series with rich metadata for uncertainty modelling: strategy and documentation of the PalMod 130k marine palaeoclimate data synthesis Jonkers, Lukas Cartapanis, Olivier Langner, Michael Mckay, Nick Mulitza, Stefan Strack, Anne Kucera, Michal Oeschger Centre for Climate Change Research (OCCR) Universität Bern / University of Bern (UNIBE) Centre européen de recherche et d'enseignement des géosciences de l'environnement (CEREGE) Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Collège de France (CdF (institution))-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) Center for Marine Environmental Sciences Bremen (MARUM) Universität Bremen School of Earth and Sustainability Northern Arizona University Flagstaff Bundesministerium fur Bildung und Forschung (PalMod grant) - Swiss National Science FoundationPP00P2-144811 2020 https://hal.inrae.fr/hal-03311934 https://hal.inrae.fr/hal-03311934/document https://hal.inrae.fr/hal-03311934/file/essd-12-1053-2020.pdf https://doi.org/10.5194/essd-12-1053-2020 en eng HAL CCSD Copernicus Publications info:eu-repo/semantics/altIdentifier/doi/10.5194/essd-12-1053-2020 hal-03311934 https://hal.inrae.fr/hal-03311934 https://hal.inrae.fr/hal-03311934/document https://hal.inrae.fr/hal-03311934/file/essd-12-1053-2020.pdf doi:10.5194/essd-12-1053-2020 WOS: 000531893900001 http://creativecommons.org/licenses/by/ info:eu-repo/semantics/OpenAccess ISSN: 1866-3508 Earth System Science Data https://hal.inrae.fr/hal-03311934 Earth System Science Data, 2020, 12 (2), pp.1053-1081. ⟨10.5194/essd-12-1053-2020⟩ Sea-surface Temperature Atlantic Deep-water Eastern Equatorial Pacific South China Sea Northern-hemisphere Climate Millennial-scale Changes Southeastern New-zealand Western Iberian Margin Intermediate Water Llast Deglaciation [SDU.STU.AG]Sciences of the Universe [physics]/Earth Sciences/Applied geology [SDU]Sciences of the Universe [physics] info:eu-repo/semantics/article Journal articles 2020 ftinsu https://doi.org/10.5194/essd-12-1053-2020 2024-01-10T17:25:36Z International audience Abstract. Palaeoclimate data hold the unique promise of providing a long-term perspective on climate change and as such can serve as an important benchmark for climate models. However, palaeoclimate data have generally been archived with insufficient standardisation and metadata to allow for transparent and consistent uncertainty assessment in an automated way. Thanks to improved computation capacity, transient palaeoclimate simulations are now possible, calling for data products containing multi-parameter time series rather than information on a single parameter for a single time slice. Efforts are underway to simulate a complete glacial–interglacial cycle using general circulation models (https://www.palmod.de/, last access: 6 May 2020), and to confront these simulations with palaeoclimate data, we have compiled a multi-parameter marine palaeoclimate data synthesis that contains time series spanning 0 to 130 000 years ago. We present the first version of the data product that focuses exclusively on time series for which a robust chronology based on benthic foraminifera δ18O and radiocarbon dating is available. The product contains 896 time series of eight palaeoclimate parameters from 143 individual sites, each associated with rich metadata, age–depth model ensembles, and information to refine and update the chronologies. This version contains 205 time series of benthic foraminifera δ18O; 169 of benthic foraminifera δ13C; 131 of seawater temperature; 174 and 119 of planktonic foraminifera δ18O and δ13C; and 44, 38 and 16 of carbonate, organic carbon and biogenic silica content, respectively. The data product is available in three formats (R, LiPD and netCDF) facilitating use across different software and operating systems and can be downloaded at https://doi.org/10.1594/PANGAEA.908831 (Jonkers et al., 2019). This data descriptor presents our data synthesis strategy and describes the contents and format of the data product in detail. It ends with a set of recommendations for data ... Article in Journal/Newspaper Planktonic foraminifera Institut national des sciences de l'Univers: HAL-INSU New Zealand Pacific Earth System Science Data 12 2 1053 1081 |