Integrating palaeoclimate time series with rich metadata for uncertainty modelling: strategy and documentation of the PalMod 130k marine palaeoclimate data synthesis

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 However, palaeoclimate data have generally been archived with insufficient standardisation and metadata to allow for transparent and c...

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Published in:Earth System Science Data
Main Authors: L. Jonkers, O. Cartapanis, M. Langner, N. McKay, S. Mulitza, A. Strack, M. Kucera
Format: Article in Journal/Newspaper
Language:English
Published: Copernicus Publications 2020
Subjects:
geo
Online Access:https://doi.org/10.5194/essd-12-1053-2020
https://www.earth-syst-sci-data.net/12/1053/2020/essd-12-1053-2020.pdf
https://doaj.org/article/709f12cab7f24c93827a00077dc724bb
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spelling fttriple:oai:gotriple.eu:oai:doaj.org/article:709f12cab7f24c93827a00077dc724bb 2023-05-15T18:01:06+02:00 Integrating palaeoclimate time series with rich metadata for uncertainty modelling: strategy and documentation of the PalMod 130k marine palaeoclimate data synthesis L. Jonkers O. Cartapanis M. Langner N. McKay S. Mulitza A. Strack M. Kucera 2020-05-01 https://doi.org/10.5194/essd-12-1053-2020 https://www.earth-syst-sci-data.net/12/1053/2020/essd-12-1053-2020.pdf https://doaj.org/article/709f12cab7f24c93827a00077dc724bb en eng Copernicus Publications doi:10.5194/essd-12-1053-2020 1866-3508 1866-3516 https://www.earth-syst-sci-data.net/12/1053/2020/essd-12-1053-2020.pdf https://doaj.org/article/709f12cab7f24c93827a00077dc724bb undefined Earth System Science Data, Vol 12, Pp 1053-1081 (2020) geo envir Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2020 fttriple https://doi.org/10.5194/essd-12-1053-2020 2023-01-22T17:50:13Z 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 However, palaeoclimate data have generally been archived with insufficient standardisation and metadata to allow for transparent and consistent uncertainty assessment in an automated way.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 archiving. Article in Journal/Newspaper Planktonic foraminifera Unknown Earth System Science Data 12 2 1053 1081
institution Open Polar
collection Unknown
op_collection_id fttriple
language English
topic geo
envir
spellingShingle geo
envir
L. Jonkers
O. Cartapanis
M. Langner
N. McKay
S. Mulitza
A. Strack
M. Kucera
Integrating palaeoclimate time series with rich metadata for uncertainty modelling: strategy and documentation of the PalMod 130k marine palaeoclimate data synthesis
topic_facet geo
envir
description 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 archiving.
format Article in Journal/Newspaper
author L. Jonkers
O. Cartapanis
M. Langner
N. McKay
S. Mulitza
A. Strack
M. Kucera
author_facet L. Jonkers
O. Cartapanis
M. Langner
N. McKay
S. Mulitza
A. Strack
M. Kucera
author_sort L. Jonkers
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 Copernicus Publications
publishDate 2020
url https://doi.org/10.5194/essd-12-1053-2020
https://www.earth-syst-sci-data.net/12/1053/2020/essd-12-1053-2020.pdf
https://doaj.org/article/709f12cab7f24c93827a00077dc724bb
genre Planktonic foraminifera
genre_facet Planktonic foraminifera
op_source Earth System Science Data, Vol 12, Pp 1053-1081 (2020)
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https://www.earth-syst-sci-data.net/12/1053/2020/essd-12-1053-2020.pdf
https://doaj.org/article/709f12cab7f24c93827a00077dc724bb
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