An ensemble reconstruction of global monthly sea surface temperature and sea ice concentration 1000-1849.
Journal
Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192
Informations de publication
Date de publication:
04 Oct 2021
04 Oct 2021
Historique:
received:
05
01
2021
accepted:
01
09
2021
entrez:
5
10
2021
pubmed:
6
10
2021
medline:
6
10
2021
Statut:
epublish
Résumé
This paper describes a global monthly gridded Sea Surface Temperature (SST) and Sea Ice Concentration (SIC) dataset for the period 1000-1849, which can be used as boundary conditions for atmospheric model simulations. The reconstruction is based on existing coarse-resolution annual temperature ensemble reconstructions, which are then augmented with intra-annual and sub-grid scale variability. The intra-annual component of HadISST.2.0 and oceanic indices estimated from the reconstructed annual mean are used to develop grid-based linear regressions in a monthly stratified approach. Similarly, we reconstruct SIC using analog resampling of HadISST.2.0 SIC (1941-2000), for both hemispheres. Analogs are pooled in four seasons, comprising of 3-months each. The best analogs are selected based on the correlation between each member of the reconstructed SST and its target. For the period 1780 to 1849, We assimilate historical observations of SST and night-time marine air temperature from the ICOADS dataset into our reconstruction using an offline Ensemble Kalman Filter approach. The resulting dataset is physically consistent with information from models, proxies, and observations.
Identifiants
pubmed: 34608148
doi: 10.1038/s41597-021-01043-1
pii: 10.1038/s41597-021-01043-1
pmc: PMC8490424
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
261Subventions
Organisme : EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council)
ID : 787574
Organisme : EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council)
ID : 787574
Informations de copyright
© 2021. The Author(s).
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