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  Reconstructing Historical Climate Fields With Deep Learning

Bochow, N., Poltronieri, A., Rypdal, M., Boers, N. (in press): Reconstructing Historical Climate Fields With Deep Learning. - Science Advances.
https://doi.org/10.1126/sciadv.adp0558

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Item Permalink: https://publications.pik-potsdam.de/pubman/item/item_32008 Version Permalink: https://publications.pik-potsdam.de/pubman/item/item_32008_1
Genre: Journal Article

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Bochow_2025_2311.18348v1.pdf (Preprint), 18MB
 
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https://zenodo.org/records/10512175 (Supplementary material)
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https://doi.org/10.48550/arXiv.2311.18348 (Preprint)
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 Creators:
Bochow, Nils1, Author           
Poltronieri, Anna2, Author
Rypdal, Martin2, Author
Boers, Niklas1, Author           
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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Language(s): eng - English
 Dates: 2025-03-10
 Publication Status: Accepted / In Press
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1126/sciadv.adp0558
MDB-ID: No MDB - stored outside PIK (see locators/paper)
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
OATYPE: Gold Open Access
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Title: Science Advances
Source Genre: Journal, SCI, Scopus, p3, oa
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/161027
Publisher: American Association for the Advancement of Science (AAAS)