Regional sensitivity patterns of Arctic Ocean acidification revealed with machine learning
An unsupervised machine learning technique clustering carbonate outputs from two climate models indicates geographically consistent boundaries to ocean acidification patterns in the Arctic Ocean, with projected boundaries being sensitive to sea ice extent.
| Published in: | Communications Earth & Environment |
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| Main Authors: | , , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
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Nature Portfolio
2022
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| Subjects: | |
| Online Access: | https://doi.org/10.1038/s43247-022-00419-4 https://doaj.org/article/c71c3338d426428ca82192201d931718 |
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fttriple:oai:gotriple.eu:oai:doaj.org/article:c71c3338d426428ca82192201d931718 2023-05-15T14:35:32+02:00 Regional sensitivity patterns of Arctic Ocean acidification revealed with machine learning John P. Krasting Maurizia De Palma Maike Sonnewald John P. Dunne Jasmin G. John 2022-04-01 https://doi.org/10.1038/s43247-022-00419-4 https://doaj.org/article/c71c3338d426428ca82192201d931718 en eng Nature Portfolio doi:10.1038/s43247-022-00419-4 2662-4435 https://doaj.org/article/c71c3338d426428ca82192201d931718 undefined Communications Earth & Environment, Vol 3, Iss 1, Pp 1-11 (2022) geo info Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2022 fttriple https://doi.org/10.1038/s43247-022-00419-4 2023-01-22T17:51:31Z An unsupervised machine learning technique clustering carbonate outputs from two climate models indicates geographically consistent boundaries to ocean acidification patterns in the Arctic Ocean, with projected boundaries being sensitive to sea ice extent. Article in Journal/Newspaper Arctic Arctic Ocean Arctic Ocean Acidification Ocean acidification Sea ice Unknown Arctic Arctic Ocean Communications Earth & Environment 3 1 |
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English |
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geo info |
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geo info John P. Krasting Maurizia De Palma Maike Sonnewald John P. Dunne Jasmin G. John Regional sensitivity patterns of Arctic Ocean acidification revealed with machine learning |
| topic_facet |
geo info |
| description |
An unsupervised machine learning technique clustering carbonate outputs from two climate models indicates geographically consistent boundaries to ocean acidification patterns in the Arctic Ocean, with projected boundaries being sensitive to sea ice extent. |
| format |
Article in Journal/Newspaper |
| author |
John P. Krasting Maurizia De Palma Maike Sonnewald John P. Dunne Jasmin G. John |
| author_facet |
John P. Krasting Maurizia De Palma Maike Sonnewald John P. Dunne Jasmin G. John |
| author_sort |
John P. Krasting |
| title |
Regional sensitivity patterns of Arctic Ocean acidification revealed with machine learning |
| title_short |
Regional sensitivity patterns of Arctic Ocean acidification revealed with machine learning |
| title_full |
Regional sensitivity patterns of Arctic Ocean acidification revealed with machine learning |
| title_fullStr |
Regional sensitivity patterns of Arctic Ocean acidification revealed with machine learning |
| title_full_unstemmed |
Regional sensitivity patterns of Arctic Ocean acidification revealed with machine learning |
| title_sort |
regional sensitivity patterns of arctic ocean acidification revealed with machine learning |
| publisher |
Nature Portfolio |
| publishDate |
2022 |
| url |
https://doi.org/10.1038/s43247-022-00419-4 https://doaj.org/article/c71c3338d426428ca82192201d931718 |
| geographic |
Arctic Arctic Ocean |
| geographic_facet |
Arctic Arctic Ocean |
| genre |
Arctic Arctic Ocean Arctic Ocean Acidification Ocean acidification Sea ice |
| genre_facet |
Arctic Arctic Ocean Arctic Ocean Acidification Ocean acidification Sea ice |
| op_source |
Communications Earth & Environment, Vol 3, Iss 1, Pp 1-11 (2022) |
| op_relation |
doi:10.1038/s43247-022-00419-4 2662-4435 https://doaj.org/article/c71c3338d426428ca82192201d931718 |
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undefined |
| op_doi |
https://doi.org/10.1038/s43247-022-00419-4 |
| container_title |
Communications Earth & Environment |
| container_volume |
3 |
| container_issue |
1 |
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1766308342934798336 |