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.
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-04-01
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| Series: | Communications Earth & Environment |
| Online Access: | https://doi.org/10.1038/s43247-022-00419-4 |
| _version_ | 1818060610597814272 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-10T13:35:09Z |
| format | Article |
| id | doaj.art-c71c3338d426428ca82192201d931718 |
| institution | Directory Open Access Journal |
| issn | 2662-4435 |
| language | English |
| last_indexed | 2024-12-10T13:35:09Z |
| publishDate | 2022-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Earth & Environment |
| spelling | doaj.art-c71c3338d426428ca82192201d9317182022-12-22T01:46:51ZengNature PortfolioCommunications Earth & Environment2662-44352022-04-013111110.1038/s43247-022-00419-4Regional sensitivity patterns of Arctic Ocean acidification revealed with machine learningJohn P. Krasting0Maurizia De Palma1Maike Sonnewald2John P. Dunne3Jasmin G. John4NOAA / OAR / Geophysical Fluid Dynamics LaboratorySchool of Environmental and Sustainability Science (SESS), Kean UniversityCooperative Institute for Modeling the Earth System (CIMES), Princeton UniversityNOAA / OAR / Geophysical Fluid Dynamics LaboratoryNOAA / OAR / Geophysical Fluid Dynamics LaboratoryAn 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.https://doi.org/10.1038/s43247-022-00419-4 |
| spellingShingle | 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 Communications Earth & Environment |
| title | 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_short | Regional sensitivity patterns of Arctic Ocean acidification revealed with machine learning |
| title_sort | regional sensitivity patterns of arctic ocean acidification revealed with machine learning |
| url | https://doi.org/10.1038/s43247-022-00419-4 |
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