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.

Bibliographic Details
Published in:Communications Earth & Environment
Main Authors: John P. Krasting, Maurizia De Palma, Maike Sonnewald, John P. Dunne, Jasmin G. John
Format: Article in Journal/Newspaper
Language:English
Published: Nature Portfolio 2022
Subjects:
geo
info
Arctic
Arctic Ocean
Arctic Ocean Acidification
Ocean acidification
Sea ice
Online Access:https://doi.org/10.1038/s43247-022-00419-4
https://doaj.org/article/c71c3338d426428ca82192201d931718
Description
Summary: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.

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