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Regional sensitivity patterns of Arctic Ocean acidification revealed with machine learning

Earth Sciences

Regional sensitivity patterns of Arctic Ocean acidification revealed with machine learning

J. P. Krasting, M. D. Palma, et al.

Dive into the effects of ocean acidification on Arctic marine life as researchers John P. Krasting, Maurizia De Palma, Maike Sonnewald, John P. Dunne, and Jasmin G. John use cutting-edge machine learning to uncover critical patterns in acidification simulations across sub-regions. Their findings reveal alarming trends in the central Arctic that highlight the urgency of understanding these changes.

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Playback language: English
Abstract
Ocean acidification, driven by anthropogenic carbon emissions, significantly impacts marine life, particularly in vulnerable Arctic regions. This study employs unsupervised machine learning to analyze Arctic surface acidification simulations from two advanced climate models. Four sub-regions are identified, their boundaries influenced by current and projected sea ice patterns, consistent across models and emission scenarios. The central Arctic shows stronger trends towards corrosive waters due to early summer warming and late summer freshening. Salinity and total alkalinity reductions are key drivers of Arctic pH changes, highlighting the importance of sub-regional analysis of surface water properties.
Publisher
Communications Earth & Environment
Published On
Apr 19, 2022
Authors
John P. Krasting, Maurizia De Palma, Maike Sonnewald, John P. Dunne, Jasmin G. John
Tags
ocean acidification
Arctic regions
machine learning
marine life
sea ice
pH changes
salinity

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