Articles | Volume 21, issue 2
https://doi.org/10.5194/os-21-587-2025
https://doi.org/10.5194/os-21-587-2025
Research article
 | 
13 Mar 2025
Research article |  | 13 Mar 2025

Detection and tracking of carbon biomes via integrated machine learning

Sweety Mohanty, Lavinia Patara, Daniyal Kazempour, and Peer Kröger

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Short summary
Climate change impacts the ocean carbon cycle, demanding methods to monitor ocean carbon uptake. We developed a machine learning tool applied to a global ocean biogeochemistry model to identify and track marine carbon biomes both seasonally and from 1958 to 2018. Distinct carbon biomes with varied ocean dynamics were detected. Changes in biome coverage revealed responses to seasonal and long-term shifts, offering insights into the impacts of climate change.
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