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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Cited articles

Agarap, A. F.: Deep learning using rectified linear units (relu), arXiv [preprint], https://doi.org/10.48550/arXiv.1803.08375, 2018. a
Behncke, J., Landschützer, P., and Tanhua, T.: A detectable change in the air-sea CO2 flux estimate from sailboat measurements, Sci. Rep., 14, 3345, https://doi.org/10.1038/s41598-024-53159-0, 2024. a
Binder, A., Montavon, G., Lapuschkin, S., Müller, K.-R., and Samek, W.: Layer-wise relevance propagation for neural networks with local renormalization layers, in: Proceedings, Part II 25, Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, 6–9 September 2016, Barcelona, Spain, Springer, 63–71, https://doi.org/10.1007/978-3-319-44781-0_8, 2016. a
Bulgin, C. E., Merchant, C. J., and Ferreira, D.: Tendencies, variability and persistence of sea surface temperature anomalies, Sci. Rep., 10, 7986 https://doi.org/10.1038/s41598-020-64785-9, 2020. a
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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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