Articles | Volume 16, issue 10
https://doi.org/10.5194/essd-16-4793-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-4793-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
AIGD-PFT: the first AI-driven global daily gap-free 4 km phytoplankton functional type data product from 1998 to 2023
Yuan Zhang
State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, China
State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, China
Renhu Li
State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, China
Mengyu Li
State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, China
Zhaoxin Li
State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, China
Songyu Chen
State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, China
Xuerong Sun
Centre for Geography and Environmental Science, Department of Earth and Environmental Sciences, Faculty of Environment, Science and Economy, University of Exeter, Cornwall, United Kingdom
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Phytoplankton contribute to half of Earth’s primary production, but not a lot is known about subsurface phytoplankton, living at the base of the sunlit ocean. We develop a two-layered box model to simulate phytoplankton seasonal and interannual variations in different depth layers of the ocean. Our model captures seasonal and long-term trends of the two layers, explaining how they respond to a warming ocean, furthering our understanding of how phytoplankton are responding to climate change.
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Because of the large diversity of case 2 waters and the complexity of light transfer, retrieving main biogeochemical parameters in these waters is still challenging. By providing optical and biogeochemical parameters for 180 sampling stations with turbidity and chlorophyll-a concentration ranging from low to extreme values, the HYPERMAQ dataset will contribute to a better description of marine optics in optically complex water bodies and can help the scientific community to develop algorithms.
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Short summary
This work describes AIGD-PFT, the first AI-driven global daily gap-free 4 km phytoplankton functional type (PFT) product from 1998 to 2023. AIGD-PFT enhances the accuracy and spatiotemporal coverage quantification of eight major PFTs (i.e. diatoms, dinoflagellates, haptophytes, pelagophytes, cryptophytes, green algae, prokaryotes, and Prochlorococcus).
This work describes AIGD-PFT, the first AI-driven global daily gap-free 4 km phytoplankton...
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