Video for learning-based prediction of the particles catchment area of PAP sediment traps ...
The ocean biological carbon pump plays a major role in climate and biogeochemical cycles. Photosynthesis at the surface produces particles that are exported to the deep ocean by gravity. Sediment traps, which measure the deep carbon fluxes, help to quantify the carbon stored by this process. However...
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Online Access: | https://dx.doi.org/10.5281/zenodo.10261826 https://zenodo.org/doi/10.5281/zenodo.10261826 |
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ftdatacite:10.5281/zenodo.10261826 2024-01-28T10:07:37+01:00 Video for learning-based prediction of the particles catchment area of PAP sediment traps ... Picard, Théo 2023 https://dx.doi.org/10.5281/zenodo.10261826 https://zenodo.org/doi/10.5281/zenodo.10261826 unknown Zenodo https://dx.doi.org/10.5281/zenodo.10261827 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 MediaObject article Audiovisual 2023 ftdatacite https://doi.org/10.5281/zenodo.1026182610.5281/zenodo.10261827 2024-01-04T21:04:21Z The ocean biological carbon pump plays a major role in climate and biogeochemical cycles. Photosynthesis at the surface produces particles that are exported to the deep ocean by gravity. Sediment traps, which measure the deep carbon fluxes, help to quantify the carbon stored by this process. However, it is challenging to precisely identify the surface origin of particles trapped thousands of meters deep because of the influence of ocean circulation on the carbon sinking path. In this study, we conducted a series of numerical Lagrangian experiments in the Porcupine Abyssal Plain region of the North Atlantic and developed a machine learning approach to predict the surface origin of particles trapped in a deep sediment trap.This new tool provide a better link between satellite-derived sea surface observations and deep sediment trap measurements, ultimately improving our understanding of the biological carbon pump mechanism. ... Article in Journal/Newspaper North Atlantic DataCite Metadata Store (German National Library of Science and Technology) |
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DataCite Metadata Store (German National Library of Science and Technology) |
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description |
The ocean biological carbon pump plays a major role in climate and biogeochemical cycles. Photosynthesis at the surface produces particles that are exported to the deep ocean by gravity. Sediment traps, which measure the deep carbon fluxes, help to quantify the carbon stored by this process. However, it is challenging to precisely identify the surface origin of particles trapped thousands of meters deep because of the influence of ocean circulation on the carbon sinking path. In this study, we conducted a series of numerical Lagrangian experiments in the Porcupine Abyssal Plain region of the North Atlantic and developed a machine learning approach to predict the surface origin of particles trapped in a deep sediment trap.This new tool provide a better link between satellite-derived sea surface observations and deep sediment trap measurements, ultimately improving our understanding of the biological carbon pump mechanism. ... |
format |
Article in Journal/Newspaper |
author |
Picard, Théo |
spellingShingle |
Picard, Théo Video for learning-based prediction of the particles catchment area of PAP sediment traps ... |
author_facet |
Picard, Théo |
author_sort |
Picard, Théo |
title |
Video for learning-based prediction of the particles catchment area of PAP sediment traps ... |
title_short |
Video for learning-based prediction of the particles catchment area of PAP sediment traps ... |
title_full |
Video for learning-based prediction of the particles catchment area of PAP sediment traps ... |
title_fullStr |
Video for learning-based prediction of the particles catchment area of PAP sediment traps ... |
title_full_unstemmed |
Video for learning-based prediction of the particles catchment area of PAP sediment traps ... |
title_sort |
video for learning-based prediction of the particles catchment area of pap sediment traps ... |
publisher |
Zenodo |
publishDate |
2023 |
url |
https://dx.doi.org/10.5281/zenodo.10261826 https://zenodo.org/doi/10.5281/zenodo.10261826 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_relation |
https://dx.doi.org/10.5281/zenodo.10261827 |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_doi |
https://doi.org/10.5281/zenodo.1026182610.5281/zenodo.10261827 |
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1789335533117243392 |