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| Rosso, Isabella; Mazloff, Matthew R.; Talley, Lynne D.; Purkey, Sarah G.; Freeman, Natalie M.; Maze, Guillaume. |
| The Southern Ocean (SO) is one of the most energetic regions in the world, where strong air‐sea fluxes, oceanic instabilities, and flow‐topography interactions yield complex dynamics. The Kerguelen Plateau (KP) region in the Indian sector of the SO is a hotspot for these energetic dynamics, which result in large spatio‐temporal variability of physical and biogeochemical (BGC) properties throughout the water column. Data from Argo floats (including biogeochemical) are used to investigate the spatial variability of intermediate and deep water physical and BGC properties. An unsupervised machine learning classification approach is used to organize the float profiles into five SO frontal zones based on their temperature and salinity structure between 300 and... |
| Tipo: Text |
Palavras-chave: Southern Ocean; Kerguelen Plateau; Argo; Unsupervised clustering; Machine learning. |
| Ano: 2020 |
URL: https://archimer.ifremer.fr/doc/00613/72471/71438.pdf |
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