Water Mass and Biogeochemical Variability in the Kerguelen Sector of the Southern Ocean: A Machine Learning Approach for a Mixing Hotspot

• Published in: Journal of Geophysical Research: Oceans
• Main Authors: Rosso, Isabella, Mazloff, Matthew R., Talley, Lynne D., Purkey, Sarah G., Freeman, Natalie M., Maze, Guillaume
• Other Authors: Laboratoire d'Océanographie Physique et Spatiale (LOPS), Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2020
• Subjects: [SDU]Sciences of the Universe [physics]; Indian; Kerguelen; Southern Ocean
• Online Access: https://hal.science/hal-04202539; https://hal.science/hal-04202539/document; https://hal.science/hal-04202539/file/JGR%20Oceans%20-%202020%20-%20Rosso%20-%20Water%20Mass%20and%20Biogeochemical%20Variability%20in%20the%20Kerguelen%20Sector%20of%20the%20Southern%20Ocean%20A.pdf; https://doi.org/10.1029/2019JC015877

International audience 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 900 m, revealing not only the location of frontal zones and their boundaries, but also the variability of water mass properties relative to the zonal mean state. We find that the variability is property‐dependent and can be more than twice as large as the mean zonal variability in intense eddy fields. In particular, we observe this intense variability in the intermediate and deep waters of the Subtropical Zone; in the Subantarctic Zone just west of and at KP; east of KP in the Polar Frontal Zone, associated with intense eddy variability that enhances deep waters convergence and mixing; and, as the deep waters upwell to the upper 500 m and mix with the surface waters in the southernmost regimes, each property shows a large variability.Plain Language SummaryThe Southern Ocean strongly influences the global climate system, by absorbing, storing and redistributing heat and carbon across the different ocean basins. Thanks to an increasing number of observations from autonomous instruments, called Argo floats, our understanding of this harsh environment has deepened in the last two decades. Here we use a machine learning technique to automatically classify the float measurements and sort them in regimes with similar properties based on their ...