气候变化领域集成服务门户(CCPortal): Water Mass and Biogeochemical Variability in the Kerguelen Sector of the Southern Ocean: A Machine Learning Approach for a Mixing Hot Spot

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DOI	10.1029/2019JC015877
	Water Mass and Biogeochemical Variability in the Kerguelen Sector of the Southern Ocean: A Machine Learning Approach for a Mixing Hot Spot
	Rosso I.; Mazloff M.R.; Talley L.D.; Purkey S.G.; Freeman N.M.; Maze G.
发表日期	2020
ISSN	21699275
卷号	125 期号:3
英文摘要	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 hot spot for these energetic dynamics, which result in large spatiotemporal variability of physical and biogeochemical 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 biogeochemical 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. © 2020. American Geophysical Union. All Rights Reserved.
英文关键词	Argo; Kerguelen Plateau; machine learning; Southern Ocean; unsupervised clustering
语种	英语
来源期刊	Journal of Geophysical Research: Oceans
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/186938
作者单位	Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, United States; Ifremer, University of Brest, CNRS, IRD, Laboratoire d'Océanographie Physique et Spatiale, IUEM, 29280, Plouzané, France
推荐引用方式 GB/T 7714	Rosso I.,Mazloff M.R.,Talley L.D.,et al. Water Mass and Biogeochemical Variability in the Kerguelen Sector of the Southern Ocean: A Machine Learning Approach for a Mixing Hot Spot[J],2020,125(3).
APA	Rosso I.,Mazloff M.R.,Talley L.D.,Purkey S.G.,Freeman N.M.,&Maze G..(2020).Water Mass and Biogeochemical Variability in the Kerguelen Sector of the Southern Ocean: A Machine Learning Approach for a Mixing Hot Spot.Journal of Geophysical Research: Oceans,125(3).
MLA	Rosso I.,et al."Water Mass and Biogeochemical Variability in the Kerguelen Sector of the Southern Ocean: A Machine Learning Approach for a Mixing Hot Spot".Journal of Geophysical Research: Oceans 125.3(2020).