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Your search keyword '"Mazloff, Matthew R."' showing total 41 results
Start Over Author "Mazloff, Matthew R." Publisher american geophysical union (agu)
41 results on '"Mazloff, Matthew R."'

15. Eddy‐Induced Acceleration of Argo Floats

Author

Wang, Tianyu, Gille, Sarah T., Mazloff, Matthew R., Zilberman, Nathalie V., Du, Yan, Wang, Tianyu, Gille, Sarah T., Mazloff, Matthew R., Zilberman, Nathalie V., and Du, Yan

Abstract

Float trajectories are simulated using Lagrangian particle tracking software and eddy‐permitting ocean model output from the Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2) project. We find that Argo‐like particles near strong mean flows tend to accelerate while at their parking depth. This effect is pronounced in western boundary current regions and in the Antarctic Circumpolar Current system. The acceleration is associated with eddy‐mean flow interactions: Eddies converge particles toward regions with stronger mean currents. Particles do not accelerate when they are advected by the eddy or mean flow alone. During a 9‐day parking period, speed increases induced by the eddy‐mean flow interactions can be as large as 2 cm s−1, representing roughly 10% of the mean velocity. If unaccounted for, this acceleration could bias velocities inferred from observed Argo float trajectories. Plain Language Summary Ocean instruments called floats are carried by ocean currents. Tests carried out using output from a numerical simulation of the ocean show that near strong currents, eddies tend to bump floats into the currents. As a result, on average, at the end of a 10‐day sampling period, a float is likely to end up in water that is moving faster than the water where it started 10 days earlier. This effect should be considered when using particles to estimate mean velocities.

Published
2020
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16. Water Mass and Biogeochemical Variability in the Kerguelen Sector of the Southern Ocean: A Machine Learning Approach for a Mixing Hotspot

Author

Rosso, Isabella, Mazloff, Matthew R., Talley, Lynne D., Purkey, Sarah G., Freeman, Natalie M., Maze, Guillaume, Rosso, Isabella, Mazloff, Matthew R., Talley, Lynne D., Purkey, Sarah G., Freeman, Natalie M., and Maze, Guillaume

Abstract

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 Summary The 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 pro

Published
2020
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