Surface circulation properties in the eastern Mediterranean emphasized using machine learning methods

Georges Baaklini

doi:10.5194/os-18-1491-2022

Surface circulation properties in the eastern Mediterranean emphasized using machine learning methods

2022-10-20

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DOI：10.5194/os-18-1491-2022

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The eastern Mediterranean surface circulation is highly energetic and composed of structures interacting stochastically. However, some main features are still debated, and the behavior of some fine-scale dynamics and their role in shaping the general circulation is yet unknown. In the following paper, we use an unsupervised neural network clustering method to analyze the long-term variability of the different mesoscale structures. We decompose 26 years of altimetric data into clusters reflecting different circulation patterns of weak and strong flows with either strain or vortex-dominated velocities. The vortex-dominated cluster is more persistent in the western part of the basin, which is more active than the eastern part due to the strong flow along the coast, interacting with the extended bathymetry and engendering continuous instabilities. The cluster that reflects a weak flow dominated the middle of the basin, including the Mid-Mediterranean Jet (MMJ) pathway. However, the temporal analysis shows a frequent and intermittent occurrence of a strong flow in the middle of the basin, which could explain the previous contradictory assessment of MMJ existence using in-situ observations. Moreover, we prove that the Levantine Sea is becoming more and more energetic as the activity of the main mesoscale features is showing a positive trend.

Keywords:

SELF-ORGANIZING MAPS

LEVANTINE BASIN

GEOSTROPHIC CIRCULATION

MESOSCALE EDDIES

HF RADAR

SEA

VARIABILITY

EDDY

PATTERNS

TOPEX/POSEIDON

Surface circulation properties in the eastern Mediterranean emphasized using machine learning methods

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