Unsupervised clustering of oceanic Lagrangian particles: identification of the main pathways of the Labrador Current

• Main Authors: Jutras, Mathilde, Planat, Noémie, Dufour, Carolina, Talbot, Lauryn C
• Format: Other/Unknown Material
• Language: unknown
• Published: Authorea, Inc. 2023
• Subjects: Belle Isle; Labrador Sea
• Online Access: http://dx.doi.org/10.22541/essoar.169008281.14450975/v1

Modelled geospatial Lagrangian trajectories are widely used in Earth Science, including in oceanography, atmospheric science and marine biology. The typically large size of these dataset makes them arduous to analyze, and their underlying pathways challenging to identify. Here, we show that a Machine Learning unsupervised k-means++ clustering method can successfully identify the pathways of the Labrador Current from a large set of modelled Lagrangian trajectories. The presented method requires simple pre-processing of the data, including a Cartesian correction on longitudes and a PCA reduction. The clustering is performed in a kernalized space and uses a larger number of clusters than the number of expected pathways. During post-processing, similar clusters are grouped into pathway categories by experts in the circulation of the region of interest. We find that the Labrador Current mainly follows a westward-flowing and an eastward retroflecting pathway (20% and 50% of the flow, respectively) that compensate each other through time in a see-saw behaviour. These pathways experience a strong variability of up to 96\%. We find that two thirds of the retroflection occurs at the tip of the Grand Banks, and one quarter at Flemish Cap. The westward pathway is mostly fed by the on-shelf branch of the Labrador Current, and the eastward pathway by the shelf-break branch. Pathways of secondary importance feed the Labrador Sea, the Gulf of St. Lawrence through the Belle Isle Strait, and the subtropics across the Gulf Stream.