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\begin{document}

\title{Unsupervised clustering of oceanic Lagrangian particles: identification
of the main pathways of the Labrador Current}



\author[1]{Mathilde Jutras}%
\author[1]{Noémie Planat}%
\author[1]{Carolina Dufour}%
\author[1]{Lauryn C Talbot}%
\affil[1]{McGill University}%


\vspace{-1em}



  
  \date{June 04, 2024}


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\begin{abstract}
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\textbackslash{}\%. 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.%
\end{abstract}%



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