Machine Learning‐Based Clustering of Oceanic Lagrangian Particles: Identification of the Main Pathways of the Labrador Current

Modeled geospatial Lagrangian trajectories are widely used in Earth Science, including in oceanography, atmospheric science and marine biology. The typically large size of these data sets makes them arduous to analyze, and their underlying pathways challenging to identify. Here, we show that we can use a machine learning unsupervised k‐means++ clustering method combined with expert aggregation of clusters to identify the pathways of the Labrador Current from a large set of modeled Lagrangian trajectories. The presented method requires simple pre‐processing of the data, including a Cartesian correction on longitudes and a principal component analysis reduction. The clustering is performed in a kernelized space and uses a larger number of clusters than the number of expected pathways. To identify the main pathways, 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 behavior. These pathways experience a strong variability (representing through time 4%–42% and 24%–73% of the flow, respectively). 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. Among the pathways of secondary importance, we identify a previously unreported one that feeds the subtropics across the Gulf Stream. Plain Language Summary Lagrangian trajectories, in which parcels of a fluid or objects are tracked as they move, are widely used in Earth Science, including in oceanography, atmospheric science and marine biology. They typically come in very large data sets containing chaotic trajectories, from which it is difficult to identify the main pathways of the flow. Here, we use a machine learning based algorithm, more specifically an unsupervised clustering algorithm, to identify the main pathways of the Labrador Current in the North Atlantic based on a large set of Lagrangian trajectories obtained from an ocean model. This study shows the power of such a method to help analyze this type of data, and provides a detailed description of the method so it can be used by a broad community on various applications. We find that, when it reached the Grand Banks of Ne