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

Abstract

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.