Ocean Surface Connectivity in the Arctic: Capabilities and Caveats of Community Detection in Lagrangian Flow Networks

Abstract

Different regions of the global ocean are connected by currents and eddies. From a Lagrangian perspective, these currents and eddies facilitate the exchange of fluid parcels, that move along chaotic trajectories which change through space and time. Lagrangian ocean analysis enables studying the pathways of objects suspended in fluid (van Sebille et al., 2018), such as plastics (Hardesty et al., 2017) or the larvae of marine species (Jacobi et al., 2012; Rossi et al., 2014). With knowledge of how particles travel through different geographical areas of a fluid domain, we can investigate the connectivity of these areas. In the context of the ocean, connectivity is a widely used term in marine ecology and marine spatial planning, relating to the larval exchange of a species between different, geographically separated subpopulations (Cowen & Sponaugle, 2009; Rossi et al., 2014). The modeling of larval dispersal has been simplified by considering larvae as passive particles (Andrello et al., 2013), sometimes also neglecting vertical effects by modeling them as buoyant particles (Rossi et al., 2014). With these simplifications, the definition of connectivity becomes more general, and relates to the exchange of any passive particle between geographical Abstract To identify barriers to transport in a fluid domain, community detection algorithms from network science have been used to divide the domain into clusters that are sparsely connected with each other. In a previous application to the closed domain of the Mediterranean Sea, communities detected by the Infomap algorithm have barriers that often coincide with well-known oceanographic features. We apply this clustering method to the surface of the Arctic and subarctic oceans and thereby show that it can also be applied to open domains. First, we construct a Lagrangian flow network by simulating the exchange of Lagrangian particles between different bins in an icosahedral-hexagonal grid. Then, Infomap is applied to identify groups of well-connected bins. The resolved transport barriers include naturally occurring structures, such as the major currents. As expected, clusters in the Arctic are affected by seasonal and annual variations in sea-ice concentration. An important caveat of community detection algorithms is that many different divisions into clusters may qualify as good solutions. Moreover, while certain cluster boundaries lie consistently at the same location between different good solutions, other boundary locations vary significantly, making it difficult to assess the physical meaning of a single solution. We therefore consider an ensemble of solutions to find persistent boundaries, trends, and correlations with surface velocities and sea-ice cover. Plain Language Summary To assess which surface regions of the Arctic Ocean are connected to one another, we create a division into clusters based on the exchange of virtual particles between regions. To do so, we divide the Arctic Ocean into boxes. Then, we release particles in each box, simulate their movement, ...

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Characteristic signatures of Northern Hemisphere blocking events in a Lagrangian flow network representation of the atmospheric circulation

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Characteristic signatures of Northern Hemisphere blocking events in a Lagrangian flow network representation of the atmospheric circulation

“…A similar connectivity analysis was recently performed in the Arctic. 23 Other related studies analyzed the surface connectivity in the Mediterranean derived from drifter data 24 and studied the transition paths of marine debris. 25 Applying the geometric or probabilistic methodologies to the atmosphere is even more challenging than in the ocean.…”