Ocean Surface Connectivity in the Arctic: Capabilities and Caveats of Community Detection in Lagrangian Flow Networks. Issue 1 (6th January 2021)

• Record Type: Journal Article
• Authors: Reijnders, Daan; van Leeuwen, Erik Jan; van Sebille, Erik
• Is Part Of: Journal of geophysical research. Volume 126:Issue 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-01-06
• Subjects: Arctic Ocean -- Connectivity -- Community Detection -- Hydrodynamic Provinces -- Lagrangian particle simulations -- Ocean Transport; Oceanography -- Periodicals; 551.4605
• DOI: 10.1029/2020JC016416
• Languages: English
• ISSNs: 2169-9275

Abstract

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, and investigate the exchange between boxes. Through the computer algorithm Infomap, we group boxes into clusters based on their particle exchange. Knowledge about this regional connectivity is important for planning areas of conservation. We find that the boundaries between clusters often coincide with ocean currents, showing that currents provide barriers to ocean transport. We also find that the cluster division is affected by sea ice. Since the amount of sea ice in the Arctic differs across seasons and since it is decreasing over the years due to climate change, cluster divisions also change seasonally and over multiple years. Because Infomap returns different cluster divisions of similar quality each time it is run, we consider the common properties of multiple solutions in our analysis. Key Points: Community detection in the Arctic using the Infomap algorithm identifies currents as flow barriers Solutions found by Infomap are degenerate but ensembles of solutions are relevant The quality of solutions is correlated with physical quantities such as sea ice area