| dc.description.abstract | Community detection algorithms from the field of network theory have been used to divide a fluid domain into clusters that are sparsely connected with each other and to identify barriers to transport, for example in the context of larval dispersal. Communities detected by the community detection algorithm Infomap have barriers that have been shown to often coincide with well-known oceanographic features. Thus far, this method has only been applied to closed domains such as the Mediterranean. We apply this method to the surface of the Arctic and subarctic oceans and show that it can 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 decadal variations in sea-ice concentration. We also discuss several caveats of this method. Firstly, there is no single definition of what makes a cluster, since this is dependent on a preferred balance of internally high connectivity, sparse connectivity between clusters, and the spatial scale of investigation. Secondly, many different divisions into clusters may qualify as good solutions and it may thus be misleading to only consider the solution that optimizes a certain quality parameter the most. Finally, 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. Particularly in the context of practical applications like planning Marine Protected Areas, it is important to consider an ensemble of qualifying solutions to find persistent boundaries. | |
