Complex networks in climate dynamics
Comparing linear and nonlinear network construction methods
Potsdam Institute for Climate Impact Research, PO Box 60 12 03, 14412 Potsdam, Germany
2 Institute of Physics, University of Potsdam, Karl-Liebknecht-Str. 24/25, 14476 Potsdam-Golm, Germany
3 Department of Physics, Humboldt University, Newtonstr. 15, 12489 Berlin, Germany
Corresponding author: firstname.lastname@example.org
Complex network theory provides a powerful framework to statistically investigate the topology of local and non-local statistical interrelationships, i.e. teleconnections, in the climate system. Climate networks constructed from the same global climatological data set using the linear Pearson correlation coefficient or the nonlinear mutual information as a measure of dynamical similarity between regions, are compared systematically on local, mesoscopic and global topological scales. A high degree of similarity is observed on the local and mesoscopic topological scales for surface air temperature fields taken from AOGCM and reanalysis data sets. We find larger differences on the global scale, particularly in the betweenness centrality field. The global scale view on climate networks obtained using mutual information offers promising new perspectives for detecting network structures based on nonlinear physical processes in the climate system.
© EDP Sciences, Springer-Verlag, 2009