  We propose a method to reconstruct and analyze a complex network from data
generated by a spatio-temporal dynamical system, relying on the nonlinear
mutual information of time series analysis and betweenness centrality of
complex network theory. We show, that this approach reveals a rich internal
structure in complex climate networks constructed from reanalysis and model
surface air temperature data. Our novel method uncovers peculiar wave-like
structures of high energy flow, that we relate to global surface ocean
currents. This points to a major role of the oceanic surface circulation in
coupling and stabilizing the global temperature field in the long term mean
(140 years for the model run and 60 years for reanalysis data). We find that
these results cannot be obtained using classical linear methods of multivariate
data analysis, and have ensured their robustness by intensive significance
testing.
