Reconstruction of network connectivity by the interplay between complex structure and dynamics to discover climate networks

Characterizing network connectivity from measures of correlation and causality is of utmost importance to study the Earth’s climate system through the statistical analysis of collective dynamics. A major challenge is to infer functional connectivity from the observations, and different methods were applied to linear and nonlinear dynamics simulated from different complex topologies widely observed in empirical systems to quantify the limits on reconstruction of functional connectivity. Climate networks were constructed from the global air temperature on the grid points of surface and pressure level of 850 hPa in monthly time step based on the robust statistical analysis with the null hypothesis of uncorrelated dynamics. Mutual information method was chosen after examining on the artificial cases consisting of desirable features of the climate system utilizing complex networks. Analyzing imbalanced results persuaded further investigation as cross-validation to have a satisfied comparison against ground truth with the employed statistical tools. The results indicated that the interplay between the structure and dynamics led to very important differences in reconstructing the underlying network connectivity, especially when different methods were considered, even under the most rigorous statistical tests. Analyzing structural characteristics of the constructed climate network on the surface level unraveled large-scale climate oscillations with the significant connectivities over the oceans. It was also noticed that the constructed climate network on the pressure level revealed more extended connectivities providing useful information to identify underlying physical interactions between ocean and atmosphere with coupling pattern on different pressure levels.