I just bumped into this interesting webpage:

* Deng Research Group, [Graphical models and climate networks](http://deng.eas.gatech.edu/node/18), Georgia Institute of Technology.

This project uses Bayesian networks to study how climate patterns influence each other. It sounds really interesting, and more truly "network-theoretic" than just computing an "average link strength" between lots of nodes.

> **Abstract:** This project seeks to recover cause-effect relationships from observational/reanalysis data using graphical models. We have applied causal discovery methods, particularly constraint-based structure learning to understanding the dynamical interactions among four prominent modes of atmospheric low-frequency variability in boreal winter including the Western Pacific Oscillation (WPO), Eastern Pacific Oscillation (EPO), Pacific North America Pattern (PNA) and North Atlantic Oscillation (NAO). The role of ENSO in these interactions is also examined. The results are shown as static and temporal independence graphs also known as Bayesian Networks. Ongoing efforts include the construction of a new type of climate network based on cause-effect-relation (information flow) in the atmosphere.



> A summary graph showing the causal-relationship among WPO, EPO, NAO and PNA. Arrows indicate the direction of information flow and numbers correspond to the time-lag in days. ([Ebert-Uphoff and Deng 2012a](http://deng.eas.gatech.edu/sites/default/files/files/Ebert-Uphoff_Deng_2012_CausalDiscoveryForClimateResearch.pdf))