Jim - thanks for pointing this out. It makes sense that there should be interesting interactions between the Indian Ocean monsoon system and ENSO. This is captured in a _rough_ way in the *backbone of the climate network*. The idea there is to find the locations on Earth whose weather has the strongest correlations with the most other locations on Earth: the "movers and shakers" of the climate world.
Below, see a picture of that "backbone" and how the whole thing is affected by El Niños. Not surprisingly, the backbone is a bunch of hot water near the equator. It would be cool to build up a much clearer picture of the whole "backbone". Susanne Still has a nice machine learning method that can in some sense objectively estimates how many states a model of a random process should have. Using ideas like this, developed quite a bit further, we could try to use machine learning to take data and build up a model of the Earth's climate as a network of interacting components. (Just a dream.)
* A. Tsonis and K. L. Swanson, [Topology and predictability of El Niño and La Niña networks](https://pantherfile.uwm.edu/aatsonis/www/publications/2008-06_Tsonis-AA_TopologyandPredictabilityofElNinoandLaNinaNetworks-2.pdf), _[Phys. Rev. Lett.](http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.100.228502)_ **100** (2008) 228502.
> **Abstract.** We construct the networks of the surface temperature field for El Niño and for La Niña years and investigate their structure. We find that the El Niño network possesses significantly fewer links and lower clustering coefficient and characteristic path length than the La Niña network, which indicates that the former network is less communicative and less stable than the latter. We conjecture that because of this, predictability of temperature should decrease during El Niño years. Here we verify that indeed during El Niño years predictability is lower compared to La Niña years