  We show that the climate phenomena of El Nino and La Nina arise naturally as
states of macro-variables when our recent causal feature learning framework
(Chalupka 2015, Chalupka 2016) is applied to micro-level measures of zonal wind
(ZW) and sea surface temperatures (SST) taken over the equatorial band of the
Pacific Ocean. The method identifies these unusual climate states on the basis
of the relation between ZW and SST patterns without any input about past
occurrences of El Nino or La Nina. The simpler alternatives of (i) clustering
the SST fields while disregarding their relationship with ZW patterns, or (ii)
clustering the joint ZW-SST patterns, do not discover El Nino. We discuss the
degree to which our method supports a causal interpretation and use a
low-dimensional toy example to explain its success over other clustering
approaches. Finally, we propose a new robust and scalable alternative to our
original algorithm (Chalupka 2016), which circumvents the need for
high-dimensional density learning.
