Abstract
To discover patterns in historical data, climate scientists
have applied various clustering methods with the goal of
identifying regions that share some common climatological
behavior. However, past approaches are limited by the fact
that they either consider only a single time period (snapshot)
of multivariate data, or they consider only a single variable
by using the time series data as multi-dimensional feature
vector. In both cases, potentially useful information may be
lost. Moreover, clusters in high-dimensional data space can
be dicult to interpret, prompting the need for a more e ective
data representation. We address both of these issues by
employing a complex network (graph) to represent climate
data, a more intuitive model that can be used for analysis
while also having a direct mapping to the physical world
for interpretation. A cross correlation function is used to
weight network edges, thus respecting the temporal nature
of the data, and a community detection algorithm identi es
multivariate clusters. Examining networks for consecutive
periods allows us to study structural changes over time. We
show that communities have a climatological interpretation
and that disturbances in structure can be an indicator of climate
events (or lack thereof). Finally, we discuss how this
model can be applied for the discovery of more complex concepts
such as unknown teleconnections or the development
of multivariate climate indices and predictive insights.