  Complex network theory provides an important tool for the analysis of complex
systems such as the Earth's climate. In this context, functional climate
networks can be constructed using a spatiotemporal climate dataset and a
suitable time series distance function. The resulting coarse-grained view on
climate variability consists of representing distinct areas on the globe (i.e.,
grid cells) by nodes and connecting pairs of nodes that present similar time
series. One fundamental concern when constructing such a functional climate
network is the definition of a metric that captures the mutual similarity
between time series. Here we study systematically the effect of 29 time series
distance functions on functional climate network construction based on global
temperature data. We observe that the distance functions previously used in the
literature commonly generate very similar networks while alternative ones
result in rather distinct network structures and reveal different long-distance
connection patterns. These patterns are highly important for the study of
climate dynamics since they generally represent pathways for the long-distance
transportation of energy and can be used to forecast climate variability on
subseasonal to interannual or even decadal scales. Therefore, we propose the
measures studied here as alternatives for the analysis of climate variability
and to further exploit their complementary capability of capturing different
aspects of the underlying dynamics that may help gaining a more holistic
empirical understanding of the global climate system.
