The effect of time series distance functions on functional climate networks

Abstract

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.

Network structure from a characterization of interactions in complex systems

“…We observed exceptions for assortativity of random coupling topologies but these were to be expected. Problematically, topological aspects have been repeatedly used to characterize natural and man-made systems in the past 68 – 73 and have already been shown to be sensitive to a number of influencing factors, such as, e.g., constraints on the spatial 95 – 97 and temporal 98 sampling of a system.…”

“…However, since failure to correctly identify even a single edge can drastically alter the appearance of a structural network (topologically, the difference between, e.g., a line and a ring of coupled units is just one edge), it remains unclear if the studied structural networks and the ones derived from data had a similar organization. In light of functional networks being used to characterize systems in nature 68 – 73 , this ambiguity (and possible concomitant dissimilarities) might prove problematical in various situations.…”

Dynamical phenomena in complex networks: fundamentals and applications

“…Among the applications to neuroscience, there are promising ones using statistical physics methods to deal with stochastic processes, from intracellular calcium spikes [17] and single neuron firing [20] to sequential decision-making behavior [18]. The contributions from the Earth science application field clearly demonstrate the predictive power of climate networks to study challenging Earth processes and phenomena [21,26,28]. Among the possible further developments, the applications of complex dynamical networks to machine learning can play an important role.…”

“…Ferreira et al [21] study functional climate networks. The patterns in functional climate networks are highly important for the study of climate dynamics since they generally represent pathways for the long-distance transportation of energy.…”

“…A substantial part of this special issue is also devoted to Earth sciences. Special emphasis has been placed on functional climate networks [21,26,28]. Also, here it is shown how complex climate networks can be used as a tool to study various processes and phenomena, such as equatorial wave interactions associated with the Madden-Julian oscillation [23], dominant global covariability patterns between sea surface temperatures and precipitation [24], fires in tropical forests over space and time [25], Amazon rainfalls [27], South American low-level circulation [29], climate-induced hysteresis in pan-tropical forests [32], and others.…”