Eur. Phys. J. Spec. Top. (2021) 230: 2973-2998
https://doi.org/10.1140/epjs/s11734-021-00274-y

Regular Article

The effect of time series distance functions on functional climate networks

¹ Associated Laboratory for Computing and Applied Mathematics, National Institute for Space Research, Av. dos Astronautas, 1758, Jardim da Granja, 12227-010, São José Dos Campos, SP, Brazil
² Department of Physics, Humboldt University, Newtonstraße 15, 12489, Berlin, Germany
³ Research Department IV-Complexity Science, Potsdam Institute for Climate Impact Research (PIK)-Member of the Leibniz Association, Telegrafenberg A56, 14473, Potsdam, Germany
⁴ Center for Weather Forecast and Climatic Studies, National Institute for Space Research, Rodovia Presidente Dutra, Km 40, 12630-000, Cachoeira Paulista, SP, Brazil
⁵ Institute of Science and Technology, Federal University of São Paulo, Av. Cesare Monsueto Giulio Lattes, 1201-Eugênio de Melo, São José dos Campos, SP, Brazil
⁶ Department of Water, Environment, Construction and Safety, Magdeburg-Stendal University of Applied Sciences, Breitscheidstr. 2, 39114, Magdeburg, Germany

^a ferreira@leonardonascimento.com

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.