The effect of time series distance functions on functional climate networks

Author(s)

Ferreira, Leonardo N. ^{[1, 2, 3, 4]} ; Ferreira, Nicole C. R. ^[5] ; Macau, Elbert E. N. ^{[3, 6]} ; Donner, V, Reik

Affiliation

^[1] Humboldt Univ, Dept Phys, Newtonstr 15, D-12489 Berlin - Germany

^[2] V, Leibniz Assoc, Telegrafenberg A56, D-14473 Potsdam - Germany

^[3] Natl Inst Space Res, Associated Lab Comp & Appl Math, Av Astronautas 1758, BR-12227010 Sao Jose Dos Campos, SP - Brazil

^[4] V, Potsdam Inst Climate Impact Res PIK, Res Dept Complex Sci 4, Telegrafenberg A56, D-14473 Potsdam - Germany

^[5] Natl Inst Space Res Rodovia Presidente Dutra, Ctr Weather Forecast & Climat Studies, Km 40, BR-12630000 Cachoeira Paulista, SP - Brazil

^[6] Univ Fed Sao Paulo, Inst Sci & Technol, Av Cesare Monsueto Giulio Lattes 1201, Sao Jose Dos Campos, SP - Brazil

• Document type: Journal article
• Source: European Physical Journal-Special Topics; v. 230, n. 14-15 SEP 2021.
• Web of Science Citations: 2

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. (AU)

• FAPESP's process: 15/50122-0 - Dynamic phenomena in complex networks: basics and applications
• Grantee: Elbert Einstein Nehrer Macau
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 17/05831-9 - Analysis of climate indexes influence on wildfires using complex networks and data mining
• Grantee: Leonardo Nascimento Ferreira
• Support Opportunities: Scholarships in Brazil - Post-Doctoral