2019/02/08 by Leonardo N. Ferreira, Ferreira, Leonardo N., Elbert E. N. Macau +4
Economics, Econometrics and Finance · Environmental Science · Physics and Astronomy · #Atmospheric and Oceanic Physics (physics.ao-ph) #Complex Network Analysis Techniques #Complex Systems and Time Series Analysis #Data Analysis #Ecosystem dynamics and resilience #FOS: Physical sciences #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.1902.03298
openalex publication_date 2019/02/08 · openalex created_date 2022/07/29 · openalex updated_date 2026/07/28
Complex network theory provides an important tool for the analysis of complex\nsystems such as the Earth's climate. In this context, functional climate\nnetworks can be constructed using a spatiotemporal climate dataset and a\nsuitable time series distance function. The resulting coarse-grained view on\nclimate variability consists of representing distinct areas on the globe (i.e.,\ngrid cells) by nodes and connecting pairs of nodes that present similar time\nseries. One fundamental concern when constructing such a functional climate\nnetwork is the definition of a metric that captures the mutual similarity\nbetween time series. Here we study systematically the effect of 29 time series\ndistance functions on functional climate network construction based on global\ntemperature data. We observe that the distance functions previously used in the\nliterature commonly generate very similar networks while alternative ones\nresult in rather distinct network structures and reveal different long-distance\nconnection patterns. These patterns are highly important for the study of\nclimate dynamics since they generally represent pathways for the long-distance\ntransportation of energy and can be used to forecast climate variability on\nsubseasonal to interannual or even decadal scales. Therefore, we propose the\nmeasures studied here as alternatives for the analysis of climate variability\nand to further exploit their complementary capability of capturing different\naspects of the underlying dynamics that may help gaining a more holistic\nempirical understanding of the global climate system.\n