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• W2922425700 abstract "Network theory has established itself as an important tool for the analysis of complex systems such as the climate. In this context, climate networks are constructed using a spatiotemporal climate dataset and a time series distance function. It consists of representing each spatial area by a node and connecting nodes that present similar time series. One fundamental concern when building climate network is the definition of a metric that captures similarity between time series. The majority of papers in the literature use Pearson correlation with or without lag. Here we study the influence of 29 time series distance functions on climate network construction using global temperature data. We observed that the distance functions used in the literature generate similar networks in general while alternative ones generate distinct networks and exhibit different long-distance connection patterns (teleconnections). These patterns are highly important for the study of climate dynamics since they generally represent long-distance transportation of energy and can be used to forecast climatological events. Therefore, we consider that the measures here studied represent an alternative for the analysis of climate systems due to their capability of capturing different underlying dynamics, what may provide a better understanding of global climate." @default.
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• W2922425700 date "2019-02-08" @default.
• W2922425700 modified "2023-09-27" @default.
• W2922425700 title "The Influence of Time Series Distance Functions on Climate Networks" @default.
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