@Article{FerreiraFerrMacaDonn:2021:EfTiSe,
               author = "Ferreira, Leonardo Nascimento and Ferreira, Nicole Costa Resende 
                         and Macau, Elbert Einstein Nehrer and Donner, Reik V.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade Federal 
                         de S{\~a}o Paulo (UNIFESP)} and {Magdeburg-Stendal University of 
                         Applied Sciences}",
                title = "The effect of time series distance functions on functional climate 
                         networks",
              journal = "European Physical Journal: Special Topics",
                 year = "2021",
               volume = "230",
               number = "14/15",
                pages = "2973--2998",
                month = "Oct.",
             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.",
                  doi = "10.1140/epjs/s11734-021-00274-y",
                  url = "http://dx.doi.org/10.1140/epjs/s11734-021-00274-y",
                 issn = "1951-6355 and 1951-6401",
             language = "en",
           targetfile = "Ferreira2021_Article_TheEffectOfTimeSeriesDistanceF.pdf",
        urlaccessdate = "10 maio 2024"
}