An approach to research the topology of Chinese temperature sequence based on complex network

Abstract

To analyze the dynamics of the temperature data, using homogenous partition of coarse graining process, the series of Chinese daily main temperature from 1961 to 2002 is transformed into symbolic sequences consisting of 5 characters {R,r,e,d,D}. The vertices of the temperature fluctuation network is 125 3-symbol strings (i.e., 125 fluctuation patterns in durations of 4 days), linked in the network's topology by time sequence. It contains Integrated information about interconnections and interactions between fluctuation patterns of temperature in network topology. Random fluctuant network and chaos fluctuant network by using random sequence and chaos sequence of Lorenz system were built. We calculated the dynamical statistics of the degrees and the distribution of degrees, clustering coefficient and the shortest path length, and compared the difference of these three sequences from the view point of network. The result is that. The main vertices of temperature fluctuant network generally contain the 3 characters R, r and e, and corresponding the background of global warming, the feature of temperature fluctuation is mainly ascending. On all accounts, the temperature fluctuant network is similar with chaos network in its dynamical statistics, but is markedly different from random network, which reflects the complexity of temperature fluctuation. And besides, some vertices in temperature fluctuant network have high betweenness centrality (BC), 4% of vertices bear 71.9% of betweenness centrality of networks, these vertices of importance in topological statistics are helpful to understanding the inherent law and information transmission. The vertices' BC in chaos network is similar with that of the temperature network, but vertices in random network almost have identical betweenness centrality. As a result, the patterns of temperature fluctuation corresponding to the process of weather change have similar property with chaos rather than the random fluctuation process.