Gómez-Gómez Javier, Carmona-Cabezas Rafael, Sánchez-López Elena, Gutiérrez de Ravé Eduardo, Jiménez-Hornero Francisco José
GEPENA Research Group, Campus Rabanales, University of Cordoba, Gregor Mendel Building (3rd Floor), 14071 Cordoba, Spain.
Entropy (Basel). 2021 Feb 8;23(2):207. doi: 10.3390/e23020207.
The last decades have been successively warmer at the Earth's surface. An increasing interest in climate variability is appearing, and many research works have investigated the main effects on different climate variables. Some of them apply complex networks approaches to explore the spatial relation between distinct grid points or stations. In this work, the authors investigate whether topological properties change over several years. To this aim, we explore the application of the horizontal visibility graph (HVG) approach which maps a time series into a complex network. Data used in this study include a 60-year period of daily mean temperature anomalies in several stations over the Iberian Peninsula (Spain). Average degree, degree distribution exponent, and global clustering coefficient were analyzed. Interestingly, results show that they agree on a lack of significant trends, unlike annual mean values of anomalies, which present a characteristic upward trend. The main conclusions obtained are that complex networks structures and nonlinear features, such as weak correlations, appear not to be affected by rising temperatures derived from global climate conditions. Furthermore, different locations present a similar behavior and the intrinsic nature of these signals seems to be well described by network parameters.
在过去几十年中，地球表面温度持续升高。人们对气候变率的兴趣日益浓厚，许多研究工作都探讨了其对不同气候变量的主要影响。其中一些研究采用复杂网络方法来探究不同网格点或站点之间的空间关系。在这项工作中，作者研究了拓扑性质在数年间是否会发生变化。为此，我们探索了水平可见性图（HVG）方法的应用，该方法将时间序列映射为复杂网络。本研究中使用的数据包括伊比利亚半岛（西班牙）多个站点60年的日平均温度异常数据。分析了平均度、度分布指数和全局聚类系数。有趣的是，结果表明，与呈现出特征性上升趋势的异常年平均值不同，它们在缺乏显著趋势这一点上是一致的。得出的主要结论是，复杂网络结构和非线性特征，如弱相关性，似乎不受全球气候条件导致的气温上升影响。此外，不同地点呈现出相似的行为，这些信号的内在性质似乎可以通过网络参数得到很好的描述。
