气候动力学中的复杂网络：比较线性和非线性网络构造方法
Complex networks in climate dynamics - Comparing linear and nonlinear network construction methods
摘要：
复杂网络理论提供了一个强有力的框架来统计研究气候系统中局部和非局部统计相互关系的拓扑结构，即遥相关。利用线性Pearson相关系数或非线性互信息作为区域间动力相似性的度量，从局部、中观和全球拓扑尺度上系统地比较了由同一全球气候数据集构建的气候网络。从AOGCM和再分析数据集的地面空气温度场的局部和中观拓扑尺度上观察到高度的相似性。我们发现在全球范围内的差异较大，特别是在介数中心领域。利用互信息获得的气候网络的全球尺度视图提供了有前途的新的视角来检测网络结构的基础上非线性物理过程的气候系统。
英文摘要：
Complex network theory provides a powerful framework to statistically investigate the topology of local and non-local statistical interrelationships, i.e. teleconnections, in the climate system. Climate networks constructed from the same global climatological data set using the linear Pearson correlation coefficient or the nonlinear mutual information as a measure of dynamical similarity between regions, are compared systematically on local, mesoscopic and global topological scales. A high degree of similarity is observed on the local and mesoscopic topological scales for surface air temperature fields taken from AOGCM and reanalysis data sets. We find larger differences on the global scale, particularly in the betweenness centrality field. The global scale view on climate networks obtained using mutual information offers promising new perspectives for detecting network structures based on nonlinear physical processes in the climate system.
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