The backbone of the climate network
The backbone of the climate network
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DOI：
10.1209/0295-5075/87/48007
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发表时间：
2009-08-01
期刊：
影响因子：
1.8
通讯作者：
Kurths, J.
中科院分区：
文献类型：
--
作者：
Donges, J. F.;Zou, Y.;Kurths, J.
基于时间序列分析的非线性互信息和复杂网络理论的介数中心性，提出了一种从时空动态系统产生的数据重构和分析复杂网络的方法.我们表明，这种方法揭示了丰富的内部结构，在复杂的气候网络再分析和模式地面气温数据。我们的新方法揭示了高能量流的特殊波浪状结构，我们与全球表面洋流有关。这表明，海洋表面环流在长期平均（模式运行140年，再分析数据60年）耦合和稳定全球温度场方面发挥着重要作用。我们发现，这些结果不能得到使用经典的多元数据分析的线性方法，并已确保其鲁棒性密集的显着性检验。版权所有（c）EPLA，2009
We propose a method to reconstruct and analyze a complex network from data generated by a spatio-temporal dynamical system, relying on the nonlinear mutual information of time series analysis and betweenness centrality of the complex network theory. We show that this approach reveals a rich internal structure in complex climate networks constructed from reanalysis and model surface air temperature data. Our novel method uncovers peculiar wave-like structures of high-energy flow, that we relate to global surface ocean currents. This points to a major role of the oceanic surface circulation in coupling and stabilizing the global temperature field in the long-term mean (140 years for the model run and 60 years for reanalysis data). We find that these results cannot be obtained using classical linear methods of multivariate data analysis, and have ensured their robustness by intensive significance testing. Copyright (c) EPLA, 2009