Reliability of Inference of Directed Climate Networks Using Conditional Mutual Information
Reliability of Inference of Directed Climate Networks Using Conditional Mutual Information
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DOI：
10.3390/e15062023
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发表时间：
2013-06-01
期刊：
影响因子：
2.7
通讯作者：
Palus, Milan
中科院分区：
文献类型：
--
作者：
Hlinka, Jaroslav;Hartman, David;Palus, Milan
在地球科学中，许多研究的现象都与由错综复杂的相互作用的子系统组成的特定复杂系统有关。这种动态复杂系统可以用有向图来表示，其中每个链接表示因果关系的存在，或节点之间的信息交换。对于全球气候等地球物理系统，这些关系通常在理论上是未知的，而是使用因果分析方法根据记录的数据进行估计的。其中包括基于信息论的双变量非线性方法及其线性对应方法。非线性方法对更一般的相互作用的宝贵敏感性与线性方法潜在的更高数值可靠性之间的权衡可能会影响有关气候网络的结构和变异性的推断。我们使用对 60 年全球气候记录进行再分析的降维地表气温数据的平稳化模型，研究了通过选定的方法和参数设置检测到的定向气候网络的可靠性。总体而言，所有研究的双变量因果关系方法都提供了气候因果关系网络的可重复估计，线性近似显示出比所研究的非线性方法更高的可靠性。在示例数据集上，在可靠性方面优化所研究的非线性方法增加了检测到的网络与其线性对应网络的相似性，支持了表面气温再分析数据的近线性的特定假设。
Across geosciences, many investigated phenomena relate to specific complex systems consisting of intricately intertwined interacting subsystems. Such dynamical complex systems can be represented by a directed graph, where each link denotes an existence of a causal relation, or information exchange between the nodes. For geophysical systems such as global climate, these relations are commonly not theoretically known but estimated from recorded data using causality analysis methods. These include bivariate nonlinear methods based on information theory and their linear counterpart. The trade-off between the valuable sensitivity of nonlinear methods to more general interactions and the potentially higher numerical reliability of linear methods may affect inference regarding structure and variability of climate networks. We investigate the reliability of directed climate networks detected by selected methods and parameter settings, using a stationarized model of dimensionality-reduced surface air temperature data from reanalysis of 60-year global climate records. Overall, all studied bivariate causality methods provided reproducible estimates of climate causality networks, with the linear approximation showing higher reliability than the investigated nonlinear methods. On the example dataset, optimizing the investigated nonlinear methods with respect to reliability increased the similarity of the detected networks to their linear counterparts, supporting the particular hypothesis of the near-linearity of the surface air temperature reanalysis data.