How complex climate networks complement eigen techniques for the statistical analysis of climatological data
How complex climate networks complement eigen techniques for the statistical analysis of climatological data
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DOI：
10.1007/s00382-015-2479-3
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发表时间：
2015-11-01
期刊：
影响因子：
4.6
通讯作者：
Kurths, Juergen
中科院分区：
文献类型：
--
作者：
Donges, Jonathan F.;Petrova, Irina;Kurths, Juergen
特征技术，如经验正交函数（EOF）或耦合模式(CP)/最大协方差分析，经常用于检测多变量气候数据集的模式。近年来，源自复杂网络理论的统计方法已被用于时空分析的目的。这种气候网络（CN）分析通常基于与经典EOF或CP分析中使用的相同的一组相似矩阵，例如，单个气候场的相关矩阵或两个不同气候场之间的互相关矩阵。在本研究中，本征方法和网络方法之间的形式关系以及概念上的差异得到了推导，并使用全球降水、蒸发和地表气温数据集进行了说明。这些结果使我们能够确定CN分析可以补充经典特征技术，并提供有关气候数据中统计相互关系的高阶结构的额外信息。因此，神经网络是对气候学家统计工具箱的一个有价值的补充，特别是对于从非常大的数据集（如由卫星观测和气候模式相互比较练习产生的数据集）中获得意义。
Eigen techniques such as empirical orthogonal function (EOF) or coupled pattern (CP)/maximum covariance analysis have been frequently used for detecting patterns in multivariate climatological data sets. Recently, statistical methods originating from the theory of complex networks have been employed for the very same purpose of spatio-temporal analysis. This climate network (CN) analysis is usually based on the same set of similarity matrices as is used in classical EOF or CP analysis, e.g., the correlation matrix of a single climatological field or the cross-correlation matrix between two distinct climatological fields. In this study, formal relationships as well as conceptual differences between both eigen and network approaches are derived and illustrated using global precipitation, evaporation and surface air temperature data sets. These results allow us to pinpoint that CN analysis can complement classical eigen techniques and provides additional information on the higher-order structure of statistical interrelationships in climatological data. Hence, CNs are a valuable supplement to the statistical toolbox of the climatologist, particularly for making sense out of very large data sets such as those generated by satellite observations and climate model intercomparison exercises.