标题： 复杂气候网络如何补充特征值技术用于气候数据的统计分析 显示英文标题

标题： How complex climate networks complement eigen techniques for the statistical analysis of climatological data

摘要： 像经验正交函数（EOF）或耦合型模式（CP）/最大协方差分析等特征技术，经常被用于检测多元气候数据集中的模式。最近，源自复杂网络理论的统计方法也被用于相同的空间-时间分析目的。 这种气候网络（CN）分析通常基于与经典EOF或CP分析相同的相似性矩阵集合，例如单一气候场的相关矩阵或两个不同气候场之间的交叉相关矩阵。 本研究通过使用全球降水、蒸发和地表空气温度等示例数据集，推导并说明了这两种特征值和网络方法之间的形式关系及概念差异。 这些结果表明，CN分析可以补充经典的特征技术，并提供关于气候数据中统计相互关系更高阶结构的额外信息。 因此，气候网络对于气候学家的统计工具箱来说是一个有价值的补充，特别是在处理卫星观测和气候模型对比实验所产生的大型数据集时尤为如此。 显示英文摘要

摘要： 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 exemplary global precipitation, evaporation and surface air temperature data sets. These results allow 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.