Deza J I, Barreiro M, Masoller C
Departament de Física i Enginyeria Nuclear, Universitat Politècnica de Catalunya, Colom 11, E-08222 Terrassa, Barcelona, Spain.
Instituto de Física, Facultad de Ciencias, Universidad de la República, Iguá 4225, Montevideo, Uruguay.
Chaos. 2015 Mar;25(3):033105. doi: 10.1063/1.4914101.
An estimate of the net direction of climate interactions in different geographical regions is made by constructing a directed climate network from a regular latitude-longitude grid of nodes, using a directionality index (DI) based on conditional mutual information (CMI). Two datasets of surface air temperature anomalies-one monthly averaged and another daily averaged-are analyzed and compared. The network links are interpreted in terms of known atmospheric tropical and extra-tropical variability patterns. Specific and relevant geographical regions are selected, the net direction of propagation of the atmospheric patterns is analyzed, and the direction of the inferred links is validated by recovering some well-known climate variability structures. These patterns are found to be acting at various time-scales, such as atmospheric waves in the extratropics or longer range events in the tropics. This analysis demonstrates the capability of the DI measure to infer the net direction of climate interactions and may contribute to improve the present understanding of climate phenomena and climate predictability. The work presented here also stands out as an application of advanced tools to the analysis of empirical, real-world data.
通过基于条件互信息（CMI）的方向性指数（DI），从规则的经纬度节点网格构建有向气候网络，来估计不同地理区域气候相互作用的净方向。分析并比较了两个地表气温异常数据集，一个是月平均值，另一个是日平均值。根据已知的大气热带和温带变率模式来解释网络链接。选择特定且相关的地理区域，分析大气模式传播的净方向，并通过恢复一些知名的气候变率结构来验证推断链接的方向。发现这些模式在不同时间尺度上起作用，比如温带的大气波或热带的更长范围事件。该分析证明了DI度量推断气候相互作用净方向的能力，可能有助于增进目前对气候现象和气候可预测性的理解。这里展示的工作作为先进工具在实证、现实世界数据分析中的应用也很突出。
