Community structure analysis in big climate data

The analysis of climate data as a complex network has shown a great deal of promise over the last decade. Because of the massive size of the resulting network structure, much of the literature in this area has focused on the analysis of a single network snapshot generalized for a single period of time. Therefore, existing analysis methods typically focus on network properties instead of structural nature of the network to find interesting patterns. Data analysis and mining in complex climate networks at a higher temporal granularity, as a result, creates a challenging yet computationally prohibitive big data problem. In this paper, we extend the analysis of complex climate networks by studying their underlying community structures, an important property that is commonly exhibited in most complex networks. In particular, we first propose a new two-layer network model that captures community structures at both generalized and hierarchical levels where sub-communities are contained within the resulting generalized communities. Our two-layer network significantly reduces the complexity of modeling climate networks, and therefore reduces the computational complexity associated with analyzing big climate data. To mine meaningful climate patterns, we further extend this approach and suggest algorithms to find persistent communities that are stable over a long period of time. Our methods were tested on global air temperature data and we found signature communities that are verified by well known global temperature patterns. The results also produced some interesting discoveries related to known climate events such as the 1997/1997 El Niño and the eruption of Mt. Pinatubo in 1991.