Using Multivariate Clustering to Characterize Ecoregion Borders
Computing in Science and Engineering
Discovery of climate indices using clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Finding Spatio-Temporal Patterns in Climate Data Using Clustering
CW '05 Proceedings of the 2005 International Conference on Cyberworlds
Comparing predictive power in climate data: clustering matters
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
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Climate change is a pressing focus of research, social and economic concern, and political attention. According to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), increased frequency of extreme events will only intensify the occurrence of natural hazards, affecting global population, health, and economies. It is of keen interest to identify "regions" of similar climatological behavior to discover spatial relationships in climate variables, including long-range teleconnections. To that end, we consider a complex networks-based representation of climate data. Cross correlation is used to weight network edges, thus respecting the temporal nature of the data, and a community detection algorithm identifies multivariate clusters. Examining networks for consecutive periods allows us to study structural changes over time. We show that communities have a climatological interpretation and that disturbances in structure can be an indicator of climate events (or lackthereof). Finally, we discuss how this model can be applied for the discovery of more complex concepts such as unknown teleconnections or the development of multivariate climate indices and predictive insights.