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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union

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Nonlin. Processes Geophys., 21, 1127-1132, 2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
27 Nov 2014
Correlations between climate network and relief data
T. K. D. Peron1, C. H. Comin1, D. R. Amancio2, L. da F. Costa1, F. A. Rodrigues2, and J. Kurths3,4,5 1São Carlos Institute of Physics, University of São Paulo, São Carlos, São Paulo, Brazil
2Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, São Paulo, Brazil
3Potsdam Institute for Climate Impact Research (PIK), 14473 Potsdam, Germany
4Department of Physics, Humboldt University, 12489 Berlin, Germany
5Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen AB24 3UE, UK
Abstract. In the last few years, the scientific community has witnessed an ongoing trend of using ideas developed in the study of complex networks to analyze climate dynamics. This powerful combination, usually called climate networks, can be used to uncover non-trivial patterns of weather changes throughout the years. Here we investigate the temperature network of the North American region and show that two network characteristics, namely degree and clustering, have marked differences between the eastern and western regions. We show that such differences are a reflection of the presence of a large network community on the western side of the continent. Moreover, we provide evidence that this large community is a consequence of the peculiar characteristics of the western relief of North America.
Citation: Peron, T. K. D., Comin, C. H., Amancio, D. R., da F. Costa, L., Rodrigues, F. A., and Kurths, J.: Correlations between climate network and relief data, Nonlin. Processes Geophys., 21, 1127-1132,, 2014.
Publications Copernicus
Short summary
In the past few years, complex networks have been extensively applied to climate sciences, yielding the new field of climate networks. Here, we generalize climate network analysis by investigating the influence of altitudes in network topology. More precisely, we verified that nodes group into different communities corresponding to geographical areas with similar relief properties. This new approach may contribute to obtaining more complete climate network models.
In the past few years, complex networks have been extensively applied to climate sciences,...