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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 21, issue 3
Nonlin. Processes Geophys., 21, 651–657, 2014
https://doi.org/10.5194/npg-21-651-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Complex network approaches to analyzing and modeling nonlinear...

Nonlin. Processes Geophys., 21, 651–657, 2014
https://doi.org/10.5194/npg-21-651-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 03 Jun 2014

Research article | 03 Jun 2014

On the influence of spatial sampling on climate networks

N. Molkenthin1,2, K. Rehfeld1,3, V. Stolbova1,2, L. Tupikina1,2, and J. Kurths1,2 N. Molkenthin et al.
  • 1PIK Potsdam Institute of Climate Impact Research, P.O. Box 601203, 14412 Potsdam, Germany
  • 2Department of Physics, Humboldt-Universität zu Berlin, Newtonstr. 15, 12489 Berlin, Germany
  • 3Alfred-Wegner Institute for Polar and Marine Research, Telegrafenberg A43, 14473 Potsdam, Germany

Abstract. Climate networks are constructed from climate time series data using correlation measures. It is widely accepted that the geographical proximity, as well as other geographical features such as ocean and atmospheric currents, have a large impact on the observable time-series similarity. Therefore it is to be expected that the spatial sampling will influence the reconstructed network. Here we investigate this by comparing analytical flow networks, networks generated with the START model and networks from temperature data from the Asian monsoon domain. We evaluate them on a regular grid, a grid with added random jittering and two variations of clustered sampling. We find that the impact of the spatial sampling on most network measures only distorts the plots if the node distribution is significantly inhomogeneous. As a simple diagnostic measure for the detection of inhomogeneous sampling we suggest the Voronoi cell size distribution.

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