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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 | Copyright

Special issue: Physics-driven data mining in climate change and weather...

Nonlin. Processes Geophys., 21, 605-615, 2014
https://doi.org/10.5194/npg-21-605-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 23 May 2014

Research article | 23 May 2014

Trend analysis using non-stationary time series clustering based on the finite element method

M. Gorji Sefidmazgi1, M. Sayemuzzaman2, A. Homaifar1, M. K. Jha3, and S. Liess4 M. Gorji Sefidmazgi et al.
  • 1North Carolina A&T State University, Dept. of Electrical Engineering, Greensboro, USA
  • 2North Carolina A&T State University, Dept. of Energy and Environmental Systems, Greensboro, USA
  • 3North Carolina A&T State University, Dept. of Civil, Architectural and Environmental Engineering, Greensboro, USA
  • 4University of Minnesota, Department of Soil, Water and Climate, St. Paul, USA

Abstract. In order to analyze low-frequency variability of climate, it is useful to model the climatic time series with multiple linear trends and locate the times of significant changes. In this paper, we have used non-stationary time series clustering to find change points in the trends. Clustering in a multi-dimensional non-stationary time series is challenging, since the problem is mathematically ill-posed. Clustering based on the finite element method (FEM) is one of the methods that can analyze multidimensional time series. One important attribute of this method is that it is not dependent on any statistical assumption and does not need local stationarity in the time series. In this paper, it is shown how the FEM-clustering method can be used to locate change points in the trend of temperature time series from in situ observations. This method is applied to the temperature time series of North Carolina (NC) and the results represent region-specific climate variability despite higher frequency harmonics in climatic time series. Next, we investigated the relationship between the climatic indices with the clusters/trends detected based on this clustering method. It appears that the natural variability of climate change in NC during 1950–2009 can be explained mostly by AMO and solar activity.

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