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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 15, issue 2 | Copyright
Nonlin. Processes Geophys., 15, 339-363, 2008
https://doi.org/10.5194/npg-15-339-2008
© Author(s) 2008. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

  22 Apr 2008

22 Apr 2008

ENSO dynamics in current climate models: an investigation using nonlinear dimensionality reduction

I. Ross1, P. J. Valdes1, and S. Wiggins2 I. Ross et al.
  • 1School of Geographical Sciences, University of Bristol, University Road, Bristol BS8 1SS, UK
  • 2School of Mathematics, University of Bristol, University Walk, Bristol BS8 1TW, UK

Abstract. Linear dimensionality reduction techniques, notably principal component analysis, are widely used in climate data analysis as a means to aid in the interpretation of datasets of high dimensionality. These linear methods may not be appropriate for the analysis of data arising from nonlinear processes occurring in the climate system. Numerous techniques for nonlinear dimensionality reduction have been developed recently that may provide a potentially useful tool for the identification of low-dimensional manifolds in climate data sets arising from nonlinear dynamics. Here, we apply Isomap, one such technique, to the study of El Niño/Southern Oscillation variability in tropical Pacific sea surface temperatures, comparing observational data with simulations from a number of current coupled atmosphere-ocean general circulation models. We use Isomap to examine El Niño variability in the different datasets and assess the suitability of the Isomap approach for climate data analysis. We conclude that, for the application presented here, analysis using Isomap does not provide additional information beyond that already provided by principal component analysis.

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