Linear dynamical modes as new variables for data-driven ENSO forecast

  • Andrey Gavrilov
  • Aleksei Seleznev
  • Dmitry Mukhin
  • Evgeny Loskutov
  • Alexander Feigin
  • Juergen Kurths
Article

Abstract

A new data-driven model for analysis and prediction of spatially distributed time series is proposed. The model is based on a linear dynamical mode (LDM) decomposition of the observed data which is derived from a recently developed nonlinear dimensionality reduction approach. The key point of this approach is its ability to take into account simple dynamical properties of the observed system by means of revealing the system’s dominant time scales. The LDMs are used as new variables for empirical construction of a nonlinear stochastic evolution operator. The method is applied to the sea surface temperature anomaly field in the tropical belt where the El Nino Southern Oscillation (ENSO) is the main mode of variability. The advantage of LDMs versus traditionally used empirical orthogonal function decomposition is demonstrated for this data. Specifically, it is shown that the new model has a competitive ENSO forecast skill in comparison with the other existing ENSO models.

Keywords

Empirical modeling Data dimensionality reduction Nonlinear stochastic modeling ENSO forecast 

Notes

Acknowledgements

The results (Sects. 34) were obtained and analyzed under support of the Government of Russian Federation (Agreement #14.Z50.31.0033 with the Institute of Applied Physics of RAS). The methods (Sect. 2) were developed under support of the Russian Science Foundation (Grant #16-12-10198).

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© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Applied Physics of RASNizhny NovgorodRussia
  2. 2.Potsdam Institute for Climate Impact ResearchPotsdamGermany

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