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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 10, issue 6
Nonlin. Processes Geophys., 10, 493–501, 2003
https://doi.org/10.5194/npg-10-493-2003
© Author(s) 2003. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

Special issue: Quantifying Predictability

Nonlin. Processes Geophys., 10, 493–501, 2003
https://doi.org/10.5194/npg-10-493-2003
© Author(s) 2003. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

  31 Dec 2003

31 Dec 2003

Conditional nonlinear optimal perturbation and its applications

M. Mu1, W. S. Duan1, and B. Wang2 M. Mu et al.
  • 1LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
  • 2Department of Meteorology, School of Ocean and Earth Science and Technology, University of Hawaii, Honolulu, USA

Abstract. Conditional nonlinear optimal perturbation (CNOP) is proposed to study the predictability of numerical weather and climate prediction. A simple coupled ocean-atmosphere model for ENSO is adopted as an example to show its applicability. In the case of climatological mean state being the basic state, it is shown that CNOP tends to evolve into El Niño or La Niña event more probably than linear singular vector (LSV) on the condition that CNOP and LSV are of the same magnitude of norm. CNOP is also employed to study the prediction error of El Niño and La Niña events. Comparisons between CNOP and LSV demonstrate that CNOP is more applicable in studying the predictability of the models governing the nonlinear motions of oceans and atmospheres.

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