A simple method for estimating variations in the predictability of ENSO

Youmin Tang, Richard Kleeman, Andrew M. Moore

Research output: Contribution to journalArticle

Abstract

Using a linear stochastic dynamical system, we further develop a recently proposed criteria of measuring variations in the predictability of ENSO. It is found that model predictability is intrinsically related to how the initial signal variance (ISV) projects on to its eigenmode space. When the ISV is large, the corresponding prediction is found to be reliable, whereas when the ISV is small, the prediction is likely to be less reliable. This finding was validated by results from a more realistic model prediction system for the period 1964-1998. A comparison of model skill and ISV for prediction made with and without data assimilation reveals that the role of data assimilation in improving model predictability may be mainly due to a further increase of ISV. Furthermore, model skill may result mainly from a few successful predictions associated with large ISV.

Original languageEnglish (US)
Pages (from-to)L17205 1-5
JournalGeophysical Research Letters
Volume31
Issue number17
DOIs
StatePublished - Sep 16 2004

Keywords

  • 3309 Meteorology and Atmospheric Dynamics: Climatology (1620)
  • 4215 Oceanography: General: Climate and interannual variability (3309)
  • 4263 Oceanography: General: Ocean prediction
  • 4504 Oceanography: Physical: Air/ Sea interactions (0312)
  • 4522 Oceanography: Physical: El Nino

ASJC Scopus subject areas

  • Geophysics
  • Earth and Planetary Sciences(all)

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