Influence of stochastic forcing on ENSO prediction

Javier Zavala-Garay, Andy M. Moore, Richard Kleeman

Research output: Contribution to journalArticle

Abstract

The basic philosophy behind data assimilation forecasting is that accurate knowledge of the initial conditions at the beginning of the forecast will produce reliable predictions. In this work we explore this idea under the assumption that El Niño-Southern Oscillation (ENSO) behaves as a stochastically forced phenomenon using the Kleeman intermediate model. This work provides a quantification of how the stochastic forcing (SF) is a limiting factor in increasing the predictability of ENSO via data assimilation in this intermediate model. Different cases are considered corresponding to the presence or absence of SF during different stages of the forecast process (i.e., assimilation and prediction or validation). Within each case, different strong- constraint assimilation methodologies are used. These methodologies show that better results can be obtained when information about the thermal structure of the upper ocean is considered and when interaction between the atmosphere and ocean is allowed in the assimilation process. Considering the best possible scenario, where observations of heat content are available at every grid point and time step, the presence of SF decreases the skill of the forecasts to about 12 months. This is similar to the skill reported by many operational and experimental seasonal prediction systems that use strong-constraint data assimilation. Therefore our results suggest that there is little room for improvement in forecast skill as a result of strong-constraint data assimilation.

Original languageEnglish (US)
JournalJournal of Geophysical Research: Space Physics
Volume109
Issue number11
DOIs
StatePublished - Nov 15 2004

Fingerprint

Southern Oscillation
assimilation
data assimilation
forecasting
prediction
predictions
methodology
upper ocean
thermal structure
oceans
limiting factor
Enthalpy
forecast
atmosphere
ocean
enthalpy
grids
atmospheres

Keywords

  • Data assimilation
  • ENSO prediction
  • Stochastic model

ASJC Scopus subject areas

  • Geochemistry and Petrology
  • Geophysics
  • Earth and Planetary Sciences (miscellaneous)
  • Space and Planetary Science
  • Atmospheric Science
  • Astronomy and Astrophysics
  • Oceanography

Cite this

Influence of stochastic forcing on ENSO prediction. / Zavala-Garay, Javier; Moore, Andy M.; Kleeman, Richard.

In: Journal of Geophysical Research: Space Physics, Vol. 109, No. 11, 15.11.2004.

Research output: Contribution to journalArticle

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