Influence of stochastic forcing on ENSO prediction

Javier Zavala-Garay, Andy M. Moore, R. 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)
Pages (from-to)C11007 1-10
JournalJournal of Geophysical Research: Oceans
Volume109
Issue number11
DOIs
StatePublished - Nov 15 2004

Keywords

  • Data assimilation
  • ENSO prediction
  • Stochastic model

ASJC Scopus subject areas

  • Geophysics
  • Forestry
  • Oceanography
  • Aquatic Science
  • Ecology
  • Water Science and Technology
  • Soil Science
  • Geochemistry and Petrology
  • Earth-Surface Processes
  • Atmospheric Science
  • Earth and Planetary Sciences (miscellaneous)
  • Space and Planetary Science
  • Palaeontology

Fingerprint Dive into the research topics of 'Influence of stochastic forcing on ENSO prediction'. Together they form a unique fingerprint.

  • Cite this