Seasonal and Decadal Prediction

  • Oscar Alves
  • Debra Hudson
  • Magdalena Balmaseda
  • Li Shi
Chapter

Abstract

Dynamical seasonal prediction has grown rapidly over the last decade or so. At present, a number of operational centres issue routine seasonal forecasts produced with coupled ocean-atmosphere models. These require real-time knowledge of the state of the global ocean since the potential for climate predictability at seasonal time scales resides mostly in information provided by the ocean initial conditions, in particular the upper thermal structure. The primary aim of the coupled model is to predict sea surface temperature variability and how this variability impacts regional climate through large scale teleconnections.

This paper reviews recent advances in dynamical seasonal prediction using coupled ocean-atmosphere models. It discusses the sources of predictability at seasonal time scales, the probabilistic nature of seasonal forecasts, the ensemble methods used to deal with it, and the current levels of skill. The ocean initialisation receives special focus, with a discussion on initialisation strategies, ocean data assimilation methods, and the role of the observing system in seasonal forecast skill.

Assimilation of observations into an ocean model forced by prescribed atmospheric fluxes is the most common practice for initialisation of the ocean component of a coupled model. Assimilation of ocean data reduces the uncertainty in the ocean estimation arising from the uncertainty in the forcing fluxes and from model errors. Although data assimilation also usually improves the skill of seasonal forecasts, its impact is often overshadowed by errors in the coupled models.

The paper also briefly discusses decadal prediction, for which there is growing demand, particularly in the context of climate change adaptation. Although decadal prediction is still in its infancy, recent development shows promising results, highlighting the role of ocean initial conditions. The initialisation of the ocean for decadal predictions is a major challenge for the next decade.

Keywords

Indian Ocean Dipole Seasonal Forecast Seasonal Prediction Indian Ocean Dipole Event Decadal Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowlegements

The authors would like to acknowledge Eun-Pa Lim, Claire Spillman, Guomin Wang and Yonghong Yin for providing some of the figures used in this paper.

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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Oscar Alves
    • 1
  • Debra Hudson
    • 1
  • Magdalena Balmaseda
    • 2
  • Li Shi
    • 1
  1. 1.Bureau of MeteorologyCentre for Australian Weather and Climate Research (CAWCR)MelbourneAustralia
  2. 2.European Centre for Medium-Range Weather Forecasts (ECMWF)MelbourneAustralia

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