Prediction skill of the Madden and Julian Oscillation in dynamical extended range forecasts
- Cite this article as:
- Jones, C., Waliser, D., Schemm, JK. et al. Climate Dynamics (2000) 16: 273. doi:10.1007/s003820050327
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The Madden and Julian Oscillation (MJO) is the most prominent mode of intraseasonal variations in the tropical region. It plays an important role in climate variability and has a significant influence on medium-to-extended ranges weather forecasting in the tropics. This study examines the forecast skill of the oscillation in a set of recent dynamical extended range forecasts (DERF) experiments performed by the National Centers for Environmental Prediction (NCEP). The present DERF experiments were done with the reanalysis version of the medium range forecast (MRF) model and include 50-day forecasts, initialized once-a-day (0Z) with reanalyses fields, for the period between 1 January, 1985, and 31 December, 1989. The MRF model shows large mean errors in representing intraseasonal variations of the large-scale circulation, especially over the equatorial eastern Pacific Ocean. A diagnostic analysis has considered the different phases of the MJO and the associated forecast skill of the MRF model. Anomaly correlations on the order of 0.3 to 0.4 indicate that skillful forecasts extend out to 5 to 7 days lead-time. Furthermore, the results show a slight increase in the forecast skill for periods when convective anomalies associated with the MJO are intense. By removing the mean errors, the analysis shows systematic errors in the representation of the MJO with weaker than observed upper level zonal circulations. The examination of the climate run of the MRF model shows the existence of an intraseasonal oscillation, although less intense (50–70%) and with faster (nearly twice as fast) eastward propagation than the observed MJO. The results indicate that the MRF model likely has difficulty maintaining the MJO, which impacts its forecast. A discussion of future work to improve the representation of the MJO in dynamical models and assess its prediction is presented.