Climate Dynamics

, Volume 36, Issue 3–4, pp 649–661 | Cite as

Prediction of the Madden–Julian oscillation with the POAMA dynamical prediction system

  • Harun A. Rashid
  • Harry H. Hendon
  • Matthew C. Wheeler
  • Oscar Alves


Predictions of the Madden–Julian oscillation (MJO) are assessed using a 10-member ensemble of hindcasts from POAMA, the Australian Bureau of Meteorology coupled ocean–atmosphere seasonal prediction system. The ensemble of hindcasts was initialised from observed atmosphere and ocean initial conditions on the first of each month during 1980–2006. The MJO is diagnosed using the Wheeler-Hendon Real-time Multivariate MJO (RMM) index, which involves projection of daily data onto the leading pair of eigenmodes from an analysis of zonal winds at 200 and 850 hPa and outgoing longwave radiation (OLR) averaged about the equator. Forecasts of the two component (RMM1 and RMM2) index are quantitatively compared with observed behaviour derived from NCEP reanalyses and satellite OLR using the bivariate correlation skill, root-mean-square error (RMSE), and measures of the MJO amplitude and phase error. Comparison is also made with a simple vector autoregressive (VAR) prediction model of RMM as a benchmark. Using the full hindcast set, we find that the MJO can be predicted with the POAMA ensemble out to about 21 days as measured by the bivariate correlation exceeding 0.5 and the bivariate RMSE remaining below ~1.4 (which is the value for a climatological forecast). The VAR model, by comparison, drops to a correlation of 0.5 by about 12 days. The prediction limit from POAMA increases by less than 2 days for times when the MJO has large initial amplitude, and has little sensitivity to the initial phase of the MJO. The VAR model, on the other hand, shows a somewhat larger increase in skill for times of strong MJO variability and has greater sensitivity to initial phase, with lower skill for times when MJO convection is developing in the Indian Ocean. The sensitivity to season is, however, greater for POAMA, with maximum skill occurring in the December–January–February season and minimum skill in June–July–August. Examination of the MJO amplitudes shows that individual POAMA members have slightly above observed amplitude after a spin-up of about 10 days, whereas examination of the MJO phase error reveals that the model has a consistent tendency to propagate the MJO slightly slower than observed. Finally, an estimate of potential predictability of the MJO in POAMA hindcasts suggests that actual MJO prediction skill may be further improved through continued development of the dynamical prediction system.


Madden–Julian Oscillation Tropical intraseasonal variability Predictability Ensemble hindcasts RMM index 



Development of the forecast skill assessment for the MJO stemmed from the participation of HHH and MCW in the CLIVAR MJO Working Group. Drs. Debbie Hudson and Andrew Marshall reviewed an earlier version of the manuscript and provided useful comments. Comments from two anonymous reviewers have led to improvements in presentation of the results.


  1. Agudelo PA, Hoyos CD, Webster PJ, Curry JA (2009) Application of a serial extended forecast experiment using the ECMWF model to interpret the predictive skill of tropical intraseasonal variability. Clim Dyn 32:855–872CrossRefGoogle Scholar
  2. Alves O, Wang G, Zhong A, Smith N, Tseitkin F, Warren G, Schiller A, Godfrey S, Meyers G (2003) POAMA: Bureau of Meteorology operational coupled model seasonal forecast system. In: Proceedings of national drought forum, Brisbane, April 2003, pp 49–56. Available from DPI Publications, Department of Primary Industries, GPO Box 46, Brisbane, Qld 4001, AustraliaGoogle Scholar
  3. Anderson J, van den Dool H, Barnston A, Chen W, Stern W, Ploshay J (1999) Present-day capabilities of numerical and statistical models for atmospheric extratropical seasonal simulation and prediction. Bull Am Meteorol Soc 80:1349–1361CrossRefGoogle Scholar
  4. Barlow M, Wheeler M, Lyon B, Cullen H (2005) Modulation of daily precipitation over Southwest Asia by the Madden–Julian oscillation. Mon Weather Rev 133:3579–3594CrossRefGoogle Scholar
  5. Bechtold P, Kohler M, Jung T, Doblas-Reyes F, Leutbecher M, Rodwell MJ, Vitart F, Balsamo G (2009) Advances in simulating atmospheric variability with the ECMWF model: from synoptic to decadal time-scales. Q J R Meteorol Soc 134:1337–1351CrossRefGoogle Scholar
  6. Ferranti L, Palmer TN, Molteni F, Klinker K (1990) Tropical–extratropical interaction associated with the 30–60-day oscillation and its impact on medium and extended range prediction. J Atmos Sci 47:2177–2199CrossRefGoogle Scholar
  7. Goswami BN (2005) South Asian monsoon. In: Lau WKM, Waliser DE (eds) Intraseasonal variability in the atmosphere-ocean climate system. Springer, New York, pp 19–62Google Scholar
  8. Gottschalck J, Wheeler M, Weickmann K, Vitart F, Savage N, Lin H, Hendon H, Waliser D, Sperber K, Prestrelo C, Nakagawa M, Flatau M, Higgins W (2010) A framework for assessing operational model MJO forecasts: a project of the CLIVAR Madden-Julian oscillation working group. Bull Amer Meteor Soc (accepted)Google Scholar
  9. Hall JD, Matthews AJ, Karoly DJ (2001) The modulation of tropical cyclone activity in the Australian region by the Madden–Julian oscillation. Mon Weather Rev 129:2970–2982CrossRefGoogle Scholar
  10. Hendon HH, Liebmann B (1990) A composite study of onset of the Australian summer monsoon. J Atmos Sci 47:2227–2240CrossRefGoogle Scholar
  11. Hendon HH, Salby ML (1994) The life cycle of the Madden Julian oscillation. J Atmos Sci 51:2225–2237CrossRefGoogle Scholar
  12. Hendon HH, Liebmann B, Newman M, Glick JD, Schemm JE (2000) Medium range forecast errors associated with active episodes of the MJO. Mon Weather Rev 128:69–86CrossRefGoogle Scholar
  13. Higgins RW, Mo KC (1997) Persistent north Pacific circulation anomalies and the tropical intraseasonal oscillation. J Clim 10:223–244CrossRefGoogle Scholar
  14. Hollingsworth A, Arpe K, Tiedtke M, Capaldo M, Savijärvi H (1980) The performance of a medium-range forecast model in winter-impact of physical parameterizations. Mon Weather Rev 108:1736–1773CrossRefGoogle Scholar
  15. Hudson D, Alves O (2007) The impact of land-atmosphere initialisation on dynamical seasonal prediction. CAWCR research report no. 133, Bur. Met., Melbourne, Australia, 4 ppGoogle Scholar
  16. Inness PM, Slingo JM, Guilyardi E, Cole J (2003) Simulation of the Madden–Julian oscillation in a coupled general circulation model. Part II: the role of the basic state. J Clim 16:365–382CrossRefGoogle Scholar
  17. Jiang X, Waliser DE, Wheeler MC, Jones C, Lee MI, Schubert SD (2008) Assessing the skill of an all-season statistical forecast model for the Madden–Julian oscillation. Mon Weather Rev 136:1940–1956CrossRefGoogle Scholar
  18. Jones C, Waliser DE, Schemm J-KE, Lau WKM (2000) Prediction skill of the Madden and Julian oscillation in dynamical extended range forecasts. Clim Dyn 16:273–289CrossRefGoogle Scholar
  19. Jones C, Carvalho LMV, Higgins RW, Waliser DE, Schemm J-KE (2004) A statistical forecast model of tropical intraseasonal convective anomalies. J Clim 17:2078–2095CrossRefGoogle Scholar
  20. Leroy A, Wheeler MC (2008) Statistical prediction of weekly tropical cyclone activity in the Southern Hemisphere. Mon Weather Rev 136:3637–3654CrossRefGoogle Scholar
  21. Liebmann B, Smith CA (1996) Description of a complete (interpolated) OLR dataset. Bull Am Meteorol Soc 77:1275–1277Google Scholar
  22. Liebmann B, Hendon HH, Glick JD (1994) The relationship between tropical cyclones of the western Pacific and Indian Oceans and the Madden–Julian oscillation. J Meteorol Soc Jpn 72:401–412Google Scholar
  23. Lin J-L et al (2006) Tropical intraseasonal variability in 14 IPCC AR4 climate models. Part I: convective signals. J Clim 19:2665–2690CrossRefGoogle Scholar
  24. Lin H, Brunet G, Derome J (2008) Forecast skill of the Madden–Julian oscillation in two Canadian atmospheric models. Mon Weather Rev 136:4130–4149CrossRefGoogle Scholar
  25. Lo F, Hendon HH (2000) Empirical prediction of the Madden–Julian oscillation. Mon Weather Rev 128:2528–2543CrossRefGoogle Scholar
  26. Maharaj EA, Wheeler MC (2005) Forecasting an index of the Madden-oscillation. Int J Climatol 25:1611–1618CrossRefGoogle Scholar
  27. Marshall AG, Alves O, Hendon HH (2009) A coupled GCM analysis of MJO activity at the onset of El Niño. J Atmos Sci 66:966–983CrossRefGoogle Scholar
  28. Matthews AJ (2008) Primary and successive events in the Madden-Julian oscillation. Q J R Meteorol Soc 134:439–453CrossRefGoogle Scholar
  29. Reichler T, Roads JO (2005) Long-range predictability in the tropics. Part II: 30–60-day variability. J Clim 18:634–650CrossRefGoogle Scholar
  30. Schiller A, Godfrey JS, McIntosh P, Meyers G (1997) A global ocean general circulation model climate variability studies. CSIRO Marine Research Report No. 227, 60 ppGoogle Scholar
  31. Schiller A, Godfrey JS, McIntosh PC, Meyers G, Smith NR, Alves O, Wang G, Fiedler R (2002) A new version of the Australian community ocean model for seasonal climate prediction. CSIRO Marine Research Report No. 240Google Scholar
  32. Seo K-H (2009) Statistical-dynamical prediction of the Madden-Julian oscillation using NCEP Climate Forecast System (CFS). Int J Climatol. doi: 10.1002/joc.1845
  33. Slingo JM et al (1996) Intraseasonal oscillations in 15 atmospheric general circulation models: results from an AMIP diagnostic subproject. Clim Dyn 12:325–357CrossRefGoogle Scholar
  34. Smith NR, Blomley JE, Meyers G (1991) A univariate statistical interpolation scheme for subsurface thermal analyses in the tropical oceans. Prog Oceanogr 28:219–256CrossRefGoogle Scholar
  35. Uppala SM, Kallberg PW, Simmons AJ, Andrae U, Da Costa Bechtold V, Fiorino M, Gibson JK, Haseler J, Hernandez A, Kelly GA (2005) The ERA-40 re-analysis. Q J R Meteorol Soc 131(612):2961–3012CrossRefGoogle Scholar
  36. Valcke S, Terray L, Piacentini A (2000) OASIS 2.4 Ocean Atmospheric Sea Ice Soil users guide, version 2.4. CERFACS technical report, CERFACS TR/CMGC/00-10, 85 ppGoogle Scholar
  37. Vitart F, Woolnough S, Balmaseda MA, Tompkins AM (2007) Monthly forecast of the Madden–Julian oscillation using a coupled GCM. Mon Weather Rev 135:2700–2715CrossRefGoogle Scholar
  38. Waliser DE, Lau KM, Kim JH (1999) The influence of coupled sea surface temperatures on the Madden Julian oscillation: a model perturbation experiment. J Atmos Sci 56:333–358CrossRefGoogle Scholar
  39. Waliser DE, Lau KM, Stern W, Jones C (2003) Potential predictability of the Madden-Julian oscillation. Bull Am Meteorol Soc 84:33–50CrossRefGoogle Scholar
  40. Wang G, Alves O, Hudson D, Hendon H, Liu G, Tseitkin F (2008) SST skill assessment from the new POAMA-1.5 system. BMRC Res Lett 8:2–6Google Scholar
  41. Weickmann KM, Lussky GR, Kutzbach JE (1985) Intraseasonal (30–60 day) fluctuations of outgoing longwave radiation and 250 mb streamfunction during northern winter. Mon Weather Rev 113:941–961CrossRefGoogle Scholar
  42. Wheeler MC, Hendon HH (2004) An all-season real-time multivariate MJO index: development of an index for monitoring and prediction. Mon Weather Rev 132:1917–1932CrossRefGoogle Scholar
  43. Wheeler MC, McBride JL (2005) Australian–Indonesian monsoon. In: Lau WKM, Waliser DE (eds) Intraseasonal variability in the atmosphere-ocean climate system. Springer-Verlag, New York, pp 125–173Google Scholar
  44. Wheeler M, Weickmann KM (2001) Real-time monitoring and prediction of modes of coherent synoptic to intraseasonal tropical variability. Mon Weather Rev 129:2677–2694CrossRefGoogle Scholar
  45. Wheeler MC, Hendon HH, Cleland S, Meinke H, Donald A (2009) Impacts of the Madden–Julian oscillation on Australian rainfall and circulation. J Clim 22:1482–1498CrossRefGoogle Scholar
  46. Zhang C (2005) Madden-Julian Oscillation. Rev Geophys 43. doi: 10.1029/2004RG000158
  47. Zhang C, Dong M, Gualdi S, Hendon HH, Maloney ED, Marshall A, Sperber KR, Wang W (2006) Simulations of the Madden-Julian oscillation in four pairs of coupled and uncoupled global models. Clim Dyn 27:573–592CrossRefGoogle Scholar
  48. Zhao M, Hendon HH (2009) Representation and prediction of the Indian Ocean dipole in the POAMA seasonal forecast model. Q J R Meteorol Soc 135:337–352CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Harun A. Rashid
    • 1
    • 2
  • Harry H. Hendon
    • 1
  • Matthew C. Wheeler
    • 1
  • Oscar Alves
    • 1
  1. 1.Centre for Australian Weather and Climate Research (A partnership between CSIRO and the Bureau of Meteorology)MelbourneAustralia
  2. 2.Centre for Australian Weather and Climate Research, CSIRO Marine and Atmospheric ResearchAspendaleAustralia

Personalised recommendations