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Assessing the simulation and prediction of rainfall associated with the MJO in the POAMA seasonal forecast system

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Abstract

We assess the ability of the Predictive Ocean Atmosphere Model for Australia (POAMA) to simulate and predict weekly rainfall associated with the MJO using a 27-year hindcast dataset. After an initial 2-week atmospheric adjustment, the POAMA model is shown to simulate well, both in pattern and in intensity, the weekly-mean rainfall variation associated with the evolution of the MJO over the tropical Indo-Pacific. The simulation is most realistic in December–February (austral summer) and least realistic in March–May (austral autumn). Regionally, the most problematic area is the Maritime Continent, which is a common problem area in other models. Coupled with our previous demonstration of the ability of POAMA to predict the evolution of the large-scale structure of the MJO for up to about 3 weeks, this ability to simulate the regional rainfall evolution associated with the MJO translates to enhanced predictability of rainfall regionally throughout much of the tropical Indo-Pacific when the MJO is present in the initial conditions during October–March. We also demonstrate enhanced prediction skill of rainfall at up to 3 weeks lead time over the north-east Pacific and north Atlantic, which are areas of pronounced teleconnections excited by the MJO-modulation of tropical Indo-Pacific rainfall. Failure to simulate and predict the modulation of rainfall in such places as the Maritime Continent and tropical Australia by the MJO indicates, however, there is still much room for improvement of the prediction of the MJO and its teleconnections.

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References

  • 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 32:855–872

    Article  Google Scholar 

  • Alves O, Wang G, Zhong A, Smith N, Tzeitkin F, Warren G, Schiller A, Godfrey S, Meyers G (2003) POAMA: Bureau of Meteorology Operational Coupled Model 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, Australia

  • Burgers G, Balmaseda MA, Vossepoel FC, Oldenborgh G, Leeuwen P (2002) Balanced ocean-data assimilation near the equator. J Phys Ocean 32:2509–2519

    Article  Google Scholar 

  • Cassou C (2008) Intraseasonal interaction between the Madden–Julian oscillation and the North Atlantic oscillation. Nature 455:523–527

    Article  Google Scholar 

  • Colman R, Deschamps L, Naughton M, Rikus L, Sulaiman A, Puri K, Roff G, Sun Z, Embury G (2005) BMRC Atmospheric Model (BAM) version3.0: comparison with mean climatology. BMRC Research Report no. 108, Bur. Met., Melbourne, Australia

  • Donald A, Meinke H, Power B, Maia AHN, Wheeler MC, White N, Stone RC, Ribbe J (2006) Near-global impact of the Madden–Julian oscillation on rainfall. Geophys Res Lett 33:L09704. doi:10.1029/2005GL025155

    Article  Google Scholar 

  • Ferranti L, Palmer TN, Molteni F, Klinker E (1990) Tropical-extratropical interaction associated with the 30–60 day oscillation and its impact on medium and extended range prediction. J Atmos Sci 125:2177–2199

    Article  Google Scholar 

  • Goswami BN (2005) South Asian monsoon. In: Lau WKM, Waliser DE (eds) Intraseasonal variability in the atmosphere-ocean climate system. Springer Berlin Heidelberg, Praxis, pp 19–61

    Chapter  Google Scholar 

  • Hendon HH, Liebmann B (1990) The intraseasonal (30–50 day) oscillation of the Australian summer monsoon. J Atmos Sci 47:2909–2924

    Article  Google Scholar 

  • 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 128:69–86

    Article  Google Scholar 

  • Higgins RW, Mo KC (1997) Persistent North Pacific circulation anomalies and the tropical intraseasonal oscillation. J Clim 10:223–244

    Article  Google Scholar 

  • Hudson D, Alves O, Hendon HH, Wang G (2010) The impact of atmospheric initialisation on seasonal prediction of tropical Pacific SST. Clim Dyn. doi:10.1007/s00382-010-0763-9

  • Inness PM, Slingo JM (2003) Simulation of the Madden–Julian oscillation in a coupled general circulation model. Part I: comparison with observations and an atmosphere-only GCM. J Clim 16:345–364

    Article  Google Scholar 

  • Jones C, Carvalho LMV, Higgins RW, Waliser DE, Schemm J-KE (2004) A statistical forecast model of tropical intraseasonal convective anomalies. J Clim 17:2078–2095

    Article  Google Scholar 

  • Jung T, Miller MJ, Palmer TN (2010) Diagnosing the origin of extended-range forecast errors. Mon Weather Rev 138:2434–2446

    Google Scholar 

  • Kang I-S, Kim HM (2010) Assessment of MJO predictability for boreal winter with various statistical and dynamical models. J Clim 23:2368–2378

    Article  Google Scholar 

  • Lin H, Brunet G, Derome J (2008) Forecast skill of the Madden–Julian oscillation in two Canadian atmospheric models. Mon Weather Rev 136:4130–4149

    Article  Google Scholar 

  • Lo F, Hendon HH (2000) Empirical prediction of the Madden–Julian oscillation. Mon Weather Rev 128:2528–2543

    Article  Google Scholar 

  • Lorenc AC (1984) The evolution of planetary scale 200 mb divergent flow during the FGGE year. Q J R Meteorol Soc 110:427–442

    Article  Google Scholar 

  • Madden RA, Julian PR (1971) Detection of a 40–50 day oscillation in the zonal wind in the tropical Pacific. J Atmos Sci 28:702–708

    Article  Google Scholar 

  • Madden RA, Julian PR (1972) Description of global-scale circulation cells in the tropics with a 40,250 day period. J Atmos Sci 29:1109–1123

    Article  Google Scholar 

  • Madden RA, Julian PR (1994) Observations of the 40–50 day tropical oscillation–a review. Mon Weather Rev 122:814–837

    Article  Google Scholar 

  • Manabe S, Holloway JL (1975) The seasonal variation of the hydrological cycle as simulated by a global model of the atmosphere. J Geophys Res 80:1617–1649

    Article  Google Scholar 

  • Mason SJ, Graham NE (1999) Conditional probabilities relative operating characteristics, and relative operating levels. Weather Forecast 14:713–725

    Article  Google Scholar 

  • Matthews AJ, Hoskins BJ, Slingo JM, Blackburn M (1996) Development of convection along the SPCZ within a Madden–Julian oscillation. Q J R Meteorol Soc 122:669–688

    Article  Google Scholar 

  • Matthews AJ, Hoskins BJ, Masutani M (2004) The global response to tropical heating in the Madden–Julian oscillation during northern winter. Q J R Meteorol Soc 130:1991–2011

    Article  Google Scholar 

  • Mo KC, Higgins RW (1998) Tropical influences on California precipitation. J Clim 11:412–430

    Article  Google Scholar 

  • Nordeng T-E (1994) Extended versions of the convective parameterization scheme at ECMWF and their impact upon the mean climate and transient activity of the model in the tropics. Research Dept Technical Memorandum No. 206, ECMWF, Shinfield Park Reading RG2 9AX, United Kingdom

  • Oke PR, Schiller A, Griffin DA, Brassington GB (2005) Ensemble data assimilation for an eddy-resolving ocean model of the Australian region. Q J R Meteorol Soc 131:3301–3311

    Article  Google Scholar 

  • Rashid H, Hendon HH, Wheeler M, Alves O (2010) Prediction of the Madden–Julian oscillation with the POAMA dynamical seasonal prediction system. Clim Dyn. doi:10.1007/s00382-010-0754-x

  • Reynolds RW, Rayner NA, Smith TM, Stokes DC, Wang W (2002) An improved in situ and satellite SST analysis for climate. J Clim 15:1609–1625

    Article  Google Scholar 

  • Schiller A, Godfrey JS, McIntosh P, Meyers G (1997) A global ocean general circulation model climate variability studies. CSIRO Marine Research Report No. 227

  • 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. 240

  • Seo K-H (2009) Statistical-dynamical prediction of the Madden–Julian oscillation using NCEP Climate Forecast System (CFS). Int J Climatol 29:2146–2155

    Article  Google Scholar 

  • Smith NR, Blomley JE, Meyers G (1991) A univariate statistical interpolation scheme for subsurface thermal analyses in the tropical oceans. Prog Oceanogr 28:219–256

    Article  Google Scholar 

  • Stanski HR, Wilson LJ, Burrowa WR (1989) Survey of common verification methods in meteorology. World Weather Watch Tech Rep. 8, WMO tech. DOC. 358, pp 114

  • Stockdale TN (1997) Coupled ocean–atmosphere forecasts in the presence of climate drift. Mon Weather Rev 125:809–818

    Article  Google Scholar 

  • Tiedke M (1989) A comprehensive mass flux scheme for cumulus parameterisation in large-scale models. Mon Weather Rev 117:1779–1800

    Article  Google Scholar 

  • 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 131:2961–3012

    Article  Google Scholar 

  • Valcke S, Terray L, Piacentini A (2000) OASIS 2.4 Ocean Atmospheric Sea Ice Soil user’s guide, Version 2.4. CERFACS technical report, CERFACS TR/CMGC/00-10, pp 85

  • Vitart F, Molteni F (2010) Simulation of the Madden–Julian oscillation and its teleconnections in the ECMWF forecast system. Q J R Meteorol Soc 136:842–855

    Article  Google Scholar 

  • Vitart F, Woolnough S, Balmaseda MA, Tompkins AM (2007) Monthly forecast of the Madden–Julian oscillation using a coupled GCM. Mon Weather Rev 135:2700–2715

    Article  Google Scholar 

  • Vitart F, Leroy A, Wheeler MC (2010) A comparison of dynamical and statistical predictions of weekly tropical cyclone activity in the Southern Hemisphere. Mon Wea Rev (in press)

  • Wang G, Alves O, Smith N (2005) BAM3.0 tropical surface flux simulation and its impact on SST drift in a coupled model. BMRC Research Report No. 107, Bur. Met., Melbourne, Australia

  • Wheeler MC, Hendon HH (2004) An all-season real-time multivariate MJO index: development of an index for monitoring and prediction. Mon Weather Rev 132:1917–1932

    Article  Google Scholar 

  • Wheeler MC, McBride JL (2005) Australian-Indonesian monsoon. In: Lau WKM, Waliser DE (eds) Intraseasonal variability in the atmosphere-ocean climate system. Springer Berlin Heidelberg, Praxis, pp 125–173

    Chapter  Google Scholar 

  • Wheeler M, Weickmann KM (2001) Real-time monitoring and prediction of modes of coherent synoptic to intraseasonal tropical variability. Mon Weather Rev 129:2677–2694

    Article  Google Scholar 

  • Wheeler MC, Hendon HH, Cleland S, Meinke H, Donald A (2009) Impacts of the Madden–Julian oscillation on Australian rainfall and circulation. J Clim 22:1482–1498

    Article  Google Scholar 

  • Xie P, Arkin PA (1997) Global precipitation: a 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull Am Meteorol Soc 78:2539–2558

    Article  Google Scholar 

  • Zhang C (2005) Madden–Julian oscillation. Rev Geophys 43. doi:10.1029/2004RG000158

  • Zhao M, Hendon HH (2009) Representation and prediction of the Indian Ocean dipole in the POAMA seasonal forecast model. Q J R Meteorol Soc 135:337–352

    Article  Google Scholar 

  • Zhao M, Wang G, Hendon HH, Alves O (2010) Impact of including surface currents on simulation of Indian Ocean variability with the POAMA coupled model. Clim Dyn. doi:10.1007/s00382-010-0823-1

  • Zhong A, Alves O, Hendon H, Rikus L (2006) On aspects of the mean climatology and tropical interannual variability in the BMRC Atmospheric Model (BAM 3.0). BMRC Research Report No. 121, Bur. Met., Melbourne, Australia

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Acknowledgments

This work was supported by the Managing Climate Variability Program of Grains Research and Development Corporation. We would like to thank Dr. Harun Rashid for providing the RMM data used in this study. Drs. James Risbey and Li Shi reviewed an earlier version of the manuscript and provided useful comments. Thanks also to Drs. Peter McIntosh, Mike Pook, Jaclyn Brown and Gary Meyers for useful discussions throughout the course of this work. Finally, we thank the three anonymous reviewers for suggested revisions to the manuscript. The CMAP dataset was provided by the NOAA/OAR/ESRL PSD in Boulder USA, and the ERA-40 data was provided by the ECMWF in Reading UK.

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Authors and Affiliations

  1. Centre for Australian Weather and Climate Research, CSIRO Marine and Atmospheric Research, Castray Esplanade, Hobart,, TAS 7000, Australia

    Andrew G. Marshall

  2. Centre for Australian Weather and Climate Research, Bureau of Meteorology, 700 Collins St, Melbourne, VIC 3008, Australia

    Debra Hudson, Matthew C. Wheeler, Harry H. Hendon & Oscar Alves

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  1. Andrew G. Marshall
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  2. Debra Hudson
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  4. Harry H. Hendon
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Correspondence to Andrew G. Marshall.

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Marshall, A.G., Hudson, D., Wheeler, M.C. et al. Assessing the simulation and prediction of rainfall associated with the MJO in the POAMA seasonal forecast system. Clim Dyn 37, 2129–2141 (2011). https://doi.org/10.1007/s00382-010-0948-2

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