Identifying a key physical factor sensitive to the performance of Madden–Julian oscillation simulation in climate models



A key physical factor in regulating the performance of Madden–Julian oscillation (MJO) simulation is examined by using 26 climate model simulations from the World Meteorological Organization’s Working Group for Numerical Experimentation/Global Energy and Water Cycle Experiment Atmospheric System Study (WGNE and MJO-Task Force/GASS) global model comparison project. For this, intraseasonal moisture budget equation is analyzed and a simple, efficient physical quantity is developed. The result shows that MJO skill is most sensitive to vertically integrated intraseasonal zonal wind convergence (ZC). In particular, a specific threshold value of the strength of the ZC can be used as distinguishing between good and poor models. An additional finding is that good models exhibit the correct simultaneous convection and large-scale circulation phase relationship. In poor models, however, the peak circulation response appears 3 days after peak rainfall, suggesting unfavorable coupling between convection and circulation. For an improving simulation of the MJO in climate models, we propose that this delay of circulation in response to convection needs to be corrected in the cumulus parameterization scheme.


Madden–Julian oscillation YOTC/MJO-TF Climate models Zonal wind convergence Coupling between convection and circulation Cumulus parameterization scheme 


  1. Bechtold P, Chaboureau JP, Beljaars ACM, Betts AK, Köhler M, Miller M, Redelsperger JL (2004) The simulation of the diurnal cycle of convective precipitation over land in global models. Q J R Meteorol Soc 130:3119–3137CrossRefGoogle Scholar
  2. Benedict JJ, Randall DA (2007) Observed characteristics of the MJO relative to maximum rainfall. J Atmos Sci 64:2332–2354CrossRefGoogle Scholar
  3. Benedict JJ, Randall DA (2011) Impacts of idealized air-sea coupling on Madden-Julian oscillation structure in the superparameterized CAM. J Atmos Sci 68:1990–2008CrossRefGoogle Scholar
  4. Benedict JJ, Maloney ED, Sobel AH, Frierson DMW (2014) Gross moist stability and MJO simulation skill in three full-physics GCMs. J Atmos Sci 71:3327–3349CrossRefGoogle Scholar
  5. Bougeault P (1985) A simple parameterization of the large-scale effects of cumulus convection. Mon Weather Rev 113:2108–2121CrossRefGoogle Scholar
  6. Chikira M, Sugiyama M (2010) A cumulus parameterization with state-dependent entrainment rate. Part I: description and sensitivity to temperature and humidity profiles. J Atmos Sci 67:2171–2193CrossRefGoogle Scholar
  7. Dee DP et al (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597CrossRefGoogle Scholar
  8. Del Genio AD, Yao MS (1993) Efficient cumulus parameterization for long-term climate studies: the GISS scheme. In: Emanuel K, Raymond D (eds) The representation of cumulus convection in numerical models, Meteorological Monographs, vol 24. American Meteorological Society, Boston, pp 181–184Google Scholar
  9. DeMott CA, Stan C, Randall DA, Branson MD (2014) Intraseasonal variability in coupled GCMs: the roles of ocean feedbacks and model physics. J Clim 27:4970–4995. doi:10.1175/jcli-d-13-00760.1 CrossRefGoogle Scholar
  10. DeMott CA, Klingaman NP, Woolnough SJ (2015) Atmosphere-ocean coupled processes in the Madden-Julian oscillation. Rev Geophys 53:1099–1154. doi:10.1002/2014RG000478 CrossRefGoogle Scholar
  11. Fritsch JM, Chappell CF (1980) Numerical prediction of convectively driven mesoscale pressure systems. Part I: Convective parameterization. J Atmos Sci 37:1722–1733CrossRefGoogle Scholar
  12. Gregory D, Rowntree PR (1990) A mass flux convection scheme with representation of cloud ensemble characteristics and stability-dependent closure. Mon Weather Rev 118:1483–1506CrossRefGoogle Scholar
  13. Guo Y, Waliser DE, Jiang X (2015) A systematic relationship between the representations of convectively coupled equatorial wave activity and the Madden-Julian oscillation in climate model simulations. J Clim 28:1881–1904CrossRefGoogle Scholar
  14. Hendon HH, Wheeler MC, Zhang C (2007) Seasonal dependence of the MJO-ENSO relationship. J Clim 20:531–543CrossRefGoogle Scholar
  15. Hong S-Y, Pan H-L (1998) Convective trigger function for a mass-flux cumulus parameterization scheme. Mon Weather Rev 126:2599–2620CrossRefGoogle Scholar
  16. Huffman GJ, Adler RF, Morrissey MM, Bolvin DT, Curtis S, Joyce R, McGavock B, Susskind J (2001) Global precipitation at one-degree daily resolution from multisatellite observations. J Hydrometeor 2:36–50CrossRefGoogle Scholar
  17. Huffman GJ, Adler RF, Bolvin DT, Gu G, Nelkin EJ, Bowman KP, Hong Y, Stocker EF, Wolff DB (2007) The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeor 8:38–55CrossRefGoogle Scholar
  18. Hung M-P, Lin J-L, Wang W, Kim D, Shinoda T, Weaver SJ (2013) MJO and convectively coupled equatorial waves simulated by CMIP5 climate models. J Clim 26:6185–6214CrossRefGoogle Scholar
  19. Jiang X et al (2015) Vertical structure and physical processes of the Madden-Julian oscillation: exploring key model physics in climate simulations. J Geophys Res Atmos 120:4718–4748. doi:10.1002/2014JD022375 CrossRefGoogle Scholar
  20. Jones C, Waliser DE, Lau KM, Stern W (2004) Global occurrences of extreme precipitation and the Madden-Julian oscillation: observations and predictability. J Clim 17:4575–4589CrossRefGoogle Scholar
  21. Khairoutdinov MF, Randall DA (2001) A cloud resolving model as a cloud parameterization in the NCAR Community Climate System Model: preliminary results. Geophys Res Lett 28:3617–3620CrossRefGoogle Scholar
  22. Khairoutdinov MF, Randall DA (2003) Cloud-resolving modeling of ARM Summer 1997 IOP: model formulation, results, uncertainties and sensitivities. J Atmos Sci 60:607–625CrossRefGoogle Scholar
  23. Kim D et al (2009) Application of MJO simulation diagnostics to climate models. J Clim 22:6413–6436CrossRefGoogle Scholar
  24. Kim D et al (2014) Process-oriented MJO simulation diagnostic: moisture sensitivity of simulated convection. J Clim 27:5379–5395CrossRefGoogle Scholar
  25. Kiranmayi L, Maloney ED (2011) Intraseasonal moist static energy budget in reanalysis data. J Geophys Res Atmos 116:D21117. doi:10.1029/2011JD016031 CrossRefGoogle Scholar
  26. 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
  27. Madden RA, Julian PR (1994) Observations of the 40-50-day tropical oscillation: a review. Mon Weather Rev 122:814–837CrossRefGoogle Scholar
  28. Maloney ED (2009) The moist static energy budget of a composite tropical intraseasonal oscillation in a climate model. J Clim 22:711–729CrossRefGoogle Scholar
  29. Maloney ED, Hartmann DL (2000) Modulation of eastern North Pacific hurricanes by the Madden-Julian oscillation. J Clim 13:1451–1460CrossRefGoogle Scholar
  30. Maloney ED, Jiang X, Xie SP, Benedict JJ (2014) Process-oriented diagnosis of East Pacific warm pool intraseasonal variability. J Clim 27:6305–6324CrossRefGoogle Scholar
  31. Moncrieff MW, Waliser DE, Miller MJ, Shapiro MA, Asrar GR, Caughey J (2012) Multiscale convective organization and the YOTC virtual global field campaign. Bull Am Meteorol Soc 93:1171–1187CrossRefGoogle Scholar
  32. Moorthi S, Suarez MJ (1992) Relaxed Arakawa–Schubert—a parameterization of moist convection for general circulation models. Mon Weather Rev 120:978–1002CrossRefGoogle Scholar
  33. Neale RB, Richter JH, Jochum M (2008) The impact of convection on ENSO: from a delayed oscillator to a series of events. J Clim 21:5904–5924CrossRefGoogle Scholar
  34. Nordeng TE (1994) Extended versions of the convective parameterization scheme at ECMWF and their impact on the mean and transient activity of the model in the tropics. In: ECMWF research department, technical memorandum. ECMWF, reading No 206. October 1994, p 41Google Scholar
  35. Pan HL, Wu WS (1995) Implementing a mass flux convection parameterization package for the NMC medium-range forecast model. NMC office note 409, p 40Google Scholar
  36. Richter JH, Rasch PJ (2008) Effects of convective momentum transport on the atmospheric circulation in the Community Atmosphere Model, version 3. J Clim 21:1487–1499CrossRefGoogle Scholar
  37. Seo K-H, Son S-W (2012) The global atmospheric circulation response to tropical diabatic heating associated with the Madden-Julian oscillation during northern winter. J Atmos Sci 69:79–96CrossRefGoogle Scholar
  38. Seo K-H, Wang W (2010) The Madden-Julian oscillation simulated in the NCEP Climate Forecast System model: the importance of stratiform heating. J Clim 23:4770–4793CrossRefGoogle Scholar
  39. Seo K-H, Schemm JKE, Wang W, Kumar A (2007) The boreal summer intraseasonal oscillation simulated in the NCEP Climate Forecast System (CFS): the effect of sea surface temperature. Mon Weather Rev 135:1807–1827CrossRefGoogle Scholar
  40. Seo K-H, Lee H-J, Frierson DMW (2016) Unraveling the teleconnection mechanisms that induce wintertime temperature anomalies over the Northern Hemisphere continents in response to the Madden-Julian oscillation. J Atmos Sci 73:3557–3571CrossRefGoogle Scholar
  41. Sperber KR, Kim D (2012) Simplified metrics for the identification of the Madden-Julian oscillation in models. Atmos Sci Lett 13:187–193. doi:10.1002/asl.378 CrossRefGoogle Scholar
  42. Thayer-Calder K, Randall DA (2009) The role of convective moistening in the Madden-Julian oscillation. J Atmos Sci 66:3297–3312CrossRefGoogle Scholar
  43. Tiedtke M (1989) A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon Weather Rev 117:1779–1800CrossRefGoogle Scholar
  44. Waliser DE et al (2012) The “year” of tropical convection (May 2008–April 2010): climate variability and weather highlights. Bull Am Meteorol Soc 93:1189–1218CrossRefGoogle Scholar
  45. Wheeler MC, McBride JL (2011) Australian–Indonesian monsoon. In: Lau WKM, Waliser DE (eds) Intraseasonal variability in the atmosphere-ocean climate system, 2nd edn. Springer, New York, pp 147–198Google Scholar
  46. Yoo C, Lee S, Feldstein SB (2012) Mechanisms of extratropical surface air temperature change in response to the Madden-Julian oscillation. J Clim 25:5777–5790CrossRefGoogle Scholar
  47. Yukimoto S et al (2011) Meteorological Research Institute Earth System Model Version 1 (MRI-ESM1): model description. Technical Report of the Meteorological Research Institute, No. 64, p 83Google Scholar
  48. Zhang C (2005) Madden-Julian oscillation. Rev Geophys 43:RG2003. doi:10.1029/2004RG000158 Google Scholar
  49. Zhang GJ, McFarlane NA (1995) Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian Climate Centre general circulation model. Atmos Ocean 33:407–446CrossRefGoogle Scholar
  50. Zhang GJ, Mu M (2005) Effects of modifications to the Zhang-McFarlane convection parameterization on the simulation of the tropical precipitation in the National Center for Atmospheric Research Community Climate Model, version 3. J Geophys Res Atmos 110(D09):109Google Scholar
  51. Zhang GJ, Wu X (2003) Convective momentum transport and perturbation pressure field from a cloud-resolving model simulation. J Atmos Sci 60:1120–1139CrossRefGoogle Scholar
  52. Zhao C, Li T, Zhou T (2013) Precursor signals and processes associated with MJO initiation over the tropical Indian Ocean. J Clim 26:291–307CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Department of Atmospheric SciencesPusan National UniversityBusanSouth Korea

Personalised recommendations