Climate Dynamics

, Volume 33, Issue 6, pp 795–816 | Cite as

A PDF-based hybrid prognostic cloud scheme for general circulation models

  • Masahiro Watanabe
  • S. Emori
  • M. Satoh
  • H. Miura


A new cloud parameterization based on prognostic equations for the subgrid-scale fluctuations in temperature and total water content is introduced for global climate models. The proposed scheme, called hybrid prognostic cloud (HPC) parameterization, employs simple probability density functions (PDFs) to the horizontal subgrid-scale inhomogeneity, allowing them to vary in shape in response to small-scale processes such as cumulus detrainment and turbulent mixing. Simple tests indicate that the HPC scheme is highly favorable as compared to a diagnostic scheme in terms of the cloud fraction and cloud water content under either uniform or non-uniform forcing. The relevance of the HPC scheme is investigated by implementing it in an atmospheric component model of the climate model MIROC with a coarse resolution of T42. A comparison of the short-term integrations between the T42 model and a global cloud resolving model (GCRM) reveals that the HPC scheme can reproduce, to a certain degree, the subgrid-scale variance and skewness of temperature and total water content simulated in the GCRM. It is also found that the HPC scheme significantly alters the climatological distributions in cloud cover, precipitation, and moisture, which are all improved from the model using a conventional diagnostic cloud scheme.


Cloud Parameterization GCM Prognostic scheme Subgrid-scale PDF 



The authors are grateful to Damian Wilson and two anonymous reviewers for their constructive comments. This work was partly supported by the Global Environmental Research Fund RF-061 by the Ministry of the Environment, Japan. The GCRM simulations were performed using the Earth Simulator, supported by CREST/JST and the Kakushin project.


  1. Berry EX (1967) Cloud droplet growth by collection. J Atmos Sci 24:688–701 doi: 10.1175/1520-0469(1967)024<0688:CDGBC>2.0.CO;2CrossRefGoogle Scholar
  2. Bony S, Dufresne JL (2005) Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models. Geophys Res Lett 32:L20806. doi: 10.1029/2005GL023851 CrossRefGoogle Scholar
  3. Bougeault P (1981) Modeling the trade-wind cumulus boundary layer. Part I: testing the ensemble cloud relations against numerical data. J Atmos Sci 38:2414–2428 doi: 10.1175/1520-0469(1981)038<2414:MTTWCB>2.0.CO;2CrossRefGoogle Scholar
  4. Bougeault P (1982) Cloud-ensemble relations based on the Gamma probability distribution for the higher-order models of the planetary boundary layer. J Atmos Sci 39:2691–2700 doi: 10.1175/1520-0469(1982)039<2691:CERBOT>2.0.CO;2CrossRefGoogle Scholar
  5. Bushell AC, Wilson DR, Gregory D (2003) A description of cloud production by non-uniformly distributed processes. Q J R Meteorol Soc 129:1435–1455. doi: 10.1256/qj.01.110 CrossRefGoogle Scholar
  6. Del Genio AD, Yao MS, Kovari W, Lo K (1996) A prognostic cloud water parameterization for global climate models. J Clim 9:270–304 doi: 10.1175/1520-0442(1996)009<0270:APCWPF>2.0.CO;2CrossRefGoogle Scholar
  7. K-1 Model Developers (2004) K-1 coupled model (MIROC) description. In: Hasumi H, Emori S (eds) K-1 technical report. Center for Climate System Research, University of Tokyo, 34 pp. Available at
  8. Gregory D, Wilson DR, Bushell AC (2002) Insights into cloud parameterization provided by a prognostic approach. Q J R Meteorol Soc 128:1485–1504. doi: 10.1002/qj.200212858305 CrossRefGoogle Scholar
  9. Klein SA, Pincus R, Hannay C, Xu KM (2005) How might a statistical cloud scheme be coupled to a mass-flux convection scheme? J Geophys Res 110:D15S06. doi: 10.1029/2004JD005017 CrossRefGoogle Scholar
  10. Lappen CL, Randall DA (2001) Toward a unified parameterization of the boundary layer and moist convection. Part I. A new type of mass-flux model. J Atmos Sci 58:2021–2036CrossRefGoogle Scholar
  11. Larson VE, Golaz JC (2005) Using probability density functions to derive consistent closure relationships among higher-order moments. Mon Weather Rev 133:1023–1042CrossRefGoogle Scholar
  12. Le Treut H, Li ZX (1991) Sensitivity of an atmospheric general circulation model to prescribed SST changes: feedback effects associated with the simulation of cloud optical properties. Clim Dyn 5:175–187Google Scholar
  13. Lewellen WS, Yoh S (1993) Binormal model of ensemble partial cloudiness. J Atmos Sci 50:1228–1237CrossRefGoogle Scholar
  14. Lohmann U, McFarlane N, Levkov L, Abdella K, Albers F (1999) Comparing different cloud schemes of a single column model by using mesoscale forcing and nudging technique. J Clim 12:438–461CrossRefGoogle Scholar
  15. Mellor GL (1977) The Gaussian cloud model relations. J Atmos Sci 34:356–358CrossRefGoogle Scholar
  16. Mellor GL, Yamada T (1982) Development of a turbulence closure model for geophysical fluid problems. Rev Geophys Space Phys 20:851–875CrossRefGoogle Scholar
  17. Miura H, Satoh M, Nasuno T, Noda A, Oouchi K (2007) A Madden-Julian oscillation event realistically simulated by a global cloud-resolving model. Science 318:1763–1765CrossRefGoogle Scholar
  18. Nakanishi M, Niino H (2004) An improved Mellow-Yamada level-3 model with condensation physics: its design and verification. Bound Layer Meteorol 112:1–31CrossRefGoogle Scholar
  19. Pan DM, Randall DA (1998) A cumulus parameterization with a prognostic closure. Q J R Meteorol Soc 124:949–981Google Scholar
  20. Price JD, Wood R (2002) Comparison of probability density functions for total specific humidity and saturation deficit humidity, and consequences for cloud parameterization. Q J R Meteorol Soc 128:2059–2072CrossRefGoogle Scholar
  21. Rasch PJ, Kristjansson JE (1998) A comparison of the CCM3 model climate using diagnosed and predicted condensate parameterizations. J Clim 11:1587–1614CrossRefGoogle Scholar
  22. Ricard JL, Royer JF (1993) A statistical cloud scheme for use in an AGCM. Ann Geophys 11:1095–1115Google Scholar
  23. Rossow WB, Dueñas EN (2004) The International Satellite Cloud Climatology Project (ISCCP) web site-An online resource for research. Bull Amer Meteorol Soc 85:167–176CrossRefGoogle Scholar
  24. Satoh M, Matsuno T, Tomita H, Miura H, Nasuno T, Iga S (2008) Nonhydrostatic icosahedral atmospheric model (NICAM) for global cloud resolving simulations. J Comp Phys 227:3486–3514CrossRefGoogle Scholar
  25. Slingo J (1987) The development and verification of a cloud prediction scheme for the ECMWF model. Q J R Meteorol Soc 113:899–927CrossRefGoogle Scholar
  26. Smith RNB (1990) A scheme for predicting layer clouds and their water content in a general circulation model. Q J R Meteorol Soc 116:435–460CrossRefGoogle Scholar
  27. Sommeria G, Deardorff JW (1977) Subgrid-scale condensation in models of nonprecipitating clouds. J Atmos Sci 34:344–355CrossRefGoogle Scholar
  28. Sundqvist H (1978) A parameterization scheme for non-convective condensation including prediction of cloud water content. Q J R Meteorol Soc 104:677–690CrossRefGoogle Scholar
  29. Sundqvist H, Berge E, Kristjansson JE (1989) Condensation and cloud parameterization studies with a mesoscale numerical weather prediction model. Mon Weather Rev 117:1641–1657CrossRefGoogle Scholar
  30. Tiedke M (1993) Representation of clouds in large-scale models. Mon Weather Rev 121:3040–3061CrossRefGoogle Scholar
  31. Tompkins AM (2002) A prognostic parameterization for the subgrid-scale variability of water vapor and clouds in large-scale models and its use to diagnose cloud cover. J Atmos Sci 59:1917–1942CrossRefGoogle Scholar
  32. Tompkins AM (2005) The parameterization of cloud cover. ECMWF Technical Memorandum. p 23. Available at
  33. Uppala SM et al (2005) The ERA-40 re-analysis. Q J R Meteorol Soc 131:2961–3012CrossRefGoogle Scholar
  34. Wang S, Wang Q (1999) On condensation and evaporation in turbulence cloud parameterizations. J Atmos Sci 56:3338–3344CrossRefGoogle Scholar
  35. Weisman ML, Klemp JB (1982) The dependence of numerically simulated convective storms on vertical wind shear and buoyancy. Mon Weather Rev 110:504–520CrossRefGoogle Scholar
  36. Wilson DR, Ballard SP (1999) A microphysically based precipitation scheme for the UK Meteorological Office unified model. Q J R Meteorol Soc 125:1607–1636CrossRefGoogle Scholar
  37. Wilson DR, Gregory D (2003) The behaviour of large-scale model cloud schemes under idealized forcing. Q J R Meteorol Soc 129:967–986CrossRefGoogle Scholar
  38. Wood R, Field PR (2000) Relationships between total water, condensed water, and cloud fraction in stratiform clouds examined using Aircraft data. J Atmos Sci 57:1888–1905CrossRefGoogle Scholar
  39. Xu KM, Randall DA (1996) A semiempirical cloudiness parameterization for use in climate models. J Atmos Sci 53:3084–3102CrossRefGoogle Scholar
  40. Zhang MH et al (2005) Comparing clouds and their seasonal variations in 10 atmospheric general circulation models with satellite measurements. J Geophys Res 110. doi: 10.1029/2004JD05021

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Masahiro Watanabe
    • 1
  • S. Emori
    • 1
    • 2
    • 3
  • M. Satoh
    • 1
    • 3
  • H. Miura
    • 3
  1. 1.Center for Climate System ResearchUniversity of TokyoKashiwa, ChibaJapan
  2. 2.National Institute for Environmental StudiesTsukubaJapan
  3. 3.Frontier Research Center for Global ChangeJAMSTECYokohamaJapan

Personalised recommendations