Cloud Microphysics Across Scales for Weather and Climate

  • Andrew GettelmanEmail author
  • Hugh Morrison
  • Greg Thompson
Part of the Springer Atmospheric Sciences book series (SPRINGERATMO)


Cloud microphysics describes the evolution of condensed water in the atmosphere and is critical for weather and climate. This chapter describes the methods used for representing microphysical processes in weather and climate models, from explicit bin schemes used for small-scale simulation up to bulk treatments often used in global models. Of particular importance is how the cloud microphysical treatments are coupled to the rest of the cloud schemes in a numerical model that includes clouds. The key issues include the presentation of sub-grid inhomogeneity in humidity and dynamics. In addition, treatment of cold clouds in a “mixed phase” where liquid and ice may co-exist is important. We discuss current approaches including more comprehensive representations of ice and snow, treatment of rimed ice (graupel or hail), and coupling to unified turbulence schemes. Finally, we discuss possible paths forward for simulating cloud microphysics.


Clouds Ice Microphysics 


  1. Bogenschutz, P.A., A. Gettelman, H. Morrison, V.E. Larson, C. Craig, and D.P. Schanen. 2013. Higher-order turbulence closure and its impact on climate simulation in the community atmosphere model. Journal of Climate 26 (23): 9655–9676. Scholar
  2. Bogenschutz, P.A., A. Gettelman, C. Hannay, V.E. Larson, R.B. Neale, C. Craig, and C.-C. Chen. 2018. The path to CAM6: Coupled simulations with CAM5.4 and CAM5.5. Geoscientific Model Development 11 (1): 235–255. Scholar
  3. Eidhammer, Trude, Hugh Morrison, David Mitchell, Andrew Gettelman, and Ehsan Erfani. 2016. Improvements in global climate model microphysics using a consistent representation of ice particle properties. Journal of Climate 30 (2): 609–629. Scholar
  4. Gettelman, A., and H. Morrison. 2015. Advanced two-moment bulk microphysics for global models. Part I: Off-line tests and comparison with other schemes. Journal of Climate 28 (3): 1268–1287. Scholar
  5. Gettelman, A., X. Liu, S.J. Ghan, H. Morrison, S. Park, A.J. Conley, S.A. Klein, J. Boyle, D.L. Mitchell, and J.-L. F. Li. 2010. Global simulations of ice nucleation and ice supersaturation with an improved cloud scheme in the community atmosphere model. Journal of Geophysical Research 115 (D18216).
  6. Gettelman, A., H. Morrison, S. Santos, P. Bogenschutz, and P.M. Caldwell. 2015. Advanced two-moment bulk microphysics for global models. Part II: Global model solutions and aerosol-cloud interactions. Journal of Climate 28 (3): 1288–1307. Scholar
  7. Golaz, J.-C., V.E. Larson, and W.R. Cotton. 2002. A PDF-based model for boundary layer clouds. Part I: Method and model description. JAS 59: 3540–3551.Google Scholar
  8. Hashino, T., and G.J. Tripoli. 2007. The spectral ice habit prediction system (SHIPS). Part I: Model description and simulation of the vapor deposition process. Journal of the Atmospheric Sciences 64: 2210–2237.CrossRefGoogle Scholar
  9. Hong, Song-You, and Jeong-Ock Jade Lim. 2006. The WRF single-moment 6-class microphysics scheme (WSM6). Asia-Pacific Journal of Atmospheric Sciences.
  10. Hoose, C., and O. Möhler. 2012. Heterogeneous ice nucleation on atmospheric aerosols: A review of results from laboratory experiments. Atmospheric Chemistry and Physics 12 (20): 9817–9854. Scholar
  11. Kessler, Edwin. 1969. On the distribution and continuity of water substance in atmospheric circulations. In On the Distribution and Continuity of Water Substance in Atmospheric Circulations. Edited by Edwin Kessler, 1–84. Meteorological Monographs. Boston, MA: American Meteorological Society. Scholar
  12. Khain, A., A. Pokrovsky, M. Pinsky, A. Seifert, and V. Phillips. 2004. Simulation of effects of atmospheric aerosols on deep turbulent convective clouds using a spectral microphysics mixed-phase cumulus cloud model. Part I: Model description and possible applications. Journal of the Atmospheric Sciences 61 (24): 2963–82. Scholar
  13. Khain, A.P., K.D. Beheng, A. Heymsfield, A. Korolev, S.O. Krichak, Z. Levin, M. Pinsky, et al. 2015. Representation of microphysical processes in cloud-resolving models: Spectral (bin) microphysics versus bulk parameterization. Reviews of Geophysics 2014RG000468. Scholar
  14. Khairoutdinov, M.F., and Y. Kogan. 2000. A new cloud physics parameterization in a large-Eddy simulation model of marine stratocumulus. Monthly Weather Review 128: 229–243.CrossRefGoogle Scholar
  15. Kogan, Yefim. 2013. A cumulus cloud microphysics parameterization for cloud-resolving models. Journal of the Atmospheric Sciences 70 (5): 1423–1436. Scholar
  16. Korolev, Alexei. 2007. Limitations of the Wegener–Bergeron–Findeisen mechanism in the evolution of mixed-phase clouds. Journal of the Atmospheric Sciences 64 (9): 3372–3375. Scholar
  17. Korolev, Alexei V., George A. Isaac, Stewart G. Cober, J. Walter Strapp, and John Hallett. 2003. Microphysical characterization of mixed-phase clouds. Quarterly Journal of the Royal Meteorological Society 129 (587): 39–65. Scholar
  18. Lebsock, Matthew, Hugh Morrison, and Andrew Gettelman. 2013. Microphysical implications of cloud-precipitation covariance derived from satellite remote sensing. JGR 118 (12): 6521–6533. Scholar
  19. Lin, Y.-L., R.D. Farley, and H.D. Orville. 1983. Bulk parameterization of the snow field in a cloud model. Journal of Applied Meteorology and Climatology 22: 1065–1092.CrossRefGoogle Scholar
  20. Lohmann, U., and E. Roeckner. 1996. Design and performance of a new cloud microphysics scheme developed for the ECHAM general circulation model. Climate Dynamics 12: 557–572.CrossRefGoogle Scholar
  21. Lohmann, U., J. Feichter, C.C. Chuang, and J.E. Penner. 1999. Prediction of the number of cloud droplets in the ECHAM GCM. Journal Geophysical Research 104: 9169–9198.CrossRefGoogle Scholar
  22. Lohmann, U., P. Stier, C. Hoose, S. Ferrachat, E. Roeckner, and J. Zhang. 2007. Cloud microphysics and aerosol indirect effects in the global climate Model ECHAM5-HAM. Atmospheric Chemistry and Physics 7 (2): 3245–3446.CrossRefGoogle Scholar
  23. Milbrandt, J.A., and M.K. Yau. 2005. A multimoment bulk microphysics parameterization. Part I: Analysis of the role of the spectral shape parameter. Journal of the Atmospheric Sciences 62 (9): 3051–3064. Scholar
  24. Milbrandt, J.A., S. Belair, M. Faucher, M. Vallee, M.L. Carrera, and A. Glazer. 2016. The pan-Canadian high resolution (2.5 km) deterministic prediction system. Weather and Forecasting 31: 1791–1816.CrossRefGoogle Scholar
  25. Morrison, H., and A. Gettelman. 2008. A new two-moment bulk stratiform cloud microphysics scheme in the NCAR community atmosphere model (CAM3), Part I: Description and numerical tests. Journal of Climate 21 (15): 3642–3659.CrossRefGoogle Scholar
  26. Morrison, Hugh, and Jason A. Milbrandt. 2015. Parameterization of cloud microphysics based on the prediction of bulk ice particle properties. Part I: Scheme description and idealized tests. Journal of the Atmospheric Sciences 72 (1): 287–311. Scholar
  27. Morrison, H., J.A. Curry, and V.I. Khvorostyanov. 2005. A new double-moment microphysics parameterization for application in cloud and climate models. Part I: Description. JAS 62: 1665–1677.Google Scholar
  28. Morrison, Hugh, Renata B. McCoy, Stephen A. Klein, Shaocheng Xie, Yali Luo, Alexander Avramov, Mingxuan Chen, et al. 2009. Intercomparison of model simulations of mixed-phase clouds observed during the ARM mixed-phase arctic cloud experiment. II: Multilayer cloud. Quarterly Journal of the Royal Meteorological Society 135 (641): 1003–1019. Scholar
  29. Neale, Richard B., C.C. Chen, A. Gettelman, P.H. Lauritzen, S. Park, D.L. Williamson, A.J. Conley, et al. 2010. Description of the NCAR community atmosphere model (CAM5.0). Boulder, CO, USA: National Center for Atmospheric Research.Google Scholar
  30. Ose, T. 1993. An examination of the effects of explicit cloud water in the UCLA GCM. Journal of the Meteorological Society of Japan 71: 93–109.CrossRefGoogle Scholar
  31. Rasch, P.J., and J.E. Kristjansson. 1998. A comparison of CCM3 model climate using diagnosed and predicted condensate parameterizations. JOC 11: 1587–1614.Google Scholar
  32. Rotstayn, L.D., B.F. Ryan, and J.J. Katzfey. 2000. A scheme for calculation of the liquid fraction in mixed-phase stratiform clouds in large-scale models. MWR 128 (4): 1070–1088.CrossRefGoogle Scholar
  33. Rutledge, S.A., and P.V. Hobbs. 1984. The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. XII: A diagnostic modeling study of precipitation development in narrow cold-frontal rainbands. Journal of the Atmospheric Sciences 41: 2949–2972.CrossRefGoogle Scholar
  34. Seifert, A., and K.D. Beheng. 2001. A double-moment parameterization for simulating autoconversion, accretion, and self-collection. Atmospheric Research 59–60: 265–281.CrossRefGoogle Scholar
  35. Shima, S., K. Kusano, A. Kawano, T. Sugiyama, and S. Kawahara. 2009. The super-droplet method for the numerical simulation of clouds and precipitation: A particle-based and probabilistic microphysics model coupled with a non-hydrostatic model. Quarterly Journal of the Royal Meteorological Society 135 (642): 1307–1320. Scholar
  36. Shipway, B.J., and A.A. Hill. 2012. Diagnosis of systematic differences between multiple parametrizations of warm rain microphysics using a kinematic framework. Quarterly Journal of the Royal Meteorological Society 138 (669): 2196–2211. Scholar
  37. Slingo, A. (ed.). 1985. Handbook of the meteorological office 11-layer atmospheric general circulation model. Rep. DCTN, 29, Meteorol. Pff., Bracknell, U.K.Google Scholar
  38. Song, X., and G.J. Zhang. 2011. Microphysics parameterization for convective clouds in a global climate model: Description and single column model tests. JGR 116 (D02201).
  39. Song, X., G.J. Zhang, and J.L.F. Li. 2012. Evaluation of Microphysics Parameterization for Convective Clouds in the NCAR Community Atmosphere Mode CAM5. J. Climate 25, no. 24 (2012): 8568–8590.
  40. Stier, P., and others. 2005. The aerosol-climate model ECHAM5-HAM. Atmospheric Chemistry and Physics 5: 1125–56.Google Scholar
  41. Thayer-Calder, K., A. Gettelman, C. Craig, S. Goldhaber, P.A. Bogenschutz, C.-C. Chen, H. Morrison, et al. 2015. A unified parameterization of clouds and turbulence using CLUBB and subcolumns in the community atmosphere model. Geoscientific Model Development 8 (12): 3801–3821. Scholar
  42. Thompson, Gregory, and Trude Eidhammer. 2014. A study of aerosol impacts on clouds and precipitation development in a large winter cyclone. Journal of the Atmospheric Sciences 71 (10): 3636–3658. Scholar
  43. Thompson, Gregory, Paul R. Field, Roy M. Rasmussen, and William D. Hall. 2008. Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Monthly Weather Review 136 (12): 5095–5115. Scholar
  44. Wetherald, R.T., and S. Manabe. 1988. Cloud feedback processes in a general circulation model. Journal of the Atmospheric Sciences 45: 1397–1415.CrossRefGoogle Scholar
  45. Wood, R. 2005. Drizzle in stratiform boundary layer clouds. Part II: Microphysical aspects. JAS 62 (9): 3034–3050.Google Scholar
  46. Wilson, D.R., and S.P. Ballard. 1999. A Microphysically Based Precipitation Scheme for the UK Meteorological Office Unified Model. Qjrms 125: 1607–1636.CrossRefGoogle Scholar
  47. Zhang, Junhua, Ulrike Lohmann, and Philip Stier. 2005. A microphysical parameterization for convective clouds in the ECHAM5 climate model: Single-column model results evaluated at the Oklahoma atmospheric radiation measurement program site. Journal of Geophysical Research: Atmospheres 110 (D15): D15S07.
  48. Zhao, Q., T.L. Black, and M.E. Baldwin. 1997. Implementation of the cloud prediction scheme in the Eta model at NCEP. Weather and Forecasting 12: 697–712.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Andrew Gettelman
    • 1
    Email author
  • Hugh Morrison
    • 1
  • Greg Thompson
    • 1
  1. 1.National Center for Atmospheric Research (NCAR)BoulderUSA

Personalised recommendations