Cloud Microphysics Across Scales for Weather and Climate
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.
KeywordsClouds Ice Microphysics
- 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. https://doi.org/10.1175/JCLI-D-13-00075.1.CrossRefGoogle Scholar
- 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. https://doi.org/10.1175/JCLI-D-16-0050.1.CrossRefGoogle Scholar
- 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). https://doi.org/10.1029/2009JD013797.
- 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. https://doi.org/10.1175/JCLI-D-14-00103.1.CrossRefGoogle Scholar
- 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
- Hong, Song-You, and Jeong-Ock Jade Lim. 2006. The WRF single-moment 6-class microphysics scheme (WSM6). Asia-Pacific Journal of Atmospheric Sciences. http://www.dbpia.co.kr.
- 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. https://doi.org/10.1007/978-1-935704-36-2_1.CrossRefGoogle Scholar
- 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. https://doi.org/10.1175/JAS-3350.1.CrossRefGoogle Scholar
- 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. https://doi.org/10.1002/2014RG000468.CrossRefGoogle Scholar
- 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. https://doi.org/10.1175/JAS-D-14-0065.1.CrossRefGoogle Scholar
- 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
- 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. https://doi.org/10.1002/qj.415.CrossRefGoogle Scholar
- 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
- 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
- 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
- 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. https://doi.org/10.1002/qj.441.CrossRefGoogle Scholar
- 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
- 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). https://doi.org/10.1029/2010JD014833.
- 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. https://doi.org/10.1175/JCLI-D-11-00563.
- Stier, P., and others. 2005. The aerosol-climate model ECHAM5-HAM. Atmospheric Chemistry and Physics 5: 1125–56.Google Scholar
- 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. https://doi.org/10.5194/gmd-8-3801-2015.CrossRefGoogle Scholar
- 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. https://doi.org/10.1175/2008MWR2387.1.CrossRefGoogle Scholar
- Wood, R. 2005. Drizzle in stratiform boundary layer clouds. Part II: Microphysical aspects. JAS 62 (9): 3034–3050.Google Scholar
- 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. https://doi.org/10.1029/2004JD005128.