Application of cloud microphysics to NCAR community climate model

Abstract

Abstract. The Colorado State University Regional Atmospheric Modeling System bulk cloud microphysics parameterization has been applied to the treatment of stratiform clouds in the National Center for Atmospheric Research community climate model. Predicted cloud properties are mass concentrations of cloud water, cloud ice, rain, and snow and number concentration of ice. Microphysical processes treated include condensation of water vapor and evaporation of cloud water and rain, nucleation of ice crystals, vapor deposition and sublimation of cloud ice and snow, autoconversion and accretion of cloud water, aggregation and collection of cloud ice, melting of ice and snow, timing on ice and snow, and gravitational settling of ice, rain, and snow. Although the parameterization is more detailed and hence more computationally demanding than other cloud microphysics parameterizations in climate models, it treats the Bergeron-Findeisen process explicitly and hence does not require an ad hoc parameterization to distinguish liquid water and ice. A variety of simulations were performed, testing sensitivity to horizontal and vertical resolution, the treatment of ice number, droplet number, and parameterization of cumulus convection. The simulated planetary radiation balance is found to be particularly sensitive to the treatment of ice number and cumulus convection.

Global indirect aerosol effects: a review

“…Because the aerosol indirect effect is based on the change in cloud droplet number concentration, some GCMs predict cloud droplet number concentrations in addition to the cloud water mass mixing ratio using one of the above described physically based aerosol activation schemes as a source term for cloud droplets (Ghan et al, 1997a;Lohmann et al, 1999). Likewise the number of ice crystals needs to be predicted in addition to the ice water mass mixing ratio in order to estimate the effect of aerosols on mixed-phase and ice clouds (Ghan et al, 1997b;Lohmann, 2002b). It has been shown by Gierens and Spichtinger (pers.…”

Simulations of midlatitude frontal clouds by single‐column and cloud‐resolving models during the Atmospheric Radiation Measurement March 2000 cloud intensive operational period

“…Other variations of the AS scheme are used in CSU, which uses a prognostic closure based on the cumulus kinetic energy [Pan and Randall, 1998], and in GFDL and McRAS, which use a relaxed AS scheme developed by Moorthi and Suarez [1992] with several modifications to convective triggers (CTR) and inhibitors (CIN) for the existence of convection (see relevant references listed in Table 1 for these models). Various bulk mass flux schemes, which use one single cloud model to describe an average over all cloud types within [Fowler et al, 1996] 0 or 1 revised AS scheme [Ding and Randall, 1998] ECHAM5 separate prognostic equations for cloud liquid and ice and diagnostic rain and snow [Lohmann and Roeckner, 1996] statistical cloud fraction scheme [Tompkins, 2002] modified from Tiedtke [1989]; mass flux scheme [Nordeng, 1994] GFDL separate prognostic equations for cloud liquid and ice and diagnostic rain and snow [Rotstayn, 1997;Rotstayn et al, 2000] prognostic cloud fraction [Tiedtke, 1993] relaxed AS scheme [Moorthi and Suarez, 1992] GISS one prognostic equation for both cloud liquid and ice and diagnostic rain and snow [Del Genio et al, 1996] diagnostic cloud fraction [Del Genio et al, 1996] mass flux [Del Genio and Yao, 1993;Del Genio et al, 2005] McRAS one prognostic equation for both cloud liquid and ice and diagnostic rain and snow Walker, 1999a, 1999b] prognostic cloud fraction Walker, 1999a, 1999b] relaxed AS scheme PNNL separate prognostic equations for cloud water, cloud ice, droplet number, and crystal number [Ghan et al, 1997a[Ghan et al, , 1997b statistical cloud fraction and autoconversion [Menon et al, 2003] mass flux [Hack, 1994] SCAM one prognostic equation for both cloud liquid and ice and diagnostic rain and snow [Rasch and Kristjánsson, 1998] and Zhang et al [2003].…”

Ice crystal number concentration versus temperature for climate studies

“…It may affect precipitation amount, radiative fluxes, and the heat and moisture budget of the earth's atmosphere (Donner et al, 1997;Ghan et al, 1997). Often, the N i in general circulation models (GCM) and meso-scale models is initially assumed to be constant or to increase with decreasing temperature (T), and it is also used as a function of supersaturation with respect to ice (S i ).…”

“…As shown by Gultepe et al (1998) a 50% uncertainty in equivalent radius (r eq ) for ice crystals may cause a 10 W m − 2 change in net radiative cloud forcing. Using a prognostic scheme for microphysics in a National Center for Atmospheric Research (NCAR) Community Climate Model (CCM2), Ghan et al (1997) stated that changing N i = 100 to 1000 m − 3 resulted in the short wave cloud forcing (SWCF) changing from −44.5 to − 31.9 W m − 2 . Long wave cloud forcing orders of magnitude lower than values given by the typical 'Fletcher curve' at temperatures around − 30°C.…”