A Comparison of the CCM3 Model Climate Using Diagnosed and Predicted Condensate Parameterizations

A parameterization is introduced for the prediction of cloud water in the National Center for Atmospheric Research Community Climate Model version 3 (CCM3). The new parameterization makes a much closer connection between the meteorological processes that determine condensate formation and the condensate amount. The parameterization removes some constraints from the simulation by allowing a substantially wider range of variation in condensate amount than in the standard CCM3 and tying the condensate amount to local physical processes. The parameterization also allows cloud drops to form prior to the onset of grid-box saturation and can require a significant length of time to convert condensate to a precipitable form, or to remove the condensate. The free parameters of the scheme were adjusted to provide reasonable agreement with top of atmosphere and surface fluxes of energy. The parameterization was evaluated by a comparison with satellite and in situ measures of liquid and ice cloud amounts. The effect of the parameterization on the model simulation was then examined by comparing long model simulations to a similar run with the standard CCM and through comparison with climatologies based upon meteorological observations. Global ice and liquid water burdens are higher in the revised model than in the control simulation, with an accompanying increase in height of the center of mass of cloud water. Zonal averages of cloud water contents were 20%‐50% lower near the surface and much higher above. The range of variation of cloud water contents is much broader in the new parameterization but was still not as large as measurements suggest. Differences in the simulation were generally small. The largest significant changes found to the simulation were seen in polar regions (winter in the Arctic and all seasons in the Antarctic). The new parameterization significantly changes the Northern Hemisphere winter distribution of cloud water and improves the simulation of temperature and cloud amount there. Small changes were introduced in the cloud fraction to improve consistency of the meteorological parameterizations and to attempt to alleviate problems in the model (in particular, in the marine stratocumulus regime). The small changes did not make any appreciable improvement to the model simulation. The new parameterization adds significantly to the flexibility in the model and the scope of problems that can be addressed. Such a scheme is needed for a reasonable treatment of scavenging of atmospheric trace constituents, and cloud aqueous or surface chemistry. The addition of a more realistic condensate parameterization provides opportunities for a closer connection between radiative properties of the clouds, and their formation and dissipation. These processes must be treated for many problems of interest today (e.g., anthropogenic aerosol‐ climate interactions).

[1]  James J. Hack,et al.  The Energy Budget of the NCAR Community Climate Model: CCM3* , 1998 .

[2]  H. D. Orville,et al.  Bulk Parameterization of the Snow Field in a Cloud Model , 1983 .

[3]  Ulrike Lohmann,et al.  Design and performance of a new cloud microphysics scheme developed for the ECHAM general circulation model , 1996 .

[4]  James J. Hack,et al.  The Hydrologic and Thermodynamic Characteristics of the NCAR CCM3 , 1998 .

[5]  E. Kessler.  On the distribution and continuity of water substance in atmospheric circulations , 1969 .

[6]  H. Treut,et al.  Precipitation and radiation modeling in a general circulation model: Introduction of cloud microphysical processes , 1995 .

[7]  Johann Feichter,et al.  Simulation of the tropospheric sulfur cycle in a global climate model , 1996 .

[8]  H. Kapitza,et al.  3D mesoscale numerical studies of cirrus and stratus clouds by their time and space evolution , 1992 .

[9]  James J. Hack,et al.  Sensitivity of the Simulated Climate to a Diagnostic Formulation for Cloud Liquid Water , 1998 .

[10]  A. Heymsfield.  Microphysical structures of stratiform and cirrus clouds , 1993 .

[11]  Anthony D. Del Genio,et al.  A Prognostic Cloud Water Parameterization for Global Climate Models , 1996 .

[12]  Erik Berge,et al.  Coupling of wet scavenging of sulphur to clouds in a numerical weather prediction model , 1993 .

[13]  H. Sundqvist.  Parameterization of Clouds in Large-Scale Numerical Models , 1993 .

[14]  Darren L. Jackson,et al.  A physical retrieval of cloud liquid water over the global oceans using special sensor microwave/imager (SSM/I) observations , 1993 .

[15]  I. Gultepe,et al.  Liquid Water Content and Temperature Relationship from Aircraft Observations and Its Applicability to GCMs , 1997 .

[16]  Variability in concentrations of cloud condensation nuclei in the marine cloud–topped boundary layer , 1993 .

[17]  S. Klein,et al.  The Seasonal Cycle of Low Stratiform Clouds , 1993 .

[18]  D. Randall,et al.  Liquid and Ice Cloud Microphysics in the CSU General Circulation Model , 1996 .

[19]  K. Liou,et al.  The role of cloud microphysical processes in climate: an assessment from a one-dimensional perspective , 1989 .

[20]  Robert Pincus,et al.  Precipitation in Stratocumulus Clouds: Observational and Modeling Results. , 1995 .

[21]  I. Mazin.  Cloud water content in continental clouds of middle latitudes , 1995 .

[22]  William R. Cotton,et al.  A Numerical Investigation of Several Factors Contributing to the Observed Variable Intensity of Deep Convection over South Florida , 1980 .

[23]  Olivier Boucher,et al.  The sulfate‐CCN‐cloud albedo effect , 1995 .

[24]  Andrew J. Heymsfield,et al.  High Albedos of Cirrus in the Tropical Pacific Warm Pool: Microphysical Interpretations from CEPEX and from Kwajalein, Marshall Islands , 1996 .

[25]  Kuo-Nan Liou,et al.  Ice microphysics and climatic temperature feedback , 1995 .

[26]  U. Lohmann,et al.  The sulfate-CCN-cloud albedo effect: a sensitivity study with two general circulation models , 1996 .

[27]  Hilding Sundqvist,et al.  A parameterization scheme for non-convective condensation including prediction of cloud water content , 1978 .

[28]  Kuan-Man Xu,et al.  Evaluation of cloudiness parameterizations using a cumulus ensemble model , 1991 .

[29]  R. Chervin,et al.  Global distribution of total cloud cover and cloud type amounts over the ocean , 1988 .

[30]  J. Slingo.  A cloud parametrization scheme derived from GATE data for use with a numerical model , 1980 .

[31]  K. Trenberth,et al.  Earth's annual global mean energy budget , 1997 .

[32]  F. Weng,et al.  Retrieval of cloud liquid water using the special sensor microwave imager (SSM/I) , 1994 .

[33]  G. Bonan.  The Land Surface Climatology of the NCAR Land Surface Model Coupled to the NCAR Community Climate Model , 1998 .

[34]  David H. Bromwich,et al.  Polar Radiation Budgets of the NCAR CCM3 , 1998 .

[35]  James J. Hack,et al.  The Dynamical Simulation of the NCAR Community Climate Model Version 3 (CCM3) , 1998 .

[36]  Bing Lin,et al.  Seasonal Variation of Liquid and Ice Water Path in Nonprecipitating Clouds over Oceans , 1996 .

[37]  W. Cotton,et al.  The Physics of the Marine Stratocumulus-Capped Mixed Layer. , 1987 .

[38]  Greg Michael McFarquhar,et al.  Microphysical Characteristics of Three Anvils Sampled during the Central Equatorial Pacific Experiment , 1996 .

[39]  J. Kristjánsson,et al.  Condensation and Cloud Parameterization Studies with a Mesoscale Numerical Weather Prediction Model , 1989 .

[40]  D. Legates,et al.  Mean seasonal and spatial variability in global surface air temperature , 1990 .

[41]  N. McFarlane,et al.  Sensitivity of Climate Simulations to the Parameterization of Cumulus Convection in the Canadian Climate Centre General Circulation Model , 1995, Data, Models and Analysis.