• Austin, P., Y. Wang, R. Pincus, and V. Kujala, 1995: Precipitation in stratocumulus clouds: Observational and modeling results. J. Atmos. Sci.,52, 2329–2352.

  • Baker, M. B., 1993: Variability in concentrations of cloud condensation nuclei in the marine cloud-topped boundary layer. Tellus,45B, 458–472.

  • Berge, E., 1993: Coupling of wet scavenging of sulphur to clouds in a numerical weather predition model. Tellus,45B, 1–22.

  • Bonan, G. B., 1998: The land surface climatology of the NCAR Land Surface Model (LSM) coupled to the NCAR Community Climate Model (CCM3). J. Climate,11, 1307–1326.

  • Boucher, O., and U. Lohmann, 1995: The sulfate-CCN-cloud albedo effect. A sensitivity study with two general circulation models. Tellus,47B, 281–300.

  • ——, H. Le Treut, and M. B. Baker, 1995: Precipitation and radiation modeling in a general circulation model: Introduction of cloud microphysical processes. J. Geophys. Res.,100 (D8), 16 395–16 414.

  • Briegleb, B. P., and D. H. Bromwich, 1998: Polar radiation budgets of the NCAR CCM3. J. Climate,11, 1246–1269.

  • Chen, C., and W. R. Cotton, 1987: The physics of the marine stratocumulus-capped mixed layer. J. Atmos. Sci.,44, 2951–2977.

  • Del Genio, A., M.-S. Yao, W. Kovari, and L. W. Lo, 1996: A prognostic cloud water parameterization for global climate models. J. Climate,9, 270–304.

  • Feichter, J., E. Kjellström, H. Rodhe, F. Dentener, J. Lelieveld, and G.-J. Roelofs, 1996: Simulation of the tropospheric sulfur cycle in a global climate model. Atmos. Environ.,30, 1693–1707.

  • Feigelson, E. M., 1978: Preliminary radiation model of a cloudy atmosphere. Part I: Structure of clouds and solar radiation. Beitr. Phys. Atmos.,51, 203–229.

  • Fowler, L., D. A. Randall, and S. A. Rutledge, 1996: Liquid and ice cloud microphysics in the CSU general circulation model. Part I: Model description and simulated microphysical processes. J. Climate,9, 489–529.

  • Ghan, S. J., and R. C. Easter, 1992: Computationally efficient approximations to stratiform cloud microphysics parameterization. Mon. Wea. Rev.,120, 1572–1582.

  • ——, L. R. Leung, and Q. Hu, 1997: Application of cloud microphysics to NCAR CCM2. J. Geophys. Res.,102, 21777–21799.

  • Greenwald, T. J., G. L. Stephens, and D. L. Jackson, 1993: A physical retrieval of cloud liquid water over global oceans using Special Sensor Microwave/Imager (SSM/I) observations. J. Geophys. Res.,98, 18 471–18 488.

  • Gultepe, I., and G. A. Isaac, 1997: Liquid water content and temperature relationship from aircraft observations and its applicability to GCMs. J. Climate,10, 446–452.

  • Hack, J. J., 1994: Parameterization of moist convection in the NCAR Community Climate Model, CCM2. J. Geophys. Res.,99, 5551–5568.

  • ——, 1998: Sensitivity of the simulated climate to a dianostic formulation for cloud liquid water. J. Climate,11, 1497–1515.

  • ——, J. T. Kiehl, and J. Hurrell, 1998: The hydrologic and Thermodynamic Characteristics of the NCAR CCM3. J. Climate,11, 1179–1206.

  • Heymsfield, A., 1993: Microphysical structures of stratiform and cirrus clouds. Aerosol-Cloud-Climate Interactions, P. V. Hobbs, Ed., Vol. 1, Academic Press, 97–119.

  • ——, and G. M. McFarquhar, 1996: High albedos of cirrus in the tropical Pacific warm pool: Microphysical interpretations from CEPEX and from Kwajalein, Marshall Islands. J. Atmos. Sci.,53, 2424–2451.

  • Hurrell, J. W., J. T. Kiehl, and J. J. Hack, 1998: The dynamical simulation of the NCAR Community Climate Model version 3 (CCM3). J. Climate,11, 1207–1236.

  • Kessler, E., 1969: On the Distribution and Continuity of Water Substance in Atmospheric Circulations. Meteor. Monogr., No. 32, Amer. Meteor. Soc., 84 pp.

  • Kiehl, J. T., and K. T. Trenberth, 1997: Earth’s annual global mean energy budget. Bull. Amer. Meteor. Soc.,78, 197–209.

  • ——, J. Hack, G. B. Bonan, B. A. Boville, B. P. Briegleb, D. L. Williamson, and P. J. Rasch, 1996: Description of the NCAR Community Climate Model (CCM3). NCAR Tech. Note, NCAR/TN-420+STR, 151 pp. [Available from National Center for Atmospheric Research, Boulder, CO 80307.].

  • ——, ——, and J. W. Hurrell, 1998: The energy budget of the NCAR Community Climate Model (CCM3). J. Climate,11, 1151–1178.

  • Klein, S. A., and D. L. Hartmann, 1993: The seasonal cycle of low stratiform clouds. J. Climate,6, 1587–1606.

  • Legates, D. R., and C. J. Willmott, 1990: Mean seasonal and spatial variability in global surface air temperature. Theor. Appl. Climatol.,10, 11–21.

  • Levkov, L., B. Rockel, H. Kapitza, and E. Raschke, 1992: 3D mesoscale numerical studies of cirrus and stratus clouds by their time and space evolution. Beitr. Phys. Atmos.,65, 35–58.

  • Lin, B., and W. B. Rossow, 1996: Seasonal variation of liquid and ice water path in nonprecipitating clouds over oceans. J. Climate,9, 2890–2902.

  • ——, R. R. Farley, and H. D. Orville, 1983: Bulk parameterization of the snow field in a cloud model. J. Climate Appl. Meteor.,22, 1065–1092.

  • Liou, K.-N., and S.-C. Ou, 1989: The role of cloud microphysical processes in climate: An assessment from a one-dimensional perspective. J. Geophys. Res.,94, 8599–8606.

  • Lohmann, U., and E. Roeckner, 1996: Design and performance of a new cloud microphysics scheme developed for the ECHAM general circulation model. Climate Dyn.,12, 557–572.

  • Mazin, I. P., 1995: Cloud water content in continental clouds of middle latitudes. Atmos. Res.,35, 283–297.

  • McFarquhar, G. M., and A. J. Heymsfield, 1996: Microphysical characteristics of three anvils sampled during the Central Equatorial Pacific Experiment. J. Atmos. Sci.,53, 2401–2423.

  • Ou, S.-C., and K.-N. Liou, 1995: Ice microphysics and climatic temperature feedback. Atmos. Res.,35, 127–138.

  • Slingo, J. M., 1980: A cloud parameterization scheme derived from GATE data for use with a numerical model. Quart. J. Roy. Meteor. Soc.,106, 747–770.

  • ——, 1987: The development and verification of a cloud prediction scheme for the ECMWF model. Quart. J. Roy. Meteor. Soc.,113, 899–927.

  • Sundqvist, H., 1978: A parameterization scheme for non-convective condensation including prediction of cloud water content. Quart. J. Roy. Meteor. Soc.,104, 677–690.

  • ——, 1988: Parameterization of condensation and associated clouds in models for weather prediction and general circulation simulation. Physically Based Modelling and Simulation of Climate and Climatic Change, M. E. Schlesinger, Ed., Vol. 1, Kluwer Academic, 433–461.

  • ——, 1993a: Inclusion of ice phase of hydrometors in cloud parameterization for mesoscale and largescale models. Beitr. Phys. Atmos.,66, 137–147.

  • ——, 1993b: Parameterization of clouds in large-scale numerical models. Aerosol-Cloud-Climate Interactions, P. V. Hobbs, Ed., Vol. 1, Academic Press, 175–203.

  • ——, E. Berge, and J. E. Kristjánsson, 1989: Condensation and cloud parameterization studies with a mesoscale numerical weather prediction model. Mon. Wea. Rev.,117, 1641–1657.

  • Tripoli, G. J., and W. R. Cotton, 1980: A numerical investigation of several factors contributing to the observed variable intensity of deep convection over south Florida. J. Appl. Meteor.,19, 1037–1063.

  • Warren, S. G., C. J. Hahn, J. London, R. M. Chervin, and R. L. Jenne, 1988: Global distribution of total cloud cover and cloud type amounts over the ocean. NCAR Tech. Note NCAR/TN-317+STR, 140 pp. [Available from National Center for Atmospheric Research, Boulder, CO 80307.].

  • Weng, F., and N. Grody, 1994: Retrieval of cloud liquid water using the Special Sensor Microwave/Imager (SSM/I). J. Geophys. Res.,99, 25 535–25 551.

  • Xu, K.-M., and S. K. Krueger, 1991: Evaluation of cloud models using a cumulus ensemble model. Mon. Wea. Rev.,119, 342–367.

  • Zhang, G. J., and N. A. McFarlane, 1995: Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian Climate Centre general circulation model. Atmos.Ocean,33, 407–446.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 421 136 6
PDF Downloads 162 76 8

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

View More View Less
  • 1 National Center for Atmospheric Research, Boulder,* Colorado
  • | 2 University of Oslo, Oslo, Norway
Restricted access

Abstract

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).

Corresponding author address: Dr. Philip J. Rasch, National Center for Atmospheric Research, Climate and Global Dynamics Division, P.O. Box 3000, Boulder, CO 80307-3000.

Email: pjr@ncar.ucar.edu

Abstract

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).

Corresponding author address: Dr. Philip J. Rasch, National Center for Atmospheric Research, Climate and Global Dynamics Division, P.O. Box 3000, Boulder, CO 80307-3000.

Email: pjr@ncar.ucar.edu

Save