• Barker, H. W., and Coauthors, 2003: Assessing 1D atmospheric solar radiative transfer models: Interpretation and handling of unresolved clouds. J. Climate, 16 , 26762699.

    • Search Google Scholar
    • Export Citation
  • Bergman, J. W., , and P. J. Rasch, 2002: Parameterizing vertically coherent cloud distributions. J. Atmos. Sci., 59 , 21652182.

  • Donner, L. J., 1993: A cumulus parameterization including mass fluxes, vertical momentum dynamics, and mesoscale effects. J. Atmos. Sci., 50 , 889906.

    • Search Google Scholar
    • Export Citation
  • GFDL Global Atmospheric Model Development Team, 2004: The new GFDL global atmosphere and land model AM2–LM2: Evaluation with prescribed SST simulations. J. Climate, 17 , 46414673.

    • Search Google Scholar
    • Export Citation
  • Gordon, N. D., , J. R. Norris, , C. P. Weaver, , and S. A. Klein, 2005: Cluster analysis of cloud regimes and characteristic dynamics of midlatitude synoptic systems in observations and a model. J. Geophys. Res., 110 .D15S17, doi:10.1029/2004JD005027.

    • Search Google Scholar
    • Export Citation
  • Hogan, R. J., , and A. J. Illingworth, 2000: Deriving cloud overlap statistics from radar. Quart. J. Roy. Meteor. Soc., 126 , 29032909.

  • Hogan, R. J., , and A. J. Illingworth, 2003: Parameterizing ice cloud inhomogeneity and the overlap of inhomogeneities using cloud radar data. J. Atmos. Sci., 60 , 756767.

    • Search Google Scholar
    • Export Citation
  • Jakob, C., , and S. A. Klein, 2000: A parametrization of the effects of cloud and precipitation overlap for use in general-circulation models. Quart. J. Roy. Meteor. Soc., 126 , 25252544.

    • Search Google Scholar
    • Export Citation
  • Klein, S. A., , and C. Jakob, 1999: Validation and sensitivities of frontal clouds simulated by the ECMWF model. Mon. Wea. Rev., 127 , 25142531.

    • Search Google Scholar
    • Export Citation
  • Larson, V. E., , J-C. Golaz, , H. Jiang, , and W. R. Cotton, 2005: Supplying local microphysics parameterizations with information about subgrid variability: Latin hypercube sampling. J. Atmos. Sci., 62 , 40104026.

    • Search Google Scholar
    • Export Citation
  • Mace, G. G., , and S. Benson-Troth, 2002: Cloud-layer overlap characteristics derived from long-term cloud radar data. J. Climate, 15 , 25052515.

    • Search Google Scholar
    • Export Citation
  • Matsumoto, M., , and T. Nishimura, 1998: Mersenne twister: A 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans. Model. Comput. Simul., 8 , 330.

    • Search Google Scholar
    • Export Citation
  • Mlawer, E. J., , S. J. Taubman, , P. D. Brown, , M. J. Iacono, , and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102D , 1666316682.

    • Search Google Scholar
    • Export Citation
  • Morcrette, J. J., , and C. Jakob, 2000: The response of the ECMWF model to changes in the cloud overlap assumption. Mon. Wea. Rev., 128 , 17071732.

    • Search Google Scholar
    • Export Citation
  • Pincus, R., , H. W. Barker, , and J. J. Morcrette, 2003: A fast, flexible, approximate technique for computing radiative transfer in inhomogeneous cloud fields. J. Geophys. Res., 108 .4376, doi:10.1029/2002JD003322.

    • Search Google Scholar
    • Export Citation
  • Pincus, R., , C. Hannay, , S. A. Klein, , K-M. Xu, , and R. S. Hemler, 2005: Overlap assumptions for assumed probability distribution function cloud schemes in large scale models. J. Geophys. Res., 110 .D15S09, doi:10.1029/2004JD005100.

    • Search Google Scholar
    • Export Citation
  • Press, W. H., , B. P. Flannery, , S. A. Teukolsky, , and W. T. Vetterling, 1986: Numerical Recipes: The Art of Scientific Computing. Cambridge University Press, 818 pp.

    • Search Google Scholar
    • Export Citation
  • Räisänen, P., , and H. W. Barker, 2004: Evaluation and optimization of sampling errors for the Monte Carlo Independent Column Approximation. Quart. J. Roy. Meteor. Soc., 130 , 20692085.

    • Search Google Scholar
    • Export Citation
  • Räisänen, P., , H. W. Barker, , M. F. Khairoutdinov, , J. Li, , and D. A. Randall, 2004: Stochastic generation of subgrid-scale cloudy columns for large-scale models. Quart. J. Roy. Meteor. Soc., 130 , 20472067.

    • Search Google Scholar
    • Export Citation
  • Räisänen, P., , H. W. Barker, , and J. N. S. Cole, 2005: The Monte Carlo Independent Column Approximation’s conditional random noise: Impact on simulated climate. J. Climate, 18 , 47154730.

    • Search Google Scholar
    • Export Citation
  • Rossow, W. B., , and R. A. Schiffer, 1999: Advances in understanding clouds from ISCCP. Bull. Amer. Meteor. Soc., 80 , 22612287.

  • Rossow, W. B., , C. Delo, , and B. Cairns, 2002: Implications of the observed mesoscale variations of clouds for the Earth’s radiation budget. J. Climate, 15 , 557585.

    • Search Google Scholar
    • Export Citation
  • Tian, L., , and J. A. Curry, 1989: Cloud overlap statistics. J. Geophys. Res., 94D , 99259935.

  • Tiedtke, M., 1996: An extension of cloud-radiation parameterization in the ECMWF model: The representation of subgrid-scale variations of optical depth. Mon. Wea. Rev., 124 , 745750.

    • Search Google Scholar
    • Export Citation
  • Tompkins, A. M., 2002: A prognostic parameterization for the subgrid-scale variability of water vapor and clouds in large-scale models and its use to diagnose cloud cover. J. Atmos. Sci., 59 , 19171942.

    • Search Google Scholar
    • Export Citation
  • Webb, M., , C. Senior, , S. Bony, , and J. J. Morcrette, 2001: Combining ERBE and ISCCP data to assess clouds in the Hadley Centre, ECMWF and LMD atmospheric climate models. Climate Dyn., 17 , 905922.

    • Search Google Scholar
    • Export Citation
  • Yu, W., , M. Doutriaux, , G. Seze, , H. LeTreut, , and M. Desbois, 1996: A methodology study of the validation of clouds in GCMs using ISCCP Satellite observations. Climate Dyn., 12 , 389401.

    • Search Google Scholar
    • Export Citation
  • Zhang, M. H., and Coauthors, 2005: Comparing clouds and their seasonal variations in 10 atmospheric general circulation models with satellite measurements. J. Geophys. Res., 110 .D15S02, doi:10.0129/2004JD005021.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 40 40 3
PDF Downloads 33 33 2

Using Stochastically Generated Subcolumns to Represent Cloud Structure in a Large-Scale Model

View More View Less
  • 1 CIRES/Climate Diagnostics Center, and NOAA/Earth System Research Laboratory, Boulder, Colorado
  • | 2 NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey
  • | 3 Atmospheric Sciences Division, Lawrence Livermore National Laboratory, Livermore, California
© Get Permissions
Restricted access

Abstract

A new method for representing subgrid-scale cloud structure in which each model column is decomposed into a set of subcolumns has been introduced into the Geophysical Fluid Dynamics Laboratory’s global atmospheric model AM2. Each subcolumn in the decomposition is homogeneous, but the ensemble reproduces the initial profiles of cloud properties including cloud fraction, internal variability (if any) in cloud condensate, and arbitrary overlap assumptions that describe vertical correlations. These subcolumns are used in radiation and diagnostic calculations and have allowed the introduction of more realistic overlap assumptions. This paper describes the impact of these new methods for representing cloud structure in instantaneous calculations and long-term integrations. Shortwave radiation computed using subcolumns and the random overlap assumption differs in the global annual average by more than 4 W m−2 from the operational radiation scheme in instantaneous calculations; much of this difference is counteracted by a change in the overlap assumption to one in which overlap varies continuously with the separation distance between layers. Internal variability in cloud condensate, diagnosed from the mean condensate amount and cloud fraction, has about the same effect on radiative fluxes as does the ad hoc tuning accounting for this effect in the operational radiation scheme. Long simulations with the new model configuration show little difference from the operational model configuration, while statistical tests indicate that the model does not respond systematically to the sampling noise introduced by the approximate radiative transfer techniques introduced to work with the subcolumns.

Corresponding author address: Dr. Robert Pincus, CIRES/Climate Diagnostics Center, 325 Broadway, R/CDC1, Boulder, CO 80305. Email: robert.pincus@colorado.edu

Abstract

A new method for representing subgrid-scale cloud structure in which each model column is decomposed into a set of subcolumns has been introduced into the Geophysical Fluid Dynamics Laboratory’s global atmospheric model AM2. Each subcolumn in the decomposition is homogeneous, but the ensemble reproduces the initial profiles of cloud properties including cloud fraction, internal variability (if any) in cloud condensate, and arbitrary overlap assumptions that describe vertical correlations. These subcolumns are used in radiation and diagnostic calculations and have allowed the introduction of more realistic overlap assumptions. This paper describes the impact of these new methods for representing cloud structure in instantaneous calculations and long-term integrations. Shortwave radiation computed using subcolumns and the random overlap assumption differs in the global annual average by more than 4 W m−2 from the operational radiation scheme in instantaneous calculations; much of this difference is counteracted by a change in the overlap assumption to one in which overlap varies continuously with the separation distance between layers. Internal variability in cloud condensate, diagnosed from the mean condensate amount and cloud fraction, has about the same effect on radiative fluxes as does the ad hoc tuning accounting for this effect in the operational radiation scheme. Long simulations with the new model configuration show little difference from the operational model configuration, while statistical tests indicate that the model does not respond systematically to the sampling noise introduced by the approximate radiative transfer techniques introduced to work with the subcolumns.

Corresponding author address: Dr. Robert Pincus, CIRES/Climate Diagnostics Center, 325 Broadway, R/CDC1, Boulder, CO 80305. Email: robert.pincus@colorado.edu

Save