Using stochastically-generated subcolumns to represent cloud structure in a large-scale model
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 climate 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{sup 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.
- Research Organization:
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- W-7405-ENG-48
- OSTI ID:
- 898035
- Report Number(s):
- UCRL-JRNL-217661; TRN: US200706%%211
- Journal Information:
- Monthly Weather Review, vol. 134, no. 12, December 20, 2006, pp. 3644-3656, Journal Name: Monthly Weather Review, vol. 134, no. 12, December 20, 2006, pp. 3644-3656
- Country of Publication:
- United States
- Language:
- English
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