A new algorithm to interface subgrid variability and microphysics

 

Author

Vincent Larson — University of Wisconsin, Milwaukee

Category

Modeling

Description

The interfacing of subgrid-scale variability and microphysics in climate models could be improved in several respects. For instance, present-day climate models often neglect within-cloud variability and correlations between hydrometeor species.

To address these problems, we have developed a new algorithm (“SILHS”) that generates profiles of temperature, moisture, hydrometeors, and so forth that are drawn from a distribution of subgrid variability. These sub-column profiles are subsequently fed into a microphysics parameterization. Then microphysical process rates are computed for each sub-column profile, averaged together to form a grid box mean, and fed back into the mean evolution equations.

The new algorithm is tested in single-column simulations of drizzling shallow stratocumulus and cumulus clouds. Although the algorithm does inject statistical noise into the solutions, the time-averaged profiles match compare satisfactorily with a limited but exact analytic method that interfaces the subgrid variability and the microphysics.