Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Geosci. Model Dev., 8, 1-19, 2015
https://doi.org/10.5194/gmd-8-1-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Development and technical paper
06 Jan 2015
Parameterizing deep convection using the assumed probability density function method
R. L. Storer1, B. M. Griffin1, J. Höft1, J. K. Weber1, E. Raut1, V. E. Larson1, M. Wang2, and P. J. Rasch2 1University of Wisconsin – Milwaukee, Department of Mathematical Sciences, Milwaukee, WI, USA
2Pacific Northwest National Laboratory, Richland, WA, USA
Abstract. Due to their coarse horizontal resolution, present-day climate models must parameterize deep convection. This paper presents single-column simulations of deep convection using a probability density function (PDF) parameterization. The PDF parameterization predicts the PDF of subgrid variability of turbulence, clouds, and hydrometeors. That variability is interfaced to a prognostic microphysics scheme using a Monte Carlo sampling method.

The PDF parameterization is used to simulate tropical deep convection, the transition from shallow to deep convection over land, and midlatitude deep convection. These parameterized single-column simulations are compared with 3-D reference simulations. The agreement is satisfactory except when the convective forcing is weak.

The same PDF parameterization is also used to simulate shallow cumulus and stratocumulus layers. The PDF method is sufficiently general to adequately simulate these five deep, shallow, and stratiform cloud cases with a single equation set. This raises hopes that it may be possible in the future, with further refinements at coarse time step and grid spacing, to parameterize all cloud types in a large-scale model in a unified way.


Citation: Storer, R. L., Griffin, B. M., Höft, J., Weber, J. K., Raut, E., Larson, V. E., Wang, M., and Rasch, P. J.: Parameterizing deep convection using the assumed probability density function method, Geosci. Model Dev., 8, 1-19, https://doi.org/10.5194/gmd-8-1-2015, 2015.
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Short summary
Representing clouds in climate models is a challenging problem. It is particularly difficult to represent deep convective clouds and, historically, deep convective parameterization is separate from the representation of other cloud types. Here we use a single-column cloud model to simulate three deep convective cases, and two shallow cloud cases. The results look reasonable, demonstrating that it may be possible to use one parameterization within a climate model for all cloud types.
Representing clouds in climate models is a challenging problem. It is particularly difficult to...
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