Parameterizing deep convection using the assumed probability density function method
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 t...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2015-01-01
|
| Series: | Geoscientific Model Development |
| Online Access: | http://www.geosci-model-dev.net/8/1/2015/gmd-8-1-2015.pdf |
| Summary: | 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.
<br><br>
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.
<br><br>
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. |
|---|---|
| ISSN: | 1991-959X 1991-9603 |