Parameterizing deep convection using the assumed probability density function method
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.
- Publication Date:
- Research Org.:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR). Scientific Discovery through Advanced Computing (SciDAC)
- OSTI Identifier:
- 1227792
- Alternate Identifier(s):
- OSTI ID: 1195623
- Grant/Contract Number:
- SC0008668; SC0008323; AC06-76RL01830
- Resource Type:
- Published Article
- Journal Name:
- Geoscientific Model Development (Online)
- Additional Journal Information:
- Journal Name: Geoscientific Model Development (Online) Journal Volume: 8 Journal Issue: 1; Journal ID: ISSN 1991-9603
- Publisher:
- Copernicus Publications, EGU
- Country of Publication:
- Germany
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES
Citation Formats
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. Germany: N. p., 2015.
Web. doi:10.5194/gmd-8-1-2015.
Storer, R. L., Griffin, B. M., Höft, J., Weber, J. K., Raut, E., Larson, V. E., Wang, M., & Rasch, P. J. Parameterizing deep convection using the assumed probability density function method. Germany. https://doi.org/10.5194/gmd-8-1-2015
Storer, R. L., Griffin, B. M., Höft, J., Weber, J. K., Raut, E., Larson, V. E., Wang, M., and Rasch, P. J. Tue .
"Parameterizing deep convection using the assumed probability density function method". Germany. https://doi.org/10.5194/gmd-8-1-2015.
@article{osti_1227792,
title = {Parameterizing deep convection using the assumed probability density function method},
author = {Storer, R. L. and Griffin, B. M. and Höft, J. and Weber, J. K. and Raut, E. and Larson, V. E. and Wang, M. and Rasch, P. J.},
abstractNote = {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.},
doi = {10.5194/gmd-8-1-2015},
journal = {Geoscientific Model Development (Online)},
number = 1,
volume = 8,
place = {Germany},
year = {Tue Jan 06 00:00:00 EST 2015},
month = {Tue Jan 06 00:00:00 EST 2015}
}
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https://doi.org/10.5194/gmd-8-1-2015
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