Articles | Volume 8, issue 1
https://doi.org/10.5194/gmd-8-1-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-8-1-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Parameterizing deep convection using the assumed probability density function method
R. L. Storer
CORRESPONDING AUTHOR
University of Wisconsin – Milwaukee, Department of Mathematical Sciences, Milwaukee, WI, USA
B. M. Griffin
University of Wisconsin – Milwaukee, Department of Mathematical Sciences, Milwaukee, WI, USA
J. Höft
University of Wisconsin – Milwaukee, Department of Mathematical Sciences, Milwaukee, WI, USA
J. K. Weber
University of Wisconsin – Milwaukee, Department of Mathematical Sciences, Milwaukee, WI, USA
E. Raut
University of Wisconsin – Milwaukee, Department of Mathematical Sciences, Milwaukee, WI, USA
V. E. Larson
University of Wisconsin – Milwaukee, Department of Mathematical Sciences, Milwaukee, WI, USA
Pacific Northwest National Laboratory, Richland, WA, USA
P. J. Rasch
Pacific Northwest National Laboratory, Richland, WA, USA
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Cited
22 citations as recorded by crossref.
- Methodology to determine the coupling of continental clouds with surface and boundary layer height under cloudy conditions from lidar and meteorological data T. Su et al. 10.5194/acp-22-1453-2022
- Parameterizing microphysical effects on variances and covariances of moisture and heat content using a multivariate probability density function: a study with CLUBB (tag MVCS) B. Griffin & V. Larson 10.5194/gmd-9-4273-2016
- A PDF-Based Parameterization of Subgrid-Scale Hydrometeor Transport in Deep Convection M. Wong & M. Ovchinnikov 10.1175/JAS-D-16-0146.1
- A unified parameterization of clouds and turbulence using CLUBB and subcolumns in the Community Atmosphere Model K. Thayer-Calder et al. 10.5194/gmd-8-3801-2015
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- Impact of scale-aware deep convection on the cloud liquid and ice water paths and precipitation using the Model for Prediction Across Scales (MPAS-v5.2) L. Fowler et al. 10.5194/gmd-13-2851-2020
- Vertical overlap of probability density functions of cloud and precipitation hydrometeors M. Ovchinnikov et al. 10.1002/2016JD025158
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- Evaluating the relationship between topography and groundwater using outputs from a continental‐scale integrated hydrology model L. Condon & R. Maxwell 10.1002/2014WR016774
- A cloudy planetary boundary layer oscillation arising from the coupling of turbulence with precipitation in climate simulations X. Zheng et al. 10.1002/2017MS000993
- Using large eddy simulations to reveal the size, strength, and phase of updraft and downdraft cores of an Arctic mixed‐phase stratocumulus cloud E. Roesler et al. 10.1002/2016JD026055
- Linking Deep and Shallow Convective Mass Fluxes via an Assumed Entrainment Distribution in CAM5‐CLUBB: Parameterization and Simulated Precipitation Variability B. Yang et al. 10.1029/2020MS002357
- A Budget Analysis of the Variances of Temperature and Moisture in Precipitating Shallow Cumulus Convection V. Schemann & A. Seifert 10.1007/s10546-016-0230-1
- A Framework for Convection and Boundary Layer Parameterization Derived from Conditional Filtering J. Thuburn et al. 10.1175/JAS-D-17-0130.1
- A Machine Learning Assisted Development of a Model for the Populations of Convective and Stratiform Clouds S. Hagos et al. 10.1029/2019MS001798
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- Ongoing Breakthroughs in Convective Parameterization C. Rio et al. 10.1007/s40641-019-00127-w
- GTS v1.0: a macrophysics scheme for climate models based on a probability density function C. Shiu et al. 10.5194/gmd-14-177-2021
- Impact of a shallow groundwater table on the global water cycle in the IPSL land–atmosphere coupled model F. Wang et al. 10.1007/s00382-017-3820-9
- Evaluation of Subgrid-Scale Hydrometeor Transport Schemes Using a High-Resolution Cloud-Resolving Model M. Wong et al. 10.1175/JAS-D-15-0060.1
- A flexible importance sampling method for integrating subgrid processes E. Raut & V. Larson 10.5194/gmd-9-413-2016
- Aerosol Impacts on Mesoscale Convective Systems Forming Under Different Vertical Wind Shear Conditions Q. Chen et al. 10.1029/2018JD030027
22 citations as recorded by crossref.
- Methodology to determine the coupling of continental clouds with surface and boundary layer height under cloudy conditions from lidar and meteorological data T. Su et al. 10.5194/acp-22-1453-2022
- Parameterizing microphysical effects on variances and covariances of moisture and heat content using a multivariate probability density function: a study with CLUBB (tag MVCS) B. Griffin & V. Larson 10.5194/gmd-9-4273-2016
- A PDF-Based Parameterization of Subgrid-Scale Hydrometeor Transport in Deep Convection M. Wong & M. Ovchinnikov 10.1175/JAS-D-16-0146.1
- A unified parameterization of clouds and turbulence using CLUBB and subcolumns in the Community Atmosphere Model K. Thayer-Calder et al. 10.5194/gmd-8-3801-2015
- Effects of spatially distributed sectoral water management on the redistribution of water resources in an integrated water model N. Voisin et al. 10.1002/2016WR019767
- Impact of scale-aware deep convection on the cloud liquid and ice water paths and precipitation using the Model for Prediction Across Scales (MPAS-v5.2) L. Fowler et al. 10.5194/gmd-13-2851-2020
- Vertical overlap of probability density functions of cloud and precipitation hydrometeors M. Ovchinnikov et al. 10.1002/2016JD025158
- A new subgrid-scale representation of hydrometeor fields using a multivariate PDF B. Griffin & V. Larson 10.5194/gmd-9-2031-2016
- Evaluating the relationship between topography and groundwater using outputs from a continental‐scale integrated hydrology model L. Condon & R. Maxwell 10.1002/2014WR016774
- A cloudy planetary boundary layer oscillation arising from the coupling of turbulence with precipitation in climate simulations X. Zheng et al. 10.1002/2017MS000993
- Using large eddy simulations to reveal the size, strength, and phase of updraft and downdraft cores of an Arctic mixed‐phase stratocumulus cloud E. Roesler et al. 10.1002/2016JD026055
- Linking Deep and Shallow Convective Mass Fluxes via an Assumed Entrainment Distribution in CAM5‐CLUBB: Parameterization and Simulated Precipitation Variability B. Yang et al. 10.1029/2020MS002357
- A Budget Analysis of the Variances of Temperature and Moisture in Precipitating Shallow Cumulus Convection V. Schemann & A. Seifert 10.1007/s10546-016-0230-1
- A Framework for Convection and Boundary Layer Parameterization Derived from Conditional Filtering J. Thuburn et al. 10.1175/JAS-D-17-0130.1
- A Machine Learning Assisted Development of a Model for the Populations of Convective and Stratiform Clouds S. Hagos et al. 10.1029/2019MS001798
- A low-dimensional subsurface model for saturated and unsaturated flow processes: ability to address heterogeneity S. Weill et al. 10.1007/s10596-017-9613-8
- Ongoing Breakthroughs in Convective Parameterization C. Rio et al. 10.1007/s40641-019-00127-w
- GTS v1.0: a macrophysics scheme for climate models based on a probability density function C. Shiu et al. 10.5194/gmd-14-177-2021
- Impact of a shallow groundwater table on the global water cycle in the IPSL land–atmosphere coupled model F. Wang et al. 10.1007/s00382-017-3820-9
- Evaluation of Subgrid-Scale Hydrometeor Transport Schemes Using a High-Resolution Cloud-Resolving Model M. Wong et al. 10.1175/JAS-D-15-0060.1
- A flexible importance sampling method for integrating subgrid processes E. Raut & V. Larson 10.5194/gmd-9-413-2016
- Aerosol Impacts on Mesoscale Convective Systems Forming Under Different Vertical Wind Shear Conditions Q. Chen et al. 10.1029/2018JD030027
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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...