Investigating the Scale Adaptivity of a Size-Filtered Mass Flux Parameterization in the Gray Zone of Shallow Cumulus Convection

Maren Brast Institute for Geophysics and Meteorology, University of Cologne, Cologne, Germany

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Vera Schemann Institute for Geophysics and Meteorology, University of Cologne, Cologne, Germany

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Roel A. J. Neggers Institute for Geophysics and Meteorology, University of Cologne, Cologne, Germany

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Abstract

In this study, the scale adaptivity of a new parameterization scheme for shallow cumulus clouds in the gray zone is investigated. The eddy diffusivity/multiple mass flux [ED(MF)n] scheme is a bin-macrophysics scheme in which subgrid transport is formulated in terms of discretized size densities. While scale adaptivity in the ED component is achieved using a pragmatic blending approach, the MF component is filtered such that only the transport by plumes smaller than the grid size is maintained. For testing, ED(MF)n is implemented into a large-eddy simulation (LES) model, replacing the original subgrid scheme for turbulent transport. LES thus plays the role of a nonhydrostatic testing ground, which can be run at different resolutions to study the behavior of the parameterization scheme in the boundary layer gray zone. In this range, convective cumulus clouds are partially resolved. The authors find that for quasi-equilibrium marine subtropical conditions at high resolutions, the clouds and the turbulent transport are predominantly resolved by the LES. This partitioning changes toward coarser resolutions, with the representation of shallow cumulus clouds gradually becoming completely carried by the ED(MF)n. The way the partitioning changes with grid spacing matches the behavior diagnosed in coarse-grained LES fields, suggesting that some scale adaptivity is captured. Sensitivity studies show that the scale adaptivity of the ED closure is important and that the location of the gray zone is found to be moderately sensitive to some model constants.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Roel A. J. Neggers, neggers@meteo.uni-koeln.de

Abstract

In this study, the scale adaptivity of a new parameterization scheme for shallow cumulus clouds in the gray zone is investigated. The eddy diffusivity/multiple mass flux [ED(MF)n] scheme is a bin-macrophysics scheme in which subgrid transport is formulated in terms of discretized size densities. While scale adaptivity in the ED component is achieved using a pragmatic blending approach, the MF component is filtered such that only the transport by plumes smaller than the grid size is maintained. For testing, ED(MF)n is implemented into a large-eddy simulation (LES) model, replacing the original subgrid scheme for turbulent transport. LES thus plays the role of a nonhydrostatic testing ground, which can be run at different resolutions to study the behavior of the parameterization scheme in the boundary layer gray zone. In this range, convective cumulus clouds are partially resolved. The authors find that for quasi-equilibrium marine subtropical conditions at high resolutions, the clouds and the turbulent transport are predominantly resolved by the LES. This partitioning changes toward coarser resolutions, with the representation of shallow cumulus clouds gradually becoming completely carried by the ED(MF)n. The way the partitioning changes with grid spacing matches the behavior diagnosed in coarse-grained LES fields, suggesting that some scale adaptivity is captured. Sensitivity studies show that the scale adaptivity of the ED closure is important and that the location of the gray zone is found to be moderately sensitive to some model constants.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Roel A. J. Neggers, neggers@meteo.uni-koeln.de
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