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Published September 2020 | Supplemental Material + Submitted + Published
Journal Article Open

Unified Entrainment and Detrainment Closures for Extended Eddy-Diffusivity Mass-Flux Schemes

Abstract

We demonstrate that an extended eddy‐diffusivity mass‐flux (EDMF) scheme can be used as a unified parameterization of subgrid‐scale turbulence and convection across a range of dynamical regimes, from dry convective boundary layers, through shallow convection, to deep convection. Central to achieving this unified representation of subgrid‐scale motions are entrainment and detrainment closures. We model entrainment and detrainment rates as a combination of turbulent and dynamical processes. Turbulent entrainment/detrainment is represented as downgradient diffusion between plumes and their environment. Dynamical entrainment/detrainment is proportional to a ratio of a relative buoyancy of a plume and a vertical velocity scale, that is modulated by heuristic nondimensional functions which represent their relative magnitudes and the enhanced detrainment due to evaporation from clouds in drier environment. We first evaluate the closures off‐line against entrainment and detrainment rates diagnosed from large eddy simulations (LESs) in which tracers are used to identify plumes, their turbulent environment, and mass and tracer exchanges between them. The LES are of canonical test cases of a dry convective boundary layer, shallow convection, and deep convection, thus spanning a broad rangeof regimes. We then compare the LES with the full EDMF scheme, including the new closures, in a single‐column model (SCM). The results show good agreement between the SCM and LES in quantities that are key for climate models, including thermodynamic profiles, cloud liquid water profiles, and profiles of higher moments of turbulent statistics. The SCM also captures well the diurnal cycle of convection and the onset of precipitation.

Additional Information

© 2020 The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Issue Online: 14 September 2020; Version of Record online: 14 September 2020; Accepted manuscript online: 04 August 2020; Manuscript accepted: 29 July 2020; Manuscript revised: 09 July 2020; Manuscript received: 01 May 2020. This research was made possible by the generosity of Eric and Wendy Schmidt by recommendation of the Schmidt Futures program, by Earthrise Alliance, Mountain Philanthropies, the Paul G. Allen Family Foundation, and the National Science Foundation (NSF, Award AGS‐1835860). I. L. would like to thank the Resnick Sustainability Institute at Caltech for fellowship support. Parts of the research were carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004) and funded through the internal Research and Technology Development program. The comments provided by Nadir Jeevanjee and one more anonymous reviewer greatly improved the final form of this work. © 2020. California Institute of Technology. Government sponsorship acknowledged. Data Availability Statements: The PyCLES code used to generate LES results is available at this site (climate-dynamics.org/software/#pycles). The SCM code is available at this site (https://doi.org/10.5281/zenodo.3789011).

Attached Files

Published - 2020MS002162.pdf

Submitted - essoar.10502905.1.pdf

Supplemental Material - jame21196-sup-0001-2020ms002162-si.tex

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Additional details

Created:
August 22, 2023
Modified:
October 23, 2023