Postprocessed large-eddy simulation data from: Connection between mass flux transport and eddy diffusivity in convective atmospheric boundary layers
Li, Qi; Cheng, Yu; Gentine, Pierre
Turbulence parameterizations for convective boundary layer in coarse-scale atmospheric models usually consider a combination of the eddy-diffusive transport and a non-local transport, typically in the form of a mass flux term, such as the widely adopted eddy-diffusivity mass-flux (EDMF) approach. These two types of turbulent transport are generally considered to be independent of each other. Using results from large-eddy simulations, here we show that a Taylor series expansion of the updraft and downdraft mass-flux transport can be used to approximate the eddy-diffusivity transport in the atmospheric surface layer and the lower part of the mixed layer, connecting both eddy-diffusivity and mass-flux transport theories in convective conditions, which also quantifies departure from the Monin-Obukhov similarity in the surface layer. The study provides a theoretical support for a unified EDMF parameterization applied to both the surface layer and mixed layer and highlights important correction required for surface models relying on Monin-Obukhov similarity. This dataset supports that study.
PG would like to acknowledge funding from the National Science Foundation (NSF CAREER, EAR-1552304) and from the Department of Energy (DOE Early Career, DE-SC00142013). The simulations were performed on the computing clusters of the National Center of Atmospheric Research under Project UCLB0017. QL would like to acknowledge funding from the National Science Foundation (NSF-AGS 2028644 and NSF-CBET 2028842) and the computational resources provided by Cheyenne by the National Center for Atmospheric Research (UCOR00029).
large-eddy simulation; mass flux transport; conditional analysis
Li, Q., Cheng, Y., & Gentine, P. (2021). Connection between mass flux transport and eddy diffusivity in convective atmospheric boundary layers. Geophysical Research Letters, 48, e2020GL092073. https://doi.org/10.1029/2020GL092073
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