Although turbulence processes have been extensively studied for the boundary layer, there are few studies that evaluate the turbulence parameterization inside convective clouds in atmospheric models. Yet, turbulence can be strong inside cumulus and cumulonimbus. This study aims at evaluating the parameterization of subgrid turbulence in deep convective clouds simulated by numerical cloud resolving model at kilometer-scale resolution.
First, we have characterized the turbulence representation in deep convective clouds. For that, a Large-Eddy Simulation (LES) using simplified atmospheric conditions has been performed with the Meso-NH model (Lafore et al, 1997) to serve as a reference simulation of deep convection (Fig. a). This LES with a 50-m grid spacing is used to compute the turbulent fluxes at different coarser horizontal resolutions (500 m, 1 km, and 2 km). Vertical turbulent fluxes of liquid-ice water potential temperature and total non-precipitating water mixing ratio exhibit counter-gradient structures, indicative of nonlocal turbulence.
Second, a diagnostic assessment, from the reference LES fields, of the current turbulence parameterization (Cuxart et al, 2000) used in the Meso-NH model at these coarser resolutions shows that turbulent kinetic energy is largely underestimated in the clouds, related to an underestimation of thermal production. The counter-gradient structures of vertical turbulent fluxes are not reproduced, indeed, the local K-gradient formulation is not suitable. Alternative parameterizations of some turbulent fluxes are then tested. In particular, a parameterization based on horizontal gradients of resolved variables as proposed by Moeng (2014), gives a better representation of the thermal production of turbulence in the clouds (Fig. b,c).
Third, the on-line evaluation from model runs with 2-km, 1-km, and 500-m horizontal grid spacing confirms the improvement when using the modified scheme (H-gradient formulation), with an increase of subgrid turbulence and a decrease of vertical velocities in convective clouds (Verrelle et al, 2017). This modified scheme is also assessed using Meso-NH simulations at kilometer-scale resolutions for real cases of deep convection. Special focus is given on the IOP16a observed during the HyMeX campaign (Ducrocq et al, 2014). Comparison with observed data enables the impact of this new scheme on convective systems to be objectively assessed. The new scheme enhances the subgrid thermal production of turbulence with a better representation of counter-gradient areas and reduces the vertical velocity inside the clouds. The enhanced turbulent mixing modifies the entrainment and detrainment rates and produces more developed anvils with increased values of ice and snow, which are more realistic. It also affects the cold pool under the convective cells.
Cuxart, J., P. Bougeault, and J.-L. Redelsperger, 2000: A turbulence scheme allowing for mesoscale and large-eddy simulations. Quarterly Journal of the Royal Meteorological Society, 126 (562), 1–30
Ducrocq, V., and Coauthors, 2014: HyMeX-SOP1: The field campaign dedicated to heavy precipitation and flash flooding in the Northwestern Mediterranean. Bulletin of the American Meteorological Society, 95 (7), 10831100, doi:10.1175/bams-d-12-00244.1, URL http://dx.doi.org/10.1175/BAMS-D-12-00244.1
Lafore, J. P., J. Stein, N. Asencio, P. Bougeault, V. Ducrocq, J. Duron, C. Fischer, P. Hereil, P. Mascart, J. P. Pinty, J. L. Redelsperger, E. Richard, and J. Vila-Guerau de Arellano, 1998: The Meso-NH Atmospheric Simulation System. Part I: Adiabatic formulation and control simulations. Annales Geophysicae, 16, 90-109
Moeng, C.-H., 2014: A closure for updraft-downdraft representation of subgrid-scale fluxes in cloud-resolving models. Mon. Wea. Rev., 142 (2), 703–715, doi:10.1175/mwr-d-13-00166.1, URL http://dx.doi.org/10.1175/MWR-D-13-00166.1
Verrelle, A., D. Ricard, and C. Lac, 2017: Evaluation and Improvement of Turbulence Parameterization inside Deep Convective Clouds at Kilometer-Scale Resolution. Mon. Wea. Rev., 145, 3947–3967, https://doi.org/10.1175/MWR-D-16-0404.1
Figure. (a) LES (with 50-m grid spacing) of deep convection at t = 175 min: vertical cross section for the vertical velocity (m s-1, shading). The cloud boundaries are represented as the sum of cloud and ice water mixing ratios above 0.001 g kg-1 (black contours). Vertical profiles averaged inside the clouds of the subgrid turbulent vertical fluxes of (b) heat (K m s-1) and (c) total nonprecipitating water mixing ratio (g kg-1 m s-1) obtained by 1-km box filtering: reference fluxes from the LES (black lines), parameterized fluxes from the K-gradient formulation (Cuxart et al, 2000) (blue lines), and from the product of horizontal gradients with CΔx = 5Δx2 /12 as in Moeng (2014) (green lines) and with CΔx = 7Δx2/12 (gray lines).