Idealized simulations are used to determine the sensitivity of moist orographic convection to horizontal grid spacing Δh. In simulated mechanically (MECH) and thermally (THERM) forced convection over an isolated ridge, Δh is varied systematically over both the deep-convection (Δh ~ 10–1 km) and turbulence (Δh ~ 1 km–100 m) gray zones. To aid physical interpretation, a new parcel-based bulk entrainment/detrainment diagnosis for horizontally heterogeneous flows is developed. Within the deep-convection gray zone, the Δh sensitivity is dominated by differences in parameterized versus explicit convection; the former initiates convection too far upstream of the ridge (MECH) and too early in the diurnal heating cycle (THERM). These errors stem in part from a large underprediction of parameterized entrainment and detrainment. Within the turbulence gray zone, sensitivities to Δh arise from the representation of both subcloud- and cloud-layer turbulence. As Δh is decreased, MECH exhibits stronger cloud-layer entrainment to enhance the convective mass flux Mco, while THERM exhibits stronger detrainment to suppress Mco and delay convection initiation. The latter is reinforced by increased subcloud turbulence at smaller Δh, which leads to drying and diffusion of the central updraft responsible for initiating moist convection. Numerical convergence to a robust solution occurs only in THERM, which develops a fully turbulent flow with a resolved inertial subrange (for Δh ≤ 250 m). In MECH, by contrast, turbulent transition occurs within the orographic cloud, the details of which depend on both physical location and Δh.
Kirshbaum, Daniel J.. "Numerical Simulations of Orographic Convection across Multiple Gray Zones." Journal of the Atmospheric Sciences, vol. 77, no. 10, Sep. 2020. https://doi.org/10.1175/jas-d-20-0035.1
Kirshbaum, Daniel J. (2020). Numerical Simulations of Orographic Convection across Multiple Gray Zones. Journal of the Atmospheric Sciences, 77(10). https://doi.org/10.1175/jas-d-20-0035.1
Kirshbaum, Daniel J., "Numerical Simulations of Orographic Convection across Multiple Gray Zones," Journal of the Atmospheric Sciences 77, no. 10 (2020), https://doi.org/10.1175/jas-d-20-0035.1
@article{osti_1853446,
author = {Kirshbaum, Daniel J.},
title = {Numerical Simulations of Orographic Convection across Multiple Gray Zones},
annote = {Idealized simulations are used to determine the sensitivity of moist orographic convection to horizontal grid spacing Δh. In simulated mechanically (MECH) and thermally (THERM) forced convection over an isolated ridge, Δh is varied systematically over both the deep-convection (Δh ~ 10–1 km) and turbulence (Δh ~ 1 km–100 m) gray zones. To aid physical interpretation, a new parcel-based bulk entrainment/detrainment diagnosis for horizontally heterogeneous flows is developed. Within the deep-convection gray zone, the Δh sensitivity is dominated by differences in parameterized versus explicit convection; the former initiates convection too far upstream of the ridge (MECH) and too early in the diurnal heating cycle (THERM). These errors stem in part from a large underprediction of parameterized entrainment and detrainment. Within the turbulence gray zone, sensitivities to Δh arise from the representation of both subcloud- and cloud-layer turbulence. As Δh is decreased, MECH exhibits stronger cloud-layer entrainment to enhance the convective mass flux Mco, while THERM exhibits stronger detrainment to suppress Mco and delay convection initiation. The latter is reinforced by increased subcloud turbulence at smaller Δh, which leads to drying and diffusion of the central updraft responsible for initiating moist convection. Numerical convergence to a robust solution occurs only in THERM, which develops a fully turbulent flow with a resolved inertial subrange (for Δh ≤ 250 m). In MECH, by contrast, turbulent transition occurs within the orographic cloud, the details of which depend on both physical location and Δh.},
doi = {10.1175/jas-d-20-0035.1},
url = {https://www.osti.gov/biblio/1853446},
journal = {Journal of the Atmospheric Sciences},
issn = {ISSN 0022-4928},
number = {10},
volume = {77},
place = {United States},
publisher = {American Meteorological Society},
year = {2020},
month = {09}}