Abstract

Kilometer-scale grid spacing is increasingly being used in regional numerical weather prediction and climate simulation. This resolution range is in the terra incognita, where energetic eddies are partially resolved and turbulence parameterization is a challenge. The Smagorinsky and turbulence ki... Show moreKilometer-scale grid spacing is increasingly being used in regional numerical weather prediction and climate simulation. This resolution range is in the terra incognita, where energetic eddies are partially resolved and turbulence parameterization is a challenge. The Smagorinsky and turbulence kinetic energy 1.5-order models are commonly used at this resolution range, but, as traditional eddy-diffusivity models, they can only represent forward-scattering turbulence (downgradient fluxes), whereas the dynamic reconstruction model (DRM), based on explicit filtering, permits countergradient fluxes. Here we perform large-eddy simulation of deep convection with 100-m horizontal grid spacing and use these results to evaluate the performance of turbulence schemes at 1-km horizontal resolution. The Smagorinsky and turbulence kinetic energy 1.5 schemes produce large-amplitude errors at 1-km resolution, due to excessively large eddy diffusivities attributable to the formulation of the squared moist Brunt-Vaisala frequency (N-m(2)). With this formulation in cloudy regions, eddy diffusivity can be excessively increased in "unstable" regions, which produce downward (downgradient) heat flux in a conditionally unstable environment leading to destabilization and further amplification of eddy diffusivities. A more appropriate criterion based on saturation mixing ratio helps eliminate this problem. However, shallow clouds cannot be simulated well in any case at 1-km resolution with the traditional models, whereas DRM allows for countergradient heat flux for both shallow and deep convection and predicts the distribution of clouds and fluxes satisfactorily. This is because DRM employs an eddy diffusivity model that is dynamically adjusted and a reconstruction approach that allows countergradient fluxes. Show less