Atmospheric and Oceanic Sciences Departmental Seminar Series
Numerically simulated orographic convection across multiple grey zones
a talk by
Prof. Daniel Kirshbaum
Associate Professor & Chair, Department of Atmospheric and Oceanic Sciences, McGill
As weather and climate models inexorably move to finer grids, they traverse so-called “grey zones”, or ranges of horizontal grid spacings where key processes transition from fully parameterized to fully explicit. Within these grey zones, scale-separation between the process of interest and the grid scale, a fundamental assumption of most subgrid parameterizations, breaks down. For the moist convection problem, which in the real atmosphere is characterized by horizontal scales from O(10 km) down to O(100 m), two important numerical grey zones exist: (i) the deep convection grey zone [O(10 km) to O(1 km)] and the turbulence grey zone [O(1 km) to O(100 m)]. In this study, idealized numerical simulations of orographic convection crossing both grey zones are conducted to quantify the errors stemming from inadequate resolution of cloud processes. The experiments consider two different mechanisms by which orography commonly initiates moist convection: mechanical and thermal forcing. To aid the diagnosis, a new method of diagnosing entrainment/detrainment in large-eddy simulations is proposed. For both convection-initiation mechanisms, cases with parameterized convection exhibit large biases in the timing, intensity, and/or horizontal distribution of clouds and precipitation. Although the results greatly improve for “convection-permitting” grids of O(1 km) and below, they do not always converge to a robust solution as the grid spacing is decreased. Robust solutions are found only in flows with well-developed turbulence fields prior to convection initiation. By contrast, in cumulus fields undergoing turbulent transition, the transition itself is highly sensitive to the grid spacing, which prevents numerical convergence. For these cases, O(10 m) or smaller grids may be required to achieve robust solutions.