Statistically Steady State Large‐Eddy Simulations Forced by an Idealized GCM: 1. Forcing Framework and Simulation Characteristics

Abstract

Using large‐eddy simulations (LES) systematically has the potential to inform parameterizations of subgrid‐scale processes in general circulation models (GCMs), such as turbulence, convection, and clouds. Here we show how LES can be run to simulate grid columns of GCMs to generate LES across a cross section of dynamical regimes. The LES setup approximately replicates the thermodynamic and water budgets in GCM grid columns. Resolved horizontal and vertical transports of heat and water and large‐scale pressure gradients from the GCM are prescribed as forcing in the LES. The LES are forced with prescribed surface temperatures, but atmospheric temperature and moisture are free to adjust, reducing the imprinting of GCM fields on the LES. In both the GCM and LES, radiative transfer is treated in a unified but idealized manner (semigray atmosphere without water vapor feedback or cloud radiative effects). We show that the LES in this setup reaches statistically steady states without nudging to thermodynamic GCM profiles. The steady states provide training data for developing GCM parameterizations. The same LES setup also provides a good basis for studying the cloud response to global warming.

Stormscapes

“…Cloud fraction is a widely used metric in meteorology and environmental sciences to demarcate different cloud types. As inspiration to model scenes for which we calculated cloud fraction profiles we used reconstructions from Lidar and infrared satellite observations [Cesana et al 2019], as well as generated data by established simulations in meteorology [Shen et al 2020]. Cloud fraction is defined as the percentage of grid cells rendered as a cloud (q c > 0) in the whole grid space.…”

Seasonal cycle of idealized polar clouds: large eddy simulations driven by a GCM

“…The model uses an anelastic framework, and it ensures closed total water specific humidity q t and specific entropy s budgets. PyCLES has been used successfully to simulate subtropical marine BL clouds (Tan et al, 2016(Tan et al, , 2017Pressel et al, 2017;Schneider et al, 2019) and deep convective clouds (Shen et al, 2020).…”

“…In order to include large-scale dynamics in the limited-domain of LES, we use timevarying large-scale fluxes simulated by the GCM. The details of the forcing framework are described in Shen et al (2020). In summary, we use LES to simulate a single grid column of a GCM, but with processes that are parameterized in the GCM (e.g., convection, condensation, and boundary layer turbulence) resolved in the LES.…”

“…As a first step, we choose an idealized approach that only captures essential processes, including large-scale circulations, a closed surface energy budget, sea ice, and mixedphase microphysics. Following Shen et al (2020), we use a GCM with simple radiation and convection schemes but without clouds, to provide horizontal and vertical advection of heat and moisture resolved by the GCM as forcing terms in the LES. Therefore, we can treat each LES as an idealized single GCM column, with turbulent fluxes resolved rather than being parameterized as in the GCM.…”

“…The simplification in radiation allows the two models to achieve closed energy budgets easily so that they have energetically consistent, though not necessarily realistic, mean state climates. The LES can also provide training data for developing and refining GCM parameterizations (Schneider et al, 2017;Shen et al, 2020).…”