Cloud-Resolving Model Simulations of Year-long Cloud Properties and Radiative Effects of Cloud Distributions over ARM SGP

General circulation models (GCMs) predict cloud cover fractions and hydrometeor concentrations only in discrete vertical layers where clouds are assumed to be horizontally homogeneous in a coarse grid. They do not explicitly specify vertical geometric associations or horizontal optical variations of clouds. Subsequently, clouds within a GCM grid are simulated as a single effective volume that impacts radiation using various vertical overlap assumptions. The parameterization of cloud vertical overlap and horizontal inhomogeneity in the radiation schemes of GCMs has been a long-standing challenge. The inclusion of subgrid cloud variability in the radiation calculation for GCMs requires the knowledge of cloud distribution under different climate regimes, which is not yet available from observations. The year-long cloud-resolving model (CRM) simulation forced with the Atmospheric Radiation Measurement (ARM) large-scale forcing provides a unique approach to generating the long-term thermodynamic and dynamic consistent cloud properties, documenting the characteristics of cloud horizontal inhomogeneity and vertical overlap, and evaluating their effects on the radiative fluxes and heating rates over a GCM grid. In this study, statistical analysis is conducted to evaluate the year-long CRM simulations against several value added products from ARM. The CRM-produced cloud liquid and ice water paths for overcast and non-precipitating clouds agree with observations in terms of monthly and diurnal cloud occurrence frequency. The vertical distribution of liquid and ice water simulated by CRM is also generally consistent with that from observational estimates. Significant radiative effects of cloud horizontal inhomogeneity are quantified by the diagnostic radiation calculation using the CRM outputs. The assumption of homogeneous clouds overestimates the total cloud albedo and underestimates the outgoing longwave flux. The redistribution of CRM cloud fraction profile based on three existing overlap assumptions (maximum, minimum, and random) indicates that none of them is able to reproduce the CRM total cloud fraction. The maximum overlap assumption systematically underestimates the total cloud fraction, while the random and minimum overlap assumptions systematically overestimate the total cloud fraction.