The Sub-Adiabatic Model as a Conceptual Model for Evaluating the Radiative Effects of Low-Level Clouds in a High-Resolution Model

Clouds and their interaction with radiation yet comprise a crucial source of uncertainty in climate model predictions, partly due to limited understanding regarding their properties and their vertical and horizontal distributions (IPCC, 2013). Most General Circulation Models (GCMs) employ the plane-parallel model for the parametrization of clouds, not fully taking into consideration the vertical and horizontal inhomogeneities. In this work, the highly-resolved ICON-LEM (ICOsahedral Nonhydrostatic Large Eddy Model) atmospheric model (Heinze et al., 2017) has been employed and a special emphasis on low-level clouds is given. Our aim is to demonstrate that the vertical structure of the modeled cloud properties is on average well approximated by the sub-adiabatic model and it can be used as a concept for evaluating the radiative effects of clouds. According to the sub-adiabatic model, the fundamental quantities are the sub-adiabatic fraction, the droplet number concentration, the cloud geometrical thickness, and the liquid water path approximates the cloud structure and enables the determination of the cloud optical depth. The foregoing quantities comprise the minimum set of parameters for the representation of low-level clouds towards the calculation of the radiative effects. Firstly, we explore the uncertainties in the derivation of these properties and their impact on the calculation of the radiative effects. In addition, the influence of different vertical cloud models on the resulting radiative fluxes is assessed. Considering the cloud parameterizations for radiative transfer simulations (i.e., cloud properties constant or linearly stratified for a given cloud geometrical depth), the sub-adiabatic model is confronted against the vertical homogeneous model (vertical homogeneous properties) and the pure adiabatic one (liquid water content linearly increases with height). The aforementioned efforts allow to regard errors in the cloud radiative effects to errors in the model representation of clouds. For the computation of the cloud radiative effects the Rapid Radiative Transfer Model for GCM applications (RRTMG) have been utilized. Simulations were conducted in an offline mode.