Open AccessUncertainty of SW Cloud Radiative Effect in Atmospheric Models Due to the Parameterization of Liquid Cloud Optical Properties
Author(s) -
Jahangir E.,
Libois Q.,
Couvreux F.,
Vié B.,
SaintMartin D.
Publication year - 2021
Publication title -
journal of advances in modeling earth systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.03
H-Index - 58
ISSN - 1942-2466
DOI - 10.1029/2021ms002742
Subject(s) - radiative transfer , shortwave , liquid water content , effective radius , parametrization (atmospheric modeling) , radiative forcing , optical depth , environmental science , computational physics , cloud computing , atmospheric sciences , absorptance , physics , meteorology , aerosol , reflectivity , optics , computer science , astrophysics , galaxy , operating system
Clouds are largely responsible for the spread of climate models predictions. Here we focus on the uncertainties in cloud shortwave radiative effect due to the parameterization of liquid cloud single scattering properties (SSPs) from liquid water content (LWC) and droplet number concentration ( N ), named parameterization of cloud optical properties. Uncertainties arise from not accounting for the droplet size distribution (DSD)—which affects the estimation of the effective radius ( r eff ) and modulates the r eff ‐dependency of the SSPs—and from averaging SSPs over wide spectral bands. To assess these uncertainties a series of r eff ‐dependent SSPs parameterizations corresponding to various DSDs and spectral averaging methods are derived and implemented in a radiative code. Combined with the DSD‐dependent estimation of r eff they are used to compute the bulk radiative properties (reflectance, transmittance, absorptance) of various clouds (defined in terms of LWC and N ), including a homogeneous cloud, more realistic case studies, and outputs of a climate model. The results show that the cloud radiative forcing can vary up to 20 % depending on the assumed DSD. Likewise, differences up to 20% are obtained for heating rates. The estimation of r eff is the main source of uncertainty, while the SSPs parameterization contributes to around 20% of the total uncertainty. Spectral averaging is less an issue, except for atmospheric absorption. Overall, global shortwave cloud radiative effect can vary by 6 W m −2 depending on the assumed DSD shape, which is about 13 % of the best observational estimate.