Parameterization Of Cloud Optical Properties Research Articles

Uncertainty of SW Cloud Radiative Effect in Atmospheric Models Due to the Parameterization of Liquid Cloud Optical Properties

AbstractClouds 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 (reff) and modulates the reff‐dependency of the SSPs—and from averaging SSPs over wide spectral bands. To assess these uncertainties a series of reff‐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 reff 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 reff 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.

Parameterization of cloud optical properties for semidirect radiative forcing

[1] A parameterization of liquid cloud optical properties with a mixture of black carbon is proposed. It is found that the changes in cloud optical properties due to mixture of black carbon can be treated as a perturbation to existing cloud optical property parameterizations in climate models. The advantage of the proposed scheme is that current cloud optical property parameterizations used in climate models can be kept. It is shown that the dominant factor with respect to radiative forcing due to the inclusion of black carbon in cloud droplets is the resulting change in single-scattering albedo values. Therefore, a simple scheme to modify only the single-scattering albedo is considered. The additional consideration of the modification of asymmetry factor only applies to the case of very large black carbon volume fraction. The results in a one-dimensional radiation model show that internal mixtures of black carbon can have a significant impact on solar flux and heating rates. For a black carbon volume fraction of 10−7, the reduction in solar flux at the top of the atmosphere can be over 0.5 Wm−2 and the heating rate can increase by about 0.08 Kd−1 for a solar zenith angle of 53°.

A method for the parameterization of cloud optical properties in bulk and bin microphysical models. Implications for arctic cloudy boundary layers

Computationally efficient and numerically accurate methods for computing band-averaged cloud optical properties for radiative transfer interactions with various microphysical parameterizations are described. Parameterizations for bulk microphysical models employing generalized gamma distribution representations of the size spectra and binned representations, in which the size spectra fluctuate with time, are discussed. It is shown that simple exponential fits and look-up tables may be used with minimal computational cost and high accuracy for bulk microphysical models. Binned microphysical representations may be parameterized using mean properties for each bin, if averaged appropriately. The implications for the radiative scheme are discussed in comparison with the computed radiative budget of fall/spring season mixed-phase Arctic stratus clouds (ASC). Compared to liquid clouds of the same water path, mixed-phase ASC absorb and reflect less radiation, and transmit more radiation to the surface. This results in greater cooling (warming) of the surface, by up to 60 W m −2, in the infrared (solar) by mixed-phase clouds. The radiative properties of mixed-phase clouds show a significant sensitivity to crystal habit for clouds with ice water paths ≳25 g m −2. Surface net fluxes and cloud absorption may vary by up to 15 W m −2, depending on the ice habit. It is also shown that mixed-phase clouds are more sensitive to the choice of ice effective radius ( r e,i) than liquid clouds are to r e. Using values of from the literature, it is shown that the surface net fluxes can vary by as much as 50 W m −2 depending on the value of r e,i. Furthermore, it is shown that the sign of the surface net flux (i.e. warming or cooling) may be dependent on the value of r e,i selected.

Cirrus cloud optical properties in far infrared

Abstract A new parameterization of cloud optical properties in the Far Infrared (FIR) (10–200 μ) has been carried out. The forward and scattered transmissivity and the scattered reflectivity are all included in the emissivity expression. Results are compared with the ones calculated by other authors and with Mie theory. The parameterization developed has been implemented in a two-stream approximation radiative transfer code and the TOA upward flux variations as function of cirrus clouds optical and physical parameters are discussed. Results show that the scattering effects added generally increase the optical depth of the cloud and decreases the OLR, with most relevant effects on medium optical depth clouds.

Precipitation and radiation modeling in a general circulation model: Introduction of cloud microphysical processes

Cloud microphysical processes are introduced in the precipitation parameterization of a general circulation model (GCM). Three microphysical processes are included in this representation of warm cloud precipitation: autoconversion of droplets, collection of droplets by falling raindrops, and evaporation of raindrops falling in clear sky. The mean droplet radius, r, is calculated from the cloud water mixing ratio, which is computed in the model, and the cloud droplet number concentration, N, which is prescribed. The autoconversion rate is set to zero for r < r0, a prescribed threshold mean droplet radius. We investigate the model sensitivity to r0 and to N, the cloud droplet concentration, which is linked to the concentration of cloud condensation nuclei and is likely to vary. We find that an increase in N leads to an increase in the amount of cloud water stored in the atmosphere. In further experiments the mean droplet radius used in the parameterization of cloud optical properties is calculated in the same way as in the precipitation parameterization in order to bring more consistency between the different schemes. We again investigate the model sensitivity to r0 and to N and we find that an increase in N significantly enhances cloud albedo.