Abstract
Abstract. Conventional satellite retrievals can only provide information on cloud-top droplet effective radius (re). Given the fact that cloud ensembles in a satellite snapshot have different cloud-top heights, Rosenfeld and Lensky (1998) used the cloud-top height and the corresponding cloud-top re from the cloud ensembles in the snapshot to construct a profile of re representative of that in the individual clouds. This study investigates the robustness of this approach in shallow convective clouds based on results from large-eddy simulations (LES) for clean (aerosol mixing ratio Na = 25 mg−1), intermediate (Na = 100 mg−1), and polluted (Na = 2000 mg−1) conditions. The cloud-top height and the cloud-top re from the modeled cloud ensembles are used to form a constructed re profile, which is then compared to the in-cloud re profiles. For the polluted and intermediate cases where precipitation is negligible, the constructed re profiles represent the in-cloud re profiles fairly well with a low bias (about 10 %). The method used in Rosenfeld and Lensky (1998) is therefore validated for nonprecipitating shallow cumulus clouds. For the clean, drizzling case, the in-cloud re can be very large and highly variable, and quantitative profiling based on cloud-top re is less useful. The differences in re profiles between clean and polluted conditions derived in this manner are however, distinct. This study also investigates the subadiabatic characteristics of the simulated cumulus clouds to reveal the effect of mixing on re and its evolution. Results indicate that as polluted and moderately polluted clouds develop into their decaying stage, the subadiabatic fraction fad becomes smaller, representing a higher degree of mixing, and re becomes smaller (~10 %) and more variable. However, for the clean case, smaller fad corresponds to larger re (and larger re variability), reflecting the additional influence of droplet collision-coalescence and sedimentation on re. Finally, profiles of the vertically inhomogeneous clouds as simulated by the LES and those of the vertically homogeneous clouds are used as input to a radiative transfer model to study the effect of cloud vertical inhomogeneity on shortwave radiative forcing. For clouds that have the same liquid water path, re of a vertically homogeneous cloud must be about 76–90 % of the cloud-top re of the vertically inhomogeneous cloud in order for the two clouds to have the same shortwave radiative forcing.
Highlights
Aerosol-cloud interactions are recognized as one of the largest uncertainties in the prediction of climate change
Representation of shallow convection in climate models is a major challenge because the relevant spatiotemporal scales are on the order of tens to hundreds of meters and seconds, i.e., scales much smaller than those that can be resolved by climate models, both and in the foreseeable future (e.g., Lohmann and Feichter, 2005; Wang and Penner, 2009)
Recent studies have shown that the manner in which warm clouds and their interaction with aerosol particles are represented by climate models has a marked effect on climate sensitivity – i.e., the Earth’s temperature response to a doubling of CO2
Summary
Aerosol-cloud interactions are recognized as one of the largest uncertainties in the prediction of climate change. The fact that many field observations and satellite measurements to date have shown that ACI is highly variable (e.g., Feingold et al, 2003; Breon et al, 2002) suggests that there is large uncertainty in cloud albedo forcing This is partly because physical mechanisms may vary under different conditions and locations, but ACI is quite sensitive to the method of remote re retrieval (Rosenfeld and Feingold, 2003) and to the aerosol proxy for cloud condensation nuclei (McComiskey et al, 2009). It should be noted that the stratified cloud model has been used to develop procedures for the retrieval of cloud geometrical thickness, liquid water content, and drop number concentration from the measurement of cloud radiances for stratiform clouds (Schuller et al, 2005) How to use this kind of cloud model for parameterization and retrieval in shallow convective clouds remains uncertain because the mixing process can lead to significant changes in cloud microphysical properties (Warner, 1955).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
