The effects of aerosol and meteorological conditions on low-level shallow clouds over East Asia
A bin-based meso-scale cloud model has been employed to explore the aerosol and meteorological influences on shallow stratocumulus clouds in East Asia in March 2005, with newly constructed aerosol size distribution and chemical composition. This study of physical controls on cloudiness was composed not only of aerosol controls (aerosol (i.e., CN) number concentration) but of meteorological controls (dynamic and thermodynamic states) to understand the change of cloud microphysical and macrophysical properties and resulting precipitation by these controlling parameters.
Based on the sensitivity experiments by the variation of column CN number concentration, quantitative evaluations of the sensitivity factor between column CN number and cloud microphysical parameters revealed a large sensitivity values in the target area compared to the previously reported values, demonstrating the strong aerosol-cloud interaction. Interestingly, cloud fraction (CF), one of cloud macrophysical properties, seemed to be invariant with CN number concentration, but it was verified that CF was slightly increasing with CN increase in humid condition and decreasing in dry condition. Additional sensitivity experiments by meteorological perturbations, with relative humidity, wind shear, and low-tropospheric static stability, showed various responses in the cloud microphysical and macrophysical properties, and precipitation amount of the target cloud system, indicating interrelationship between cloud properties.
Various changes of cloud properties and precipitation efficiency by both changes in aerosol and meteorological conditions could magnify or diminish the aerosol indirect effect, which makes the understanding of aerosol indirect effect challenging. Thorough investigations on aerosol-cloud interactions with high-resolution simulations implemented by bin-based full cloud microphysics would be expected to assess aerosol indirect effects accurately to reduce existing large uncertainty.