4.4 Article

Comparison of bulk and bin warm-rain microphysics models using a kinematic framework

Journal

JOURNAL OF THE ATMOSPHERIC SCIENCES
Volume 64, Issue 8, Pages 2839-2861

Publisher

AMER METEOROLOGICAL SOC
DOI: 10.1175/JAS3980

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This paper discusses the development and testing of a bulk warm-rain microphysics model that is capable of addressing the impact of atmospheric aerosols on ice-free clouds. Similarly to previous two-moment bulk schemes, this model predicts the mixing ratios and number concentrations of cloud droplets and drizzle/ raindrops. The key elements of the model are the relatively sophisticated cloud droplet activation scheme and a comprehensive treatment of the collision-coalescence mechanism. For the latter, three previously published schemes are selected and tested, with a detailed ( bin) microphysics model providing the benchmark. The unique aspect of these tests is that they are performed using a two-dimensional prescribed-flow ( kinematic) framework, where both advective transport and gravitational sedimentation are included. Two quasi-idealized test cases are used, the first mimicking a single large eddy in a stratocumulus-topped boundary layer and the second representing a single shallow convective cloud. These types of clouds are thought to be the key in the indirect aerosol effect on climate. Two different aerosol loadings are considered for each case, corresponding to either pristine or polluted environments. In general, all three collision coalescence schemes seem to capture key features of the bin model simulations ( e. g., cloud depth, droplet number concentration, cloud water path, effective radius, precipitation rate, etc.) for the polluted and pristine environments, but there are detailed differences. Two of the collision-coalescence schemes require specification of the width of the cloud droplet spectrum, and model results show significant sensitivity to the specification of the width parameter. Sensitivity tests indicate that a one-moment version of the bulk model for drizzle/rain, which predicts rain/drizzle mixing ratio but not number concentration, produces significant errors relative to the bin model.

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