Stratocumulus occur in closed- or open-cell states, which tend to be associated with high or low cloud cover and the absence or presence of precipitation, respectively. Thus, the transition between these states has substantial implications for the role of this cloud type in Earth’s radiation budget. In this study, we analyze transitions between these states using an ensemble of 127 large-eddy simulations, covering a wide range of conditions. Our analysis is focused on the behavior of these clouds in a cloud fraction (f_c) scene albedo (A) phase space, which has been shown in previous studies to be a useful framework for interpreting system behavior. For the transition from closed to open cells, we find that precipitation creates narrower clouds and scavenges cloud droplets for all f_c. However, precipitation decreases the cloud depth for f_c > 0.8 only, causing a rapid decrease in A. For f_c < 0.8, the cloud depth actually increases due to mesoscale organization of the cloud field. As the cloud deepening balances the effects of cloud droplet scavenging in terms of influence on A, changes in A are determined by the decreasing f_c only, causing a linear decrease in A for f_c < 0.8. For the transition from open to closed cells, we find that longwave radiative cooling drives the cloud development, with cloud widening dominating for f_c < 0.5. For f_c > 0.5, clouds begin to deepen gradually due to the decreasing efficiency of lateral expansion. The smooth switch between cloud widening and deepening leads to a more gentle change in A compared to the transitions under precipitating conditions.@en

Data-driven quantification and parameterization of cloud physics in general, and of aerosol-cloud interactions in particular, rely on input data from observations or detailed simulations. These data sources have complementary limitations in terms of their spatial and temporal coverage and resolution; simulation data has the advantage of readily providing causality but cannot represent the full process complexity. In order to base data-driven approaches on comprehensive information, we therefore need ways to integrate different data sources.  We discuss how the classical statistical technique of Gaussian-process emulation can be combined with specifically initialized ensembles of detailed cloud simulations (large-eddy simulations, LES) to provide a framework for evaluating data-driven descriptions of cloud characteristics and processes across different data sources. We specifically illustrate this approach for integrating LES and satellite data of aerosol-cloud interactions in subtropical stratocumulus cloud decks. We furthermore explore the extension of our framework to ground-based observations of Arctic mixed-phase clouds.@en

The effect of anthropogenic aerosol on the reflectivity of stratocumulus cloud decks through changes in cloud amount is a major uncertainty in climate projections. In frequently occurring nonprecipitating stratocumulus, cloud amount can decrease through aerosol-enhanced cloud-top mixing. The climatological relevance of this effect is debated because ship exhaust only marginally reduces stratocumulus amount. By comparing detailed numerical simulations with satellite analyses, we show that ship-track studies cannot be generalized to estimate the climatological forcing of anthropogenic aerosol. The ship track-derived sensitivity of the radiative effect of nonprecipitating stratocumulus to aerosol overestimates their cooling effect by up to 200%. The offsetting warming effect of decreasing stratocumulus amount needs to be taken into account if we are to constrain the cloud-mediated radiative forcing of anthropogenic aerosol.

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