Drivers of cloud droplet number variability in the summertime in the southeastern United States

Bougiatioti, Aikaterini; Nenes, Athanasios; Lin, Jack J.; Brock, Charles A.; de Gouw, Joost A.; Liao, Jin; Middlebrook, Ann M.; Welti, André

Here we analyze regional-scale data collected on board the NOAA WP-3D aircraft during the 2013 Southeast Nexus (SENEX) campaign to study the aerosol–cloud droplet link and quantify the sensitivity of droplet number to aerosol number, chemical composition, and vertical velocity. For this, the observed aerosol size distributions, chemical composition, and vertical-velocity distribution are introduced into a state-of-the-art cloud droplet parameterization to show that cloud maximum supersaturations in the region range from 0.02 % to 0.52 %, with an average of inline-formula0.14±0.05 %. Based on these low values of supersaturation, the majority of activated droplets correspond to particles with a dry diameter of 90 inline-formulanm and above. An important finding is that the standard deviation of the vertical velocity (inline-formulaσw) exhibits considerable diurnal variability (ranging from 0.16 inline-formulam s−1 during nighttime to over 1.2 inline-formulam s−1 during day), and it tends to covary with total aerosol number (inline-formulaNa). This inline-formulaσwinline-formulaNa covariance amplifies the predicted response in cloud droplet number (inline-formulaNd) to inline-formulaNa increases by 3 to 5 times compared to expectations based on inline-formulaNa changes alone. This amplified response is important given that droplet formation is often velocity-limited and therefore should normally be insensitive to aerosol changes. We also find that inline-formulaNd cannot exceed a characteristic concentration that depends solely on inline-formulaσw. Correct consideration of inline-formulaσw and its covariance with time and inline-formulaNa is important for fully understanding aerosol–cloud interactions and the magnitude of the aerosol indirect effect. Given that model assessments of aerosol–cloud–climate interactions do not routinely evaluate for overall turbulence or its covariance with other parameters, datasets and analyses such as the one presented here are of the highest priority to address unresolved sources of hydrometeor variability, bias, and the response of droplet number to aerosol perturbations.

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Bougiatioti, Aikaterini / Nenes, Athanasios / Lin, Jack J. / et al: Drivers of cloud droplet number variability in the summertime in the southeastern United States. 2020. Copernicus Publications.

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