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

• Main Authors: A. Bougiatioti, A. Nenes, J. J. Lin, C. A. Brock, J. A. de Gouw, J. Liao, A. M. Middlebrook, A. Welti
• Format: Article
• Language: English
• Published: Copernicus Publications 2020-10-01
• Series: Atmospheric Chemistry and Physics
• Online Access: https://acp.copernicus.org/articles/20/12163/2020/acp-20-12163-2020.pdf

<p>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&thinsp;% to 0.52&thinsp;%, with an average of <span class="inline-formula">0.14±0.05</span>&thinsp;%. Based on these low values of supersaturation, the majority of activated droplets correspond to particles with a dry diameter of 90&thinsp;<span class="inline-formula">nm</span> and above. An important finding is that the standard deviation of the vertical velocity (<span class="inline-formula"><i>σ</i>_w</span>) exhibits considerable diurnal variability (ranging from 0.16&thinsp;<span class="inline-formula">m s⁻¹</span> during nighttime to over 1.2&thinsp;<span class="inline-formula">m s⁻¹</span> during day), and it tends to covary with total aerosol number (<span class="inline-formula"><i>N</i>_a</span>). This <span class="inline-formula"><i>σ</i>_w</span>–<span class="inline-formula"><i>N</i>_a</span> covariance amplifies the predicted response in cloud droplet number (<span class="inline-formula"><i>N</i>_d</span>) to <span class="inline-formula"><i>N</i>_a</span> increases by 3 to 5 times compared to expectations based on <span class="inline-formula"><i>N</i>_a</span> 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 <span class="inline-formula"><i>N</i>_d</span> cannot exceed a characteristic concentration that depends solely on <span class="inline-formula"><i>σ</i>_w</span>. Correct consideration of <span class="inline-formula"><i>σ</i>_w</span> and its covariance with time and <span class="inline-formula"><i>N</i>_a</span> 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.</p>