Precipitation and Coalescence Scavenging in Shallow Southern Ocean Clouds

海洋边界层中的云层(比方说海洋表面上方的大气千米)具有全球降温效应，因为它们将阳光反射回太空，但云层太低，无法有效地捕获发出的红外辐射。这些云层的气候效应已成为气候变化研究中的一个重要问题，因为温度升高可能会改变云层的反射率或其典型寿命，而这些因素决定了它们冷却效果的强度。海洋边界层云的反射率在很大程度上是由云凝结核(CCN)的丰度决定的，CCN是一种微小的气溶胶粒子，它从大气中吸收水分，为云滴的生长提供种子。当CCN更丰富时，可用云水扩散到更多更小的液滴上，导致比CCN更少时更具反射性的云，因此更少的更大液滴。由较小的液滴组成的云持续时间也更长，因为当云滴较小时，碰撞和合并形成雨滴的过程需要更长的时间，因此需要更多的液滴才能形成雨滴，这意味着足够大的液滴可以从云中落下。PIS之前的工作为CCN丰度的变化制定了一个简化的预算方程，将CCN丰度的变化与源(包括表层海洋中的生物过程产生的气溶胶)和汇(包括降水)联系起来。降水是从液态云中去除CCN的非常有效的方法，因为雨滴中的大约百万个液滴中的每一个都包含一个CCN。公共投资机构利用其预算分析表明，通过降水清除CCN，即所谓的降水清扫或合并清除，是CCN清除的主要机制，并解释了海洋上CCN丰度的很大地理变异性。他们还利用预算推导了海洋边界层云中液滴数量浓度(ND)的公式，假设CCN的源和汇是平衡的，并且云中的所有CCN都有播种液滴。这里所做的工作是利用2018年南大洋云、辐射、气溶胶、输送实验研究(苏格拉底)期间收集的观测数据，将预算方程和ND公式应用于南大洋风暴路径上的边界层云，该实验研究使用一架考察机对塔斯马尼亚以南海洋上空飞行的云进行采样(见AGS-1660609)。飞机测量了云中、云上和云下的CCN浓度，并使用雷达和激光雷达观测云滴和雨滴，这些信息可以与卫星数据和气象分析相结合，确定CCN浓度并测量CCN源和汇。预算研究的结果与大涡模拟(LES)模式的输出进行了比较，该模式基于大尺度气象输入和云上CCN浓度的测量生成详细的云、CCN和降水模拟。该项目还处理和分析来自飞机雷达和激光雷达的数据，以检查SO云产生的降雨量，雨滴的大小分布，以及冰相降水的产生程度。这项工作具有社会和科学意义，因为低云提供的冷却程度的变化是估计全球气温对温室气体增加的敏感性的最大不确定性之一。苏格拉底的竞选活动在很大程度上是因为担心，在用于预测未来气候变化的气候模型中，SO云没有得到很好的代表。该项目还产生了云、降水和CCN属性的数据集，可供全球气候研究人员使用。此外，该奖项还为研究生提供支持和培训，从而促进该研究领域未来的劳动力队伍。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Clouds in the marine boundary layer (say the kilometer of the atmosphere just above the ocean surface) have a global cooling effect as they reflect sunlight back to space but are too low to effectively trap outgoing infrared radiation. The climatic effect of these clouds has become an important issue in climate change research, as warmer temperatures may alter either the reflectivity of the clouds or their typical lifetimes, the factors which determine the strength of their cooling effect. The reflectivity of marine boundary layer clouds is determined in large part by the abundance of cloud condensation nuclei (CCN), the tiny aerosol particles which absorb moisture from the atmosphere to seed the growth of cloud droplets. When CCN are more abundant the available cloud water is spread over a larger number of smaller droplets, leading to a more reflective cloud than would occur with fewer CCN and hence a smaller number of larger droplets. Clouds made of smaller droplets also last longer, as the process of collision and coalescence that combines cloud droplets (perhaps a million or so) to form a raindrop takes longer when the droplets are smaller and thus more are needed to make a raindrop, meaning a drop big enough to fall from the cloud.Previous work by the PIs developed a simplified budget equation for CCN relating changes in CCN abundance to sources, including the generation of aerosols by biological processes in the surface ocean, and sinks, including precipitation. Precipitation is quite effective in removing CCN from liquid clouds as each one of the million or so droplets in a raindrop contains a CCN. The PIs used their budget analysis to show that CCN removal by precipitation, referred to as precipitation scavenging or coalescence scavenging, is the dominant mechanism for CCN removal and accounts for much of the geographic variability of CCN abundance over the oceans. They also used their budget to derive a formula for the droplet number concentration (Nd) in marine boundary layer clouds under the assumption that the CCN sources and sinks are balanced and all the CCN in a cloud have seeded droplets.Work performed here applies the budget equation and the Nd formula to the boundary layer clouds in the storm track over the Southern Ocean using observations collected during the 2018 Southern Ocean Clouds, Radiation, Aerosol, Transport Experimental Study (SOCRATES), a field campaign that used a research aircraft to sample clouds on flights over the ocean south of Tasmania (see AGS-1660609). The aircraft measured CCN concentrations in, above, and below the clouds, and used radar and lidar to observe cloud droplets and raindrops, information which can be combined with satellite data and meteorological analysis to determine CCN concentrations and measure CCN sources and sinks. Results of the budget study are compared with output from a Large Eddy Simulation (LES) model which generates detailed clouds, CCN, and precipitation simulations based on large-scale meteorological inputs and measurements of above-cloud CCN concentrations. The project also processes and analyzes data from the aircraft radar and lidar to examine the precipitation produced by the SO clouds, looking at the amount of precipitation produced, the size distribution of raindrops, and the extent to which ice phase precipitation is also produced.The work is of societal as well as scientific interest as change in the extent of cooling provided by low clouds is among the largest uncertainties in estimates of the sensitivity of global temperature to greenhouse gas increases. The SOCRATES campaign was largely motivated by concern that the SO clouds are poorly represented in climate models used to make projections of future climate change. The project also produces datasets on cloud, precipitation, and CCN properties that can be used by the worldwide community of climate researchers. In addition, the award provides support and training for a graduate student, thereby promoting the future workforce in this research area.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.