Peak Rain Rate Sensitivity to Observed Cloud Condensation Nuclei and Turbulence in Continental Warm Shallow Clouds During CACTI. Issue 16 (24th August 2022)

• Record Type: Journal Article
• Authors: Borque, Paloma; Varble, Adam; Hardin, Joseph
• Is Part Of: Journal of geophysical research. Volume 127:Issue 16(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-08-24
• Subjects: shallow clouds -- drizzle -- turbulence -- aerosols -- cloud radar -- CACTI; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2022JD036864
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: Warm clouds strongly affect Earth's energy budget but remain imperfectly represented in climate models, partly due to the complexity and covariability of relevant processes influencing warm rain. This work presents a detailed analysis of different factors affecting rain rate peak intensity (RR) in continental warm clouds. Clouds were identified with vertically pointing radar and lidar observations and categorized via a temperature‐based cloud type classification algorithm from which warm clouds were isolated. Observations and retrievals of liquid water path (LWP), cloud condensation nuclei concentration (NCCN ), cloud depth, and cloud duration of more than 3, 000 separate warm clouds sampled during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign are analyzed in this work. Multiple linear regression (MLR) and random forest (RF) models are applied to assess the relative impact of these variables on RR. Overall, RR tends to increase as cloud depth, LWP, and cloud duration increase, or NCCN decreases. Cloud depth affects RR the most while NCCN impacts it the least. When considering over 170 warm clouds observed at least 1 hr in which in‐cloud turbulence is retrieved, the effect of NCCN on RR remains most likely suppressive, but it is not significant at a 75% level for MLR and is highly uncertain for RF. The impact of in‐cloud turbulence depends on the moment and location it is sampled. Cloud base turbulence around the time of RR suppresses RR, while cloud top turbulence effects are inconclusive. Possible difficulties in isolating robust CCN and turbulence effects on RR are discussed. Plain Language Summary: Liquid clouds significantly affect Earth's energy budget but remain imperfectly represented in climate models, partly due to complex processes influencing rain rates. Here, we present analyses of key factors affecting rain rate peak intensity in liquid clouds observed during a field campaign in central Argentina. Weather balloon soundings are combined with vertically pointing radar, lidar, and radiometer observations to define cloud elements passing overhead in time and height space along with their properties. Statistical models are applied to assess the relative impacts on rain rate of the liquid amount in the cloud, the aerosol particle concentration ingested into the cloud, and the cloud depth and duration. Rain rate increases as cloud depth, liquid amount, and cloud duration increase, or as aerosol concentration decreases. Cloud depth affects rain rate the most while aerosol concentration impacts it the least with significant uncertainty. The impact of in‐cloud turbulence on rain rate depends on time and location. Turbulence in the bottom of the cloud around the time of peak rain rate suppresses rain rate, while turbulence in the top of the cloud has variable effects. Key Points: Multiple linear regression and random forest models are used to assess controls on continental warm cloud peak rain rate Peak rain rate increases with cloud depth, liquid water path, and cloud duration, while it decreases with CCN concentration but with high uncertainty Turbulence variably affects rain rate depending on timing and location with cloud base turbulence at time of peak rain rate suppressing it