The ATom, A-LIFE, and FIREX-AQ data set: automatic cloud detection and properties of aerosol-impacted cirrus

Clouds remain one of the largest uncertainties connected to future climate predictions, due partly to existing gaps in the understanding of cloud microphysical processes. Airborne in situ observations provide relevant data sets to investigate these cloud microphysical processes. However, the process of selecting and classifying cloud sequences can be very time consuming if done manually and algorithms developed by the community are numerous and prone to misclassification, particularly when coarse aerosol is present.We developed a novel cloud indicating algorithm, automatically detecting and classifying clouds in airborne in situ observations, based on data of three international airborne field campaigns, including ATom (Atmospheric Tomography; 2016-2018), A-LIFE (Absorbing aerosol layers in a changing climate: aging, lifetime and dynamics; 2017) and FIREX-AQ (Fire Influence on Regional to Global Environments and Air Quality; 2019). The algorithm automatically detects flight sequences in clouds and classifies the cloud type using size distribution measurements, combined with measurements of relative humidity and temperature. In addition, the cloud indicator algorithm was tuned and evaluated to successfully differentiate cloud sequences to those with enhanced concentrations of coarse mode particles (e.g. mineral dust, sea salt, or biomass burning layers).In this study, we introduce the cloud indicator algorithm and present its ability to differentiate aerosol layers from clouds with a detailed analysis of unique measurements of ice clouds imbedded in a Saharan dust layer. Furthermore, we show first results of the analysis of the combined global-scale data set of ATom, A-LIFE and FIREX-AQ with regard to properties of aerosol-impacted cirrus clouds as well as properties of cirrus clouds in pristine environments.