Aerosol-cloud interactions in cirrus clouds based on global-scale airborne observations and machine learning models

This study developed near global-scale in situ aircraft observational datasets based on 12 field campaigns that spanned from the polar regions to the tropics from 2008 to 2016. Cirrus cloud microphysical properties were investigated at temperatures ≤ −40 °C, including ice water content (IWC), ice crystal number concentration (Ni​​​​​​​), and number-weighted mean diameter (Di).

Positive correlations were found between the fluctuations of these ice microphysical properties and the fluctuations of aerosol number concentrations for larger (> 500 nm) and smaller (> 100 nm) aerosols (i.e. Na500 and Na100, respectively). Steeper linear regression slopes were seen for large aerosols compared with smaller aerosols. Machine learning (ML) models showed that using relative humidity with respect to ice (RHi) as a predictor significantly increased the accuracy of predicting cirrus occurrences compared with temperature, vertical velocity (w), and aerosol number concentrations.