Aerosol Indirect Effects on Cirrus Clouds Based on Global-Scale Airborne Observations and Machine Learning Models

Abstract. Cirrus cloud formation and evolution are subject to the influences of thermodynamic and dynamic conditions and aerosol indirect effects (AIEs). 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 between the fluctuations of 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) were found, with stronger AIE from larger aerosols than smaller ones. Machine learning (ML) models showed that using relative humidity with respect to ice (RHi) as a predictor significantly increases the accuracy of predicting cirrus occurrences compared with temperature, vertical velocity (w), and aerosol number concentrations. The ML predictions of IWC fluctuations showed higher accuracies when larger aerosols were used as an predictor compared with smaller aerosols, indicating the stronger AIE from larger aerosols than smaller ones, even though their AIEs are more similar when predicting the occurrences of cirrus. It is also important to capture the spatial variabilities of large aerosols at smaller scales as well as those of smaller aerosols at coarser scales to accurately simulate IWC in cirrus. These results can be used to improve understanding of aerosol-cloud interactions and evaluate model parameterizations of cirrus cloud properties and processes.