Machine Learning-Based Observational Constraints on Cloud Condensation Nuclei Responses to Australian Wildfire Aerosols over Remote Oceans

Abstract. Australian “Black Summer” wildfires in 2019–2020 released large amounts of smoke that affected aerosols and clouds over the South Pacific. Here we quantified wildfire perturbation on aerosol loading and cloud condensation nuclei (CCN, particles that can act as seeds for cloud droplets) using a machine learning (ML) method. We trained ML models with meteorological datasets to represent counterfactual “no-wildfire” conditions, which were contrasted against satellite observations of “wildfire” conditions of aerosol optical depth (AOD), Aerosol Index, and CCN. We found strong and robust aerosol and CCN responses during the wildfire months over the South Pacific downwind plume region (140° E–100° W, 10° S–50° S). The wildfire perturbation substantially increased AOD by 36 % and 68 % in December 2019 and January 2020, respectively, and Aerosol Index by 21 % and 53 %, respectively. In comparison, CCN increased by 40 % in December 2019 and 20 % in January 2020. We find that the AOD and Aerosol Index enhancements form a broad band following the main smoke plume and extend across the full studied region over the South Pacific, while the CCN response is weaker and more localized, with enhancements mainly confined to the plume central-line along the main smoke transport pathway with a rapid decay further downwind. This difference suggests that transport and aging processes could reduce wildfire aerosol capability in enhancing CCN number concentration. Moreover, our results provide further observational evidence that Aerosol Index can provide a useful complement to AOD when interpreting smoke impacts on CCN.