On the Prediction of Aerosol-Cloud Interactions Within a Data-Driven Framework

Aerosol-cloud interactions (ACI) pose the largest uncertainty for climate projection. Among many challenges of understanding ACI, the question of whether ACI can be deterministically predicted has not been explicitly answered. Here we attempt to answer this question by predicting cloud droplet number concentration (Formula presented.) from aerosol number concentration (Formula presented.) and ambient conditions using a data-driven framework. We use aerosol properties, vertical velocity fluctuations, and meteorological states from the ACTIVATE field observations (2020–2022) as predictors to estimate (Formula presented.). We show that the campaign-wide (Formula presented.) can be successfully predicted using machine learning models despite the strongly nonlinear and multi-scale nature of ACI. However, the observation-trained machine learning model fails to predict (Formula presented.) in individual cases while it successfully predicts (Formula presented.) of randomly selected data points that cover a broad spatiotemporal scale. This suggests that, within a data-driven framework, the (Formula presented.) prediction is uncertain at fine spatiotemporal scales.