Machine learning significantly improves the simulation of hourly-to-yearly scale cloud nuclei concentration and radiative forcing in polluted atmosphere

Abstract. The accurate prediction of cloud condensation nuclei (CCN) number concentration (N_CCN) on a large spatiotemporal scale is challenging but critical to evaluate the aerosol cloud interaction (ACI) effect. Combining multi-source dataset and the N_CCN simulated by the Weather Research and Forecasting coupled with Chemistry (WRF-Chem) model, we have developed a new machine learning-based model which predicts well both regional and hourly-to-yearly scale N_CCN at typical supersaturations in the North China Plain (NCP). We show that the prediction bias of N_CCN compared to observations is reduced from -39 % with the WRF-Chem model to approximately -8 % with the new model. The greatest improvement is seen in polluted cases. The new model captures well the spatial variation and better describes long-term trends of N_CCN than the WRF-Chem. More importantly, the study reveals a significant long-term decreasing trend of N_CCN in NCP due to a rapid reduction in aerosol concentrations from 2014 to 2018, during which a series of strict emission reduction measures were implemented by the Chinese government. This reflects the climate benefit of pollution control. Our study further illustrates that the new model reduces the uncertainty in simulating cloud radiative forcing from an overestimation of 1.07±0.76 W m^-2 to only 0.18±0.65 W m^-2, illustrating the high sensitivity of climate forcing to changes in N_CCN. This work offers a new modeling framework that has the potential to greatly improve the assessment of the ACI effect in current models, and guides the way to simulate CCN in other regions around the world.