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Atmospheric aerosols and their interaction with clouds are the largest uncertainty in the human forcing of the climate system. Anthropogenic emissions have increased aerosol concentrations, increasing the concentration of cloud droplets and leading to reductions in droplet size and increases in cloud reflectivity (a negative radiative forcing). Central to this climate impact is the susceptibility of cloud droplet number to aerosol. This susceptibility varies widely with the method and data used to estimate it and within global climate models, explaining much of the variation in estimates of the radiative forcing from aerosol-cloud interactions (RFaci). Better constraints on the susceptibility have been a key target for recent observation-based constraints on the aerosol forcing. Previous work has shown that the aerosol burden of the clean, pre-industrial atmosphere has been demonstrated as a key uncertainty for the aerosol forcing. Here we show that the behaviour of clouds under these clean conditions is of equal importance for understanding the spread in radiative forcing estimates between models and observations. This means that the uncertainty in the aerosol impact on clouds is, counterintuitively, driven by situations with little aerosol. Removing these clean conditions from observational estimates of the susceptibility produces a close agreement between different model and observational estimates of the cloud response to aerosol, but does not provide a strong constraint on the RFaci. If we are to produce tighter constraints on the radiative forcing from aerosol-cloud interactions, better constraints on the behaviour of cloud in keen conditions are vital.

In this study we evaluate simulated surface SO2 and sulfate (SO42-) concentrations from the United Kingdom Earth System Model (UKESM1) against observations from ground-based measurement networks in the USA and Europe for the period 1987–2014. We find that UKESM1 captures the historical trend for decreasing concentrations of atmospheric SO2 and SO42- in both Europe and the USA over the period 1987–2014. However, in the polluted regions of the eastern USA and Europe, UKESM1 over-predicts surface SO2 concentrations by a factor of 3 while under-predicting surface SO42- concentrations by 25 %–35 %. In the cleaner western USA, the model over-predicts both surface SO2 and SO42- concentrations by factors of 12 and 1.5 respectively. We find that UKESM1’s bias in surface SO2 and SO42- concentrations is variable according to region and season. We also evaluate UKESM1 against total column SO2 from the Ozone Monitoring Instrument (OMI) using an updated data product. This comparison provides information about the model's global performance, finding that UKESM1 over-predicts total column SO2 over much of the globe, including the large source regions of India, China, the USA, and Europe as well as over outflow regions. Finally, we assess the impact of a more realistic treatment of the model's SO2 dry deposition parameterization. This change increases SO2 dry deposition to the land and ocean surfaces, thus reducing the atmospheric loading of SO2 and SO42-. In comparison with the ground-based and satellite observations, we find that the modified parameterization reduces the model's over-prediction of surface SO2 concentrations and total column SO2. Relative to the ground-based observations, the simulated surface SO42- concentrations are also reduced, while the simulated SO2 dry deposition fluxes increase.