Understanding how air pollution events may change in the future is of key importance to decision makers. Multi-model intercomparison projects focusing on atmospheric chemistry and air quality have been performed to inform the latest IPCC assessments. Future anthropogenic emission changes have generally been the foci of such model experiments, envisaged as the dominant driver of future atmospheric composition. The latest model assessments such as AerChemMIP utilize multi-model ensembles but also have limited individual model ensembles which permit different sources of uncertainty to be characterized. The recent study by Fiore et al. (2022, https://doi.org/10.1029/2021JD035985) specifically considers a multi-model and multi-member ensemble approach. It adds to the quantification of uncertainty in future projections through delineating uncertainty due to model diversity and due to internal or natural climate variability within the climate system, for mean and high PM2.5 air pollution events over the Eastern USA in the 21st century. Exploring the separate roles of internal climate variability and model diversity adds further value to the important research issue of quantifying how future anthropogenic climate change impacts air quality. Future multi-model intercomparisons need to balance the additional knowledge gained from research into understanding multiple sources of uncertainty that can inform decision making vs. the resource costs of performing these experiments using Earth System Models with interactive chemistry
