The influence of clouds on photochemistry remains a significant uncertainty in global chemistry models. Variability in cloud fraction, morphology, phase and optical properties provides significant challenges to models with horizontal resolutions that far exceed the scale of most clouds. Measured photolysis frequencies derived from the Charged-coupled device Actinic Flux Spectroradiometers (CAFS) on board the NASA DC-8 during the Atmospheric Tomography (ATom) mission in 2016 provide an extensive set of statistics on how clouds alter the photolytic rates throughout remote ocean basins. Here we focus on north and tropical pacific transects during the first deployment (ATom-1) in August 2016 including regular profiles through cloudy, partly cloudy and clear conditions. Nine global chemistry–climate or –transport models provide similar statistics on J-values for regional domains encompassing the measured flight path. The statistical picture of the impact of clouds on J-values emerges through the distribution of the ratio of the cloud influenced models and measurement to corresponding cloud free model runs (J-cloudy/J-clear). The models all reproduce general patterns of enhancement above and shading below cloud, but diverge in distribution patterns and clear sky prevalence.

Risk assessments of air pollution impacts on human health and ecosystems would ideally consider a broad set of climate and emission scenarios and the role of natural internal climate variability within a single scenario. We analyze initial condition chemistry-climate ensembles to gauge the significance of greenhouse-gas-induced air pollution changes relative to internal climate variability, and response differences in two models. To quantify the effects of climate change on the frequency and duration of summertime regional-scale pollution episodes over the Eastern United States (EUS), we apply an Empirical Orthogonal Function (EOF) analysis to a 3-member GFDL-CM3 ensemble with prognostic ozone and aerosols and a 12-member NCAR-CESM1 ensemble with prognostic aerosols under a 21st century RCP8.5 scenario with air pollutant emissions frozen in 2005. Correlations between GFDL-CM3 principal components for ozone, PM2.5 and temperature represent spatiotemporal relationships discerned previously from observational analysis. Over the Northeast region, both models simulate summertime surface temperature increases of over 5 °C from 2006-2025 to 2081-2100 and PM2.5 of up to 1-4 μg m-3. The ensemble average decadal incidence of upper quartile Northeast PM2.5 events lasting at least five days doubles in GFDL-CM3 and increases >50% in NCAR-CESM1. In other EUS regions, inter-model differences in PM2.5 responses to climate change cannot be explained by internal climate variability. Our EOF-based approach anticipates future opportunities to data-mine initial condition chemistry-climate model ensembles for probabilistic assessments of changing frequency and duration of regional-scale pollution and heat events while obviating the need to bias-correct concentration-based thresholds separately in individual models.