The mechanisms underlying decadal variability in Arctic sea ice remain actively debated. Here we show that variability in boreal biomass burning (BB) emissions strongly influences simulated Arctic sea ice on multi-decadal timescales. In particular, we find that a strong acceleration in sea ice decline in the early 21st century in the Community Earth System Model version 2 (CESM2) is related to increased variability in prescribed CMIP6 BB emissions through summertime aerosol-cloud interactions. Furthermore, we find that more than half of the reported improvement in sea ice sensitivity to CO2 emissions and global warming from CMIP5 to CMIP6 can be attributed to the increased BB variability, at least in the CESM. These results highlight a new kind of uncertainty that needs to be considered when incorporating new observational data into model forcing, while also raising questions about the role of BB emissions on the observed Arctic sea ice loss.

Multiple 50-member ensemble simulations with the Community Earth System Model version 2 are performed to estimate the coupled climate responses to the 2019-2020 Australian wildfires and COVID-19 pandemic policies. The climate response to the pandemic is found to be weak generally, with net top-of-atmosphere radiative anomalies of +0.23+-0.14 W m/2driving a gradual global warming of 0.05+-0.04 K by the end of 2022. While regional anomalies are detectible in aerosol burdens and clear-sky radiation, few significant anomalies exist in other fields due to internal variability. In contrast, the simulated response to Australian wildfires is a strong and rapid cooling, peaking at -0.95+-0.15 W m/2 in late 2019 with an anomalous global cooling of 0.06+-0.04 K by mid-2020. Transport of fire aerosols throughout the Southern Hemisphere increases albedo and drives a strong interhemispheric radiative contrast, with simulated responses that are consistent generally with those to a Southern Hemisphere volcanic eruption.

Stratospheric dynamics and chemistry can impact the tropospheric climate through changing radiatively active atmospheric constituents and stratosphere-troposphere interactions. The impact of stratospheric dynamics and chemistry on the Last Glacial Maximum (LGM) climate is not well studied and remains an uncertain aspect of glacial-interglacial climate change. Here we perform coupled LGM simulations using the Community Earth System Model version 2 (CESM2), with a high-top atmosphere—the Whole Atmosphere Community Climate Model version 6 with a middle atmosphere chemistry mechanism (WACCM6ma). The CESM2(WACCM6ma) LGM simulations show a weaker stratospheric circulation than the preindustrial, 10–35% less tropospheric ozone and 10–50% more ozone in the lower stratosphere. These stratospheric dynamics and chemistry changes cause slightly colder (by <5%) LGM surface and tropospheric temperatures than parallel simulations using a low-top atmosphere model without active chemistry. The results suggest that stratospheric dynamics and chemistry have little direct effect on the glacial-interglacial climate change.

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.