Electric vehicle (EV) adoption promises potential air pollutant and greenhouse gas (GHG) reduction co-benefits. As such, China has aggressively incentivized EV adoption, however much remains unknown with regard to EVs’ mitigation potential, including optimal vehicle type prioritization, power generation contingencies, effects of Clean Air regulations, and the ability of EVs to reduce acute impacts of extreme air quality events. Here, we present a suite of scenarios with a chemistry-climate model that assess the potential co-benefits of EVs during an extreme winter air quality event. We find that regardless of power generation source, heavy-duty vehicle (HDV) electrification consistently improves air quality in terms of NO2 and fine particulate matter (PM2.5), potentially avoiding 562 deaths due to acute pollutant exposure during the infamous January 2013 pollution episode (~1% of total premature mortality). However, HDV electrification does not reduce GHG emissions without enhanced emission-free electricity generation. In contrast, due to differing emission profiles, light-duty vehicle (LDV) electrification in China consistently reduces GHG emissions (~2 Mt CO2), but results in fewer air quality and human health improvements (145 avoided deaths). The calculated economic impacts for human health endpoints and CO2 reductions for LDV electrification are nearly double those of HDV electrification in present-day (155M vs. 87M US$), but are within ~25% when enhanced emission-free generation is used to power them. Overall we find only a modest benefit for EVs to ameliorate severe wintertime pollution events, and that continued emission reductions in the power generation sector will have the greatest human health and economic benefits.

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.

The U.S. and much of the world sit on the cusp of an electrification revolution – a moment driven largely by the need to reduce the emission of greenhouse gases to limit the impacts of anthropogenic climate change. The electrify everything movement aims to transition combustion-powered sectors into technologies powered solely by electricity, with the idea that the electric grid – currently comprised of a mix of combustion, renewable, and nuclear generation units – will become cleaner and greener over time. Studies indicate that electrifying high-efficiency devices, appliances, and vehicles will reduce greenhouse gas emissions regardless of the grid’s composition, however ancillary air quality benefits and tradeoffs remain poorly resolved, particularly at impact- and equity-relevant scales. Here, I use a fine-scale CONUS-wide climate and air quality co-benefit and tradeoff analysis framework (i.e., 4 km2 SMOKE-CMAQ-WRF simulations) to assess sustainable climate solutions. Analyses utilize emission scenarios that account for increased grid demand and uncertainties in grid evolution, simulate the interaction of meteorological and chemical processes, characterize changes in greenhouse gases and air pollutants, and assess economic, social, and public health consequences of sustainable transitions over two key, yet methodologically disparate, residential/commercial sectors: transportation: via the replacement of internal combustion vehicles with electric vehicles and lighting: via the replacement of low efficiency bulbs with high-efficiency LEDs.

The southern Lake Michigan region of the United States, home to Chicago, Milwaukee, and other densely populated Midwestern cities, frequently experiences high pollutant episodes with unevenly distributed exposure and health burdens. Using the two-way coupled Weather Research Forecast and Community Multiscale Air Quality Model (WRF-CMAQ), we investigate criteria pollutants over a southern Lake Michigan domain using 1.3 and 4 km resolution hindcast simulations. We assess WRF-CMAQ’s performance using data from the National Climate Data Center and EPA Air Quality System. Our 1.3 km simulation slightly improves on the 4 km simulation’s meteorological and chemical performance while also resolving key details in areas of high exposure and impact, i.e., urban environments. At 1.3 km, we find that most air quality-relevant meteorological components of WRF-CMAQ perform at or above community benchmarks. WRF-CMAQ’s chemical performance also largely meets community standards, with substantial nuance depending on the performance metric and component assessed. For example, hourly simulated NO2 and O3 are highly correlated with observations (r > 0.6) while PM2.5 is less so (r = 0.4). Similarly, hourly simulated NO2 and PM2.5 have low biases (<10%), whereas O3 biases are larger (<30%). Simulated spatial pollutant patterns show distinct urban-rural footprints, with urban NO2 and PM2.5 20-60% higher than rural, and urban O3 6% lower. We use our 1.3 km simulations to resolve high-pollution areas within individual urban neighborhoods and characterize changes in O3 regimes across tight spatial gradients. Our findings demonstrate both the benefits and limitations of high-resolution simulations, particularly over urban settings.

We describe the baseline model configuration and simulation characteristics of GFDL’s Atmosphere Model version 4.1 (AM4.1), which builds on developments at GFDL over 2013–2018 for coupled carbon-chemistry-climate simulation as part of the sixth phase of the Coupled Model Intercomparison Project. In contrast with GFDL’s AM4.0 development effort, which focused on physical and aerosol interactions and which is used as the atmospheric component of CM4.0, AM4.1 focuses on comprehensiveness of Earth system interactions. Key features of this model include doubled horizontal resolution of the atmosphere (~200 km to ~100 km) with revised dynamics and physics from GFDL’s previous-generation AM3 atmospheric chemistry-climate model. AM4.1 features improved representation of atmospheric chemical composition, including aerosol and aerosol precursor emissions, key land-atmosphere interactions, comprehensive land-atmosphere-ocean cycling of dust and iron, and interactive ocean-atmosphere cycling of reactive nitrogen. AM4.1 provides vast improvements in fidelity over AM3, captures most of AM4.0’s baseline simulations characteristics and notably improves on AM4.0 in the representation of aerosols over the Southern Ocean, India, and China—even with its interactive chemistry representation—and in its manifestation of sudden stratospheric warmings in the coldest months. Distributions of reactive nitrogen and sulfur species, carbon monoxide, and ozone are all substantially improved over AM3. Fidelity concerns include degradation of upper atmosphere equatorial winds and of aerosols in some regions.