| Autor: |
Fiore, Arlene M., Milly, George P., Hancock, Sarah E., Quiñones, Laurel, Bowden, Jared H., Helstrom, Erik, Lamarque, Jean‐François, Schnell, Jordan, West, J. Jason, Xu, Yangyang |
| Předmět: |
|
| Zdroj: |
Journal of Geophysical Research. Atmospheres; 5/16/2022, Vol. 127 Issue 9, p1-19, 19p |
| Abstrakt: |
Risk assessments of air pollution impacts on human health and ecosystems would ideally consider a broad set of climate and emission scenarios, as well as natural internal climate variability. We analyze initial condition chemistry‐climate ensembles to gauge the significance of greenhouse‐gas‐induced air pollution changes relative to internal climate variability, and consider 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 4°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 three days doubles in GFDL‐CM3 and increases by ∼50% in CESM1. In other EUS regions, inter‐model differences in PM2.5 responses to climate change cannot be explained solely 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 regional‐scale pollution and heat event frequency and duration, while obviating the need to bias‐correct concentration‐based thresholds separately in individual models. Plain Language Summary: Prior studies concluded climate change will worsen air quality in some polluted regions but typically neglected the role of climate variability. Uncertainty also arises from differences in climate model responses. Differentiating the relative contributions of these uncertainties to inter‐model differences in projected air pollution responses to climate change is becoming possible with initial‐condition climate model ensembles. We analyze day‐by‐day variations in air pollution over five eastern U.S. regions to quantify changes in frequency and duration of regional‐scale high pollution and heat events with small initial‐condition ensembles from two different models. Under a 21st century climate change scenario in which air pollutant emissions are fixed at 2005 levels, both models simulate longer‐lasting and more frequent Northeast PM2.5 episodes, which could exacerbate public health burdens, especially given correlations with temperature and ozone. Projecting changes in other Eastern United States regions is limited by inter‐model differences that exceed the uncertainty attributable to climate variability. While our ensembles are small relative to those generated now with physical climate models, our findings add to a growing recognition that climate variability complicates the detection and attribution of observed and simulated air pollution trends. Key Points: Frequency and duration of Northeast U.S. pollution events increase along with heat events under a high‐warming scenarioEmpirical Orthogonal Function approach enables rapid assessment of regional‐scale changes in pollution events without needing to bias correct models individuallyLarger uncertainty in Eastern United States PM2.5 from different model responses to climate change than from climate variability [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
Plný text