2022 journal article

Characterizing Changes in Eastern US Pollution Events in a Warming World

JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 127(9).

co-author countries: United States of America 🇺🇸
Source: Web Of Science
Added: May 31, 2022

Abstract 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, PM 2.5 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 PM 2.5 of up to 1–4 μg m −3 . The ensemble average decadal incidence of upper quartile Northeast PM 2.5 events lasting at least three days doubles in GFDL‐CM3 and increases by ∼50% in CESM1. In other EUS regions, inter‐model differences in PM 2.5 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.