Supporting data from: Characterizing changes in eastern U.S. pollution events in a warming world

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.