Characterizing Changes in Eastern U.S. Pollution Events in a Warming
World
Abstract
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.