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, 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 chemistryclimate 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 SummaryPrior 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 initialcondition 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 PM 2.5 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 ...

Characterizing and quantifying uncertainty in projections of climate change impacts on air quality

“…In contrast, internal variability in climate-induced changes to PM 2.5 remains consistent across climate sensitivities explored. While several studies have reported climate penalty on the upper tails of projected future air pollution distributions [5,13,23,48,54], further work investigating high pollution events can shed additional light on the interconnections different of climate in simulations of air quality impacts.…”

“…However, sub-decadal simulations have proven insufficient to filter out the noise of internal variability in projections of climate impacts on O 3 and PM 2.5 and the associated health impacts [25,26,52,53]. Although large ensembles offer an effective approach to capture internal variability in climate projections [19], few studies have used initial condition ensembles to simulate climate change impacts on air quality [23,50,51,53]. Here, we rely on multiple model initializations and 30 year periods to represent a range of internal variability and weigh its influence on projected climate change impacts on pollutant concentrations.…”

“…Uncertainty in climate projections stems from three major sources: greenhouse gas (GHG) emissions scenario, climate model response, and internal variability [18]. Internal variability arises from natural variations in the climate system and is most effectively captured by initial condition ensembles [19][20][21][22][23]. Model response uncertainty is the differing response of climate models to the same radiative forcing and has been assessed by quantifying variability in projections across multi-model ensembles [21].…”

“…Understanding how these drivers of uncertainty propagate to projections of climate impacts is critical for policy analyses considering air quality impacts and co-benefits of mitigation strategies. While climate uncertainties in air quality projections have been explored [23,[25][26][27][28], these uncertainties have not been systematically weighed or compared.…”

Projections of Future Air Quality Are Uncertain. But Which Source of Uncertainty Is Most Important?

“…Another major application of initialized ensembles is to determine a robust anthropogenic climate change signal, and hence inform approaches in the field of the detection and attribution of anthropogenic climate change. At its simplest, the anthropogenic climate change “signal” is represented by the ensemble mean whilst the internal climate variability “noise” emanates from the initialized ensemble range (Fiore et al., 2022).…”