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Studies of the effect of climate change on fine particulate matter (PM2.5 air quality using general circulation models (GCMs) show inconsistent results including in the sign of the effect. This reflects uncertainty in the GCM simulations of the regional meteorological variables affecting PM2.5. Here we use the CMIP3 archive of data from fifteen different IPCC AR4 GCMs to obtain improved statistics of 21st-century trends in the meteorological modes driving PM2.5 variability over the contiguous US. We analyze 1999–2010 observations to identify the dominant meteorological modes driving interannual PM2.5 variability and their synoptic periods T. We find robust correlations (r > 0.5) of annual mean PM2.5 with T, especially in the eastern US where the dominant modes represent frontal passages. The GCMs all have significant skill in reproducing present-day statistics for T and we show that this reflects their ability to simulate atmospheric baroclinicity. We then use the local PM2.5-to-period sensitivity (dPM2.5/dT) from the 1999–2010 observations to project PM2.5 changes from the 2000–2050 changes in T simulated by the 15 GCMs following the SRES A1B greenhouse warming scenario. By weighted-average statistics of GCM results we project a likely 2000–2050 increase of ~ 0.1 μg m−3 in annual mean PM2.5 in the eastern US arising from less frequent frontal ventilation, and a likely decrease albeit with greater inter-GCM variability in the Pacific Northwest due to more frequent maritime inflows. Potentially larger regional effects of 2000–2050 climate change on PM2.5 may arise from changes in temperature, biogenic emissions, wildfires, and vegetation, but are still unlikely to affect annual PM2.5 by more than 0.5 μg m−3

atmospheric chemistry and physics

Impact of 2000–2050 climate change on fine particulate matter (PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt;) air quality inferred from a multi-model analysis of meteorological modes