Meteorological modes of variability for fine particulate matter (PM 2.5 ) air quality in the United States: implications for PM 2.5 sensitivity to climate change

We applied a multiple linear regression model to understand the relationships of PM2.5 with meteorological variables in the contiguous US and from there to infer the sensitivity of PM2.5 to climate change. We used 2004-2008 PM2.5 observations from 1000 sites ( 200 sites for PM2.5 components) and compared to results from the GEOS-Chem chemical transport model (CTM). All data were deseasonal- ized to focus on synoptic-scale correlations. We find strong positive correlations of PM2.5 components with temperature in most of the US, except for nitrate in the Southeast where the correlation is negative. Relative humidity (RH) is gen- erally positively correlated with sulfate and nitrate but neg- atively correlated with organic carbon. GEOS-Chem results indicate that most of the correlations of PM2.5 with tempera- ture and RH do not arise from direct dependence but from co- variation with synoptic transport. We applied principal com- ponent analysis and regression to identify the dominant mete- orological modes controlling PM2.5 variability, and show that 20-40 % of the observed PM2.5 day-to-day variability can be explained by a single dominant meteorological mode: cold frontal passages in the eastern US and maritime inflow in the West. These and other synoptic transport modes drive most of the overall correlations of PM2.5 with temperature and RH except in the Southeast. We show that interannual variability of PM2.5 in the US Midwest is strongly correlated with cy- clone frequency as diagnosed from a spectral-autoregressive analysis of the dominant meteorological mode. An ensem- ble of five realizations of 1996-2050 climate change with the GISS general circulation model (GCM) using the same climate forcings shows inconsistent trends in cyclone fre- quency over the Midwest (including in sign), with a likely decrease in cyclone frequency implying an increase in PM2.5. Our results demonstrate the need for multiple GCM realiza- tions (because of climate chaos) when diagnosing the effect of climate change on PM2.5, and suggest that analysis of meteorological modes of variability provides a computation- ally more affordable approach for this purpose than coupled GCM-CTM studies.

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