Abstract
Abstract. 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 deseasonalized 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 generally positively correlated with sulfate and nitrate but negatively correlated with organic carbon. GEOS-Chem results indicate that most of the correlations of PM2.5 with temperature and RH do not arise from direct dependence but from covariation with synoptic transport. We applied principal component analysis and regression to identify the dominant meteorological 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 cyclone frequency as diagnosed from a spectral-autoregressive analysis of the dominant meteorological mode. An ensemble 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 frequency 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 realizations (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 computationally more affordable approach for this purpose than coupled GCM-CTM studies.
Highlights
Air pollution is highly dependent on weather, and it follows that climate change could significantly impact air quality
We examined the correlations of PM2.5 and its components with meteorological variables for 2004–2008 (EPA-AQS) and 2005–2007 (GEOS-Chem) by applying a standardized multiple linear regression (MLR) model: elemental carbon (EC) emissions are from Cooke et al (1999)
For each realization we examined the change in median cyclone frequency between the presentday (1996–2010) and the future (2036–2050), by applying the spectral-autoregressive method to the dominant cyclone principal components (PCs) for each 15-year time series, and using a Monte Carlo method to diagnose the probability distribution and significance of the change based on variability of the AR2 parameters
Summary
Air pollution is highly dependent on weather, and it follows that climate change could significantly impact air quality. Studies using chemical transport models (CTMs) driven by general circulation models (GCMs) consistently project a worsening of ozone air quality in a warming climate (Weaver et al, 2009) This finding is buttressed by observed correlations of ozone with temperature that are well reproduced by models (Jacob et al, 1993; Sillman and Samson, 1995; Rasmussen et al, 2012). Interpretation is complicated by the covariation of meteorological variables with synoptic transport To address this issue, we use PCA and regression to determine the dominant meteorological modes of observed daily PM2.5 variability in different US regions, and show how spectral analysis of these modes enables a robust estimate of the effect of climate change on PM2.5 air quality
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