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| Titel |
Meteorological modes of variability for fine particulate matter (PM2.5) air quality in the United States: implications for PM2.5 sensitivity to climate change |
| VerfasserIn |
A. P. K. Tai, L. J. Mickley, D. J. Jacob, E. M. Leibensperger, L. Zhang, J. A. Fisher, H. O. T. Pye |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 12, no. 6 ; Nr. 12, no. 6 (2012-03-30), S.3131-3145 |
| Datensatznummer |
250010953
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| Publikation (Nr.) |
 copernicus.org/acp-12-3131-2012.pdf |
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| Zusammenfassung |
| 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. |
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