AbstractThe spatial and temporal variability of atmospheric aerosols is not well understood, as most studies have been constrained to data sets that include few stations and are of short duration. Furthermore, all methods for quantifying atmospheric turbidity suffer from a major constraint in that they require cloudless sky conditions. This restriction produces gaps in the turbidity record and sampling bias, which has led to questionable inferences about the variability of aerosols.In this research, we address these concerns via analyses at scales broader than all previous studies. We analyzed the spectral aerosol optical depth at 500 nm (τa5) and Ångström's wavelength exponent (α), which represents the relative size distribution of aerosols. A total of 27 sites, with a mean period of record of 7.3 years, are included. Beyond seasonal and spatial summaries of aerosol variability, we have divided observations by synoptic condition, utilizing the Spatial Synoptic Classification (SSC).Our results show that atmospheric turbidity across North America is greatest over the east. Seasonality of both parameters was shown, most notably a greater τa5 during summertime. Utilizing the SSC, we have uncovered significant differences across weather types. Moist weather types, especially moist tropical, display considerably higher turbidity, while the colder, drier dry polar weather type is associated with low aerosol optical depth. Certain weather types show considerable seasonal variability; the dry tropical weather type is associated with relatively low values in winter, but high values in summer, when convection is significant. Cluster analyses of stations yielded three general regions, each with similar synoptic variability: a western cluster with low aerosol optical depth and minimal synoptic variability, an eastern cluster with higher turbidity and variability, and a cluster located on the periphery of the eastern cluster, associated with moderate levels of turbidity but very high variability, suggesting a varied influence of nearby industrial areas. Copyright © 2006 Royal Meteorological Society.
Read full abstract