Abstract
Abstract. Knowledge of aerosol size and composition is important for determining radiative forcing effects of aerosols, identifying aerosol sources and improving aerosol satellite retrieval algorithms. The ability to extrapolate aerosol size and composition, or type, from intensive aerosol optical properties can help expand the current knowledge of spatiotemporal variability in aerosol type globally, particularly where chemical composition measurements do not exist concurrently with optical property measurements. This study uses medians of the scattering Ångström exponent (SAE), absorption Ångström exponent (AAE) and single scattering albedo (SSA) from 24 stations within the NOAA/ESRL Federated Aerosol Monitoring Network to infer aerosol type using previously published aerosol classification schemes.Three methods are implemented to obtain a best estimate of dominant aerosol type at each station using aerosol optical properties. The first method plots station medians into an AAE vs. SAE plot space, so that a unique combination of intensive properties corresponds with an aerosol type. The second typing method expands on the first by introducing a multivariate cluster analysis, which aims to group stations with similar optical characteristics and thus similar dominant aerosol type. The third and final classification method pairs 3-day backward air mass trajectories with median aerosol optical properties to explore the relationship between trajectory origin (proxy for likely aerosol type) and aerosol intensive parameters, while allowing for multiple dominant aerosol types at each station.The three aerosol classification methods have some common, and thus robust, results. In general, estimating dominant aerosol type using optical properties is best suited for site locations with a stable and homogenous aerosol population, particularly continental polluted (carbonaceous aerosol), marine polluted (carbonaceous aerosol mixed with sea salt) and continental dust/biomass sites (dust and carbonaceous aerosol); however, current classification schemes perform poorly when predicting dominant aerosol type at remote marine and Arctic sites and at stations with more complex locations and topography where variable aerosol populations are not well represented by median optical properties. Although the aerosol classification methods presented here provide new ways to reduce ambiguity in typing schemes, there is more work needed to find aerosol typing methods that are useful for a larger range of geographic locations and aerosol populations.
Highlights
It is well established that aerosol particles affect the radiative forcing of climate both directly by scattering and absorbing sunlight and indirectly by influencing cloud formation and precipitation, aerosols still remain a primary source of uncertainty in assessing the Earth’s radiative budget (Boucher et al, 2013)
Surface in situ aerosol optical properties obtained at 24 stations in the NOAA/ESRL Federated Aerosol Monitoring Network were used to classify aerosol type at the site, using aerosol classification schemes from the literature, cluster analyses, and general knowledge of station location and characteristics
The monitoring sites utilized for the analysis offered a diverse range of station locations and aerosol types, www.atmos-chem-phys.net/17/12097/2017/
Summary
It is well established that aerosol particles affect the radiative forcing of climate both directly by scattering and absorbing sunlight and indirectly by influencing cloud formation and precipitation, aerosols still remain a primary source of uncertainty in assessing the Earth’s radiative budget (Boucher et al, 2013) This uncertainty arises from a large range of aerosol chemical and physical properties as well as from the high spatiotemporal variability in aerosol particles. High SSA values near 1 indicate low- or nonabsorbing “white” aerosols, while low SSA values (below 0.85) indicate “darker” highly absorbing aerosols, and an SSA value can be used to characterize the aerosol type (Bergstrom et al, 2002; Russell et al, 2010; Gyawali et al, 2012) Equations for calculating these properties from extensive optical parameters are found in Sect. Many studies have used the information inherent in these optical properties to predict aerosol type; Table 1 provides a review of previous studies that have utilized intensive optical property thresholds to identify aerosol type
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