Abstract
Abstract. Remote sensing of aerosols provides important information on atmospheric aerosol abundance. However, due to the hygroscopic nature of aerosol particles observed aerosol optical properties are influenced by atmospheric humidity, and the measurements do not unambiguously characterize the aerosol dry mass and composition, which complicates the comparison with aerosol models. In this study we derive aerosol water and chemical composition by a modeling approach that combines individual measurements of remotely sensed aerosol properties (e.g., optical thickness, single-scattering albedo, refractive index and size distribution) from an AERONET (Aerosol Robotic Network) Sun–sky radiometer with radiosonde measurements of relative humidity. The model simulates water uptake by aerosols based on the chemical composition (e.g., sulfates, ammonium, nitrate, organic matter and black carbon) and size distribution. A minimization method is used to calculate aerosol composition and concentration, which are then compared to in situ measurements from the Intensive Measurement Campaign At the Cabauw Tower (IMPACT, May 2008, the Netherlands). Computed concentrations show good agreement with campaign-average (i.e., 1–14 May) surface observations (mean bias is 3% for PM10 and 4–25% for the individual compounds). They follow the day-to-day (synoptic) variability in the observations and are in reasonable agreement for daily average concentrations (i.e., mean bias is 5% for PM10 and black carbon, 10% for the inorganic salts and 18% for organic matter; root-mean-squared deviations are 26% for PM10 and 35–45% for the individual compounds). The modeled water volume fraction is highly variable and strongly dependent on composition. During this campaign we find that it is >0.5 at approximately 80% relative humidity (RH) when the aerosol composition is dominated by hygroscopic inorganic salts, and <0.1 when RH is below 40%, especially when the composition is dominated by less hygroscopic compounds such as organic matter. The scattering enhancement factor (f(RH), the ratio of the scattering coefficient at 85% RH and its dry value at 676 nm) during 1–14 May is 2.6 ± 0.5. The uncertainty in AERONET (real) refractive index (0.025–0.05) is the largest source of uncertainty in the modeled aerosol composition and leads to an uncertainty of 0.1–0.25 (50–100%) in aerosol water volume fraction. Our methodology performs relatively well at Cabauw, but a better performance may be expected for regions with higher aerosol loading where the uncertainties in the AERONET inversions are smaller.
Highlights
Atmospheric aerosol particles interact directly and indirectly, i.e., through cloud albedo and lifetime, with radiation (Lohmann and Feichter, 2005)
The modeled water volume fraction is highly variable and strongly dependent on composition. During this campaign we find that it is > 0.5 at approximately 80 % relative humidity (RH) when the aerosol composition is dominated by hygroscopic inorganic salts, and < 0.1 when RH is below 40 %, especially when the composition is dominated by less hygroscopic compounds such as organic matter
Aerosol and Cloud Lidar; Apituley et al, 2009) data showed that low refractive index (RRI) values seem to occur both on clear days (e.g., 7 and 8 May) as well as on days with a slight lidar backscatter signal between 8 and 12 km altitude
Summary
Atmospheric aerosol particles interact directly and indirectly, i.e., through cloud albedo and lifetime, with radiation (Lohmann and Feichter, 2005). Large uncertainties are associated with the aerosol radiative. Estimates of radiative forcing from models range from −0.2 to −0.9 W m−2 for the direct effect and −0.5 and −1.5 W m−2 for the indirect effect (Forster et al, 2007; Quaas et al, 2009), while remote sensing estimates yield between −0.9 and −1.9 W m−2 for the direct effect (Bellouin et al, 2005; Quaas et al, 2008) and −0.2 ± 0.1 W m−2 for the indirect effect (Quaas et al, 2008). More recent estimates of the aerosol radiative effects from models and remote sensing tend to converge (e.g., Bellouin et al, 2008; Myhre, 2009; Lohmann et al, 2010), but the uncertainty remains large (Loeb and Su, 2010; Schulz et al, 2010; Kahn, 2012; Bellouin et al, 2013; Myhre et al, 2013a). The uncertainty in aerosol forcing leads to large uncertainties in the estimates of climate sensitivity and future projections of climate change (Andreae et al, 2005; Myhre et al, 2013b)
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