Remote sensing of soot carbon – Part 2: Understanding the absorption Ångström exponent

Atmos. Chem. Phys., 16, 1587–1602, 2016 www.atmos-chem-phys.net/16/1587/2016/ doi:10.5194/acp-16-1587-2016 © Author(s) 2016. CC Attribution 3.0 License. Remote sensing of soot carbon – Part 2: Understanding the absorption Ångström exponent G. L. Schuster1 , O. Dubovik2 , A. Arola3 , T. F. Eck4,5 , and B. N. Holben5 1 NASA Langley Research Center, Hampton, VA, USA 2 Laboratoire d’Optique Atmosphérique, Université de Lillé 1, CNRS, Villeneuve d’Ascq, France 3 Finnish Meteorological Institute, P.O. Box 1627, 70211 Kuopio, Finland 4 Universities Space Research Association, Columbia, MD, USA 5 NASA Goddard Space Flight Center, Greenbelt, MD, USA Correspondence to: G. L. Schuster () Received: 24 March 2015 – Published in Atmos. Chem. Phys. Discuss.: 5 August 2015 Revised: 17 November 2015 – Accepted: 25 November 2015 – Published: 11 February 2016 Abstract. Recently, some authors have suggested that the absorption Ångström exponent (AAE) can be used to deduce the component aerosol absorption optical depths (AAODs) of carbonaceous aerosols in the AERONET database. This AAE approach presumes that AAE  1 for soot carbon, which contrasts the traditional small particle limit of AAE = 1 for soot carbon. Thus, we provide an overview of the AERONET retrieval, and we investigate how the microphysics of carbonaceous aerosols can be interpreted in the AERONET AAE product. We find that AAE  1 in the AERONET database requires large coarse mode fractions and/or imaginary refractive indices that increase with wavelength. Neither of these characteristics are consistent with the current definition of soot carbon, so we explore other possibilities for the cause of AAE  1. AAE is related to particle size, and coarse mode particles have a smaller AAE than fine mode particles for a given aerosol mixture of species. We also note that the mineral goethite has an imaginary refractive index that increases with wavelength, is very common in dust regions, and can easily contribute to AAE  1. We find that AAE  1 can not be caused by soot carbon, unless soot carbon has an imaginary refractive index that increases with wavelength throughout the visible and near-infrared spectrums. Finally, AAE is not a robust parameter for separating carbonaceous absorption from dust aerosol absorption in the AERONET database. 1 Introduction The aerosol robotic network (AERONET) is composed of hundreds of sun–sky scanning radiometers located at surface sites throughout the world, which are robotically controlled, solar powered, and weather hardened so that they may operate autonomously (Holben et al., 1998). These narrow-fieldof-view instruments provide frequent aerosol optical depth measurements in the 0.340–1.02 µm spectral range throughout the day. The radiometers also measure sky radiances at a wide range of scattering angles, and these data are incorporated into a radiometric retrieval algorithm to infer aerosol absorption (Dubovik and King, 2000). The absorption Ångström exponent and the absorption aerosol optical depth represent two of the aerosol absorption products available in the AERONET database at http://aeronet.gsfc.nasa. gov. The absorption Ångström exponent (AAE) is defined by a power law relationship with the absorption aerosol optical depth (AAOD) or the aerosol absorption coefficient (Cabs ): ξ(λ) = ξ(1)λ−AAE . (1) Here, ξ is a generic variable for absorption that can represent either AAOD or Cabs , λ represents wavelength, and ξ(1) is the power law value for ξ at a reference wavelength of 1 µm. If AAOD(λ) or Cabs (λ) are known at two or more wavelengths, then AAE can be obtained by linear regression of the logarithm of Eq. (1). (Note that some authors use a reference wavelength of 0.55 µm instead of 1 µm, but this does not Published by Copernicus Publications on behalf of the European Geosciences Union. 1588 G. L. Schuster et al.: Understanding the AAE for remote sensing of soot carbon affect the derived value of AAE. Additionally, some authors have noted that AAE is not necessarily constant throughout the visible and near-infrared wavelengths at East Asian AERONET sites; Eck et al., 2010; Li et al., 2015). The spectral dependence of absorption (i.e., AAE) depends upon particle size and the imaginary refractive index, and excellent overviews of the relationship between AAE and aerosol microphysics are provided by Moosmüller et al. (2009, 2011). Generally, AAE is obtained from photoacoustic measurements (Lewis et al., 2008; Chow et al., 2009; Gyawali et al., 2009; Flowers et al., 2010; Lack et al., 2012b; Chakrabarty et al., 2013; Utry et al., 2014), particle soot absorption photometer measurements (Roden et al., 2006), aethalometer measurements (Ganguly et al., 2005), the solar-flux–AOD technique (Russell et al., 2010), integrating plate methods (Schnaiter et al., 2003), differencing extinction and scattering coefficients (Schnaiter et al., 2006), or aerosol robotic network (AERONET) retrievals (Bahadur et al., 2012; Chung et al., 2012b; Giles et al., 2012; Cazorla et al., 2013; Xu et al., 2013; Russell et al., 2014). The AAE parameter is appealing because AAE = 1 in the Rayleigh limit for small particles that have wavelengthindependent refractive indices throughout the visible and near-infrared wavelengths (Bohren and Huffman, 1983; Moosmüller et al., 2009, 2011). Since soot carbon (sC) has a spectrally invariant imaginary refractive index at visible wavelengths (Bond and Bergstrom, 2006; Bond et al., 2013) and primary spherule sizes are less than about 50 nm (Mulholland and Mountain, 1999; Pósfai et al., 2003), we expect AAE = 1 when externally mixed soot carbon is the only absorbing aerosol present (Bergstrom et al., 2002; Andreae, 2006; Bergstrom et al., 2007; Moosmüller et al., 2009). Other absorbing aerosols typically exhibit AAE > 1, so aerosol scientists can use AAE to determine whether soot carbon is the dominant absorbing aerosol in their measurements (Bergstrom et al., 2002; Russell et al., 2010). Recently, some authors have suggested that AAE can also be used to deduce the component AAODs of dust, brown carbon (BrC), and soot carbon in the atmosphere (Bahadur et al., 2012; Chung et al., 2012b; Bond et al., 2013; Cazorla et al., 2013; Xu et al., 2013). The premise behind this AAE approach is that AAE is a species-dependent aerosol property that does not depend upon particle size or mass, that absorbing aerosol species are externally mixed with one another, and that AAE  1 for soot carbon. Other authors have found that AAE does not contain enough information to unambiguously speciate the absorbing aerosols (Bergstrom et al., 2007; Gyawali et al., 2009; Lack and Cappa, 2010; Giles et al., 2012; Lack and Langridge, 2013). Thus, we explore this topic here, and we point out some theoretical inconsistencies associated with using the AAE approach to deduce component AAODs from the AERONET retrievals. We begin by defining our choice of nomenclature in Sect. 2, which has become an (...truncated)