## 1. Introduction

[2] Mineral dust aerosol is an important component of the Earth's atmosphere. Dust affects the Earth's radiation balance through direct absorption and scattering of light [*Ramaswamy et al.*, 2001; *Satheesh and Moorthy*, 2005; *Sokolik and Toon*, 1996]. Atmospheric dust particles also serve as nucleation sites for cloud formation [*DeMott et al.*, 2003] and provide reactive surfaces for heterogeneous chemistry that can alter the chemical balance for key atmospheric species such as SO_{2}, thus indirectly affecting radiative forcing [*Dentener et al.*, 1996]. Modeling the full impact of dust on climate and atmospheric chemistry requires accurate information about aerosol loading and dust particle composition, size, and shape distributions. This information is often derived from optically based remote sensor measurements using satellite or ground-based instruments [*Chowdhary et al.*, 2001; *Sokolik*, 2002; *Schepanski et al.*, 2007; *Kalashnikova et al.*, 2005; *Koven and Fung*, 2006; *Cattrall et al.*, 2005; *DeSouza-Machado et al.*, 2006], but these methods require accurate modeling of dust optical properties [*Dubovik et al.*, 2006; *Mishchenko et al.*, 2003; *Thomas and Gauthier*, 2009].

[3] It is well established that light scattering and absorption by aerosol can be strongly influenced by particle shape [*Bohren and Huffman*, 1983], and this constitutes a major source of uncertainty in aerosol retrievals from remote sensor measurements [*Kalashnikova and Sokolik*, 2002; *Dubovik et al.*, 2006; *Baran*, 2009; *Kahnert and Kylling*, 2004]. There has been great progress in modeling the effect of particle nonsphericity through the use of advanced light scattering theories such as T-matrix theory, discrete dipole approximation (DDA), finite-difference time domain (FDTD), or geometrical optics methods (GOM), and for weak scattering, Rayleigh-Debye-Gans (RDG) theory [*Draine and Flatau*, 1994; *Kalashnikova and Sokolik*, 2004; *Mishchenko et al.*, 1997; *Yang and Liou*, 1996a; *Yang and Liou*, 1996b; *Wang and Sorenson*, 2002]. For recent reviews of progress in light scattering from nonspherical particles see, for example, *Mishchenko et al.* [1996], *Nousiainen* [2009], *Baran* [2009], and *Yang et al.* [2007]. Such methods, however, still involve significant approximations and uncertainties. DDA or RDG approaches, for instance, allow tremendous flexibility in constructing model particles that are inhomogeneous and irregularly shaped, but are restricted to smaller particles [*Kalashnikova and Sokolik*, 2004; *Wang and Sorenson*, 2002].

[4] The T-matrix approach, based on a distribution of randomly oriented, homogeneous spheroids, has been one of the most broadly tested and applied methods, in part because of its computational efficiency [*Mishchenko et al.*, 1997; *Dubovik et al.*, 2006; *Mishchenko et al.*, 2003]. A spheroid is an ellipse of revolution about a symmetry axis whose shape can be characterized by a single parameter, the axial ratio (*AR*), the ratio of major-to-minor axis lengths. While the T-matrix approach has proven extremely useful, it remains an open question whether calculations based on a uniform spheroid approximation can accurately model the optical properties of real atmospheric mineral dust, which generally consists of inhomogeneous mixtures of particles with highly irregular shapes.

[5] In addition, all theoretical models require assumptions about the range of particle sizes, shapes, and refractive index values that may best approximate “real” atmospheric dust. Indeed, one of the primary challenges to modeling the optical properties of atmospheric dust in any theoretical approach is to determine an effective particle shape distribution for use in the simulations. For the most part, modelers have assumed particle shape distributions inferred from electron microscope images of dust [*Mishchenko et al.*, 1997; *Kalashnikova and Sokolik*, 2004]. For example, a particle shape model commonly used in T-matrix simulations is based on electron microscope image analysis of field samples of dust from collected from various source regions; this model assumes a uniform (flat) distribution of oblate and prolate spheroids with a range of moderate axial ratio parameters, *AR* ≤ 2.4 [*Mishchenko et al.*, 1997; *Dubovik et al.*, 2006].

[6] Recently, however, it has been suggested that it may be necessary to include more highly eccentric particles in the simulations. For example, *Veihelmann et al.* [2004] have used T-matrix methods to model particle shape effects on both laboratory scattering measurements from mineral dust, and on simulated satellite observational data. Their results suggest that accurate modeling of polarimetric observations of light scattered by irregular dust may require particle shape models that include extreme axis ratios, corresponding to highly eccentric spheroids. *Dubovik et al.* [2006] have also employed T-matrix-based methods to model both laboratory scattering data of mineral dust and dust retrievals from AERONET observations [http://aeronet.gsfc.nasa.gov/]. They find good fits for retrievals of aerosol scattering phase function and polarization data for particle shape distributions with moderate axis ratios. However, it is interesting to note that the shape distributions inferred from their data also show extended tails that extrapolate to more extreme axial ratios, *AR* > 3.

[7] Highly eccentric particles present a special challenge for T-matrix-based theoretical simulations because the calculations can fail to converge for larger and more asymmetric particles. For example, even the most advanced T-matrix code can fail to converge for a particle aspect ratio of *AR* = 3 with dimensionless size parameters X = 2*π*D/*λ* > ∼50, where D is the particle diameter and *λ* the wavelength of light [*Mishchenko and Travis*, 1998]. For particle size and shape distributions that include large and highly asymmetric particles, GOM, or hybrid approaches that combine T-matrix theory with GOM may prove useful [*Yang and Liou*, 1996b; *Dubovik et al.*, 2006].

[8] Particle shape effects can also lead to significant changes in IR resonance spectral line shapes, even for particles that fall in the Rayleigh regime, D ≪ *λ* [*Bohren and Huffman*, 1983; *Hudson et al.*, 2008a, 2008b]. These spectral shifts arise primarily from enhancements in the IR extinction cross section that result from surface scattering resonances and are very sensitive to particle shape [*Bohren and Huffman*, 1983]. Neglecting particle shape effects in spectral modeling could lead to errors in atmospheric dust loading or composition measurements that are based on spectral analysis of IR extinction data [*Hudson et al.*, 2008a, 2008b; *Thomas and Gauthier*, 2009].

[9] In recent experiments, we explored these issues by collecting aerosol IR extinction spectra for a range of important components of atmospheric mineral dust, while simultaneously measuring the full aerosol size distributions [*Hudson et al.*, 2007, 2008a, 2008b]. We could then directly compare experimental spectra with different theoretical model simulations [*Hudson et al.*, 2008a, 2008b; *Kleiber et al.*, 2009]. For example, in *Kleiber et al.* [2009], we explored the use of T-matrix theory to analyze IR spectral resonance line profiles for a series of well-characterized mineral aerosol samples. In particular, we investigated the “inverse problem,” i.e., whether IR spectral line profiles could be used, in conjunction with T-matrix theory, to infer the general characteristics of the aerosol particle shape distribution. Our results showed that accurately modeling IR resonance line profiles for mineral dust often required particle shape distributions that include highly eccentric spheroids, with axial ratio parameters, *AR* > 3. These results raise the question whether shape distributions based on analyses of two-dimensional electron microscope images are truly representative of the actual shape distributions for three-dimensional particles.

[10] Of course, it is a valid question whether a shape distribution derived from fitting the IR spectra has any broader significance or usefulness. In this paper, we explore this question for the case of quartz dust aerosol; specifically, do the particle shape distributions derived from analysis of IR resonance spectra provide for more accurate simulations of visible light scattering phase function and polarimetry data for the sample? These results serve as an additional internal consistency check on the shape distributions inferred from IR spectral data. We also compare the experimental results with model simulations based on particle shape distributions determined directly from electron microscope images of our quartz dust samples.

[11] Light scattering from irregularly shaped particles has been studied experimentally for many years. Previous laboratory studies have often been limited by uncertainties in the particle size distribution, shape distribution, or optical constants [*Jaggard et al.*, 1981; *Hill et al.*, 1984; *West et al.*, 1997; *Volten et al.*, 2001]. Our experimental approach enables a rigorous test of the validity of the uniform spheroid approximation in modeling the optical properties of quartz dust. Since the quartz optical constants are known and the particle size and shape distributions have been determined, simulations of the light scattering can be directly and quantitatively compared with experiment with few adjustable parameters.

[12] The work we present here is restricted to quartz dust particles that fall in the accumulation mode size range, with particle diameters in the range ∼0.1–3 *μ*m. This mode is characteristic of particles with long atmospheric lifetimes that can undergo long-range transport. Field studies have shown that the mass median size for mineral dust aerosol over the oceans is typically in the 2–3 *μ*m diameter range [*Prospero*, 1999]. Further, recent studies have estimated that up to 30% of submicron aerosol may be associated with mineral dust in a dust event [*Arimoto et al.*, 2006].