Journal of Geophysical Research: Atmospheres

RESEARCH ARTICLE Key Points: A new algorithm that uses POLDER satellite data that retrieves the aerosol SSA over clear-sky ocean has been developed The spatial and temporal variability of the aerosol single scattering albedo at 865 nm has been estimated over clear-sky ocean for 2006 Taking absorption into account improves the spatial continuity for the aerosol properties retrieved over clear-sky and cloudy scenes Correspondence to: F. Waquet, fabien.waquet@univ-lille1.fr Citation: Waquet, F., J.-C. Péré, F. Peers, P. Goloub, F. Ducos, F. Thieuleux, and D. Tanré (2016), Global detection of absorbing aerosols over the ocean in the red and near-infrared spectral region, J. Geophys. Res. Atmos., 121, 10,902 10,918, doi:. Received 29 MAR 2016 Accepted 11 AUG 2016 Accepted article online 15 AUG 2016 Published online 28 SEP 2016 2016. American Geophysical Union. All Rights Reserved. Global detection of absorbing aerosols over the ocean in the red and near-infrared spectral region F. Waquet 1, J.-C. Péré 1, F. Peers 1,2, P. Goloub 1, F. Ducos 1, F. Thieuleux 1, and D. Tanré 1 1 Laboratory of Atmospheric Optics, University of Lille 1, Villeneuve d Ascq, France, 2 Now at: College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK Abstract The spatial and temporal variability of the aerosol single scattering albedo (SSA at 865 nm) has been estimated over clear-sky ocean for 2006 by using measurements acquired by POLDER (Polarization and Directionality of Earth Reflectances). Our estimates are correlated with Sun photometer retrievals (R = 0.63). Differences in SSA are generally around 0.05 and systematically fall below 0.055 for optical thicknesses 0.3 (at 865 nm) and modeling errors 3.0%. Fine absorbing aerosols (radius 0.16 μm) are detected in many coastal regions. The lowest SSAs are retrieved over the southeast Atlantic during summer (0.80), whereas nonabsorbing fine particles ( 0.98) are observed over the North Pacific. During winter, fine absorbing aerosols are detected together with mineral dust near the coasts of western Africa (0.90), over the tropical Atlantic (0.88), and around India (0.88). Long-range transport of absorbing species is also detected, as for instance over the Arctic. This study could help to constrain aerosol absorption and radiative forcing in models. 1. Introduction Aerosols play an important role in climate. They directly affect the Earth s radiative budget by the scattering and the absorption of solar and telluric radiation (direct radiative effect) [Toll and Männik, 2015] and indirectly by changing the reflectance and persistence of clouds (semidirect and indirect effects) [Wilcox, 2012; Goren and Rosenfeld, 2014]. Absorbing particles are of particular interest as they may exert a warming of the atmosphere where they are located and simultaneously cause a surface cooling. These opposite effects can modify the atmospheric dynamics, the precipitation regime, and the temperature gradient [Ott et al., 2010; Wang, 2013; Péré et al., 2014]. The main absorbing components of aerosols are black and brown carbon. The former refers to soot particles formed by combustion, while the latter corresponds to organic materials of various origins. Soil dust is also shown to have light-absorbing efficiency, depending on their size and mineral composition [Knippertz and Stuut, 2014]. As black/brown carbon and mineral dust potentially represent a large fraction of the aerosol composition, the contribution of absorption in the particle-light interaction can be important. The relative proportion of scattering and absorption are expressed through the single scattering albedo (SSA), which is defined by the ratio between the scattering and total extinction (scattering + absorption) coefficient. This optical parameter can be deduced from in situ and laboratory measurements [Liu et al., 2014] or from ground remote-sensing techniques [Dubovik et al., 1998; Buchard et al., 2011]. However, the temporally varying spatial distribution of aerosols and the diversity of their chemical composition make the magnitude of aerosol absorption at the global scale subject to considerable uncertainty [Boucher et al., 2013]. In that sense, the use of satellite represents a unique opportunity to overcome this issue. Current passive remote-sensing techniques provide several global aerosol properties over ocean [Kaufman et al., 2002] but not all of them. For instance, the spectral radiances provided between 0.55 and 2.13 μm by the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument are used to estimate the aerosol optical thickness (AOD) and the particle size [Tanré et al., 1997]. However, the MODIS spectral radiances do not provide enough information to retrieve the SSA over uniform and dark surfaces like the ocean. While the potential to retrieve aerosol extinction from satellite sensors is now clearly demonstrated, the global distribution of the SSA has been much less documented, in spite of its major importance for the aerosol radiative forcing [Boucher et al., 2013]. Kaufman [1987] and Kaufman et al. [2001, 2002] have already shown that it is possible to retrieve aerosol SSA from passive measurements. They developed two methods to retrieve the aerosol absorption with passive WAQUET ET AL. GLOBAL OBSERVATION OF ABSORBING AEROSOLS 10,902

satellite instruments. Both technics rely on the attenuation of the signal above a bright surface. On the one hand, the first one [Kaufman, 1987; Kaufman et al., 2001] allows the evaluation of the SSA of dust aerosols above a bright surface as long as the scattering phase function and the surface reflectance are known (derived during a clear day, for instance). This technique has been demonstrated with MODIS observations and evaluated with ground-based aerosol observations from the Aerosol Robotic Network (AERONET) [Zhuetal., 2011]. On the other hand, Kaufman et al. [2002] suggest using the sunglint to retrieve the aerosol absorption above the ocean. Based on the capacity of aerosols to absorb UV radiations, different approaches were developed using Total Ozone Mapping Spectrometer (TOMS) [Torres et al., 1998, 2005] and Ozone Monitoring Instrument (OMI) [Torres et al., 2007]. Torres et al. [2013] have evaluated an algorithm to operationally retrieve, under some assumptions, the AOD and SSA in the UV with OMI. Extensive evaluation analyses of these retrieved quantities using AERONET observations have been carried out [Ahn et al., 2014; Jethva et al., 2014a]. Research algorithms were successfully applied to photopolarimetric spaceborne and aircraft data to retrieve aerosol absorption and SSA for case studies [Chowdhary et al., 2002; Hasekamp et al., 2011]. Recently, Peers et al. [2015] developed an algorithm to estimate the SSA of above-cloud aerosols using the total and polarized radiances provided by POLDER (Polarization and Directionality of Earth Reflectances) on board the satellite PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar). The method uses total and polarized radiances in a complementary way since total radiances are mostly sensitive to the absorption of the above-cloud aerosols, whereas polarized radiances mainly inform on the scattering process of the aerosol layer above liquid clouds [Waquet et al., 2009, 2013]. Hasekamp et al. [2011] developed a retrieval algorithm that also uses measurements acquired by POLDER to retrieve the SSA over clear-sky ocean. This algorithm uses all available total and polarized radiance measurements provided by POLDER at 490, 670, 865, and 1020 nm. The algorithm is based on a variational method and therefore considers a continuous parameter space for aerosol microphysical properties. The particle absorption is retrieved separately for the two modes. This method aims to simultaneously retrieve the aerosol and surface properties, including the wind speed and direction as well as the chlorophyll concentration. Global results obtained with this method were compared with AERONET data and global climate model results in Lacagnina et al. [2015]. In our work, we present a new algorithm for the retrieval of the SSA that only uses the red and near-infrared spectral bands of the POLDER instrument (670 and 865 nm). We are using less information, but the number of unknowns is also reduced through the limitation of the spectral domain. The method has the advantage to use only off-glint observations to retrieve aerosol properties and does not require a simultaneous retrieval of the surface and aerosol properties. We demonstrate that the polarized and total radiance measurements acquired at 670 and 865 nm over clear-sky ocean can be often well modeled using this assumption and that a look-up table (LUT) algorithm allows for a robust retrieval of the SSA at such wavelengths. First, we present the method and a sensivity study. Then, a case study analysis and a comparison against AERONET Sun photometer retrievals are presented. Finally, we describe the spatial and temporal variability in the AOD and SSA retrieved at 865 nm for the year 2006 and compare our results with previous works. 2. Data and Method We use normalized total and polarized radiance measurements obtained from the Stokes parameters acquired at 670 and 865 nm by POLDER to derive the aerosol properties [Herman et al., 2005]. Cloud-free pixels at POLDER s native resolution (6 6 km 2 ) are first selected according to the cloud-screening algorithm of Bréon and Colzy [1999]. Then, the data are spatially aggregated at a resolution of 18 18 km 2. The data are corrected from the effects of foam and gaseous absorption using the approach developed by Deuzé et al. [2000]. In many ways, the present algorithm is similar to the operational one developed for POLDER over ocean [Deuzé et al., 2000; Herman et al., 2005]. The two main differences with the operational method are a better description of the fine mode of particles and the inclusion of the aerosol absorption. To a lesser extent, the use of spheroids to represent the scattering properties of nonspherical dust particles is another improvement with respect to Herman et al. [2005]. In this latter study an empirical model was used to model the scattering properties of nonspherical coarse particles. We describe here below the calculations and models considered for the new method. A LUT of radiances is generated using the successive order of scattering code [Lenoble et al., 2007] for different proportions of fine and coarse mode aerosols, various aerosol optical thicknesses, and viewing geometries. The code also takes into account the influence of molecules and ocean surface properties. To generate the LUT, nine values of ratio between fine and total AOD (at 865 nm), namely, η, are used. The optical properties of fine aerosols are computed using the Mie theory for three values of imaginary refractive WAQUET ET AL. GLOBAL OBSERVATION OF ABSORBING AEROSOLS 10,903

Table 1. Lognormal Parameters Used for the Particle Size Distribution Considered in the LUT a ParticleSize rm (μm) σ reff (μm) Fine 0.06, 0.08, 0.10, 0.12, 0.14, 0.16 0.40 0.09, 0.12, 0.15, 0.18, 0.21, 0.24 Coarse 0.7 0.69 2.30 a Geometric mean radius (rm), geometric standard deviation (σ) and effective radius (reff). The formula used for the lognormal is the one given in Deuzé et al. [2000]. index (mi). The imaginary refractive index values are adjusted for each fine aerosol model in order to get three values of fine-mode SSA at 865 nm (1.00, 0.86, and 0.72) which cover the entire range of SSA natural variability at 865 nm. A linear interpolation process is then used in the LUT to create a fine step for the fine-mode SSA at 865 nm (0.0175). Six values of the real refractive index (1.35, 1.40, 1.45, 1.50, 1.55, and 1.60) are considered for the fine aerosol models. The imaginary and real refractive index values are both assumed spectrally constant between 670 and 865 nm. We consider six values of effective radius (from 0.06 to 0.16 μm) and six values of real refractive index (from 1.35 to 1.60) for the fine mode. Coarse aerosols consist in a mixture of spherical and spheroid particles (with varying proportion) at a single effective radius (2.3 μm), which seems sufficient in regard to the POLDER spectral range (0.44 0.865 μm). The lognormal parameters used for the particle size distribution are reported in Table 1. Coarse spheroids are assigned a real refractive index of 1.51 (dust), while three different values (1.33, 1.35, and 1.37) are considered for the spherical coarse mode (i.e., detection of cloud bows for large hydrated particles). In our algorithm, only fine aerosols have absorption capability as coarse particles (such as maritime and mineral dust particles) are shown to be generally nonabsorbing or weakly absorbing at the considered wavelengths [Müller et al., 2008, 2012]. Different assumptions are used to build the LUT and are described hereafter. Their impacts on the aerosol retrievals are evaluated in section 3. The altitude of the aerosol layer affects radiance measurements through Rayleigh scattering effect. Because the latter is much stronger in the UV than in the near infrared, our algorithm has a small sensitivity to the aerosol profile. A constant vertical repartition is assumed, which follows a Gaussian distribution with a variance of 0.75 and a mean aerosol altitude of 2.25 km. We use the median between 0.5 and 4.5 km, which is the typical range of variability for this parameter over ocean for dust and carbonaceous aerosol layers [Torres et al., 2013]. To compute the diffuse and multiple interactions between the surface and the atmosphere, the specular reflection model of the sunlight on the ocean wave facets (glitter) of Cox and Munk [1954] is used with a wind speed value of 5ms 1. According to meteorological data, the wind speed values ranging between 3 m s 1 and8ms 1 correspond to most situations (85% of our retrieval shown in Figure 9), which justifies the choice of an intermediate value of5ms 1 ). The contribution of the water leaving radiance is typically small at 670 nm and is negligible at 865 nm. This contribution is assumed lambertian and unpolarized for these wavelengths. We used the MODIS ocean color products called remote-sensing reflectance at 667 nm to estimate the ocean surface reflectance in the 670 nm POLDER band. We use a single value of 0.001 for the ocean surface reflectance at 670 nm since the typical range of variability for this parameter is between 0.0005 and 0.002, according to MODIS ocean color products. The retrieval process is divided into two steps, described below. In a first step, we only use measurements acquired for off-glint viewing geometries to retrieve the aerosol properties. Observations for which the modeled glitter contribution is larger than one twentieth of the measured total radiance measurements are considered contaminated and excluded in this part of the retrieval. The solution of the algorithm is the couple of fine and coarse mode and the associated optical properties that minimize a weighted error term ε w computed between measured and calculated radiances, as follows: [ 2 n (Lcal (i, j) L ε w = meas (i, j)) 2 + (Lp cal (i, j) Lp ] meas (i, j))2 w(i, j) wp(i, j) j=1 i=1 (1) wherein j is the index for the wavelength, i is the index for the viewing geometry, and n denotes the number of viewing geometries used for the retrieval. L calc and L meas, respectively, stand for the calculated and measured radiances. Lp stands for the polarized radiances. The quantity w is a weight defined as w(i, j) =(cal(j) L(i, j)) 2 + L noise (j) 2 (2) WAQUET ET AL. GLOBAL OBSERVATION OF ABSORBING AEROSOLS 10,904

Figure 1. Schematic view of the algorithm used to retrieve the aerosol single scattering albedo (SSA) and other properties over clear-sky ocean from POLDER total and polarized radiances acquired at 670 and 865 nm. The quantity wp is computed in a similar way for the polarized radiance measurements. The coefficient cal is the relative calibration error fixed to 2% at 670 and 865 nm for both total and polarized radiances. The noise equivalent radiance for total (L noise ) and polarized radiances (Lp noise ) are given in Fougnie et al. [2007]. When the range of scattering angles sampled for off-glint viewing geometries is narrow, the sensitivity to particle microphysics decreases and multiple solutions appear (i.e., different combinations of optical and microphysical properties that equivalently fit the data). For such cases, if the glitter is available, we use the glitter to reduce the space of solutions and to improve the aerosol retrievals. Practically, once the retrieval process is ended, the direct contribution of the glitter is added to the LUT s radiances, as follows: L = L LUT + t.t + L GLITTER (w s,φ w ) (3) In equation (3), the dependences on the viewing geometry and wavelength are not indicated for the sake of simplicity. The contribution of the glitter (L GLITTER ) is modeled with a two-parameter anisotropic model [Bréon and Henriot, 2006]. The downward and upward direct transmission terms (t and t + ) and the LUTs radiances (L LUT ) are computed with the aerosol optical parameters and models that have been retrieved with data acquired for off-glint viewing geometries. The second part of the retrieval process consists in minimizing the error term (equation (1)) computed with all available viewing geometries by adjusting the wind speed (w s ) and wind direction (φ w ). Equivalent formulae as equation (3) stand for the Stokes parameters Q and U. These equations are used to compute the polarized radiances, which are also included in this part of the retrieval process. If different solutions best fit the POLDER data with the same accuracy (i.e., within the measurements errors), then mean values of these solutions are retained. Practically, another error term ε is computed between measured and calculated radiances with weights w and wp, respectively, equal to 1.0 in equation (1). We compare WAQUET ET AL. GLOBAL OBSERVATION OF ABSORBING AEROSOLS 10,905

Table 2. Comparison of Aerosols Retrievals Obtained With Our Improved Algorithm and the Operational One for a Biomass Burning Case Study a AOD AOD AOD SSA Fraction of Nonspherical AOD Real Refractive Index rm (μm) (Total) (Fine) (Coarse) (Coarse) (Fine) (Fine) Solution 1 0.43 0.34 0.09 0.79 0.75 1.55 0.12 Research algorithm (with absorption) Solution 2 0.43 0.14 0.29 1.0 1.0 1.35 0.17 Operational algorithm (no absorption) Solution 3 0.27 0.14 0.13 1.0 1.0 1.55 0.08 Research algorithm (without absorption) a The rm is the geometric mean radius. this latter error term with the one computed for the error measurements expected for POLDER, ε polder, defined as follows: 2 n [ (cal(j) L(i, ε polder = j)+lnoise (j) ) 2 ( + cal(j) Lp(i, j)+lpnoise (j) ) ] 2 (4) j=1 i=1 Then, we compute the mean values of the aerosol parameters retrieved for all solutions with ε ε polder and report them as the solution of our algorithm. If no solution fits the POLDER data within the error measurements, we report the aerosol parameters associated with the solution that best fits the data. Finally, we found that the sensibility of our method to the SSA is lost when the range of scattering angles sampled for off-glint views becomes too narrow and when the AOD becomes small. To ensure the quality of the retrieval, the SSA is not reported when the range of scattering angles is smaller than 20 and the AOD is smaller than 0.05 at 865 nm. Figure 1 summarizes the different steps of the algorithm in a schematic way. 3. Sensitivity Analysis In this section, we evaluate the impacts of various assumptions and approximations used in our method on the aerosol retrievals. We generated synthetic data to test our algorithm. The microphysical properties of the particles used in the simulations are reported in Table 2. The simulations are representative of an oceanic scene with a particle size distribution dominated by biomass burning particles. The selected viewing geometries allow sampling a large range of scattering angles for off-glint views. We use the viewing geometries Figure 2. Simulated total radiances in function of the fine-mode SSA at 865 nm for different viewing geometries (Θ is the scattering angle). Simulations performed for a total AOD of 0.3 at 865 nm. WAQUET ET AL. GLOBAL OBSERVATION OF ABSORBING AEROSOLS 10,906

Table 3. Microphysical and Optical Parameters Used for the Aerosol Particles Considered in the Sensitivity Study a Parameters Values Reff (fine) (μm) 0.15 Real refractive index (fine) 1.50 Reff (coarse) (μm) 2.30 Real refractive index (coarse) 1.35 Fraction of spherical AOD (within coarse mode at 865 nm) 1.0 RATIO (at 865 nm) 0.833 a RATIO is the ratio of fine-mode AOD to total AOD. corresponding to the data associated with the POLDER pixel shown in Figure 7. In the following, the aerosol optical parameters are given at 865 nm. We first evaluate the retrieval errors associated with the use of interpolation processes for the calculation of the radiances. Figure 2 shows simulations of total radiances for various viewing geometries (Θ is the scattering angle) in a function of the fine-mode SSA for a constant AOD of 0.30. The total radiance decreases with decreasing fine-mode SSA (or increasing absorption AOD). This process is the basis of the aerosol absorption retrieval using POLDER data acquired for off-glint viewing geometries and is due to the fact that aerosol absorption reduces the multiple scattering terms that largely contribute to total radiance measurements. This effect is almost linear, and we therefore use a simple linear interpolation with three nodal points for the interpolation of the radiances in a function of the fine-mode SSA (1.00, 0.86, and 0.72). The final product is the SSA for the entire particle size distribution (π), which is computed using the fine-mode SSA (π f ), the AOD (τ), and the ratio of the fine AOD to the total AOD (η, called RATIO hereafter), as follows: π = η τ πf +(1 η) τ (5) τ Linear interpolation processes are also considered for the retrieval of AOD and RATIO. We performed synthetic simulations to evaluate the errors introduced by the use of interpolation processes in our algorithm. Synthetic simulations were performed for various values of fine-mode SSA (including SSA values between nodal points). Computations were performed for a total AOD of 0.30 and for the aerosol layer altitude, the wind speed, and the ocean surface reflectance used to build the LUT. Table 3 shows that the interpolation error for the SSA is typically less than 0.02 and interpolation errors for the AOD and the RATIO do not exceed 0.015 and 0.02, respectively. In Figure 3, the aerosol parameters retrieved (black solid lines) and used in the reference states (i.e., input simulations, dashed black lines) are plotted against the AOD. For large AOD values ( 0.05), interpolation errors for the SSA, AOD, and RATIO remain below 0.03, 0.05, and 0.03, respectively, whatever the AOD is. The use of additional nodal points for the SSA could help to partially reduce the SSA uncertainty. This modification could be considered in future works as a further improvement of the method. For low AOD values ( 0.05), large errors are found for the SSA. Our method becomes insensitive to the SSA for AOD smaller than 0.05. This result stands for the most favorable scenario tested with synthetic data; i.e., a large range of scattering angles is sampled and the aerosol profile, surface reflectance, and wind speed are the ones considered in the LUT. We therefore consider that this AOD value is the lower AOD limit for which a meaningful SSA retrieval is possible with our method. The assumptions made for the aerosol layer altitude and for the modeling of the surface are other sources of potential errors in our modeling and retrievals. Considering a fixed value of wind speed to model the multiple interaction terms between the ocean surface and the atmosphere is the main source of error in our modeling. For instance, considering a wind speed of 5 m s 1 instead of 10 m s 1 leads to relative maximal errors of 4.5% for total radiances at 865 nm. Simulations show that the effect of the aerosol layer altitude is almost negligible for the spectral band centered at 865 nm and is typically small at 670 nm (few percent for some viewing geometries when changing the aerosol layer altitude from 0.5 to 4.5 km). To a lesser extent, the choice of a fixed value for the ocean surface reflectance at 670 nm is another source of error in our modeling. For example, increasing the ocean surface reflectance from 0.001 to 0.002 slightly increases the total radiances at 670 nm by 1%, at the top of the atmosphere. The grey area shown in Figure 3 corresponds to the space of retrieved parameters obtained when we perturb the algorithm with synthetic data computed for different assumptions WAQUET ET AL. GLOBAL OBSERVATION OF ABSORBING AEROSOLS 10,907

Figure 3. Sensitivity of the research algorithm to properties of aerosols over clear-sky ocean scenes. From left to right: total AOD, SSA, and ratio of fine-mode AOD to total AOD (RATIO) at 865 nm. (a, b, and c) The simulations are representative of an oceanic scene with a particle size distribution dominated by biomass burning particles with significant absorption properties. (d, e, and f) The particle size distribution is dominated by rather scattering pollutant aerosols with different microphysical parameters. Black dashed lines correspond to the properties of the actual modeled conditions and solid lines to those retrieved by the algorithm when the wind speed (w s ), the ocean surface reflectance (r o ), and the aerosol layer altitude (z a ) are the ones considered in the LUT (w s = 5 ms 1, r o = 0.001 at 670 nm and z a = 2.25 km). The light and dark grey areas correspond to the properties retrieved by the algorithm when different values of wind speed, ocean surface reflectance, and aerosol layer altitude are considered in the synthetic simulations (0.005 r o 0.002 at 670 nm ; 0.5 z a 4.5 km; dark grey: 1.0 w s 10.0 ms 1 ; light grey: 2.5 w s 7.5 ms 1 ). made on the aerosol layer altitude, wind speed, and ocean surface reflectance. Simulations were performed for a combination of five values of wind speed (1.0, 2.5, 5.0, 7.5, and 10 m s 1 ), three values of ocean surface reflectance (0.0005, 0.001, and 0.002), and three values for the aerosol layer altitude (0.5, 2.25, and 4.5 km). These different sources of retrieval errors often cancel each other. We then reported the results obtained for the combination of the parameters (i.e., wind speed, ocean surface reflectance, and aerosol layer altitude), which give the maximal difference between the aerosol parameters retrieved and used in the reference states. We also added another type of particle in our sensitivity study. These particles have rather scattering properties (SSA of about 0.97) and have different microphysical parameters. These particles are representative of pollutant aerosols that can be detected, for instance, over the North Pacific during spring or summer. The maximal differences observed between the aerosol parameters retrieved and used in the reference states are rather similar for the two types of particles and are summed up below. The errors on the AOD retrievals are almost negligible and remain below 10% (see Figure 3b). Errors for the RATIO parameter decrease with the AOD increasing. These errors remain below 12% for AODs larger than 0.2. Errors for the SSA also decrease with the AOD increasing (see Figure 3a). Absolute errors for the SSA typically remain below 0.055 for AODs larger than 0.30. Therefore, the different assumptions evaluated here should have limited impacts in our SSA retrievals, as long as the AOD remains large enough ( 0.30). It should be noted that this result stands for the worst case scenario (i.e., a large range of wind speed values is considered; 1.0 m s 1 w s 10.0 m s 1 ). For most cases, an AOD lower than 0.3 is needed to reach an SSA accuracy of 0.055. As already mentioned above, WAQUET ET AL. GLOBAL OBSERVATION OF ABSORBING AEROSOLS 10,908

Journal of Geophysical Research: Atmospheres Figure 4. Map of AERONET stations used in the comparison between POLDER and AERONET level 2.0 aerosol retrievals. The size of the circles represents the number of colocated measurements for AODs, as indicated in the legend. wind speed values ranging between 3 m s 1 and 8 m s 1 are the most frequent ones. For wind speeds ranging from 2.5 to 7.5 m s 1 (83% of the cases in Figure 9), an SSA accuracy of 0.055 is reached for an AOD of 0.20 (see Figures 3a and 3d ; the dark grey areas). For AOD lower than 0.075, a broad range of variability is found (see Figure 3a) and the accuracy on our SSA retrievals then should be more variable. 4. Results and Discussions 4.1. Comparison of POLDER s Retrievals With AERONET Data POLDER s retrievals were collocated with ground-based aerosol observations from the AERONET stations during 2005 and 2013. Only AERONET sites located less than 50 km from POLDER pixels are used. For the AOD, we use AERONET observations performed within 1 h of the satellite overpass. For the SSA, maximal temporal variation between AERONET and POLDER retrievals do not exceed 3 h. This gives a total of around 6000 collocated data for AOD. For SSA, the AERONET inversion quality is only ensured (level 2.0) when AOD (440 nm) is above 0.4, which gives a total of only 624 coincident data for SSA and 817 data for AOD. The AERONET stations used in this AOD comparison are shown in Figure 4. Figure 5a shows an excellent correlation between the photometric and the satellite AOD. For SSA, some differences are found between AERONET and POLDER but these discrepancies decrease with increasing AOD and with the quality of the fit. In particular, there is a good agreement when discrepancies between modeled and POLDER radiances fall within 3.0% for both total and polarized data (in relative value). A 3% residual is a sufficient accuracy that enables to provide a large number of retrievals. Then, most of the SSA differences do not exceed 0.05 for AOD (865 nm) larger than 0.3, which is fully consistent with our sensitivity study. Regardless of the AODs, most of our SSA retrievals (73%) show difference with AERONET SSA retrievals lower than 0.05 at 865 nm. This percentage reaches 87%, when we only consider retrievals associated with POLDER relative errors within 3.0%. Figure 5c shows POLDER SSA retrievals in a function of AERONET SSA retrievals. POLDER tends to retrieve higher SSA values than AERONET. The dispersion is nonnegligible, but the coefficient of correlation (R) is equal to 0.63 (see Figure 5c). All these results are encouraging, considering that sensitivities of space-based and ground-based instruments are different and that the accuracy on the AERONET SSA retrievals is not better than ±0.03. It is also worth mentioning that most of the SSA retrievals considered in our comparison corresponds to rather high SSA values : 90% and 70% of the AERONET SSA retrievals are higher than 0.90 and 0.95, respectively. This is probably due to the fact that most of the strong absorbing aerosols detected by POLDER (SSA 0.90 at 865 nm) are located in oceanic regions with few available AERONET stations and persistent cloud covers (e.g., southeastern tropical Atlantic Ocean and Arctic Ocean; see Figures 4 and 9b). 4.2. Application on an African Biomass Burning Event A case study of biomass burning aerosols transported off the southwest coast of Africa on 4 August 2008, and previously investigated by Waquet et al. [2013] and Peers et al. [2015], has been chosen (Figure 6a). It mainly WAQUET ET AL. GLOBAL OBSERVATION OF ABSORBING AEROSOLS 10,909

Figure 5. Comparison between POLDER and AERONET retrievals at 865 nm for (a) AOD and (b and c) SSA. allows to illustrate the method and to show the spatial variability of the daily retrieved parameters. The analysis of POLDER AOD (Figure 6b) indicates a transport of the biomass burning plume both over clear-sky ocean and above low-level clouds ( 1 km), and based on the lidar data, the aerosol layer is located between 2 and 4 km. Moreover, a boundary aerosol layer was reported over cloud-free ocean scenes ( 1 km). To retrieve aerosol properties for this specific case, we selected data acquired by POLDER for a cloud-free pixel associated with a large AOD and no glitter pattern (Figure 7). Results of our improved algorithm are compared with the operational one initially developed by Herman et al. [2005] that uses an empirical model for nonspherical dust [Volten et al., 2001] (Table 4). Both algorithms retrieve an AOD of 0.43 and nonspherical particles for the coarse mode (see Table 4). Discrepancies are, however, obtained between modeled and measured radiances, especially for polarization (Figure 7a). The polarization signal is primarily controlled by the product of the polarized phase function and the scattering AOD (primary scattering). In contrast, total radiances depend on high order of scattering. For this reason total radiances are more sensitive to absorption than polarized ones. The solution provided by the operational algorithm underestimates the fine-mode AOD (see Table 4) and therefore does not reproduce the magnitude of the polarized radiances (Figure 7a). The only way to fit Figure 6. (a) Visible image of a biomass burning transport off the southwest coast of Africa in August 2008 and associated (b) fine and total AOD and (c) SSA at 865 nm. WAQUET ET AL. GLOBAL OBSERVATION OF ABSORBING AEROSOLS 10,910

Figure 7. Examples of measured and simulated normalized total and polarized radiances for one POLDER pixel (red cross in Figure 6) in a function of scattering angles. The dots are for the measurements, and the vertical crosses account for the errors measurements (calibration and noise). The simulations obtained with (a and b) the research algorithm with aerosol absorption (solid colored lines), the operational algorithm that assumes nonabsorbing particles (dashed lines, Figure 7a), and the research algorithm when assuming nonabsorbing particles (dashed lines, Figure 7b). the data is to simultaneously increase the fine-mode AOD and the absorption to properly attenuate the computed total radiances. By doing so, our improved algorithm improves the modeling accuracy for polarization (and also for total radiances to a lesser extent) and retrieves a particle size distribution dominated by a fine mode with strong absorption properties, which is expected for biomass burning aerosols. The selected microphysical aerosol model (i.e., real part of the refractive index and radius) is also different (see Table 4). Finally, we applied our research algorithm to the data shown in Figure 7 assuming nonabsorbing particles (SSA fixed to 1.0). Our research algorithm includes a large set of real refractive indices (1.35 1.60) and more values of fine-mode effective radius than the operational algorithm. It also includes theoretical nonspherical models for mineral dust particles. In this configuration (SSA of 1.00), the solution given by the research algorithm (dashed line, Figure 7b) allows to significantly reduce the departures between simulations and measurements; this solution, however, fails to reproduce the data within the error measurements for some viewing geometries. This latter test demonstrates that a purely scattering model is not sufficient to accurately fit the total and polarized radiance measurements acquired at 670 and 865 nm for this case study. In Figures 6b and 6c, the SSA and the fine-mode AOD retrieved over cloud-free pixels with our improved algorithm are compared with the respective ones retrieved above clouds with POLDER [Peers et al., 2015], assuming that most of the fine-mode particles are located in the elevated biomass burning aerosol layer. The sensitivity of total radiances to the aerosol absorption is very important above bright scenes, such as clouds. SSA retrievals are therefore expected to be more accurate over bright clouds than over dark ocean targets. Table 4. Retrieval Errors (Δ) Due to the Interpolation Processes Considered in the Algorithm for the Aerosol Model Given in Table 3 and for Different Values of SSA, Fine-Mode SSA, an AOD of 0.30 at 865 nm, and a RATIO of 0.833 at 865 nm SSA ΔSSA (fine) SSA ΔAOD ΔRATIO (fine) ΔSSA 1.000 1.000 0.000 0.005 0.000 0.000 0.930 0.942 0.000 0.026 0.017 0.017 0.895 0.912 0.000 0.010 0.017 0.016 0.860 0.883 0.000 0.021 0.000 0.003 0.825 0.854 0.000 0.010 0.017 0.017 0.755 0.796 0.015 0.021 0.035 0.023 0.720 0.767 0.015 0.010 0.000 0.003 WAQUET ET AL. GLOBAL OBSERVATION OF ABSORBING AEROSOLS 10,911

Journal of Geophysical Research: Atmospheres The accuracy of the above-cloud algorithm was carefully evaluated using synthetic data in Peers et al. [2015]. Moreover, comparisons of above-cloud AOD retrieved by POLDER, OMI, and MODIS were performed for the region of interest and a close agreement was reported [Jethva et al., 2014b], which gives confidence in the above-cloud AOD retrieved with POLDER. An excellent agreement is found for the AODs and SSA retrieved with the two methods, both in terms of intensity and spatial pattern, with strong absorbing properties (SSA around 0.80). In the upper right corner of the AOD map (Figure 6b), where the largest AOD values are observed, differences in AOD for adjafigure 8. SSA and AOD retrieved over clear-sky and cloudy scenes at 865 nm in a function of longitude for the transect shown in Figure 7a cent cloudy and clear pixels are typically (see the red line). lower than 0.05 (at 865 nm). We found a maximal difference of about 0.10 for a couple of adjacent pixels, which might be due to remaining cloud contamination. For low AODs, the sensitivity of the clear-sky algorithm to the SSA largely decreases and the comparison of the two products is not meaningful. This probably explains the large discontinuities in SSA in the lower right portion of the image Figure 9. (a) Total and (c) fine AOD along with (b) SSA at 865 nm averaged for the year 2006 and (d) associated number of retrievals. WAQUET ET AL. GLOBAL OBSERVATION OF ABSORBING AEROSOLS 10,912

Journal of Geophysical Research: Atmospheres Figure 10. (left column) Total and (middle column) fine AOD along with (right column) SSA at 865 nm seasonally averaged for the year 2006. (around 25 in latitude and 10 in longitude), where the retrieved fine-mode AOD is lower than 0.03. Figure 8 shows more clearly the SSA and AOD retrieved over cloud-free and cloudy scenes. Retrievals were performed along a northwest-southeast transect including (from the point of coordinate 5.75 longitude and 11.25 latitude to the point of coordinate 8.9 and 11.25 latitude; see Figure 6a). A gradient is found in AOD over the entire transect. AODs vary between 0.7 and 0.3 above-cloud-free pixels. For cloudy scenes, AODs vary between 0.2 and 0.3 at 865 nm. For AODs ranging between 0.2 and 0.3, the retrieval accuracy for the above-cloud SSA is below 0.045 [Peers et al., 2015], whereas errors on SSA should not exceed 0.055 for clear-sky scenes (see section 3). The SSA is rather constant and varies between 0.77 and 0.81 over the entire transect. Taking into account the uncertainties of each method, an excellent agreement is found between SSA retrievals from the clear-sky algorithm compared to the above-cloud algorithm. 4.3. Global Results This part focuses on the analysis of total and fine-mode AOD and SSA (at 865 nm) retrieved over clear-sky ocean during 1 year at global scale. The annual mean values of these parameters are reported in Figures 9a 9c, respectively. Note that we averaged the scattering and absorption AODs separately and computed the SSA afterward. Results are presented on a seasonal-average basis in Figure 10: winter (December 2005 and January and February 2006, line 1), spring (March, April, and May 2006, line 2), summer (June, July, and August 2006, line 3), and autumn (September, October, and November 2006, line 4). Only results for relative errors between measured and modeled total and polarized radiances less than 3.0% are presented to ensure the quality of the retrieved aerosol properties. This quality criterion reduces the number of events (50%) but does not modify the tendencies and patterns observed on the AOD and SSA maps. The number of observations for the year 2006 is shown in Figure 9d. The results obtained for the different ocean basins are analyzed separately. WAQUET ET AL. GLOBAL OBSERVATION OF ABSORBING AEROSOLS 10,913

4.3.1. Atlantic Ocean Our algorithm retrieves a particle size distribution dominated by nonspherical coarse particles and SSA around 1.00 along the well-known transatlantic transport of Saharan dust that is maximal during spring and summer. A different situation is found during winter over areas close to the Sahara, with absorbing fine biomass burning aerosols mixed with coarse Saharan dust particles (mainly nonspherical) [Johnson et al., 2008] resulting in a mean SSA of 0.90. Similar aerosol mixture is transported over the Gulf of Guinea toward South Africa and the coasts of Brazil but with a slightly lower SSA due to a lesser contribution of mineral dust. During summer and autumn, high loads of fine-mode absorbing aerosols, coming from southern Africa savannah s fires, are retrieved over the southeast Atlantic Ocean with low SSA (below 0.80) and maximum AOD values over the coasts of Gabon and Cameroon. It is worth noticing that these particles are associated with smaller effective radius (reff 0.11 μm) and higher real refractive indices (mr 1.48) than biomass burning aerosols detected over the tropical Atlantic Ocean during winter (reff 0.11 μm and mr 1.42), suggesting different aging processes. In Figure 10, we can also see that during summer and autumn, fine absorbing biomass burning particles from forest fires in Alaska and Canada are transported over the North Atlantic (for latitudes 40 ), over the Hudson Bay and near the coasts of northeastern America [Real E. et al., 2007]. 4.3.2. Pacific Ocean Our algorithm is able to detect the well-known outflow of Asian anthropogenic pollutants and dust from Mongolia and northeastern deserts that crosses the North Pacific Ocean during springtime (roughly between 25 and 40 in latitudes). Anthropogenic pollutants are also detected near Japan and China during summer with SSA values close to 1.00, suggesting the predominance of scattering species. During spring, winter, and autumn, fine-mode absorbing aerosols (SSA of 0.9) are detected at high latitudes over the North Pacific, the Okhotsk Sea, and the Sea of Japan. They might correspond to biomass burning aerosols originated from wild fires that frequently occur over siberia [Peers et al., 2015]. During spring, these particles cross the North Pacific toward North America and have smaller effective radius (reff 0.11 μm) and higher refractive indices (1.50 mr 1.60) than the Asian pollutants (reff 0.11 μm and 1.42 mr 1.48), which is expected for fresh biomass burning aerosols. Fine-mode absorbing aerosols are also detected off the coasts of Southeast Asia (SSA of 0.90) mainly during spring. They likely correspond to smoke emitted from the rice straw burning occurring in Southeast Asia such as in Vietnam, Cambodia, and Laos [Fu et al., 2012]. During this season, nonspherical coarse particles are detected almost everywhere over the North Pacific (between 20 and 60 in latitudes), including the coasts of Asia (maximal AOD of 0.45 over the Yellow Sea), Siberia, and North America. 4.3.3. Indian Ocean During winter and spring, a mixture of coarse nonspherical dust and fine absorbing carbonaceous particles [Ramanathan et al., 2001] is observed over the North Indian Ocean, which results in low SSA values (0.88). This period is associated with both fine-mode aerosols largely widespread over the Arabian Sea and the Gulf of Bengal and coarse particles near the coasts of India, Pakistan, Iran, and Bangladesh. In contrast, nonspherical coarse aerosols are detected during spring over a large area including the Arabia Sea, the Indian Ocean, and the Gulf of Bengal leading to a mean SSA higher during the wintertime (SSA 0.9). During autumn, a transport of biomass burning aerosols between South Africa and Australia is detected. At the end of the summer, another biomass burning transport is revealed near the coasts of North Australia and is associated with low AOD values ( 0.05). This result is consistent with the spatial and temporal patterns of fire emissions observed in North Australian savannas [Beringer et al., 2014]. Biomass burning aerosols are also detected in South Australia, but events are less frequent. 4.3.4. Other Oceanic Basins At high latitudes, very little retrievals are made due to the presence of clouds and sea ice, but absorbing fine-mode aerosols are even so detected near the coasts of Scandinavia (spring and autumn) and Western Siberia and might be originated from boreal forest fires [Warneke et al., 2009]. Over the Mediterranean region, absorbing aerosols are mainly detected during winter and autumn with SSA of 0.90 0.95, which is in good agreement with AERONET retrievals [Mallet et al., 2013]. During spring and summer, nonspherical dust dominate (SSA around 1.00), with some exceptions over the northeastern part of the basin (SSA of 0.93). During springtime, fine-mode absorbing biomass burning particles are detected over Central and South America, crossing over the tropical Pacific Ocean or reaching the eastern U.S. A mixture of dust and biomass burning aerosols is also detected over the Gulf of Mexico (SSA of 0.95 on average for the spring). Over the open ocean (when excluding regions influenced by the transport of aerosols), our algorithm generally retrieves low amount of scattering spherical coarse particles (SSA around 1) that are likely to be maritime aerosols. WAQUET ET AL. GLOBAL OBSERVATION OF ABSORBING AEROSOLS 10,914

4.3.5. Comparison With Previous Studies First, we compare our global mean results (see Figure 9) with the ones described in Lacagnina et al. [2015]. This latter study shows mean SSA maps for POLDER and for the Aerosol Model Intercomparison (AeroCom) models at 550 nm and averaged for the year 2006 [see Lacagnina et al., 2015, Figures 9a and 9b]. We recall that Lacagnina et al. [2015] uses a different retrieval method and different assumptions to retrieve the SSA over clear-sky ocean with POLDER [Hasekamp et al., 2011]. Because the SSA depends on the wavelength, the comparison presented here is largely qualitative since our SSA maps are given at 865 nm. Our map of yearly mean SSA retrievals (see Figure 9c) shows different regions with values deviating from 1.00, indicating absorbing aerosols: the southeastern Atlantic Ocean, the areas over the Indian Ocean bordering India, the coastal regions in southeastern Africa and northwestern Australia, the Bay of Bengal, the Okhotsk Sea, and the Hudson Bay. All these areas are also associated with SSA values smaller than 1.0 in both the AeroCom models and the POLDER estimation shown in Lacagnina et al. [2015]. AeroCom models also identify regions with SSA deviating from 1.0, where POLDER SSA retrievals are of about 1.0 (in both studies). These regions are the Yellow Sea, the Sea of Japan, and the seas surrounding Indonesia. Absorbing particles are often detected over these regions by POLDER. This suggests that other particles with rather scattering properties dominate the AOD over these regions throughout the year, resulting in SSA values close to 1.0 in our annual average. The main differences in the POLDER SSA retrievals between the POLDER SSA from Lacagnina et al. [2015] and our study concern the following areas: the area located between the coasts of southern Africa and Australia for latitudes higher than 30, the area located over the North Pacific Ocean between longitudes 80 and 180, and the area located over the tropical Pacific Ocean for longitudes smaller than 180. For these regions, our POLDER SSA retrievals are generally close to 1.0, whereas the POLDER SSA retrievals shown in Lacagnina et al. [2015] are of about 0.90. These regions are associated with low AODs, and thus the SSA retrievals are less accurate. Again, since the SSA is reported at a different wavelength in the two studies, it is impossible to draw definitive conclusions regarding the meaning of these differences. Finally, the two retrieval methods detect absorbing aerosol layers at high northern latitudes during spring (see Figure 10, line 2), whereas the AeroCom models do not reproduce these events. When excluding these events, a good qualitative agreement (at least in terms of spatial patterns and temporal variability) is generally found between our SSA retrievals and the SSA AeroCom models estimates. It also applies for the remote ocean regions where we retrieve SSAs typically higher than 0.97 and often close to 1.0 (see Figure 9b), as in the climate models. Finally, we compared POLDER and OMI SSA retrievals. We used the OMI product called Level-2 OMI Near-UV Aerosol Optical Depth and Single Scattering Albedo OMAERUV (V003) that provides daily OMI retrievals at a resolution of 13 24 km 2. We used the parameter called Best Aerosol Single Scattering Albedo, for which the retrievals are performed with an aerosol layer altitude adjusted based on a monthly climatology of aerosol layer height. The OMI data were spatially aggregated at the POLDER spatial resolution (18 18 km 2 ) for the purpose of comparison. OMI pixels overlapping a POLDER pixel were identified and aggregated. In 2006, the two instruments were together within the A-train, and the temporal variation between the two acquisitions is less than 30 min. As shown in Figure 11, very few coincidences were found for 2006. This is probably due to the fact that the two methods have different sensibilities to SSA and therefore used different quality filters for the selection of successful retrievals. Nerveless, interesting case studies can be observed and allow to perform a qualitative comparison. Along the coasts of Angola, OMI and POLDER both detected absorbing particles. The SSA shows values below 0.85 at 865 nm associated with a small spectral dependence; at 354 nm, SSA is between 0.85 and 0.90. POLDER indicates that these particles mainly contribute to the fine mode. These particles are likely to be biomass burning aerosols emitted from fires in southern Africa. Along the coasts of Sahara, POLDER detects nonspherical coarse particles, which typically indicates mineral dust particles. The SSA shows a strong spectral dependency; the POLDER SSA is around 1.00, whereas the OMI SSA in the UV ranges between 0.85 and 0.90. Such a spectral dependence in SSA for mineral dust aerosols is expected, and it is explained by their mineral composition (i.e., iron is responsible for aerosol absorption in UV). Rather similar spectral SSAs associated with nonspherical AOD are retrieved over the Mediterranean Sea, the Arabian Peninsula, and over the Yellow Sea and the Sea of Japan. These detected particles are likely to be mineral dust aerosols. Finally, OMI confirms the presence of absorbing aerosol layers at high northern latitudes. The spectral dependence of the SSA is rather flat between the UV and the near infrared (NIR) for these events; SSA is slightly lower in the NIR than in the UV but remains higher than 0.90. These particles mainly contribute to the fine mode, according to POLDER retrievals. These particles might be biomass burning aerosols emitted from boreal forest fires since these aerosols are typically rather scattering and associated with high SSA values [Peers et al., 2015]. WAQUET ET AL. GLOBAL OBSERVATION OF ABSORBING AEROSOLS 10,915

Figure 11. Examples of collocated (a) POLDER SSA retrievals (at 865 nm) and (b) OMI SSA retrievals (at 354 nm) for some mineral dust and biomass burning aerosol events sampled during the year 2006. The SSA is much higher than the one for African biomass burning aerosol, suggesting the presence of different absorbing species in these biomass burning aerosol layers. In a general way, the spectral behavior in SSA found by OMI and POLDER for biomass burning aerosols and mineral dust is in good qualitative agreement with previous works [Bergstrom et al., 2007]. Acknowledgments The first author is grateful to CNRS for supporting his research. We are grateful to CNES and the ICARE data and services center for the POLDER/PARASOL data. We are grateful to NASA for the OMI data. Data used in this paper are available upon request from the corresponding author: fabien.waquet@univ-lille1.fr. 5. Conclusions In this work, 1 year of POLDER/PARASOL data are reanalyzed with a new algorithm that retrieves the aerosol SSA over clear-sky ocean. We have shown that the absorption of fine-mode aerosols needs to be considered in order to accurately model the total and polarized radiances at 670 and 865 nm from POLDER. This inclusion of absorption in the POLDER retrievals modifies the relative contribution of fine and coarse particles to the total AOD, suggesting that the fine-mode AOD might be underestimated in previous works for regions with absorbing particles. Taking into account the absorption in the retrieval scheme also improves the spatial continuity for the aerosol properties retrieved over clear-sky and cloudy ocean scenes, which is promising in the frame of the calculation of the all-sky aerosol forcing. At global scale, a correlation of 0.63 between POLDER and AERONET SSA has been found. Most of the SSA differences between POLDER and AERONET retrievals do not exceed 0.05. SSA differences between POLDER and AERONET retrievals systematically fall below 0.055 for AOD (865 nm) larger than 0.3 (at 865 nm) and modeling errors 3.0%. A sensitivity study performed with synthetic data confirmed this result and indicated that for most cases, a SSA accuracy of 0.055 is expected for lower AOD values (0.2 at 865 nm). The largest contribution of aerosol absorption is detected over the southeast Atlantic Ocean during summer (SSA of 0.80). During winter, strong events of fine absorbing particles are detected together with mineral dust near the coasts of western Africa (0.90), over the tropical Atlantic Ocean (0.85 0.90), and around India (0.88). Long-range transport of absorbing particles occurs over the North Pacific and Atlantic Oceans, high latitudes seas (Arctic), and the southern Indian Ocean. Such species are also detected in many other coastal regions including Southeast Asia, Australia, Indonesia, Central America, South America, and the Mediterranean basin. Many of these regions are under the influence of mineral dust which tends to increase our estimation of the SSA estimation. The SSA is a crucial parameter to calculate the aerosol forcing at the top of the atmosphere and at the surface, as well as the heating rates. The retrieval uncertainties taken into account, this novel algorithm could help to constrain aerosol absorption and forcing in climate models and better identify the nature of chemical species responsible for this absorption. References Ahn, C., O. Torres, and H. Jethva (2014), Assessment of OMI near-uv aerosol optical depth over land, J. Geophys. Res. Atmos., 119, 2457 2473, doi:10.1002/2013jd020188. Beringer, J., L. B. Hutley, D. Abramson, S. K. Arndt, P. Briggs, M. Bristow, J. G. Canadell, L. A. Cernusak, D. Eamus, and A. C. Edwards (2014), Fire in Australian savannas: From leaf to landscape, Global Change Biol., 21, 62 81, doi:10.1111/gcb.12686. Bergstrom, R. W., P. Pilewskie, P. B. Russell, J. Redemann, T. C. Bond, P. K. Quinn, and B. Sierau (2007), Spectral absorption properties of atmospheric aerosols,atmos. Chem. Phys., 7, 5937 5943. WAQUET ET AL. GLOBAL OBSERVATION OF ABSORBING AEROSOLS 10,916