We compared CALIPSO column aerosol optical depths at 0.532 μm to measurements at 147 AERONET sites, synchronized to within 30 min of satellite overpass times during a 3-yr period. We found 677 suitable overpasses, and a CALIPSO bias of −13% relative to AERONET for the entire data set; the corresponding absolute bias is −0.029, and the standard deviation of the mean (SDOM) is 0.014. Consequently, the null hypothesis is rejected at the 97% confidence level, indicating a statistically significant difference between the datasets. However, if we omit CALIPSO columns that contain dust from our analysis, the relative and absolute biases are reduced to −3% and −0.005 with a standard error of 0.016 for 449 overpasses, and the statistical confidence level for the null hypothesis rejection is reduced to 27%. We also analyzed the results according to the six CALIPSO aerosol subtypes and found relative and absolute biases of −29% and −0.1 for atmospheric columns that contain the dust subtype exclusively, but with a relatively high correlation coefficient of R = 0.58; this indicates the possibility that the assumed lidar ratio (40 sr) for the CALIPSO dust retrievals is too low. Hence, we used the AERONET size distributions, refractive indices, percent spheres, and forward optics code for spheres and spheroids to compute a lidar ratio climatology for AERONET sites located in the dust belt. The highest lidar ratios of our analysis occur in the non-Sahel regions of Northern Africa, where the median lidar ratio at 0.532 μm is 55.4 sr for 229 retrievals. Lidar ratios are somewhat lower in the African Sahel (49.7 sr for 929 retrievals), the Middle East (42.6 sr for 489 retrievals), and Kanpur, India (43.8 sr for 67 retrievals). We attribute this regional variability in the lidar ratio to the regional variability of the real refractive index of dust, as these two parameters are highly anti-correlated (correlation coefficients range from −0.51 to −0.85 for the various regions). The AERONET refractive index variability is consistent with the variability of illite concentration in dust across the dust belt.

We describe a method of using the Aerosol Robotic Network (AERONET) size distributions and complex refractive indices to retrieve the relative proportion of carbonaceous aerosols and free iron minerals (hematite and goethite). We assume that soot carbon has a spectrally flat refractive index and enhanced imaginary indices at the 440 nm wavelength are caused by brown carbon or hematite. Carbonaceous aerosols can be separated from dust in imaginary refractive index space because 95 % of biomass burning aerosols have imaginary indices greater than 0.0042 at the 675–1020 nm wavelengths, and 95 % of dust has imaginary refractive indices of less than 0.0042 at those wavelengths. However, mixtures of these two types of particles can not be unambiguously partitioned on the basis of optical properties alone, so we also separate these particles by size. Regional and seasonal results are consistent with expectations. Monthly climatologies of fine mode soot carbon are less than 1.0 % by volume for West Africa and the Middle East, but the southern African and South American biomass burning sites have peak values of 3.0 and 1.7 %. Monthly averaged fine mode brown carbon volume fractions have a peak value of 5.8 % for West Africa, 2.1 % for the Middle East, 3.7 % for southern Africa, and 5.7 % for South America. Monthly climatologies of free iron volume fractions show little seasonal variability, and range from about 1.1 to 1.7 % for coarse mode aerosols in all four study regions. Finally, our sensitivity study indicates that the soot carbon retrieval is not sensitive to the component refractive indices or densities assumed for carbonaceous and free iron aerosols, and the retrieval differs by only 15.4 % when these parameters are altered from our chosen baseline values. The total uncertainty of retrieving soot carbon mass is ∼ 50 % (when uncertainty in the AERONET product and mixing state is included in the analysis).

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.