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Abstract

This paper presents new laboratory measurements of the mass absorption efficiency (MAE) between 375 and 850 nm for 12 individual samples of mineral dust from different source areas worldwide and in two size classes: PM10. 6 (mass fraction of particles of aerodynamic diameter lower than 10.6 µm) and PM2. 5 (mass fraction of particles of aerodynamic diameter lower than 2.5 µm). The experiments were performed in the CESAM simulation chamber using mineral dust generated from natural parent soils and included optical and gravimetric analyses. The results show that the MAE values are lower for the PM10. 6 mass fraction (range 37–135  ×  10−3 m2 g−1 at 375 nm) than for the PM2. 5 (range 95–711  ×  10−3 m2 g−1 at 375 nm) and decrease with increasing wavelength as λ−AAE, where the Ångström absorption exponent (AAE) averages between 3.3 and 3.5, regardless of size. The size independence of AAE suggests that, for a given size distribution, the dust composition did not vary with size for this set of samples. Because of its high atmospheric concentration, light absorption by mineral dust can be competitive with black and brown carbon even during atmospheric transport over heavy polluted regions, when dust concentrations are significantly lower than at emission. The AAE values of mineral dust are higher than for black carbon (∼ 1) but in the same range as light-absorbing organic (brown) carbon. As a result, depending on the environment, there can be some ambiguity in apportioning the aerosol absorption optical depth (AAOD) based on spectral dependence, which is relevant to the development of remote sensing of light-absorbing aerosols and their assimilation in climate models. We suggest that the sample-to-sample variability in our dataset of MAE values is related to regional differences in the mineralogical composition of the parent soils. Particularly in the PM2. 5 fraction, we found a strong linear correlation between the dust light-absorption properties and elemental iron rather than the iron oxide fraction, which could ease the application and the validation of climate models that now start to include the representation of the dust composition, as well as for remote sensing of dust absorption in the UV–vis spectral region.