20 Feb 2023
 | 20 Feb 2023
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Modeling dust mineralogical composition: sensitivity to soil mineralogy atlases and their expected climate impacts

María Gonçalves Ageitos, Vincenzo Obiso, Ron L. Miller, Oriol Jorba, Martina Klose, Matt Dawson, Yves Balkanski, Jan Perlwitz, Sara Basart, Enza Di Tomaso, Jerónimo Escribano, Francesca Macchia, Gilbert Montané, Natalie Mahowald, Robert O. Green, David R. Thompson, and Carlos Pérez García-Pando

Abstract. Soil dust aerosols are a key component of the climate system, as they interact with short- and long-wave radiation, alter cloud formation processes, affect atmospheric chemistry and play a role in biogeochemical cycles by providing nutrient inputs such as iron and phosphorus. The influence of dust on these processes depends on its physico-chemical properties, which far from being homogeneous, are shaped by its regionally varying mineral composition. The relative amount of minerals in dust depends on the source region and shows a large geographical variability. However, many state-of-the-art Earth System Models (ESMs), upon which climate analyses and projections rely, still consider dust mineralogy as invariant. The explicit representation of minerals in ESMs is more hindered by our limited knowledge of the global soil composition along with the resulting size-resolved airborne mineralogy than by computational constraints. In this work, we introduce an explicit mineralogy representation within the state-of-the-art atmosphere-chemistry model MONARCH. We review and compare two existing soil mineralogy datasets, which remain a source of uncertainty for dust mineralogy modelling, and provide an evaluation of multi-annual simulations against available mineralogy observations. Soil mineralogy datasets are based on measurements performed after wet sieving, which breaks the aggregates found in the parent soil. Our model predicts the emitted particle size distribution (PSD) in terms of its constituent minerals based on Brittle Fragmentation Theory (BFT), which reconstructs the emitted mineral aggregates destroyed by wet sieving. Our simulations broadly reproduce the most abundant mineral fractions, independently of the soil composition data used. Feldspars and calcite are highly sensitive to the soil mineralogy map, mainly due to the different assumptions made in each soil dataset to extrapolate a handful of soil measurements to arid and semiarid regions worldwide. For the least abundant or more difficult to determine minerals, such as the iron oxides, uncertainties in soil mineralogy yield differences in annual mean aerosol mass fractions of up to ∼100 %. Although BFT restores coarse aggregates including phyllosilicates that usually break during soil analysis, we still identify an overestimation of coarse quartz mass fractions (above 2 µm in diameter). In a dedicated experiment, we estimate the fraction of dust with undetermined composition as given by a soil map, which makes a ∼10 % of the emitted dust mass at the global scale, and can be regionally larger. Changes in the underlying soil mineralogy impact our estimates of climate-relevant variables, particularly affecting the regional variability of the single scattering albedo at solar wavelengths, or the total iron deposited over oceans. All in all, this assessment represents a baseline for future model experiments including new mineralogical maps constrained by high quality spaceborne hyperspectral measurements, such as those arising from the NASA EMIT mission.

María Gonçalves Ageitos et al.

Status: open (until 18 Apr 2023)

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María Gonçalves Ageitos et al.

María Gonçalves Ageitos et al.


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Short summary
Dust aerosols affect our climate differently depending on their mineral composition. We include dust mineralogy in an atmospheric model considering two existing soil maps, which still have large associated uncertainties. The soil data and the distribution of the minerals in different aerosol sizes are key to our model performance. We find significant regional variations in climate-relevant variables, which supports including mineralogy in our current models and the need for improved soil maps.