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Mineral dust effects upon climate are strongly affected by its particle size distribution (PSD). In particular, the emitted dust PSD partly controls the dust lifetime and its global distribution. Despite the extensive research performed on this topic over the last decades, there are still substantial gaps in our understanding of the emitted PSD along with its potential variability and associated causes. In this study, we provide insights into the saltation and size-resolved dust emission process based on measurements obtained during a comprehensive wind erosion and dust emission field campaign that took place in the Moroccan Sahara in September 2019 in the context of the FRontiers in dust minerAloGical coMposition and its Effects upoN climaTe (FRAGMENT) project. The measurement site located in a remote ephemeral lake, consisting of a smooth hard-crusted paved sediment surface surrounded by small sand dunes, is characterized by strong and frequent saltation and dust emission conditions, and relatively low sandblasting efficiencies. Our study, which thoroughly analyses the number and mass PSDs of both the concentration and diffusive flux (the latter typically assumed to be equivalent to the emitted dust PSD), detects statistically significant dependencies upon friction velocity (u*), wind direction and type of event (regular events vs haboob events). We discuss the potential underlying causes of such variability, including the effect of dry deposition, an enhanced fragmentation of aggregates, and the impact of the haboob gust front. We clearly identify and quantify the major role played by dry deposition in shaping the diffusive flux PSD variations, modulated by the wind direction-dependent fetch length of our measurement location and u*. Our estimates show the importance of dry deposition relative to emission, representing up to ∼40 % for super-coarse particles (> 10 μm) and up to ∼20 % for particles as small as ∼5 μm in diameter. While we attribute the enhancement (reduction) in submicron (supermicron) particles with u* to the effect of dry deposition, an enhanced fragmentation of aggregates with u* could still play a complementary yet arguably smaller role. We additionally find clear differences in the PSDs associated to haboob events in comparison with the regular events, i.e., a higher (lower) proportion of supermicron (submicron) particles for equivalent or higher u* values, and more vigorous dry deposition and variability in the coarse and super-coarse dust mass fractions. We hypothesize that these differences are due to 1) a smaller horizontal (spatial) extent of the haboob events (which is equivalent to the effect of a smaller fetch), 2) the effect of the moving haboob gust front, where u* and dust emission are maximized, along with its changing proximity to the measurement site (which is equivalent to a variable fetch), and/or 3) the increased resistance of soil aggregates to fragmentation associated to the observed increases in relative humidity along the haboob outflow. We finally compare the obtained PSDs with both the PSDs predicted by the original and a recently updated version Brittle Fragmentation Theory (BFT), the latter accounting for super-coarse dust emission. For the comparison with the updated BFT we transform our optical diameters into geometric diameter PSDs, assuming dust particles are tri-axial ellipsoids with an index of refraction consistent with measured optical properties during the campaign. We obtain a substantially lower (higher) proportion of submicron (supermicron) particles in the diffusive flux PSDs in comparison with the original BFT PSDs. Also, our PSDs show a higher proportion of particles above ∼2 μm and a higher mass fraction of super-coarse particles, despite large effect of dry deposition upon this fraction. All in all, our results indicate that dry deposition needs to be adequately considered to estimate the emitted PSD, even in studies limited to the fine and coarse size ranges (< 10 μm), and particularly in measurement locations with long fetches.

Mineral dust emissions play a fundamental role in the simulation of the dust cycle in numerical models. The emission of dust depends on a number of atmospheric and surface conditions that span a large range of time and spatial scales. Due to the inherent difficulties to physically represent this complexity in a simplified way, the emission of mineral dust is usually parameterized in the atmospheric numerical models. The heterogeneity of available dust emission parametrizations, along with the soil characteristics and meteorological information, the atmospheric models themselves, their tuning, and their boundary and initial conditions, contribute to the large spread of net dust flux estimated with different modeling frameworks.This work presents a novel approach to estimate dust emissions through the assimilation of dust optical depth filtered retrievals from satellite measurements, by means of an ensemble-based data assimilation scheme. Because of the lagged nature of the emission inversion problem, the assimilation is produced with a slightly modified version of the ensemble Kalman Filter algorithm. We show results of the inversion for 5-year global numerical experiments (2017 to 2021), by using dust-only simulations with three of the available state-of-the-art dust emission schemes implemented in the chemical MONARCH model.In these three experiments, we assimilate dust optical depth obtained from the SNPP-VIIRS Deep Blue retrievals. The control vector consists of model dust emissions at native spatial resolution (1.4 by 1 degrees) and a 3-days time resolution. We find regional and temporal corrections in the estimated emissions after assimilation that are consistent across the different dust emission scheme experiments, making our findings robust. We compare the dust optical depth of our simulations with the assimilated observations, as well as with independent dust-filtered optical depth from ground-based AERONET sun-photometers. The dust optical depth resulting from the simulations that use the corrected emissions show substantial improvements in the skill scores than the dust optical depth simulated with the uncorrected emissions. Our work paves the road toward quantifying and eventually reducing uncertainties in dust emission schemes and toward better constraining the contribution to climate of the dust sources at sub-regional scale.

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.

Mineral dust is the major continental contributor to the global atmospheric aerosol burden with important effects on the climate system. Regionally, a large fraction of the emitted dust is produced in North Africa, however the total emission flux from this region is still highly uncertain. In order to reduce these uncertainties, emission estimates through top-down approaches (i.e., usually models constrained by observations) had been successfully developed and implemented. Such studies usually rely on a single observational dataset and propagate the possible observational errors of this dataset onto the emission estimates. In this study, aerosol optical depth (AOD) products from five different satellites are assimilated one by one in a source inversion system to estimate dust emission fluxes over northern Africa and the Arabian Peninsula. We estimate mineral dust emissions for the year 2006 and discuss the impact of the assimilated dataset on the analysis. We find a relatively large dispersion in flux estimates among the five experiments, which can likely be attributed to differences in the assimilated observation datasets and their associated error statistics. We also show how the assimilation of a variety of AOD products can help to identify systematic errors in models.

We explore the relationship between satellite retrievals of aerosol optical depth (AOD) and surface aerosol mass concentrations over a subtropical urban area, namely, Santiago, Chile (33.5°S, 70.6°W, 500 m.a.s.l.). We compare 11 years of AOD from the MODerate resolution Imaging Spectroradiometer (MODIS) with in situ particulate matter mass concentrations (PM). MODIS AOD reaches its maximum in summer and minimum in winter, the opposite of the annual cycle of surface PM. To improve our understanding of the relevant governing processes, we use a simple model that estimates the boundary layer (BL) AOD based on measured PM, relative humidity and BL height (BLH) as well as best estimates of aerosol composition, size distribution, and optical properties. Model results indicate that a weak annual AOD cycle is due to the opposite annual cycles in BLH and PM, which is largely supported by the Aerosol Robotic NETwork (AERONET) data collected in 2001 and 2002 in Santiago. We identify a possible bias linked to the operational estimate of surface reflectance that may lead to a spurious summer maximum in MODIS AOD over Santiago. This misfit in surface reflectance appears to affect not only Santiago but also a significant area of the semi-arid Southern South America. Sensitivity experiments with the simple model indicate an underestimate of simulated AOD as compared to AERONET data. This underestimate points to the possible role of residual aerosol layers in the AOD measured at the surface (not included in the simple model). Cirrus clouds appear not to play a significant role in explaining the MODIS AOD seasonality. The need for improved characterizations of aerosol properties and their temporal and spatial distribution in cities such as Santiago is emphasized.

We use concurrent morning peak observations of carbon monoxide (CO) and nitrogen oxides (NOx) to evaluate mobile emissions estimates for CO and NOx at Bogota (Colombia), Buenos Aires (Argentina), Santiago (Chile) and Sao Paulo (Brazil). In all cities, molar ratios of CO to NOx decrease over the last 10–15 years. These ratios are not captured by available inventories. Comparison among inventories suggests that major uncertainties are linked to inadequate emission factors for CO and inadequate activity data for NOx. These results, in combination with previous studies, suggest that current NOx emissions are overestimated by a factor of up to 3 in Santiago and Sao Paulo, and Buenos Aires shows a slight overestimate by 20%. In the case of Bogota we suspect that the current CO emission inventory is overestimated. Available observations provide valuable information, as exemplified hereby, but more careful attention must be paid to calibration and continuity of the stations.