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Atmospheric mineral dust aerosol constitutes a threat to aircraft engines from deterioration of internal components. Here we fulfil an overdue need to quantify engine dust ingestion at airports worldwide. The vertical distribution of dust is of key importance since ascent/descent rates and engine power both vary with altitude and affect dust ingestion. We use representative jet engine power profile information combined with vertically and seasonally varying dust concentrations to calculate the “dust dose” ingested by an engine over a single ascent or descent. Using the Copernicus Atmosphere Monitoring Service (CAMS) model reanalysis, we calculate climatological and seasonal dust dose at 10 airports for 2003–2019. Dust doses are mostly largest in Northern Hemisphere summer for descent, with the largest at Delhi in June–August (JJA; 6.6 g) followed by Niamey in March–May (MAM; 4.7 g) and Dubai in JJA (4.3 g). Holding patterns at altitudes coincident with peak dust concentrations can lead to substantial quantities of dust ingestion, resulting in a larger dose than the take-off, climb, and taxi phases. We compare dust dose calculated from CAMS to spaceborne lidar observations from two dust datasets derived from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP). In general, seasonal and spatial patterns are similar between CAMS and CALIOP, though large variations in dose magnitude are found, with CAMS producing lower doses by a factor of 1.9 to 2.8, particularly when peak dust concentration is very close to the surface. We show that mitigating action to reduce engine dust damage could be achieved, firstly by moving arrivals and departures to after sunset and secondly by altering the altitude of the holding pattern away from that of the local dust peak altitude, reducing dust dose by up to 44 % and 41 % respectively. We suggest that a likely low bias of dust concentration in the CAMS reanalysis should be considered by aviation stakeholders when estimating dust-induced engine wear.

A new four-dimensional, multiyear, and near-global climate data record of the fine-mode (submicrometer in terms of diameter) and coarse-mode (supermicrometer in terms of diameter) components of atmospheric pure dust is presented. The separation of the two modes of dust in detected atmospheric dust layers is based on a combination of (1) the total pure-dust product provided by the well-established European Space Agency (ESA) “LIdar climatology of Vertical Aerosol Structure” (LIVAS) database and (2) the coarse-mode component of pure dust provided by the first step of the two-step POlarization LIdar PHOtometer Networking (POLIPHON) technique, developed in the framework of the European Aerosol Research Lidar Network (EARLINET). Accordingly, the fine-mode component of pure dust is extracted as the residual between the LIVAS total pure dust and the coarse-mode component of pure dust. Intermediate steps involve the implementation of regionally dependent lidar-derived lidar ratio values and AErosol RObotic NETwork (AERONET)-based climatological extinction-to-volume conversion factors, facilitating conversion of dust backscatter into extinction and subsequently extinction into mass concentration. The decoupling scheme is applied to observations from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) at 532 nm. The final products consist of the fine mode and coarse mode of atmospheric pure dust, quality-assured profiles of backscatter coefficient at 532 nm, extinction coefficient at 532 nm, and mass concentration for each of the two components. The datasets are established primarily with the original L2 horizontal (5 km) and vertical (60 m) resolution of the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) along the CALIPSO orbit path and secondly in averaged profiles of seasonal–temporal resolution, 1° × 1° spatial resolution, and the original vertical resolution of CALIPSO, focusing on the latitudinal band extending between 70° S and 70° N and covering more than 15 years of Earth observations (June 2006–December 2021). The quality of the CALIPSO-based fine-mode and coarse-mode dust products is assessed through the use of AERONET fine-mode and coarse-mode aerosol optical thickness (AOT) interpolated to 532 nm and the AERosol properties – Dust (AER-D) campaign airborne in situ particle size distributions (PSDs) as reference datasets during atmospheric conditions characterized by dust presence. The near-global fine-mode and coarse-mode pure-dust climate data record is considered unique with respect to a wide range of potential applications, including climatological, time series, and trend analysis over extensive geographical domains and temporal periods, validation of atmospheric dust models and reanalysis datasets, assimilation activities, and investigation of the role of airborne dust in radiation and air quality.

Most of the dust models underestimate the load of the large dust particles, consider spherical shapes instead of irregular ones, and have to deal with a wide range of the dust refractive index (RI) to be used. This leads to an incomplete assessment of the dust radiative effects and dust-related impacts on climate and weather. The current work aims to provide an assessment, through a sensitivity study, of the limitations of models to calculate the dust direct radiative effect (DRE) due to the underrepresentation of its size, RI, and shape. We show that the main limitations stem from the size and RI, while using a more realistic shape plays only a minor role, with our results agreeing with recent findings in the literature. At the top of the atmosphere (TOA) close to dust sources, the underestimation of size issues an underestimation of the direct warming effect of dust of ∼ 18–25 W m−2, for DOD = 1 (dust optical depth) at 0.5 µm, depending on the solar zenith angle (SZA) and RI. The underestimation of the dust size in models is less above the ocean than above dust sources, resulting in an underestimation of the direct cooling effect of dust above the ocean by up to 3 W m−2, for aerosol optical depth (AOD) of 1 at 0.5 µm. We also show that the RI of dust may change its DRE by 80 W m−2 above the dust sources and by 50 W m−2 at downwind oceanic areas for DOD = 1 at 0.5 µm at TOA. These results indicate the necessity of including more realistic sizes and RIs for dust particles in dust models, in order to derive better estimations of the dust DRE, especially near the dust sources and mostly for studies dealing with local radiation effects of dust aerosols.

Land–atmosphere interactions govern the process of dust emission and transport. An accurate depiction of these physical processes within numerical weather prediction models allows for better estimating the spatial and temporal distribution of the dust burden and the characterisation of source and recipient areas. In the presented study, the ECMWF-IFS (European Centre for Medium-Range Weather Forecast – Integrated Forecasting System) outputs, produced with and without the assimilation of Aeolus quality-assured Rayleigh–clear and Mie–cloudy horizontal line-of-sight wind profiles, are used as initial or boundary conditions in the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to simulate 2-month periods in the spring and autumn of 2020, focusing on a case study in October. The experiments have been performed over the broader eastern Mediterranean and Middle East (EMME) region, which is frequently subjected to dust transport, as it encompasses some of the most active erodible dust sources. Aerosol- and dust-related model outputs (extinction coefficient, optical depth and concentrations) are qualitatively and quantitatively evaluated against ground- and satellite-based observations. Ground-based columnar and vertically resolved aerosol optical properties are acquired through AERONET sun photometers and PollyXT lidar, while near-surface concentrations are taken from EMEP. Satellite-derived vertical dust and columnar aerosol optical properties are acquired through LIVAS (LIdar climatology of Vertical Aerosol Structure) and MIDAS (ModIs Dust AeroSol), respectively. Overall, in cases of either high or low aerosol loadings, the model predictive skill is improved when WRF-Chem simulations are initialised with the meteorological fields of Aeolus wind profiles assimilated by the IFS. The improvement varies in space and time, with the most significant impact observed during the autumn months in the study region. Comparison with observation datasets saw a remarkable improvement in columnar aerosol optical depths, vertically resolved dust mass concentrations and near-surface particulate concentrations in the assimilated run against the control run. Reductions in model biases, either positive or negative, and an increase in the correlation between simulated and observed values was achieved for October 2020.

Since 2018, the Aeolus satellite of the European Space Agency (ESA) has acquired wind HLOS (horizontal line-of-sight) profiles throughout the troposphere and up to the lower stratosphere, filling a critical gap in the Global Observing System (GOS). Aeolus, carrying ALADIN (Atmospheric LAser Doppler INstrument), the first UV HSRL (High Spectral Resolution Lidar) Doppler lidar ever placed in space, provides also vertically resolved optical properties of particulates (aerosols and clouds). The present study focuses on the assessment of Aeolus L2A particulate backscatter coefficient (baseline 2A11), retrieved by the Standard Correct Algorithm (SCA), in the eastern Mediterranean, a region hosting a variety of aerosol species. Ground-based retrievals acquired by lidar instruments operating in Athens (central Greece), Thessaloniki (northern Greece) and Antikythera (southwestern Greece) serve as reference. All lidar stations provide routine measurements to the PANACEA (PANhellenic infrastructure for Atmospheric Composition and climatE chAnge) network. A set of ancillary data, including sun-photometric observations (AERONET), reanalysis products (CAMS and MERRA-2), satellite observations (MSG-SEVIRI and MODIS Aqua) and backward-trajectories modelling (FLEXPART), is utilized towards an optimum characterization of the probed atmospheric conditions under the absence of a classification scheme in Aeolus SCA profiles. First, emphasis is given on the assessment of Aeolus SCA backscatter coefficient under specific aerosol scenarios over Antikythera island. Due to the misdetection of the cross-polar component of the backscattered lidar signal, Aeolus underestimates the aerosol backscatter coefficient by up to 33 % when non-spherical mineral particles are recorded (10 July 2019). A good performance is revealed on 3 July 2019, when horizontally homogeneous loads of fine spherical particles are confined below 4 km. For other two cases (8 July 2020 and 5 August 2020), due to noise issues, the SCA performance degrades in terms of depicting the stratification of aerosol layers composed of particles of different origin. According to the statistical assessment analysis of 43 identified cases, a poor-to-moderate performance is revealed for the unfiltered (aerosols plus clouds) SCA profiles, which improves substantially when cloud-contaminated profiles are excluded from the collocated sample. This improvement is evident at both Aeolus vertical scales (regular scales have 24 bins and mid-bin scales have 23 bins), and it is justified by the drastic reduction in the bias (from 0.45 to 0.27 Mm−1 sr−1 for SCA and from 0.69 to 0.37 Mm−1 sr−1 for SCA mid-bin) and root mean square error (from 2.00 to 1.65 Mm−1 sr−1 for SCA and from 1.88 to 1.00 Mm−1 sr−1 for SCA mid-bin) scores. In the vertical, the SCA performance degrades at the lowermost bins due to either the contamination from surface signals or the increased noise levels for the aerosol retrievals. Among the three PANACEA stations, the best agreement is found at the remote site of Antikythera with respect to the urban sites of Athens and Thessaloniki. Finally, all key Cal/Val (calibration and validation) aspects necessary for future relevant studies, the recommendations for a possible Aeolus follow-on mission and an overview of the ongoing related activities are thoroughly discussed.

Dust particles larger than 20 µm in diameter have been regularly observed to remain airborne during long-range transport. In this work, we modify the parameterization of the mineral dust cycle in the GOCART-AFWA dust scheme of WRFV4.2.1 to also include such coarse and giant particles, and we further discuss the underlying misrepresented physical mechanisms which hamper the model in reproducing adequately the transport of the coarse and giant mineral particles. The initial particle size distribution is constrained by observations over desert dust sources. Furthermore, the Stokes drag coefficient has been updated to account for realistic dust particle sizes (Re < 105). The new code was applied to simulate dust transport over Cabo Verde in August 2015 (AER-D campaign). Model results are evaluated against airborne dust measurements and the CALIPSO-LIVAS pure dust product. The results show that the modeled lifetimes of the coarser particles are shorter than those observed. Several sensitivity runs are performed by reducing artificially the particles' settling velocities in order to compensate underrepresented mechanisms, such as the non-spherical aerodynamics, in the relevant parameterization schemes. Our simulations reveal that particles with diameters of 5.5–17 and 40–100 µm are better represented under the assumption of an 80 % reduction in the settling velocity (UR80), while particles with sizes ranging between 17 and 40 µm are better represented in a 60 % reduction in settling velocity (UR60) scenario. The overall statistical analysis indicates that the best agreement with airborne in situ measurements downwind (Cabo Verde) is achieved with a 40 % reduction in settling velocity (UR40). Moreover, the UR80 experiment improves the representation of the vertical structure of the dust layers as those are captured by the CALIPSO-LIVAS vertically resolved pure dust observations. The current study highlights the necessity of upgrading the existing model parameterization schemes of the dust life-cycle components towards improving the assessment of the dust-related impacts within the Earth–atmosphere system.

Quantifying the dust optical depth (DOD) and its uncertainty across spatiotemporal scales is key to understanding and constraining the dust cycle and its interactions with the Earth System. This study quantifies the DOD along with its monthly and year-to-year variability between 2003 and 2017 at global and regional levels based on the MIDAS (ModIs Dust AeroSol) dataset, which combines Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua retrievals and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), reanalysis products. We also describe the annual and seasonal geographical distributions of DOD across the main dust source regions and transport pathways. MIDAS provides columnar mid-visible (550 nm) DOD at fine spatial resolution (0.1∘×0.1∘), expanding the current observational capabilities for monitoring the highly variable spatiotemporal features of the dust burden. We obtain a global DOD of 0.032±0.003 – approximately a quarter (23.4 %±2.4 %) of the global aerosol optical depth (AOD) – with about 1 order of magnitude more DOD in the Northern Hemisphere (0.056±0.004; 31.8 %±2.7 %) than in the Southern Hemisphere (0.008±0.001; 8.2 %±1.1 %) and about 3.5 times more DOD over land (0.070±0.005) than over ocean (0.019±0.002). The Northern Hemisphere monthly DOD is highly correlated with the corresponding monthly AOD (R2=0.94) and contributes 20 % to 48 % of it, both indicating a dominant dust contribution. In contrast, the contribution of dust to the monthly AOD does not exceed 17 % in the Southern Hemisphere, although the uncertainty in this region is larger. Among the major dust sources of the planet, the maximum DODs (∼1.2) are recorded in the Bodélé Depression of the northern Lake Chad Basin, whereas moderate-to-high intensities are encountered in the Western Sahara (boreal summer), along the eastern parts of the Middle East (boreal summer) and in the Taklamakan Desert (spring). Over oceans, major long-range dust transport is observed primarily along the tropical Atlantic (intensified during boreal summer) and secondarily in the North Pacific (intensified during boreal spring). Our calculated global and regional averages and associated uncertainties are consistent with some but not all recent observation-based studies. Our work provides a simple yet flexible method to estimate consistent uncertainties across spatiotemporal scales, which will enhance the use of the MIDAS dataset in a variety of future studies.

Atmospheric mineral dust has a rich tri-dimensional spatial and temporal structure that is poorly constrained in forecasts and analyses when only column-integrated aerosol optical depth (AOD) is assimilated. At present, this is the case of most operational global aerosol assimilation products. Aerosol vertical distributions obtained from spaceborne lidars can be assimilated in aerosol models, but questions about the extent of their benefit upon analyses and forecasts along with their consistency with AOD assimilation remain unresolved. Our study thoroughly explores the added value of assimilating spaceborne vertical dust profiles, with and without the joint assimilation of dust optical depth (DOD). We also discuss the consistency in the assimilation of both sources of information and analyse the role of the smaller footprint of the spaceborne lidar profiles in the results. To that end, we have performed data assimilation experiments using dedicated dust observations for a period of 2 months over northern Africa, the Middle East, and Europe. We assimilate DOD derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board Suomi National Polar-Orbiting Partnership (SUOMI-NPP) Deep Blue and for the first time Cloud-Aerosol Lidar with Orthogonal Polarisation (CALIOP)-based LIdar climatology of Vertical Aerosol Structure for space-based lidar simulation studies (LIVAS) pure-dust extinction coefficient profiles on an aerosol model. The evaluation is performed against independent ground-based DOD derived from AErosol RObotic NETwork (AERONET) Sun photometers and ground-based lidar dust extinction profiles from the Cyprus Clouds Aerosol and Rain Experiment (CyCARE) and PREparatory: does dust TriboElectrification affect our ClimaTe (Pre-TECT) field campaigns. Jointly assimilating LIVAS and Deep Blue data reduces the root mean square error (RMSE) in the DOD by 39 % and in the dust extinction coefficient by 65 % compared to a control simulation that excludes assimilation. We show that the assimilation of dust extinction coefficient profiles provides a strong added value to the analyses and forecasts. When only Deep Blue data are assimilated, the RMSE in the DOD is reduced further, by 42 %. However, when only LIVAS data are assimilated, the RMSE in the dust extinction coefficient decreases by 72 %, the largest improvement across experiments. We also show that the assimilation of dust extinction profiles yields better skill scores than the assimilation of DOD under an equivalent sensor footprint. Our results demonstrate the strong potential of future lidar space missions to improve desert dust forecasts, particularly if they foresee a depolarization lidar channel to allow discrimination of desert dust from other aerosol types.