Modern-Era Retrospective analysis for Research and Applications v.2 (MERRA-2), Copernicus Atmosphere Monitoring Service Operational Analysis (CAMS-OA), and a high-resolution regional Weather Research and Forecasting model coupled with chemistry (WRF-Chem) were used to evaluate natural and anthropogenic particulate matter (PM) air pollution in the Middle East (ME) during 2015–2016. Two Moderate Resolution Imaging Spectrometer (MODIS) retrievals – combined product Deep Blue and Deep Target (MODIS-DB&DT) and Multi-Angle Implementation of Atmospheric Correction (MAIAC) – and Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) observations as well as in situ PM measurements for 2016 were used for validation of the WRF-Chem output and both assimilation products. MERRA-2 and CAMS-OA assimilate AOD observations. WRF-Chem is a free-running model, but dust emission in WRF-Chem is tuned to fit AOD and aerosol volume size distributions obtained from AERONET. MERRA-2 was used to construct WRF-Chem initial and boundary conditions both for meteorology and chemical and aerosol species. SO₂ emissions in WRF-Chem are based on the novel OMI-HTAP SO₂ emission dataset. The correlation with the AERONET AOD is highest for MERRA-2 (0.72–0.91), MAIAC (0.63–0.96), and CAMS-OA (0.65–0.87), followed by MODIS-DB&DT (0.56–0.84) and WRF-Chem (0.43–0.85). However, CAMS-OA has a relatively high positive mean bias with respect to AERONET AOD. The spatial distributions of seasonally averaged AODs from WRF-Chem, assimilation products, and MAIAC are well correlated with MODIS-DB&DT AOD product. MAIAC has the highest correlation (R=0.8), followed by MERRA-2 (R=0.66), CAMS-OA (R=0.65), and WRF-Chem (R=0.61). WRF-Chem, MERRA-2, and MAIAC underestimate and CAMS-OA overestimates MODIS-DB&DT AOD. The simulated and observed PM concentrations might differ by a factor of 2 because it is more challenging for the model and the assimilation products to reproduce PM concentration measured within the city. Although aerosol fields in WRF-Chem and assimilation products are entirely consistent, WRF-Chem is preferable for analysis of regional air quality over the ME due to its higher spatial resolution and better SO₂ emissions. The WRF-Chem’s PM background concentrations exceed the World Health Organization (WHO) guidelines over the entire ME. Mineral dust is the major contributor to PM (≈75 %–95 %) compared to other aerosol types. Near and downwind from the SO₂ emission sources, nondust aerosols (primarily sulfate) contribute up to 30 % to PM_2.5. The contribution of sea salt to PM in coastal regions can reach 5 %. The contributions of organic matter, black carbon and organic carbon to PM over the Middle East are insignificant. In the major cities over the Arabian Peninsula, the 90th percentile of PM₁₀ and PM_2.5 (particles with diameters less than 10 and 2.5 µm, respectively) daily mean surface concentrations exceed the corresponding Kingdom of Saudi Arabia air quality limits. The contribution of the nondust component to PM_2.5 is <25 %, which limits the emission control effect on air quality. The mitigation of the dust effect on air quality requires the development of environment-based approaches like growing tree belts around the cities and enhancing in-city vegetation cover. The WRF-Chem configuration presented in this study could be a prototype of a future air quality forecast system that warns the population against air pollution hazards.

Oil recovery, power generation, water desalination, gas flaring, and traffic are the main contributors to SO emissions in the Middle East (ME). Satellite observations suggest that the traditional emission inventories do not account for multiple SO emission sources in the ME. This study aims to evaluate the most frequently used SO emission data sets over the ME by comparing high‐resolution regional model simulations and meteorology/chemistry assimilation products, MERRA‐2 and CAMS, with satellite and available ground‐based air‐quality observations. Here, we employ the WRF‐Chem‐3.7.1 regional meteorology‐chemistry model and conduct simulations for the period 2015–2016 with 10 km grid spacing using HTAP‐2.2 emission data sets and the new OMI‐HTAP data, which is based on the combination of the near‐surface SO emissions taken from the HTAP‐2.2 inventory with strong (>30 kt/year) SO point sources obtained from the satellite Ozone Monitoring Instrument (OMI) observations. We find that conventional emission inventories (EDGAR‐4.2, MACCity, and HTAP‐2.2) have uncertainties in the location and magnitude of SO sources in the ME and significantly underestimate SO emissions in the Arabian Gulf. The WRF‐Chem, run in conjunction with the new OMI‐HTAP emissions, improves comparisons between the satellite and ground‐based SO observations. Our simulations show that SO surface concentrations in Jeddah and Riyadh frequently exceed European air‐quality limits. The ME generates about 10% of global anthropogenic SO emissions, on par with India. Therefore, the development of effective emission controls and improvement of air‐quality monitoring in the ME are urgently needed.

Water is the single most important element of life. Rainfall plays an important role in the spatial and temporal distribution of this precious natural resource, and it has a direct impact on agricultural production, daily life activities, and human health. One of the important elements that govern rainfall formation and distribution is atmospheric aerosol, which also affects the Earth's radiation balance and climate. Therefore, understanding how dust compositions and distributions affect the regional rainfall pattern is crucial, particularly in regions with high atmospheric dust loads such as the Middle East. Although aerosol and rainfall research has garnered increasing attention as both an independent and interdisciplinary topic in the last few decades, the details of various direct and indirect pathways by which dust affects rainfall are not yet fully understood. Here, we explored the effects of dust on rainfall formation and distribution as well as the physical mechanisms that govern these phenomena, using high-resolution WRF-Chem simulations (∼ 1.5 km × 1.5 km) configured with an advanced double-moment cloud microphysics scheme coupled with a sectional eight-bin aerosol scheme. Our model-simulated results were realistic, as evaluated from multiple perspectives including vertical profiles of aerosol concentrations, aerosol size distributions, vertical profiles of air temperature, diurnal wind cycles, and spatio-temporal rainfall patterns. Rainfall over the Red Sea coast is mainly caused by warm rain processes, which are typically confined within a height of ∼ 6 km over the Sarawat mountains and exhibit a strong diurnal cycle that peaks in the evening at approximately 18:00 local time under the influence of sea breezes. Numerical experiments indicated that dust could both suppress or enhance rainfall. The effect of dust on rainfall was calculated as total, indirect, and direct effects, based on 10-year August-average daily-accumulated rainfall over the study domain covering the eastern Red Sea coast. For extreme rainfall events (domain-average daily-accumulated rainfall of ≥ 1.33 mm), the net effect of dust on rainfall was positive or enhancement (6.05 %), with the indirect effect (4.54 %) and direct effect (1.51 %) both causing rainfall increase. At a 5 % significance level, the total and indirect effects were statistically significant whereas the direct effect was not. For normal rainfall events (domain-average daily-accumulated rainfall < 1.33 mm), the indirect effect enhanced rainfall (4.76 %) whereas the direct effect suppressed rainfall (−5.78 %), resulting in a negative net suppressing effect (−1.02 %), all of which were statistically significant. We investigated the possible physical mechanisms of the effects and found that the rainfall suppression by dust direct effects was mainly caused by the scattering of solar radiation by dust. The surface cooling induced by dust weakens the sea breeze circulation, which decreases the associated landward moisture transport, ultimately suppressing rainfall. For extreme rainfall events, dust causes net rainfall enhancement through indirect effects as the high dust concentration facilitates raindrops to grow when the water vapor is sufficiently available. Our results have broader scientific and environmental implications. Specifically, although dust is considered a problem from an air quality perspective, our results highlight the important role of dust on sea breeze circulation and associated rainfall over the Red Sea coastal regions. Our results also have implications for cloud seeding and water resource management.

In desert regions like the Middle East, dust has a profound impact on the environment, climate, air quality, and solar devices. The size of dust particles determines the extent of these effects. Dust deposition (DD) measurements show that coarse dust particles with geometric radius r > 10 μm comprise most of the deposited mass. Still, these particles are not represented in the current models that are tuned to fit the observed aerosol optical depth (AOD). As a result, the existing models and reanalysis products underestimate DD and dust emission (DE) almost 3 times. This is the first study to constrain the dust simulations by both AOD and DD measurements to quantify the effect of coarse and fine dust using the WRF‐Chem model. We found that, on average, coarse dust contributes less than 10% to dust shortwave radiative forcing (RF) at the surface but comprises more than 70% of DE. Annual mean net RF over the Arabian Peninsula and regional seas locally reaches −25 W m⁻². Airborne fine dust particles with radii r < 3 μm are mainly responsible for the significant dimming (5%–10%) of solar radiation, cooling the surface, and hampering solar energy production. However, dust mass deposition is primarily linked to coarse particles, decreasing the efficiency of photovoltaic panels by 2%–5% per day. Therefore, incorporating coarse dust in model simulations and data assimilation would improve the overall description of the dust mass balance and its impact on environmental systems and solar devices.

Strong mesoscale haboob dust storms in April 2007 in the central Arabian Peninsula were studied using the cloud‐resolving Weather Research and Forecasting‐Chemistry (WRF‐Chem) modeling system and observations collected during an intensive atmospheric field campaign. The field campaign provided the valuable aircraft and Doppler weather radar measurements. Active convection persisted for several days during the study period. Dust generation was caused by both strong large‐scale winds and locally produced density currents. Because of insufficient spatial resolution, the event was not resolved accurately by the conventional reanalyses. However, the WRF‐Chem model did successfully capture the primary features of the convection, its location, and precipitation patterns. Although the amount of rainfall in the model was slightly underestimated compared to the satellite measurements, it was approximately double the rainfall in the reanalysis. The convection‐associated dust outbreaks were simulated well, with the aerosols optical depth magnitude and the temporal variability being in good agreement with both the ground‐based and satellite aerosol retrievals. The model captured the major dust generation patterns, transport pathways, and several of the largest haboobs identified from the satellite observations. About 25 Tg of dust was emitted in the Arabian Peninsula during the 10‐day period. Approximately 40% of the locally deposited dust was subject to wet removal processes. During periods of high local dust production, the WRF‐Chem model underestimated the PM₁₀ mass concentration (associated mostly with dust particles larger than 3 μm in diameter) by nearly a factor of 2. This suggests that the current dust parameterizations, which prescribe the size distribution of the emitted dust, underestimate the number of large particles that increases at strong wind conditions.

Modern-Era Retrospective analysis for Research and Applications v.2 (MERRA-2), Copernicus Atmosphere Monitoring Service Operational Analysis (CAMS-OA) data assimilation products, and a regional Weather Research and Forecasting model (10 km resolution) coupled with Chemistry (WRF-Chem) were used to evaluate natural and anthropogenic aerosol air pollution in the ME during 2015–2016. Satellite and ground-based AOD observations, as well as in-situ Particulate Matter (PM) measurements for 2016, were used for validation. WRF-Chem code was modified to correct the calculation of dust gravitational settling and aerosol optical properties. The dust emission in WRF-Chem is calibrated to fit Aerosol Optical Depth (AOD) and aerosol volume size distributions obtained from Aerosol Robotic Network (AERONET) observations. MERRA-2 was used to construct WRF-Chem initial and boundary conditions both for meteorology and chemical/aerosol species. SO₂ emissions in WRF-Chem are based on the novel NASA SO₂ emission dataset that reveals unaccounted sources over the ME. Although aerosol fields in WRF-Chem and assimilation products are quite consistent, WRF-Chem, due to its higher spatial resolution and better SO₂ emissions, is preferable for analysis of regional air-quality over the ME. The WRF-Chem's PM background concentrations exceed the World Health Organization (WHO) guidelines over the entire ME. The major contributor to PM (~ 75–95 %) is mineral dust. In the ME urban centers and near oil recovery fields, non-dust aerosols (primarily sulfate) contribute up to 26 % into PM_2.5. The contribution of sea salt into PM can rich up to 5 %. The contribution of organic matter into PM prevails over black carbon.