Journal of Geophysical Research: Atmospheres

RESEARCH ARTICLE Observations and Cloud-Resolving Modeling of Haboob Dust 10.1029/2018JD028486 Storms Over the Arabian Peninsula Key Points: Anatolii Anisimov1 , Duncan Axisa2,3 , Paul A. Kucera2 , Suleiman Mostamandi1 , and • An online convective-permitting 1 WRF-Chem model successfully Georgiy Stenchikov

captured a prolonged convective 1 event and associated haboobs over Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, 2 3 the Arabian Peninsula National Center for Atmospheric Research, Boulder, CO, USA, Droplet Measurement Technologies, Longmont, CO, USA • A 25 Tg of dust were emitted during the 10-day period of convective activity, 40% of which was removed Abstract Strong mesoscale haboob dust storms in April 2007 in the central Arabian Peninsula were by rainfall • When local dust production was studied using the cloud-resolving Weather Research and Forecasting-Chemistry (WRF-Chem) modeling high, the WRF-Chem model system and observations collected during an intensive atmospheric field campaign. The field campaign underestimated the PM10 mass provided the valuable aircraft and Doppler weather radar measurements. Active convection persisted for concentration by a factor of 1.5-2 several days during the study period. Dust generation was caused by both strong large-scale winds and fi Supporting Information: locally produced density currents. Because of insuf cient spatial resolution, the event was not resolved • Supporting Information S1 accurately by the conventional reanalyses. However, the WRF-Chem model did successfully capture the • Movie S1 primary features of the convection, its location, and precipitation patterns. Although the amount of rainfall in • Movie S2 • Movie S3 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 Correspondence to: aerosols optical depth magnitude and the temporal variability being in good agreement with both the G. Stenchikov, [email protected] 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 Citation: of dust was emitted in the Arabian Peninsula during the 10-day period. Approximately 40% of the locally Anisimov, A., Axisa, D., Kucera, P. A., deposited dust was subject to wet removal processes. During periods of high local dust production, the Mostamandi, S., & Stenchikov, G. (2018). Observations and cloud-resolving WRF-Chem model underestimated the PM10 mass concentration (associated mostly with dust particles larger modeling of haboob dust storms over than 3 μm in diameter) by nearly a factor of 2. This suggests that the current dust parameterizations, the Arabian peninsula. Journal of which prescribe the size distribution of the emitted dust, underestimate the number of large particles that Geophysical Research: Atmospheres, 123, 12,147–12,179. https://doi.org/10.1029/ increases at strong wind conditions. 2018JD028486 Plain Language Summary In this study, we use a sophisticated numerical model of atmospheric Received 2 MAR 2018 circulation with an aerosol component to simulate a series of local-scale haboob dust storms that Accepted 6 OCT 2018 occurred in the central Arabian Peninsula during April 2007. This type of dust storm is associated with a cold Accepted article online 12 OCT 2018 fl fi Published online 6 NOV 2018 air out ow from thunderstorms and is speci c to the wet spring season in this region. We compare the model results with the data obtained from aircraft and meteorological radar during a field campaign Author Contributions: conducted at that time. The high-resolution model produces good results that capture the atmospheric Conceptualization: Georgiy convection, rainfall features, and major dust outbreaks recorded at a ground station in Riyadh. We Stenchikov Data curation: Duncan Axisa, Paul A. demonstrate that it performs better than other global reanalysis products. However, our results show that Kucera the aerosol component of the numerical model needs to be improved, as the model underestimates the Formal analysis: Anatolii Anisimov, concentration of coarse dust particles. This is caused by the uncertainties introduced when prescribing the Duncan Axisa, Georgiy Stenchikov Funding acquisition: Georgiy physical properties of dust aerosols generated from the surface. Stenchikov Investigation: Anatolii Anisimov, Duncan Axisa, Georgiy Stenchikov (continued) 1. Introduction The role of mineral dust aerosol in global systems is one of the core themes in geoscience (Shao et al., 2011). Mineral dust influences the Earth’s radiative balance by scattering and absorbing solar and terrestrial radia- ©2018. The Authors. tion (Balkanski et al., 2007; García et al., 2012; R. L. Miller et al., 2014; H. Yu et al., 2006). It has an impact on This is an open access article under the the microphysics of clouds, leading to changes in their radiative properties, extent, and associated precipita- terms of the Creative Commons Attribution-NonCommercial-NoDerivs tion (Andreae & Rosenfeld, 2008; Karydis et al., 2017; Lohmann & Feichter, 2005). Dust deposited on glaciers License, which permits use and distri- and snow-covered areas changes their albedo and leads to increased melting rates (Gabbi et al., 2015). bution in any medium, provided the Mineral dust transported to remote regions affects ocean and terrestrial biogeochemical processes and influ- original work is properly cited, the use is non-commercial and no modifications ences bioproductivity (Mahowald et al., 2005; Schulz et al., 2012; H. Yu, Chin, et al., 2015). Depending on the or adaptations are made. source region, the physical and chemical properties of dust are highly variable (Perlwitz et al., 2015; Zhang,

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Methodology: Anatolii Anisimov, Mahowald, et al., 2015). Moreover, during its lifecycle, airborne dust is involved in complex processes of Duncan Axisa, Paul A. Kucera, Georgiy Stenchikov transformation and interaction with other aerosols and chemical components of the atmosphere, which in Resources: Georgiy Stenchikov turn leads to significant changes in its properties (Abdelkader et al., 2017; Baker et al., 2014; Reitz et al., Supervision: Paul A. Kucera, Georgiy 2011). Finally, dust storms affect human health (Goudie, 2014; Morman & Plumlee, 2014), solar energy Stenchikov Validation: Anatolii Anisimov production (Schroedter-Homscheidt & Oumbe, 2013), and transportation safety (Middleton, 2017). Despite Visualization: Anatolii Anisimov the many studies focused on these processes over the last decade, our understanding and ability to Writing - original draft: Anatolii model them is still quite limited (Evan et al., 2014). Recent model estimates of global dust production vary Anisimov Writing – review & editing: Anatolii by a factor of 10, although this variability strongly depends on the range of resolved particle sizes Anisimov, Duncan Axisa, Paul A. Kucera, (Huneeus et al., 2011). This provides us with an incentive to better understand dust processes by combining Georgiy Stenchikov models with observations. While anthropogenically driven sources contribute significantly to global dust emission, mineral dust is the most abundant natural aerosol. The Earth’s major dust sources are located predominantly in arid regions of subtropical desert. The biggest and most studied dust sources are in the Sahara (Knippertz & Todd, 2012). The estimated contribution from North African sources to global dust production is highly variable (Engelstaedter et al., 2006), but at least 50% (Huneeus et al., 2011). Among the other prominent dust- producing regions, Arabian Peninsula (AP) is one of the most important (Ginoux et al., 2012; Shao et al., 2011). Several dust sources have been identified in the region. Most studies identify the eastern areas of the AP as the most significant for dust emission. Those are the dry riverbeds (wadis) east of the Jebel Tuwaiq escarpment in Saudi Arabia, the Ad-Dahna desert, the Rub’ al Khali desert, and the Tigris-Euphrates alluvial plain, which stretches from Iraq to the Arabian Gulf coasts of Kuwait and northeastern Saudi Arabia (Gherboudj et al., 2017; Ginoux et al., 2012; Goudie & Middleton, 2006; Notaro et al., 2013; Shao, 2008). Other local sources include the coastal plain of the Red Sea (Anisimov et al., 2017) and the southern coastal regions of Yemen and Oman (Edgell, 2006; Prospero et al., 2002). A number of studies were conducted recently on the seasonal and interannual variability of dust events in the AP, and their synoptic mechanisms were identified (Hamidi et al., 2013; Nabavi et al., 2016). For most areas in the central and northern AP, spring to early summer is the peak season for dust activity (Notaro et al., 2013; Sabbah & Hasan, 2008; Y. Yu et al., 2013). Further east, the peak shifts to June, conditioned by the strong Shamal winds bringing dust from the Iraqi and Syrian deserts (Bou Karam Francis et al., 2017; Goudie & Middleton, 2006; Y. Yu et al., 2016). In the western AP, dust activity is relatively weak; the cold season maxima are strongly influenced by the Mediterranean cyclones and their associated frontal passages (Y. Yu, Notaro, et al., 2015), and by the Sharav cyclones, which transport dust from the Sahara and the African Red Sea coast (Goudie & Middleton, 2006; Israelevich et al., 2003; Kalenderski & Stenchikov, 2016). Overall, local sources of dust are important throughout the entire season, especially in the areas with the highest dust activity (Notaro et al., 2013). On interannual timescales, the quantity of dust produced locally over the AP during spring is controlled by the amount of rainfall associated with El Niño–Southern Oscillation (Y. Yu, Notaro, et al., 2015). La Niña influences the Shamal onset and termination time (Y. Yu et al., 2016). Dust strengthens the heat low over Saudi Arabia and affects local circulation via radiative effects (Mohalfi et al., 1998). When transported over the Arabian Sea, this dust influences the Indian monsoon and precipitation in southern India (Solmon et al., 2015; Vinoj et al., 2014). Although haboobs are recognized as an important mechanism of dust generation, only one of the abovementioned studies (S. D. Miller et al., 2008) focused on Arabian haboobs. Traditionally, a haboob event is defined as a dust storm that is caused by a gust front forming from a thunderstorm downdraft. Downdrafts are associated with the melting and evaporation of hydrometeors from moist convection. Downdrafts form cold pools with divergent surface wind patterns that can lead to localized dust generation. The dry subcloud environment of a desert generally favors downdrafts reaching the ground and forming gust fronts, even in the absence of precipitation at the surface (Knippertz et al., 2009; S. D. Miller et al., 2008 and references therein). Haboobs may develop in virtually any region of the world where moist convection occurs and alluvial sediments are available (Roberts & Knippertz, 2014, and references therein). The spatial extent of a gust front can vary considerably, from ~100 to more than 1,000 km. The Saharan haboobs associated with mesoscale convective system (MCS) cold pools, which are caused by northward surges of the West African Monsoon, are often very large (Bou Karam et al., 2014; Cavazos-Guerra & Todd,

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2012; Roberts & Knippertz, 2014). Depending on the season and location, dust uplifted by cold pool outflows is estimated to have a contribution to total dust production of 20%–30% (S. D. Miller et al., 2008; Pantillon et al., 2016), which reaches 40%–50% in areas influenced by West African Monsoon surges (Allen et al., 2013; Heinold et al., 2013; Marsham et al., 2011, 2013). However, this estimate is highly dependent on detection and partitioning techniques. Cold pools propagate cold and moist air at high wind speeds (Bou Karam et al., 2014; Knippertz et al., 2007; Marsham et al., 2013; S. D. Miller et al., 2008). Within a cold-pool density current, dust is produced by strong horizontal winds and high turbulent mixing along the frontal line. The vortex-like circulation along the leading edge of a density current forms a prominent dust wall, which is characterized by a sharp gradient in the dust concentration with respect to its environment (Solomos et al., 2012). Several factors must be considered when studying haboobs. First, haboobs are challenging to observe given the scattered surface observation networks common in sparsely populated desert areas. In addition, they are often masked by upper level clouds in satellite imagery (Allen et al., 2015; Heinold et al., 2013; Kocha et al., 2013; Williams, 2008). Second, the spatial resolution of the model is a crucial element in haboob simulations. A study by (Reinfried et al., 2009) showed that the cold pool production is strongly dependent on the convec- tive parameterization. A realistic representation of moist convection in dust simulations has been confirmed as essential by several studies (Heinold et al., 2013; Marsham et al., 2008; Solomos et al., 2012). The most rea- listic simulations are those that have explicitly resolved convection; models with parameterized convection have substantial biases due to systematic errors in both the modeled moist convection and the associated cold pools (Cavazos-Guerra & Todd, 2012; Garcia-Carreras et al., 2013; Pantillon et al., 2015; Pope et al., 2016; Sodemann et al., 2015). Large-scale reanalyses that assimilate observations can capture some large- scale processes of deep convection events, such as dust loading, by including an aerosol component. Although it is very computationally demanding, including an air quality or aerosol component in a modeling framework greatly improves its research capabilities and offers an additional means for model verification. An aerosol component is not vital for studies of meteorological drivers of dust storms, but it is highly desirable for any dust forecast research, as implementing high-resolution, cloud-resolving interactive aerosol, and meteorology models increases the reliability of mesoscale dust event forecasts. However, these reanalyses products still systematically underestimate the wind speeds observed during deep convective events (Largeron et al., 2015) and, thus, underpredict the dust generation from haboobs. Thus, the preferred approach to studying haboobs includes a combination of numerical simulations and detailed ground, airborne, and remote measurements (Allen et al., 2015; McConnell et al., 2008).

The need for detailed surface observational networks and airborne measurements has been reflected in a number of observational campaigns conducted recently, mostly in the central and western Sahara. For a review of dust observational campaigns, refer to Figure 1 in Todd et al. (2013) and Table 1 in Ryder et al. (2013). Several modeling case studies were accomplished under the scope of Saharan mineral dust experi- ment campaign (Heinold et al., 2009; Laurent et al., 2010), which focused on haboobs caused by outflows from convection initiated over the Atlas Mountains in Morocco. The models used by Knippertz et al. (2009) and Reinfried et al. (2009) did not include a dust component and were focused on simulations of the convec- tion and its associated cold pools. Later, two studies with online dust models complemented that field campaign (Khan et al., 2015; Solomos et al., 2012). The African Monsoon Multidisciplinary Analysis campaign (Redelsperger et al., 2006) was complemented by a modeling study (Cavazos-Guerra & Todd, 2012) of 30- and 10-km nested Weather Research and Forecasting-Chemistry (WRF-Chem) simulations with parameterized convection and a bulk aerosol scheme. Several MCS with cold pools causing dramatic haboob dust fronts were described in the Sahara over Mali and Nigeria. Studies by Heinold et al. (2013) and Marsham et al. (2011) performed within the Cascade project aimed at assessing the relative contribution of haboobs to dust formation, with the latter study featuring an offline dust emission component. Another interesting case study of even larger haboobs in this area was performed using a 10-km MesoNH model (Bou Karam et al., 2014). A long-lived Saharan MCS was studied with nested (30 to 10 to 3.3 km) WRF simulations (without dust compo- nent) (Roberts & Knippertz, 2014). Another important case study was based on the results of the most recent Fennec campaign (Ryder et al., 2013), which performed extensive ground measurements at a supersite near the Algeria-Mali border and aircraft measurements in southern Morocco and Mauritania (Sodemann et al., 2015). The authors compared particle size distribution (PSD) simulations from the Lagrangian particle- dispersion model with aircraft observations of an aged, dusty cold pool. Fully interactive, high-resolution

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haboob case studies were also performed for the United States (Vukovic et al., 2014), Sudan (Kalenderski & Stenchikov, 2016), and a severe, record-breaking dust event of 2015 that was caused by a series of cold pool outflows from MCS in the Eastern Mediterranean (Gasch et al., 2017; Solomos et al., 2017). To conclude, very few fully interactive modeling studies of haboobs have been conducted, with none so far specifically investigating AP haboobs. Several modeling studies targeted large-scale dust storms (Basart et al., 2016; Jish Prakash et al., 2015; Kalenderski & Stenchikov, 2016; Zhang, Liu, et al., 2015). The only observational campaign UAE2 was performed in the southeastern Arabian Peninsula in 2004 (United Arab Emirates Unified Aerosol Experiment; Reid et al., 2008). A very dense, ground-based network was used, with a number of instruments that allowed for an observational description of haboob formation and structure (S. D. Miller et al., 2008). The study presented a detailed analysis and a significant contribution to the understanding of wet downdrafts but used only a simple, idealized model and did not consider the properties of mineral dust aerosols. In the current study, we focus on mesoscale haboob dust storms. We combined high-resolution model simu- lations with high-quality observations to assess the physical characteristics of haboobs in the central AP. Specifically, we used the cloud-resolving Weather Research and Forecasting-Chemistry (WRF-Chem) model with an online aerosol and air quality module (Grell et al., 2005; Skamarock et al., 2008) to perform case study simulations of a series of convective events and their associated haboob dust storms that were observed in the central AP during April 2007. Spring is the season of maximum convective activity in the central region of the AP and dust events caused by cold pools are very common, making this area a prominent natural source of haboob develop- ment. We incorporate the observations obtained during the Kingdom of Saudi Arabia Assessment of Rainfall Augmentation research program, which studied the feasibility of rainfall augmentation in Saudi Arabia (National Center for Atmospheric Research, 2010). The research aircraft flights were conducted in the vicinity of Riyadh. A subset of these flights targeted convection associated with elevated dust loading and, during one flight, the aircraft flew into a cold pool front where extremely high dust loading was measured (Pósfai et al., 2013). The resulting data represent a unique case of airborne observations of a freshly generated haboob dust storm. We complemented the aircraft measurements with data from Doppler radar that was operating in the central AP region during the same period. These measurements allowed us to perform a more detailed model verification and to better understand the structure and evolution of convective cells. We also utilized many other available ground-based observations, reanalysis products, and remote sensing data to better describe dust events, model verification, and quantitative characteristics of dust properties. By combining observational data sets with the model simulations, we were able to address a number of questions. We present a representative case of convective activity and precipitation in the desert environ- ment. We show that the large-scale reanalysis products failed to represent the convection and its associated precipitation, producing an inaccurate meteorological background for the haboob development. For this reason, we tested and evaluated a high-resolution coupled model and documented its ability to capture con- vective activity. We also assessed the model’s representation of haboobs and quantified the spatiotemporal characteristics of the aerosol burden. The aircraft data allowed us to also evaluate the aerosol PSD. Correct representation of PSD is crucial in modeling the size-dependent dust deposition process and, consequently, its long-range transport and radiative and microphysical impacts (Mahowald et al., 2014). The approach chosen to represent PSD can alter the radiative effect of the dust aerosol by up to a factor of 3, presenting a major uncertainty (Zhao, Chen, et al., 2013a). Therefore, the primary goals of the study were the following: 1. Reproduce in the model the intense convection in the central AP that led to the formation of a series of mesoscale haboob dust storms; 2. Evaluate the WRF-Chem performance; 3. Assess the spatiotemporal characteristics of simulated haboobs; and 4. Evaluate the model PSDs compared to airborne measurements and ground retrievals. The paper is organized as follows. The model configuration is described and the data sets are introduced in section 2. The synoptic conditions during the convective event and the basic features of the dust generation patterns are discussed in section 3. Verification of the simulated meteorology and convection characteristics are given in section 4. Evaluation of the modeled aerosol components, haboob structure, and PSD are discussed in section 5. The results of the study conclude the paper in section 6.

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Figure 1. WRF-Chem nested domains, AERONET stations used in the study, major dust source areas, and surface topography (m).

2. Model and Data 2.1. Model Configuration The open-source community WRF-Chemistry (WRF-Chem, version 3.7.1) model (Grell et al., 2005; Skamarock et al., 2008) is one of the most advanced atmosphere-land-chemistry-aerosol coupled modeling systems currently available. We ran simulations of the model for the convection event of 1–14 April 2007, with convective-resolving spatial resolutions of 4.5 km (parent domain) and 1.5 km (child domain) in the AP and the surrounding region (Figure 1). The configurations of the meteorological and chemical/aerosol WRF-Chem components are outlined with references in Table 1 and Table 2, respectively. Several important remarks should be made here. First, using the model with online aerosols restricted our choice of meteorological parameterization scheme. For example, only the Lin and Morrison microphysical modules and the RRTMG radiation scheme are fully coupled with the online aerosol schemes. Second, we decided to run the model without any convection parameterization in either domain, partly because it better reproduced the cold pools and also because there are no fully aerosol-aware convective parameterization schemes in the model (as of version 3.7.1). Therefore, the meteorological boundary conditions were taken from the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analysis, which is the data set with the highest spatial resolution (~25 km) available for 2007. Two test runs were performed: with and without spectral nudging towards the driving meteorological boundary conditions. The nudging coefficients are given in Table 1. The Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) scheme is one of the most developed aerosol options in WRF-Chem. This 8-bin sectional aerosol scheme (bin boundaries are 0.039–0.078, 0.078–0.156, 0.156–0.312, 0.312–0.625, 0.625–1.25, 1.25–2.5, 2.5–5.0, and 5.0–10.0 μm) allows for the most detailed representation of aerosol PSD (Archer-Nicholls et al., 2015; Tsikerdekis et al., 2017; Zhao et al., 2010; Zhao, Chen, et al., 2013a), a key objective of this study. MOSAIC is coupled with the Carbon Bond

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Table 1 WRF-Chem Meteorological Component Configuration Process WRF-Chem option

Horizontal resolution 4.5 km (670 × 600) + 1.5 km (330 × 330), one-way nesting Vertical levels 40 Boundary conditions ECMWF operational analysis IFS v. CY31R2 T799 spectral resolution (~25 km) 91 vertical levels Simulation period 1–14 April 2007 Spectral nudging (sensitivity experiment only) Nudging wind velocities and geopotential above PBL (nudging À strength = 0.0001 s 1, nudging time = 2.7 hr, xwavenum = 6, ywavenum = 6) Microphysics Modified Lin with prognostic cloud droplet number coupled to aerosol module (Chapman et al., 2009; Lin et al., 1983) Shortwave and longwave radiation Aerosol and droplet number-aware RRTMG (Iacono et al., 2008) Cumulus parameterization Off Surface layer Eta (Janjić, 1994) Land surface model Noah LSM (Tewari et al., 2016) PBL MYJ (Janjić, 1994) Note. WRF-Chem = Weather Research and Forecasting-Chemistry; ECMWF = European Centre for Medium-Range Weather Forecasts; PBL = planetary boundary layer; LSM = Land Surface Model; MYJ = Mellor-Yamada-Janjić.

(CMB-Z) photochemical mechanism module, which includes 67 prognostic gas species and 164 reactions, allowing this model configuration to be used for advanced aerosol and air quality studies (Archer-Nicholls et al., 2015; Fast et al., 2006; Raut et al., 2017; Ritter et al., 2013; Zhang et al., 2013). This model configuration has been used for a large number of global (Hu et al., 2016; Ridley et al., 2016; Zhao, Chen, et al., 2013a) and regional (e.g., North Africa, Smoydzin et al., 2012; Zhao et al., 2010; Iceland, Blechschmidt et al., 2012; Australia, Alizadeh-Choobari et al., 2012; and China, S. Chen et al., 2013) dust-targeted studies. MOSAIC gives a prognostic treatment to eight aerosol species: five inorganic ions (sulfate, nitrate, ammonium, chloride, and sodium) and three unreactive aerosol species (black carbon [BC], primary organic mass [OC], and dust [other

Table 2 WRF-Chem Aerosol/Chemistry Component Configuration Process WRF-Chem option

Aerosols MOSAIC 8 bin with aqueous chemistry (eight aerosol species) (chem_opt = 10) (Fast et al., 2006; Zaveri et al., 2008) Gas-phase mechanism CBM-Z (67 prognostic species) (Zaveri & Peters, 1999) Photolysis scheme Fast-J (Barnard et al., 2004; Wild et al., 2000) Anthropogenic emissions Hemispheric Transport of Air Pollution version 2 (0.1 × 0.1; Janssens-Maenhout et al., 2015) Chemical/aerosol boundary conditions, Off biomass burning emissions Dust emission GOCART emission scheme (Chin et al., 2002) MODIS source function (Ginoux et al., 2012) Prescribed PSD at emission (Kok, 2011a) Dry deposition (gases) (Wesely, 1989) Dry deposition (aerosols) (Binkowski & Shankar, 1995) Wet deposition (gases and aerosols) In-cloud and below-cloud immediate removal by precipitation (without resuspension) (Easter et al., 2004) Aerosol-cloud interaction Activation and resuspension for new prognostic set of aerosol variables in cloud borne stage (Chapman et al., 2009; S. J. Ghan & Easter, 2006) Aqueous chemistry (Fahey & Pandis, 2001) Aerosol-radiation interaction (Zhao, Ruby Leung, et al., 2013b) Aerosol optical properties Internal mixing, volume-averaged refractive index (Steven J. Ghan & Zaveri, 2007) Dust refractive index 1.53 + 0.003i (Zhao et al., 2010) Note. WRF-Chem = Weather Research and Forecasting-Chemistry; PSD = particle size distribution; GOCART = Goddard Chemistry Aerosol Radiation and Transport.

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inorganics]). Aerosol water content, hysteresis water content, and total aerosol number are treated as separate variables (strictly speaking, the total aerosol number variable is a product of others, but it is saved separately for convenience). The model also accounts for the complex mechanisms of gas-to-particle conver-

sion (e.g., H2SO4, HNO3, HCl, and NH3 gas species might transition to particle phase), aerosol nucleation, coagulation, and cloud-stage chemical transformations. Aerosol direct radiative effects are implemented by calculating their optical properties following Mie theory, assuming that the aerosols are internally mixed. The optical properties of each model layer are then used in the radiation module. Aerosols indirectly affect the cloud droplet number density, which is also introduced to the radiation module. With a set of cloud-borne variables, the total number of prognostic aerosol species is 160. For a more detailed description of the implementation of aerosol-cloud interactions and feedbacks, we refer the reader to Chapman et al. (2009), Grell et al. (2011), and Gustafson et al. (2007). Dust and OC are considered weakly hygroscopic with a hygroscopicity parameter of 0.14, and black carbon is À almost hydrophobic (hygroscopicity = 10 6). Therefore, cloud-borne dust does not significantly influence the total aerosol PSD. We limited our analysis to only those aerosols in the interstitial stage, the analysis of indirect effects being outside the scope of this study. As long as the dust aerosol is unreactive, the particles do not transit between the bins, and the primary source and sinks of the dust are natural emission and dry and wet deposition, respectively (Table 2). Dry deposition includes gravitational settling and turbulent deposition in the boundary layer. Wet deposition includes in-cloud scavenging and below-cloud removal. The details of the emission scheme are discussed in the next section.

2.2. Anthropogenic and Natural Emissions Dust emission is implemented in the model following simple Goddard Chemistry Aerosol Radiation and Transport (GOCART) schemeThe key element of GOCART is the source function, which allows a more realistic spatial distribution of the potentially dust-producing soils that could be inferred from a variety of principles. The statistical source function was based on the frequency of high dust aerosol optical depth (AOD) values, which were determined from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observa- tions. This source function was already tested in the AP within WRF-Chem (Kalenderski & Stenchikov, 2016). The tuning constant of the emission scheme was set to 0.7, a value close to the default option (0.65). The total dust flux generated by the GOCART scheme was then redistributed into the MOSAIC size bins following (Zhao, Chen, et al., 2013a), with the bin-weighting coefficients corresponding to brittle fragmenta- tion theory (Kok, 2011a). Sea salt emissions follow Zhao, Chen, et al. (2013a) and were partitioned between chloride, sodium, and OC aerosols. Anthropogenic gaseous and aerosol emissions followed the widely used Hemispheric Transport of Air Pollution version 2 data set. The inventory components used in the model are the four major gaseous species

(SO2,NOx, CO, and NH3), and four primary aerosol species: BC, OC, and unspecified PM10 and PM2.5. Unspecified emissions were redistributed to dust (other inorganics) bins. In this study, the contribution from these unspecified anthropogenic emissions was small compared to natural dust emissions.

2.3. Data The data sources used in our study are listed in Table 3. The main data used to verify the model meteorology are the station weather observations, assembled in the Integrated Surface Data set (ISD; Smith et al., 2011). Temperature, dew point, and wind velocity biases and root mean square errors were calculated for each of 83 ground stations. Most of the observations were reported hourly, while precipitation was reported twice daily. Precipitation was compared with two stations near Riyadh, Riyadh International Airport (RUH) and Riyadh Air Base (RAB). We also compared our results with precipitation estimates from Tropical Rainfall Measuring Mission (TRMM) satellite remote measurements, driving operational analysis, ERA-Interim reanalysis, and The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis. To test the modeled convection, we used Doppler radar data from Riyadh. Three-dimensional radar horizontal reflectivity (dBZ) measurements and column vertically integrated liquid content (VIL) were inferred from the raw data. The radar-equivalent WRF-Chem reflectivity factor was calculated by the default built-in algorithm (do_radar_ref = 1 namelist option was set), which was implemented within the Lin microphysical parameterization routine. We did not rely on the rainfall estimates from the radar, as it was not calibrated for rainfall assessment at the time of operation.

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Table 3 Data Sources and Products Instrument/model Data product Horizontal resolution

AERONET V2 Level 1.5 AOD at 500 nm (Holben et al., 1998) Point measurements Column-integrated PSD inversion product (Dubovik et al., 2006; Dubovik & King, 2000) ERA-Interim reanalysis (Dee et al., 2011) 3-hourly precipitation ~80 km MERRA-2 aerosol reanalysis (Randles et al., 2017) 1-hourly precipitation, AOD at 550 nm 0.5° × 0.625° SEVIRI instrument onboard Meteosat-9 Dust AOD at 550 nm (daytime only) (Banks & Brindley, 2013; 3 km (nadir) geostationary satellite Brindley & Russell, 2009) Pink dust (IR channels based, 24 hr) and natural color (visible and NIR-channels based, daytime only) RGB composites (Lensky & Rosenfeld, 2008) MODIS AQUA MYD04_L2 (Sayer et al., 2014) Collection 6 combined Dark Target (ocean and dark land) and 10 km (nadir) Deep Blue (bright land) AOD at 550 nm TRMM 3B42RT (Liu et al., 2012) 3-hourly precipitation 0.25° Doppler weather radar in Riyadh 5 min radar reflectivity (dBZ) at 10 fixed elevation levels 0.25 km Aircraft PSD measurements (Pósfai et al., 2013) 0.01–0.4 μm: Differential Mobility Analyzer, 85-s frequency Trajectory 0.1–3 μm: Passive Cavity Aerosol Spectrometer Probe, 1-s frequency 3–47 μm: Forward Scattering Spectrometer Probe, 1-s frequency Integrated Surface Dataset (Smith et al., 2011) Weather stations hourly temperature, dew point, wind, visibility & Point measurements weather code reports Note. PSD = particle size distribution; AOD = aerosol optical depth; IR = infrared; NIR = near infrared.

AOD is a primary variable that can be inferred from ground and satellite measurements. AOD is the key obser- vation for verification of a model’s aerosol simulation. We utilized the data from four Aerosol Robotic Network (AERONET) stations in the AP to assess the magnitude and temporal variability of the modeled dust loading. The measurements from Solar Village, a station located 50-km northwest of Riyadh and in the center of dust generation, indicated high dust loading at the time of the convection event. The other three stations, located in the UAE, did not detect high dust loadings. To assess the spatial AOD patterns, we used satellite tools. The polar-orbiting MODIS TERRA and AQUA satellites provide measurements twice daily, in the morning and early afternoon. However, haboobs usually develop later in the day (Kocha et al., 2013; Schepanski, Tegen, Todd, et al., 2009b) and might be missed by these polar-orbiting satellites (Schepanski et al., 2012). Therefore, we also used an AOD product based on measurements from the SEVIRI instrument on board the geostationary Meteorological Satellite (METEOSAT) satellite (Banks & Brindley, 2013; Brindley & Russell, 2009). Despite its lower spatial resolution over the AP, the Spinning Enhanced Visible and Infrared Imager (SEVIRI) measurements are updated every 15 min; we used it as a proxy for model verification together with the AERONET measurements. Another pro- duct based on the SEVIRI instrument is the RGB pink dust composite (Lensky & Rosenfeld, 2008), which is based on infrared channels and, thus, available 24 hr a day (i.e., not limited to daytime hours like AOD retrieved in the solar spectral range) This product is a convenient tool for understanding dust generation pat- terns, studying the spatial structure of cloud systems that produce cold pools and haboobs, and even devel- oping dust source maps (Schepanski et al., 2012). The recent MERRA-2 reanalysis is part of a new generation of global aerosol products that incorporates an aerosol component. The key feature of MERRA-2 is that it assimilates the AERONET and MODIS AOD measure- ments, allowing for a continuous, observation-synchronized AOD time series. The relatively coarse spatial resolution can be partly compensated by assimilation techniques. We used the reanalysis as an additional source to verify the WRF-Chem aerosol component. 2.4. Research Aircraft Flights Data from four Piper Cheyenne II research aircraft flights, which operated from Riyadh Airport and sampled atmospheric conditions on 7–9 and 11 April 2007, were analyzed. As the research campaign was focused on rainfall augmentation, the flights were performed in the presence of enhanced convection with the goal of measuring the microphysical properties of the clouds. The aircraft sampled aerosol and cloud PSD over a wide diameter range (0.01–47 μm) based on three instruments, Differential Mobility Analyzer (DMA),

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Figure 2. ECMWF operational analysis: mean sea level pressure (hPa, color) and 10 m wind velocity (m/s, vectors) at 12.00 UTC on (a) 7 April, (b) 8 April, (c) 9 April, (d) 10 April, (e) 11 April, (f) 12 April 2007.

Passive Cavity Aerosol Spectrometer Probe (PCASP), and Forward Scattering Spectrometer Probe (FSSP; Table 3). In contrast to most other observational campaigns aimed at measuring aerosol properties, the aircraft did not perform long flight legs at fixed altitudes. Instead, the aircraft ascended to the cloud base and penetrated the cloud at different heights. A detailed description of the flights, analysis of the aerosol chemical composition, vertical aerosol profiles, and backward trajectories were given by Pósfai et al. (2013). Here we focused on comparisons between these observations and the model and studied two primary characteristics: the vertical profiles of the aerosol mass concentrations and the column-integrated PSD. Given that we limited our interest to the aerosol properties, we performed cloud filtering and only considered the cloud-free segments of the flights. A cloud flag was formulated and activated to in-cloud 20 s before and 20 s after each of the aircraft trajectory data points that show either liquid water content À above 0.02 g/m3 or a total FSSP particle concentration above 25 cm 3. In addition to cloud filtering, this also excluded the near-cloud areas, where high relative humidity values might contribute to aerosol hygroscopic growth and, thus, bias the measurements (Konwar et al., 2015). Regardless, we found that in most cases, the major contribution to column-integrated PSD was made by the cloud-free planetary boundary layer (PBL) segments of the flight, where relative humidity remained well below 80%. The contribution of the flight segments in the free troposphere, where relative humidity could occasionally reach higher values, was insignificant due to a substantially lower concentration (about an order of magnitude) of aerosols.

3. Synoptic Situation and Dust Generation Patterns In this section, we discuss the synoptic conditions that lead to rainfall and dust generation. We based our analysis on the ECMWF operational analysis data set, which we used as the meteorological boundary condi- tions for WRF-Chem. The weather conditions during the research flights described in the previous section were characterized by low surface pressure, strong southwesterly winds, cloudiness, occasional rainfall, and a series of dust events. The low-pressure area stretched from equatorial Africa to cover the whole AP during the event, favoring low-level convergence (Figure 2). On 7 April, a surface low formed over the Eastern Mediterranean, and a well-developed convection is overseen over northern Saudi Arabia, Jordan, and Iraq. That same day, moder- ate convective activity started to develop in the central and southwestern AP and persisted until 12 April. As the low-pressure system propagated eastward on 8 April, an associated cold front passed through the northern AP, bringing moisture from the Mediterranean Sea and initiating strong surface winds, reaching over 15 m/s. Beginning on 9 April, an upper level trough developed over the northern Red Sea (Figure 3),

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Figure 3. Same as Figure 2 but for 1,000 hPa to 500 hPa thickness (gpdm, color) and 500 hPa isosurface geopotential height (gpdm, contour lines).

causing a divergence zone that further reinforced the low-level depression. As the upper layer trough deepened over the next 2 days (10–11 April), the jet streak reached the central AP (Figures S1d–S1f), further enhancing the daytime instability that already existed due to surface heating and low-level convergence. Strong, eastward moving convective storms formed across a broad area of the central AP, sometimes enhanced on lee-side of the Hedjaz Mountains, likely due to the advancing upper wave (Houze, 1993). Despite the thermal low persisting over the Rub’ al Khali, the deep convection was not initiated so far to the south. The strongest deep precipitating cumulus more likely formed in the area under the trough, while only scattered middle- or low-level cloudiness was seen in the Rub’ al Khali (appearing green or yellow in the RGB dust images, Movie S1 in the supporting information). The low- pressure system was clearly identified at the 850-hPa level, reaching its maximum on 11 April (Figure S2). On 12 April, the 850-hPa low retreated to the north, and the upper level trough over the AP weakened. On 11 and 12 April, following the position of the surface low, significant precipitation was generated over Kuwait and the Zagros Mountains. Significant accumulated rainfall was measured by TRMM in the southern Asir mountain range, central AP, Arabian Gulf, and Zagros Mountains on 5–14 April (Figure 4a). The maximum amount was more than 60 mm in some areas of the central AP. Unfortunately, rain gauge observations in this area are irregular and sparse, so it was difficult to use them for detailed verification. The station in Dawadmi reported a single measurement of 70 mm on 14 April. The RAB station reported a total of 23 mm on 9–13 Apri, and only 15 mm was reported at RUH. No flooding was recorded in the nearby cities. Given that annual rainfall in Riyadh is measured to be 90–110 mm and around 150 mm in Gassim (Hasanean & Almazroui, 2015), with the peak months being March and April (Al-Saleh, 1997), we concluded that approximately 50% of the annual norm precipitated during this 10-day event. In general, the precipitation pattern is consistent with spring climatol- ogy, in that the rainfall reached its maxima over the Asir Mountains and formed a wide belt stretching across the central AP (Almazroui, 2011; Babu et al., 2016). A number of recent studies investigated the meteorological processes leading to extreme precipitation in the AP (de Vries et al., 2016; Deng et al., 2015; Kumar et al., 2015). One of the main phenomena leading to extreme precipitation in the Levant and sometimes western Saudi Arabia is the Active Red Sea Trough (ARST) (de Vries et al., 2013; Krichak et al., 2012, and references therein). ARST is a low-level pressure trough, extending from equatorial Africa over the Red Sea toward the eastern Mediterranean. When accompanied by an upper level trough and the Arabian Anticyclone on the east, it causes an anomalous transport of moisture from the Red and Arabian Seas to the north. Upper-air cold advection over moist, unstable air masses causes extreme rain- fall and devastating floods. However, the presence of Red Sea Trough is not a necessary condition for rainfall formation in the Middle East (Tsvieli & Zangvil, 2007). Kumar et al. (2015) has shown that upper-air forcing coupled with low-level cyclonic circulation (not necessarily confined to the Red Sea surroundings) are the

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Figure 4. A 10-day accumulated precipitation (5–14 April 2007) from (a) TRMM, (b) ECMWF operational analysis, (c) ERA-Interim reanalysis, (d) WRF-Chem run without spectral nudging, (e) WRF-Chem run with spectral nudging, (f) MERRA-2 reanalysis.

main drivers or preconditions for heavy rainfall in central AP. The position of the Arabian Anticyclone regulates the moisture transport pathways and the rainfall maxima. In case of an absent or weak Arabian Anticyclone, moist airflow is not channeled alongside the Red Sea but instead spreads with westerlies across most of the AP (de Vries et al., 2016). Apparently, similar mechanisms were present during the April 2007 convection event. In-depth investigation of the moisture sources and pathways requires additional analysis beyond the scope of our current study. However, the backward trajectories shown in Pósfai et al. (2013) for 11 April suggest that the southern Red Sea might be an important moisture source. Therefore, we conclude that the upper-level forcing and surface low-pressure system were the main factors leading to convective activity during the research flights, with the rainfall maxima controlled by the position of the upper air trough and its associated subtropical jet streak. The animation of the METEOSAT RGB pink dust product (Movie S1) reveals the primary patterns of dust gen- eration and transport. Following convection in the northern AP, a cold pool outflow could be traced in the cloud-free area on the evening of 7 April. Dust was likely lifted by the strong winds along the surface front and transported southward by the density current. In the late afternoon the next day, again, strong frontal winds led to forming of a thick dust cloud near the city of Hail that was transported eastward with the winds (SEVIRI retrieved AOD above 1.8). Starting from 8 April and continuing until the end of the event, a series of cold pools and arcus clouds were seen all around the AP. Due to the overcast conditions, it was difficult to trace each individual dust outbreak using satellite imagery. However, in many cases, high dust loadings were identified at the edges of the anvils, which were confirmed by the occasional successful retrievals of high AOD values by SEVIRI. A prominent haboob with an arc cloud transporting dust from the Rub’ al Khali to Yemen was seen in the southern AP at night on 10 April (Figure 5b). Later that same night, another huge dust storm formed near Hail. The dust cloud traveled eastward through Iraq and Kuwait, reaching Iran on 11 April, as documented by Taghavi and Asadi (2008). We cannot definitively attribute the forcing mechanism of this particular dust storm to density currents alone, as it developed in the presence of strong, synoptically driven surface winds and the sharp edge of a gust front was not present. However, cold pools very likely contributed to its development. In the evening on 10 April, a strong haboob was clearly seen passing through central AP and advancing southeast (Figure 5d). The date 11 April was characterized by the strongest convection that led to haboob formation and conditioned AOD above 2.4 in Solar Village

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Figure 5. WRF-Chem aerosol optical depth (AOD) fields (left panel) and METEOSAT dust RGB composites (right panel) with haboob fronts delineated on (a, b) 01.00 UTC 10 April, (c, d) 19.00 UTC 10 April, (e, f) 00.00 UTC 12 April, (g, h) 23.00 UTC 12 April.

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Figure 6. Biases (a–c) and root-mean-square errors (RMSE; d–f) for (a, d) wind speed, (b, e) 2-m temperature, and (c, f) dew point from WRF-Chem run without spectral nudging for 5–14 April 2007.

(Figure 5f). Due to the strong winds reaching 20 m/s, dust was seen forming before the convection developed. On 12 April, as the convective activity reached its southernmost position, a haboob was observed in the Rub’ al Khali (Figure 5h). The combined forcing mechanisms of dust generation in the AP could also be deducted from previous studies. Frontal passages reportedly drive dust generation in the northern AP during springtime (Notaro et al., 2013; Y. Yu, Notaro, et al., 2015). The prevalence of haboob-like dust outbreaks during spring in the central AP was also discussed by Houssos et al. (2015). The authors performed the clustering of synoptic patterns leading to dust outbreaks registered in Solar Village. The main springtime (or, more specifically, March–June) cluster was characterized by the thermal low over most of AP, which is a more common feature of summertime circulation, induced by the influence of South Asian Monsoon (de Vries et al., 2013). Reinforced by the upper layer trough over the northwestern AP and a narrow jet streak, favorable conditions are set for convective storms development. Therefore, Houssos et al. (2015) suggested that the springtime dust outbreaks in the central AP were actually haboobs.

4. Results: Meteorology 4.1. Model Meteorology Verification The primary aim of the modeling study was a realistic simulation of dust events. As we have seen, several synoptic mechanisms are involved in dust generation and transport. Therefore, we need to perform detailed verification of the various model variables. We treat the first 4 days of the simulation as a spin-up period to evaluate the model performance for the 10-day period, 5–14 April. The 10-day model biases and root- mean-square error (RMSE) for temperature, wind speed, and dew point are shown in Figure 6. The model demonstrated an acceptable performance, with slight warm and dry biases. Surface temperature was over- estimated by 0.7 °C and dew point was underestimated by 1.6 °C. The wind speed was overestimated with a mean bias of around 1.6 m/s. The highest errors for temperature and dew point were seen at the coastal stations in the UAE, which did substantially impact the dust evolution in the central AP. More important was the overestimation of the wind speed at the stations in the Sarawat Mountains (Taif, Mecca, Al Baha, Abha, and Bisha). Of the stations in Saudi Arabia, the strongest dry biases were found in the south (Najran) and near the border with Iraq in the north (Arar and Rafha). In the nudged experiment (not shown), the dew point and temperature biases were higher (À1.9 °C and 0.8 °C), whereas RMSE for wind velocity was slightly lower (about 0.2 m/s and 0.2 °C).

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Time series of wind speed, temperature, and dew point at the Riyadh sta- tion are shown in Figure 7. No marked differences between the model per- formances for large and small domains were seen. The model generally overestimated wind speed, but the observed peaks were captured. The model correctly represented the main temperature variations, including a cooling trend at the end of the simulation period. Again, there was a dry bias starting from 9 April, but the variability was in line with the obser- vations. The dew point and temperature biases were partly caused by insufficient local evaporation due to the delayed rainfall onset in the model, which will be discussed later (Figure 7d). High-frequency fluctua- tions were seen in the temperature time series for 7–11 Apri, coinciding with the highest wind speeds (20 m/s). Strong winds bringing cold and moist air reflect cold pool passages, marking the period of convective activity. Visibility reductions and dust storms were reported during this period. Accurate location and timing of convection are requisites for the correct representation of cold pools (Knippertz et al., 2009; Reinfried et al., 2009). As evaporative cooling is the main mechanism driving cold pool outflow formation, the amount of precipitation reaching the ground is of second- ary importance. Knippertz et al. (2009) demonstrated the profound impact of microphysical parameterization on the development of precipitation, whereas the impact of density currents on propagation was much smaller. Indeed, virtually all of the hydrometeors might evaporate before reaching the ground in the deep desert boundary layer (i.e., a virga event), forming an even stronger cold pool (Marsham et al., 2013; Solomos et al., 2012). However, extensive rainfall has the potential to moisten the soil, reducing dust generation (Y. Yu, Notaro, et al., 2015) and to wash out the airborne dust, although it has been postulated that haboobs are not likely to be sca- venged by the parent storm (Williams, 2008). Given that significant rainfall was recorded in our case study, we considered this parameter to be an important indicator of model performance. Figure 7. Time series of (a) wind speed (m/s), (b) 2-m temperature (°C), (c) dew point temperature (°C), (d) accumulated precipitation (mm) from We evaluate the precipitation patterns in WRF-Chem by comparing them WRF-Chem large (BD) and small (SD) domains and RUH station for 5–14 April with those from TRMM and the reanalyses. Figures 4b, 4c, and 4f show that 2007. The precipitation data from TRMM, ECMWF operational analysis, the amount of precipitation over the central AP was not captured in the and RAB station are also shown. large-scale reanalyses. Both ERA-Interim and the ECMWF operational ana- lysis simulated a reasonable amount of rainfall over the Asir Mountains but dramatically underestimated it in the central regions, producing less than 10 mm (averaged across the area of the small model domain). Underestimation of precipitation by ERA-Interim compared to TRMM was not a surprising result as this was also reported in other studies (de Vries et al., 2016). The underestimation of precipitation by MERRA-2 was even larger. The spatial patterns in WRF-Chem simulations were reasonable and in good agreement with both the driving model and TRMM. The averaged amounts from the large and small model domains were 13 and 15 mm for spectrally nudged run, and 20 and 21 mm for the free model run, respectively, that is significantly larger compared to 9 mm from the driving ECMWF operational analysis. The estimate from TRMM was 35 mm. The station measurements from the Riyadh area were significantly lower than the TRMM estimates (15 and 25 mm). The averaged data from gridded data sets covering an area with a 30-km radius, including both the RUH and RAB stations, are shown in Figure 7d. Rainfall in WRF-Chem was approximately double that of the driving reanalysis, falling between the TRMM and station measurements. Given the above, we concluded that free-running 4.5-km convective-resolving regional model simulation successfully represented the amount and spatial pattern of precipitation, doing a good job of increasing the rainfall compared to the driving model. In the nudged simulation, although RMSE for wind and temperature was slightly better, the amount of precipitation was substantially (around 30%) lower. Therefore, we based our analysis on the results from the WRF-Chem simulation without spectral nudging.

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4.2. Convection Statistics The simulated precipitation patterns compared well with observations, suggesting that the model captures the localization of convective activity. A brief examination of the METEOSAT RGB pink dust composite anima- tion and the WRF-Chem evolution fields (the column-integrated hydrometeor content was used to reflect the convective activity) revealed that the model successfully reproduced most of the cloud systems present in observations (Movie S2). The convective activity was possibly underestimated in the eastern Rub’ al Khali, although this is difficult to verify due to a lack of observations. The RGB composite plots did not allow for a quantitative assessment of the convection intensity. To better understand the processes leading to cold pool formation, we performed a fine-scale evaluation of the vertical extent and strength of the convection. Assessing the convection characteristics not only is relevant in the scope of the current study on dust-related processes but also is itself of interest to the modeling community, as a rare case of convection in desert envir- onment hardly resolved by large-scale models.

We applied rather simple methods to evaluate the horizontal and vertical extent of convective cells and assess the vertically integrated liquid, that is, the column water path, in both the model and observations. We did not aim compare individual time frames; instead, we only considered the 10-day integrated charac- teristics within a small, 1.5-km model domain. Ground weather radar is a straightforward and convenient tool for testing the representation of clouds in numerical weather prediction models. It has been used to test microphysical (Caine et al., 2013; Min et al., 2015) and convective parameterization schemes (Niemelä et al., 2005). As the viewing area of the radar was narrow below 1.7 km, we limited our analysis to the model layers centered above this level. The blind cone area of the radar was also excluded from analysis. We started by examining the areal coverage of the convective activity in the model and the radar data. We calculated the average areal fraction of the cells, with the dBZ maxima falling in eight ranges (Figure 8). The most relevant are the dBZ ranges above 40, which are usually associated with well-developed precipitating storms (which was the case for our study as well). The model significantly (by a factor of 3–4) overestimated the areal extent of the intense cells, although this overestimation was smaller for cells >55 dBZ. To test whether the maxi- mum dBZ value was representative of the column-integrated characteristics, we calculated the average VIL values for each dBZ range. Model VIL was defined as the sum of three hydrometeor types (rainwater, snow, and graupel); this estimate was almost equal to the total hydrometeor content of the column introduced earlier, as cloud water and cloud ice constituted only a small mass fraction. The VIL values of dBZ ranges above 40 were similar in the model and the radar data, suggesting that these quantities could be used interchangeably. Accepting 40 dBZ as the threshold for shower onset, the corresponding VIL value was around 3 kg/m2.

The next step was to assess the vertical extent of convection. For this purpose, we applied the Contoured Frequency by Altitude Diagrams (CFAD) method (Yuter et al., 1995). CFADs are the contour lines conjoining the percentiles of the dBZ probability distribution functions calculated at fixed altitudes (Figure 9). The threshold of 25 dBZ corresponds to areas of moderately developed clouds, and 45 dBZ describes the core of precipitating thunderstorm. We calculated CFADs based on the hourly data from profiles whose column maximum dBZ exceeded the threshold value and filtered the data to exclude points with reflectivity values below À16 dBZ. Following Hamada et al. (2015), we also calculated joint histograms of reflectivity against height. The colored values in Figure 9 represent the frequency of occurrence in a single histogram bin.

The CFADs of the model and radar match reasonably well, suggesting that the vertical buildup of the well-developed high storms was correctly represented. The reflectivity maximum was reached at a level of 3–4 km. The model had a much wider reflectivity probability distribution functions than the radar at lower altitudes for 25 dBZ CFAD; the reflectivity tended to have more small values. The histogram plots reflect that the convection height was relatively low over a large portion of the areas where the threshold reflectivity was reached. This was more pronounced for the 45 dBZ threshold: of all the cells where the threshold was reached, the model developed convection above an 8-km altitude in less than 2% of cases; in the radar data, it exceeded this altitude in more than 20%. An analysis of the spatial distribution of the echo top heights (not shown) revealed that the model simulated extensive high reflectivity-low vertical extent zones around the storm cores, and this is one of the constituents of the total 45 dBZ cell areas overestimation (Figure 8). The overestimation of the area of the storms with 45-dBZ echo tops occurring below 4 km was almost 2 times greater than for echo tops above 4 km. The relative underestimation of the 25-dBZ cell heights was much less.

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Figure 8. Areal fractions (with respect to total radar catchment area) of zones with dBZ values falling within 8 ranges and the corresponding vertically-integrated liquid content (VIL) values from radar and WRF-Chem data, averaged over 5–14 April 2007.

Figure 9. Horizontal reflectivity Contoured Frequency by Altitude Diagrams (CFAD) (contour lines) and reflectivity-elevation joint probability histograms (color) from radar (a and b) and WRF-Chem (c and d) for two thresholds: (a, c) dBZ > 25 and (b, d) dBZ > 45 averaged for 5–14 April 2007.

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The reason for these artifact convective structures is unclear. They might be caused by deficiencies in the microphysical parameterization or an evaporation/condensation imbalance. Despite the model’s overestimation of the updraft area, the amount of precipitation reaching the ground was less than estimated by TRMM, suggesting extensive evaporation. This indicates stronger cold pools and is a likely reason the wind velocity was overestimated in Riyadh (Figure 7a) and at the stations in the eastern AP mountains (Figure 6a and 6d). There was no significant difference between the simulated convection heights with and without spectral nudging, despite the precipitation amount being nearly double in the latter. Enhanced rainfall in the model without spectral nudging was likely caused by the increased cell area. Studying this mechanism in more detail requires a more sophisticated observational framework and is beyond the scope of the current paper.

5. Results: Dust Aerosol 5.1. Model AOD Evaluation and Large-Scale Dust Generation and Transport In this section, we analyze the spatial patterns and temporal variability of AOD and the dust generation mechanisms in WRF-Chem and provide a more detailed description of the observational records. To analyze the characteristics of simulated haboobs, we continued to examine the evo- lution of the model in the large domain. Figure 5 shows that the model was able to capture the general features of the dust generation patterns and the largest, well-developed haboobs. We based our quantitative assessment on the ground AOD retrievals. Figure 10 shows the temporal evolution of AOD at four AERONET stations in the AP. MERRA-2 assimi- lates and, thus, was in perfect agreement with AERONET. Three stations in the UAE did not feature strong aerosol loadings, representing almost constant AODs below 0.8, with weak daily cycles and only slight increases in the second half of the period of interest. WRF-Chem captured this increasing trend and was generally in very good agreement with the AERONET/MERRA-2 estimate, only slightly underestimating AOD. The SEVIRI instrument was close to AERONET on average but contained several outlying dust outbreaks, reflecting its tendency to retrieve higher AOD. However, the AOD retrieval in the UAE was performed by SEVIRI at almost-threshold zenith angles. The AOD time series from Solar Village was complemented with visibility measurements and weather code reports from the RUH and RAB stations. Visibility measurements at the two stations were quantitatively equivalent, the only exception being 7 April, confirming their reliability. The evolution Figure 10. Time series of a visibility (m) and primary weather code report of AOD recorded at Solar Village that recorded a number of short-term type (dust [star] or rain [triangle]) in RUH (red line) and RAB (blue line) stations. Time series of aerosol optical depth (AOD) in (a) Solar Village, dust outbreaks was substantially different from that at the UAE stations. (b) Abu Dhabi, (c) Dhadnah, (d) Hamim from AERONET (green circles), SEVIRI Seven peaks were identified in the continuous MERRA-2 record, with most (yellow squares), MERRA-2 (blue line), WRF-Chem large domain (red line) of the peaks mirrored by reduced visibility. Weather code reports indicated and small domain (yellow line). that visibility reductions were all due to dust-no mist or fog was reported. Again, the SEVIRI retrieval was quite high compared to AERONET/MERRA-2. The weather stations reported dust pollution almost every day, but rainfall or drizzling conditions were only observed on 7–11 April, consistent with the previously discussed period of highest wind speeds and temperature and humidity fluctuations. The detected convection indicates that these dust outbreaks should be attributed to local cold pool passages, that is, haboobs. Together with that, the role of background wind forcing cannot be neglected. The dust outbreaks on 8–11 April were the strongest, with AOD reaching over 1.6 in MERRA-2 and SEVIRI, and about 2.4 in AERONET on April 11. During this period, the weather stations

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Figure 11. Aerosol optical depth (AOD) spatial patterns for 7 April 2007 (left panel) and 11 April 2007 (right panel) from (a, e) WRF-Chem, (b, f) MERRA-2, (c, g) SEVIRI, and (d, h) MODIS Aqua.

reported dusty conditions followed by rainfall, and a clear period before dust-conditioned visibility reduction the following day. The model simulated a similar sequence of events, presented in the next section. WRF-Chem successfully captured all of the identified AOD peaks at the same range of magnitudes, with the maximum AOD exceeding 2 on 11 April. The model simulated a slightly early haboob passage and a more rapid decay of dust loading. The model time series had a stronger high-frequency component than expected because the small-scale motions were better captured. In contrast, MERRA-2 was not likely to resolve haboobs. The discrepancy between WRF-Chem and MERRA-2 was within the instrumentation uncertainty of the AERONET and SEVIRI retrievals. Similar to the meteorological variables, no significant difference was inferred between the AOD series of large and small domains. For comparison of simulated AOD spatial patterns with satellite retrievals, we selected two less cloudy cases on 7 and 11 April corresponding to MODIS passage, to have another independent AOD retrieval estimate together with the one from SEVIRI (Figure 11). At 10.00 UTC on 7 April, the developing convection could be identified on the lee side of the Sarawat Mountains (note the blank areas in the satellite AOD). No haboobs were seen, as it was too early for the cold pools to develop. The WRF-Chem AOD pattern was in a very good agreement with both MERRA-2 and the satellites. Widespread dust generation was seen, with dust present all around the AP and extending through the Rub’ al Khali to Oman. Even small-scale outbreaks over the Levant and the western Red Sea coast were captured. The differences between the MODIS and SEVIRI retrievals were significant and demonstrate the instrument uncertainty. As in comparison with AERONET, SEVIRI tended to retrieve higher AODs, while the typical AOD values over the eastern AP in WRF-Chem and MERRA-2 were 0.6–0.7, in MODIS and SEVIRI they were 0.5–0.6 and 0.8–0.9, respectively. The simulated AOD pattern on the peak dust loading day (11 April) was also consistent with the observations and reanalysis. Dust transported from the AP was present over Iraq and eastern Iran. Freshly generated dust could be traced over central Saudi Arabia and seen in the SEVIRI retrieval. MODIS possibly missed it, which would explain the lower AOD in MERRA-2. Several storms were observed later in the day in the vicinity of Riyadh. The haboobs merged, strengthened, and rapidly propagated southeast across large distances of flat desert terrain. Therefore, the largest dust loadings were seen on the eastern flanks of the density currents, where considerable AOD levels were preserved through the following day. Indeed, a prominent feature of our event was series of rapidly moving precipitating storms in a desert environment merging and losing propagation velocity. Similar conditions with multiple superimposed and merged gust fronts were encoun- tered in other studies (S. D. Miller et al., 2008; Raman et al., 2014). Evolving haboobs were easily identified in the model evolution fields by their concentric structures in the AOD distribution (Movie S3). Notably, these concentric structures were completely masked when overridden by hydrometeors (Movie S2). One of these

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Figure 12. A 10-day accumulated (5–14 April 2007) WRF-Chem (a) dust emission, (b) dust dry deposition, and (c) dust wet deposition (g/m2). Total values for area inside the rectangular box are shown in figure title.

haboobs traveled 700–800 km in 12 hr on 11 April (Figure 5e and 5f), and the extent of the front was 400–500 km. This suggests an estimated propagation velocity of over 16 m/s, which is slightly higher than reported for haboobs in the Atlas Mountains (Knippertz et al., 2009) and Sudan (Kalenderski & Stenchikov, 2016) and reflected the presence of an environmental flow. Some weaker haboobs that evolved the next day (12 April) were seen propagating in the same direction. Both MERRA-2 and WRF-Chem underestimated AOD over the western part of Rub’ al Khali, possibly due to undersampling of the previous day’s shallow convection over the desert. Again, significant discrepancies in AOD over Iraq were seen between SEVIRI and MODIS. Accumulated dust emissions and deposition patterns are shown on Figure 12. In total, 25.5 Tg of dust was generated during this 10-day period of convective activity in the central-northern AP. Over 50 g/m2 was generated in the hot spot areas. This estimate is quite high; the annual dust generation from the hot spot areas in the Red Sea coastal plain was recently estimated to be around 100 g/m2. The tendency of the GOCART-MOSAIC scheme to produce higher-end estimates of dust generation is known (Zhao, Chen, et al., 2013a). Of the total, 10.6 Tg of dust was subject to dry deposition, while 7.3 Tg was scavenged by rainfall, suggesting that around 30% of the total generated dust was transported outside of the area of interest. This relatively high fraction of remote transport was due to high wind velocities. The spatial patterns reveal the two primary transport mechanisms that were already discussed. Briefly, a significant amount of dust was transported north to Iraq, Kuwait, and eastern Iran, where it was a subject to both wet and dry deposition. A similar amount was transported southeast to the Rub’ al Khali, where dry deposition was the main removal process. The contribution of wet deposition to total dust removal was over 40%, and its spatial pattern was fully consistent with precipitation. Dust emission and deposition in the small model domain did not differ significantly from those in the equivalent area of the parent domain. Here we do not assess the contribution from haboobs to total dust generation, as this would require a multi- year study applying haboob and cold pool detection methods (Redl et al., 2015). Moreover, the detection methods may be ineffective in an environment where the density currents develop under strong background wind conditions.

5.2. Fine-Scale Structure of Haboobs To describe the fine-scale haboob structure and evolution and the environmental conditions during aircraft flights, we zoom in to the small model domain (Movie S4). We compared the METEOSAT pink dust and radar reflectivity to the model reflectivity, AOD, precipitation, 10-m wind velocity, 2-m temperature, and dust generation. We followed the approach by Knippertz et al. (2009) and marked the vertical velocities exceeding 4 m/s in the lower 700 mbar, which represent the updraft on a leading edge of the density current, to highlight the haboobs. We plotted the aircraft trajectory for each of the four research flights (Figures 13, S3, S4, and S5). Each flight lasted about 2 hr, so the satellite image corresponds to its central time. The goal of the flights was to sample clouds, but they took place before the cumulus reached mature stages.

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Figure 13. (a) METEOSAT dust RGB and aircraft trajectory elevation (m), (b) radar column-maximum reflectivity, (c) WRF-Chem column-maximum reflectivity, (d) WRF-Chem aerosol optical depth (AOD; color) and 10 m/s (blue contour line) 20 m/s (black contour line) 10-m wind speed isotaches, (e) WRF-Chem 1-hr accumulated precipitation (mm, color), wind velocity (vectors), and areas where vertical velocity exceeds 4 m/s in the lowest 700 hPa (red dots), (f) WRF-Chem 2 m temperature (°C, color) and 1-hr accumulated dust generation (g/m2, colored contour lines) at 12.00 UTC on 9 April 2007.

The reflectivity distributions clearly show the overestimation of the area of convective activity in the model that has been already seen in radar statistics (Figure 8). The model captures the observed convection, but the area of the cells is generally higher than in the radar echo. In both the model and the observations, con- vection began on 7 April, when a haboob passed through Riyadh. The research flight that took place in the early afternoon of 7 April (Figure S3) before the haboob arrived, and the flight in the late afternoon of 8 April were referenced in Pósfai et al. (2013) as representing background aerosol loading. However, the conditions on 8 April especially are difficult to characterize as background, as suspended dust was present during the whole day (Figure 10a) and a strong haboob had covered Riyadh for several hours after the flight, which took place during the short, clear window between the passages of cloud systems (Figure S4). Two colliding cold pools were seen in the evening of 8 April, and some haboobs generated in outer large domain were also seen entering and passing through the small domain area. The convection activity on 9 April (Figure 13) and 11 April (Figure S5) is the most interesting, as it occurred in the presence of strong, freshly generated dust loading. Convection occupied most of the area of the small model domain. A haboob passed over Riyadh slightly earlier in the model than it was observed; according to the satellite, the area was still cloud free at 11.00 UTC on 9 April, while it was already overcast from the strong system south of Riyadh in the model. Reflectivity exceeded 50 dBZ in the cores of the strongest cells. Radar-derived reflectivity was apparently lower, but it was shown to be better vertically developed. There is significant amount of precipitation accumulated during previous hour, over 5 mm in some areas. A number of developed density currents were identified by their divergent structures in the 10-m wind fields, with an updraft in the leading edge. Even a small amount of precipitation can cause a cold pool, but here the tem- perature gradient, which ranged from 3 to 4 °C to more than 10 °C, seemed to be dependent on the intensity of rainfall. Wind speeds reached over 20 m/s (Figure 13f). As noted earlier, dust was transported predomi- nantly eastward, and the eastern flanks of the density currents were more clearly pronounced. AOD exceeded 1.8 in the narrow haboob front, and reduced back down to background values after the haboob passed. In

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Figure 14. WRF-Chem vertical cross section along the black line in Figure 13d showing (left panel) dust concentration (μg/m3, color) and wind velocity (vectors), and (right panel) virtual potential temperature (°C, color) and vertical velocity isotaches (every 1 m/s, dashed lines for negative values) at (a, b) 11.00 UTC, (c, d) 12.00 UTC, and (e, f) 13.00 UTC on 9 April 2007.

the case of a significantly precipitating system, the passage of a haboob precedes rainfall. Indeed, the cold pools quickly detached from the precipitation area, suggesting that an immediate washout was not likely À À À À (Heinold et al., 2013). Over 3 g m 2 hr 1 (800 μmm 2 s 1) of dust was generated by the density current passage. Consistent with the weather reports, the air after the haboob passage was clear due to washout by rain. On 11 April (Figure S5), widespread dust generation under strong wind condition was observed. This day is an illustration of a mixture of dust generation mechanisms by both environmental flow and density cur- rents. A strong convective system with a well-defined cold pool manifested on 12 April, but the local dust À À À À generation was weak compared to previous days (less than 1 g m 2 hr 1 [270 μmm 2 s 1]). One likely explanation is that a significant amount of soil moisture had accumulated and limited dust generation. However, a moderate-intensity haboob was seen entering the area from the northeast, both in the model and the observations. To visualize the typical vertical structure of a haboob, the virtual potential temperature, dust concentration, and wind velocities along a cross section (black line on Figure 13d) of the eastward propagating haboob are plotted for three time frames on 9 April (Figure 14). A cold pool began to develop at 11.00 UTC. To the east, there was a developed 3-km PBL with well-mixed dust concentrations of 400–500 μm/m3. A density current formed an hour later (12.00 UTC). The temperature gradient along its frontal line was around 8 °C, and the wind velocity exceeded 20 m/s. Vertical velocity on a haboob front reached 10 m/s, forming a wall of dust that extended 4,000-m high. In this example, the maximum dust concentration (essentially the equivalent of 3 PM10, as other aerosols made negligible contributions) reached 6,000 μm/m . In some other cases, it sur- passed 8,000 μm/m3. The downdraft following the frontal head was due to evaporative cooling and marked the rainfall area. After the haboob passage an hour later, most of the dust was removed by wet deposition, and only a residual layer could be traced. The structure and circulation within a haboob front and the mechanisms of particle uplift and mixing with environmental air were discussed in previous studies (Simpson, 1997; Solomos et al., 2012). We did not aim at a precise analysis of the density current, as it is beyond our scope and requires a separate modeling and observational frameworks. Similar to Solomos et al. (2012) and Vukovic et al. (2014) we note that the maximum dust concentration of a haboob is reached at the surface level within the cold pool and in the frontal head of a density current. Strong updrafts ahead of the frontal head lift environmental air and lead to the formation of a small vortex (i.e., a Kelvin-Helmholtz billow), sometimes appearing as an arc cloud from satellite images (Weckwerth & Wakimoto, 1992). However, neither a flow reversal nor a returning undercur- rent in the lower levels were present in our example. The maximum dust concentration in the frontal head is explained by the combined mechanisms of emission, that is, the density current itself and the uplifting of preexisting environmental particles (Solomos et al., 2012). At the top of the frontal head, enhanced

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Figure 15. Vertical profiles of dust concentration from WRF-Chem fine mode (thin green line), coarse mode (thin blue line), their sum (PM10; thin black line), and total aerosol concentration from aircraft fine mode (thick green line), coarse mode (thick blue line), their sum (PM10) (thick black line), and giant mode (thick red line) on (a) 7 April, (b) 8 April, (c) 9 April, and (d) 11 April 2007. Model data were averaged over the white cross-section line in Figure 13d for 9 April, and over the rectangular box in Figure 13d for 7, 8, and 11 April. PM10 column loading (CL10) and total column loading (CLtot) are shown in figure legend (g/m2).

turbulent updrafts facilitated the injection of dust particles into the free troposphere. Emitted particles were immediately uplifted by turbulent updrafts along the frontal head, which supports prolonged lifetimes of large particles. In the next section, we address the issue of PSD in the model and observations in more detail.

Our model estimate of surface PM10 is much higher than reported in other studies for remote regions (Middleton, 2017) but compares well to PM10 maximum measured during the haboob in Arizona (Vukovic et al., 2014, 9,000 μm/m3) and record-breaking dust storm in Syria (Mamouri et al., 2016, 7,600 μm/m3; Gasch et al., 2017, 6,000 μm/m3). In the next section, we also demonstrate that our model estimates are in good agreement with the aircraft measurements.

5.3. Vertical Profiles of Dust Concentration Vertical profiles of the dust aerosol concentrations along the aircraft trajectory are shown in Figure 15; the blank areas correspond to cloud contamination. Similar to Sodemann et al. (2015), the mass concentrations were calculated separately for three size ranges: 0.1–3 μm (accumulation or fine mode), 3–10 μm (coarse mode), and 10–50 μm (giant mode). The separation between coarse mode and giant mode is done at 10 μm (not 30 μm) due to the MOSAIC cutoff diameter. Thus, the model does not reproduce the giant mode. Moreover, the FSSP maximum diameter range of 50 μm also limited the possible comparisons with TSP (total suspended particle) concentrations reported by some studies. The concentration profiles recorded by the aircraft are shown for the ascending leg of the flight trajectory. For the days of 7, 8, and 11 April, the model profiles were calculated by averaging within the rectangular boxes in Figures S3–S5. The profile on 9 April was calculated along the narrow cross section having the maximum dust concentration (shown with white color in Figure 13). The aircraft measurements of aerosol concentrations varied significantly day to day, reflecting the changing environmental conditions discussed above. Sharp gradients in the profiles denote transitions from the

well-mixed PBL to the free troposphere. Within PBL, PM10 was generally comprised of coarse mode aerosols. 3 On 7 and 8 April, background PM10 values within PBL were around 500 μg/m . The coarse mode concentra- tions tended to drop rapidly in the free troposphere, and the contribution to PM10 (i.e., concentrations <200 μg/m3 and decreasing with altitude) from both fine and coarse modes became roughly equal. On 9 3 3 April, PM10 was the highest and increased with altitude from 800 μg/m at the surface to 2000 μg/m within 3 the PBL. On 10 April 10, PM10 was around 1,000 μm/m , and the dust was well mixed up to 5,500 m, where

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even coarse mode particles existed. Above 5,500 m, the giant mode particles decreased even more rapidly

compared to coarse mode; in PBL, they were at least equal (on 7 and 11 April) or even exceeded PM10 (on 8 and 9 April). The lower bound estimate of the total concentration was, thus, more than 2 times higher than

PM10. Unfortunately, a more accurate estimate of the TSP to PM10 ratio was not possible. The ratio values reported in other studies range from 1.25–1.3 (Mamouri et al., 2016) and 1.2–2.4 (Kandler et al., 2011) in remote regions, and 10–100 near the ground in source regions (W. Chen & Fryrear, 2002; Kandler et al., 2009). The main features of model profiles of aerosol concentration are in line with measurements. The modeled above-PBL gradient is not very sharp because the profiles were obtained by averaging a large number of grid

boxes with variable mixing heights. The typical PM10 concentration from the model in PBL was 200– 300 μg/m3 on background days, and 600–700 μg/m3 on 11 April. Within the haboob on 9 April, we averaged a smaller number of grid boxes and the PBL gradient on 4-km could be identified more clearly. In the model, 3 3 PM10 decreased from 6,000 μm/m at the surface to 600–700 μg/m within the PBL. In the observations, the PM10 increased within PBL, reflecting that the aircraft entered the approaching gust front after takeoff. This range of discrepancy was expected; as the aircraft profiles were point measurements, direct correspondence with the model could not be achieved. It could be seen that whereas the fine mode concentration was well captured by the model, the coarse mode

(and, therefore, PM10) was underestimated. The absolute underestimation of PM10 became more evident under high dust loading conditions on 9 April. Further analysis revealed that PM10 underestimation was due to lower coarse-to-fine mode ratio in the model compared to the aircraft measurements. Indeed, it was quite stable within PBL both in the model (2.5–3.7) and in the observations (4.0–6.2), suggesting that

the coarse mode was underestimated by a factor of 1.5–2. Model PM10 might reach high values, and even exceed those observed from the aircraft in some segments of the aircraft trajectory (like the profiles below 2,000 m on 9 and 11 April). However, the coarse-to-fine mode ratio underestimation stayed within the same

ranges, reflecting that local PM10 overestimation is related to the magnitude of PSD, not its shape. These find- ings are further elaborated in the next section, where normalized PSDs are analyzed. The dust column loadings were also calculated for each of the cases (shown on figure legend); however, the aircraft measurements were uncertain due to cloud contamination, and the column loading calculations were highly sensitive to the MOSAIC cutoff diameter. For example, on 9 April, the total column loading 2 2 (17.3 g/m ) was almost three times higher than the PM10 column loading (6.0 g/m ). The lower bound of model PM10 column loading underestimation is a factor of 1.5, which it consistent with aforementioned coarse-to-fine mode ratio underestimation. Making robust conclusions about the dust concentrations in the free troposphere is difficult based on our data, as the flights were not aimed at sampling concentration profiles far above 5,000 m.

5.4. Column-Integrated Particle Size Distributions For a more detailed analysis of the aerosol properties, we calculated the column-integrated number PSDs from the same data samples used in Figure 15, and complemented it with PSD taken from the AERONET inversion product at the times closest to the flight. The raw PSDs for the full instrument size range are given in Figure S6. We combined PSD from all three aircraft instruments. In the overlapping DMA and PCASP size ranges (0.1–0.4 μm), only the DMA data are shown. Due to cloud contamination, PSD from AERONET was not available for 11 April. MOSAIC PSDs are shown for all aerosols and for dust aerosol separately. PSD from the haboob front on 9 April (as in Figure 15c) is shown together with the PSD averaged from the data within the rectangular box on Figure 13c. Pósfai et al. (2013) concentrated on PSD from two samples taken during the campaign on 8 and 9 April. In their report, PSD was modeled using four lognormal modes that corresponded to the distribution peaks. The same peaks are seen in the column-averaged PSD (Figure S6). Three of the coarsest modes (peaks at ~0.8, 2, and 10 μm) likely corresponded to different populations of dust aerosols (Kandler et al., 2011), includ- ing aged dust, chemically transformed by interaction with other species. The model formulation did not allow us to capture these PSD modes present in the aircraft data, as dust aging was not reproduced. In the model data, the particles less than 0.3 μm in diameter were primarily of nondust origins, whereas particles above 0.3 μm were fully comprised of dust. Two peaks were identified in the dust PSD from the model. The peak

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Figure 16. Normalized particle size distribution (PSD) from AERONET (blue), aircraft (green), WRF-Chem (red), haboob front (cyan), and prescribed at emission (black) on (a) 7 April, (b) 8 April, (c) 9 April, and (d) 11 April 2007. Model data were averaged over the white cross-section line in Figure 13d for the haboob case, and over the rectangular box in Figure 13d for the other cases. Vertical lines denote standard deviation intervals.

in the finest bin was due to the contribution of nucleated sulfate particles from anthropogenic emissions. PSD of dust aerosol peaked in the fourth (~0.3–0.6 μm) and fifth bins (~0.6–1.25 μm). AERONET measurements are point measurements and have a large number of instrumental uncertainties (especially important for the measurements in the coarse range) (McConnell et al., 2008). Aircraft observa- tions of aerosol PSD may have artifacts in cloud segments. Therefore, direct comparison with the model PSD in terms of absolute values was not possible. The absolute number concentrations were constrained by the total AOD, which was shown to be in good agreement between model and observations. Given the aims of our study, we focused our attention on the size range where dust aerosol made its primary contribu- tion; thus, we normalized the data by the total number of particles in the size range 0.5–10 μm. The normal- ized PSD are shown in Figure 16. The plot for 9 April was complemented by the normalized theoretical size distribution prescribed at emission (Kok, 2011a). According to all data sources, PSD did not vary significantly during 7–11 April. However, under high loading conditions on 9 April, the contribution from the coarse particles became stronger in the aircraft sample in

agreement with the increase in PM10 concentrations discussed in the previous paragraph. PSD from AERONET broadly agreed with the aircraft data in the optically active range but failed to capture the observed maxima. The tail at the high end of the PSD size range was greatly underestimated, especially under the high loading conditions. The tendency of PSD obtained from AERONET inversion to be small was widely discussed in other studies (McConnell et al., 2008; Müller et al., 2010; Ryder et al., 2015; Schepanski, Tegen, & Macke, 2009a, and references therein). One of the additional explanations relevant to our case is that the AERONET retrievals were done in the early phase of the flights, before the daily dust maximum.

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The main PSD features from the model are in agreement with the aircraft data. However, as it has been mentioned, the model formulation did not allow the complex modal structure of the observed PSDs to be reproduced. The model PSD followed the one prescribed at emission (Kok, 2011a) (Figure 16c) and lacked the peak at 2–3 μm, which is present in aircraft data. The particle concentrations from the model in the

coarsest size bin (5–10 μm) were too low, and it is the reason behind the underestimation of PM10, which became most pronounced on 9 April. When we shifted to the volume size distribution (not shown), we saw that the coarsest size bin made a primary contribution to the dust mass loading. Figure 16c reveals that this is primarily caused by the fixed theoretical PSD of emitted dust. The model PSD within the haboob (Figure 16c) was closer to the observed one, but even the theoretical emission PSD underestimated the number of coarse particles. However, despite these significant discrepancies, the model PSD was closer to the aircraft measurements than the AERONET inversion PSD. Significant amounts of coarse particles (>5–10 μm) in desert dust samples were recorded by most observa- tional campaigns, both from airborne (McConnell et al., 2008; Ryder et al., 2013; Weinzierl et al., 2009) and ground measurements (Allen et al., 2013; Kandler et al., 2009). However, since coarse particles have much larger sedimentation velocities, their concentrations are subject to a great variability and may vary by several orders of magnitude even within the same campaign (Kandler et al., 2009; Mahowald et al., 2014). At the same time, modeling studies have a general tendency to underestimate the coarse aerosol mode and, conse-

quently, the PM10 concentrations in both source and remote regions (Gasch et al., 2017; Laurent et al., 2008; Sodemann et al., 2015; Vukovic et al., 2014). A number of explanations for this underestimation have been proposed, such as uncertainties in the parameterization of dust sources may lead to erroneous dust transport pathways, or imperfect sedimentation parameterizations. However, our results showed that the main cause might be related to the PSD fixed at emission. Indeed, according to the brittle fragmentation theory, the upper bound of the particle diameter with fixed emission PSD invariant to soil type and state is uncertain, though Kok (2011b) estimated the threshold particle diameter of 5 μm, and Mahowald et al. (2014) compiled in situ measurements from a number of campaigns that confirmed this estimate. The PSDs for coarser particles were highly variable and were claimed to exhibit a stronger dependence on soil properties and state. Our results agreed with these findings: the model underestimated the concentration of coarse particles (>5 μm) compared to the observational samples, especially under strong wind conditions.

6. Conclusions In this study, we presented cloud-resolving aerosol-interactive simulations of intense convective events and their associated haboob dust storms over the central AP in April 2007. The spring precipitation maximum is a steady climatic feature in the AP, and the AOD peak during spring and early summer is likely associated with haboobs caused by density current outflows resulting from evaporating precipitation. Haboob dust storms have long been recognized as responsible for a substantial fraction of dust emission; however, they are not well represented in most of the current modeling studies. There are not many studies on haboobs in general, and none so far on haboobs in the AP. Few of the existing studies have used fully interactive aerosol models. Here we aimed to reduce this gap. We documented the environmental conditions that led to a series of convective events and haboob formation and applied an advanced set of observations to perform an in-depth evaluation of one of the most sophisticated coupled models currently available. We combined aircraft and radar measurements collected during a rainfall enhancement campaign and made use of most of the available satellite products and ground measurements, to analyze the spatiotemporal characteristics of convection and associated haboobs. We tested two WRF-Chem model configurations: with and without spectral nudging towards the driving reanalysis fields. Both model configurations used two one-way nested convective-resolving domains with spatial resolution of 4.5 and 1.5 km. While we targeted dust storms caused by cold pool outflows, our primary interest was to properly capture the convection location and the amount of precipitation. We demonstrated that coarse-resolution reanalysis products were either not able to reproduce the observed amount of rainfall (ERA-Interim and the driving ECMWF operational analysis), or completely missed the event (MERRA-2). We found out that both WRF-Chem configurations demonstrated an acceptable performance with a similar range of meteorological biases and satisfactorily reproduced the amount and pattern of rainfall well compared to the TRMM observations. However, the free-running WRF-Chem simulation performed better

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in capturing the amount of rainfall. Although the amount of rainfall was still underestimated compared to the TRMM observations, it was approximately double that of the driving reanalysis, and about 30% higher that of the nudged WRF-Chem simulation. The underestimation was likely to be linked to the dry bias in the model: the dew point was underestimated by 1.6 °C, and the surface temperature was overestimated by 0.7 °C. The wind speed was slightly overestimated with a mean bias of around 1.6 m/s1. To evaluate the three-dimensional structure of the convective clouds, we examined Doppler radar measure- ments. We found out that the model significantly overestimated the area of the storms, primarily due to an overestimation of the weakly developed convective areas (below 4 km) surrounding the deep cells. The area of well-developed updrafts was also moderately overestimated. However, the model CFAD matched reason- ably well with the radar data, suggesting that the vertical build-up of deep storms was correctly represented, with reasonable VIL values. Large-scale spatial patterns of cloud systems were also successfully simulated. The underestimation of precipitation compared to the observations in the presence of larger amount of hydrometeors might suggest stronger evaporation and, consequently, stronger cold pools, which would explain the wind velocity bias. We conclude that WRF-Chem was able to successfully capture the major dust outbreaks present in the AERONET record, the SEVIRI satellite measurements, and the MERRA-2 aerosol reanalysis. The model simu- lated a reasonable range of AOD magnitudes both in Solar Village and remote stations in the UAE. The timing of the maximum AOD values (>2) on 11 April was also captured. In general, the discrepancy between WRF- Chem and the MERRA-2 AOD time series was within instrumentation uncertainty between the AERONET and SEVIRI retrievals; the SEVIRI retrievals measured substantially larger AOD values. WRF-Chem also showed a good representation of the AOD spatial patterns, being consistent with MERRA-2, MODIS, and SEVIRI. The model simulated major dust transport pathways (to Kuwait, Iran, and the southern AP) and captured the spatial characteristics of haboobs when they could be distinguished from the satellite data. The fine-scale structure of convection and its associated cold pools creates a complex, rapidly changing local pattern of colliding and merging haboobs, which leads to substantial variability in modeled AOD time series. Even small amounts of precipitation can cause a cold pool with the potential to elevate dust. In WRF-Chem, the temperature gradient on a leading edge seemed dependent on the rainfall intensity and ranged from 3 to 4 °C up to more than 10 °C, with wind speeds reaching over 20 m/s1. The vertical velocity on a haboob front 1 reached 10 m/s , forming a wall of dust that extended vertically to more than 4,000 m. The maximum PM10 values of 6,000–8,000 μm/m3 might occur at the surface, but only for a very short period of time. Although the model tended to predict an earlier onset of convection, the course of events in the model reasonably corresponded to the observations. The model captured the combined mechanisms of dust generation, that is, large-scale winds and density currents, as well as the rainfall that cleaned the air after haboob passage and restored AOD to its background values. The distance traveled by the largest haboobs was up to 700–800 km in 12 hr, and the extent of the front was 400–500 km. The estimated propagation velocity was over 16 m/s toward the east and southeast, reflecting the environmental flow. The large number of haboobs of various strengths and sizes caused by this multiday convective event was an inherent feature of our case study. Most of the other haboob studies aimed to model single or few individual events. No significant differences were found between the child (1.5 km) and parent (4.5 km) model domains, con- sistent with Solomos et al. (2012), who claimed that a 4.8-km resolution was sufficient to resolve convective downdrafts and that a grid spacing of 8 km was the borderline resolution in the presence of weak density currents. They reported that switching to a 2.4-km or 800-m resolution did not significantly improve down- draft intensity and cold pool strength, although it could lead to better fine-scale density current dynamics and the associated dust generation. However, it is unclear whether a 4- to 5--km spatial resolution is sufficient to represent convective downdrafts in a general case. Spatial resolutions of 1 km or better are desirable for a more detailed convection representation, although the added value is strongly case dependent and difficult to evaluate (Schwartz et al., 2009, 2017). In our case, we demonstrated that a 4.5-km resolution was sufficient to capture the convection timing and location, which are the main prerequisites for a reliable simulation of haboob evolution, confirming the findings by Knippertz et al. (2009) and Reinfried et al. (2009). Despite its simplicity, the bulk dust emission scheme generated a reasonable amount of dust in the high-resolution modeling setup very well. However, our ability to validate the local-scale dust patterns was limited, as our observational framework did not feature enough ground stations.

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Comparisons between the modeled dust concentrations and observed PSD measured by the aircraft were complicated by the very high spatial and temporal heterogeneity of dust loading in the model. In contrast to most of the dust observational campaigns, the flight trajectory did not feature long horizontal legs on fixed altitudes but rather performed vertical profiling in a limited, horizontal area. Therefore, we decided to com- pare the observed vertical profiles and column-integrated size distribution with the model results and the AERONET inversion PSD product.

The typical measured values of PM10 within PBL (above the surface layer) under high local dust production were around 2,000 μg/m3, compared to 500 μg/m3 in the background (suspended dust) conditions, with the primary contribution coming from coarse mode dust (>3 μm). While the model captured the fine mode

(<3 μm, almost equivalent to PM2.5) dust concentration reasonably well, the concentration of the coarse mode (3–10 μm, almost equivalent to PM10) was underestimated by a factor of 1.5–2. PM10 underestimation was mainly manifested in the coarsest model bin (5–10 μm), which became the most pronounced under high loading conditions. Also, the model formulation did not allow the PSD modes present in the aircraft data, corresponding to different population of dust aerosols, to be captured. The reason is likely twofold. First, we did not use the aerosol boundary condition and might have underestimated the remotely transported dust component. However, there were no major remote dust transport from Sahara during the time of the campaign. Second, which is probably more important, the model did not have dust aging mechanisms, and the water uptake did not transfer particles among bins. The reason behind the underestimation of the coarse mode particles is the prescribed emission PSD. We attribute this feature to the limitation of the brittle fragmentation theory, which has an uncertain upper bound for particle diameter (estimated to be 5 μm) for which a fixed emission PSD invariant to soil type and state could be formulated. Except for the aforemen- tioned discrepancies, the model and aircraft PSD were in good agreement and within the range of differences between the aircraft and AERONET data. We estimate that around 25.5 Tg of dust was emitted in the central-northern AP during the 10-day period of convective activity. Over 50 g/m2 was generated in the hot spot areas. The 10.6 Tg of dust was subject to dry Acknowledgments The research reported in this deposition, while 7.3 Tg was scavenged by rainfall, suggesting that around 30% of the total dust generated publication was supported by funding was transported outside the area of interest. The contribution of wet deposition to total dust removal was from King Abdullah University of over 40%. We have shown that the contribution from particles >10 μm to the total amount of dust generated Science and Technology (KAUST). For computer time, this research used the in the source regions was substantial. In our case study, the mass concentration of particles >10 μm was at resources of the Supercomputing least equal to or greater than PM10. The lower bound of the maximum TSP column dust loading was Laboratory at KAUST. We kindly thank 2 2 Helen Brindley from Imperial College, measured to be 17 g/m compared to 6 g/m for PM10. Thus, we stress that these estimates should be treated London, for providing the SEVIRI AOD very carefully when compared to other modeling studies. When dust generation in the model is discussed or data. The WFR-Chem source code, concentrations are compared to observations in the source region, the model cutoff diameter and highly initial, chemical, and boundary condi- tions, and a sample of output files uncertain coarse mode PSD should be taken into account. necessary to test and reproduce the simulations are publicly available through KAUST Repository at http://hdl. References handle.net/10754/628030. ERA-Interim Abdelkader, M., Metzger, S., Steil, B., Klingmüller, K., Tost, H., Pozzer, A., et al. (2017). Sensitivity of transatlantic dust transport to chemical and ECMWF operational analysis data aging and related atmospheric processes. Atmospheric Chemistry and Physics, 17(6), 3799–3821. https://doi.org/10.5194/acp-17-3799- were obtained through the ECMWF data 2017 portal:http://apps.ecmwf.int/archive- Alizadeh-Choobari, O., Zawar-Reza, P., & Sturman, A. (2012). Atmospheric forcing of the three-dimensional distribution of dust particles over catalogue/?class=ei. MERRA-2 data were Australia: A case study. Journal of Geophysical Research, 117, D11206. https://doi.org/10.1029/2012JD017748 obtained using the Web-based subset Allen, C. J. T., Washington, R., & Engelstaedter, S. (2013). Dust emission and transport mechanisms in the Central Sahara: Fennec ground- tool: https://disc.gsfc.nasa.gov/daac- based observations from Bordj Badji Mokhtar, June 2011. Journal of Geophysical Research: Atmospheres, 118, 6212–6232. https://doi.org/ bin/FTPSubset2.pl. AERONET data were 10.1002/jgrd.50534 obtained from: https://aeronet.gsfc. Allen, C. J. T., Washington, R., & Saci, A. (2015). Dust detection from ground-based observations in the summer global dust maximum: Results nasa.gov/. MODIS data were obtained from Fennec 2011 and 2012 and implications for modeling and field observations. Journal of Geophysical Research: Atmospheres, 120, from LAADS website: https://ladsweb. 897–916. https://doi.org/10.1002/2014JD022655 modaps.eosdis.nasa.gov/. TRMM RT Almazroui, M. (2011). Calibration of TRMM rainfall climatology over Saudi Arabia during 1998–2009. Atmospheric Research, 99(3–4), 400–414. data were obtained from https://disc2. https://doi.org/10.1016/J.ATMOSRES.2010.11.006 gesdisc.eosdis.nasa.gov/data/TRMM_ Al-Saleh, M. A. (1997). Variability and frequency of daily rainfall in Riyadh, Saudi Arabia. Geographical Bulletin - Gamma Theta Upsilon, 39(1), RT/TRMM_3B42RT.7/. Weather station 48–57. data were retrieved using the NCDC Andreae, M. O., & Rosenfeld, D. (2008). Aerosol–cloud–precipitation interactions. Part 1. The nature and sources of cloud-active aerosols. Web tool: https://gis.ncdc.noaa.gov/ Earth-Science Reviews, 89(1–2), 13–41. https://doi.org/10.1016/j.earscirev.2008.03.001 maps/ncei/cdo/hourly. SEVIRI data for Anisimov, A., Tao, W., Stenchikov, G., Kalenderski, S., Prakash, P. J., Yang, Z.-L., et al. (2017). Quantifying local-scale dust emission from the RGB components were obtained from Arabian Red Sea coastal plain. Atmospheric Chemistry and Physics, 17(2), 993–1015. https://doi.org/10.5194/acp-17-993-2017 EUMETSAT data portal: https://eoportal. Archer-Nicholls, S., Lowe, D., Darbyshire, E., Morgan, W. T., Bela, M. M., Pereira, G., et al. (2015). Characterising Brazilian biomass burning eumetsat.int/. SEVIRI AOD data were emissions using WRF-Chem with MOSAIC sectional aerosol. Geoscientific Model Development, 8(3), 549–577. https://doi.org/10.5194/gmd- prepared at Imperial College by request. 8-549-2015

ANISIMOV ET AL. 12,173 Journal of Geophysical Research: Atmospheres 10.1029/2018JD028486

Babu, C. A., Jayakrishnan, P. R., & Varikoden, H. (2016). Characteristics of precipitation pattern in the Arabian Peninsula and its variability associated with ENSO. Arabian Journal of Geosciences, 9(3), 186. https://doi.org/10.1007/s12517-015-2265-x Baker, A. R., Laskina, O., & Grassian, V. H. (2014). Processing and ageing in the atmosphere. In P. Knippertz, & J.-B. W. Stuut (Eds.), Mineral Dust: A Key Player in the Earth System, (pp. 75–92). Dordrecht: Springer Netherlands. doi:https://doi.org/10.1007/978-94-017-8978-3_4 Balkanski, Y., Schulz, M., Claquin, T., & Guibert, S. (2007). Reevaluation of mineral aerosol radiative forcings suggests a better agreement with satellite and AERONET data. Atmospheric Chemistry and Physics, 7(1), 81–95. https://doi.org/10.5194/acp-7-81-2007 Banks, J. R., & Brindley, H. E. (2013). Evaluation of MSG-SEVIRI mineral dust retrieval products over North Africa and the Middle East. Remote Sensing of Environment, 128,58–73. https://doi.org/10.1016/j.rse.2012.07.017 Barnard, J. C., Chapman, E. G., Fast, J. D., Schmelzer, J. R., Slusser, J. R., & Shetter, R. E. (2004). An evaluation of the FAST-J photolysis algorithm for predicting nitrogen dioxide photolysis rates under clear and cloudy sky conditions. Atmospheric Environment, 38(21), 3393–3403. https://doi.org/10.1016/j.atmosenv.2004.03.034 Basart, S., Vendrell, L., & Baldasano, J. M. (2016). High-resolution dust modelling over complex terrains in West Asia. Aeolian Research, 23, 37–50. https://doi.org/10.1016/j.aeolia.2016.09.005 Binkowski, F. S., & Shankar, U. (1995). The regional particulate matter model: 1. Model description and preliminary results. Journal of Geophysical Research, 100(D12), 26191. https://doi.org/10.1029/95JD02093 Blechschmidt, A.-M., Kristjánsson, J. E., Ólafsson, H., Burkhart, J. F., Hodnebrog, Ø., & Rosenberg, P. D. (2012). Aircraft-based observations and high-resolution simulations of an Icelandic dust storm. Atmospheric Chemistry and Physics, 12(22), 10,649–10,666. https://doi.org/10.5194/ acp-12-10649-2012 Bou Karam, D., Williams, E., Janiga, M., Flamant, C., McGraw-Herdeg, M., Cuesta, J., et al. (2014). Synoptic-scale dust emissions over the Sahara Desert initiated by a moist convective cold pool in early August 2006. Quarterly Journal of the Royal Meteorological Society, 140(685), 2591–2607. https://doi.org/10.1002/qj.2326 Bou Karam Francis, D., Flamant, C., Chaboureau, J.-P., Banks, J., Cuesta, J., Brindley, H., et al. (2017). Dust emission and transport over Iraq associated with the summer Shamal winds. Aeolian Research, 24,15–31. https://doi.org/10.1016/J.AEOLIA.2016.11.001 Brindley, H. E., & Russell, J. E. (2009). An assessment of Saharan dust loading and the corresponding cloud-free longwave direct radiative effect from geostationary satellite observations. Journal of Geophysical Research, 114, D23201. https://doi.org/10.1029/2008JD011635 Caine, S., Lane, T. P., May, P. T., Jakob, C., Siems, S. T., Manton, M. J., et al. (2013). Statistical assessment of tropical convection-permitting model simulations using a cell-tracking algorithm. Monthly Weather Review, 141(2), 557–581. https://doi.org/10.1175/MWR-D-11- 00274.1 Cavazos-Guerra, C., & Todd, M. C. (2012). Model simulations of complex dust emissions over the sahara during the west african monsoon onset. Advances in Meteorology, 2012,1–17. https://doi.org/10.1155/2012/351731 Chapman, E. G., Gustafson, W. I., Easter, R. C., Barnard, J. C., Ghan, S. J., Pekour, M. S., et al. (2009). Coupling aerosol-cloud-radiative processes in the WRF-Chem model: Investigating the radiative impact of elevated point sources. Atmospheric Chemistry and Physics, 9(3), 945–964. https://doi.org/10.5194/acp-9-945-2009 Chen, S., Huang, J., Zhao, C., Qian, Y., Leung, L. R., & Yang, B. (2013). Modeling the transport and radiative forcing of Taklimakan dust over the Tibetan Plateau: A case study in the summer of 2006. Journal of Geophysical Research: Atmospheres, 118, 797–812. https://doi.org/10.1002/ jgrd.50122 Chen, W., & Fryrear, D. W. (2002). Sedimentary characteristics of a haboob dust storm. Atmospheric Research, 61(1), 75–85. https://doi.org/ 10.1016/S0169-8095(01)00092-8 Chin, M., Ginoux, P., Kinne, S., Torres, O., Holben, B. N., Duncan, B. N., et al. (2002). Tropospheric aerosol optical thickness from the GOCART model and comparisons with satellite and sun photometer measurements. Journal of the Atmospheric Sciences, 59(3), 461–483. https://doi. org/10.1175/1520-0469(2002)059<0461:TAOTFT>2.0.CO;2 de Vries, A. J., Feldstein, S. B., Riemer, M., Tyrlis, E., Sprenger, M., Baumgart, M., et al. (2016). Dynamics of tropical-extratropical interactions and extreme precipitation events in Saudi Arabia in autumn, winter and spring. Quarterly Journal of the Royal Meteorological Society, 142(697), 1862–1880. https://doi.org/10.1002/qj.2781 de Vries, A. J., Tyrlis, E., Edry, D., Krichak, S. O., Steil, B., & Lelieveld, J. (2013). Extreme precipitation events in the Middle East: Dynamics of the Active Red Sea Trough. Journal of Geophysical Research: Atmospheres, 118, 7087–7108. https://doi.org/10.1002/jgrd.50569 Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., et al. (2011). The ERA-interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137(656), 553–597. https://doi.org/ 10.1002/qj.828 Deng, L., McCabe, M. F., Stenchikov, G., Evans, J. P., Kucera, P. A., Deng, L., et al. (2015). Simulation of flash-flood-producing storm events in Saudi Arabia using the weather research and forecasting model. Journal of Hydrometeorology, 16(2), 615–630. https://doi.org/10.1175/ JHM-D-14-0126.1 Dubovik, O., & King, M. D. (2000). A flexible inversion algorithm for retrieval of aerosol optical properties from Sun and sky radiance mea- surements. Journal of Geophysical Research, 105(D16), 20,673–20,696. https://doi.org/10.1029/2000JD900282 Dubovik, O., Sinyuk, A., Lapyonok, T., Holben, B. N., Mishchenko, M., Yang, P., et al. (2006). Application of spheroid models to account for aerosol particle nonsphericity in remote sensing of desert dust. Journal of Geophysical Research, 111, D11208. https://doi.org/10.1029/ 2005JD006619 Easter, R. C., Ghan, S. J., Zhang, Y., Saylor, R. D., Chapman, E. G., Laulainen, N. S., et al. (2004). MIRAGE: Model description and evaluation of aerosols and trace gases. Journal of Geophysical Research, 109, D20210. https://doi.org/10.1029/2004JD004571 Edgell, H. S. (2006). Arabian deserts. Arabian Deserts: Nature, origin, and evolution. Dordrecht: Springer Netherlands. doi:https://doi.org/ 10.1007/1-4020-3970-0 Engelstaedter, S., Tegen, I., & Washington, R. (2006). North African dust emissions and transport. Earth-Science Reviews, 79(1–2), 73–100. https://doi.org/10.1016/j.earscirev.2006.06.004 Evan, A. T., Flamant, C., Fiedler, S., & Doherty, O. (2014). An analysis of aeolian dust in climate models. Geophysical Research Letters, 41, 5996–6001. https://doi.org/10.1002/2014GL060545 Fahey, K. M., & Pandis, S. N. (2001). Optimizing model performance: Variable size resolution in cloud chemistry modeling. Atmospheric Environment, 35(26), 4471–4478. https://doi.org/10.1016/S1352-2310(01)00224-2 Fast, J. D., Gustafson, W. I., Easter, R. C., Zaveri, R. A., Barnard, J. C., Chapman, E. G., et al. (2006). Evolution of ozone, particulates, and aerosol direct radiative forcing in the vicinity of Houston using a fully coupled meteorology-chemistry-aerosol model. Journal of Geophysical Research, 111, D21305. https://doi.org/10.1029/2005JD006721 Gabbi, J., Huss, M., Bauder, A., Cao, F., & Schwikowski, M. (2015). The impact of Saharan dust and black carbon on albedo and long-term mass balance of an Alpine glacier. The Cryosphere, 9(4), 1385–1400. https://doi.org/10.5194/tc-9-1385-2015

ANISIMOV ET AL. 12,174 Journal of Geophysical Research: Atmospheres 10.1029/2018JD028486

García, O. E., Díaz, J. P., Expósito, F. J., Díaz, A. M., Dubovik, O., Derimian, Y., et al. (2012). Shortwave radiative forcing and efficiency of key aerosol types using AERONET data. Atmospheric Chemistry and Physics, 12(11), 5129–5145. https://doi.org/10.5194/acp-12-5129-2012 Garcia-Carreras, L., Marsham, J. H., Parker, D. J., Bain, C. L., Milton, S., Saci, A., et al. (2013). The impact of convective cold pool outflows on model biases in the Sahara. Geophysical Research Letters, 40, 1647–1652. https://doi.org/10.1002/grl.50239 Gasch, P., Rieger, D., Walter, C., Khain, P., Levi, Y., Knippertz, P., et al. (2017). Revealing the meteorological drivers of the September 2015 severe dust event in the eastern Mediterranean. Atmospheric Chemistry and Physics, 17(22), 13,573–13,604. https://doi.org/10.5194/acp- 17-13573-2017 Ghan, S. J., & Easter, R. C. (2006). Impact of cloud-borne aerosol representation on aerosol direct and indirect effects. Atmospheric Chemistry and Physics, 6(12), 4163–4174. https://doi.org/10.5194/acp-6-4163-2006 Ghan, S. J., & Zaveri, R. A. (2007). Parameterization of optical properties for hydrated internally mixed aerosol. Journal of Geophysical Research, 112, D10201. https://doi.org/10.1029/2006JD007927 Gherboudj, I., Naseema Beegum, S., & Ghedira, H. (2017). Identifying natural dust source regions over the Middle-East and North-Africa: Estimation of dust emission potential. Earth-Science Reviews, 165, 342–355. https://doi.org/10.1016/j.earscirev.2016.12.010 Ginoux, P., Prospero, J. M., Gill, T. E., Hsu, N. C., & Zhao, M. (2012). Global-scale attribution of anthropogenic and natural dust sources and their emission rates based on MODIS deep blue aerosol products. Reviews of Geophysics, 50, RG3005. https://doi.org/10.1029/2012RG000388 Goudie, A. S. (2014). Desert dust and human health disorders. Environment International, 63, 101–113. https://doi.org/10.1016/j. envint.2013.10.011 Goudie, A. S., & Middleton, N. J. (2006). Desert dust in the global system. Berlin Heidelberg: Springer. doi:https://doi.org/10.1007/3-540-32355-4 Grell, G. A., Freitas, S. R., Stuefer, M., & Fast, J. (2011). Inclusion of biomass burning in WRF-Chem: Impact of wildfires on weather forecasts. Atmospheric Chemistry and Physics, 11(11), 5289–5303. https://doi.org/10.5194/acp-11-5289-2011 Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock, W. C., et al. (2005). Fully coupled “online” chemistry within the WRF model. Atmospheric Environment, 39(37), 6957–6975. https://doi.org/10.1016/j.atmosenv.2005.04.027 Gustafson, W. I., Chapman, E. G., Ghan, S. J., Easter, R. C., & Fast, J. D. (2007). Impact on modeled cloud characteristics due to simplified treatment of uniform cloud condensation nuclei during NEAQS 2004. Geophysical Research Letters, 34, L19809. https://doi.org/10.1029/ 2007GL030021 Hamada, A., Takayabu, Y. N., Liu, C., & Zipser, E. J. (2015). Weak linkage between the heaviest rainfall and tallest storms. Nature Communications, 6(1), 6213. https://doi.org/10.1038/ncomms7213 Hamidi, M., Kavianpour, M. R., & Shao, Y. (2013). Synoptic analysis of dust storms in the Middle East. Asia-Pacific Journal of Atmospheric Sciences, 49(3), 279–286. https://doi.org/10.1007/s13143-013-0027-9 Hasanean, H., & Almazroui, M. (2015). Rainfall: Features and variations over Saudi Arabia, a review. Climate, 3(3), 578–626. https://doi.org/ 10.3390/cli3030578 Heinold, B., Knippertz, P., Marsham, J. H., Fiedler, S., Dixon, N. S., Schepanski, K., et al. (2013). The role of deep convection and nocturnal low- level jets for dust emission in summertime West Africa: Estimates from convection-permitting simulations. Journal of Geophysical Research: Atmospheres, 118, 4385–4400. https://doi.org/10.1002/jgrd.50402 Heinold, B., Tegen, I., Esselborn, M., Kandler, K., Knippertz, P., MüLLER, D., et al. (2009). Regional Saharan dust modelling during the SAMUM 2006 campaign. Tellus Series B: Chemical and Physical Meteorology, 61(1), 307–324. https://doi.org/10.1111/j.1600- 0889.2008.00387.x Holben, B. N., Eck, T. F., Slutsker, I., Tanré, D., Buis, J. P., Setzer, A., et al. (1998). AERONET—A federated instrument network and data archive for aerosol characterization. Remote Sensing of Environment, 66(1), 1–16. https://doi.org/10.1016/S0034-4257(98)00031-5 Houssos, E. E., Chronis, T., Fotiadi, A., Hossain, F., Houssos, E. E., Chronis, T., et al. (2015). Atmospheric circulation characteristics favoring dust outbreaks over the Solar Village, central Saudi Arabia. Monthly Weather Review, 143(8), 3263–3275. https://doi.org/10.1175/MWR-D-14-00198.1 Houze, R. A. J. (1993). Cloud dynamics. San Diego: Academic Press. doi:https://doi.org/10.1016/0377-0265(87)90017-0 Hu, Z., Zhao, C., Huang, J., Leung, L. R., Qian, Y., Yu, H., et al. (2016). Trans-Pacific transport and evolution of aerosols: Evaluation of quasi- global WRF-Chem simulation with multiple observations. Geoscientific Model Development, 9(5), 1725–1746. https://doi.org/10.5194/gmd- 9-1725-2016 Huneeus, N., Schulz, M., Balkanski, Y., Griesfeller, J., Prospero, J., Kinne, S., et al. (2011). Global dust model intercomparison in AeroCom phase I. Atmospheric Chemistry and Physics, 11(15), 7781–7816. https://doi.org/10.5194/acp-11-7781-2011 Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., & Collins, W. D. (2008). Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. Journal of Geophysical Research, 113, D13103. https://doi.org/10.1029/ 2008JD009944 Israelevich, P. L., Ganor, E., Levin, Z., & Joseph, J. H. (2003). Annual variations of physical properties of desert dust over Israel. Journal of Geophysical Research, 108(D13), 4381. https://doi.org/10.1029/2002JD003163 Janjić, Z. I. (1994). The step-mountain Eta coordinate model: Further developments of the convection, viscous sublayer and turbulence closure schemes. Monthly Weather Review, 122(5), 927–945. https://doi.org/10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2 Janssens-Maenhout, G., Crippa, M., Guizzardi, D., Dentener, F., Muntean, M., Pouliot, G., et al. (2015). HTAP_v2.2: A mosaic of regional and global emission grid maps for 2008 and 2010 to study hemispheric transport of air pollution. Atmospheric Chemistry and Physics, 15(19), 11,411–11,432. https://doi.org/10.5194/acp-15-11411-2015 Jish Prakash, P., Stenchikov, G., Kalenderski, S., Osipov, S., & Bangalath, H. (2015). The impact of dust storms on the Arabian Peninsula and the Red Sea. Atmospheric Chemistry and Physics, 15(1), 199–222. https://doi.org/10.5194/acp-15-199-2015 Kalenderski, S., & Stenchikov, G. (2016). High-resolution regional modeling of summertime transport and impact of African dust over the Red Sea and Arabian Peninsula. Journal of Geophysical Research: Atmospheres, 121, 6435–6458. https://doi.org/10.1002/2015JD024480 Kandler, K., Schütz, L., Deutscher, C., Ebert, M., Hofmann, H., Jäckel, S., et al. (2009). Size distribution, mass concentration, chemical and mineralogical composition and derived optical parameters of the boundary layer aerosol at Tinfou, Morocco, during SAMUM 2006. Tellus Series B: Chemical and Physical Meteorology, 61(1), 32–50. https://doi.org/10.1111/j.1600-0889.2008.00385.x Kandler, K., Schütz, L., Jäckel, S., Lieke, K., Emmel, C., Müller-Ebert, D., et al. (2011). Ground-based off-line aerosol measurements at Praia, Cape Verde, during the Saharan Mineral Dust Experiment: Microphysical properties and mineralogy. Tellus Series B: Chemical and Physical Meteorology, 63(4), 459–474. https://doi.org/10.1111/j.1600-0889.2011.00546.x Karydis, V. A., Tsimpidi, A. P., Bacer, S., Pozzer, A., Nenes, A., & Lelieveld, J. (2017). Global impact of mineral dust on cloud droplet number concentration. Atmospheric Chemistry and Physics, 17(9), 5601–5621. https://doi.org/10.5194/acp-17-5601-2017 Khan, B., Stenchikov, G., Weinzierl, B., Kalenderski, S., & Osipov, S. (2015). Dust plume formation in the free troposphere and aerosol size distribution during the Saharan Mineral Dust Experiment in North Africa. Tellus Series B: Chemical and Physical Meteorology, 67(1), 27170. https://doi.org/10.3402/tellusb.v67.27170

ANISIMOV ET AL. 12,175 Journal of Geophysical Research: Atmospheres 10.1029/2018JD028486

Knippertz, P., Deutscher, C., Kandler, K., Müller, T., Schulz, O., & Schütz, L. (2007). Dust mobilization due to density currents in the Atlas region: Observations from the Saharan Mineral Dust Experiment 2006 field campaign. Journal of Geophysical Research, 112, D21109. https://doi. org/10.1029/2007JD008774 Knippertz, P., & Todd, M. C. (2012). Mineral dust aerosols over the Sahara: Meteorological controls on emission and transport and implications for modeling. Reviews of Geophysics, 50, RG1007. https://doi.org/10.1029/2011RG000362 Knippertz, P., Trentmann, J., & Seifert, A. (2009). High-resolution simulations of convective cold pools over the northwestern Sahara. Journal of Geophysical Research, 114, D08110. https://doi.org/10.1029/2008JD011271 Kocha, C., Tulet, P., Lafore, J.-P., & Flamant, C. (2013). The importance of the diurnal cycle of Aerosol Optical Depth in West Africa. Geophysical Research Letters, 40, 785–790. https://doi.org/10.1002/grl.50143 Kok, J. F. (2011a). A scaling theory for the size distribution of emitted dust aerosols suggests climate models underestimate the size of the global dust cycle. Proceedings of the National Academy of Sciences of the United States of America, 108(3), 1016–1021. https://doi.org/ 10.1073/pnas.1014798108 Kok, J. F. (2011b). Does the size distribution of mineral dust aerosols depend on the wind speed at emission? Atmospheric Chemistry and Physics, 11(19), 10,149–10,156. https://doi.org/10.5194/acp-11-10149-2011 Konwar, M., Panicker, A. S., Axisa, D., & Prabha, T. V. (2015). Near-cloud aerosols in monsoon environment and its impact on radiative forcing. Journal of Geophysical Research: Atmospheres, 120, 1445–1457. https://doi.org/10.1002/2014JD022420 Krichak, S. O., Breitgand, J. S., Feldstein, S. B., Krichak, S. O., Breitgand, J. S., & Feldstein, S. B. (2012). A conceptual model for the identification of Active Red Sea Trough synoptic events over the Southeastern Mediterranean. Journal of Applied Meteorology and Climatology, 51(5), 962–971. https://doi.org/10.1175/JAMC-D-11-0223.1 Kumar, K. N., Entekhabi, D., & Molini, A. (2015). Hydrological extremes in hyperarid regions: A diagnostic characterization of intense preci- pitation over the central Arabian Peninsula. Journal of Geophysical Research: Atmospheres, 120, 1637–1650. https://doi.org/10.1002/ 2014JD022341 Largeron, Y., Guichard, F., Bouniol, D., Couvreux, F., Kergoat, L., & Marticorena, B. (2015). Can we use surface wind fields from meteor- ological reanalyses for Sahelian dust emission simulations? Geophysical Research Letters, 42, 2490–2499. https://doi.org/10.1002/ 2014GL062938 Laurent, B., Marticorena, B., Bergametti, G., Léon, J. F., & Mahowald, N. M. (2008). Modeling mineral dust emissions from the Sahara desert using new surface properties and soil database. Journal of Geophysical Research, 113, D14218. https://doi.org/10.1029/2007JD009484 Laurent, B., Tegen, I., Heinold, B., Schepanski, K., Weinzierl, B., & Esselborn, M. (2010). A model study of Saharan dust emissions and distri- butions during the SAMUM-1 campaign. Journal of Geophysical Research, 115, D21210. https://doi.org/10.1029/2009JD012995 Lensky, I. M., & Rosenfeld, D. (2008). Clouds-Aerosols-Precipitation Satellite Analysis Tool (CAPSAT). Atmospheric Chemistry and Physics, 8(22), 6739–6753. https://doi.org/10.5194/acp-8-6739-2008 Lin, Y.-L., Farley, R. D., & Orville, H. D. (1983). Bulk parameterization of the snow field in a cloud model. Journal of Climate and Applied Meteorology, 22(6), 1065–1092. https://doi.org/10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2 Liu, Z., Ostrenga, D., Teng, W., Kempler, S., Liu, Z., Ostrenga, D., et al. (2012). Tropical Rainfall Measuring Mission (TRMM) precipitation data and services for research and applications. Bulletin of the American Meteorological Society, 93(9), 1317–1325. https://doi.org/10.1175/ BAMS-D-11-00152.1 Lohmann, U., & Feichter, J. (2005). Global indirect aerosol effects: A review. Atmospheric Chemistry and Physics, 5(3), 715–737. https://doi.org/ 10.5194/acp-5-715-2005 Mahowald, N. M., Albani, S., Kok, J. F., Engelstaeder, S., Scanza, R., Ward, D. S., et al. (2014). The size distribution of desert dust aerosols and its impact on the Earth system. Aeolian Research, 15,53–71. https://doi.org/10.1016/j.aeolia.2013.09.002 Mahowald, N. M., Baker, A. R., Bergametti, G., Brooks, N., Duce, R. A., Jickells, T. D., et al. (2005). Atmospheric global dust cycle and iron inputs to the ocean. Global Biogeochemical Cycles, 19, GB4025. https://doi.org/10.1029/2004GB002402 Mamouri, R.-E., Ansmann, A., Nisantzi, A., Solomos, S., Kallos, G., & Hadjimitsis, D. G. (2016). Extreme dust storm over the eastern Mediterranean in September 2015: Satellite, lidar, and surface observations in the Cyprus region. Atmospheric Chemistry and Physics, 16(21), 13,711–13,724. https://doi.org/10.5194/acp-16-13711-2016 Marsham, J. H., Dixon, N. S., Garcia-Carreras, L., Lister, G. M. S., Parker, D. J., Knippertz, P., et al. (2013). The role of moist convection in the West African monsoon system: Insights from continental-scale convection-permitting simulations. Geophysical Research Letters, 40, 1843–1849. https://doi.org/10.1002/grl.50347 Marsham, J. H., Knippertz, P., Dixon, N. S., Parker, D. J., & Lister, G. M. S. (2011). The importance of the representation of deep convection for modeled dust-generating winds over West Africa during summer. Geophysical Research Letters, 38, L16803. https://doi.org/10.1029/ 2011GL048368 Marsham, J. H., Parker, D. J., Grams, C. M., Taylor, C. M., & Haywood, J. M. (2008). Uplift of Saharan dust south of the intertropical discontinuity. Journal of Geophysical Research, 113, D21102. https://doi.org/10.1029/2008JD009844 McConnell, C. L., Highwood, E. J., Coe, H., Formenti, P., Anderson, B., Osborne, S., et al. (2008). Seasonal variations of the physical and optical characteristics of Saharan dust: Results from the Dust Outflow and Deposition to the Ocean (DODO) experiment. Journal of Geophysical Research, 113, D14S05. https://doi.org/10.1029/2007JD009606 Middleton, N. J. (2017). Desert dust hazards: A global review. Aeolian Research, 24,53–63. https://doi.org/10.1016/J.AEOLIA.2016.12.001 Miller, R. L., Knippertz, P., Pérez García-Pando, C., Perlwitz, J. P., & Tegen, I. (2014). Impact of dust radiative forcing upon climate. In P. Knippertz, & J.-B. W. Stuut (Eds.), Mineral dust: A key player in the Earth system, (pp. 327–357). Dordrecht: Springer Netherlands. doi:https:// doi.org/10.1007/978-94-017-8978-3_13 Miller, S. D., Kuciauskas, A. P., Liu, M., Ji, Q., Reid, J. S., Breed, D. W., et al. (2008). Haboob dust storms of the southern Arabian peninsula. Journal of Geophysical Research, 113, D01202. https://doi.org/10.1029/2007JD008550 Min, K.-H., Choo, S., Lee, D., Lee, G., Min, K.-H., Choo, S., et al. (2015). Evaluation of WRF cloud microphysics schemes using radar observations. Weather and Forecasting, 30(6), 1571–1589. https://doi.org/10.1175/WAF-D-14-00095.1 Mohalfi, S., Bedi, H. S., Krishnamurti, T. N., Cocke, S. D., Mohalfi, S., Bedi, H. S., et al. (1998). Impact of shortwave radiative effects of dust aerosols on the summer season heat low over Saudi Arabia. Monthly Weather Review, 126(12), 3153–3168. https://doi.org/10.1175/1520- 0493(1998)126<3153:IOSREO>2.0.CO;2 Morman, S. A., & Plumlee, G. S. (2014). Dust and human health. In P. Knippertz, & J.-B. W. Stuut (Eds.), mineral Dust: A key player in the Earth system, (pp. 385–409). Dordrecht: Springer Netherlands. doi:https://doi.org/10.1007/978-94-017-8978-3_15 Müller, D., Ansmann, A., Freudenthaler, V., Kandler, K., Toledano, C., Hiebsch, A., et al. (2010). Mineral dust observed with AERONET Sun photometer, Raman lidar, and in situ instruments during SAMUM 2006: Shape-dependent particle properties. Journal of Geophysical Research, 115, D11207. https://doi.org/10.1029/2009JD012523

ANISIMOV ET AL. 12,176 Journal of Geophysical Research: Atmospheres 10.1029/2018JD028486

Nabavi, S. O., Haimberger, L., & Samimi, C. (2016). Climatology of dust distribution over West Asia from homogenized remote sensing data. Aeolian Research, 21,93–107. https://doi.org/10.1016/j.aeolia.2016.04.002 National Center for Atmospheric Research. (2010). Kingdom of Saudi Arabia assessment of rainfall augmentation—2009 Field Season. Boulder, Colorado USA. Niemelä, S., Fortelius, C., Niemelä, S., & Fortelius, C. (2005). Applicability of large-scale convection and condensation parameterization to meso-γ-scale HIRLAM: A case study of a convective event. Monthly Weather Review, 133(8), 2422–2435. https://doi.org/10.1175/ MWR2981.1 Notaro, M., Alkolibi, F., Fadda, E., & Bakhrjy, F. (2013). Trajectory analysis of Saudi Arabian dust storms. Journal of Geophysical Research: Atmospheres, 118, 6028–6043. https://doi.org/10.1002/jgrd.50346 Pantillon, F., Knippertz, P., Marsham, J. H., Birch, C. E., Pantillon, F., Knippertz, P., et al. (2015). A parameterization of convective dust storms for models with mass-flux convection schemes. Journal of the Atmospheric Sciences, 72(6), 2545–2561. https://doi.org/10.1175/JAS-D-14- 0341.1 Pantillon, F., Knippertz, P., Marsham, J. H., Panitz, H.-J., & Bischoff-Gauss, I. (2016). Modeling haboob dust storms in large-scale weather and climate models. Journal of Geophysical Research: Atmospheres, 121, 2090–2109. https://doi.org/10.1002/2015JD024349 Perlwitz, J. P., Pérez García-Pando, C., & Miller, R. L. (2015). Predicting the mineral composition of dust aerosols – Part 1: Representing key processes. Atmospheric Chemistry and Physics, 15(20), 11,593–11,627. https://doi.org/10.5194/acp-15-11593-2015 Pope, R. J., Marsham, J. H., Knippertz, P., Brooks, M. E., & Roberts, A. J. (2016). Identifying errors in dust models from data assimilation. Geophysical Research Letters, 43, 9270–9279. https://doi.org/10.1002/2016GL070621 Pósfai, M., Axisa, D., Tompa, É., Freney, E., Bruintjes, R., & Buseck, P. R. (2013). Interactions of mineral dust with pollution and clouds: An individual-particle TEM study of atmospheric aerosol from Saudi Arabia. Atmospheric Research, 122, 347–361. https://doi.org/10.1016/J. ATMOSRES.2012.12.001 Prospero, J. M., Ginoux, P., Torres, O., Nicholson, S. E., & Gill, T. E. (2002). Environmental characterization of global sources of atmospheric soil dust identified with the NIMBUS 7 Total Ozone Mapping Spectrometer (TOMS) absorbing aerosol product. Reviews of Geophysics, 40(1), 1002. https://doi.org/10.1029/2000RG000095 Raman, A., Arellano, A. F., & Brost, J. J. (2014). Revisiting haboobs in the southwestern United States: An observational case study of the 5 July 2011 Phoenix dust storm. Atmospheric Environment, 89, 179–188. https://doi.org/10.1016/J.ATMOSENV.2014.02.026 Randles, C. A., da Silva, A. M., Buchard, V., Colarco, P. R., Darmenov, A., Govindaraju, R., et al. (2017). The MERRA-2 aerosol reanalysis, 1980 onward. Part I: System description and data assimilation evaluation. Journal of Climate, 30(17), 6823–6850. https://doi.org/10.1175/JCLI-D- 16-0609.1 Raut, J.-C., Marelle, L., Fast, J. D., Thomas, J. L., Weinzierl, B., Law, K. S., et al. (2017). Cross-polar transport and scavenging of Siberian aerosols containing black carbon during the 2012 ACCESS summer campaign. Atmospheric Chemistry and Physics, 17(18), 10,969–10,995. https:// doi.org/10.5194/acp-17-10969-2017 Redelsperger, J.-L., Thorncroft, C. D., Diedhiou, A., Lebel, T., Parker, D. J., Polcher, J., et al. (2006). The African Monsoon Multidisciplinary Analysis (AMMA): An international research project and field campaign. Bulletin of the American Meteorological Society, 87(12), 1739–1746. https://doi.org/10.1175/BAMS-87-12-1739 Redl, R., Fink, A. H., Knippertz, P., Redl, R., Fink, A. H., & Knippertz, P. (2015). An objective detection method for convective cold pool events and its application to Northern Africa. Monthly Weather Review, 143(12), 5055–5072. https://doi.org/10.1175/MWR-D-15- 0223.1 Reid, J. S., Piketh, S. J., Walker, A. L., Burger, R. P., Ross, K. E., Westphal, D. L., et al. (2008). An overview of UAE 2 flight operations: Observations of summertime atmospheric thermodynamic and aerosol profiles of the southern Arabian Gulf. Journal of Geophysical Research, 113, D14213. https://doi.org/10.1029/2007JD009435 Reinfried, F., Tegen, I., Heinold, B., Hellmuth, O., Schepanski, K., Cubasch, U., et al. (2009). Simulations of convectively-driven density currents in the Atlas region using a regional model: Impacts on dust emission and sensitivity to horizontal resolution and convection schemes. Journal of Geophysical Research, 114, D08127. https://doi.org/10.1029/2008JD010844 Reitz, P., Spindler, C., Mentel, T. F., Poulain, L., Wex, H., Mildenberger, K., et al. (2011). Surface modification of mineral dust particles by sul- phuric acid processing: Implications for ice nucleation abilities. Atmospheric Chemistry and Physics, 11(15), 7839–7858. https://doi.org/ 10.5194/acp-11-7839-2011 Ridley, D. A., Heald, C. L., Kok, J. F., & Zhao, C. (2016). An observationally constrained estimate of global dust aerosol optical depth. Atmospheric Chemistry and Physics, 16(23), 15,097–15,117. https://doi.org/10.5194/acp-16-15097-2016 Ritter, M., Müller, M. D., Tsai, M. Y., & Parlow, E. (2013). Air pollution modeling over very complex terrain: An evaluation of WRF-Chem over Switzerland for two 1-year periods. Atmospheric Research, 132-133, 209–222. https://doi.org/10.1016/J. ATMOSRES.2013.05.021 Roberts, A. J., & Knippertz, P. (2014). The formation of a large summertime Saharan dust plume: Convective and synoptic-scale analysis. Journal of Geophysical Research: Atmospheres, 119, 1766–1785. https://doi.org/10.1002/2013JD020667 Ryder, C. L., Highwood, E. J., Rosenberg, P. D., Trembath, J., Brooke, J. K., Bart, M., et al. (2013). Optical properties of Saharan dust aerosol and contribution from the coarse mode as measured during the Fennec 2011 aircraft campaign. Atmospheric Chemistry and Physics, 13(1), 303–325. https://doi.org/10.5194/acp-13-303-2013 Ryder, C. L., McQuaid, J. B., Flamant, C., Rosenberg, P. D., Washington, R., Brindley, H. E., et al. (2015). Advances in understanding mineral dust and boundary layer processes over the Sahara from Fennec aircraft observations. Atmospheric Chemistry and Physics, 15(14), 8479–8520. https://doi.org/10.5194/acp-15-8479-2015 Sabbah, I., & Hasan, F. M. (2008). Remote sensing of aerosols over the Solar Village, Saudi Arabia. Atmospheric Research, 90(2–4), 170–179. https://doi.org/10.1016/J.ATMOSRES.2008.02.004 Sayer, A. M., Munchak, L. A., Hsu, N. C., Levy, R. C., Bettenhausen, C., & Jeong, M.-J. (2014). MODIS Collection 6 aerosol products: Comparison between Aqua’s e-Deep Blue, Dark Target, and “merged” data sets, and usage recommendations. Journal of Geophysical Research: Atmospheres, 119, 13,965–13,989. https://doi.org/10.1002/2014JD022453 Schepanski, K., Tegen, I., & Macke, A. (2009a). Saharan dust transport and deposition towards the tropical northern Atlantic. Atmospheric Chemistry and Physics, 9(4), 1173–1189. https://doi.org/10.5194/acp-9-1173-2009 Schepanski, K., Tegen, I., & Macke, A. (2012). Comparison of satellite based observations of Saharan dust source areas. Remote Sensing of Environment, 123,90–97. https://doi.org/10.1016/j.rse.2012.03.019 Schepanski, K., Tegen, I., Todd, M. C., Heinold, B., Bönisch, G., Laurent, B., et al. (2009b). Meteorological processes forcing Saharan dust emission inferred from MSG-SEVIRI observations of subdaily dust source activation and numerical models. Journal of Geophysical Research, 114, D10201. https://doi.org/10.1029/2008JD010325

ANISIMOV ET AL. 12,177 Journal of Geophysical Research: Atmospheres 10.1029/2018JD028486

Schroedter-Homscheidt, M., & Oumbe, A. (2013). Validation of an hourly resolved global aerosol model in answer to solar electricity gen- eration information needs. Atmospheric Chemistry and Physics, 13(7), 3777–3791. https://doi.org/10.5194/acp-13-3777-2013 Schulz, M., Prospero, J. M., Baker, A. R., Dentener, F., Ickes, L., Liss, P. S., et al. (2012). Atmospheric transport and deposition of mineral dust to the ocean: Implications for research needs. Environmental Science & Technology, 46(19), 10,390–10,404. https://doi.org/10.1021/ es300073u Schwartz, C. S., Kain, J. S., Weiss, S. J., Xue, M., Bright, D. R., Kong, F., et al. (2009). Next-day convection-allowing WRF model guidance: A second look at 2-km versus 4-km grid spacing. Monthly Weather Review, 137(10), 3351–3372. https://doi.org/10.1175/2009MWR2924.1 Schwartz, C. S., Romine, G. S., Fossell, K. R., Sobash, R. A., Weisman, M. L., Schwartz, C. S., et al. (2017). Toward 1-km ensemble forecasts over large domains. Monthly Weather Review, 145(8), 2943–2969. https://doi.org/10.1175/MWR-D-16-0410.1 Shao, Y. (2008). In Y. Shao (Ed.), Physics and modelling of wind erosion, (2nd ed., Vol. 37). Dordrecht: Springer Netherlands. https://doi.org/ 10.1007/978-1-4020-8895-7 Shao, Y., Wyrwoll, K.-H., Chappell, A., Huang, J., Lin, Z., McTainsh, G. H., et al. (2011). Dust cycle: An emerging core theme in Earth system science. Aeolian Research, 2(4), 181–204. https://doi.org/10.1016/j.aeolia.2011.02.001 Simpson, J. E. (1997). Gravity currents in the environment and the laboratory, (2nd ed.). Cambridge: Cambridge University Press. doi:https://doi. org/10.1017/S0022112097227527 Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda, M. G., et al. (2008). A description of the advanced research WRF version 3. NCAR/TN-475+STR NCAR Technical Note, (June), 113. doi:https://doi.org/10.5065/D68S4MVH Smith, A., Lott, N., & Vose, R. (2011). The Integrated Surface Database: Recent developments and partnerships. Bulletin of the American Meteorological Society, 92(6), 704–708. https://doi.org/10.1175/2011BAMS3015.1 Smoydzin, L., Teller, A., Tost, H., Fnais, M., & Lelieveld, J. (2012). Impact of mineral dust on cloud formation in a Saharan outflow region. Atmospheric Chemistry and Physics, 12(23), 11,383–11,393. https://doi.org/10.5194/acp-12-11383-2012 Sodemann, H., Lai, T. M., Marenco, F., Ryder, C. L., Flamant, C., Knippertz, P., et al. (2015). Lagrangian dust model simulations for a case of moist convective dust emission and transport in the western Sahara region during Fennec/LADUNEX. Journal of Geophysical Research: Atmospheres, 120, 6117–6144. https://doi.org/10.1002/2015JD023283 Solmon, F., Nair, V. S., & Mallet, M. (2015). Increasing Arabian dust activity and the Indian summer monsoon. Atmospheric Chemistry and Physics, 15(14), 8051–8064. https://doi.org/10.5194/acp-15-8051-2015 Solomos, S., Ansmann, A., Mamouri, R.-E., Binietoglou, I., Patlakas, P., Marinou, E., et al. (2017). Remote sensing and modelling analysis of the extreme dust storm hitting the Middle East and eastern Mediterranean in September 2015. Atmospheric Chemistry and Physics, 17(6), 4063–4079. https://doi.org/10.5194/acp-17-4063-2017 Solomos, S., Kallos, G., Mavromatidis, E., & Kushta, J. (2012). Density currents as a desert dust mobilization mechanism. Atmospheric Chemistry and Physics, 12(22), 11,199–11,211. https://doi.org/10.5194/acp-12-11199-2012 Taghavi, F., & Asadi, A. (2008). The Persian Gulf 12th April 2007 dust storm: Observation and model analysis. EUMETSAT Meteorological Satellite Conference 2008. Darmstadt, Germany. Tewari, M., Chen, F., Wang, W., Dudhia, J., LeMone, M. A., Mitchell, K., et al. (2016). Implementation and verification of the united NOAH land surface model in the WRF model. In 20th Conference on Weather Analysis and Forecasting/16th Conference on Numerical Weather Prediction (pp. 11–15). Todd, M. C., Allen, C. J. T., Bart, M., Bechir, M., Bentefouet, J., Brooks, B. J., et al. (2013). Meteorological and dust aerosol conditions over the western Saharan region observed at Fennec Supersite-2 during the intensive observation period in June 2011. Journal of Geophysical Research: Atmospheres, 118, 8426–8447. https://doi.org/10.1002/jgrd.50470 Tsikerdekis, A., Zanis, P., Steiner, A. L., Solmon, F., Amiridis, V., Marinou, E., et al. (2017). Impact of dust size parameterizations on aerosol burden and radiative forcing in RegCM4. Atmospheric Chemistry and Physics, 17(2), 769–791. https://doi.org/10.5194/acp-17-769-2017 Tsvieli, Y., & Zangvil, A. (2007). Synoptic climatological analysis of Red Sea Trough and non-Red Sea Trough rain situations over Israel. Advances in Geosciences, 12, 137–143. https://doi.org/10.5194/adgeo-12-137-2007 Vinoj, V., Rasch, P. J., Wang, H., Yoon, J.-H., Ma, P.-L., Landu, K., et al. (2014). Short-term modulation of Indian summer monsoon rainfall by West Asian dust. Nature Geoscience, 7(4), 308. Retrieved from https://doi.org/10.1038/ngeo2107–313. Vukovic, A., Vujadinovic, M., Pejanovic, G., Andric, J., Kumjian, M. R., Djurdjevic, V., et al. (2014). Numerical simulation of “an American haboob.”. Atmospheric Chemistry and Physics, 14(7), 3211–3230. https://doi.org/10.5194/acp-14-3211-2014 Weckwerth, T. M., & Wakimoto, R. M. (1992). The initiation and organization of convective cells atop a cold-air outflow boundary. Monthly Weather Review, 120(10), 2169–2187. https://doi.org/10.1175/1520-0493(1992)120<2169:TIAOOC>2.0.CO;2 Weinzierl, B., Petzold, A., Esselborn, M., Wirth, M., Rasp, K., Kandler, K., et al. (2009). Airborne measurements of dust layer properties, particle size distribution and mixing state of Saharan dust during SAMUM 2006. Tellus Series B: Chemical and Physical Meteorology, 61(1), 96–117. https://doi.org/10.1111/j.1600-0889.2008.00392.x Wesely, M. L. (1989). Parameterization of surface resistances to gaseous dry deposition in regional-scale numerical models. Atmospheric Environment (1967), 23(6), 1293–1304. https://doi.org/10.1016/0004-6981(89)90153-4 Wild, O., Zhu, X., & Prather, M. J. (2000). Fast-J: Accurate simulation of in- and below-cloud photolysis in tropospheric chemical models. Journal of Atmospheric Chemistry, 37(3), 245–282. https://doi.org/10.1023/A:1006415919030 Williams, E. R. (2008). Comment on “Atmospheric controls on the annual cycle of North African dust” by S. Engelstaedter and R. Washington. Journal of Geophysical Research, 113, D23109. https://doi.org/10.1029/2008JD009930 Yu, H., Chin, M., Yuan, T., Bian, H., Remer, L. A., Prospero, J. M., et al. (2015). The fertilizing role of African dust in the Amazon rainforest: A first multiyear assessment based on data from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations. Geophysical Research Letters, 42, 1984–1991. https://doi.org/10.1002/2015GL063040 Yu, H., Kaufman, Y. J., Chin, M., Feingold, G., Remer, L. A., Anderson, T. L., et al. (2006). A review of measurement-based assessments of the aerosol direct radiative effect and forcing. Atmospheric Chemistry and Physics, 6(3), 613–666. https://doi.org/10.5194/acp-6- 613-2006 Yu, Y., Notaro, M., Kalashnikova, O. V., & Garay, M. J. (2016). Climatology of summer Shamal wind in the Middle East. Journal of Geophysical Research: Atmospheres, 121, 289–305. https://doi.org/10.1002/2015JD024063 Yu, Y., Notaro, M., Liu, Z., Kalashnikova, O., Alkolibi, F., Fadda, E., et al. (2013). Assessing temporal and spatial variations in atmospheric dust over Saudi Arabia through satellite, radiometric, and station data. Journal of Geophysical Research: Atmospheres, 118, 13,253–13,264. https://doi.org/10.1002/2013JD020677 Yu, Y., Notaro, M., Liu, Z., Wang, F., Alkolibi, F., Fadda, E., et al. (2015). Climatic controls on the interannual to decadal variability in Saudi Arabian dust activity: Toward the development of a seasonal dust prediction model. Journal of Geophysical Research: Atmospheres, 120, 1739–1758. https://doi.org/10.1002/2014JD022611

ANISIMOV ET AL. 12,178 Journal of Geophysical Research: Atmospheres 10.1029/2018JD028486

Yuter, S. E., Houze, R. A., Yuter, S. E., &, Houze, Jr. R. A. (1995). Three-dimensional kinematic and microphysical evolution of Florida cumulo- nimbus. Part II: Frequency distributions of vertical velocity, reflectivity, and differential reflectivity. Monthly Weather Review, 123(7), 1941–1963. doi:https://doi.org/10.1175/1520-0493(1995)123<1941:TDKAME>2.0.CO;2 Zaveri, R. A., Easter, R. C., Fast, J. D., & Peters, L. K. (2008). Model for Simulating Aerosol Interactions and Chemistry (MOSAIC). Journal of Geophysical Research, 113, D13204. https://doi.org/10.1029/2007JD008782 Zaveri, R. A., & Peters, L. K. (1999). A new lumped structure photochemical mechanism for large-scale applications. Journal of Geophysical Research, 104(D23), 30,387–30,415. https://doi.org/10.1029/1999JD900876 Zhang, Y., Liu, Y., Kucera, P. A., Alharbi, B. H., Pan, L., & Ghulam, A. (2015). Dust modeling over Saudi Arabia using WRF-Chem: March 2009 severe dust case. Atmospheric Environment, 119, 118–130. https://doi.org/10.1016/j.atmosenv.2015.08.032 Zhang, Y., Mahowald, N., Scanza, R. A., Journet, E., Desboeufs, K., Albani, S., et al. (2015). Modeling the global emission, transport and deposition of trace elements associated with mineral dust. Biogeosciences, 12(19), 5771–5792. https://doi.org/10.5194/bg-12-5771-2015 Zhang, Y., Sartelet, K., Zhu, S., Wang, W., Wu, S.-Y., Zhang, X., et al. (2013). Application of WRF/Chem-MADRID and WRF/Polyphemus in Europe —Part 2: Evaluation of chemical concentrations and sensitivity simulations. Atmospheric Chemistry and Physics, 13(14), 6845–6875. https:// doi.org/10.5194/acp-13-6845-2013 Zhao, C., Chen, S., Leung, L. R., Qian, Y., Kok, J. F., Zaveri, R. A., et al. (2013a). Uncertainty in modeling dust mass balance and radiative forcing from size parameterization. Atmospheric Chemistry and Physics, 13(21), 10,733–10,753. https://doi.org/10.5194/acp-13-10733-2013 Zhao, C., Liu, X., Leung, L. R., Johnson, B., McFarlane, S. A., Gustafson, W. I., et al. (2010). The spatial distribution of mineral dust and its shortwave radiative forcing over North Africa: Modeling sensitivities to dust emissions and aerosol size treatments. Atmospheric Chemistry and Physics, 10(18), 8821–8838. https://doi.org/10.5194/acp-10-8821-2010 Zhao, C., Ruby Leung, L., Easter, R., Hand, J., & Avise, J. (2013b). Characterization of speciated aerosol direct radiative forcing over California. Journal of Geophysical Research: Atmospheres, 118, 2372–2388. https://doi.org/10.1029/2012JD018364

ANISIMOV ET AL. 12,179