An Analysis of the Differences Between Two Seasonal Saudi Arabian Dust Storms Using WRF-Chem

University of Nevada, Reno

A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Atmospheric Science

Yazeed Alsubhi

Dr. Eric M. Wilcox/Thesis Advisor

May, 2016

THE GRADUATE SCHOOL

We recommend that the thesis prepared under our supervision by

YAZEED ALSUBHI

Entitled

An Analysis of the Differences Between Two Seasonal Saudi Arabian Dust Storms Using WRF-Chem

be accepted in partial fulfillment of the requirements for the degree of

MASTER OF SCIENCE

Eric M. Wilcox, Ph.D., Advisor

Michael Kaplan, Ph.D., Committee Member

Mae Gustin, Ph.D., Graduate School Representative

David W. Zeh, Ph.D., Dean, Graduate School

May, 2016 i

ABSTRACT

This research focused on understanding the difference atmospheric conditions between two major dust storms in Saudi Arabia. These two types of dust storms occur annually over Saudi Arabia with different seasons, sources, and atmospheric dynamics. We analyzed the Sharav case study in the spring season from 31 March 2015 and the Shamal case study in the early summer season from 09 to 10 June 2015 using the HYSPLIT (Hybrid

Single Particle Lagrangian Integrated Trajectory) model to determine the dust source regions and the MERRA (Modern Era-Retrospective Analysis for Research and

Applications) data for the large-scale analyses. Dust particles are generated from the Sahara

Desert in the Sharav case; however, these particles are generated from the Syrian and Iraqi

Deserts in the Shamal case. The large-scale analyses show that the differences in the circulation over much of the troposphere between these two cases cause the different circulations at the surface that impact the evolution of the dust storms differently. The results showed that the Sharav dust event is primarily caused by extratropical dynamics, while the Shamal dust event is forced by more regional dynamics. The WRF-Chem

(Weather Research and Forecasting/Chemistry) model was validated against the MERRA data and simulated these cases to estimate the TKE at low-levels for both the Sharav and

Shamal case studies which caused high dust concentrations with a maximum magnitude of

≅ 250 and ≅ 1000 , respectively. The model calculated the dust emissions within the domain to show the simulated daily rates of dust emissions for both the Sharav ii and Shamal case studies: a maximum daily average of ≅ 2.5x10 and ≅

9x10 , respectively.

iii

ACKNOWLODGMENTS

First of all, I would like to thank my parents who have been a source of inspiration and encouragement to me for my entire life. A very special thanks goes to my wife, Alaa

Alhazmi who has helped me and provided a lovely life and a quiet environment to study to achieve my goals. My gratitude goes to my brothers, including Sahlan Almuntashiri, and my sisters for their love, support, and everything they have done for me over the years. My deepest appreciation goes to my government for supporting me to pursue my education to achieve my Master’s degree in United States.

Second of all, I would like to express my biggest appreciation for my thesis committee members, Dr. Eric Wilcox, Dr. Michael Kaplan, and Dr. Mae Gustin for the nice and friendly attitude which assisted me throughout my study and research. I am really grateful for their patience and guidance while teaching me the applicability of what I have learned from my course work that shaped my research. Their motivation, support and vast knowledge have motivated me throughout my graduate career to accomplish my Master degree.

Lastly, I would like to thank professors, faculty, and my fellow graduate students at the University of Nevada, Reno (UNR) and the Desert Research Institute (DRI). I am especially grateful to Lan Gao, Marco Giordano, Robert David, and Chiranjivi Bhattarai for providing me valuable information, suggestions, and help over my graduate career. It iv has been my pleasure to work with you all, including those I have not named. My thesis would not have been possible without everyone’s contributions that I have mentioned.

TABLE OF CONTENTS

Abstract ...... i

Acknowledgements ...... iii

Table of contents ...... v

List of Tables ...... vii

List of Figures ...... viii

1. Introduction ...... 1

2. Data and Methodology ...... 7

2.1 NASA Dataset ...... 7

2.2 WRF-Chem Model ...... 8

3. Research Findings ...... 15

3.1 Observational Analyses ...... 15

3.1.1 The Aerosol Optical Depth (AOD) ...... 15

3.1.2 The Large-Scale Structure of the Atmosphere ...... 18

3.1.2a The Sharav case ...... 20 vi

3.1.2b The Shamal case ...... 25

3.2 The Modeled Results ...... 29

3.2.1 The Large-Scale structure of the atmosphere for the Sharav case ....29

3.2.2 The Large-Scale structure of the atmosphere for the Shamal case ...32

3.2.3 The Meteorological conditions for both cases ...... 35

3.2.4 The Mesoscale boundary layer details ...... 38

3.2.4a The Sharav case ...... 38

3.2.4b The Shamal case ...... 42

4. Discussion/Conclusion ...... 47

5. References ...... 52

vii

LIST OF TABLES

Table2.1: The parameters of the model.

viii

LIST OF FIGURES Figure 1.1: Map to show the location of the area of interest. Figure 2.1: The domain configuration.

Figure 3.1: HYSPLIT- derived 72 hours back-trajectories ending in one location over Northern Saudi Arabia 29° N and 43°E at two atmospheric heights (100 m and 1500 m) at 09:00 UTC for the Sharav case on 01 April 2015 and the Shamal case on 10 June.

Figure 3.2: The AOD values at 550 nm from MODIS Deep Blue measured over time for (Left) the Sharav case and (Right) the Shamal case. Figure 3.3: The jet streaks location at the 300 hPa for the Sharav case. (in the top-left corner): 18:00 UTC 31 March 2015, (in the top-right corner): 21:00 UTC 31 March 2015, (in the bottom-left corner): 03:00 UTC 01 April 2015, and (in the bottom-right corner): 09:00 UTC 01 April 2015.

Figure 3.4: The jet streaks location at the 300 hPa for the Shamal case. (in the top-left corner): 12:00 UTC 09 June 2015, (in the top-right corner): 21:00 UTC 09 June 2015, (in the bottom-left corner): 15:00 UTC 10 June 2015, and (in the bottom-right corner): 21:00 UTC 10 June 2015. Figure 3.5: The Geopotential Heights, wind speed, and direction at 500 hPa for the Sharav case. (in the top-left corner): at 18:00 UTC on 31 March 2015, (in the top-right corner): at 21:00 UTC on 31 March 2015, (in the bottom-left corner): at 03:00 UTC on 01 April 2015, and (in the bottom-right corner): at 09:00 UTC on 01 April 2015. Figure 3.6: Geopotential Heights at the 850 hPa for the Sharav case. (in the top-left corner): at 18:00 UTC on 31 March 2015, (in the top-right corner): at 21:00 UTC on 31 March 2015, (in the bottom-left corner): at 03:00 UTC on 01 April 2015, and (in the bottom-right corner): at 09:00 UTC on 01 April 2015. Figure 3.7: Mean sea level pressure (Pa) for the Sharav case. (in the top-left corner): at 18:00 UTC on 31 March 2015, (in the top-right corner): at 21:00 UTC on 31 March 2015, (in the bottom-left corner): at 03:00 UTC on 01 April 2015, and (in the bottom-right corner): at 09:00 UTC on 01 April 2015. Figure 3.8: The surface air temperature for the Sharav case. (in the top-left corner): at 18:00 UTC on 31 March 2015, (in the top-right corner): at 21:00 UTC on 31 March 2015, (in the bottom-left corner): at 03:00 UTC on 01 April 2015, and (in the bottom-right corner): at 09:00 UTC on 01 April 2015. ix

Figure 3.9: Wind speed and direction at 10 m above the surface for the Sharav case. (in the top-left corner): at 18:00 UTC on 31 March 2015, (in the top-right corner): at 21:00 UTC on 31 March 2015, (in the bottom-left corner): at 03:00 UTC on 01 April 2015, and (in the bottom-right corner): at 09:00 UTC on 01 April 2015.

Figure 3.10: Geopotential heights and wind direction at the 500 hPa for the Shamal case. (in the top-left corner): at 12:00 UTC on 09 June 2015, (in the top-right corner): at 21:00 UTC on 09 June 2015, (in the bottom-left corner): at 15:00 UTC on 10 June 2015, and (in the bottom-right corner): at 21:00 UTC on 10 June 2015. Figure 3.11: Geopotential heights and wind direction at 850 hPa for the Shamal case. (in the top-left corner): at 12:00 UTC on 09 June 2015, (in the top-right corner): at 21:00 UTC on 09 June 2015, (in the bottom-left corner): at 15:00 UTC on 10 June 2015, and (in the bottom-right corner): at 21:00 UTC on 10 June 2015. Figure 3.12: Mean sea level pressure for the Shamal case. (in the top-left corner): at 12:00 UTC on 09 June 2015, (in the top-right corner): at 21:00 UTC on 09 June 2015, (in the bottom-left corner): at 15:00 UTC on 10 June 2015, and (in the bottom-right corner): at 21:00 UTC on 10 June 2015. Figure 3.13: The surface air temperature for the Shamal case. (in the top-left corner): at 12:00 UTC on 09 June 2015, (in the top-right corner): at 21:00 UTC on 09 June 2015, (in the bottom-left corner): at 15:00 UTC on 10 June 2015, and (in the bottom-right corner): at 21:00 UTC on 10 June 2015. Figure 3.14: Wind speed and direction at 10 m above the surface for the Shamal case. (in the top-left corner): at 12:00 UTC on 09 June 2015, (in the top-right corner): at 21:00 UTC on 09 June 2015, (in the bottom-left corner): at 15:00 UTC on 10 June 2015, and (in the bottom-right corner): at 21:00 UTC on 10 June 2015. Figure 3.15: The geopotential heights, wind speeds, and direction at 500 hPa for the Sharav case. a): 18:00 UTC on 31 March 2015, b): 21:00 UTC on 31 March 2015, c): 03:00 UTC on 01 April, and d): 09:00 UTC on 01 April 2015. Figure 3.16: Mean sea level pressure for the Sharav case. a): 18:00 UTC on 31 March 2015, b): 21:00 UTC on 31 March 2015, c): 03:00 UTC on 01 April 2015, and d): 09:00 UTC on 01 April 2015. Figure 3.27: Wind speed and direction at 10 m above the surface on 01 April 2015 for the Sharav case. a): 18:00 UTC on 31 March 2015, b): 21:00 UTC on 31 March 2015, c): 03:00 UTC on 01 April 2015, and d): 09:00 UTC on 01 April 2015. x

Figure 3.18: The geopotential heights on the 500 hPa surface simulated by the WRF-Chem for the Shamal case. a): 12:00 UTC 09 June 2015, b): 21:00 UTC 09 June 2015, c): 15:00 UTC 10 June 2015, and d): 21:00 UTC 10 June 2015. Figure 3.19: Mean sea level pressure from the WRF-Chem model for the Shamal case. a): 12:00 UTC 09 June 2015, b): 21:00 UTC 09 June 2015, c): 15:00 UTC 10 June 2015, and d): 21:00 UTC 10 June 2015. Figure 3.20: Wind speed and direction at 10 m above the surface on 01 April 2015 for the Shamal case. a): 12:00 UTC 09 June 2015, b): 21:00 UTC 09 June 2015, c): 15:00 UTC 10 June 2015, and d): 21:00 UTC 10 June 2015. Figure 3.21: The trajectories at 100 m, 500 m, 1000 m, 1500 m, 2000 m, 2500 m, and 3000 m for the Sharav case and the Shamal case. Figure 3.22: The soundings over the Iraqi desert during the strongest dust signals for the Sharav case and the Shamal case. Figure 3.23: The source function for the area of study. Figure 3.24: The air temperature (black line) and dew-point temperature (blue line) in the skew-T sounding from the model on 01 April 2015 for a) Sakaka, Saudi Arabia at the latitude 30° and longitude 40° at 03:00 UTC, b) Rafha, Saudi Arabia at the latitude 29.63° and longitude 43.5° at 03:00 UTC, c) Sakaka, Saudi Arabia at 09:00 UTC, and d) Rafha, Saudi Arabia at 09:00 UTC. Figure 3.25: Turbulence kinetic energy for the Sharav case on 01 April 2015 at 03:00 UTC (left) and 09:00 UTC (right).

Figure 3.26: The dust concentration and wind direction at 10 m for the Sharav case. a): 18:00 UTC on 31 March 2015, b): 21:00 UTC on 31 March 2015, c): 03:00 UTC on 01 April 2015, and d): 09:00 UTC on 01 April 2015. Figure 3.27: The dust emissions for the Sharav case. a): at 18:00 UTC on 31 March 2015, b): at 21:00 UTC on 31 March 2015, c): at 03:00 UTC on 01 April 2015, and d): at 09:00 UTC on 01 April 2015. Figure 3.28: The daily rate of dust emissions for the Sharav case. a) 31 March 2015 and b) 01 April 2015. Figure 3.29: The air temperature (black line) and dew-point temperature (blue line) in the skew-T sounding from the model on 09 June 2015 for a) Sakaka, Saudi Arabia at the latitude 30° and longitude 40° at 12:00 UTC, b) Rafha, Saudi Arabia at the latitude xi

29.63° and longitude 43.5° at 12:00 UTC. On 10 June 2015 c) Sakaka, Saudi Arabia at 15:00 UTC, and d) Rafha, Saudi Arabia at 15:00 UTC. Figure 3.30: Turbulence kinetic energy for the Shamal case on a) 09 June 2015 12:00 UTC, b) 09 June 2015 21:00 UTC, c) 10 June 2015 15:00 UTC, and d) 10 June 2015 21:00 UTC. Figure 3.31: The dust concentration and wind direction at 10 m for the Shamal case. a): 18:00 UTC 31 March 2015, b): 21:00 UTC 31 March 2015, c): 03:00 UTC 01 April 2015, and d): 09:00 UTC 01 April 2015. Figure 3.32: The dust emission and wind direction at 10 m for the Shamal case. a) 12:00 UTC 09 June 2015, b) 21:00 UTC 09 June 2015, c) 15:00 UTC 10 June 2015, and d) 21:00 UTC 10 June 2015. Figure 3.33: The daily rate dust emissions over 24 h for the Shamal case. (left) on 09 June 2015 and (right) on 10 June 2015.

1.0 Introduction:

Aerosol particles are divided into two categories: primary (natural) and secondary

(anthropogenic) sources with different sizes (Haywood and Boucher, 2000). They have a strong influence on the climate, and they are dominated by dust minerals (Buseck and

Posfai, 1999). Dust minerals make up 30 to 50 % of the aerosols in the troposphere

(d’Almedia et al., 1991), and are one of the main aerosols that affect the climate system by altering the transmission of solar and infrared radiation through the atmosphere (Prakash et al., 2015).

The primary sources of dust are regions with arid and/or semi-desert areas, and remote areas can be affected as well (Prakash et al., 2015). The main dust source region in the world is the Sahara, and it is also the largest (Washington, 2003). Dust particles that are developed in the Saharan Desert can be lifted to 2.5 km or more in the atmosphere

(Escudero et al., 2005). In severe dust storms, these particles can be carried from the

Saharan Desert through Saudi Arabia to Eastern Asia within a week (Tanaka et al., 2005).

Moreover, dust transport from the Saharan Desert can affect the Mediterranean, Europe,

North America, and South America (Prospero, 2001). The amount of dust particles released into the planetary boundary layer is in the millions of tons (Prakash et al., 2015). The health and environmental problems during dust events are eye inflammation, low visibility, more car accidents, and respiratory diseases (Chung and Yoon, 1996; Wilkerson, 1991). 2

Researchers have investigated the direct and indirect effects of dust minerals on the climate system and the reason for them. Dust particles both absorb and scatter solar radiation and also weakly absorb longwave radiation. As a result of these direct effects of dust, the air in the dust layers is heated and the air below the dust layer, as well as the surface are cooled (direct effects) (Haywood and Boucher, 2000; Mohalfi et al., 1998;

Ackerman and Cox, 1982; Miller et al., 2004). When the clouds interact with dust particles, the dust concentration indirectly affects the climate by increasing the number of cloud condensation nuclei (CCN) as well as the lifetime and the albedo of the clouds (indirect effects) (Penner, 2001; Haywood and Boucher, 2000; Ackerman and Cox, 1982). These effects will further cool the surface beneath dust layers.

Many studies have been done to investigate the universal average of dust emission per year, and the value of their studies are between about 1 to 3 (Miller et al., 2004; Mahowald, 1999; Tegen et al., 2002; Ginoux et al., 2001; Luo, 2003; Zender,

2003a). The strength of dust concentration is inversely proportional to the amount of precipitation in the regions where dust is emitted. Therefore, climate variation, such as El

Niño events, that impact drought conditions over the Saharan Desert might play an important role in causing variations in dust emissions (Prospero, 2001). The highest values of the aerosol optical thickness (AOT), a proxy for the dust concentration that can be observed globally with satellites, are observed over the Sahara Desert and Taklimakan-

Gobi (Chin et al., 2002). 3

The Arabian Peninsula is located in the Middle East, and it is surrounded by Syria and Iraq in the north, the Mediterranean in the southwest, the Red Sea in the west, the

Arabian Sea in the north-northeast, and the Arabian Gulf on the east side. It has three local dust regions, and the largest local desert is located in the south and it is called Al-Rub Al-

Khali with an area of about (600,000 ). On the east side, its smallest primary desert which is Ad Dahna with an area of about (40,000 ), and An Nafud desert in the northwest with an area of about (65,000 ). The Sarawat Mountains (Sarat Mountains) are located on the west side of the Arabian Peninsula, and they cover part of Jordan to the north of Saudi Arabia to Yemen to the south of Saudi Arabia with the highest point of 3300 m. Besides the three local desert regions, the Arabian Peninsula can be affected by remote dust regions which are the Syrian and Iraqi deserts in the north and the Saharan Desert in the west (Figure 1.1). 4

Figure 3.1: Map to show the location of the area of interest Many studies have been done on Arabian Peninsula dust storms, such as their impacts, dynamical processes and the global and/or local average of dust emissions.

Prakash et al. (2015) quantified the effects of dust storms on the vertical temperature profile and the circulation over the Arabian Peninsula by using the Weather Research and Forecast model with the chemistry section WRF-Chem. This study explored the short-wave (SW) and long-wave (LW) radiative fluxes in the presence of the dust particles and showed that the direct radiative effects for SW are cooling the surface and the direct radiative effects for LW are heating the surface. The net effect on temperature at the surface due to the dust effects was found to be a cooling of -6.70 K in the area of study. WRF-Chem model was used by Kumar et al. (2014) to simulate the dust radiative effects, and the characteristics of its plumes were investigated over the north of India from the period of 17 April to 22 5

April, 2010 and found that dust particles reduced the shortwave and long wave radiation in that region by an average of -10.1 and 5.8 at the surface, respectively.

Middleton (1986a) studied the spatial characteristics and the temporal characteristics of dust storms over the Middle East, but Middleton’s study was limited to just a few countries for a short-term period of time. Kutiel and Furman (2003) expanded his study with longer spatial and temporal characteristics also investigating the reduction in visibility caused by dust storms. Their study included northern Saudi Arabia which has the strongest dust storms and low visibility for 15% of the time on average in spring seasons. These characteristics were studied over Saudi Arabia by Notaro et al. (2013) by using observational data from MODIS, aerosol optical thickness (AOT) data, and back trajectories to trace the dust particles for 13 local stations. They identified the dust storms time period in Saudi Arabia which is from February to June and the main local/remote dust regions, the location and season of the peaks of dust concentration, and the synoptic systems which produce the dust storms. Moreover, the spring peak is found over Northern

Saudi Arabia, while the location of the summer peak is on the east side of Saudi Arabia and the highest values of AOT are generated over the Iraqi and Al-Rub Al-Khali Deserts.

The Saharan cyclone (the Sharav cyclone) in spring seasons plays an important role in causing dust storms over Saudi Arabia. In spring seasons, the jet stream over the coast of North Africa and the Mediterranean supports the moderate baroclinic layer in the upper levels to be enhanced by the strong baroclinicity in the atmospheric boundary layer to 6 produce the Sharav cyclone. Alpert and Ziv (1989) found these mechanisms by tracking the path, movements, and velocities of this cyclone and showed that the dust particles which are associated with a warm front can be lifted up to 5500 m or more. According to

Bou Karam et al. (2010), the horizontal and vertical characteristics of the Sharav cyclone and the environmental conditions were studied by using observational data, such as

(ECMWF), (CALIPSO), and (CloudSat), and they used images of (SEVIRI) to describe the dust storms over that region and found that the horizontal and vertical scales of the cyclone were 800-1000 and 8 , respectively, at the surface. The dynamical processes, path, and time periods of the cyclone were described by Meso-NH model which was validated against aerosol optical depths (AOD) data from (MODIS) website and

CALIPSO-CloudSat and showed this cyclone associated with the cold front to produce dust emissions and reduced the visibility to 0 for more than 24 hours. During a severe dust storm on 14-17 March, 1998, Alpert and Ganor (2001) studied the properties of dust particles, the movements of the dust plumes which are associated with the Sharav cyclone from the Sahara to the eastern Mediterranean. They also examined surface observational data, synoptic images and satellite aerosol index (AI) values over the Middle East region.

The AI is determined from observations of reflected ultraviolet radiation by the Total

Ozone Mapping Spectrometer (TOMS) satellite instrument and is roughly proportional to the vertically integrated concentration of dust in the atmosphere. They found that the magnitude of AI from the center of the cyclone eastward to the edge decreased from 0.9 to 7

2.1 and 2.5 AI value that corresponded to the average daily concentration of 1900 and 1000

at the surface, respectively.

The Shamal dust storms annually happen over the Middle East in the summer time, and most of them occur due to a monsoon trough on the south side of Iran and a high pressure system over the Mediterranean. It is one of the two main dust storms events over the Middle East and the south west of Asia that were recognized by Hamidi et al. (2013).

They found the values of AI increased from the west (Libyan source) to the east (Saudi

Arabian source), so the high values in May-August over the Saudi Arabian source. While the Sharav cyclone event has been well described, it is not the only circulation that contributes major dust storm events over the Arabian Peninsula. Our hypothesis is that the

Sharav cyclone case is substantially different dynamically from a summertime Shamal dust storm case. We will employ observation data and the WRF-Chem model to illustrate how the sources of dust and the atmospheric dynamics that transport the dust are distinct between these two types of dust storm systems.

In this study, we compare two cases of dust storms in different seasons over Saudi

Arabia. The cases are the Shamal case in the summer and the Saharan (Sharav) cyclone in the spring, and they have different patterns in the Synoptic Scale environment. Most of this work focuses on Northern Saudi Arabia between the two remote dust regions which are the

Syrian and Iraqi deserts and the two local dust regions which are An Nafud and Ad Dahna 8 deserts to detect the different mechanisms between the dynamical processes of the cases by using WRF-Chem model.

2.0 Data and Methodology:

In this research, we use upper air analyses from NASA datasets including satellite imagery, surface observational data, and a mesoscale numerical model to simulate the two cases.

The simulation data is validated against the NASA satellite data and the surface observational data in order to know how well the model compares.

2.1 NASA Datasets:

In this study, observational data at high spatial and temporal resolutions are needed for the study periods to characterize the meteorological conditions at the surface, the synoptic features at upper levels, and the comparisons between the model results and the datasets.

The Deep Blue Aerosol Optical Depth (AOD) at the wavelength of 550 nm were obtained from the Moderate Resolution Imaging Spectroradimeter (MODIS) instrument aboard the

Terra satellite using level 2.0 products and the collection 6 over the Middle East region.

This Deep Blue product is used over bright regions, such as deserts, to distinguish the 9 properties of dust aerosols from other particles. It uses the SeaWIFS satellite sensor (Sea-

Viewing Wide Field-of-View Sensor) along with MODIS data. However, these properties aren’t characterized by the standard MODIS AOD data in bright surfaces (Zhao et al., 2006;

Hsu et al., 2004). The Deep Blue product was validated against the AERONET data over

King Abdullah University of Science and Technology (KAUST) on 19 March 2012, and the

Deep Blue AOD value was ≅ 10 % higher than the AERONET AOD data (Prakash et al.,

2015). The comparison between these two AOD data from 2002 to 2012 was described by Sayer et al., (2013). They found that the correlation coefficient between the collection

6 Deep Blue AOD data with the highest confidence retrievals and AERONET AOD data was

0.82 over Saudi Arabia. More details about this satellite can be found online at

(http://terra.nasa.gov/about/terra-instruments/modis). The tracks of air masses were traced from the dust source regions to the area of study by using the back-trajectory method at two heights (100 m and 1500 m) from the Hybrid Single Particle Lagrangian

Integrated Trajectory (HYSPLIT) model using the archived observational data from the

Global Data Assimilation System (GDAS) every 50 km. More information about this model can be found at (http://ready.arl.noaa.gov/HYSPLIT.php). To test the accuracy of the synoptic processes in the numerical simulations, the numerical model data should be validated against the analyses from MERRA data. MERRA stands for the Modern Era-

Retrospective Analysis for Research and Applications, and NASA produces this data by 10 using the data assimilation system, GEOS (the Goddard Earth Observing System)

(Rienecker et al., 2011). The MERRA data products have global data for long periods of time, and it helps researchers and/or students to reanalyze meteorological variables

(Saha et al., 2010). Furthermore, it has a very high spatial resolution and temporal resolution, and the data are generated for every 3 hours at 0.5° latitude and 0.5° longitude and has 42 vertical levels (Rienecker et al., 2011). More information about MERRA data products can be found online at (http://gmao.gsfc.nasa.gov/research/merra/intro.php).

2.2 WRF-Chem model:

The model that has been used in this research is WRF-Chem that is the chemistry part of the third version of the Weather Research and Forecasting system (WRF). The WRF model is a Numerical Weather Prediction (NWP) system that is supported by some institutes such as the National Center for Atmospheric Research (NCAR), and it is used by researchers and/or students who are interested in, for example, simulating synoptic features or understanding a specific atmospheric phenomenon (Skamarock et al., 2008).

This system is used to understand and produce the weather forecasts. WRF-Chem has 11 been used in a number of other studies of dust storms (Chen et al., 2014; Kang et al.,

2011). More details about WRF can be found online at (http://www.wrf- model.org/index.php). One of the most important goals of using WRF (online or offline) is the chemistry component so that the air quality can be investigated. The WRF model has the capability to simulate the quality, the movements, the concentration of gases and aerosols (Grell et al., 2005; Skamarock et al., 2008 ). More details about WRF can be found online at (http://ruc.noaa.gov/wrf/WG11/).

The dust emission scheme that has been used in this project is the Goddard global ozone chemistry aerosol radiation and transport (GOCART) (Ginoux et al., 2001).

This scheme was validated against the satellite data (Ginoux et al., 2001; Chin et al., 2002) and the AERONET data over the Arabian Peninsula with a good agreement (Prakash et al.,

2015; Zhang et al., 2015).

The scheme can be shown as:

= CSU −U if >

where is the dust emission flux in units of , C is the constant with the value of 1 , S is the source function which varies by location according to the properties of the soil, and it is dimensionless. This scheme has eight size bins and s is the 12 fraction of the total emission for each size bin, p. U is the wind at the height of 10 meters above the surface, and is the threshold velocity of wind erosion.

For this study, the entire area of interest should be covered in the domain of the model simulation in order to precisely investigate the synoptic features and how they relate to the small scale dynamics and dust emissions. Figure 2.1 shows that the configuration of

WRF-Chem has two nests (three domains), and the center of the domain is located at the latitude 27°N and longitude 42°E. The first nest (the domain) covered the entire area of the Arabian Peninsula, the eastern Mediterranean Sea, part of the Saharan Desert, the

Arabian Sea, and part of the Middle East between latitudes (49°N to 17°N) and longitudes

(60°E to 24°E), the number of the grid nodes is 100 X 100 and the horizontal resolution is

45 . The second nest has 100 X 100 grid nodes between latitudes (33°N to 19.9°N) and longitudes (48°E to 34.7°E) with 15 horizontal resolution, and the third nest covered northern Saudi Arabia between latitudes (29.8°N to 24°N) and longitudes (45°E to 38.5°E) with 121 X 121 grid nodes and 5 horizontal resolution. 13

Figure 2.1: The domain configuration

The details of the domain configuration, such as the horizontal resolution, dimensions, vertical levels, boundary conditions, time step, dust-radiative feedback, simulation period, physical parameterizations, and the emission scheme are shown in the following table:

Table2.2: The parameters of the model

Model name: WRF-Chem (Grell et al., 2005) The number of nests 1st, 2nd, and 3rd Horizontal resolution 45 Km, 15 Km, and 5 Km (respectively) Dimensions in X-axis and (100, 100), (100, 100), and (121, 121) Y-axis Vertical levels 35 Boundary conditions FNL from NCAR/UCAR (NCEP Data) Time step 180s Dust-radiative feedback Yes Simulation period The Sharav case: 31st of March, 2015 – 01st of April, 2015 The Shamal case: 9th-10th of June, 2015 Physical schemes Microphysics (Lin et al., 1983) Planetary Boundary Layer (PBL) (Janjic, 2011) Cumulus convection (Grell and Devvenyi, 2002) Emission scheme GOCART scheme (Ginoux et al., 2001)

3.0 Research Findings

3.1 Observational Analyses

The two case studies of dust storms, which are the Sharav case from 31 March 2015 to 01

April 2015 and the Shamal case from 09 June 2015 to 10 June 2015, covered the entire area of Saudi Arabia and affected many countries which surround Saudi Arabia, such as

Syria, Lebanon, Jordan, Iraq, and Kuwait. Many car accidents happened, some flights were delayed, and some health and environmental problems occurred due to these storms (Dolce,

2015).

Figure 3.1 shows the back trajectories, which are calculated by the HYSPLIT model, for the dust events at two atmospheric heights (100 m and 1500 m) initiated at one synoptic station located over Rafha City in northern Saudi Arabia, which is surrounded by the 16

Syrian and Iraqi Deserts on the north side and the local desert regions on the south side, at the latitude 29° and the longitude 43°. In order to trace the air masses at these heights for 72 hours before the dust storm happened, we integrate backward the trajectory of the air parcel using observational analyses. This was performed employing data at 12 hour intervals. The values of the Aerosol Optical Depth (AOD) from the MODIS measured over time were shown in Figure 3.2. The small boxes on the trajectory in

Figure 3.1 at the height of 100 m and the small circles on the trajectory at the height of

1500 m represent the estimated AOD values by superimposing the AOD values from the

MODIS with the exact location of the air parcels along the trajectories. For the Sharav case, the trajectory at 100 m shows the path of a northeast to southwest wind that means the winds were coming from the southern Mediterranean Sea at an elevation of approximately 1400 m. The AOD value on that day was about 0.4. On 31 March 2016 at

07:25 UTC, the winds close to the surface were crossing the Syrian and Iraqi Deserts that caused an increase in the values of AOD, and these values are proportional to the intensity of the dust storm (Figure 3.2). 17

Figure 3.2: The AOD values at 550 nm from MODIS Deep Blue measured over time for (Left) the Sharav case and (Right) the Shamal case. The highest AOD values, which are associated with locations that are strongly affected by desert regions, were measured during the dust event over the Iraqi desert and the synoptic station at 07:30 UTC with a magnitude of 1.7 and 1.2, respectively. Furthermore, the trajectory at the higher level (1500 m) shows that the winds were coming from the same source regions as the lower trajectory and displays the same path (Figure 2.1). As a result, these trajectories and AOD values estimate that the dust particles in this case were generated and transported from the Saharan Desert. During their movements across Syria and Iraq, particulate concentration was enhanced and strengthened by the Syrian and Iraqi

Deserts increasing the intensity of the dust storm over northern Saudi Arabia.

In the Shamal case, a northeast to south path showed that the winds were coming from the

Eastern Mediterranean Sea at the lower trajectory and moving across the Syrian and Iraqi

Deserts to produce the AOD values of 0.9 over both the Iraqi desert on 09 June 2016 at 18

07:30 UTC, and the station during the dust event on 01 April 2016 at 07:25 UTC. At the higher trajectory level (1500 m) with the path of a southeast to south wind, the source region of the air masses was the Saharan Desert with an AOD magnitude of 1.1 on 08 June

2016 at 07:25 UTC. On the next day, some particles were lost due to dry and wet deposition which decreased the AOD values (Figure 3.2).

3.1.2 The Large-Scale Structure of the Atmosphere:

For the Sharav case, a jet streak at the 300 hPa level moved southeastwards to cover the area between latitude 27° 34°N and longitude 30° 50°, during the time period of the study (Figure 3.3). In the Shamal case, however, the feature was located at latitude

30° 37° and longitude 26° 38° as shown in Figure 3.4.

As discussed further below, these jet streaks play an important role in developing low and high pressure systems at the surface, and they determine the stability of the atmosphere in several different ways. The jet streaks contain circulations that represent vertical motions when they are in thermal wind balance. 19

Figure 3.3: The jet streaks location at the 300 hPa for the Sharav case. (in the top-left corner): 18:00 UTC 31 March 2015, (in the top-right corner): 21:00 UTC 31 March 2015, (in the bottom-left corner): 03:00 UTC 01 April 2015, and (in the bottom-right corner): 09:00 UTC 01 April 2015.

These circulations are thermally direct in the entrance region with divergence on the southwest side and convergence on the northwest side of the jet entrance region and thermally indirect with divergence on the northeast side and convergence on the southeast side of the jet exit region. The differences in the 300 hPa circulation between the Sharav and Shamal case studies cause differences in the circulation at the surface that impact storm evolution differently in the two cases.

Figure 3.5: The Geopotential Heights, wind speed, and direction at 500 hPa for the Sharav case. (in the top-left corner): at 18:00 UTC on 31 March 2015, (in the top-right corner): at 21:00 UTC on 31 March 2015, (in the bottom-left corner): at 03:00 UTC on 01 April 2015, and (in the bottom-right corner): at 09:00 UTC on 01 April 2015. 3.1.2a The Sharav case

For the Sharav case, the jet streak propagated southeastward to Northern Saudi Arabia and also covered Syria and Iraq. On 01 April 2015 at 09:00, it was situated on northeastern

Saudi Arabia with the eastwards exit region, and a strong divergence aloft was in place 21 over Rafha City in northern Saudi Arabia due to a thermally indirect circulation. The north side of Saudi Arabia was affected by the strong vertical motion. As a result of this movement, the evolution of the geopotential heights at 500 hPa shows a very strong trough was located directly beneath the region of the strongest divergence aloft at the 300 hPa.

Furthermore, this trough was traveling from the Southern Mediterranean Sea southeastwards to northern Saudi Arabia and a very weak ridge passed southern Saudi

Arabia northeastwards to western India (Figure 3.5).

By looking at the geopotential heights at the 850 hPa, the ridge was reduced southeastwards from Northern Africa due to the strength of the divergence aloft at the 300 hPa (Figure

3.6). This Figure shows that a relative trough, at the same level and time, was strengthened and moved with the upper jet streak to develop a low pressure system at northern Saudi

Arabia on the day of the dust storm at 09:00 UTC. 22

Figure 3.6: Geopotential Heights at the 850 hPa for the Sharav case. (in the top-left corner): at 18:00 UTC on 31 March 2015, (in the top-right corner): at 21:00 UTC on 31 March 2015, (in the bottom-left corner): at 03:00 UTC on 01 April 2015, and (in the bottom-right corner): at 09:00 UTC on 01 April 2015. During this time period, mean sea level pressure data from the MERRA reanalysis shows a strong low pressure system that developed over northwestern Saudi Arabia and moved southeastwards (Figure 3.7). 23

Figure 3.7: Mean sea level pressure (Pa) for the Sharav case. (in the top-left corner): at 18:00 UTC on 31 March 2015, (in the top-right corner): at 21:00 UTC on 31 March 2015, (in the bottom-left corner): at 03:00 UTC on 01 April 2015, and (in the bottom-right corner): at 09:00 UTC on 01 April 2015. Figure 3.8 shows a strong cold front was moving through Northern Africa to northern Saudi

Arabia and associated with this storm. On 01 April 2016, a cyclone was generated over northern Saudi Arabia, on the west side of the Arabian (Persian) Gulf, and associated with this cold front, and strong winds occurred behind the cold front with a maximum speed of

≅ 13 , as shown in Figure 3.9. 24

Figure 3.8: The surface air temperature for the Sharav case. (in the top-left corner): at 18:00 UTC on 31 March 2015, (in the top-right corner): at 21:00 UTC on 31 March 2015, (in the bottom-left corner): at 03:00 UTC on 01 April 2015, and (in the bottom-right corner): at 09:00 UTC on 01 April 2015.

As a consequence of this wind, dust particles were strongly emitted, and the dust concentration was increased. These figures display the relationships between the upper trough and the low pressure system at the surface.

In contrast, for the Shamal case, the geopotential heights at the 500 hPa in Figure 3.10 show that a large ridge was in place over Saudi Arabia with northeasterly winds surrounding it. These wind systems were propagating southwestward over the Arabian Sea to Saudi Arabia, which enhanced the longitudinal circulation over that region. This circulation was associated with the winds that facilitated the transport of dust particles 26 southwestward. A relative monsoon trough over the Arabian Sea, which moved westwards to the southeastern Arabian Peninsula, is shown in the 850 hPa geopotential heights in

Figure 3.11. A high pressure system built up over the Mediterranean Sea due to the upper ridge, and the monsoon trough developed a relative low pressure system in place over the coast of Oman, which plays a significant role in the transport of dust particles (Figure 3.12).

Figure 3.11: Geopotential heights and wind direction at 850 hPa for the Shamal case. (in the top-left corner): at 12:00 UTC on 09 June 2015, (in the top-right corner): at 21:00 UTC on 09 June 2015, (in the bottom-left corner): at 15:00 UTC on 10 June 2015, and (in the bottom-right corner): at 21:00 UTC on 10 June 2015. 27

Figure 3.12: Mean sea level pressure for the Shamal case. (in the top-left corner): at 12:00 UTC on 09 June 2015, (in the top-right corner): at 21:00 UTC on 09 June 2015, (in the bottom-left corner): at 15:00 UTC on 10 June 2015, and (in the bottom-right corner): at 21:00 UTC on 10 June 2015. When the sun heats up the land and the ocean surface, especially in the summer, the land absorbs and releases the sun’s radiation much faster than the ocean due to the differences between their heat capacity. In the day time, an air parcel right above the land will be heated faster than an air parcel that is right above the ocean surface. This process develops a low pressure system over the land and a high pressure system over the ocean, respectively. Figure 3.13 shows the differences in temperature between the

Mediterranean Sea, the Arabian Sea, and the Arabian Peninsula which caused the longitudinal circulation with a maximum wind of 13 to enhance the intensity of the dust storm over the region (Figure 3.14). 28

Figure 3.13: The surface air temperature for the Shamal case. (in the top-left corner): at 12:00 UTC on 09 June 2015, (in the top-right corner): at 21:00 UTC on 09 June 2015, (in the bottom-left corner): at 15:00 UTC on 10 June 2015, and (in the bottom-right corner): at 21:00 UTC on 10 June 2015.

Figure 3.14: Wind speed and direction at 10 m above the surface for the Shamal case. (in the top-left corner): at 12:00 UTC on 09 June 2015, (in the top-right corner): at 21:00 UTC on 09 June 2015, (in the bottom-left corner): at 15:00 UTC on 10 June 2015, and (in the bottom-right corner): at 21:00 UTC on 10 June 2015. 29

3.2 The Modeled Results: The WRF-Chem model was utilized in this study because it allows us to examine the sources of dust, and it has higher resolution in space and time than the MERRA data. The modeled results compare well with the NASA datasets.

3.2.1 The Large-Scale structure of the atmosphere for the Sharav case:

For the Sharav case, Figure 3.15 displays the geopotential heights at the 500 hPa which are produced by the WRF-Chem model, and the geopotential heights, at the same level and time, from MERRA reanalysis are shown in Figure 3.5. The images from WRF-

Chem are similar to the images from MERRA, which means the model compared well to the reanalysis constrained by satellite data. The upper-level trough that is shown in these figures produced a low pressure system at the surface over northern Saudi Arabia with a magnitude of 1007 hPa in the center of the system (Figure 3.16b). Figure 3.16 shows a high pressure system built up at the surface over Northern Iran, Afghanistan, and

Tajikistan with a central magnitude of 1020 hPa. 30

Figure 3.15: The geopotential heights, wind speeds, and direction at 500 hPa for the Sharav case. a): 18:00 UTC on 31 March 2015, b): 21:00 UTC on 31 March 2015, c): 03:00 UTC on 01 April, and d): 09:00 UTC on 01 April 2015. These two pressure systems combined together to produce significant northwesterly winds at 10 meters above the surface on the west side of the cyclone, as shown in Figure

3.17d. On the other hand, Figure 3.17c shows that the maximum wind speed over northern Saudi Arabia was about 10 that was not enough to produce strong dust emissions over that region. 31

Figure 3.16: Mean sea level pressure for the Sharav case. a): 18:00 UTC on 31 March 2015, b): 21:00 UTC on 31 March 2015, c): 03:00 UTC on 01 April 2015, and d): 09:00 UTC on 01 April 2015. Within a time of 6 hours at the same location, the wind speeds increased to reach a maximum of 14 and were associated with high temperatures that reduced the stability of the atmosphere near the surface. These winds, which were consistent with the

300 hPa jet streaks in Figure 3.3, moved southeastwards crossing An Nafud desert in northwestern Saudi Arabia (Figure 3.17). As a result of these circumstances, dust particles were emitted and transported southeastwards from Northern Africa and the southern Mediterranean Sea to reach northeastern Saudi Arabia. 32

Figure 3.47: Wind speed and direction at 10 m above the surface on 01 April 2015 for the Sharav case. a): 18:00 UTC on 31 March 2015, b): 21:00 UTC on 31 March 2015, c): 03:00 UTC on 01 April 2015, and d): 09:00 UTC on 01 April 2015.

3.2.2 The Large-Scale structure of the atmosphere for the Shamal case:

For the Shamal case, the evolution of the geopotential heights on the 500 hPa surface from the WRF-Chem model, that is shown in Figure 3.18, is consistent with the geopotential heights, at the same level and time, from the MERRA reanalysis in Figure

3.10. In this case study, the data from the model was validated against the NASA MERRA, and they are consistent with each other. Figure 3.18 shows the sequence of a large ridge which was situated over northern Saudi Arabia that propagated within a time period of about two days towards the monsoon trough in southeastern Saudi Arabia. The juxtaposition of these two systems created a pressure gradient force which caused strong 33 northerly and northwesterly winds. This circulation on the 500 hPa level affected the environment at the Earth’s surface and developed a high pressure system over the eastern

Mediterranean Sea with a maximum value of approximately 1018 hPa and a low pressure system on the southeast side of Saudi Arabia with a minimum value of 998 hPa in its center

(Figure 3.19).

Figure 3.18: The geopotential heights on the 500 hPa surface simulated by the WRF-Chem for the Shamal case. a): 12:00 UTC 09 June 2015, b): 21:00 UTC 09 June 2015, c): 15:00 UTC 10 June 2015, and d): 21:00 UTC 10 June 2015. The northerly and northwesterly winds, which were generated by the juxtaposition between these two pressure systems, were strengthened by the longitudinal circulation over the area of study. Significant winds at 10 m above the Earth’s surface were produced by the model to show a maximum speed of approximately 15 m over Rafha city, on the 34 west side of the Persian Gulf, that means dust emissions and dust concentration are likely increased here (Figure 3.20).

Figure 3.19: Mean sea level pressure from the WRF-Chem model for the Shamal case. a): 12:00 UTC 09 June 2015, b): 21:00 UTC 09 June 2015, c): 15:00 UTC 10 June 2015, and d): 21:00 UTC 10 June 2015. 35

These strong winds, which are enhanced by the longitudinal circulations in this season, generated a deep well-mixed layer above the surface that caused severe dust storms over the area of study.

Figure 3.20: Wind speed and direction at 10 m above the surface on 01 April 2015 for the Shamal case. a): 12:00 UTC 09 June 2015, b): 21:00 UTC 09 June 2015, c): 15:00 UTC 10 June 2015, and d): 21:00 UTC 10 June 2015. 3.2.3 The Meteorological conditions for both cases:

Figure 3.21 shows the differences in the trajectories between the Sharav and the

Shamal case studies at the height of 100 m, 500 m, 1000 m, 1500 m, 2000 m, 2500 m, and 3000 m. In the Shamal case study, the trajectories show that the strong curvature to the north and east decreases with respect to height, this curvature is a reflection of the 36 large ridge in the upper-level atmosphere over that region, while the trajectories in the

Sharav case study are very shallow with lower curvature.

Figure 3.21: The trajectories at 100 m, 500 m, 1000 m, 1500 m, 2000 m, 2500 m, and 3000 m for the Sharav case and the Shamal case. The soundings in Figure 3.22 shows the different meteorological conditions between these two types of dust storms, and the yellow arrows on the trajectories represent the locations of these soundings over the Iraqi desert during the strongest dust signals in both cases. These soundings indicate that there is a well-mixed layer in the Shamal case study with a maximum wind speed of ≅15 from the north which is the deeper mixed layer with higher temperatures, while the well-mixed layer in the Sharav case study with a maximum wind speed of ≅12 from the west direction is not as deep. The soundings show that the differences between the temperature and the dew-point temperature in the Shamal case is bigger than in the Sharav case that means the Shamal 37 case is drier than the Sharav case. The results in the soundings suggest that the dust emission in the Shamal case is higher than in the Sharav case because the dust emission flux is a function of the wind speed at the surface, the dust emission sources, and the surface wetness. These soundings indicate that the deeper well-mixed layer in the Shamal case is consistent with the larger amount of turbulence kinetic energy at the surface as well as the greater wind shear in the soundings and trajectory structure.

Figure 3.22: The soundings over the Iraqi desert during the strongest dust signals for the Sharav case and the Shamal case The characteristics of the area within this domain, which is a source function variable (S) in the GOCART scheme, were utilized to characterize dust emission source regions (Figure

3.23). 38

Figure 3.23: The source function for the area of study 3.2.4 The Mesoscale boundary layer details:

3.2.4a The Sharav case:

Figure 3.24 shows the air temperature and dew-point temperature in the Skew-T sounding from the model to show the sequence of the lapse rate within a time period of 6 hours for two stations. These stations are Sakaka in Saudi Arabia at the latitude 30° and longitude

40° and Rafha in Saudi Arabia at the latitude 29.63° and longitude 43.5° on 01 April

2015 at 03:00 and 09:00 UTC. This sounding shows the dry adiabatic layer went from about 970 hPa to 700 hPa, and this deep layer represents the well-mixed layer above the surface. This sounding often occurs near the dry land, especially, the land with a little moisture sources due to the difference between the temperature and the dew point temperature. This convection can help to transport the dust particles into the atmosphere and generate the turbulence kinetic energy (Figure 3.25). 39

Figure 3.24: The air temperature (black line) and dew-point temperature (blue line) in the skew-T sounding from the model on 01 April 2015 for a) Sakaka, Saudi Arabia at the latitude 30° and longitude 40° at 03:00 UTC, b) Rafha, Saudi Arabia at the latitude 29.63° and longitude 43.5° at 03:00 UTC, c) Sakaka, Saudi Arabia at 09:00 UTC, and d) Rafha, Saudi Arabia at 09:00 UTC.

Figure 3.25: Turbulence kinetic energy for the Sharav case on 01 April 2015 at 03:00 UTC (left) and 09:00 UTC (right) 40

Figure 3.26: The dust concentration and wind direction at 10 m for the Sharav case. a): 18:00 UTC on 31 March 2015, b): 21:00 UTC on 31 March 2015, c): 03:00 UTC on 01 April 2015, and d): 09:00 UTC on 01 April 2015. In this study, dust concentrations, wind speed and wind direction at 10 meters were simulated to show that some dust source regions were active at the same time (Figure 3.26).

High dust concentrations with a maximum of 190 were produced in Southeastern

Saudi Arabia due to the transport of the dust particles and the dust emissions by the strong northwesterly winds in that region (Figure 3.26).

The GOCART dust emissions scheme was utilized to calculate dust emissions in the area of study to show which time has the most intense dust emissions within the time period

(Figure 3.27). During the beginning of the time period, the dust emission source regions on the west side of the Red Sea were active and combined with the winds to transport some particles southeastwards. On 01 April 2015 at 09:00 UTC, dust emissions 41 intensified and prevailed in northern and northeastern Saudi Arabia that means they were a reflection of the strong northwesterly winds at the same location (Figure 3.27d).

Figure 3.27: The dust emissions for the Sharav case. a): at 18:00 UTC on 31 March 2015, b): at 21:00 UTC on 31 March 2015, c): at 03:00 UTC on 01 April 2015, and d): at 09:00 UTC on 01 April 2015. The daily rate simulation of dust emissions within an average of 24 hours for 31 March

2015 and 01 April 2015 are shown in Figure 3.28. On 31 March 2015, Northern Africa had the most intense dust emissions due to the location of the cyclone at the same time, and in northern Saudi Arabia the simulated daily rate of dust emissions on 01 April 2015 exceeded ≅ 2.5x10 . As a result of the movement of this cyclone, the intensity of the dust emissions was linked by the cyclone to be place in over northern and northeastern Saudi Arabia (Figure 3.28b). 42

Figure 3.28: The daily rate of dust emissions for the Sharav case. a) 31 March 2015 and b) 01 April 2015.

3.2.4b The Shamal case:

The Skew-T sounding from the model for the two stations in the Shamal case is shown in Figure 3.29. The sequence of the deep well-mixed layer shows that the dry adiabatic lapse rate extended from the surface to ≅ 400 hPa with very high temperatures at the surface. This deep well-mixed layer generates strong dry convective overturning that causes a high dust emission rate and strong transport of the dust particles into the atmosphere.

Figure 3.29: The air temperature (black line) and dew-point temperature (blue line) in the skew-T sounding from the model on 09 June 2015 for a) Sakaka, Saudi Arabia at the latitude 30° and longitude 40° at 12:00 UTC, b) Rafha, Saudi Arabia at the latitude 29.63° and longitude 43.5° at 12:00 UTC. On 10 June 2015 c) Sakaka, Saudi Arabia at 15:00 UTC, and d) Rafha, Saudi Arabia at 15:00 UTC. This unstable condition generates strong turbulence kinetic energy (TKE) which led to a strong dust storm. Figure 3.30 displays the movement of the TKE at the surface with a maximum of 3 in the Shamal case study with northerly and northwesterly winds. Figure 3.31 shows the dust concentration over the area of study and these values are proportional to the strong northwesterly winds, that are shown in Figure 3.20. At low levels over the desert regions, the TKE caused high dust concentrations with a significant magnitude of ≅ 1000 on 09 June 2015 at 12:00 UTC (Figure 3.31a). Also, the 44 model calculated the dust emissions within the domain and they were consistent with the sequence of the dust concentrations in this case study.

Figure 3.30: Turbulence kinetic energy for the Shamal case on a) 09 June 2015 12:00 UTC, b) 09 June 2015 21:00 UTC, c) 10 June 2015 15:00 UTC, and d) 10 June 2015 21:00 UTC

The sequence of the dust emissions is shown in Figure 32 with a maximum of ≅

7000 on 09 June 2015 at 12:00 UTC. The simulated daily rates of dust emissions for this case study were calculated and the most intense daily rate was on 09

June 2015 with a magnitude of ≅ 9x10 on the northwest side of the Persian

Gulf and ≅ 5x10 on 10 June 2015 over the same region (Figure 3.33). 45

Figure 3.31: The dust concentration and wind direction at 10 m for the Shamal case. a): 18:00 UTC 31 March 2015, b): 21:00 UTC 31 March 2015, c): 03:00 UTC 01 April 2015, and d): 09:00 UTC 01 April 2015

Figure 3.32: The dust emission and wind direction at 10 m for the Shamal case. a) 12:00 UTC 09 June 2015, b) 21:00 UTC 09 June 2015, c) 15:00 UTC 10 June 2015, and d) 21:00 UTC 10 June 2015.

Figure 3.33: The daily rate dust emissions over 24 h for the Shamal case. (left) on 09 June 2015 and (right) on 10 June 2015

4.0 Discussion/Conclusion:

This current study defines and analyzes two major Saudi Arabian dust storms associated with the 300 hPa jet streaks. The backward-trajectories for these dust events were calculated based on the archived observational data at one synoptic station which is situated over Rafha City in Northern Saudi Arabia to determine the dust source regions for each case study. The Saharan, Syrian, Iraqi and An Nafud deserts are the major dust sources for the Sharav case study. For the Shamal case study, however, the major sources are the

Syrian and Iraqi deserts. Furthermore, the AOD values from MODIS were superimposed on these trajectories to assess the processes by comparing to their exact locations on the trajectories for these two case studies in order to recognize the impact of the dust sources.

The 100 m elevation Sharav trajectory showed that the air mass moved over the

Iraqi desert toward the synoptic station with a maximum AOD of 1.7, which indicates a highly concentrated dust plume over the Iraqi desert at 07:30 and its magnitude became weaker away from the source with a value of 1.2 due to dry depositional processes. These results strongly suggest that the Sahara Desert produced the dust particles that were enhanced by the Syrian and Iraqi Deserts while these particles were transported toward

Northern Saudi Arabia within a time period of 72 hours. The peaks of AOD in the Shamal case for both trajectories at 100 m and 1500 m were 0.9 and 1.1, respectively, which were lower than the highest AOD value in the Sharav case. These two dust events had different 48 dust source regions based on their trajectories which, to a large extent, caused the differences of the AOD values between these events.

This study considered the large-scale atmospheric circulation structure to distinguish the different features associated with these events. The maximum upper-level jet wind speed was observed in the Sharav case with a value of approximately 70 compared to the maximum speed of about 40 in the Shamal case. These two jet streaks had different impacts on the surface due to their locations and seasons. For the

Sharav case study, Northern Saudi Arabia was affected by the divergence aloft accompanying this jet which contributed to the development of a low pressure system over the northern part of Saudi Arabia. At low levels this jet was also associated with a relative high pressure system over Northern Africa and included the region on the east side of the

Mediterranean Sea resulting in northeasterly low-level winds with a maximum speed of 13

over northwestern Saudi Arabia. Moreover, these results support the concept that there was a cyclone associated with the cold front that propagated from the west to the east and then southeast. In this case, the strong pressure gradients over Northern Saudi Arabia played a significant role in strengthening the winds over the region. In comparison, the jet streak in the Shamal case caused a large anticyclone that was situated over Saudi Arabia to develop in a combination with a relatively weak low pressure system that was in response to the monsoon trough over southern and southeastern parts of Saudi Arabia. Also, strong northerly and northwesterly winds with a maximum speed of about 15 around this 49 ridge were enhanced by the longitudinal circulation in this case study to transport the dust particles over the Syrian and Iraqi Deserts. As a result, the instability of the atmosphere increased due to the turbulence which was generated by these circumstances. The connection between this instability and turbulence comes from the fact that cooling above the surface reduced the static stability and increased the buoyant generation of turbulence kinetic energy (TKE) in proximity to accelerating low-level flow. The accelerating low- level flow provided wind shear also favorable for planetary boundary layer (PBL) turbulence.

The WRF-Chem model was used in this study to create a more accurate quantitative dataset for dust emission and dust loading than the satellite data alone could provide due to the different spatial and temporal resolutions of the model and satellite. The synoptic features which were produced by the model closely match the same variables from the

MERRA reanalysis. The model identified the dust source regions within the domain by using the source function variable in the dust emission scheme. The 10-meter wind fields were simulated for both Sharav and Shamal case studies to depict the path of dust transport.

During the Sharav dust event, the northeasterly winds in northwestern Saudi Arabia were accelerating with a maximum velocity of approximately 14 and subsequently veered to become progressively more easterly near Rafha City, that is in the northern part of Saudi

Arabia, causing in part the well-mixed turbulent PBL from the surface to about 700 hPa, while the Shamal case had northwesterly flow from northern and northeastern Saudi Arabia 50 toward southern and southwestern Saudi Arabia with the highest wind velocity of ≅15

. Furthermore, the Skew-T soundings for both the Sharav and Shamal case studies show that the Shamal case has a deeper well-mixed PBL with higher temperatures at the surface which caused larger TKE with a maximum magnitude of 3 for this event, while the Sharav case has a TKE value of approximately 2.5 . In northern Saudi

Arabia, the simulated dust concentrations at the surface are higher in the Shamal case study with a maximum value of ≅ 1000 , while the dust concentration in the Sharav case study reached a maximum magnitude of ≅ 250 . The modeled results show that the highest daily rate of dust emissions over 24-hour was simulated in the Shamal case study with maximum values of 9x10 and 5x10 on 09 and 10 June

2015, respectively. However, the averages of dust emissions over 24-hour in the Sharav case study were 2.5x10 and 3x10 on 31 March 2015 and 01 April

2015, respectively.

The major dust source regions of the Sharav and Shamal case studies emit and transport dust particles that affect Saudi Arabia in the spring and summer seasons. The hot dry climate in Saudi Arabia can potentially cause increasing dust emissions in the area of study. This paper sheds some light on the importance of understanding the different large- scale features in the two cases of dust storms and how they relate to the mesoscale features that impact the evolution of dust storms differently. The different radiative effects of dust particles and the interactions of these particles with clouds in these two case studies have 51 not been studied in this paper, but these can have different impacts on Saudi Arabia and need to be a focus of our future research. In our future research, we will do a study of more dust storms cases to establish how common the meteorological features are that characterize the Sharav and Shamal cases, and how common it is for these conditions to coincide with enhanced dust loading over Saudi Arabia.

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