Monitoring the changes in dust storms and their relationship with the North Atlantic Oscillation index (NAO) in selected stations in the west and southwest of Iran

Document Type : Research Paper

Authors

1 Ph.D. in Climatology, Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili

2 Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili

Abstract

Extended Abstract



1. Introduction

Dust storms play a crucial role in the Earth’s atmospheric cycle. In arid and semi-arid regions, the rapid change in air temperature causes the formation of pressure differences in different parts of the region and the creation of strong and permanent winds. Deserts and dry regions and strong winds are the two causes of the dust phenomenon. One of the most effective ways to identify dust source areas is by using remote sensing techniques. The AOD (Aerosol Optical Depth) index provides the possibility of temporal and spatial monitoring of dust over a wide area in terms of detection accuracy and temporal and spatial extent.



2. Research Methodology

In this research, we investigated the relationship between the North Atlantic Oscillation index (NAO) and the occurrence of dust storms from the data related to the horizontal visibility of less than 1000 m and the current weather code expressing the dust storm (codes 6 to 9 and 30 to 35) 6 The synoptic station located in the west and southwest of Iran (Abadan, Bostan, Ilam, Dehloran, Kermanshah and Sarpol-E-Zahab) was used for some time (1987-2022) and the values related to the index (NAO) were also obtained from www.cdc.noaa .gov.htm was removed. To control the quality of the data, correlation coefficients were calculated along with the negative and positive phases of the North Atlantic oscillations and the frequency of dust storms. To check the temporal distribution of dust, satellite data of the AOD index produced by MODIS and MISR measurements obtained from Giovanni’s website at http://gdata1.sci.gsfc.nasa.gov were used, and the graph of the index changes in time scale Annually, the North Atlantic Oscillation Index was examined in two positive and negative phases. Due to the high ratio of the number of days in the positive phase compared to the negative phase in the years when the positive phase prevailed, dust storm routing was conducted with the HYSPLIT model for 6 h and at three altitude levels of 200, 1000 and 1500 m from the ground for the period Statistics were made for 2011-2022 and wind direction was assessed using WRPLOT software.



3. Results and discussion

The results of examining the relationship between the North Atlantic Oscillation and dust storms at selected stations showed that in all stations, between 51% and 70% of dust storms occurred in the positive phase. In general, it seems that the conditions for the occurrence of dust storms show more compliance with the positive phase. The number of correlation coefficients between the North Atlantic Oscillation index and the annual dust storms in the desired stations is inversely and significantly correlated with the NAO index in the negative phase, and it is insignificant and insignificant in the negative phase. The results of the study of the graphs of the changes in the AOD index produced by the MISR sensor at the same time as the positive phase in the stations of Ilam, Kermanshah, and Sarpol-E-Zahab show low amounts of aerosols and significant amounts in the three stations of Abadan, Bostan, and Dehloran. In the negative phase of the NAO index, changes in the amount of suspended particles in the atmosphere from years 2001 to 2006 and 2010 decreased at all stations. While it has increased in year 2008. Along with the predominance of the positive phase according to the AOD index obtained from the MODIS sensor, there has been an increasing trend in the annual average slope of the AOD index at all stations. Along with the dominance of the negative phase, AOD values showed that from 2002 to 2006, the average trend of the AOD index was decreasing, and from 2008 to 2016, the index value increased in all stations. The routing of dust particle transport during the positive phase of the North Atlantic Oscillation Index in the stations investigated with the HYSPLIT model shows the western and southwestern part of the region where dust enters these stations. In other words, the dust originated from eastern Syria, Iraq, and Saudi Arabia and entered Iran and reached these stations. The results of the annual Wind Rose show that in Abadan and Bostan stations, the most erosive winds were from the northwest direction, in Ilam from the southwest direction, and in the rest of the stations, they were from the west side.

4. Conclusion

The results of dust storm monitoring using satellite data showed that these techniques can play a major role in dust monitoring because of their wide coverage. The study of the results of the correlation coefficient between the positive phase of the North Atlantic Oscillation Index and the occurrence of dust storms indicates that there is a direct relationship between the occurrence of dust storms and the positive phase at the same time. in the negative phase, the opposite relationship is established. analysis of annual average dust change graphs concerning the positive and negative phases of the North Atlantic Oscillation Index showed that the largest annual changes in the AOD index produced by MISR and MODIS sensors in the positive and negative phases are related to the Abadan, Bostan, and Ilam stations, respectively. Tracking the paths of dust entry has shown the entry of dust from neighboring western countries and western Chenob to these areas.

Keywords

Main Subjects


Ahmadi, M., & dadashiroudbari, A. (2000). Spatio-Temporal Distribution of Particulate Matter (PM2.5) with an Environmental Approach in West and Southwest of Iran Based on SeaWifs, MISR and MODIS Sensors. Journal of Environmental Studies45(3), 379-394. (in Persian). doi: 10.22059/jes.2019.282101.1007867.
Alam, K., Qureshi, S., & Blaschke, T. (2011). Monitoring spatio-temporal aerosol patterns over Pakistan based on MODIS, TOMS and MISR satellite data and a HYSPLIT model. Atmospheric Environment, 45(27), 4641-4651. doi:10.1016/j.atmosenv.2011.05.055.
Alipour, N., Mesbahzadeh, T., Ahmadi, H., Jafari, M., Karami, S., & Tahmasebi Birgani, A. (2022). Evaluation of the effect of dust storm on heat flux and radiation balance in Hirmand basin. Journal of Water and Soil Conservation, 29(1), 53-73. (in Persian). doi: 10.22069/jwsc.2022.19967.3539.
Aliabadi, K., Asadi Zangeneh, M., & Dadashi Roudbari, A. (2015). Evaluation and monitoring dust storm by using remote sensing (Case study: west and southwest of Iran). Jorar, 7 (1).
(in Persian). http://jorar.ir/article-1-207-fa.html.
Almazroui, M., Alobaidi, M., Saeed, S., Mashat, A., & Assiri, M. (2018). The possible impact of the circumglobal wave train on the wet season dust storm activity over the northern Arabian Peninsula. Climate Dynamics, volume 50, 2257–2268. doi: 10.1007/s00382-017-3747-1.
Arjmand, M., Sargazi, H., & Rashki, A. R. (2018). Monitoring of spatial and temporal variability of desert dust over the Hamoun e Jazmurian, Southeast of Iran based on the Satellite Data. Journal of Geographical Information, 27(106), 153-168. (in Persian). https://doi.org/10.22131/sepehr.2018.32339.
Ayazi, Z., Mesbahzadeh, T., Ahmadi, H., & mashhad, N. (2015). An Application of Wind Rose, Storm Rose, and Sand Rose in the Analysis of Wind Erosion and Determining the Direction of Moving Sands (Case Study Area: Kashan – Aran). Conference: International Specialized Congress of Science and Earth, volume 34, 1-8. (in Persian)
Baddock, M. C., Bullard, J. E., & Bryant, R. G. (2009). Dust source identification using MODIS: A comparison of techniques applied to the Lake Eyre Basin, Australia. Remote Sensing of Environment, 113(7), 1511-1528. https://doi.org/10.1016/j.rse.2009.03.002.
Baghbanan, P. (2020). The analysis of large scale oceanic-atmospheric patterns role in spatiotemporal variations of dust storms in Iran. Ph.D thesis. Faculty of Humanities Department of Physical Geography, Tarbiat Modares University. 321. (in Persian).
Bartina, H., Sayyad, G. A., Matinfar, H. R. & Hojati, S. (2014). Spatio-temporal distribution of atmospheric aerosols in western part of Iran based on MODIS spectral data. Journal of Water and Soil Conservation, 21(4), 119-137. (in Persian).
Dadashi Roudbari, A. A., Ahmadi, M., & Shakiba, A. R. (2020). Evaluation Seasonal Trend of Iran Aerosol Index (AI) Based on Nimbus 7, Earth Probe and Aura Satellite Data. Journal of natural geography research, 52(1), 51-65. (in Persian). doi:10.22059 JPHGR.2020.279630.1007366.
Darvand, S., Khosravi, H., & Eskandari Damaneh, H. (2020). Investigation and spatio-temporal analysis of the AOD index of the dust phenomenon using remote sensing during the years 2000-2019 (case study: west and southwest of Iran). The 4th National Conference on Soil Protection and Watershed Management with a Focus on Dust. Tehran - Research Institute of Soil Protection and Watershed Management, 1-9. (in Persian).
Di, A., She, L., Xue, Y., Yang, X., Leys, J., Guang, J., Mei, L., Wang, J., Hu, Y., He, X., Che, Y., & Fan, C. (2016). Dust Aerosol Optical Depth Retrieval and Dust Storm Detection for Xinjiang Region Using Indian National Satellite Observations. Journal of Remote Sensing, volume 8, 702. doi:10.3390/rs8090702.
Ekhtesasi, M. R., Ahmadi, H., Khlili, A., Saremi Naeini, M. A., Rajabi, M. (2006). An Application of Wind Rose, Storm Rose, and Sand Rose in the Analysis of Wind Erosion and Determining the Direction of Moving Sands (Case Study Area: Yazd – Ardakan Basin). Journal of the Iranian Natural Res, 59(3), 533-541. (in Persian)
Fountoukis, C., Harshvardhan, H., Gladich, I., Ackermann, L., & Ayoub, M.A. (2020). Anatomy of a severe dust storm in the Middle East: Impacts on aerosol optical properties and radiation budget, Aerosol and Air Quality Research. 20(1), 155-165. https://doi.org/10.4209/aaqr.2019.04.0165.
Filonchyk, M., Yan, H., & Zhang, Z. (2018). Analysis of spatial and temporal variability of aerosol optical depth over China using MODIS combined Dark Target and Deep Blue product. Theoretical and Applied Climatology, 137(1), 2271-2288. https://ui.adsabs.harvard.edu/link_gateway/2019ThApC.137.2271F/doi:10.1007/s00704-018-2737-5.
Ginoux, P., Garbuzov, D., & Hsu, N. C. )2010(. Identification of anthropogenic and natural dust sources using Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue level 2 data. Journal of Geophysical Research Atmospheres, 115 (5), 1-10. https://doi10.1029/2009JD012398,2010.
Cheki Forak, M., Doostan, R., & Minaei, M. (2023). Identification of Dust Centers in Birjand City. Geography and Territorial Spatial Arrangement13(46), 61-84. (in Persian). doi: 10.22111/gaij.2023.42530.3034.
Ghaderi, B., & Azizi, Z. (2020). Using Modis satellite imagery in source finding and path determining of dust storms in western and southwestern Iran. Journal of Meteorology and Atmospheric Science, 3(2), 148-160. (in Persian). doi: 10.22034/jmas.2021.291587.1144.
Hsu, N. h., Jeong, M.J., Bettenhausen, C., Sayer, A., Hansell, R., & Seftor, C. (2013). Enhanced Deep Blue aerosol retrieval algorithm: The second generation. Journal of Geophysical Research, Atmospheres, 11 (16), 9296-9315, https://doi10.1002/jgrd.50712.
Hurrell, J.W. (1995). Decadal trends in the North Atlantic Oscillation: regional temperatures and precipitation. Science, 269(5224), 676-679. https://doi10.1126/science.269.5224.676.
Halos, S. H., Al-Taai, O., Al-Jiboori, M. (2017). Impact of dust events on aerosol optical properties over Iraq. Arabian Journal Geo science, 10(263). https://doi10.1007/s12517-017-3020-2.
Jafari, R., & Alidadi, S. (2021). Desert Dust Mapping and Identification Using MODIS Level 1 and AOD- AI Indices in South West of Iran. Journal of Desert Ecosystem Engineering, 10(33), 53-64. (in Persian). doi:10.22052/DEEJ.2021.10.33.39.
Javidniya, M. (2020). Investigation of Relationship Between Dust Storms and Teleconnection Indices in the Kerman Province. Thesis submitted For the degree of Master of Science. Humanities and Social Sciences Campus. Yazd University, 143. (in Persian).
Jiawei, L., Han. Z., Zhang, R. (2011). Model study of atmospheric particulates during dust storm period in March 2010 over East Asia. Atmospheric Environment, 45(24), 3954-3964. doi:10.1016/J.ATMOSENV.2011.04.068.
Kasturi, D.K., N. Yaso. (2010). Preliminary analysis of the spatial and temporal patterns of aerosols and their impact on climate in Malaysia using MODIS satellite data. International Archives of the Photogrammetry, Remote Sensing and Spatial. Information Science, XXXVIII(8), Kyoto Japan.
Ghochizadeh, A. L., Moin Aldini, M., Shahbazi, R., Ahmadi, N., & Naver Noiri, M. (2018). Investigation of the importance of Quaternary period dust emission sources on Qom city air quality. Quaternary Journal of Iran, 4(3), 341 -360. (in Persian). doi: 10.22034/irqua.2018.702078
Ghouse, B., Venkat, M., Ratnama, K., Niranjan, K., Kishored, P., & Isabella, V. (2019). Long-term variation of dust episodes over the United Arab Emirates. Journal of Atmospheric and Solar-Terrestrial Physics, 7(187), 33-39. https://Doi.org/10.1016/j.jastp.2019.03.006.
Iraji, F., Memarian, M. H., Joghataei M., Ghafarian Malamiri, H. R. (2021). Determining the source of dust storms with use of coupling WRF and HYSPLIT models: A case study of Yazd province in central desert of Iran. Dynamics of Atmospheres and Oceans, 93, 101197. doi:10.1016/j.dynatmoce.2020.101197.
Mehrshami, D., & Nekunam, Z. (2009). statistical investigation of dust phenomenon and analysis of dust-causing wind blowing pattern in Sabzevar city. Iranian Geographical Society Scientific Research Journal, 7(22), 83-104. (in Persian).
Mirakbari, M., & Ebrahimi Khusfi, Z. (2020). Investigation of spatial and temporal changes in atmospheric aerosol using aerosol optical depth in Southeastern Iran. RS & GIS for Natural Resources, 11(3), 87-105. (in Persian). doi:10.30495/GIRS.2020.674954.
Moradi, M., & Rezazadeh, P. (2020). Investigation the Sand and Salt Drift Potential in Orumia Lake. Journal of Climatology Research, 11(41), 71-89. (in Persian).
Omidvar, K. (2017). Natural Hazards, Yazd University Publications, third edition, 312. (in Persian).
pourahmad, M., karampour, M., & nasiri, B. (2023). Optical depth changes of dust in connection with land use changes in Central Zagros. Journal of Geography and Planning27(85), 13-25. (in Persian). doi: 10.22034/gp.2022.51303.2994
Poordehghan ardekani, F., Tazeh, M., Kalantari, S., & Ebrahimi khosfi, Z. (2022). Investigating the relationship between dustiness indices and the aerosols optical depth around the Horulazim wetland. Journal of Arid Biome, 12(1), 141-158. (in Persian). doi: 10.29252/aridbiom.2023.19686.1923.
Raispour K. (2018). Analysis of events of dust using satellite monitoring and synoptic analysis in southwest Iran. E.E.R. 2018; 8 (1):74-93
Rashki, A., Kaskaoutis, D. G., Goudie, A. S., & Kahn, R. A. (2013). Dryness of ephemeral lakes and consequences for dust activity: the case of the Hamoun drAAInage basin, southeastern Iran. Science of the Total Environment, 463, 552-564. https://doi.org/10.1016/j.scitotenv.2013.06.045.
Ramanathan, V., & Crutzen, P. J. (2003). New directions: Atmospheric brown clouds. Atmospheric Environment, 37(28), 4033-4035. doi:10.1016/S1352-2310(03)00536.
Razavizadeh., S, Abbasi, H., & Dargahian, F, (2021). Investigation of Dust Phenomenon in Golestan Province, with Emphasis on Aerosol Optical Depth Index and Wind Direction and Speed. Jwmseir, 15 (53), 58-67. (in Persian). http://jwmsei.ir/article-1-993-fa.html.
Sehat Kashani, S., Rahnama, M., Karami, S., RanjbarSaadatabadi, A., & Khoddam, N. (2022). Evaluation of Environmental Parameters Influencing Dust Sources Activation over Ilam Province. Physical Geography Research, 54(3), 403-427. (in Persian). doi: 10.22059/jphgr.2022.340430.1007687.
Soleimani Sardoo, F., Karami, S., & Hossein Hamzeh, N. (2021). Determining and analyzing the temporal and spatial trend of dust and its effect on vegetation and precipitation (Case study of Jazmourian Basin). E.E.R., 43(3), 64-81. (in Persian). doi:20.1001.1.22517812.1400.11.3.7.9.
Srivastava A, Saran S. 2017. Comprehensive study on AOD trends over the Indian subcontinent: a statistical approach. International Journal of Remote Sensing, 38(18): 5127-5149. doi:https://doi.org/10.1080/01431161.2017.1323284.
Valizadeh Kamran, K., & Namdari, S. (2020). Temporal-Spatial Analysis of Aerosols Trend in the Zone of Influence Urmia Aerosols by Processing of Satellite Imageries in 2000-2015 (Case Study: East Azerbaijan and West Azerbaijan). Journal of geography and planning, 24(72), 427-446. (in Persian). doi:10.22034GP.2020.10826.
World Meteorological Organization (WMO), United Nations Environment Programme (UNEP), (2011). Establishing a WMO Sand and Dust Storm Warning Advisory and Assessment System Regional Node for West Asia: Current Capabilities and Needs, pp. 1121 (Accessed on April 13, 2016).
Wang, D., Zhang, F., Yang, S., Xia, N., & Ariken, M. (2020). Exploring the spatial-temporal characteristics of the aerosol optical depth (AOD) in Central Asia based on the moderate resolution imaging spectroradiometer (MODIS). Environmental monitoring and assessment, 192, 1-15. https://doi.org/10.1007/s10661-020-08299-x.
Yarmoradi, Z., Nasiri, B., Mohamadi, G. M., & Karampour, M. (2019). Analysis and tracking dust storms routes entering to east of Iran using the particle diffusion HYSPLIT model. E.E.R., 1(33), 27-44. (in Persian). doi:20.1001.1.22517812.1398.9.1.3.7.
Zamani, S., Mahmoodabadi, M., Yazdanpanah, N., & Farpoor, M. H. (2019). Analysis of wind erosivity at synoptic stations of Kerman province using wind rose, storm rose and sand rose. Journal of Soil Management and Sustainable. 9(2), 23-43. (in Persian). doi:10.22069/ejsms.2019.14813.1810.
Zoulfaghri, H. (2014). Climate of the Earth: Razi University Publications. first edition. Kermanshah, 282. (in Persian).