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Dr. Mostafa Karimi, ُsir Seyfollah Kaki, Dr. Somayeh Rafati,
Volume 5, Issue 3 (12-2018)

Global temperatures have increased in the past 100 years by an average of 0.74°C (IPCC, 2013), with minimum temperatures increasing faster than maximum temperatures and winter temperatures increasing faster than summer temperatures (IPCC, 2013). Total annual rainfall tends to increase at the higher latitudes and near the equator, while rainfall in the sub-tropics is likely to decline and become more variable (Asseng et al., 2016). Considering probability of occurrence climate change and its hazardous impacts, it seems essential to clarify future climate. General Circulation Models is widely used to assess future climate and its probable changes. Although the outputs of these models are not appropriate for small-scale regions because of its coarse resolution. Thus, statistical or dynamical techniques are used to downscaling the outputs of these models using observed data in weather stations. Despite the fact that frequent researches has done in relation with climate and climate change, but it is unclear yet future climate, especially climate change, in Iran. The goal of this study was to present the results of climate change predictions which has been done so far in Iran, in order to help prospective studies in this field. This step can be important to consider new questions and challenges. In this study, we assessed future climate change in Iran using results of statistical downscaling studies of atmospheric-oceanic General Circulation Model’s outputs. To do this, studies on prediction of precipitation and temperature parameters in Iran by different emission scenarios, atmospheric-oceanic General Circulation Model’s outputs and statistical downscaling techniques were gathered. Then a comprehensive view about Iran's future climate and specifically the climate changes presented by descriptive-content based analysis and comparison of their results. Used downscaling techniques in these researches were included: LARS-WG, SDSM, ASD, Clim-Gen and used General Circulation Models were: HADCM3, BCM2, IPCM4, MIHR, CGCM3, CCSM4 and finally used emission scenarios were A1B, A1, A2, B1, B2, RCP4.5. Based on climatically geographical differences in Iran, the results discussed separately in six different regions across Iran. The results of various regions are different because of usage of different models and different climatological and geographical conditions. These models simulate temperature more accurate than precipitation, because of more variability and temporal discontinuity of the precipitation relative to temperature. Assessment of results in 30-year periods from 2011 to 2099 showed that in North West of Iran (Ardebil, Azarbayejan- Sharqi and Azarbayejan- Qarbi provinces), precipitation will be decreasing, decreasing- oscillating, decreasing- transitional and temperature will be increasing. Decreasing- transitional trend, in other words decrease precipitation in cold seasons and increase of it in warm seasons, lead to a decrease in the snow occurrence and an increase in the rainfall occurrence. Thus, it can affect the frequency of floods occurrence. In west and southwest region of Iran precipitation has been predicted to have different changes in various sections of it. It will be decreasing-oscillating in Kermanshah and Kordestan provinces and oscillating in Hamedan province. Precipitation will increase in Lorestan and finally it expected to decrease in Khoozestan, Chaharmahal-va-Bakhtiari, and Ilam. However Temperature will rise across this region. In south and south east region of Iran (Fars, Hormozgan, Kerman and sistan-va-Baloochestan provinces), precipitation will be decreasing, decreasing-oscillating, oscillating and increasing-oscillating. Also in this region, temperature expected to increase similar to other regions. In east and north‌ east of Iran (Khorasan Shomali, Khorasan Razavi and Khorasan Jonobi provinces), temperature predicted to be increasing-oscillating, that it is different with other regions. Changes in precipitation will be oscillating and decreasing-oscillating. In the northern coasts of Iran (Gilan, Mazandaran and Golestan provinces), precipitation changes will be decreasing and increasing-oscillating and temperature changes expected to be increasing and increasing-oscillating. Thus, it expected to increase heat wave, drought, and aridness condition as the results of these changes. Precipitation changes in south of Alborz region and center of Iran (Semnan, Tehran, Qazvin, Markazi, Esfahan and Yazd provinces), will be decreasing, oscillating, increasing-oscillating. Also temperature will be increasing in this region. Considering the decreasing trend of precipitation and the increasing trend of temperature in the most of Iran, it is probable to increase the occurrence of climatic and environmental hazards such as flood, drought and heat waves in the future. These events can have serious effects on water resources, agriculture and tourism, especially in regions such as Iran where have sensitive environment.
Dr Somayeh Rafati,
Volume 7, Issue 4 (2-2021)

Extended Abstract
Mesoscale Convective Systems (MCSs) are the convective precipitation structure that is most frequently associated with floods at mid-latitudes, mainly due to the high degree of organisation, which allows the structure to be maintained for a longer period of time and to become more extensive. Moreover, MCSs are an important link between atmospheric convection and larger-scale atmospheric circulation. Based on the results of previous studies, it can be claimed that Sudanese low pressure systems in many cases are the cause of the formation of MCSs, especially in southwestern Iran. Although many studies have been done in Iran on these systems and how they are formed, but the role of some environmental components of their formation and intensification, such as vertical wind shear, High and Low Level Jets (HLJ and LLJ) has received less attention. Therefore, the purpose of this study is to investigate the role of these factors in addition of the known factors that cause the formation of these systems. For this purpose, the flood of 24 and 25 march 2019 in the south and southwest of Iran has been selected as a case study.
To track and investigate the spatial characteristics of MCSs in this study, IR channel of the second-generation Meteosat imagery (MSG) on March 24 and 25, 2019, with a spatial resolution of 3 km and a temporal resolution of 15 minutes from Eumetsat site was extracted. After calibration and georeference of the images, the brightness temperature was calculated. The exact choice of temperature threshold for the identification of convective systems is optional and depends on the spatial resolution and wavelength of imagery. The size distribution obtained from the 207 or 218 k thresholds are not very different, especially for larger convection systems. Therefore, in this study, a threshold of 218 degrees Kelvin was used. Also, there is no agreement among researchers on the criterion of minimum length or area in the definition of MCSs, and this criterion is mostly determined by the characteristics of the region and the selected temperature threshold. In this study we select a threshold of 10 thousand square kilometers. In other words, the system was identified as MCSs, which at some point in life had an area of more than 10,000 square kilometers. The daily precipitation data of GPCC database were used to investigate the scattering of precipitation produced by these systems. Also, to understand the synoptic and environmental conditions of occurrence of MCSs on studied days, first geopotential height data, zonal and meridional wind components, potential temperature, relative humidity, vertical velocity and CAPE from ECMWF database were extracted and then the required maps and diagrams were prepared to synoptic and environmental analyses.
In general, the results of this study showed that three MCSs on March 24 and 25, 2019 affected different parts of Iran. The maximum area of ​​the cold core of the first system is about 73,000 square kilometers and has traveled from west to north of Iran. The second system, which affected Iran from the west to the northeast, had a maximum area of ​​about 660 thousand square kilometers. The cold core of the third quasi-stable system with a linear extension (northeast-southwest) and a maximum area of ​​about 440 thousand square kilometers, has moved slightly to the southeast.
The synoptic conditions of the formation of these systems have been the same as the common pattern of the formation of Sudanese low pressure systems and MCSs. In this pattern, Azores high pressure can bring the cold air of the high latitudes to the middle latitudes and hot and humid air is injected by the high pressure over the Oman Sea and the Arabian Sea, which activates the Red Sea convergence zone along with the Mediterranean system. These conditions have led to the formation of the minimum potential temperature zone in the eastern Mediterranean with significant temperature and pressure differences compared to its environment, resulting in the formation of LLJ. This LLJ has been very effective in transferring hot and humid air to western Iran. So that in the peak hours of convective activity in the center of Iran, a potential temperature difference of about 30 degrees Kelvin with the environment has created that has played an effective role in the formation of convective storms. The transfer of hot and humid air by the LLJ has led to the formation and continuation of convection and the release of latent heat to enhance the convergence and longer life of convection systems. On the other hand, the coupling of LLJ and HLJ, by strengthening the MCSs in the western part of Iran and strengthening the divergent flow at higher levels, has strengthened the HLJ, which in turn has led to strengthening the convective system. Vertical wind shear probably also led to the formation of new convective cells in areas far from the origin of the primary convective cells. During the peak hours, unstable convective activity was observed over a large part of Iran, especially the southern and western parts, and its maximum was observed from the southern half of the Red Sea along the convergence zone to the west of Iran.
Therefore, various components of the Sudanese low pressure system play an important role in the formation, continuity and development of mseoscale convective systems. It seems that low-level jet, vertical wind shear and its interaction with the Red Sea convergence zone and the outflow of primary convective cells have a very effective role in the occurrence of this phenomenon. Thus, more detailed studies of this issue using mesoscale numerical models will probably identify unknown aspects of Iran's climate.


Dr. Mostafa Karimi, Ms Sousan Heidari, Dr. Somayeh Rafati,
Volume 8, Issue 2 (9-2021)

The role of environmental and climatic environment on the transport and emission of carbon monoxide pollutants Iran in 2018
Air pollution, as one of the most important environmental hazards in urban areas, is closely related to weather conditions. Today, pollution in metropolitan areas has become an important issue that requires the study and presentation of practical solutions to improve living conditions in this area. Therefore, understanding the relationship between synoptic systems and air pollutants helps a lot in how to solve environmental problems and future planning. Therefore, in this study, compression algorithms of carbon monoxide emission and transfer from domestic and foreign sources were analyzed. For this purpose, GEOS-5 / GMAO / NASA satellite images were used. The results showed that the highest amount of pollution from the seasonal point of view is related to the cold and early morning seasons and the lowest is related to the early afternoon and hot season of the year. And Khuzestan are densely populated carbon monoxide cores. Low pressures of the eastern Mediterranean play an important role in reducing pollutants in the southwest of the country and in the south of the country, under the influence of atmospheric currents from the topographic cut of Bandar Abbas, air streams polluted with carbon monoxide are able to penetrate into the interior to the southern half of Kerman. Increased by low pressure systems in Afghanistan and Pakistan. The Zagros Mountains also play an important role in preventing the entry of pollutants produced by western neighbors into Iran. In summer, Iran is polluted by carbon monoxide carriers by monsoon currents from central and southern Africa to Iran and has caused a lot of pollution.        
materials and Method
The geographical location we study in this study is Iran. Iran is the 16th largest country in the world. Iran is located in the northern hemisphere, the eastern hemisphere in Asia and in the western part of the Iranian plateau and is one of the Middle Eastern countries. Meridian 5 44 passes east of the westernmost point of Iran and meridian 18 63 passes east of the easternmost point of Iran. 1648195 sq km is bordered by Armenia, Azerbaijan, and Turkmenistan to the north, Afghanistan and Pakistan to the east, Turkey and Iraq to the west, the Persian Gulf and the Sea of ​​Oman to the south. Iran is one-fifth the size of the United States and almost three times France. . Iran is a mountainous country. More than half of the country is covered by mountains and heights, and less than 1/4 of it is arable land. In general, Iran's heights can be divided into four mountain ranges: North, West, South and Central Mountains. East divided, which is therefore the twenty-third highest mountain in the world.                                        
This study is based on the method of environmental analysis to focus on circulation, so that based on the concentration of carbon monoxide in 2018, synoptic patterns of this phenomenon have been identified. Satellite imagery of surface carbon monoxide was then obtained from three GEOS-5 / GMAO / NASA organizations. Also for synoptic analysis, MSLP and WS satellite images were received and analyzed from GFS / NCEP / US National Weather Service organizations and also one of the sensors used for pollutant studies is MOPITT. The MOPITT sensor is a tool for measuring troposphere pollution that can detect atmospheric pollution. This sensor is the first satellite sensor designed for use in gas correlation spectroscopy and is part of NASA's Operational Program (ESE), which has been operating since 1999 and is installed on three satellites Terra, Aura, Aqua Depending on the type of mission in space, it acts as an orbiter. This sensor measures only two variables of methane and carbon monoxide in the atmosphere of the troposphere of the atmosphere, for which purpose 3 bands and 8 channels for measuring monoxide with a size of 62.4 microns (using 4 channels), 33.2 It uses microns (using 2 channels) and methane measuring 26.2 microns (using 2 channels). The MOPITT sensor is specifically designed to measure carbon monoxide. The geographical boundaries of the study area were also selected to include all atmospheric systems affecting the study area.     
The meteorological condition and the physical and dynamic properties of the atmosphere can play an important role in the level of air protection. The main factor that can cause the scattering and transmission of air forces is the use of the ground and the levels of reception of the atmosphere, and the synoptic systems as a service provider providing services for upward movement and distribution of air pollutants, as well as the definition of chalk. As a decision made in this field, Iran can use its images in this field in 2018 2018, MSLP, WS will provide you with GFS / NCEP / US National Weather Service. With great intensity you can go to Tehran and southwest to destroy yourself and access your officials. In the imagination carbon monoxide is possible and used in the southwest of the country. Now in your country and change the status of lists proposed by Coriolis, increase the high pressure of carbon monoxide in Mr. Tropical from the Middle East and Iran. This program allows you to modify your suggested lists. Carbon monoxide pollutants sent to a drawer in the international province of the country and available in Bandar Abbas, a road nest free from high mountains and as a corridor company you can get from this par of the air pollution as carbon monoxide through the air to this one Use the land up to the Kerman province.          
Keywords: Carbon monoxide, Compression systems, Monson, Atmospheric pollution, Topography
Mr Alireza Sadeghinia, Mrs Somayeh Rafati, Mr Mehdi Sedaghat,
Volume 8, Issue 4 (3-2022)

Climate change is the greatest price society is paying for decades of environmental neglect. The impact of global warming is most visible in the rising threat of climate-related natural disasters. Globally, meteorological disasters more than doubled, from an average of forty-five events a year to almost 120 events a year (Vinod, 2017). Climate change refers to changes in the distributional properties of climate characteristics like temperature and precipitation that persist across decades (Field et al., 2014). Because precipitation is related to temperature, scientists often focus on changes in global temperature as an indicator of climate change. Valipour et al. (2021) reported the mean of monthly the global mean surface temperature (GMST) anomalies in 2000–2019 is 0.54 C higher than that in 1961–1990. Many studies have been done on climate change in Iran. These studies have mostly studied the mean and extreme temperature trends (Alijani et al., 2011; Masoudian and Darand, 2012). In general, the results of previous studies showed that the statistics of mean, maximum and minimum air temperature in most parts of the Iranian plateau have increased in recent decades. Also, the increase of minimum temperature is greater than maximum temperature.
A review of the research background shows that we need to understand more about regional climate change in Iran. Therefore, present study performs the climate change of 14 extreme temperature indices using multivariate statistical methods at the regional scale.

Data and methodology
Historical climate observations including daily maximum and minimum temperature were obtained from the Iranian Meteorology Organization for the period 1968 to 2017 at 39 stations. In this paper, 14 extreme temperature indices defined by ETCCDI were analyzed. The indices are as follows: (1) Annual maxima of daily maximum temperature (TXx); (2) Annual maxima of daily minimum temperature (TNx); (3) Annual minima of daily maximum temperature (TXn); (4) Annual minima of daily minimum temperature (TNn); (5) Cold nights (TN10p); (6) Cold days (TX10p); (7) Warm night (TN90p); (8) Warm day (TX90p); (9) Frost days (FD); (10) Icing days (ID); (11) Summer day (SU); (12) Tropical nights (TR); (13) The warm spell duration index (WSDI) and (14) the cold spell duration index (CSDI). The extreme temperature indices were extracted using R software environment, RclimDex extension. The Mann–Kendall Test and Sen’s Slope Method was employed to assess the trends in 14 extreme temperature indices. To identify homogeneous groups of stations with similar annual thermal regimes, Principal Component analysis (PCA) and Clustering (CL) was applied. Pearson correlation coefficient was used to investigate the relationship between height and trend slope.

All the extreme temperature intensity indices (TXx, TNx, TXn, and TNn) showed increasing trends during 1968 to 2017. The increasing trends of TXx, TNx, TXn, and TNn were 0.2, 0.3, 0.44, and 0.5 ° C per decade, respectively. These results indicated that the extreme warm events increased and the extreme cold events decreased. The average of the extreme temperature frequency indices over Iran showed that the frequency of warm night (TN90p) and warm day (TX90p) significantly increased with a rate of 6.9 and 4.2 day per decade, respectively. Also, the frequency of cold night (TN10p) and cold day (TX10p) significantly fell with a decrease rate of 3.8 and 3.8 day per decade, respectively. The frequency of warm nights (TN90p) was higher than that of warm days (TX90p). The result indicated that the trend of nighttime extremes were stronger than those for daytime extremes. The average of frost days (FD) and icing days (ID) indices over Iran showed decreasing trends during 1968 to 2017 with rates of 3 and 1.1 d per decade, respectively. While, the averaged of summer days (SU) and tropical days (TR) indices over Iran showed increasing trends with rates of 4.4 and 6.4 day per decade, respectively. The warm spell duration index (WSDI) indices showed a clear increase, with a rate of 2.1 per decade. In contrast the cold spell duration index (CSDI) showed a significant decrease, with a rate of 1.7 per decade. In general, the cold indices displayed decreasing trends, whereas the warm indices displayed increasing trends over most of Iran. Pearson correlation coefficient between height and Sen’s Slope was estimated to be equal to -0.62 (p < 0.01). In general, the results of this study showed that there is a negative correlation between the elevation factor and the Sen’s Slope of warm extreme indices. That is, as the altitude decreases, the Sen’s Slope increases. Therefore, the stations located in low altitude have experienced stronger increasing trends than in high altitude. The area of ​​Iran was classified into four clusters using PCA and CL methods. Cluster 1 has experienced the strongest increasing trends. The average height of cluster 1 is 535 meters. Approximately 38% of the studied stations were located in cluster 1. Cluster 2 showed a moderate heating trends. 33% of the stations were located in cluster 2. Most of the stations of cluster 2 are located in the northwest and west of Iran. Cluster 3 showed a weak increasing trends compared to clusters 1 and 2. The stations of cluster 3 did not show a special geographical concentration and were scattered in all parts of Iran. 18% of the studied stations are located in cluster 3. The stations of Cluster 4, have experienced weak decreasing trends, which was different from the other three clusters

In this study we analyzed the climate change of extreme temperature indices in Iran. The result showed that the frequency of warm nights, warm days, summer days and tropical days increased. Also, the frequency of cold nights, cold days, Frost days and icing days decreased. The warm spell duration index showed a clear increase. In contrast the cold spell duration index showed a significant decrease. In general, the extreme warm events increased and the extreme cold events decreased over most of Iran. There is a negative correlation between the elevation factor and the Sen’s Slope of extreme warm indices (R = -0.62). Therefore, the stations located in low altitude have experienced stronger increasing trends than in high altitude. The area of ​​Iran was classified into four clusters using PCA and CL methods. Cluster 1 has experienced the strongest increasing trends. The average height of cluster 1 is 535 meters. Therefore, the most heating have occurred in Low-lying areas of Iran. Cluster 2 and Cluster 3 showed a moderate and weak heating trends, respectively. The stations of Cluster 4, have not experienced clear trends.

Key words: climate change; Extreme temperature; clustering; Iran


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