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Showing 22 results for Climate Change

Omosalameh Babai Fini, Elahe Ghasemi, Ebrahim Fattahi,
Volume 1, Issue 3 (10-2014)

Global changes in extremes of the climatic variables that have been observed in recent decades can only be accounted anthropogenic, as well as natural changes. Factors are considered, and under enhanced greenhouse gas forcing the frequency of some of these extreme events is likely to change (IPCC, 2007 Alexander et al., 2007). Folland et al. (2001) showed that in some regions both temperature and precipitation extremes have already shown amplified responses to changes in mean values. Extreme climatic events, such as heat waves, floods and droughts, can have strong impact on society and ecosystems and are thus important to study (Moberg and Jones, 2005). Climate change is characterized by variations of climatic variables both in mean and extremes values, as well as in the shape of their statistical distribution (Toreti and Desiato, 2008) and knowledge of climate extremes is important for everyday life and plays a critical role in the development and in the management of emergency situations. Studying climate change using climate extremes is rather complex, and can be tackled using a set of suitable indices describing the extremes of the climatic variables.    The Expert Team on climate change detection, monitoring and indices, sponsored by WMO (World Meteorological Organization) Commission for Climatology (CCL) and the Climate Variability and Predictability project (CLIVAR), an international research program started in 1995 in the framework of the World Climate Research Programme, has developed a set of indices (Peterson et al., 2001) that represents a common guideline for regional analysis of climate.    It is widely conceived that with the increase of temperature, the water cycling process will be accelerated, which will possibly result in the increase of precipitation amount and intensity. Wang et al. (2008), show that many outputs from Global Climate Models (GCMs) indicate the possibility of substantial increases in the frequency and magnitude of extreme daily precipitation.     eneral circulation models (GCMs) are three-dimensional mathematical models based on principles of fluid dynamics, thermodynamics and radiative heat transfer. These are easily capable of simulating or forecasting present-future values of various climatic parameters. Output of GCMs can be used to analyze Extreme climate. For this study high quality time series data of key climate variables (daily rainfall totals and Maximum and minimum temperature) of 27 Synoptic stations were used across Iran from a network of meteorological stations in the country. In order to get a downscaled time series using a weather generator (LARS-WG), the daily precipitation output of HadCM3 GCM, SRES A2 and A1B scenario for 2011-2040 are estimated.     The Nine selected precipitation indices of ETCCDMI[1] core climate indices are used to assess changes in precipitation extremes and monitor their trends in Iran in the standard-normal period 1961–1990 and future (2011-2030).    Due to the purpose of this study, at first changes in extreme precipitation indices in the standard-normal period is evaluated and its results show annual maximum 1-day precipitation increased in many regions in the East of Iran. Simple measure of daily rainfall intensity (SDII), annual maximum consecutive 5-day precipitation, annual count of days with daily precipitation greater than 10mm (R10mm), annual count of days when rainfall is equal to or greater than 20 mm (R20mm) have increased in the central areas, regions in the north , north east and southern parts of Iran. Similar results are obtained for the R25mm index.    The consecutive dry days (CDD) index has generally increased across the west areas, southwest, north, northwest and southeast of Iran and indices of consecutive wet days (CWD) decreased in these areas.    Trends of extreme precipitation indices simulated by HadCM3 SRES A2 showing increases RX1Day in North West expect west Azerbaijan Province, central, southwest, north east and coasts of Caspian Sea. Similar results are obtained for the R5mm index expects northeast. There are mixed changes in R10mm across Iran, increasing in west, southwest, coasts of Caspian Sea, Hormozgan and Ardebil provinces, East Azerbaijan, Zanjan and Qazvin  provinces. Similar results are obtained for the R20, 25 mm index in northeast, south of Caspian Sea, and some parts in western and central areas. Same as HadCM3 SRES A2 pattern there are mixed changes in R10mm across the region. Positive trends are seen in part of the Isfahan, Markazi, Kuhkilue , Lorestan, Ilam, Chaharmahaland Khozestan provinces and some part of Hormozgan and Kerman and some areas in north west. Similar results are obtained for the R20mm and R25mm index and in west of Yazd to north of Khozestan provinces have increased.    Consecutive wet days (CWD) have increased over most of the west of Iran, Khorasn Razavi and Southern Khorasn provinces, In contrast consecutive dry days (CDD) index has generally increased in many parts of the region.  
[1]. Expert Team on Climate Change Detection and Monitoring Indices

Mohamad Saligheh,
Volume 2, Issue 3 (10-2015)

Tehran, in the south of Alborz Mountains, is faced with three types of weather risk, weather risk caused by geography, climatic risks caused by air resistance and weather risk due to global warming. The aim of this study is to examine the three types of risk in Tehran. The method of this study was to evaluate the changes of synoptic factors that affect global warming and urban development. In order to detect the height changes of 500 hPa two 5-year periods including 1948 to 1952 and 2010 to 2014, were studied.

     The results showed that changes in heights of 500 geopotential, there was an increased risk in the city of Tehran. The effect of climate change in recent decades,  increased the stability of  air in Tehran. Human factors in the formation of heat islands, increase LCL height and density of the air balance is transferred to a higher altitude. Changing urban wind field, atmospheric turbulence intensified, exacerbated thermodynamic gradient, fat and refugee cyclones, heat island effect of the city.

Thermal stability in the warm period will appear. The thermal stability of all levels of lower, middle and upper troposphere was intensified. Thermal stability couraged the  development of subtropical high pressure in the area. With the arrival of the atmospheric pressure during calm and humid days the stability and pollution were increased. Negative vorticity from early June  developed the intensive high pressure over the region. Compare the conditions of the two study periods  showed that  : the height of the high pressure was 100 meters higher than the second period. The number of days of intensified subtropical high increased during the second period.  The high pressure has moved to the northern areas during the second period. This change in the subtropical high pressure increased the dry periods motivating the loss of vegetation. Heat island effect was increased as well. More than 90% of the  temperature inversions occurred  at an altitude of less than 500 meters in both warm and cold periods of year. Wind direction at both stations has shown that the establishment of any pollutant source in the West of Tehran will increase the pollution.

Gelaleh Molodi, Asadolah Khorani, Abbas Moradi,
Volume 3, Issue 1 (4-2016)

Climate change is one of the most significant threats facing the world today. One of the most important consequences of climate change is increasing frequency of climate hazards, mainly heat waves. This phenomena has a robust impacts on human and other ecosystems. The aim of this study is investigating changes of heat waves in historical (1980-2014) and projected (2040-2074) data in northern cost of Persian Gulf.

The focus here is on Mean daily maximum temperature and Fujibe index to extract heat waves. For this purpose 6 weather stations locating in north coast of Persian Gulf, Iran, are used (table 1).

Table1: weather stations






30° 22' N

48° 20' E



28° 55' N

50° 55' E



27° 15' N

56° 15' E



26° 35' N

54° 58' E



26° 54' N

53° 54' E


  In addition, 4 model ensemble outputs from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are used to project future occurrence and severity of heat waves (2040 to 2070), under Representative Concentration Pathways 8.5 (RCP8.5), adopted by the Intergovernmental Panel on Climate Change for its Fifth Assessment Report (AR5) (table 2).

Table2: List of the AR5 CMIP5 Used Models


Modeling Cener



Canadian Earth System Model



Max-Planck-Institut für Meteorologie



Commonwealth Scientific and Industrial Research Organization



CMCC Carbon Earth System Model


The output of models is downscaled using artificial neural network method (ANN). A feed-forward network of multi-layer perceptron with an input layer, a hidden layer and an output layer is used for this purpose. 73 percent (1980 – 2000) of the data is used for training and 27 percent (2000-2005) for testing ANN models. Root Mean Square Error (RMSE) is used as an indicator of the accuracy of Models.


Here  AWT IMAGE is the outputs of ANN models (downscaled data) and AWT IMAGEis the observation data.

Fujibe et all (2007) used an index based on Normalized Thermal Deviation (NTD) for extracting long-term changes of temperature extremes and day to day variability using following equations:


Where N is the number of days in the summation except missing values. Then nine-day running average was applied three times in order to filter out day-to-day irregularities.

AWT IMAGE=(i,j,n)=T(i,j,n)-T(I,j)

The departure from the climatic mean is given by



If NTD >2 and at least lasts for 2 days it determine as a heat wave.


Table 3 shows the results of downscaling selected GCM models.



Average RMSE

Sigmoid function

Linear function










































Table 4 compares the frequency of heat waves for GCMs and historical data.





Historical data































For historical data, heat waves are more frequent in Abadan station than other stations. There is an increasing trend in the occurrence of heat waves in historical data and monthly frequency of heat waves show the highest amounts for summer.

For both historical and future data 2 days listening heat waves are more frequent.

Table 5 shows seasonal changes of heat waves for historical data and GCMs.


The ratio of heat waves from total historical data (percent)

The ratio of heat waves from total projected data (percent)


































































In recent years the frequency of heat waves is increasing in all studied stations. Coincide with Russia and Europe, the highest amounts of heat waves is occurred in 2010 in northern coast of Persian Gulf and this is adopted Esmaeilnezhad et all (2013), Gavidel (2015) and Azizi (2011).

S. Reza Alvankar, Farzane Nazari, Ebrahim Fattahi ,
Volume 3, Issue 2 (5-2016)

Due to the growth of industries and factories, deforestation and other environmental degradation as well as greenhouse gases have been increasing on the Earth's surface in recent decades. This increase disturbs the climate of the Earth and is called climate change. An Increase in greenhouse gases in the future could exacerbate the climate change phenomenon and have several negative consequences on different systems, including water resources, agriculture, environment, health and industry. On the other hand to evaluate the destructive effects of climate change on different systems, it is necessary to initially study the area affected by climate change phenomena. One of the most important effects of climate change on water resource is Drought.  On the other hand, one of the most serious consequences of climate change is how it will affect droughts and water resources.

Drought along with warmer temperature and less precipitation will threaten the water supplies for the crop irrigation, which will directly reduce the production of crops.The climate of the 21st century will very likely be quite different from the climate we observed in the past. The changes will continue to be large in the future period with increasing carbon dioxide emissions. Analyzing and quantifying the signal of climate change will be much in demand considering the above sectors, which are highly relating to the sustainability and human living.

In the past several decades, global climate models have been used to estimate future projections of precipitation [Intergovernmental Panel on Climate Change (IPCC), 2007], and have led to future estimation of drought, to quantify the impact of climate change and comparing the duration  and intensity of droughts under future climate conditions with current climate by using Atmospheric-Ocean General Circulation Models AOGCMs to predict future Precipitation. Global circulation models namely, coupled Atmosphere-Ocean Global Climate Models (AOGCMs) are current state of the art in climate change research. in This study aims at investigating the impact of climate change on droughts conditions in Iran using the Standard Precipitation Index (SPI).

The precipitation time series have been used for the estimation of Standardized Precipitation Index

(SPI) for three timescales, 3, 12 and 24 months, for the region. The outputs of HadCM3-A2 and A1B were applied for the assessment of climate change impact on droughts. One of the major problems in using the output of AOGCMs , is their low degree of resolution compared to the study area so to make them appropriate for use, downscaling methods are required. In this study we have used lars WG for downscaling monthly average of rainfall of AOGCM-HadCM3, and The HadCM3 outputs were downscaled statistically to the study area for a future period 2011-2040.then, was evaluated by the coefficient of determination (R2) between observed and downscaled data.  A method has been used for the estimation of annual cumulative drought severity-time scale-frequency curves. According to the rainfall results, in the 2011- 2040 period rainfall would decrease  to compared to baseline period in the study area.

The SPI time series were estimated (2011-2040) and compared with the respective time series of the historical period 1961-1990. Results revealed that there are decreases in the frequency of severe and mild droughts for the three examined SPI time series while there are increases in the duration of moderate droughts. This implies that droughts will be a concern in the future during the growing season (for the dominant crop) which should be considered in water resources management. specially in the west station of Iran.

Also, these frequency ratios were mapped by GIS on study area. Results showed that generally in the future periods, frequency of droughts ratio of three months drought time- scale will be increase in the North, North West and some parts of the south Alborz mountains and, The Ratio of long ( 24 months) drought for scenario A2 compare to the current climate shows increasing drought in the parts of the North khorasn, sistan and baluchestan and kerman provinces and parts of South West of Iran. scenario A1B shows increasing drought in the parts of the East of Mazandaran , Tehran , Horozgan and parts of Fars and Yazd  provinces.

Finally ,further  more analysis of drought, AWCDS-Timescale-Return Periods computed. These curves integrate the drought severity and frequency for various types of drought. The AWCDS time series were estimated

for basic period and 2011-2040 under scenarios A2 and A1B. The comparison indicated the three types of drought intensity increases for the three examined SPI time series in the South East of Iran.

Hamzeh Ahmadi, Gholamabass Fallah Ghalhari, Mohammad Baaghideh, Mohammaf Esmail Amiri,
Volume 5, Issue 2 (9-2018)

Climate change stand as the most important challenge in the future. Horticulture is one of the most sensitive and vulnerable sectors to the climate change. Climate change and global warming will endanger the production of agricultural products and food security. Because of required longer time to fruit production, fruit trees are heavily susceptible to damage from climate change. The purpose of this study was to investigate the impacts of climate change on thermal accumulation pattern in Apple tree cultivation regions of Iran based on the outputs of new CMIP5 models and radiative forcing (RCP) scenarios.
The present study was carried out using a statistical-analytical method. In this study, two types of data was used; baseline data for past period and model output simulation data for the future period. Observation data for baseline period for 53 weather station was extracted from the Iran meteorological organization (IMO). Afterwards, the data for the upcoming period up to the 2090 horizon were processed using the HadGEM2-ES model from the series of CMIP5 models of the MarksimGCM database based on the radiative forcing scenarios RCP8.5 and RCP4.5. The future period will be refined in the mid-term (2020-2055) and the far future (2056-2090). Afterwards, based on the thermal thresholds, thermal accumulation in Apple tree cultivation areas in Iran processed.
The results showed that based on statistical indices on the output of CMIP5 models, the output of the HadGEM2.ES general circulation model is accompanied by fewer simulation errors in illustrating the climate change of the future period than the observation or baseline period. In fact, based on the evaluation criteria or error measures, this model shows a higher compliance with observational data. In general, the model has a lower accuracy than precipitation in the simulation of rainfall, which is due to the complexity of the precipitation process as well as the structure of the climatic models. One of the fundamental issues that have emerged in recent decades is the change in the potential status or heat accumulation of different regions due to the increase in air temperature. The results showed that due to temperature increase, in the mid and far future heat accumulation will increase compared to the baseline period in Apple tree cultivation areas. Increasing of heat accumulation will reduce the length of the Apple tree growth period, and in fact the Apple tree will complete its vegetative and reproductive cycles sooner. This condition will have negative effects on the quality, taste and color of the Apple varieties. For example, according to the RCP8.5 scenario in the physiological threshold of the apple tree 4.5 C° , in the mid term (2020-2055) and far future (2056-2090) will be 1132 and 2171 active degree days respectively compared baseline period. These conditions equivalent to the  51% and 42% respectively. Based on the RCP4.5 scenario, these conditions will be 390 and 680 active degree day, equivalent to 9.3% and 15.1%, respectively, compared to the baseline period.
The results showed that the heat accumulation in Apple tree cultivation areas in the future period will increase compared to the baseline period. One of the most important effects of climate change on the Apple tree  cultivation will be due to increased heat accumulation in the upcoming period. Increasing the heat accumulation will reduce the length of fruit tree growth period, and in fact the fruit tree will complete its vegetative and reproductive cycles earlier. According to these conditions, the areas of Apple tree cultivation in the future will be extended to higher regions. These conditions are important for cold regios fruit tree such as Apple tree, in facr increase in heat accumulation will reduce the length of the growing season and, as a result, reduce the quality and yield of the fruit. Based on the spatial distribution, the least heat accumulation in the highlands, especially Northwest and central Alborz, will occured. In natural landscapes of low elevations, valleys and plains in the Northeast, central Southern part of the Zagros and around Lake Urmia, higher heat accumulation will occured in the future. Therefore, one of the effects of climate change on fruit trees will be due to increased heat accumulation in the upcoming period. Increasing the potential or heat accumulation will reduce the growth period of the fruit trees, in fact, the fruit trees will complete their vegetative and reproductive cycles sooner.
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.
, , , ,
Volume 5, Issue 4 (3-2019)

 Extended  Abstract
Cold and frost is one of the most important climatic parameters in the agricultural climate, and the damage caused by them reduces the possibility of producing many agricultural and horticultural products in vulnerable areas. Cold and frost is one of the climatic hazards that annually causes damage to various activities. The agricultural sector is the most important part of the damage that is most seriously damaged by frost. Cold and frosty weather for many crops and gardens results in harmful and destructive consequences, in some years billions of rials damage farmers, farmers and, ultimately, the national interests of the country. Considering that the northwest region of Iran suffers a lot of financial losses each year due to atmospheric hazards especially cold and frost. Identification and zoning of areas with high potential of cold and frost hazard and prediction of their occurrence can provide valuable and valuable information for preventing and mitigating damages. In this study using HadCM3 global model under two scenarios A2 and B1 and The LARS-WG microscope model is dealt with this.
It is important to check the time of occurrence and predict their future changes. For this purpose, general atmospheric circulation (GCM) models are designed that can simulate future climate parameters. In this study, the output data of the HadCM3 general circulation model under two scenarios of A2 and B1 were analyzed by LARS-WG statistical method in 21 synoptic stations located in northwest of Iran. The results of this study were based on the base period (1980-1989) and The 2020 decade (2030-2011) was evaluated for two climate variables: minimum temperature and maximum temperature. Then the history of the first and last frost and cold of autumn and spring was extracted and their date of occurrence was calculated in the future.
The monthly average of the minimum temperature of the stations studied in the course of the 2020s and the base period shows that the temperature has been increased according to both scenarios and increased in all months and at most study stations compared to the base period. The maximum changes in the minimum temperature in the study area are based on the average scenarios in this decade related to Abhar, Ardebil, Khoy and Urumieh stations at 0.8 degrees Celsius; In fact, the minimum temperatures that occurred at these stations during the base period have not been observed in the next period and the heating process has shown that its rate in the region of the study area in the 2020s is between 0.4 and 0.8 It will be in the base period. The results indicate an increase in the monthly average of the minimum and maximum daily temperatures in the upcoming period to about 0.8 degrees Celsius. The results of the first and last glacial survey in the decade of 2020 indicate that the first glacial precipitate of autumn occurs between 2 and 9 days later, with the least change in the history of frost occurring in two stations of Qazvin and Meshkinshahr each with 2 The change day is relative to the base period. The last frost of late spring also will be 3-10 days earlier on the surface of the region. However, the duration of the ice free period will be reduced at all stations, which is the highest decrease for Khoy station with 16 days, then the stations of Urmia and Ardebil each Two with 14 days and the lowest decrease is due to Meshkinshahr station for 6 days. Based on the results of changes in the date of early ice ages, changes are less than the late frost. Based on this, the study of the condition of glaciers and serma in most of the studied stations shows that the first frost and autumn frost in the coming period will start earlier and the cold and the frostbite will end sooner. The least changes were observed in the south-east of the study area, Meshkinshahr and Sarab regions, and the most changes in the glacial period were related to Khoy, Urmia, Tabriz and Ahar areas. According to the results of most studied areas, averaging between 10 and 12 days decrease in length The ice age will experience the base course.
The results indicate an increase in the monthly average of the minimum and maximum daily temperatures in the upcoming period to about 0.8 C. Based on this, the study of the condition of glaciers and serma in most of the studied stations shows that the first frost and autumn frost in the coming period will start earlier and the cold and the frostbite will end sooner. Also, the length of the cold and freezing period is decreasing, which may reflect the consequences of climate change at study stations. The results of this study are based on the studies of Grasick and Dodwilich (2015) in Poland, Medella et al. (2016) in Texas, Hosseini and Ahmadi (1395) in Saqez, Aqa Shariatmadari et al. (1395) in West Iran, Sobhani et al. (1396). ) In Ardebil and Khalili et al. (1396) in Iran.
Dr Manouchehr Farajzadeh, Miss Zahra Kazemnezhad, Dr Reza Borna,
Volume 5, Issue 4 (3-2019)


Climate change in one area has severe impacts on water resources and, consequently, agriculture in that area. Therefore, studying the extent of the vulnerability of regions to adopting policies to reduce or adapt to new conditions is of particular importance. One of the methods for assessing the extent of damage to agricultural activities is the calculation of the vulnerability index. In this study, with the aim of assessing agricultural vulnerability to climate change, The CVI index was calculated for 16 cities in Guilan province.

The results showed that the cities of Rasht (61.58) and Talesh (55.21) had the highest vulnerability and, accordingly, had the least adaptive power to climate change compared to other cities. And Langrood County (29.51) has the lowest number of vulnerabilities. The average value of the calculated index is 40.42 in Guilan province. In component R, the most vulnerable were Talesh (99.66) and lowest for Lahijan (2.27), In component M, the highest vulnerability was for Rudbar (97.21) and the lowest for Talesh (24.30), In component A, the most vulnerable were Rasht (89.99) and the lowest for Anzali (2.21), In component C, the most vulnerable were Shaft (66.66) and lowest for Anzali (1.89), In component U, the most vulnerable were Rasht (67.55) and the lowest for Astara (28.92), In component E, the highest vulnerability was for Talesh (76.49) and lowest for Lahijan (22.69), In component G, the most vulnerable was reported to Rasht (53.05) and the lowest vulnerability was reported for Sunnelk (23.24).

Mostafa Yaghoobzadeh, Abbas Khashei, Yousof Ramezani, Seyyedeh Atefeh Hosseini,
Volume 6, Issue 4 (2-2020)

Evaluation the best of selective base period of GCM models to determine meteorological variables of Birjand station in future periods
Nowadays, determining the effect of a climate change in the various aspects of human life is quite evident. In such a situation, it is very important to determine the base period, which determines the effects of a climate change than in this period. Choosing a course-based course plays an important role in choosing future courses to conduct research on the effects of climate change. Many researchers in the research use the LARS-WG dynamic downscale method or the statistical method to measure the weather variables, which should be the same for the years of the base period and the upcoming period.
This research was conducted to select the appropriate base course for estimating minimum temperature, maximum temperature and precipitation at the synoptic station in Birjand. The station is located at latitude 32 degrees and 53 degrees east and 59 degrees and 17 degrees north latitude. In order to evaluate and accuracy of the methods in this research, seven criteria for estimating root mean square error (RMSE) and mean absolute error (MAE), relative error (RD), mean relative error of the month of the year (MRDM), average relative error of the month in the year (RDMM), PBIAS and RSR. In this study, using GCM models, we assessed the selected base courses for the synoptic station in Birjand. To doing in the research, an amount of 27 base courses from 35 models of the fifth report of the change were compared with similar periods obtained from the station in Birjand.
The results showed about precipitation that the duration of the base periods such as 1960-2005 and 1960-2000 is less of the RMSE and MAE errors than the rest of the courses, and the base period of 1965-1990 between periods less than 30 years and the period The 1990-1960s are also well suited to the precipitation data of the synoptic station. The maximum temperature of the 1960-1990, 1960-1985 and 1960-1995 is the lowest RMSE error. However, short-term courses of 1980-1960 and 1965-1985 present satisfactory results.In the case of minimum temperatures, periods of 21 and 31 years 1960-1980, 1960-1985, 1960-1990 and 1965- 1985 have a percentage error of RMSE and a lower percentage of PBIAS. Variable variation range can also be used to show the appropriate base course. The result showed that the periods 1960-2005 and 1970-2005 had a lower range of rainfall variation than the other variables and seems to be more suitable. However, courses such as 1990-2000, 1975-1995, and 1995-2005 have less certainty. The more courses that go into periods with shorter periods of time, the more modest and less certainty they will be. Also, if you look at changes in the 1975-2005 periods and the 1965-1995 periods, it will be clear how much each year towards the years closest to 2005 will be deducted from the precipitation daily average.
The results also show that maximum temperature changes are better than precipitation, and all courses have less variation range. Nevertheless, the period of 1960-2005 has the highest degree of certainty and the period of 1975-2005 has the least degree of certainty compared to the rest of the courses. In contrast to precipitation, there are periods such as 1970-1990, which, if considered as the basis for research, provide more certainty than the longer period of 1965-2005 for maximum temperature. Also, what's most clear about the maximum temperature is the higher the period with years closer to 2005, the temperature increases, which will increase the temperature over time.
The process of minimum temperature variations also indicates that in addition these changes are similar to the change in temperature, with the difference that the range of variations in the minimum temperature is somewhat higher than the maximum temperature. The period of 1960-2005 has the best degree of certainty and the period from 1975-2005 has the least degree of certainty than the rest of the courses. Although long periods of time are less certain than short periods, the result is that the longer the interval between periods increases, the more precise the results will be. The result is not entirely correct, 1975-2000 is less certainty than the 1965-2000 period and has better results in minimum temperatures. Therefore, the evaluation of selected periods of GCM models with similar periods from observations of Birjand station shows that for rainfall variables, periods with a number of years yield more satisfactory results, but for two variables the minimum temperature and maximum temperature of the periods, not long or short periods, provide less risk of RMSE and PBIAS than long periods.
Keywords: climate change, GCM model, base period, meteorological variable, emotion scenario
Hassan Zohrevandi, Ali Mohamad Khorshid Dost, Behroz Sari Saraf,
Volume 7, Issue 1 (5-2020)

Prediction of Climate Change in Western of Iran using Downscaling of HadCM3 Model under Different Scenarios
Hassan Zohrehvandi 1, Ali Mohammad Khorshiddoust 2, Behrouz Sari Sarraf 3

1- Ph.D student of Climatology, University of Tabriz, Email: 
Mobile number:+989181502513
2 - Associate Professor of Climatology, University of Tabriz, Email:         

 Mobile number:
 3- Associate Professor of Climatology, University of Tabriz, Email:      
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   Considering that water resources are at risk from climate change, the study of temperature and precipitation changes in the coming years can lead to droughts such as droughts, sudden floods, high evaporation and environmental degradation. To this end, global climate models (GCMs) are designed to assess climate change. The outputs of these models have low spatial accuracy. In order to increase the spatial accuracy of this data, downscaling methods are used which are divided into statistical and dynamic methods. One of the reasons for using these models is their quick and easy operation compared to other methods. Our study area consists of Kurdistan, Kermanshah and Hamedan provinces in the west of the country. In this study, observational data of minimum temperature, maximum temperature, precipitation and radiation of 6 synoptic stations in the studied area in the statistical period of 1961 until 2005. In this study, the LARS-WG model was used for downscaling of HadCM3 global model data. The LARS-WG model is one of the most popular weather generator models that which to generation for maximum and minimum temperature, rainfall and radiation are used daily under current and future climate conditions. This model as a downscaled version of the same process less complex and simulated data input and output, high ability to predict climate change. The HadCM3 model is also a type of atmospheric- oceanic circulation model developed at the Hadley Center for Climate Prediction and Research, which has a 2.5 degree latitude network at 3.75 degrees longitude. Also, three climate change scenarios A1B, A2 and B1 have been used, each of which reflects the characteristics of the world's economic growth, the world's population and social awareness. The methodology is that the model receives the monitored data of the basic course; by examining them the statistical characteristics of the data are extracted. Then, in order to validate and ensure the model's capability for the basic statistical period, the model is implemented to re-establish a series of artificial data in the base period. Then the outputs to evaluate the performance of the model in the reconstruction of the data, the statistical characteristics of observations to test and compare various criteria. MAE, MSE, RMSE and R2 criteria were used to evaluate and analyze the performance of the downscaling model. The results showed that the accuracy of the model varies in different stations and parameters, so that the model in simulation of temperature and radiation is more suitable than rainfall simulation. Also, the model has more successful in simulation of maximum temperature in comparison with minimum temperature. In sum, the results of different evaluation criteria indicate that the LARS-WG model has a good accuracy for the downscaling of the parameters studied in the study area. After evaluating the LARS-WG model and ensuring its appropriateness, the data was generated by the model for three climate change scenarios using the HadCM3 model. The results of the monthly review of the parameters studied at the station indicate that precipitation in the 2050s at all stations except Saralpul Zahab and Sanandaj stations according to the three scenarios studied in most months except December, January And at some stations, sometimes in November and February, they were lower than the base period, and rainfall is expected to decrease over the 20 years period (2046-2065), but the situation for Sanandaj and Saralpul Zahab stations is somewhat different, which, according to some scenarios, has increased in most months of the year, and according to some scenarios, rainfall has decreased in some months and it seems that the precipitation pattern is shifted The end of the warm season. But the rainfall situation is completely different in the 2080s, and rainfall has decreased in all stations and in most months of the year. The average monthly of the minimum and maximum temperatures as well as the amount of radiation shows that all three parameters will increase in all months of the year based on all three scenarios, as well as in the two decades studied (2080 and 2050) And its rate would increase in the decade than in the previous decade. According to the results, the amount of precipitation decreases in study area and the temperature and radiation will increase as well. The rate of precipitation decrease in the following periods will be 7.7% in the region than in the base period, and the minimum and maximum temperatures in the long-term was increase at the region 3.4 and 3.4 degrees Celsius, respectively, compared to the average period of the base. The radiation increase was 0.38 mJ /m2 in Area level. The results of this research can help to solve the challenges of water resource managers and planners in future periods.
Keywords: Climate Change, downscaling, west of Iran, General Circulation model, LARS-WG
Nima Sohrabnia, Dr Bohlol Alijani, Dr Mehry Akbari,
Volume 7, Issue 2 (8-2020)

Modeling the discharge of rivers in selected watersheds of Guilan province during climate change
   In this essay, we investigated the effects of climate change on the rivers of selected basins of Guilan province, one of the northern provinces of Iran for the period 2020 to 2050 under three climate scenarios: RCP2.6, RCP4.5, RCP8.5. For this purpose, rainfall and temperature data from 45 climate data stations and 20 hydrometric stations from 1983 to 2013 were used. The average precipitation and temperature at basin level were calculated by drawing both Isohyet and Isothermal lines by usage Kriging method. Mann-Kendall and Sen’s slope estimator tests were used to determine the significance of the data trends and their slope, respectively. The results showed that temperature has increased in all catchments during the study period and this trend was significant in most of them but no significant trend was observed for precipitation. Discharge has also decreased in most basins and this trend was significant in Shafarood, Navrood and Chafrood basins. However, for future periods, precipitation is not significant in any of the climate scenarios, but the temperature is increasing in all scenarios except for the RCP2.6 scenario. Rivers discharge in the RCP2.6 scenario is not significant in any of the basins, but in the RCP4.5 scenario the Shafarood and Ghasht-Roodkan catchments have a significant reduction in the 95% confidence level. In the RCP8.5 scenario, the Chafrood and Shafarood basins have a 99% confidence reduction trend.
Population and technology growth, increased water consumption and climate change have led many researchers to study and model water resources in the present and future periods. Especially in areas like Iran that are facing a lot of water stresses. The purpose of the present study, which was carried out in the Guilan province, is to provide information on the present and future status of surface water resources, and to prepare them for facing the problems of potential water resources exploitation.
In this study 45 synoptic, evaporative and rain gauge stations and 20 hydrometric stations data with sufficient statistics were used. The period of study is also between 1983 and 2013. In this regard, after calculating the average precipitation and temperature values of each basin using Kriging model, first, the annual average of precipitation and temperature values ​​of each basin were calculated. Then, multivariate regression was used to obtain the regression equations between precipitation, temperature and discharge data, then by using SDSM model and climate scenarios (RCP2.6, RCP4.5, RCP8.5) future temperature and precipitation data were generated. By placing these generated data in the Created regression equations, the discharge of the rivers was calculated for the period 2020 to 2050. The trend of time series and their slope were analyzed respectively by Mann-Kendall and Sense tests.
   The study of the annual average precipitation trend of the selected catchments during the study period showed that all the basins had no significant trend at any of the confidence levels (95% and 99%). However, for the temperature there is an increasing trend. In Chafrood, Zilaki, Chalvand, Lavandevil, Tutkabon, Chubar, Lamir, Hawigh, Dissam, Shirabad, Ponel, Samoosh, and Polrood basins there is significant trend at 95% confidence level. For the Hawigh River basin there is significant trend at 99% confidence level. Also in most of the basins there is a downward trend of rivers discharge. In addition, in the three basins of Chafrood, Navrood and Shafarood, there is a significant decreasing trend at 95% confidence level, which is also significant at 99% confidence level for Navrood and Shafarood rivers.
Analysis of future data showed that precipitation is not significant in any of the climate scenarios, but the temperature is increasing in all scenarios except for the RCP2.6 scenario in RCP2.6 scenario. For rivers discharge there was no significant trend in any of the basins, but in RCP4.5 scenario there is a significant decrease in 95% confidence level in Shafarood and Ghasht-Roodkan. Also in the RCP8.5 scenario, a significant decreasing trend of flow discharge at 99% confidence level is observed for Chafrood and Shafarood basins. Finally, the catchments were grouped according to the level of risk involved with decreasing discharge. The results of grouping showed that most of the basins in the three scenarios were in the medium risk group but Shafarood, Chafrood and Ghasht-roodkhan watersheds have higher risk than the other watersheds, respectively.
Investigation of river discharge trends for the period 2020 to 2050 in different scenarios showed that the basins of Ghasht-roodkhan, Chafrood and Shafarood are more sensitive to climate change than other basins. Overall, escalating temperature trends in future and precipitation irregularities can create very difficult conditions in future to use these resources. Especially, this study's concordance with other studies in Iran and the study area confirms that such crises are more likely to occur..
Keywords: Climate Change Scenarios, Rivers Discharge, Man-Kendall, Sen’s Slope estimator, Guilan Province

Mr. Erfan Naseri, Mr. Alireza Massah Bavani, Mr. Tofigh Sadi,
Volume 8, Issue 1 (5-2021)

 Detection and Attribution of Changing in Seasonal variability cause of climate change (Case study: Hillsides of Central Southern Alborz Mountains)
One of the most important challenges for the human communities is Global Warming. This vital problem affected by Climate Change and corresponding effects. Thus this article attempted to assess the trend of real climate variables from synoptic stations. Daily precipitation, Daily Maximum Temperature and Daily Minimum Temperature have been selected for the Hillsides of Southern Central Alborz Mountains and have been tried to prove climate change and attribute the related forcing such as Greenhouse Gases. The Capital of Iran located in this region and this region has a special occasion, because at least a quarter of Iranian population live in these provinces (Tehran and Alborz) and four big dams located in this region. The Intergovernmental Panel on Climate Change’s defines ‘‘detection’’ of climate change as ‘‘the process of demonstrating that climate or a system affected by climate has changed in some defined statistical sense, without providing a reason for that change,’’ while ‘‘attribution’’ is defined as the process of evaluating the relative contribution of multiple causal factors to a change or event with an assignment of statistical confidence. Regional D&A studies provide an insight to local changes in natural systems and may help in planning and developing robust adaptation strategies. Previously, formal detection and attribution have been used to investigate the nature of changes in various climatological variables such as air temperature, surface specific humidity, ocean heat, sea level pressure, continental river runoff, global land precipitation and precipitation extremes. However, almost all of these studies deal with climatological or meteorological variables at the global or continental scale. Studies which have attempted to formally detect and attribute regional hydrometeorological changes to anthropogenic effects are rare. Regional-scale D&A analysis is more difficult because the detection of anthropogenic ‘‘signal’’ in natural internal climate variability ‘‘noise’’ is determined by the signal-to-noise ratio which is proportional to the spatial scale of analysis, especially for real observation data. For overcoming this issue interpolation method (IDW) has been applied to transfer point data to area (gridded) data. The point data gathered from 3 synoptic stations (Mehrabad, Karaj and Abali). Then transferred data have been Standard and Averaged for 3 years. Standard values of annual and seasonal amounts have been computed for individual stations as the average of the standard values of annual and seasonal amounts available 3 years anomaly values. Estimates of annual or seasonal variables anomalies were obtained by averaging the annual or seasonal by 12 or 3 respectively. For detecting and attributing 3 simulation signals (ALL, GHG and NAT) selected from Canadian General Circulation Model (CanESM2.0) of CMIP5 archive subcategories. Space–time series of observations and model simulated variables responses to external forcings (the “signals”) first have been compared qualitatively by computing correlation coefficients between observations and simulations. This simple method does not optimize the signal-to-noise ratio nor provide a quantitative measure of the magnitude of model simulated response relative to that in the observations. Nevertheless, it provides an easy-to-understand view of the similarity between observed and model-simulated changes. Optimal detection and attribution analysis very often requires a reduction of dimensionality. This is typically done by projecting both observations and simulations onto leading empirical orthogonal functions (EOFs) of internal variability and using the residual consistency check to determine the number of EOFs to be retained in the analysis. To produce internal variability for residual test and consistency, Pi-Ctrl Runs have been used. The Preindustrial simulations have high volume, this subject complicates calculation therefore Experimental Orthogonal Functions (EOFs) have been used to reduce the Pi-Ctrl simulations volume and provide situations for Optimal Fingerprint. Optimal Fingerprint method is the best method for Detection and Attribution. Results have been obtained by this manner indicated Global Warming affected the study region by affecting on mean cumulative winter precipitation (0.88), mean spring minimum temperature (0.78) and mean summer maximum temperature (0.76). These numbers are the beta coefficient that named scaling factor. Although the scaling factor for the mean spring minimum temperature affected from GHG signal obtained (0.73), but the GHG forcing alone didn’t have a significant effect on the precipitation and maximum temperature. Also, NAT signal didn’t have significant effect on the region alone, too. The obtained results of this study indicate the earlier studies, such as Wan et al, 2014.
Key words: Climate change, Detection, Attribution, Optimal Fingerprint, Hillsides of Central Southern Alborz Mountains
Mr Mohammad Hossein Aalinejad, Pro Saeed Jahanbakhsh Asl, Pro Ali Mohammad Khorshiddoust,
Volume 8, Issue 3 (12-2021)

Investigation of Temperature and Precipitation Changes in the Seymarreh Basin by Using CMIP5 Series Climate Models
Panel reports on climate change suggest that climate change around the world is most likely due to human factors. Temperature and precipitation are two important parameters in the climate of a region whose variations and fluctuations affect different areas such as agriculture, energy, tourism and so on. Seymareh basin is one of the most significant sub-basins of Karkheh. The purpose of this study is to predict the impact of climate change on precipitation and temperature of the Seymareh Basin in 2021-2040 period. These effects were analyzed at selected stations with uncertainties related to atmospheric general circulation models (GCMs) of CMIP5 models under two scenarios of RCP45 and RCP85 through LARS-WG statistical model. Then the uncertainties of the models and scenarios were investigated by comparing the monthly outputs of the models by the coefficients of determination coefficient (R2) in the forthcoming period (2021-2040) with the base period (1980–2010). The root mean square error (RMSE) calculations presented the best model and scenarios for generating future temperature and precipitation data.            
The Seymareh catchment is the largest and the main Karkheh sub-basin that covers parts of Kermanshah, Lorestan and Ilam provinces. The length of the largest river at the basin level to the site of the Seymareh Reservoir Dam is approximately 475 km, and the area of the basin is 26,700 km2. Geographic coordinates of the basin are from 33° 16 ́ 03 ̋to 34°59 ́ 29 ̋north latitudes and 46°6 ́9 ̋to ̋ 5 ́ 0 ° 49 Eastern longitudes, minimum basin height 698 m at the dam outlet and its maximum height 3,638 m. It is on the western highlands of Borujerd.
The information used in this study was obtained from the Meteorological Organization of the country. For this study, three synoptic stations of Kermanshah, Hamadan and Khorramabad, which had the highest statistical records and had appropriate distribution at basin level, were used. These data included daily and monthly temperature and precipitation information, and sunshine hours.
The LARS-WG fine-scale exponential model was proposed by Rasko et al., Semnoff and Barrow (1981). We used daily data at stations under current and future weather conditions. In order to select the best GCM model from the models mentioned above, minimum temperature, maximum temperature, precipitation and sunshine data were entered daily in the base period (1980–2010) and data were generated for five models under two scenarios of RCP45 and RCP85 for the period 2040–2021. The data were generated in 100 random series and the mean of required variables (minimum temperature, maximum temperature and rainfall) were extracted monthly in the period 2021-2040. Then, root mean square error (RMSE) and determination coefficient (R2) were used to evaluate the performance of the models and compare the results.
To ensure the models' ability to generate data in the coming period, computational data from the model and observational data at the stations under study should have been compared. The capability of the LARS-WG model in modeling the minimum temperature, maximum temperature, and radiation at the stations under study was completely consistent with the observed data. The model's ability to exemplify rainfall was also acceptable, however the highest modeling error was related to March rainfall.
By comparing the observed and produced data including monthly average precipitation, minimum and maximum temperatures through five mentioned models with their indices, the best model and scenario for future fabrication were determined. The results of this comparison showed that among the available models, HADGEM2-ES model under RCP 4.5 scenario had the best result for precipitation and HADGEM2-ES under RCP 8.5 scenario predicted the best result for maximum temperature. Determining the best model, precipitation data, minimum temperature and maximum temperature produced in the selected models and scenarios were analyzed to investigate the climate change temperature and precipitation for the future period.
The results of this study indicated that due to the wide range of output variations of different models and scenarios, by not taking into account the uncertainties of the models and scenarios can have a great impact on the results of the studies. It was also found in this study that the LARS-WG exponential model was capable of modeling precipitation data and baseline temperature in the study area, so that the radiation data, minimum and maximum temperatures were completely consistent with the data.
The observations are consistent and the models' ability to predict rainfall is very good and acceptable manner. In investigating the uncertainties caused by atmospheric general circulation models and existing scenarios, the best model to predict precipitation in the study area is HADGEM2-ES model under RCP 8.5 scenario, the best model for temperature estimation model HADGEM2-ES under RCP scenario No. 4.5.
The overall results of this study revealed that the average precipitation in the basin will decrease by 4.5% on average, while the minimum temperature will be 1.5° C and the maximum temperature will be 2.17° C. The highest increase will be due to the warmer months of the year. Notable are the disruptions of rainfall distribution and the high temperatures will have significantly negative consequences than rainfall reduction.
  • : Climate Change, Climate Scenarios, Uncertainty, LARS-WG, Seymareh.

Mrs Fatemeh Falahati, Dr Bohlol Alijani, Dr Mohammad Saligheh,
Volume 8, Issue 4 (1-2021)

In many areas, snow cover in the mountains is a major source of surface and underground water supply. Due to climate change and its effect on the time of melting ,it  is very important for environmental planning to predict the arrival time of water from snow melt to water consumption cycle. The purpose of this study is to investigate the volumetric changes and time distribution of snow flood flows in future by integrating remote sensing , GIS and climatic models.The studied area is the Upper Basin of Amir Kabir Dam, which is located on the southern slopes of Alborz Mountains. In this study, digital elevation maps (DEM) and GIS software were used to estimate parameters such as area, environment, main length, highest and lowest elevation points. In order to complete the snow cover data, MODIS products (MOD10A100) were extracted and the snow cover was extracted in the Upper Basin of Amir Kabir Dam. Next, runoff and snow melting models were simulated using SRM software. Calibration and validation of the model's acceptable performance were estimated. Then, in order to investigate the effects of climate change on the future of snowmelt runoff production in the basin of Amir Kabir Dam, the latest CMIP5 climatic models were used under four scenarios RCP2.6, RCP4.5, RCP6.0 and RCP8.5. A survey on the relationship between snow cover area , temperature and precipitation was used to predict snow cover in the future. The increase in temperature in the autumn and winter season has led to a reduction in the shape of precipitation in the form of snow, and as a result, the amount of snow storm is reduced. The results show that the amount of runoff in the autumn and winter increases due to increased rainfall in the form of rain, and it will be  increased late winter and spring due to the increase in the amount of water resulting from snow melting. The results of this study are based on the increase of snow melt as a result of increased runoff volume, reduction of snow reserves and maximum flow transmission to earlier than normal conditions due to early snow melting due to temperature rise. Generally, in the future, the average annual runoff will be decreased about 1.1 cubic meters per second, and the average annual melting share will be about 13.9%
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

Ali Mohammad Khorshid Doust, Ali Panahi, Farahnaz Khorramabadi, Hossein Imanipour,
Volume 9, Issue 2 (9-2022)

The effect of climatic parameters on vegetation distribution in central Iran
Climate or climate reflects the daily weather conditions in a particular place for a long time. Most climatic elements are closely related to ecological factors, which is why the analysis of the relationship between climate and plant distribution patterns has been discussed in scientific and research circles for many years. And in recent years, scientists have been using a combination of climatic characteristics with other environmental factors to describe vegetation around the world. Climate change and atmosphere condition will change the content and composition of many plant communities.

The Study Area
The geographic coordinates of the studied area are between latitudes 29°32’ to 33°59’ and 51°27’ to 55°5’. The position of the selected provinces of central Iran compared to the neighboring provinces are shown in Figure 1 The annual data of 8 stations have been analyzed during the stations period determined by the National Meteorological Organization. The stations characteristics including latitude, longitude, elevation and specific statistical period are shown in Table 3.

Data and research methods
In this study, the role of temperature changes and relative humidity on vegetation in Central Iran has been investigated using statistical models of analysis of the main components and hierarchical clustering. This research is applied and its method is slightly analytical. In order to investigate the climatic fluctuations of the center of Iran with respect to urban green space, statistical data related to average temperature and relative humidity during the 32-year period (1986 to 2018) selected central stations of Iran to come and statistical deficiencies such as Data loss was performed by reconstructing differential equations using SPSS software. The criterion for selecting stations is the availability of long-term statistics. Using statistical methods and Geographic Information System (GIS), vegetation classification was performed for Central Iran. ArcGIS, Minitab, SPSS and EXCEL software are used in this research. After identifying the stations, climatic variables including temperature and relative humidity were selected from the data of 8 meteorological stations and were analyzed using the techniques mentioned above. Then, using statistical regression analysis, the impact (topography, average temperature and average relative humidity) on how to distribute and distribute vegetation was investigated. Kendall-man non parametric test was used to investigate changes in the vegetation index trend.

Results and discussion
Analysis of temporal changes in climatic parameters and NDVI index
The results show that the distribution of relative humidity in Abadeh and Kerman stations has decreased by 3% and the temperature distribution in these stations has increased by more than one percent. Relative humidity changes in Kashan and Sirjan stations have a weak decreasing trend, while the relative humidity distribution in Isfahan station has decreased by about 2%.The temperature distribution of Shiraz and Yazd stations increased by 3%, Abadeh station increased by 2% and also Isfahan and Kerman stations increased by 1%. The distribution of vegetation in Yazd and Khor Biyabank stations has decreased by one percent, while the growth of vegetation in Isfahan, Abadeh and Sirjan stations is increasing by less than one percent.

Distribution of NDVI vegetation index in Central Iran using cluster analysis
The stations are located in three distinct areas in terms of distribution of vegetation, each group having the same climatic characteristics in the distribution of similar vegetation. Based on this, three climatic zones in the study area can be identified.

The aim of this study was to investigate the effect of climatic parameters (average temperature and relative humidity) on the distribution of vegetation in Central Iran using comparison of statistical models; by examining the distribution and density of vegetation, eight factors were identified. Among the factors, the first and second factors, with 81.57% of the total vegetation variance, have played the most important role in determining the climatic diversity of Central Iran. In total, these eight factors have justified about 100% of the vegetation behavior in the area Also, according to the analysis of images of Modis satellite measuring satellites from the vegetation situation in the last 5 years, Central Iran, the value of NDVI index in Central Iran varies between 0.2 to 0.64, the northwestern parts of Fars province have the highest vegetation density and The central parts of Isfahan, especially Yazd, lack vegetation. Based on the results, altitude has a direct and significant relationship with temperature distribution in plants, especially in the study area. However, the height of Iran's central regions has affected the distribution of vegetation.

Keywords:  climatic parameters, vegetation distribution, central Iran

Dr. Homayoun Motiee, Mrs. Saba Ahrari,
Volume 9, Issue 2 (9-2022)

Glaciers are one of the most important water resources in the world, which are heavily affected by global warming and climate change. This paper investigates the effects of global warming on the changes in the snow cover level of the Takht Suleiman region located in Mazandaran province during the warm months of the year through the past three decades using remote sensing. For this purpose, the images from June to August of the Landsat-5 and 8 satellites in the period of 1990 to 2021, as well as the data of the air temperature product of the ERA5 sensor were processed on the Google Earth Engine. In this research, NDSI index (Normalized Snow Cover Surface Index) was used to detect snow covered surfaces and the Mann-Kendall test was used to evaluate the trend of the data. The results of the overall accuracy and Kappa coefficient in the Google Earth Engine system show an overall accuracy of 94% and a Kappa coefficient of 89% in 2021, which shows the high compatibility of this method with real data.
The results obtained during the investigated period show an increase of about 1.5 degrees in temperature during the last three decades at a significant level of 95%. The snow and ice cover of the Takht Suleiman region in June month decreased from 127 square kilometers( in 1990) with a decrease of 82% to 22 square kilometers( in 2021). The trend of changes in the level of snow cover in June was analyzed with the Mann-Kendall test, which shows a decreasing trend at a significance level between 80 and 90%. In general, these results indicate an increase in temperature and a decrease in the level of this glacier during the statistical period studied, and the continuation of the gradual depletion of the glaciers of this region in the future is a serious threat to the downstream water source and the surrounding environment.

Mr Loghman Khodakarami, Dr Saeid Pourmanafi, Dr Alireza Soffianian, Dr Ali Lotfi,
Volume 9, Issue 2 (9-2022)

Space-based quantification of anthropogenic CO2 emissions in an urban area using “bottom-up” method
(Case study: Isfahan Metropolitan)
Increasing consumption of fossil fuels in urban areas emits enormous amounts of greenhouse gases into the atmosphere. Therefore, the study of carbon dioxide (CO2) emissions from urban areas has become an important research topic. The main purpose of this study is space-based quantification of carbon dioxide emissions driving from fossil fuel combustion in different source sectors in Isfahan. To achieve it, in the present study, the "bottom-up" method was used to quantify the carbon dioxide gas emission based on its production sources sectors. In this method, the amount of emission was measured distinctly for different sources of energy consumption and consequently the spatial distribution map the CO2 emission was generated. The results of this study revealed that the total amount of carbon dioxide emissions driving from fossil fuels is 13855525 tons per year in Isfahan. Separately stationary sectors of power plant, housing and commercial and mobile sources including road and railroad and existing agricultural machinery were responsible for emitting 50.61, 21.78, 17.18, 4.92, 4.37, and 1.14% of CO2, respectively. In conclusion, through applying the bottom-up method and CO2 emission distribution mapping based on different source sectors, mitigation measures can be applied more efficiently in urban planning.
Key words: Greenhouse gas (GHG), Fossil fuel combustion, Mobile and stationary source of energy consumption, climate change, Mitigation strategies
Mr Sayyed Mahmoud Hosseini Seddigh, Mr Masoud Jalali, Mr Hossein Asakereh,
Volume 9, Issue 3 (12-2022)

The expansion of the pole toward the tropical belt is thought to be due to climate change caused by human activities, in particular the increase in greenhouse gases and land use change. The variability of the tropical belt width to higher latitudes indicates the expansion of the subtropical arid region, which indicates an increase in the frequency of drought in each hemisphere. In order to change the width of the tropical belt of the Northern Hemisphere in the middle offerings, indices of  precipitation minus evaporation, wind vector orbital component, stream function, tropopause surface temperature, OLR, and SLP have been used. Findings showed that the expansion of tropical belt latitude with stream function to higher latitudes with 1° to 3° latitude and the effect of Hadley circulation subsidence has increased the amplitude of evaporation minus precipitation has shown that the fraction of precipitation minus evaporation 1° to 3° latitude geographically increased. The subtropical jet has increased the movement of the upper branches of troposphere from the Hadley circulation by 2° to 4° latitude, which can have a negative effect on transient humidification systems as well as on the amount of precipitation. The extension of the pole towards the tropical belt, which is a consequence of climate change and hazards, will lead to the displacement of the pole towards the tropical side of the river, thus providing dry tropical belts to the pole; Also, the long-wave radiation of the earth's output has increased by 1° to 2° latitude and has caused an increase in heat in the upper troposphere, which has increased the dryness and slightly reduced the clouds in the upper troposphere and also caused the tropical belt to expand to higher latitudes. Has been. In general, the research findings showed that most tropical belt indicators have been increasing since 1979.
Seyyed Mohammad Khademi Nosh Abadi, Dr Maryam Omidi Najaf Abadi, Dr Seyyed Mehdi Mirdamadi,
Volume 9, Issue 4 (3-2023)

Industrial and agricultural activities in the world have led to an increase in the concentration of greenhouse gases such as carbon dioxide, methane and nitrogen oxide and have caused the earth's climate to become warmer. This phenomenon has caused climate change and has changed the thermal and rainfall patterns. Climate change in Iran in recent years has caused a decrease in rainfall and an increase in temperature and continuous droughts. Agricultural production in Iran has been affected by climate change and has faced a decrease in the production of crops such as wheat. Therefore, according to the government's policy of self-sufficiency in wheat production and the establishment of sustainable food security in the country, it is necessary to use climate smart agricultural technologies to sustainably increase agricultural productivity, Adapting and resilience of agriculture to climate change and reduction greenhouse gases emission from agriculture. The purpose of this study was to design a behavioral model for the use of climate smart agricultural technologies with an emphasis on motivation. The research method was quantitative, in terms of practical purpose, and research data was collected through a cross-sectional survey.The conceptual model was designed using the theory of planned behavior and the theory of norm activation. Bayesian structural equation modeling was used to test the model and hypotheses. The statistical population of this research was 800 wheat farmers of Nazarabad city, Alborz province. The sample size was calculated using Cochran formula 260 people, and stratified random sampling method with proportional assignment was determined as the sampling method. A researcher-made questionnaire was used to collect research data. The validity of the questionnaire was confirmed through agricultural extension and education experts, and its reliability was also confirmed through the pre-test and calculation of Cronbach's alpha coefficient. The findings of the research show that subjective norms, personal norms and perceived behavioral control related to the use of climate smart agricultural technologies have a significant effect on the intention to use these technologies. While the attitude towards the use of climate smart agricultural technologies do not have a significant effect on the intention to use these technologies. The variable of intention to use climate smart agricultural technologies also has a significant effect on the behavior of using these technologies.

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