نشریه پژوهش های اقلیم شناسی
پیاپی 48 (زمستان 1400)

The main characteristics of climate change are the changes in mean climate parameters and the increase in the frequency of climatic extreme indices (WMO, 1998). Climatic extreme indices are phenomena that are scarce in terms of frequency and intensity, which disturb the equilibrium of climate systems. A joint project was undertaken within the World Meteorological Organization's activities between the CCL, CLIVAR and WCRP and provided specific relationships to calculate various indices resulting in the production of many softwares such as Climdex and Climdex (Zhang, 2007). Halimato et al. (2017) investigated the trends of temperature and precipitation changes in the Segou and Bamako in Mali. The results in Segou showed that the number of cold days and cold nights significantly reduced and the number of hot days and hot nights increased. One of the common characteristics of environmental factors is their continuous spatial variations. Therefore, in order to quantitatively describe the distribution patterns of environmental variables, the geographical location of the observations must be taken into account at the same time, in addition to the value specified (Buma, 1996). Thereupon, in this study mid-term data of 2017-2050 were simulated in order to investigate the influence of climate change on the trend of temperature extreme indices by means of MIROC5 climate model and Delta microscale method based on the fifth IPCC report using daily temperature data in the statistical period of 1992-2016 at 17 stations in South Khorasan Province. Afterward, 16 temperature extreme indices, which were defined by ETCCDMI expert group, were calculated using RClimdex software in R programming language environment. The non-parametric Mann-Kendall test and linear regression were applied to trend analysis process, the trend of all indices were calculated at probability levels of 0.8, 0.96, and 0.995, and finally by using the conventional Kriging geostatistical method, zoning maps were drawn.

South Khorasan Province in Iran has an area of 151,193 km2 and is located between the latitude of 30° 31' to 34° 53' N, and the longitude of 57° 03' to 60° 60' E. In this study, daily temperature data of seventeen South Khorasan meteorological stations were used during the period of 1992-1996 with different climatic conditions. The first data were controlled by RClimdex software and then the 16 temperature extreme indices defined by the ETCCDMI Expert Group were calculated using RClimdex software in the programming language environment of R on annual and monthly bases. The Mann-Kendall test and Statistical method of linear regression were utilized to examine the trends of indices. Afterwards, the probability of occurrence was calculated at 0.8, 0.96, and 0.995 probability levels to be respectively equal to 5, 25, and 200 year return period after fitting to the theoretical distributions using SMADA software, sequentially, the likelihood maps of probability were plotted. From the existing climate models, the Miroc5 model was selected and the results of previous CMIP5 data show acceptable performance of this model for simulating atmosphere, ocean, glaciers, chemistry and aerosols of the Earth and atmosphere, giving acceptable results for arid and desert areas (Dasht Bozorgi, 2015 and Ullah, 2018). In addition, from the four scenarios introduced by AR5, two scenarios namely RCP4.5 and RCP8.5 were selected for the period of 2006-2050.

The FDO index is increasing in Aryanshahr, Boshrooyeh, Khoor, Deyhook, Eshghabad, Tabas, Madan Parvadeh, Nehbandan, and Birjand, while most of these stations were shown to be reduced by RCP4.5 and RCP8.5 scenarios for this index. The IDO index is either rising or in normal conditions at most stations, while according to the RCP4.5 scenario there is only a sudden decrease at Fathabad station .TX10P has a sudden increase in the current conditions at Eresk, Deyhook and Ferdows stations, but based on the RCP4.5 and RCP8.5 scenarios there is a decreasing trend at all stations. The TXn index at Eresk, Boshrooyeh, Birjand, and Zohan declined abruptly. SU25 index has a decreasing trend at Fathabad station and is increasing at all the other stations based on RCP4.5 and RCP8.5 scenarios. It is also increasing at the probability level of 0.995 in the south of province. At all three probability levels, the TN90P is rising at RC4.5 in the east of the province and RCP8.5 in the south and west of the province. TNx is also increasing at all three probability levels in the northwest and west of the province. The TR20 index is also rising at RCP4.5, RCP8.5 and TX90P in all three in the south of the province. The TXx exponentially increases in northwest of the province for both RCP4.5 and RCP8.5 scenarios. The WSDI index is increasing at RCP4.5 in the south of the province and at 0.995 level in the east.

Examination of temperature indices, including cold (TX10P, TN10P) and hot (TX90P and TN90P) period indices in different scenarios, shows that cold indices tend to decrease and hot indices tend to increase in most parts of the province. The number of cold nights is decreasing based on the RCP4.5 and RCP8.5 scenarios in the north and northeast of the province, indicating that the future needs of trees in these areas will not be met. Based on the RCP8.5 scenario, the TN90P index at all stations has an increasing trend, which has a significant negative impact on summer crops and horticulture crops in the province (Bokwa et al. 2014). The duration of the CSDI cold index has declined throughout the province and the number of frost days is also declining in the two RCP4.5 and RCP8.5 scenarios for the next 50 years. On the other hand, some hot indices such as the TR20 index are spread in the north and northwest of the province. The SU25 extends from west and northwest to the center and south of the province. Future scenarios show the likelihood of an increase in hot extreme indices in addition to the west of the province to the south and center and the likelihood of danger of cold extreme indices extending from north to northeast of the province.

Analysis of Land Surface Temperature (LST) time series data significantly improves our understanding of long-term climate change. Since about two-thirds of Iran is covered by arid and semi-arid climates; Therefore, it is important to study temperature fluctuations in the country in order to determine and predict the resulting crises. The purpose of this study was to investigate the trend of temperature changes in Tehran province because in recent years we are facing with overpopulation, which is one of the issues affecting climate change in Tehran province because overpopulation increases consumption of fossil fuels, greenhouse gases emissions and As a result, the temperature rises. In this essay, the trend of temperature changes was investigated using the time series of MODIS sensor images of Terra satellite (since 2002) and the time series of MODIS sensor images of Aqua satellite (since 2003) until the end of 2018. The trend of temperature changes of the received images was determined using TerrSET software ETM analyzer and their significance was determined by parametric methods of correlation coefficient and non-parametric Mann-Kendall significance test (at 1% level). These methods were used to identify the trend of changes in maximum, medium and minimum temperature variables on a monthly and annual basis. The trend estimate of these changes was converted to degrees Celsius by ordinary least squares analysis (OLS). In addition, the trend of minimum temperature data changes of synoptic stations in Tehran province was obtained using regression methods and was compared and validated with trend maps of minimum temperature changes obtained from satellite images. In order to compare the performance of MODIS sensor in Terra and Aqua satellites, the time series of MODIS sensor images of Terra satellite were compared with the time series of MODIS sensor images of Aqua satellite by Linear Modeling analysis and R correlation coefficient. Also, the classified areas of significant increase, decrease and no significant trend obtained from the MODIS satellite sensor were compared with the classified areas obtained from the Aqua satellite sensor by the Kappa index. In addition, in order to identify the ecological effects of the trend of surface temperature changes, the shape file of protected areas and national parks of Tehran province was added to the maps of the trend of temperature changes in Arc Gis software. According to the results, the maximum, average and minimum monthly temperatures during the years under study were almost without a significant trend However, a significant increase in surface temperature was observed in the study of annual variables in most parts of Tehran province, especially in Tehran city, desert and plain areas. Also, the irrational or sudden increase in temperature in an area has reasons such as uncontrolled construction, proximity to industrial towns and drying of rivers, lakes, wetlands, etc. Comparison of the minimum temperature trend of satellite images with the minimum temperature trend of synoptic stations datas in Tehran province showed 98.3% similarity. Based on this, it can be said that there is an acceptable agreement between the trend of changes in satellite images and the trend of changes in terrestrial data, and because the degree of concordance of the results obtained from the Aqua satellite is more similar to the data of the synoptic stations than the results obtained from the Terra satellite, it is therefore recommended to study the temperature changes of the Aqua satellite. Comparison of MODIS sensor images time series of Terra satellite with Aqua MODIS sensor time series in most parts of Tehran province showed a high correlation coefficient (99% similarity). Except Tehran city and the east of Tehran province (Firoozkooh city) which can be the difference due to housing, human interference, pollution due to higher population density and agricultural use of land and harvest. Also, kappa index is below 0.7, which indicates the lack of similarity in the classification of significant areas in the whole province by the MODIS sensor between the two satellites. Based on this, it can be said that although MODIS sensor is recommended for long-term changes and better understanding of the change process but there seems to be uncertainty in the interpretation of the data obtained from the classification of MODIS sensor images. Therefore, care must be taken in choosing the type of satellite for the MODIS sensor. Based on the results obtained from identifying the ecological effects of temperature changes, Jajroud Protected Area has a trend of more temperature changes than other protected areas in Tehran province. Considering the trend of rising land surface temperature and its role in increasing evapotranspiration, we should look for solutions to better manage water resources and improve its exploitation methods, especially in agriculture and industry in Tehran province.

Dust storms caused by erodible surface sources, such as deserts, have been one of the major natural disasters in recent years, affecting countries in the Middle East, including Iran. One of the major factors to formation the dust storms is the strong wind flow that can be created in the presence of low pressure systems in the potential. Therefore, the study and identification the atmospheric patterns can be effective in predicting these storms. One way to study dust storms is to use numerical models that can be used even for times when appropriate data is not available and also to predict these types of storms.

After statistical study of the phenomenon of "dust" and the factors affecting it in the Jazmourian basin, a severe and widespread event of "dust" in the Jazmourian basin is investigated. Then, to investigate the prevailing atmospheric currents in the region, the HYSPLIT model is implemented as a matrix and in a leading way. In the implementation of HYSPLIT model, GDAS meteorological data with horizontal separation of 0.5 degrees have been used. Using the output of this model, it is possible to study how dust particles are transferred from this area. Finally, each of the studied dust phenomena is simulated using the WRF-Chem model to determine how the dust is emitted and transmitted in the region.

Leading HYSPLIT model output for November 28, 2016 UTC12 clock which was executed as a matrix with GDAS data with 0.5 degree horizontal separation for 36 hours at a height of 100 meters. The results showed that the particles from this region were affected by northwestern currents and were transferred to Sistan-Baluchestan province and the border between Iran and Pakistan. Aerosols optical depth of the output particles of the WRF-Chem model at UTC12 on November 24, 2016. Values higher than 0.5 indicate a high amount of suspended particles in the atmosphere in the study area. At this time, the highest light depth of particles is in the Strait of Hormuz and Qeshm Island, but the concentration of particles in a large part of Jazmourian region is between 0.4 to 0.6, which can indicate the presence of dust particles in the atmosphere. The results also show that the concentration of "dust" on the Oman Sea and the Persian Gulf is higher than 5000 micrograms per cubic meter. Also, the concentration of "dust" in Jazmorian region is high and is higher than 1500 micrograms per cubic meter. Also in some areas, the concentration of "dust" is more than 5000 micrograms per cubic meter.

In the present study, using the outputs of the WRF_Chem model, the dust phenomenon and its characteristics in the southeastern region of Iran have been identified. According to the results, the WRF_Chem model provides a reasonable estimate of the weather in the study area in terms of scale and time changes. By producing dust particle concentration distribution maps, areas of the simulation basin that have the maximum particle concentration were identified as the main sources of particle emission. In general, the performance of the WRF / Chem numerical model in this study confirms the applicability of this model in modeling and forecasting air quality, especially for air vents produced from natural emission sources such as erodible areas of deserts. The results showed that the studied dust phenomenon (November 24-27, 2016) dust particles from Jazmourian basin, were affected by northwestern currents and were transferred to Sistan and Baluchestan province and the border between Iran and Pakistan.Keywords: simulation, Dust storm, WRF/Chem model, MODIS.

In the present study, using the outputs of the WRF_Chem model, the dust phenomenon and its characteristics in the southeastern region of Iran have been identified. According to the results, the WRF_Chem model provides a reasonable estimate of the weather in the study area in terms of scale and time changes. By producing dust particle concentration distribution maps, areas of the simulation basin that have the maximum particle concentration were identified as the main sources of particle emission. In general, the performance of the WRF / Chem numerical model in this study confirms the applicability of this model in modeling and forecasting air quality, especially for air vents produced from natural emission sources such as erodible areas of deserts. The results showed that the studied dust phenomenon (November 24-27, 2016) dust particles from Jazmourian basin, were affected by northwestern currents and were transferred to Sistan and Baluchestan province and the border between Iran and Pakistan.

Since the scientific study ofdrought provides a basis for reducing the effects of this climatic phenomenon,the study of drought in Iran,especially on the basis of multivariate indicators that use other climatic parameters to estimate drought in addition to rainfall is very important.In addition, this study is based on network data with high spatial and temporal resolution such as GPCC,CRU, TRMM.Due to climate change in recent decades and increasing water demand in different parts of the country and comparing it with the results of station data is doubly important and necessary.This research,in terms of purpose,is an applied research.In terms of the nature of data,this research is a quantitative research that presents results by collecting data and analyzing them with quantitative methods.To achieve the purpose of this study,network data along with statistical methods have been used. At first,CRU networking data with spatial resolution of 0.5 × 0.5 ° was obtained from NOVA site and was extracted for Iran using the capabilities of GIS and MATLAB software during the study period.Thus, first,the received CRU data was transferred to MATLAB software and the relevant area in Iran was separated from the rest of the world.Then, using the programming index in MATLAB environment,SPEI index as a new index that besides to precipitation considers the effects of temperature and evapotranspiration in estimating drought.Drought and wet periods are calculated in season time scales and drought characteristics were extracted in different parts of the country.Finally,the results of drought calculation using GIS and office software environments were shown as maps,graphs and tables.Based on this, the whole country was divided into 7 clusters. In order to identify the spatial distribution of different drought classes in the country,the variables of each cluster were zoned in GIS software.For monthly drought assessment,the selected drought index is fitted to 50-year rainfall cultivars of 621 points of the CRU database nationwide,and considering the value (-0.5 and + 0.5) as the normal situation,the monthly conditions are checked from January to March.The drought situation of each of the cold months of the year is described below.Drought is one of the natural disasters that compared to other natural disasters in terms of magnitude, severity, duration of the event, regional expansion, casualties,economic losses and long-term effects.It is one of the most important climatic phenomena that strongly affects all aspects of human activities.Studying the characteristics of drought and predicting it can be effective in reducing the damage caused by it.But as we know,one of the most important phenomena of climate and climatology is drought,which causes their intensity, continuity and expansion on human activities, transportation, energy, environmental issues and the activities of living things.Considering that the main source of fresh water supply for agriculture is domestic and industrial consumption and can lead from mild effects of personal life to major disasters at the national level.The general trend of rainfall in winter is decreasing.As in the 90s, most of the rainfall anomalies of this season in the country are positive; However,with the onset of the 2000s,the incidence of dry periods has increased, and this situation has continued almost to the end of the period under study. On a monthly scale,the most stable rainfall regions of the country are the first (northwest) and third (north and northeast) clusters; Because these clusters have the highest frequency of normal periods in most months. Instead,the fifth (south) and seventh (southeast) clusters have the highest fluctuations and the lowest frequency of the normal period. For most months of the year,the frequency of dry periods is higher than wet periods, and it is only in December that in four clusters, the frequency of wet periods is higher than dry periods. In November, in three clusters (second, third and sixth), the frequency of wetlands is higher. The fifth and seventh clusters have the highest frequency of monthly droughts and the second and sixth clusters have the highest frequency of monthly wetlands. The duration of dry periods is more in most months of the year than wet periods. The most severe duration of the drought occurred in April and May, in the seventh and fifth clusters, respectively. In these two months, the continuation of drought in the two mentioned clusters has lasted more than 10 years, while the continuation of wet periods in most years and clusters of the country is 2 periods and the maximum duration of wet period in most months of the year is 4 periods. In the central regions (fourth cluster) and western (second cluster) the most continuous wet periods are observed. However, sometimes in the fifth dry cluster we see wet periods of 4 years. In terms of severity, a significant percentage of droughts and wetlands in the country in all clusters are mild and moderate, and severe events of low frequency and very severe events are usually accidental. In general, it can be said that the intensity of drought in the country has been more than wet season, because most wet season are mild, but in some droughts, especially in the fifth to seventh clusters, the frequency of the middle class is noticeable and even in some months is higher than mild droughts. In addition, intensity of droughts is more common than intensity of wet periods. The frequency of intensity of droughts in January, February, April and especially in December is much higher than intensity of wet periods. Spatially, intensity events of drought and wet season mainly belong to the arid regions of the country, especially the two clusters 5 and 7. In terms of time trend, the rainfall is decreasing in most months of the year and the frequency of droughts in recent years is more than wetlands. In October and November, of course, the precipitation trend has a slight upward slope and wet periods have been observed more in recent years. Therefore,the occurrence of drought has significant effects on economic,social and agricultural issues.And this requires studies to be done in this area and with proper management and careful planning to avoid potential risks and losses.

Wind energy has been considered as one of the clean energy sources. Due to the variability of wind speed and its effect on wind power plant, wind forecasting methods are of special importance. Fossil fuel consumption has destructive effects on the environment. According to Renewable Energy Policy Network for 21st century(REN21st) in 2014, nearly 20% of the total electricity was generated by wind energy, and the European Wind Energy Association (EWEA) predicts that it reaches 24.4 % in 2030. Wind speed has a great impact on increasing or decreasing air pollution and thus human health. Also it is one of the most important and effective factors in evaporation. Accurate prediction of wind speed is crucial in many social applications such as weather warnings in risk assessment and appropriate decisions in aviation, ship navigation, recreational sailing and agriculture.With the advancement of computers and the ability to perform fast calculations, it became possible to implement weather forecasting models. At first, looking at weather forecasting was only a deterministic forecast, but since numerical weather prediction models include differential equations that approximately describethe physical and dynamic laws of the atmosphere, the answer obtained from the implementation of numerical models is an approximation of the real answer and is always in error. Also, factors such as the existence of errors in the initial boundary values, the inability of the model to take into account all atmospheric processes, the lack of primary data in some areas, the inability of the model to successfully simulate subnet phenomena and the chaotic nature of the atmospheric dynamic system can be mentioned. Hence, forecasting in such a system will be accompanied by high uncertainty. Therefore, to determine how the weather will be in the future, taking into account the mentioned uncertainty, there is an approach called probability forecasting. In this method, by using probabilistic prediction the chance of possible occurrence of future states of the atmosphere is calculated and the uncertainty is quantified and can be obtained more and accurate information. In this way, instead of considering only one model with an initial value, with a physical schema and a dynamic core, it can be created a finite number of prediction models by changing each of these three cases. In this study with the change in the model physics schema, the uncertainty caused by the model physics is considered and each produced prediction is considered a member of the system which is different from others. By applying statistical methods to the members of the ensemble system, a probability distribution function will be obtained in which post-processing is also performed and describes the uncertainty of the future state of the atmosphere and includes sufficient information for the needs of different users.

In this study, probabilistic wind speed predictions are generated after applying two methods of BMA and NGR on the raw output of ensemble system for 24, 48 and 72 hour forecasts and compared with raw probability predictions of the system which are selected democratic voting. The applied ensemble system consists of eight different physical configurations, with changes in the boundary layer scheme of the WRF model with a resolution of 21 kilometers over Iran.GFS forecasts are used for the initial and boundary conditions, and the forecast start time is 12 UTC per day. Observation data of 31 synoptic meteorological stations located in the provincial capitals have been used and the corresponding values of the predictions on these stations have been interpolated by bilinear method. The model run from 1 March to 31 August 2017, and the results from 11 April to 31 August 2017 are considered as the test period. After calculating the forecast errors with different training periods, 30 days are considered as the length of training period for prediction in both BMA and NGR methods.

Verification was performed by Barrier Score(BS), Barrier Skill Score(BSS), reliability diagram, ROC diagram and Economic Value diagram for 10-meter wind speed threshold less than 3 and more than 5,10 and 13 m/s for BMA, NGR and Raw probability prediction of the system in all forecast ages. The results show that BMA and NGR have improved BS, BSS 33% to 46% from Raw probability prediction of the system and the reliability and the resolution have improved 78% to 97% and 12 to 30 times respectively. Reliability diagram and ROC diagrams of NGR and BMA have been also improved significantly. Economic Value diagram shows also that using probabilistic predictions is important to reduce the cost of meteorological hazard. In both BS and Reliability diagram the quality of NGR was better than BMA. In BSS, Roc diagram and Economic Value diagram the both two post-processing methods had the same advantage. The economic value diagram had the best performance at a wind speed threshold of 10 m/s.

Historical article: Today, one of the most important aspects of climate change is the increase in the frequency of extreme events such as heat waves, cold waves, torrential rains, droughts and other climatic conditions, which is due to increased entropy due to global warming in the climate system. The main purpose of this study is to investigate the temporal and spatial nature of hot waves as a climatic and bioclimatic hazard in the metropolitan area of Ahvaz. In this regard, the daily data of Ahvaz synoptic station during the 60-year period 1961-2019 were used to extract heat waves. Using the normalized daily deviation model from the long-term temperature of the same day, hot waves were identified each year and their time series trends were examined. To investigate the bioclimatic risk of hot waves, a typical hot wave that ruled from 7 to 13 July 2015 in the metropolis of Ahvaz was selected. The ground surface temperature of Ahvaz city was extracted by applying a single channel algorithm on the heat bands of Landsat 8 thermal sensor for July 9, 2015, and using the observation data at 09:00 AM,Ahvaz meteorological station was converted to air temperature for the same day. Using hot spot spatial analysis, critical areas of Ahvaz city were identified during the hot wave and the amount of population exposed to the line was obtained from the population census blocks of the 2016 census using cross-matrix analysis. The results showed that firstly, during the period 1961 to 2019, there was an increasing trend with a slope of 0.08 hot wave per year in hot events in Ahvaz metropolis and secondly, during the hot wave event, the temperature in Ahvaz was different from 44 to 55 degrees.The northern parts of the city and the area around the Karun River, which passes through the center of Ahvaz, as well as the green space around the Karun River, had the lowest surface temperature at this time of day. The temperature in this range varied between 44 and 46 degrees Celsius. A large part of the central areas of Ahvaz, which generally includes the urban area, had a temperature of about 49 to 51 degrees Celsius. The results showed that the total population of Ahvaz is in the critical core of the hot wave and the bioclimatic risk of the hot wave can affect the total population of this metropolis, which was about 1.2 million people. Hot waves and heat stresses caused by heatstroke in urban environments are about to become one of the most important hazards of the urban climate. Awareness of the intensity of hot waves in different urban areas and along with awareness of the vulnerable population at risk of heatstroke ( Both age groups (the elderly and children) can be very useful for organizing the spatial distribution of urban emergency facilities and services and intensive care related to heatstroke. In this study, the spatial distribution of the risk of thermal stresses due to the occurrence of a hot wave in the city of Ahvaz was investigated. In the first step, by examining the spatial distribution of air temperature during the occurrence of a hot wave in the city, satellite images of thermal sensor, TIRS Landsat 8 were used. Demographic analysis in relation to hot wave risk, showed that in general, the population of Ahvaz metropolis, according to the general population and housing census in 2016 was equal to 1184788 people and the population density of the city is 65 people per hectare. Population analysis In 212056 of the population of Ahvaz are in the age category under 10 years, which is equal to 18% of the total population of Ahvaz. These children are very vulnerable to the risk of hot flashes in the city due to low cardiovascular capacity, which has created thermal stresses between 48 and 50 degrees during the peak of the hot wave. The risk of hypothermia or heatstroke in this age group can appear in this age group in the form of dehydration, risk of dehydration or severe dehydration, shortness of breath and increased heart rate and skin burns, suffocation, especially in hot and humid conditions. . In the age group of the elderly or the population over 65, the risk of hot flashes is more acute and destructive. Hot wave is one of the most serious bioclimatic hazards in Ahvaz metropolis that puts the entire population of Ahvaz at risk of acute heatstroke and can cause deaths due to 30% of the vulnerable population of the city (elderly and children). Exacerbate severe heat stress.

Agricultural productions and management strategies can be highly affected by long term changes and variability in temperature. Agrometeorological indices are commonly used to evaluate how weather and climate conditions affect crop production. The extreme temperature-based indices are important tools for monitoring agrometeorological indices. In this study, trend analysis of extreme temperature-based agrometeorological indices for five stations including Abadeh, Fasa, Shiraz, Badjgah and Kooshkak in Fars province was studied. The Abadeh, Fasa, Shiraz stations are synoptic stations and Badjgah and Kooshkak stations are agrometeorological stations. Ten indices including frost indices (last spring frost, first fall frost, length of frost period, number of frost days and chilling hours), growing season indices (start, end and length of growing season) and annual absolute temperature indices (minimum and maximum temperature) were investigated in the present study. Using daily minimum and maximum temperature data, time series of these ten indices were separately produced over a long period. The autocorrelation function for these constructed time series were plotted and used to check serially correlated time series indices. For each station, those time series indices, which were serially correlated, were transformed to produce an uncorrelated pre-whitened time series. For example, last spring frost in Shiraz station, and start of growing season in Badjgah station were serially correlated and it is necessary to be pre-whitened. The autocorrelation function for all indices in Kooshkak station have indicated that all correlations were statistically insignificant and therefore no necessary to pre-whitening. The nonparametric Mann-Kendal test which is commonly used to trend analysis was used to determine the existence of trend in time series agrometeorological indices. The linear trend which is commonly applied to quantify time series changes was applied to determine the amount (slope) of time series agrometeorological indices. The sequential Mann-Kendal test was also applied to determine initiation of abrupt trend changes and explore in sub-periods of time series agrometeorological indices. The applied sequential Mann-Kendal test indicated a significant jump in the air temperature at Fasa station in 1984. This station had a re-location in 1984 (personal communication with Fasa station). Therefore, the Fasa station was removed for further analysis in this study. This was also applied to determine initiation of abrupt trend changes and explore in sub-periods of time the results of both Mann-Kendall and linear trend indicated that the trends of time series agrometeorological indices were different for various stations.A significant decreasing trend was observed for most frost indices in Badjgah station, which is an agrometeorological station at agriculture collage of Shiraz University. A significant decreasing trend, at 5% significance level, was observed for length of period and number of frost days, respectively, with -9 and -12 Day/Decade slope in Badjgah from 1986 such that the length of frost period and number of frost days had largest decreasing trend among frost indices. Also, a significant decreasing linear trend were observed for chilling hours in Badjgah and Shiraz stations with -68 and -66 Hour/ Decade slope. For all growing season indices trend existence was observed in Badjgah and Shiraz stations, except start of growing season in Badjgah. An earlier start, delay in end, and increasing in the length of growing season were observed in both Badjgah and Shiraz stations. For most indices, a change was occurred in 1970s and 1980s in Badjgah and Shiraz, respectively. A significant trend in the annual absolute maximum and minimum temperature was observed in Kooshkak station (near to Marvdasht) using the Mann-Kendall analysis at 5% significance level. In this station, a change had occurred in the absolute annual minimum temperature in 1992. In the other hand, after 1979 a change had occurred in the absolute annual maximum temperature in Kooshkak station. A significant downward trend for Absolute minimum temperature has been started from 1999 and this trend has continued up to the end of studied year.For each station, the studied indices were indicated different trends and beginning time. A significant trend was observed in all of frost period and growing season indices at Badjgah station. The information of first fall frost and last spring frost is vital for harvest and planning crops. Decreasing trend of chilling hours in Shiraz and Badjgah stations have indicated that dormancy period is longer and plants need more time to exit from dormancy period.

Nowadays precipitation distribution and rainfall rate are directly affected by climate change and, natural disaster, drought, agricultural product efficiency in every basin (Sharafati 2019). It is necessary to access studies related to climate change to be prepared against disadvantageous consequences. Precipitation trend studies are one of the conventional methods for evaluating climate change in specified time series on studied basins. For this purpose, parametric and non-parametric methods were presented by researchers to assess trends in datasets. Man-Kendall test is one of the most suitable methods among non-parametric trend analysis, especially in hydraulic data sets trend assessment. Access to reliable and collated precipitation datasets is a prerequisite for study and evaluate climate change but there is not precipitation data in some rain gauge stations in Iran, or unreliable observe datasets are available. Gridded precipitation data series are sort of main data sets known as “gridded” cause of their special distribution. These datasets are raster data information that showing rainfall depth or other climate data sets in certain points. The importance of gridded precipitation data is noticeable in areas without reliable observation data. and annual precipitation trends were evaluated and reported decreasing rainfall trends after 1990 in Iran.In this research, GPCC,CHIRPS data sets were evaluated to find out the performance of GPCC,CHIRPS datasets in trend analysis. At first, daily precipitation observes data of 68 synoptic stations were collected, Then GPCC,CHIRPS satellite-based precipitation of rainfall depth and rainy days were collected. Specification and description of GPCC,CHIRPS collected gridded data from 1997 to 2017 were used in this research. As far as the gridded data set was produced in network format, It was necessary to interpolate precipitation data to approach to 68 station correct data. The interpolated rainfall depth and rainy days were calculated with the inverse distance weighting method. Rainy days and rainfall depth of data sets trend was derived in all 68 stations and 12month of the year and the annual trend also calculated with the Mann-kendal test. Finally Gridded data sets trend and observation data sets trend were used to compute POD,TSS parameters in two parts; rainy days and rainfall depth. Finally for every 68 stations. The examination results of the trend of rainfall depth of GPCC datasets and observational data showed that the highest agreement in the process of GPCC data with observational data was observed in 3 stations with moderate compliance (POD parameter more than 60%) in low rainfall areas with an average rainfall of 60 mm. Also, the highest rate (TSS between 45 to 60%) was observed in the G1 region on the eastern edge of the country.The examination results of the correlation of CHIRPS data with observational data showed, the highest POD parameter was observed in the southern margin of the Zagros as well as the Persian Gulf and the Caspian Sea (ie 100%). The lowest amount was seen in the Oman Sea and north of the Alborz mountain range. Also the TSS parameter showed, with the exception of one station in the east of the country, almost all stations are located along the Zagros Mountains with 100% compliance. The lowest TSS parameter is also consistent with the POD results, meaning that the lowest TSS value is located on the shores of the Oman Sea and north of the Alborz mountain range.The examination results of the trend of rainy days of GPCC datasets and observational data showed that the total of 22 stations have a POD parameter of more than 80%. Most of these stations are located in mountainous areas with average rainfall and temperate climate. For 16 stations out of a total of 68 stations, the POD parameter was calculated to be more than 65%. Most of these stations are located at low altitudes and foothills. The TSS parameter result showed that almost all stations with TSS are 100% located in the G1 area. The lowest TSS parameters are located in stations in the northern and eastern parts of the country.The examination results of the correlation of CHIRPS data with observational data showed, most of the POD parameters, except for one station in the center of the country, the other stations are located along the Alborz mountain range. Also, areas with the lowest TSS parameter are located in the southeastern margin of the country. Most of the TSS parameters are located in the central regions of the Zagros Mountains and the northeastern part of Alborz. In this study, it was tried to determine the trend of precipitation data related to GPCC,CHIRPS gridded data in accordance with observational data.The results of rainfall dephts trend with both POD and TSS parameters on CHIRPS satellite data in comparison with ground precipitation data showed that in 28% of stations the precipitation trend is completely consistent with the observed precipitation data, which in practice ranks these data in average to acceptable rank. In terms of compliance. While the result of high compliance of GPCC data with observational data was observed in only about 8% of stations, which indicates a very low correlation of these data with observational data.GPCC data also did not have good results in examining the results of the adaptation of the number of rainy days, so that only 1.5% of the stations were in complete agreement with the observational data, which, like the results of examining the trend of rainfall depth, has very little conformity. Also, the results of the trend of the number of rainy days of CHIRPS satellite data in comparison with ground precipitation data showed that in 33% of stations, the precipitation trend is more than 80% consistent with the observed precipitation data. As a result, the trend compliance in these data is at an acceptable level.Therefore, according to the results of this study, it can be concluded that the use of GPCC data for routing in the replacement of observational data is not highly recommended. Used safely and reliably.

Since the planet Earth acts like a black body like the planet, and always in a quasi-conditioned state, as much energy as it receives from the sun, it loses energy through long-wave radiation from the earth. The solar radiation absorbed on the ground is converted to heat; however, due to the reflection of the earth, the earth is not hot and hot. The energy reflection process by the earth is called earth reflection or long infrared wavelength, which is indicated by watts per square meter (w / m2). The low OLR values are related to the cloud at high latitudes, so that high values of the long-wave radiation of the Earth's output mean smooth skies and low values of the clouds. This indicator is also used to estimate rainfall in the tropical region. OLR calculations and estimates are a key component of the MJO, MNO, Negative and Positive Phases (ENSO), North Atlantic Oscillation (NAO), and also to study the assessment of weather indicators.

In the present study, in order to calculate the long-wave IR radiation, the OLR data from 1975-2015 were daily from NCEP / NCAR databases of the National Oceanic and Oceanographic Organization of the United States with a spatial resolution of 2.5 * 2.5 degrees longitude and 4-hour time resolution (hours, 00:00, 06:00, 12:00 and 18:00) were extracted and analyzed. In order to calculate the long-wave radiation of Iran, in the region of Iran's Earth's atmosphere (from 25 to 40 degrees north and from 42.5 to 65 degrees east), using Grads and MiniTab programming facilities, weighted earth integral Watts per square meter. First, the general characteristics of the long wave were studied. In this study, linear regression (VIA) regression methods were used to analyze the trend. In this procedure, the amount of variability of the long wave of earthquake is estimated over time. In the present study, in order to better understand the data and make a more accurate decision about the level of statistical confidence, the method of analysis of the Moran model was used; also, the Moran Model and GeoDa software were used to calculate and map the corresponding graphs. In order to calculate the Moran index or index, first the z and P-value points are calculated, and in the next step, the index is evaluated and significant.

The results of this study showed that the mean long wave length of Iran is 263/3 W / m2. The highest mean longitude of the Earth's longitude is due to latitudes below 30 degrees north, especially in the southern and southeastern parts of the country. Nevertheless, it was observed that more than half of the country's average surface longitude was greater than the average. The lowest mean radiation of the long wave of earth exits was seen as a belt from the northeast to the northwest of the country, but its minimum core is in the northwest and northeast. The lowest daily spatial variation coefficient of Iran's high-tide wave is seen in the southeast and southern coast of the country and in parts of the central and eastern parts of the country. Therefore, the geographic latitudes are higher than the mean long wave of the earth and the coefficient of spatial variation increases. Spatial Distribution The temporal and spatial variations of the temporal and spatial variations of the long wave of Iran's annual output in most areas of Iran have been increasing. The most extreme slope is the increasing trend (on average, between 0.8121 and 0.696296 watts per square meter) in the southern part of the southern belt of the Persian Gulf and the Oman Sea. In order to better understand the result of the temporal and spatial changes of the long-wave IR radiation to 4 periods of 10 years (1975-2015), during the first period, the total area of the country had an insignificant increase trend . Of course, in the second period, in contrast to the first period, most of the country's area had a decreasing trend, so that the areas that had a growing trend in the first period had a decreasing trend in the second period. Also, in the third period, again, in the second period, the majority of the country's area was incrementally and statistically insignificant. Of course, in the fourth period, the long-wave radiation of the Earth's surface throughout the southeastern region of Iran has been increasing and statistically insignificant, which includes 14.3% of the country's total area; but in general, during the fourth period, 96% of the country's area has a decreasing trend, of which 50.32% is statistically significant, and 43.53% are statistically non-significant. This suggests that in all four 10-year periods, I have had a photographic process at the outlet of the tidal wave in Iran. The results of the spatial distribution of the local Moran index showed that the long wave of Iranian outbound radiation in the south-east, south, and in the east and west of the region, consisted of a high cluster pattern with 47/60 percent of the country's land area. The cluster pattern regions of the north are drawn from the northeast to the northwest and include the northeastern, north and northwest regions of the country as well as the northern heights of the Zagros Mountains of the country. The spatial self-correlation model has a similar positive correlation with the pattern of spatial autocorrelation, with the difference that the spatial spatial dependence pattern of the second period is decreasing, while the positive spatial self-dependency model from the third period to the next It will slow down. Nevertheless, it can be said that throughout the course of the model, the spatial self-sufficiency pattern negatively affects the recent periods of decline and also the positive spatial self-correlation pattern with 0.75 percent ascendance.

One of the important impacts of climate change and global warming is the increasing of extreme atmospheric phenomena. The most destructive effects of climate change are in areas with arid climate, which is highly dependent on water resources and climate which Iran is one of these areas. Generally, East Azerbaijan in North West of Iran has a cold and dry climate. Because of the diversity in topography it has different climates. The mean annual rainfall is 250 to 300 mm and the average temperature is 12 °C. The long-term mean temperature in the warm period (June, July and August) is 24-34 °C and in the cold period (December, January, February and March) 0 - -7°C respectively. Reducing the consequences of climate in the future, depends on identifying the mechanism of extreme phenomena such as heat and cold waves, the synoptic patterns of them, finding solutions to reduce its effects as well as identifying vulnerable areas. Therefore, it is necessary to conduct the studies on extreme atmospheric phenomena.

17 synoptic meteorological synoptic stations' data were used for the occurrence of extreme temperature events. Heat and cold waves were studied for warm (June, July and August) and cold (December, January, February and March) period of year respectively. Heat and cold waves were evaluated based on three indices, frequency, intensity and duration, which are defined as follows. Heat wave frequency: Heat wave is defined based on the increase in maximum temperature relative to 95% percent of long-term values over 5 days in each station. Heat duration: Number of days with heat wave, Heat wave intensity: difference between the average maximum temperature during the heatwave and long-term in the same period for each station, Cold wave frequency: Cold wave based on a significant decrease in minimum temperature compared to the previous day. One percentile of long term values for the difference in minimum temperature relative to the previous day is defined as the significant limit of the cold wave for each station. Cold duration: Number of days with cold wave, Cold wave intensity: Maximum decreasing in temperature during cold wave that leads to subzero temperature for each station. First, the trend of heat and cold waves indices were investigated for stations with long term data (Tabriz, Ahar, Jolfa, Maragheh and Sarab).Then the non-parametric Mann-Whitney comparison test was used to compare changes between decades. According to the results, the spatial distribution of extreme temperature events was presented for the East Azerbaijan. The impact of the climate change on the maximum and minimum temperature were evaluated by downscaling model SDSM to predict the probability of occurrence in the coming years and identify the affected areas in the province.

The results of the study for trend showed the increasing of heat wave events for all stations. Due to the significant difference of the last decade (2006-2018) compared to previous decades, the spatial distribution of heat and cold wave events (frequency, duration and intensity) for the recent period were compared. Examining the spatial distribution of heat wave occurrences in the last 10 years showed that, the frequency of heat wave occurred more in the southwest, duration in the western regions and intensity in the northern regions of the province compared to other regions. The results showed that the frequency of cold events in the northeast, east and part of the southwest, duration in the northeast (Arasbaran region) and north (along the Aras River) and the intensity in the east and northeast of the province are more than other areas. The results of climate change scenarios compare to baseline period (1961-2005) showed that the northwestern and central regions of the province will experience an increase in temperature between 5-6 °C and the rest of the province between 3.5-5 °C in the warm season. This indicates an increase in occurrence of heat waves. Also, according to the climate prediction in 2020-2049 the minimum temperature in the cold season will increase in the eastern, southeastern and northwestern regions of the province for 2-3 °C and the rest of the province for 0.5-2 °C.

Considering the impact of climate change in the coming years, it is predicted that East Azerbaijan province will witness extreme temperature events (heat and cold waves). The results of this research play an important role not only in seasonal forecasting but also in the provincial management planning.

According to meteorologists studying climate and atmospheric change , snow monitoring is essential; Because the surface expansion and physical properties of snow are affected by daily changes and even long-term climatic changes and sometimes affect.Snow storage in mountainous basins is the main source of surface water currents in spring. Accumulation of snow and gradual melting of snow masses, provides favorable conditions for the infiltration and feeding of groundwater and the creation of permanent and seasonal rivers in catchments. Iran is a mountainous country and is also located in arid and semi-arid territory and most of its rainfall falls in the cold season, which is snow in the mountains. Mountains hold water reserves in the form of snow for other times of the year. The Alborz mountain range, as a water dividing line on its northern and southern slopes, manages water resources. Important rivers that flow in the southern slopes of Alborz make life possible in the interior of Alborz. As the temperature decreases, this mountain range changes the type of precipitation to snow on its slopes and maintains the snow reserves as a safe bed during the dry days of the year. Climate change and global warming are causing changes in the snow border area in the mountains, resulting in drought. Border snow fluctuations are more affected by temperature changes than annual rainfall, because if there is no decrease in rainfall, increasing the temperature will increase the height of the border snow.According to meteorologists studying climate and atmospheric change, snow monitoring is essential; Because the surface expansion and physical properties of snow are affected by daily changes and even long-term climatic changes and sometimes affect. In recent years, climate change has caused temporal and spatial changes in the amount and type of precipitation in Mazandaran province, which in turn adds to the importance of this issue. Accurate estimation of cover level is considered as one of the central and basic operations in the field of water resources management, especially in areas where snowfall has a large share in precipitation. Snow storage in mountainous basins is the main source of surface water currents in spring. Accumulation of snow and gradual melting of snow masses provide favorable conditions for the infiltration and feeding of groundwater and the creation of permanent and seasonal rivers in catchments. According to studies, about 60% of the country's surface water and 57% of groundwater is fed by melting snow.The deep correlation between the height of the border snow at the end of the cold season predicts the occurrence or non-occurrence of drought for the following year In recent years , many changes in snow levels have occurred due to climate change in different parts of Iran and Mazandaran is no exception to this rule. Accurate estimation of snow spatial distribution is very useful and necessary for estimating the resilience and vulnerability of the area , water supply planning, risk management and flood crisis. Remote sensing technology provides a new opportunity to perform broader , more accurate and easier calculations than geostatistical models to estimate snow changes. In this study, the snow changes in the border of Mazandaran province in an 18-year period from 2001 to the end of 2018 were studied and the snow changes in the border of the province in winter were identified. Then , with the climatic data of the standard of the American Geological Survey, which was prepared for climate change in 2093, the trend of snow changes in the province border in 2050 for the winter was predicted using the MLP neural network model and the amount of changes compared to the present time was calculated. Took. Then, using the ROC curve method , the accuracy of the model for this chapter was evaluated 98.50%, which indicates the high accuracy of the neural network model to simulate the snow of the province border.The results show that in 2050, the height of snow in the border of the province in winter will be about 800 meters compared to the current conditions will move to higher altitudes and from the current height of about 2750 meters will reach 3560 meters and these conditions can be many challenges. Created in the province's water resources.