جستجوی مقالات مرتبط با کلیدواژه « minimum temperature » در نشریات گروه « علوم انسانی »

Today, pre-risk awareness has become an integral part of the national development management and planning system in many countries (Civiacumar et al., 2005). Agriculture is inherently sensitive to climatic conditions. The minimum temperature, which has been identified as the most vital determining factor in the distribution of plant species on the planet, can be both a limiting factor and a factor in the spread and species distribution (Rodrigo, 2000: 155). Therefore, in this study, we examined the changes in minimum temperatures in the statistical period (1980-2010) and predicted these changes in the 2050s (2065-2046) in the Northwest of the country using the LARS-WG microscale method and model output. Atmospheric pairings of HadCM3 and MPEH5 were addressed. The prediction of minimum temperature variations to determine the extent of its future changes and considering the necessary measures to minimize the adverse effects of climate change on agricultural products were of great importance. In this regard, general atmospheric circulation models (GCMs) are designed that can simulate climatic parameters. 

In the present study, the output data of two HadCM3 and MPEH5 general circulation models based on two scenarios A2 and B1 were analyzed by LARS-WG statistical method in 21 synoptic stations located in the Northwest of the country. The results were monthly and periodic on the base period (1980-1999) and the 2050s (2046-2065), thereby the minimum temperature was evaluated and analyzed. In assessing the LARS-WG model, the observational and simulation error data were evaluated using MSE, RMSE, MAE and R2, and the model was evaluated for the appropriate region. The results showed that the minimum temperature in the future period will increase compared to the base period in the study area. This increase in air temperature at the study area is based on the HadCM3 and MPEH5 models, on average, 1.9 and 1.7 degrees Celsius to 2065 horizons compared to the base period. The north-eastern part of the northwestern region of Iran will have higher temperatures than the semi-southern regions. In fact, the cooler regions of the high latitudes will face more incremental changes in the amount of minimum temperatures. The results and achievements of this research are important for long-term plans for adaptive measures in the management of fruit gardens, agricultural products and water resources management. In order to calibrate and ensure the accuracy of the LARS-WG microscale model, the model was first implemented for the basic statistical period (1980-2010); then the minimum temperature output and its standard deviation were compared with the observational data of the studied stations, which indicated a small difference between the observed and simulated values ​​and also deviated from their criteria. 

The results of evaluation of observational and simulated data by LARS-WG microscale model using RMSE, MSE and MAE error measurement indices for the studied stations indicate that there is a significant difference between the simulated values ​​and the values ​​of the observed observations. There is no critical 0.05 significance levels, and Pearson correlation values ​​between simulated and real data are acceptable at the significance level of 0.01. The obtained results show that the accuracy of the model varies in different stations. In general, the results of error measurement indices indicate that the LARS-WG model is of good accuracy for micro-scaling the parameters under study. In order to better represent and ensure the accuracy of the prediction as well as to investigate the uncertainties in the studied models, the simulated values ​​were compared and observations were made on a long-term average during the base period in the studied stations using comparative graphs. As can be seen, the observed and generated values ​​in the base period at all stations are very close to each other and the LARS-WG model has been successful in simulating the studied parameter. After evaluating the LARS-WG model and ensuring its suitability, the data predicted by the model for two scenarios A2 and B1 using HadCM3 and MPEH5 models and were examined on a monthly and long-term basis. The study of the status of minimum temperature changes of the studied stations in the future period (2065-2056) shows that the minimum temperature is based on both scenarios and in all months and stations compared to the period, the base has increased. Due to the large number of study stations, only stations located in provincial centers of this study are listed. 

Cold and frost are one of the most significant climatic hazards on fruit trees. This type of climate risk affects different parts every year, especially the cold regions of the northwest of the country. Studies show that in recent years, the rate of economic damage to fruit trees in this region has increased, so in this study, the outlook for changes in minimum temperatures in this region using the LARS-WG statistical microscale model and output two HadCM3 global model and MPEH5 were introduced in the 2050s (2065-2046). For accuracy and precision of the models, error measurement indices and coefficients of determination and correlation were used. The results showed that the LARS-WG model has a good ability to simulate the studied variables in the study area. The results of long-term prediction of the studied models show that the minimum temperature values ​​will increase in all study stations, which is based on HadCM3 and MPEH5 models on average. In the 2050s, and it will be 1.9 and 1.7 respectively, compared to the base period. The results of the studies of Kayo et al. (2016), Sharma et al. (2017), Khalil Aghdam et al. (2012), Qaderzadeh (2015), Sobhani et al. (2015) and Khalili et al. (2015) were confirmed. In general, based on the studied scenarios and models, the minimum temperatures are expected to increase in the study area in the future. By increasing it, the yield of some crops that need cold during the growing and productive period would decrease. It can also reduce snowfall, followed by frost on crops and lack of water in dry seasons. Therefore, due to the fact that following the climate changes, the conditions of the agricultural climatology are also changing, it is necessary for the relevant officials and planners in the agricultural sectors to adopt the necessary strategies to reduce the consequences and adapt to the new climate.

The purpose of this study is to investigate the temperature limit indices in Kermanshah province. For this purpose, daily temperature data of 19 synoptic stations from its establishment until 2018 and extreme temperature indices have been used. Studies have shown that glacial indices (IC) and cold waves (CW) have the highest correlation with other indices. Therefore, the mentioned indicators were considered as representative indicators and the trend and also the pattern of spatial autocorrelation were studied for these indicators. The I - Kendall method was used to examine the trend and the spatial index G * was used for the spatial autocorrelation model. The results showed that the glacial index in the eastern highlands has a maximum frequency for 1 to 6 days and has an increasing trend in the lowlands of the southwest. The cold wave index is also the most frequent in the eastern regions in all continuities and shows an increasing trend in parts of the province. The results of spatial autocorrelation pattern of G * index showed that the positive spatial autocorrelation pattern of cold and ice waves has been observed more in the eastern highlands of the province. The pattern of negative spatial autocorrelation is mostly formed in the western half of the province, i.e., the border areas of Iraq, which indicates the role of the Zagros elevation distribution in the formation of the glacial autocorrelation pattern in the province.

Frost and freezing are important and risk generating factor in the agricultural sector of the country. Frost is a phenomenon that at low temperatures which could damage or destruct the plant organs. Various studies by researchers show that last spring frost (LSF) and first autumn frost (FAF) along with gradual global warming have significant impacts on agriculture and natural resources. Teleconnection patterns represent large changes which can periodically alter other atmospheric patterns such as temperature, wind, humidity and precipitation in regional and global scales. The purpose of this study was to investigate the effect of different teleconnection indices on variability of FAF and LSF events during the last 31 years at 12 Synoptic stations of Iran.

To detect the relationships between frost events and teleconnection patterns we used two different data sources. As the first source, the screen daily minimum air temperature (Tmin) were used as the frost indicator. We also applied 10 different teleconnection indices in daily and monthly scales. Daily minimum air temperature were obtained from the Iran Meteorological Organization (IRIMO) for the historical period of 1985-2015. The teleconnection indices were also utilized for the same period (http://www.cdo.noaa.gov). After the data quality control, the outlier data were removed from the analysis. Normality of data was evaluated by Kolmogorov-Smirnov test. For time series with normal distribution (P <0.05), the Pearson’s significance test was performed. For other time series, when the normal distribution was not the case, Spearman's nonparametric test was applied.

In case of lag-free correlations in the monthly time scale the strongest correlation between teleconnection indices and minimum temperature was observed for NAO- (-0.76), in February for Isfehan Station, which can cause a delay in occurrence of last spring frost. By applying the time lag to the correlation, the strongest correlation was found for AMO+ with time lag of 11 month. In seasonal time scale, the strongest lag-free correlations were found for AO-during the last spring frost (LSF). By applying time lag-correlation, the strongest correlation was evident between Tmin temperature and AMO+, indicating that this index was the most influencing teleconnections (with 3 seasons lag). In comparison with the normal phase, the occurrence of El Niño causes earlier autumn and spring frost events. In contrast, La Niña event will postpone the dates of autumn and spring frosts. This means that if La Niña occurs in winter, the strongest effect will be appeared in the autumn of the following year. The correlation between the El Niño, La Niña and Normal phases with minimum temperature showed that the La Niña phase has the highest effect on shifting the dates of the minimum temperature event.The strongest correlation between the minimum temperature of autumn and teleconnection patterns (correlation coefficient of 0.382) was observed for AO- , highlighting the fact that the occurrence of the Negative phase of this phenomenon leads to the cause a delay in occurrence of first autumn frost (FAF). For the last spring frost (LSF), the strongest correlation was found for SOI- index (correlation coefficient of 0.665) for Hamedan station, leading to early spring cold in the region. The correlation between the phases of the El Niño, La Niña and Normal with minimum temperatures showed that the La Niña phase has the most effective phase affecting the minimum temperature. The results of this research can be used for wise managing of risk factors and arranging appropriate time of planting agricultural products, as well as insurance for agricultural products.

This study highlighted the impact of teleconnection phenomena on shifting the dates of autumn and spring frost. It was shown, that the occurrence of teleconnection can significantly shift the date of first and last frost in the study region. For some teleconnections cause the advance, but for others may postpone (up to 50 days) the frost event. We found different results for lag-free correlations compared to lag-correlations. For instance, for monthly analysis of lag-free correlations, the strongest correlation between teleconnection indices and minimum temperature was found for NAO- (-0.76), but for lag correlations, the strongest correlation was found for AMO+. In comparison with the normal phase, the occurrence of El Niño causes earlier autumn and spring frost events. The results showed that Shifting the Date of First )Autumn( and Last )Spring( Frost Events at the stations were related to a number of teleconnection patterns such as AMO, SOI, NAO, AO and MEI. In contrast, La Niña event will postpone the dates of autumn and spring frosts. Further works are required to better understanding of the impacts of teleconnection events on other meteorological parameters such as humidity which was not investigated in this research.

Climate change is nowadays a major cause of concern in water related fields because it may cause more severe, shortened or prolonged droughts or floods in the future. In this research was tried to the best model of climate change is determined from the climate change models to determining the minimum temperature, maximum temperature and precipitation for the Birjand synoptic station. For this reason, 35 models of GCM were determined for each of variables of the minimum temperature, maximum temperature and precipitation. Initially, for each of the weather variables, the values ​​of each of the fifth report models for the base period and the synoptic station of Birjand were determined and compared with the results of the synoptic station in Birjand. Results showed that the rainfall data of GISS-ES-R, CNRM-CM5, CSIROMKMK3.6 models are most similar to the data of the Birjand synoptic station. For maximum temperature, GISS-ES-R, CNRM-CM5, CSIROMKMK3.6 models and for the minimum temperature, the GISS-ES-R, GFDLCM3 and MIROC-ESM models have the minimum error values and results of these models had the best similarity with the observed data. From comparison of model data with synoptic station data showed that the highest percentage of relative error of rainfall, minimum temperature and maximum temperature is shown for 1(January), 2(February) and 5(May) months, respectively. In comparing the differences between GCM models and Birjand station data for precipitation, the maximum temperature and minimum temperature are the fifth (May), third (March) and first (January) months, respectively, than the rest of the months.

Cold and frost waves are are among the most important natural phenomena, occuring in the cold period of the year. The reason why the frost phenomenon has been considered is it's feedbacks on the nature and human life, which sometimes has been associated with negative severe tension and sometimes positive feedbacks, particularly in agriculture and gardening. This study has tried to analysis of daily, as monthly and seasonal, frosts of Southern Khorasan province. To do this, the minimum daily temperature of synoptic stations of Southern Khorasan province were used for a 27 years period (1988-2014). And to investigate the probability and frequency of the frost days, Markov chain model was used. Results illustrated that Ghaen city in the north, and Birjand city in center of Southern Khorasan province, had the maximum occurrence and frequency of the frost days, respectively. Among the stations, Nehbandan city has experienced the minimum probability of frost days. Two-days continuities in all stations, revealed the minimum return period, indicating the less continuity of frost days in Southern Khorasan province.. All the analyzed stations in the studied area, except for Nehbandan, which generally hasn't experienced any frost day, are characterized by Markov chain, first class. In other words, any occurrence of frost days, depend on climate conditions of past days. The beginning of autumn frost days of Southern Khorasan province, is in November, known as the first autumn frost; and the beginning of winter frost is concentrated in January. The maximum frosty cycle in October have been occurred in Ghaen station about 0.058, and in November in Ferdos and Boshrooye stations about 0.05 and 0.043, respectively. Results confirmed the obvious role of high and mountain regions in the frequency and continuity of frost days in the province.

Temperature is one of the most important elements of climate that its changes can alter the climate of each region and plays a major role in how to operate the environmental and agricultural powers. The objectives of this study were the identification and evaluation of temperature anomalies in Mazandaran province. The data used in this study have been considered on a daily basis for the statistical period from 1971 to 2010 including two maximum and minimumm environmental data groups from 24 meteorological stations on land, pressure data at sea level and 500 hecto-pascal along the axis 52.5 ° east. In this study, the positive deviations in temperature continuing up to eleven days are considered as an indicator of temperature anomalies. The results of this study showed that the frequency of temperature anomalies in the province stations did not show significant differences. Also it is showed that the large-scale temperature anomalies are controlled by climatic factors outside the control of the local scale.However abnormality repetition, especially in maximum temperatures at the mountain stations has partially been more than coastal stations and the plains of the province. For this purpose, the relationship between abnormalities was evaluated with the index. And it was found that the estimated index in the studied region had high correlation with the province temperature anomalies. In addition, the anomaly change by Mann-Kendall test showed a significant increase in the frequency of abnormalities occurring in most stations.

The global warming process during last century has not only affected on amount of atmospheric parameters but also affected on onset and end of each atmospheric parameters. The air temperature trend has been increasing during recent decades, especially in the regions such as Iran which is located in dry and semi-dry world belt. In this investigation, using Mann – Kendall statistical test, which is one of the proposed methods of World Meteorological Organization (WMO) for time series analysis, the trend of seasonal maximum and minimum temperatures in Iran will be studied. Hence, In this research, the daily data of maximum and minimum temperature with spatial resolution of 0.5 *0.5 degree received from cell database in the world scale, available in data base: http://hydro.engr.scu.edu/files/ , and converted to utilizable file (.TXT) in mat lab software by using Grads software. In continuation, Mann-Kendall script code, executed for a 21550*2058 matrix to calculating the threshold trend of 95% in Mat lab. We use from Arc GIS software for graphical representation of results. The results of this research indicate that the maximum temperature of northwest and southeast of Iran is without trend (trendless) in every four seasons, and in the winter , north-eastern of Iran have the least area . In the autumn, the increasing trend of maximum temperature seem more in the eastern area and Fars, whereas, the maximum temperature of autumn in without trend (trendless) in central and northwest area of Iran. The minimum temperature has increasing trend in the most regions of Iran. In this relation, the increasing trend of minimum temperature in winter is completely obvious in northwest, Zagros and southeast of Iran.

Climate change is one of the most significant issues for researchers and media in recent decades. This phenomenon is slowly expanding whole over the world and affecting all relevant parameters. Hence, the main objective of this study is evaluating the impacts of global warming on minimum temperatures of Iran. To do so, daily minimum temperature of 47 synoptic stations were extracted during the 1961-1990. In order to investigate global warming impact, output of the HADCM3 model for 2015-2045 based on A2 and B2 scenarios was downscaling using SDSM model. Results illustrated that base on the relationship between predictor and predictable variables, created model between these variables are partially capable of producing climate data for future periods. Correlation coefficient (r) between observed and generated data during the 1976-1990 was 0.95. Furthermore, results of variance and correlation tests indicated that there is significant similarity between observed and generated data. Finally, the model demonstrated that minimum temperature in studied statins will be increased during 2015-2045. Temperature will be increased about 0.8 and 0.5 ᵒC based on the A2 and B2 scenarios, respectively. The stations will experience different temperature changes.

Introduction Temperature is a factor feels by human body and body sense changes when the outside temperature changes for the reason of the heat transfer between body and the environment.
Wind chill is caused by the wind in lower temperature and the higher the wind speed and the lower the temperature, the wind chill and its effects on the creatures would increase. The WCT is important, since it’s an index which shows people how to dress up or to be optimum on energy consumption. When it feels cold due to the wind chill, it could contribute greatly to decision making in urban design and fair and optimum distribution of fuel. The main purpose of this paper is to study the effect of wind chill on reducing the temperature that body feels, also when this done, it could help decision makers in social, political issues as well as peoples clothing and etc based on the wind chill index.
Material and Methods This research has been done about the determination of the temperature which our body feels based on the wind chill index in Isfahan.Isfahan with an area of about 105937 square kilometers has been placed between 30 degrees and 43 minutes to 34 degrees and 27 minutes of north latitude of equator and 49 degrees and 36 minutes to 55 degrees and 31 minutes of east longitude of Greenwich meridian. In this article we can calculate the wind chill index (WCT) from equation 1.
WCT=13.12.6215T-11.37V0.16.3965TV0.16 (1)
V: wind velocity in terms of km (hr) and T: air temperature in terms of °c
Results and discussion From the studies we have done the table results indicated that the month of October is the beginning of the wind chill and the end of it would be in May. But since the frequency of occurrence of this phenomenon in these two months is much lower than other months, will not be discussed in this article. Therefore April, November, December, January and February and March were considered to study. The occurrence of wind chill at Knur Biabanak station was 228 times during 23 years (8395 days). The highest of the wind chill occurrence was 2257 times in February and its lowest was 39 times in May. The wind chill has occurred in Naein station 432 times in the month of February. And this number is the highest occurrence of wind chill in a month.
Conclusion In this article, we have studied the effect of wind speed on the minimum temperature reduction in Isfahan province. Wind chill reduces the emotional temperature on the human body. The results of this research showed that the wind chill factor has been felt 2196 times at Isfahan airport station and 228 times at Khur station as the highest and the lowest numbers recorded, respectively. In the month of February we had 2257 times of wind chill occurrence in the statistical period that has been the most frequency of wind chill occurrence and the lowest number was 39 times in May. The minimum temperature and wind chill in the month of January respectively was -1.6 and -5.6 degrees Celsius. On average, wind blowing will cause the minimum temperature to reduce 4 degrees in this month. The zoning results of the minimum and the wind chill temperatures in Isfahan province indicate that the vulnerable areas of the wind chill in Isfahan are located in the center and western half (overlooking the mountains) of the province . In these regions some stations due to the topographic conditions are more vulnerable to the wind chill. These stations include Isfahan airport, Daran, Ardestan and Naien that they are the most vulnerable areas of the province against the wind chill. Also the month of February is considered as the most critical month in terms of wind chill effects on human body and the most widespread zone of wind chill happens in this month.

Climate is so important for the life and development of the living organisms on Earth. Among all the effects of different atmospheric variables on human comfort, the effect of combined wind and temperature is of particular significance. In this study, using statistical data of minimum temperature and wind speed of the synoptic stations during the period 1374 to 1393 ,which were obtained from the Meteorological Organization, the chill wind was zoned in the country .Results that were obtained from data analysis of 120 to 314 synoptic stations (for different time intervals) showed that we can consider the months of Aban to Farvardin as the start and end of wind chill in Iran, respectively.For days of Aban to Farvardin, the average reduction in minimum temperature of 4 degrees Celsius in Bahman due to the wind blowing has been felt.Zoning maps of the country that were traced for the cold months (from Aban to Farvardin), showed that the difference between minimum air temperature and the temperature that can be felt on human body (wind chill) is meaningful as well(Level of significance is in the range of 0.51 to 0.74). The highest frequency of wind chill is related to the year of 1390 with 18088 cases from 330 stations that are under study and the lowest is related to the year of 1374 with 4321 cases. .It should be noted that from the base year (1374) onwards ,the frequency of the wind chill occurrence has increased and the numbers of cold days because of the wind existence in the cities of the country are increasing too. by investigating the frequency of wind chill occurrence in the months which are under the study, the month of Bahman with47219 cases from 350 stations and the month of Aban with 17728 cases from 305stations, had the highest and the lowest frequency of occurrence.

Daily weather information is currently available for about 40000 stations across the world. But, distribution of these stations is relatively uneven in some parts of the world. Moreover, there are often large amounts of missing values (Schuol and Abbaspour, 2007:301). Using generated data can help to fill missing or even to correct erroneously measured data (Fodor et al., 2010: 91). LARS-WG is a stochastic weather generator which can simulate weather data under both current and future climate conditions at a single site (Semenov and Barrow, 2002: 3).
There is another watershed modeling problem, which weather stations are often outside of/or at a long distance from the watersheds. So, recorded data may not meaningfully indicate the weather taking place over a watershed. Therefore, some researchers have developed radar data to supply precipitation inputs in watershed modeling (Fuka et al., 2013: 1). But, these data are only available in small parts of the world. So, considering additional methods to generate weather conditions over watersheds is necessary. Using reanalysis dataset (CFSR) is one option (Fuka et al., 2013: 1).
Dile and Srinivasan (2013) investigated CFSR climate data in the Lake Tana basin in the Nile basin. The results showed simulations with CFSR and conventional weather gave trivial differences in the water balance components in all except one watershed. In the four zones, both weather simulations indicated similar annual crop yields. Nevertheless the conventional weather simulation results were better than the CFSR weather simulation, but they can be applied as important option for regions where no weather stations exist such as remote subbasin of the Upper Nile basin. Soltani and Hoogenboom (2003) evaluated the weather generators WGEN and SIMMETEO for 5 Iranian locations. The results showed that WGEN was successful to generate maximum and minimum temperatures and SIMMETEO was acceptable to reproduce minimum temperature and solar radiation.
The objective of current study was assessment of accuracy of weather generator LARS-WG and CFSR data in simulation of climate parameters of Chaharmahal and Bakhtiari province.
The study was conducted in Chaharmahal and Bakhtiary province. This province, with an area of 16532 km2, is located between to 31° 09' to 32° 48' north latitude and 49° 28' to 51° 25' East longitude and provides more than 10% of the water resources of Iran.
1- LARS-WG model
LARS-WG model applies complex statistical distributions for simulation of meteorological variables. The basis of this model to simulate dry and wet periods is daily precipitation and radiation series semi-empirical distribution. The temperature is estimated by Fourier series. The output of this model includes minimum temperature, maximum temperature, precipitation and solar radiation (Babaeian et al., 2007: 62).
2- Required data for LARS-WG model
Required data for LARS-WG model includes daily maximum temperature, minimum temperature, precipitation and solar radiation (sunshine hours). These data were provided for four selected synoptic weather stations (Shahrekord, Koohrang, Boroojen and Lordegan).
3- CFSR data
Reanalysis is a systematic approach to produce data sets for climate monitoring. Reanalysis data are created through a fixed data assimilation design and models which use all available observations every 6 hours over the period being analyzed. CFSR data has a global horizontal resolution of 38 km. The CFSR adjacent stations were determined for the four mentioned stations.
Daily weather data of each station during 1991-2010 was implemented in the LARS-WG model. For assessment of both data, the comparison of statistical indices such as RMSE, MBE, MAE and R2 was used in daily, monthly, annual and decade scales.
The results showed that there is no correlation between the output of LARS-WG model and observed daily precipitation data in each of the four stations. The values of these coefficients for minimum and maximum temperatures increased in all stations. In general, due to high values of RMSE and MAE, this model was not successful in simulation of daily climate parameters. Performance of the model to simulate monthly and annual scale was better than daily. Ability of LARS-WG model in simulation of long-term period (decade) was satisfactory. Also, results indicated that monthly and annual climate parameters by CFSR data have been predicted by a more effective performance. Because statistical indices of CFSR data are lower than LARS-WG. These data underestimated the precipitation in Shahrekord station. RMSE and MAE values of monthly precipitation are 20.49 and 11.19 respectively In Shahrekord station, for CFSR data. These values for annual precipitation are 92.88 and 72.51. For LARS-WG model in monthly scale, RMSE and MAE values are 41.45and 24/75 and these values in annual scale are 164.75 and 123.43.
In recent years, it is necessary to get accurate and long-term meteorological data due to climate events and scarcity of meteorological stations across the country. So, it is a reasonable solution to use weather generators. The objective of current study was assessment of accuracy of weather generator LARS-WG and CFSR data in simulation of climate parameters of Chaharmahal and Bakhtiari province. In general, the results showed the ability of LARS-WG model in simulation of long-term period (decade) data. So, values of statistical indicators are not satisfying in short-time periods. Statistical indices of CFSR data are lower than LARS-WG in simulation of short-time period (monthly and annual). They are highly correlated with the observations and they can simulate climate parameters in short- time. Therefore, considering the purposes of any specific research, both LARS-WG model and CFSR dataset can be used. Moreover, CFSR data can be applied as valuable option for regions where there are no weather stations.

In this study, linking between the North Sea–Caspian Pattern (NCP) and minimum temperatures fluctuation in Iran for 60 years period (1950-2010) has been investigated. The data used in this study included monthly temperature data from 17 synoptic stations in different regions of Iran, the NCP index data and upper atmospheric reanalysis data (geopotential height, zonal wind, meridional wind, temperature and thickness (1000-500 Hpa)). Pearson correlation analysis as the main method was used in this study and showed an inverse significant relationship between minimum temperatures in most selected stations and NCP index. This proves a decrease of temperature in positive phase of the NCP and increase of minimum temperature in negative phase. In terms of temporal analysis, there was a significant negative correlation between the minimum temperatures of stations and the NCP in months of January, March and February. In between stations, the monthly highest calculated correlation with -0.349 (=0.01) was computed in January. Calculated seasonal correlations show that the highest significant correlation between NCP and minimum temperatures is for winter with -0.237 (=0.01). From periodical perspective, the results indicate a strong correlation between cold period and NCP in most stations. Zahedan station had the weakest correlation with the NCP. This is probably due to its remoteness from active centers of NCP. Atmospheric patterns in 500 Hpa level in positive and negative phases of positive phase, prove the settlement of a deep trough on the Caspian Sea, Iraq, Turkey and Iran, and also the location of Iran in the eastern part of the trough axis and on the other hand, the settlement of a deep ridge on the North Sea and Europe led to the advections of cold temperature from Northern Europe near polar regions in Arctic to Iran. In negative phase placement of the study area beneath the ridge axis, led to the creation of a barotropic atmosphere with weak zonal wind and advections of warm temperatures to Iran. Therefore, with advections of warm temperatures from Africa, Arabian Pennsylvania and low latitudes, temperatures rise in Iran.

Today, climate change and its anomalies, one of the important issues raised in the world. Detection of changes on temperature and precipitation as the most important climatic elements, is used as primary evidence of climate change in many parts of the world. This paper investigates the temperature trend of Iran of annual, monthly and seasonal scales. For this purpose minimum, average and maximum temperature data of 38 synoptic stations, over the period of 50 years (1960-2010) with good distribution, were obtained from Iran meteorological organization.  Then the temperature trend parameters were investigated (studied) by Mann Kendall nonparametric test. The results showed that the general temperature trend is increased in most of the stations for different time scales; however this intensity of increase decreases from the minimum to maximum temperature. On monthly, in June and July, and on seasonal, in summer, the increase of temperatures are higher than other time. Although the most frequency positive trend of the maximum and average temperature observed in the winter, but for a few of the stations was significant. While in warm seasons especially in the summer, the frequency of station with positive significantly trend is higher. In terms of spatial, the stations that located in the Alborz Mountains, South West and South East of IRAN have experienced higher temperatures than other areas of the country and in some station such as SHAREKORD, URIMA, KHORRAMABABD AND BANDAR ABASS trend of temperature in different scale is mostly decreasing.

The main purpose of this article is، showing similar fluctuation of temperature series and their consistency with what happened in the global level. In addition، it determines new estimates of regional seasonal and annual temperature trends over Iran، after the detection of artificial and natural non homogenous points of 33 synoptic stations for the period of 1960 2010. The 22 stations with shorter records are used only to confirm variability during overlapping periods as evidence. Due to similarity of the results of homogeneity tests and also to prevent of complexity، assessing of data homogeneity have been surveyed only by using the results of Standard Normal Homogeneity test (SNHT)، expert judgment and Metadata. The results showed، the country may be segmented to 10 such regions in terms of trends and variability of temperature. In addition، it was shown، the country with having different climatic type and with facing similar natural fluctuation، have experienced similar variability and fluctuation of temperatures in all regions at nearly equal rates of 0. 4-0. 5 and 0. 2-0. 3 (°C /decade) for annual minimum and maximum temperature، respectively in Iran. Non homogeneities in the temperature series relate mostly to relocation and changes of environmental conditions at individual stations. In the case of ignoring these non homogeneities، it is appeared errors in the statistical characteristics of temperature، Furthermore، it will be followed uncertainties in spatially averaged trends. The finding in earlier work of a few individual stations with negative trends is found to be due to artificial effects like relocation.

Zayanderoud Basin is one of the important agricultural areas in country level because of climatic variety and resuscitative water of Zayanderoud River. Since frost phenomenon especially late spring frost incurs great damages on fruit trees and agricultural products of this region، it is necfessary to study frost in this basin. In this research، the intensity of frost during cold period and spring season in Zayanderoud Basin in five thresholds: mild (0 to -5 0C)، moderate (-1. 5 to -10 0C)، strong (-1. 10 to -150C)، very strong (-1. 15 to -20 0C) and extraordinarily strong (lower than -200C) was studied using daily minimum temperature statistics of 13 meteorological stations during 16-years statistical period (1992-1993 to 2007-2008). Frequency zoning map of different frost intensities was also drawn in ARC MAP environment. The results of research show that all studied stations of Zayanderoud Basin experience mild to very strong frost during cold period، but the extraordinarily strong frost only is occurred within elevated regions (above 2000 m). In spring season، mild frost is occurred in until late Farvardin month and within elevated stations until third decade of Ordibehesht and moderate frost is only occurred within 2000 m stations and above in Farvardin month. Strong frost is only occurred in the studied north-western region. According to zoning maps، north-western Zayanderoud Basin is hazardous in terms of the frequency of occurrence of different frost intensities.

Global warming and its result، climate change is an important subject investigated especially in the recent decades by researchers throughout the world. In these studies، at first، climatic parameters changes are investigated. Considering many uncertainties in the parameters estimation، it is better to choose a method in order to study and analyze the uncertainty band due to different sources of uncertainty. To investigate climate change impact on different sources in the future periods، at first climatic parameters should be simulated under climate change effect. Various methods simulate the parameters، but the most valid method is application of AOGCM (Atmosphere-Ocean General Circulation Model). A general circulation model is a three-dimension mathematical model of the general circulation of a planetary atmosphere or ocean and based on the Navier–Stokes equations on a rotating sphere with thermodynamic terms for various energy sources (radiation، latent heat). These equations are the basis for complex computer programs commonly used for simulating the atmosphere or ocean of the Earth. Atmospheric and oceanic GCMs (AGCM and OGCM) are key components of global climate models along with sea ice and land-surface components. AOGCMs are widely applied for weather forecasting، understanding the climate، and projecting climate change. One of the most important uncertainty sources in climate change field is related to AOGCMs and emission scenarios which makes different outputs for climatic variables. Atmospheric carbon dioxide levels have been recorded continual increases since the 1950s، a phenomenon that may significantly alter the global and local climate characteristics، such as temperature and available water resources. IPCC (Intergovernmental Panel on Climate Change) predicted that the world mean temperature will be increased up to 1°C by the year 2050 and up to 3°C by the end of the next century. Estimates of global warming are generally based on the application of general circulation models (GCMs)، which attempt to predict the impact of increased atmospheric CO2 concentrations on the weather variables. Owing to the complex mechanism in the atmosphere motion and the uncertainty of the model structure، different GCMs produce different predictions. Study Area: In this study، Mashhad synoptic station is chosen as the study area. The station is located in north east of Iran with 59 degrees 38 minutes of eastern longitude and 36 degrees 16 minutes of northern latitude. Its elevation is 999 meters from sea level. The situation of the region is showed in Figure 1. In this paper، investigating the uncertainty band due to fifteen AOGCMs (including BCM2، CGMR، CNCM3، CSMK3، FGOALS، GFCM21، GIAOM، HADCM3، HADGEM، INCM3، IPCM4، MIHR، MPEH5، NCCCSM and NCPCM) under three emission scenarios including A1B، A2 and B1، changes of minimum temperature، maximum temperature، and rainfall parameters in Mashhad synoptic station located in Ghareghom basin are studied. Also LARS-WG model is used for downscaling. Studying outputs of different models، it is tried that emission scenarios analysis for each parameter is done and uncertainty band due to fifteen models for emission scenarios is drawn. These band show possible changes for the studied parameters in the future period to the past one. AOGCMs are the most suitable tools to study climate change phenomenon، but regarding large-scale spatial resolution of these models، regional scale is not possible. In other words، the models simulate climatic parameters in large scale whereas comparing the output for historical periods with observed data shows difference. Therefore، various methods of downscaling are created. LARS-WG is a stochastic weather generator which can be used for the simulation of weather data at a single site under both current and future climate conditions. These data are in the form of daily time-series for a suite of climate variables، namely، precipitation (mm)، maximum and minimum temperature (°C) and solar radiation (MJm-2day-1). Stochastic weather generators were originally developed for two main Firstly، providing a means of simulating synthetic weather time-series with statistical characteristics corresponding to the observed statistics at a site، but which were long enough to be used in an assessment of risk in hydrological or agricultural applications. Secondly، to provide a means of extending the simulation of weather time-series to unobserved locations، through the interpolation of the weather generator parameters obtained from running the models at neighboring sites. The most recent version of LARS-WG has undergone a complete redevelopment in order to produce a robust model capable of generating synthetic weather data for a wide range of climates. LARS-WG has been compared with another widely-used stochastic weather generator، which uses the Markov chain approach at a number of sites representing diverse climates and has been shown to perform at least as well as، if not better than، WGEN at each of these sites (Semenov et al، 1998). To validate LARS-WG model، observation data and simulated one in the base period (1976-2005) for values of minimum temperature، maximum temperature and rainfall for each month are drawn in graphs. For each three parameters the model could have suitable accuracy that maximum differences are 0. 6°c (May)، 0. 7° (November) and 0. 14 mm (January)، respectively. Maximum and minimum uncertainty band of minimum temperature parameter are for A1B (12. 5°c in year) and B1 (11. 7°c in year) scenarios، respectively. These values for maximum temperature parameter are for A1B (15. 7°c) and A2 (11. 3°c) scenarios، and finally for rainfall parameter are for A1B (3. 5 mm in year) and B1 (3. 1 mm in year). Increasing minimum temperature for the future period for A1B، A2 and B1 are between (0. 5-1. 4°c)، (0. 7-1. 6 °c) and (0. 7-1. 7°c)، respectively. The values for maximum temperature parameter in the same order are (0. 3-1. 6°c)، (0. 5-1. 5°c) and (0. 4-1. 6°c). For rainfall all the scenarios have approximately -0. 1 to +0. 2 mm changing values. LARS-WG is able to simulate climatic parameters in the base period، since the modeled parameters and observation ones have high closeness in all months. The uncertainty due to emission scenarios for different parameters is varied. The results indicate the highest uncertainty band is related to A1B scenario، but about the lowest one various conclusions are achieved. In other words، B1 scenario for minimum temperature and rainfall parameters and A2 scenario for maximum temperature parameter are introduced. Finally، Estimation of long duration changes in climatic and hydrological variables concludes necessity of appropriate management of water resources considering impacts of climatic changes، especially for the regions having arid and semi-arid climate like the studied region.

Long time series are needed for analysis of time variation, trend of extreme events, risk estimation and possible events. One of the most important time series in geographical and climatic science is daily maximum and minimum temperature. These two parameters use daily evapotranspiration estimation, determination of water balance and climate change study. Maximum and minimum temperature are measured in meteorological stations. However, different statistical years, deficiency in statistical data and error of measurement cause variation in time series. Therefore, reconstruction of time series is very important. This research evaluates reconstruction of daily extreme temperatures to nearest neighbor and artificial neural network methods for five stations in the west of Tehran Province. In the nearest neighborhood method correlation between respective maximum or minimum temperature is used. Whilst in the artificial neural network using meteorological stations network the minimum and maximum daily temperature are reconstituted. Neural network used in this research is a multilayer feed forward network with back propagation algorithm and hidden layer. Results show that artificial neural network method had least mean absolute error for all stations compared to the nearest neighbor method. With increasing distance of the station the estimated error increases in the nearest neighbor method. Accuracy of the two methods in estimating daily maximum is more than the daily minimum temperature.

The temperature phases could determine its effects on the environment. One of the important temperature phases that could be a disaster is frost. Frost has important roles on Biological activity as well as on economical. In current paper in order to analyze frost in Iran, we used database of minimum temperature of Iran. This data based is arranged for 43 years (15992 days) and 15 ×15 KM resolution (7187 pixels for all over Iran). So the database has 15992×7187 dimensions. All the operating calculations have performed on this matrix. As in classical documents zero degrees Celsius have been chosen as threshold for frost in the database. The analyses have been taking place on the temperature equal or below of this threshold. It has been done by using softwares such as Surfer and MATLAB. Time series analyses of frost shows those 9224 days out of 15992 days have experienced the frozen day. There are a strong oscillatory behavior and weak, not statistical significant trends in frost temperature time series as well as a jump in its middle. Oman Sea cost as well as Persian Gulf shores never have experienced the frost days. The height lands and high latitudes area has most experiences in freezing days as well as in extensive. The average of maximum days with frost was 145 days.

This article was conducted in order to gain more knowledge about thermal differences (Thermal Island) between Tehran and Karaj based on a five-year statistical period (1993-1997). The element of temperature as the most important climatic element has been obtained as daily minimum and maximum from the Meteorological Organization of Iran. In the study of thermal island, stations of Dushan Tappeh in the east, Aqdasieh in the north and Mehrabad in west of Tehran, as the most important study area and Karaj station in west of Tehran as complementary area, have been selected. Based on the investigations carried out on the average temperatures of three stations in Tehran and Karaj station, it was observed that Dushan Tappeh as the most important thermal region in Tehran has the highest temperature values ​​for both minimum and maximum annual temperatures with temperatures 13.33 and 22.49 ° C, respectively. Next, Mehrabad station has been determined as the second thermal zone in Tehran with the minimum annual temperature of 12.88 ° C and 12.29 ° C at maximum temperatures. Third, Aqdasieh station, with a mountainous character and higher elevation than the rest, acts as a relatively weak thermal zone, the average annual minimum temperature at this station being 10.33 and the maximum temperature 19.9 ° C. Generally, the mean annual temperatures of all three stations in Tehran have higher temperature values ​​at both minimum and maximum temperatures than Karaj station, so that Tehran has a temperature difference of more than 0.75 ° C in the mean annual maximum temperature and a temperature difference of 3.37 ° C at the average annual minimum temperature with Karaj, and so has been recognized as the warmer region.