Investigation the temperature changes of Arak station and prediction based on statistical downscaling model of SDSM

The city population, in particular at the industrialized cities and centers of provinces, has increased dramatically in Iran during recent decades. Arak city is among of those industrialized cities as a centers of Markazi province which has experienced a fast increase in population. This changes in population numbers tend to increase in consuming water resources as well as increasing in energy resources demanding. This situation accompanied with global warming caused an increase in temperature values during recent decades.
In current research, in order to understand the nature of temperature changes in Arak, the temperature trends was analyzed for previous as well as future based on SDSM. Because, according to IPCC (2014: 563) it is vital to understand the nature of climate change in order to reduce its negative effects.
In current research, in order to understand the nature of temperature changes in Arak, the temperature trends was analyzed for previous as well as future based on SDSM. Because, according to IPCC (2014: 563) it is vital to understand the nature of climate change in order to reduce its negative effects.
In order to study temperature trends during recent decades in Arak, the temperature data selected based on having sufficient temporal records for carrying out the investigation and also sufficient accuracy that extend from 1961 until the end of the 2010 which is the most length of accessible temperature data record in Iran. The data of daily temperature is derived from Meteorological Organization of Iran. An initial check was carried out in order to test the quality of data. The NCEP/NCAR data and HadcM3 under scenario A2 and B2 also are used in current study in order to modeling and predicting the temperature values.
In order to discover whether the negative/positive trend are governed the data, the temperature data subjected to Mann-Kendal trend test. In order to fit a proper model on each character of Arak's temperature, linear and non-linear regression models were used. The best models are chosen based on conformation of ordinary statistics and indices.
All the results are performed by SPSS and MATLAB softwares and depicted in figures and shapes. Statistical downscaling model is used to simulate and predict the temperature of Arak station by using SDSM software.
Results and discustion : According to our study, the best fitted models on annual mean temperature, annual minimum temperature average, and annual maximum temperature average are cubic and quadratic models, while these models are fitted on absolute maximum temperature for spring and winter. There are no non-linear model which be fitted on minimal absolute temperature, due to the huge variability in this parameter. Based on correlation and partial correlation analyses which is used in current study, the explanatory variables for annual mean temperature are Sea Level Pressure (SLP), 500 hpa geopotential height (500hpa HGT). The explanatory variables for mean maximum temperature are Vorticity at 500 hpa, 500hpa HGT, relative humidity at 500 hpa, and mean temperature at 2m. Ultimately explanatory variables for mean minimal temperature are SLP, 500hpa HGT, relative humidity at 500 hpa and also mean temperature at height of 2 meters. After calibrating with using estimated models and abovementioned variables for period of 1961 to 2010 the data is evaluated and it became clear that the difference between simulated data with recorded data is very low. Then bused on two scenario A2 and B2 the temperature variables of Arak are predicted. Based on scenario A2 and B2 during 100 years there will be about 0.24 and 0.19 degree centigrade increase in annual mean temperature, while 0.25 and 0.2 degree centigrade the mean maximum temperature will increased. The mean minimum temperature will increased by 0.19 and 0.16 degree centigrade.
According to our finding, the Arak temperature trends are non-linear during the study period (1961 to 2010). Average of minimal temperature during summer shows increasing trend. Therefore energy and water demanding are increases in summer. Absolute values of maximum temperature of winter and summer recently have increased during last two decades. So that, the snow melts will have accrued very fast during winter and spring in future. The result of current research and several other researches which are performed in Iran and also in global scale are testify on temperature increasing of cities and also certify the IPCC reports on increasing trends at least during five recent decades and continues increase at least during next two decades. This temperature increasing trends also can influence other climate variables such as evaporation, rainfall, relative humidity and so on and accordingly can effect human activities such as consuming energy, and human environment such as air pollution. Accordingly the environmental management as well as environmental planning should take this reality in their account.