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

Document Type : Full length article

Authors

1 Professor of Climatology, Department of Geography, University of Zanjan, Iran

2 PhD Candidate in Climatology, University of Zanjan, Iran

Abstract

Introduction
The city population, in particular at the industrialized cities and centers of provinces, has increased dramatically in Iran during recent decades. Arak city as center of Markazi Province is among those industrialized cities which has experienced a fast increase in population. These changes in population numbers tend to increase consuming water resources as well as increasing in energy resources demand. This situation is accompanied with global warming and 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 were analyzed for previous and future states 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.
 
Materials and Methods
In order to study temperature trends during recent decades in Arak, the temperature data selected based on having sufficient temporal records to carry out the investigation and also sufficient accuracy that extend from 1961 until the end of the 2010 as the longest period 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 are also used in current study in order to model and predict the temperature values. 
In order to discover the negative/positive trends of the data, the temperature data were analyzed by 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 applications and depicted in figures and shapes. Statistical downscaling model is used to simulate and predict the temperature of Arak station using SDSM software.
 
Results and Discussion
According to our study, the best fitted models on annual mean temperature, annual average of minimum temperature, and annual average of maximum temperature are cubic and quadratic models, while these models are fitted on absolute maximum temperature for spring and winter. There is no non-linear model to be fitted on minimal absolute temperature, due to the huge variability in this parameter. Based on correlation and partial correlation analyses which are used in current study, the explanatory variables for annual mean temperature are Sea Level Pressure (SLP), 500 hpa geopotential heights (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 were evaluated. It became clear that the difference between simulated data with recorded data is very low. Then, based 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 will increase the mean maximum temperature. The mean minimum temperature will be increased by 0.19 and 0.16 degree centigrade.
 
Conclusion
According to our findings, the Arak temperature trends are non-linear during the study period (1961 to 2010). Average of minimal temperature during summer shows an increasing trend. Therefore, energy and water demanding are increased in summer. Absolute values of maximum temperature of winter and summer have recently increased during last two decades. Therefore, the snow melts will have accrued very fast during winter and spring in future. The results of current research and several other studies performed in Iran and also in global scale have testified temperature increasing of cities and also the IPCC reports on increasing trends at least during the recent five decades and continue the increase at least during next two decades. This temperature increasing trends can also influence other climate variables such as evaporation, rainfall, relative humidity and so on and accordingly can affect human activities such as consuming energy, and human environment such as air pollution. Accordingly, the environmental management as well as environmental planning should consider this reality. 

Keywords

Main Subjects

References

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Physical Geography Research
Volume 48, Issue 2
July 2016
Pages 193-212

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History

  • Receive Date: 14 July 2015
  • Revise Date: 26 December 2015
  • Accept Date: 11 January 2016

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