According to the importance of climate change, the necessity of develop a fast and accurate tool is undeniable. Although the comparison of a statistical model with specialized models which were designed regard to non-linear complexities of a phenomenon is not common, in this study ARIMA statistical model was analyzed and evaluated with GFDL CM2.1 and CGM3 Atmosphere-Ocean General Circulation Models (AOGCMs) in order to investigate on the effects of climate change on temperature and precipitation in the Taleghan basin. The results showed although GFDL CM2.1 model showed better performance in MAE and R2 validation criteria and the predicted temperature had similar trend with the observational data, the difference between the model results and observations is significant. The CGM 3 model showed better performance in R2 for precipitation, temperature and MAE for long term average of precipitation in addition to having similar trend to the observed data. However, for long term average of both temperature and precipitation, the general predicted trend had a considerable distance with the observational values. In contrast, although the statistical ARIMA model predictions had some fluctuations, they had better conformity to the general trend of observations. These results show that contrary to popular belief, in some cases like this investigated case, even cheap statistical models can likely provide acceptable results.
| [1] | IPCC 2007a. Climate Change: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge; n.d.View Article |
| [2] | Buchdahl J. A review of contemporary and prehistoric global climate change. Chester Street, Manchester M1 5GD: Manchester Metropolitan University; 1999. PubMed PubMed |
| [3] | Karamooz, M., Araghy nejad S. Advanced Hydrology. Tehran: Amirkabir Technology University Press; 2005. |
| [4] | Mearns LO, Giorgi F, Whetton P, Pabon D, Hulme M, Lal M. Guidelines for Use of Climate Scenarios Developed from. 2003.View Article |
| [5] | Katsoulis BD. Indications of change of climate from the analysis of air temperature time series in Athens, Greece. Clim Change 1987; 10:67-79.View Article |
| [6] | Karl TR. Multi-year fluctuations of temperature and precipitation: The gray area of climate change. Clim Change 1988; 12:179-97.View Article |
| [7] | Galbraith JW, Green C. Inference about trends in global temperature data. Clim Change 1992; 22: 209-21.View Article |
| [8] | Graf H-F, Perlwitz J, Kirchner I, Schult I. Recent northern winter climate trends, ozone changes and increased greenhouse gas forcing. Contrib Atmos Phys 1995; 68: 233-248.View Article |
| [9] | Brunetti M, Buffoni L, Maugeri M, Nanni T. Trends of Minimum and Maximum Daily Temperatures in Italy from 1865 to 1996. Theor Appl Climatol 2000; 66:49-60.View Article |
| [10] | Yue S, Hashino M. Temperature trends in Japan: 1900 – 1996. Environment 2003; 27: 15-27.View Article |
| [11] | Li Q, Zhang H, Liu X, Huang J. Urban heat island effect on annual mean temperature during the last 50 years in China. Theor Appl Climatol 2004; 79: 165-74.View Article |
| [12] | Eyni S. Investigation on comparative analysis and verification of temperature and precipitation simulations of ARIMA statistical model and MAGICC-SCENGEN climatic models (Case Study: Tabriz station). Natl. Conf. Clim. Chang. Sustain. Dev. Agric. Nat. Resour. Eng. Sci. Technol., Tehran, Iran. (In Persian): Civilica; 2014. PubMed PubMed |
| [13] | 8.1 Stationarity and differencing | OTexts n.d. https://www.otexts.org/fpp/8/1 (accessed August 19, 2017).View Article |
| [14] | System TSF. Notation for ARIMA Models. SAS Inst n.d. https://support.sas.com/documentation/cdl/en/etsug/63939/HTML/default/viewer.htm#etsug_tffordet_sect016.htm (accessed August 19, 2017).View Article |
| [15] | Hyndman RJ, Athanasopoulos G. 8.9 Seasonal ARIMA models. Forecast Princ Pract n.d. https://www.otexts.org/fpp/8/9 (accessed August 19, 2017).View Article |
| [16] | Autoregressive integrated moving average. Wikipedia n.d. https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average#cite_note-1 (accessed August 19, 2017).View Article |
| [17] | Xu C. From GCMs to river flow: a review of downscaling methods and hydrologic modelling approaches. Prog Phys Geogr 1999; 23: 229-49.View Article |
| [18] | Jones Pd, Hulme M. Calculating regional climatic time series for temperature and precipitation: methods and illustrations. Int J Climatol 1996; 16: 361-77.View Article |
| [19] | Yoosefdoost A, Raisi M, Esmaeli P. Comparison of the Performance of HadCM3, GFDL CM 2.1 and CGCM3 Models in Estimating the Climate Change Effects on Rainfall and Temperature in Taleghan Basin Under SRES A2 Scenario. Int. Congr. Enviroment, Tehran, Iran. (In Persian): Civilica; 2015. |
| [20] | YoosefDoost A, Abunuri R, Naghdi E, YoosefDoost Ic. Comparison of GFDL, CM 2.1 and HadCM3 Models in Estimating The Effects of Climate Change in the Taleghan Basin. Int. Congr. Enviroment, Tehran, Iran. (In Persian): Civilica; 2015. |
| [21] | Yoosefdoost I, Rajaie M, Yoosefdoost A. Analysis and Evaluation of CGM3 Atmosphere Ocean Global Circulation Model in Comparison with ARIMA Statistical Model to Predict the Effects of Climate Change. Second Natl. Conf. Water, Human, Earth, Isfahan, Iran. (In Persian): Civilica; 2015. |
| [22] | YoosefDoost A, Sadeghian MS, Bazargan Lari MR. Analysis and Evaluation of Using Artificial Parameters Generated by Data Mining in Runoff Estimation by Neural Networks considering to the climate change. Natl. Conf. Water, Hum. Earth, Isfahan, Iran. (In Persian): Civilica; 2014. |
| [23] | YoosefDoost A, Rajaie M. Analysis and Evaluation of ARIMA model Performance in prediction the Effects of Climate Change. Second Natl. Conf. Water, Human, Earth, Isfahan, Iran. (In Persian): Civilica; 2015. |
