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Yue S, Hashino M. Temperature trends in Japan: 1900 – 1996. Environment 2003; 27: 15-27.

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Article

Comparison between Performance of Statistical and Low Cost ARIMA Model with GFDL, CM2.1 and CGM 3 Atmosphere-Ocean General Circulation Models in Assessment of the Effects of Climate Change on Temperature and Precipitation in Taleghan Basin

1Member of Young Researchers’ Elite Club, South Tehran branch, Islamic Azad University, Iran

2Islamic Azad University Central Tehran Branch, Tehran, Iran


American Journal of Water Resources. 2017, Vol. 5 No. 4, 92-99
DOI: 10.12691/ajwr-5-4-1
Copyright © 2017 Science and Education Publishing

Cite this paper:
Arash YoosefDoost, Mohammad Sadegh Sadeghian, MohammadAli NodeFarahani, Ana Rasekhi. Comparison between Performance of Statistical and Low Cost ARIMA Model with GFDL, CM2.1 and CGM 3 Atmosphere-Ocean General Circulation Models in Assessment of the Effects of Climate Change on Temperature and Precipitation in Taleghan Basin. American Journal of Water Resources. 2017; 5(4):92-99. doi: 10.12691/ajwr-5-4-1.

Correspondence to: Arash  YoosefDoost, Member of Young Researchers’ Elite Club, South Tehran branch, Islamic Azad University, Iran. Email: YoosefDoost@Gmail.com

Abstract

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.

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