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

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 R 2 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 R 2 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.

Wastewater inflow time series forecasting at low temporal resolution using SARIMA model: a case study in South Australia

“…However, it underestimated/overestimated the wastewater inflow during after midnight hours from 1–5 p.m. This could be because ARIMA family models only approximate the data patterns in the past, as the structure of the underlying data mechanism is not explained (YoosefDoost et al 2017 ).

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Predicting future dry season periods for irrigation management in West Sumatra, Indonesia

“…The test can be applied to show trends in yearly variation in the dry season parameters. Various studies have discussed predicting future climate patterns by comparing statistical and complexity dynamic approaches such as global circulation models (GCMs) (Yoosefdoost et al 2017;Alotaibi et al 2018;Hao et al 2018). Yoosefdoost et al (2017) concluded that statistical models such as the autoregressive integrated moving average (ARIMA) model can likely provide acceptable results in terms of temperature and rainfall parameters.…”

“…Various studies have discussed predicting future climate patterns by comparing statistical and complexity dynamic approaches such as global circulation models (GCMs) (Yoosefdoost et al 2017;Alotaibi et al 2018;Hao et al 2018). Yoosefdoost et al (2017) concluded that statistical models such as the autoregressive integrated moving average (ARIMA) model can likely provide acceptable results in terms of temperature and rainfall parameters. ARIMA is a widely applied method for predicting climate variables (Box et al 2016;Murat et al 2018;Soltani et al 2007;Wang et al 2013;Wei 2006) and forecasting droughts (Xu et al 2020;Han et al 2010).…”

Forecasting One-Decade Ahead Minimum Temperature And Relative Humdity for Water Resources Management in Lower Niger

“…Consequently, ARIMA (2, 0, 2)(2, 1, 2) 12 , ARIMA (0, 1, 2)(1, 1, 1) 12 , ARIMA (1, 0, 2)(2, 1, 1) 12 and ARIMA (1, 0, 2)(2, 1, 2) 12 model respectively was found to be suitable and these models were used to forecasting the monthly humidity for the upcoming two years to assist in water demand management decisions. YoosefDoost et al (2017) analyzed and evaluated ARIMA statistical model with GFDL CM2.1 and CGM3 Atmosphere-Ocean General Circulation Models (AOGCMs) to assess the effects of climate change on temperature and precipitation in the Taleghan basin.…”