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Investigating the ability of periodically correlated (PC) time series models to forecast the climate index

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Abstract

Considering the importance of climate change and its effects, especially in recent decades, the forecast of future climate conditions can be useful in managing and planning to reduce its impacts. The hypothesis of this study is that in periodic data series, such as seasonal (3-month) and monthly data, the periodically correlated time series models (PC) have a more ability to predict data series. Therefore, in this research using climatic data series of 18 synoptic stations during 1967–2017 over Iran (with different climate conditions and suitable spatial distribution), initially, the climate conditions based on united nation environmental program (UNEP) aridity index (UAI) in seasonal time scale were assessed, and then, using PC models including Periodic Autoregressive Moving Average (PARMA), Periodic Moving Average (PMA) and Periodic Autoregressive (PAR) the UAI from 2018 to 2030 were predicted and finally, for increasing the applicability of the results of the research the trend of changes in UAI data series on observed data (during 1967–2017) and observed and forecasted data (during 1967–2030) were assessed and compared. The results showed calculated UAI at all stations were periodical (significantly at 0.05% level) and among different PC time series models such as PARMA, PMA and PAR, the PAR model with order 22 [PAR (22)] was the best time series model that fitted in all data series at all stations. The R-squared between the observed and the simulated [based on PAR (22) model] UAI from 1967 to 2017 at all stations were more than 0.610 (significantly at 0.01% level) and the R-squared between the observed and the predicted [based on PAR (22) model] UAI from 2013 to 2017 for validating fitted models at all stations were more than 0.659 (significantly at 0.01% level). Trend assessment of climate conditions showed the climate conditions will be dryer at 94.44% (17 out of 18) of stations (only at Gorgan, the climate conditions will be more humid).

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Acknowledgements

Authors would like to thank meteorological organization of Iran for providing the meteorological information.

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Authors and Affiliations

  1. Department of Range and Watershed Management (Nature Engineering), College of Agricultural Science, Fasa University, Fasa, Iran

    Abdol Rassoul Zarei

  2. Department of Statistics, College of Science, Fasa University, Fasa, Iran

    Mohammad Reza Mahmoudi

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  1. Abdol Rassoul Zarei
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  2. Mohammad Reza Mahmoudi
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Correspondence to Abdol Rassoul Zarei.

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Zarei, A.R., Mahmoudi, M.R. Investigating the ability of periodically correlated (PC) time series models to forecast the climate index. Stoch Environ Res Risk Assess 34, 121–137 (2020). https://doi.org/10.1007/s00477-019-01751-6

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