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Meteorology and Atmospheric Physics

, Volume 131, Issue 5, pp 1259–1280 | Cite as

Historical trend analysis and future projections of precipitation from CMIP5 models in the Alborz mountain area, Iran

  • Mohammad ZarenistanakEmail author
Original Paper

Abstract

Precipitation changes in annual and seasonal time series over the Alborz Mountains area during 1950–2014 were analyzed using historical observations from 154 rain gauge stations. The projected changes in precipitation for the twenty-first century were evaluated using three Coupled Model Intercomparison Project Phase 5 (CMIP5) datasets. Trends in the precipitation time series were detected by linear regression and its significance was tested by t test. Mann–Kendall rank test (MK test) and Sen’s slope estimator were also employed to confirm the results. Sequential Mann–Kendall test (SQ-MK test) was also applied for change point detection in annual and seasonal precipitation time series. Pre-whitening was used to eliminate the influence of serial correlation on the MK test. Future precipitation was analyzed by GFDL-CM3, HadGEM2-AO and MPI-ESM-MR models under two representative concentration pathways (RCP) RCP 4.5 and RCP 8.5 scenarios. The analysis of the historical precipitation series indicated an insignificant trend in the annual and seasonal series at most stations. The highest numbers of stations with negative significant trends occurred in winter and with positive significant trends in summer. The results of change point detection in annual and seasonal precipitation series show that most of the significant mutation points began in the 1970s. The future projections showed that precipitation may decrease according to most of the models under the RCP 4.5 and RCP 8.5 scenarios, while the decrease may not be large, except in the summer season for the end of this century.

Notes

Acknowledgements

The author is thankful to the Islamic Republic of Iran Meteorological Organization, Iranian Water Resources Management Organization, Ministry of Energy for providing necessary data required for the study. I also acknowledge the modeling groups for providing their data for analysis, the PCMDI, Lawrence Livermore National Laboratory, USA, for collecting and archiving the model output and for providing necessary data required for the study. The author is also grateful to Dr. R. H. Kripalani and Dr. Preethi from the Indian Institute of Tropical Meteorology, Pune, India, for their suggestions on projection analysis. Also, the author would like to thank the anonymous reviewers for their comments and suggestions which have contributed to improve the manuscript.

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© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Research Institute of Shakhes PajouhIsfahanIran

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