Projection changes of extreme precipitation with different return periods in Iran based on the ensemble approach of 10 CMIP6 models in the near future

Document Type : Research Article

Authors

• Sakineh Khansalari ¹
• Seyede Atefeh Mohammadi ²
• Zahra Ghassabi ¹
• Hassan Salehi ³

¹ Assistant Professor in Research Institute of Meteorology and Atmospheric Science, Tehran, Iran

² Research Institute of Meteorology and Atmospheric Science, Tehran, Iran

³ Kohgiluyeh and Boyer-Ahmad province Meteorological Administration, Yasuj, Iran

10.22067/geoeh.2024.86464.1456

Abstract

In the current study, changes in the amount of extreme precipitation with different return periods for the period 2026–2050 were analyzed using the weighted combination of 10 CMIP6 models under different scenarios. The spatial and temporal ranking of the historical simulation performance of the 10 CMIP6 models in Iran was used to determine the weights for each model. Subsequently, a rank-based weighting method was employed to project extreme precipitation levels in the study area.
For extreme precipitation analysis, the best statistical distribution was selected for each station based on historical data. In 84% of the stations, the Log-Pearson Type 3 distribution was identified as the best fit using the chi-square test for both the historical period (1990–2014) and future scenarios.
A comparison between the results of extreme precipitation for the historical and future periods revealed that the percentage of change in projected extreme precipitation was notably higher under the SSP2-4.5 scenario compared to the other two scenarios. Moreover, with longer return periods, the percentage increase in extreme precipitation was greater, and the spatial extent of its occurrence also expanded. The greatest increases in projected extreme precipitation, exceeding 25%, were observed in the SSP1-2.6 scenario in Gorgan, Babolsar, Bojnord, Arak, Isfahan, and Zahedan stations. In the SSP2-4.5 scenario, the largest increases were recorded in Gorgan, Sanandaj, Hamedan, Arak, Kashan, Isfahan, Shiraz, Fasa, Minab, Bushehr, Bandar Abbas, Bandar Lengeh, and Abu Musi stations. Under the SSP5-8.5 scenario, the greatest increases occurred in Bojnord, Birjand, Arak, Kashan, and Semnan stations.
A significant increase in extreme precipitation was consistently observed in Arak station across all three scenarios. For the 200-year return period, extreme precipitation at Arak station increased by approximately 70% under the SSP1-2.6 and SSP2-4.5 scenarios, and by about 50% under the SSP5-8.5 scenario.

Keywords

• Extreme Precipitation Events
• Climate Projection Models
• Return Period Analysis
• CMIP6 Models
• Shared Socioeconomic Pathways (SSPs)
• Statistical Downscaling

Main Subjects

• Climate change

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References

Alijani, B., & Afshar Manesh, H. (2015). Statistical analysis of long-term precipitation data for fitting an appropriate statistical distribution (Case study: Iran). Zagros Quarterly Journal of Geography and Urban Planning, 7(25), 73-95. [In Persian] https://sanad.iau.ir/Journal/zagros/Article/937845

Alijani, B., O’Brien, J., & Yarnal, B. (2008). Spatial analysis of precipitation intensity and concentration in Iran. Theoretical and Applied Climatology, 94, 107-124. https://doi.org/10.1007/s00704-007-0344-y

Alizadeh, A. (2015). Principles of Applied Hydrology (40th ed.). Mashhad: Imam Reza University Press. [In Persian]

Allen, M. R., & Ingram, W. J. (2002). Constraints on future changes in climate and the hydrologic cycle. Nature, 419(6903), 224-232. https://doi.org/10.1038/nature01092

Asadi, A., & Akbari Azirani, T. (2021). Analysis of variations the beginning and ending of precipitations with tending models in western south of Iran. Sustainable Development of Geographical Environment, 3(4), 99-107. [In Persian] https://doi.org/10.52547/SDGE.3.4.99

Azizi, H., & Nejatian, N. (2022). Evaluation of the climate change impact on the intensity and return period for drought indices of SPI and SPEI (study area: Varamin plain). Water Supply, 22(4), 4373-4386. https://doi.org/10.2166/ws.2022.056

Berg, P., Moseley, C., & Haerter, J. O. (2013). Strong increase in convective precipitation in response to higher temperatures. Nature Geoscience, 6(3), 181-185. https://doi.org/10.1038/ngeo1731

Chen, H. (2013). Projected change in extreme rainfall events in China by the end of the 21st century using CMIP5 models. Chinese Science Bulletin, 58, 1462-1472. https://doi.org/10.1007/s11434-012-5612-2

Chen, H., Sun, J., Chen, X., & Zhou, W. (2012). CGCM projections of heavy rainfall events in China. International Journal of Climatology, 32(3), 441-450. https://doi.org/10.1002/joc.2278

Chen, W., Jiang, Z., & Li, L. (2011). Probabilistic projections of climate change over China under the SRES A1B scenario using 28 AOGCMs. Journal of Climate, 24(17), 4741-4756. https://doi.org/10.1175/2011JCLI4102.1

Dike, V. N., Lin, Z. H., & Ibe, C. C. (2020). Intensification of summer rainfall extremes over Nigeria during recent decades. Atmosphere, 11(10), 1084. https://doi.org/10.3390/atmos11101084

Hawkins, E., & Sutton, R. (2011). The potential to narrow uncertainty in projections of regional precipitation change. Climate dynamics, 37, 407-418. https://doi.org/10.1007/s00382-010-0810-6

Hong, J., Javan, K., Shin, Y., & Park, J. S. (2021). Future projections and uncertainty assessment of precipitation extremes in Iran from the CMIP6 ensemble. Atmosphere, 12(8), 1052. https://doi.org/10.3390/atmos12081052

IPCC. (2020). Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. Special Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. https://www.ipcc.ch/report/managing-the-risks-of-extreme-events-and-disasters-to-advance-climate-change-adaptation/

Javan, K., Movaghari, A., & Park, J. S. (2023). Projected changes in extreme precipitation indices over the Lake Urmia basin in Iran. Journal of Water and Climate Change, 14(8), 2564-2582. https://doi.org/10.2166/wcc.2023.447

Jiang, Z., Li, W.,  Xu, J., & Li, L. (2015). Extreme precipitation indices over China in CMIP5 models. Part I: Model evaluation. Journal of Climate, 28(21), 8603-8619. https://doi.org/10.1175/JCLI-D-15-0099.1

Khansalari, S., & Mohammadi, S. A. (2023). Projection of extreme precipitation over Iran based on the ensemble approach of CMIP6 models in the near future (2026-2050) with rank-based weighting. Journal of the Earth and Space Physics, 49(3), 727-746. [In Persian] https://doi.org/10.22059/jesphys.2023.351711.1007476

Lee, Y., Paek, J., Park, J. S., & Boo, K. O. (2020). Changes in temperature and rainfall extremes across East Asia in the CMIP5 ensemble. Theoretical and Applied Climatology, 141, 143-155. https://doi.org/10.1007/s00704-020-03180-w

Lin, Q. J., & Yu, J. Y. (2022). The potential impact of model horizontal resolution on the simulation of atmospheric cloud radiative effect in CMIP6 models. Terrestrial, Atmospheric and Oceanic Sciences, 33(1), 21. https://doi.org/10.1007/s44195-022-00021-3

Loucks, D. P., Stedinger, J. R., & Haith, D. A. (1981). Water Resource Systems Planning and Analysis. Prentice-Hall.

Mann, M. E., & Kump, L. R. (2015). Dire predictions: Understanding climate change. Pearson.

Meehl, G. A., Senior, C. A., Eyring, V., Flato, G., Lamarque, J. F., Stouffer, R. J., … & Schlund, M. (2020). Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models. Science Advances, 6(26), eaba1981. https://doi.org/10.1126/sciadv.aba1981

Peng, Y., Zhao, X., Wu, D., Tang, B., Xu, P., Du, X., & Wang, H. (2018). Spatiotemporal variability in extreme precipitation in China from observations and projections. Water, 10(8), 1089. https://doi.org/10.3390/w10081089

Ruckstuhl, C., Philipona, R., Morland, J., & Ohmura, A. (2007). Observed relationship between surface specific humidity, integrated water vapor, and longwave downward radiation at different altitudes. Journal of Geophysical Research: Atmospheres, 112(D3). https://doi.org/10.1029/2006JD007850

Sarabi, M., Dastorani, M. T., & Zarrin, A. (2020). Investigating Impact of Future Climate Changes on Temperature and Precipitation condition (Case Study: Torogh Dam Watershed, Mashhad). Journal of Meteorology and Atmospheric Science, 3(1), 63-83. doi: 10.22034/jmas.2021.278862.1129. [In Persian] https://doi.org/10.22034/jmas.2021.278862.1129

Sarabi, M., Dastorani, M. T., & Zarrin, A. (2021). The Impact of Future Climate Change on Hydrological Response in Torogh Dam Watershed, Mashhad. Journal of Meteorology and Atmospheric Science, 3(4), 310-330. [In Persian] https://doi.org/10.22034/jmas.2021.297763.1149

Shahabfar, A., & Qiyami, A. (2001). Evaluation of goodness-of-fit methods for statistical distribution functions and the use of time series for annual rainfall prediction in Mashhad. In Proceedings of the First National Conference on Water Crisis Management Strategies, Zabol, Iran. [In Persian] https://civilica.com/doc/81273/

Tang, B., Hu, W., & Duan, A. (2021). Future projection of extreme precipitation indices over the Indochina Peninsula and South China in CMIP6 models. Journal of Climate, 34(21), 8793-8811. https://doi.org/10.1175/JCLI-D-20-0946.1

Taylor, K. E. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres, 106(D7), 7183-7192. https://doi.org/10.1029/2000JD900719

Trenberth, K. E., Jones, P. D., Ambenje, P., Bojariu, R., Easterling, D., Klein Tank, A., ... & Zhai, P. (2007). Observations. surface and atmospheric climate change. Chapter 3. https://cig.uw.edu/publications/observations-surface-and-atmospheric-climate-change/

Yazdandoost, F., Moradian, S., Izadi, A., & Aghakouchak, A. (2021). Evaluation of CMIP6 precipitation simulations across different climatic zones: Uncertainty and model intercomparison. Atmospheric Research, 250, 105369. https://doi.org/10.1016/j.atmosres.2020.105369

Zareian, M. (2022). Effects of climate change on temperature and precipitation in yazd province based on combined output of CMIP6 models. JWSS-Isfahan University of Technology, 26(2), 91-105. [In Persian] http://dx.doi.org/10.47176/jwss.26.2.31501

Zarrin, A., & Dadashi Roudbari, A. (2022b). Investigating Precipitation Return Period and its Probability of Occurrence in Iran based on Multi-Source Weighted-Ensemble Precipitation (MSWEP). Journal of Geography and Environmental Hazards, 10(4), 209-227. [In Persian] https://doi.org/10.22067/geoeh.2021.71102.1079

Zarrin, A., & Dadashi-Roudbari, A. (2021b). Projection of future extreme precipitation in Iran based on CMIP6 multi-model ensemble. Theoretical and Applied Climatology, 144, 643-660. https://doi.org/10.1007/s00704-021-03568-2

Zarrin, A., & Dadashi-Roudbari, A. (2022a). Technical Note: Assessing the Effect of Climate Change on Heavy Precipitation in Iran Based on a CMIP6 Ensemble Model. Journal of Water and Sustainable Development, 8(4), 119-124. [In Persian] https://dorl.net/dor/20.1001.1.24235474.1400.8.4.14.9

Zarrin, A., & Dadashi-Roudbari, A. A. (2021a). Projected consecutive dry and wet days in Iran based on CMIP6 bias‐corrected multi‐model ensemble. Journal of the Earth and Space Physics, 47(3), 561-578. [In Persian] https://doi.org/10.22059/jesphys.2021.319270.1007295

Zarrin, A., Dadashi-Roudbari, A., & Hassani, S. (2022c). Future changes in precipitation extremes over Iran: Insight from a CMIP6 bias-corrected multi-model ensemble. Pure and Applied Geophysics, 179, 441-464. https://doi.org/10.1007/s00024-021-02904-x

Zhai, P., Zhang, X., Wan, H., & Pan, X. (2005). Trends in total precipitation and frequency of daily precipitation extremes over China. Journal of Climate, 18(7), 1096-1108. https://doi.org/10.1175/JCLI-3318.1

Zhang, W., & Zhou, T. (2019). Significant increases in extreme precipitation and the associations with global warming over the global land monsoon regions. Journal of Climate, 32(24), 8465-8488. https://doi.org/10.1175/JCLI-D-18-0662.1