پیش‌نمایی تغییرات دما و بارش در شهرهای شمال غرب ایران تحت سناریوهای SSP مدل اقلیمی NorESM2

نوع مقاله : علمی-پژوهشی

نویسندگان

1 دکتری، گروه جغرافیای طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران.

2 استاد، گروه جغرافیای طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران.

چکیده

هدف پژوهش حاضر پیش­نمایی تغییرات دما و بارش در شمال­غرب ایران تحت سناریوهای SSP مدل NorESM2 است. با بهره­گیری از آزمون تخمین­گر شیب سن، دما و بارش شهرهای منتخب طی 2014-1985 روندیابی شد. سپس با استفاده از نرم‌افزار  SDSM6.1، پارامترهای مذکور برای دوره پایه (2014-1985) شبیه­سازی و برای سال­های 2043-2015 پیش­بینی گردیدند. برای ارزیابی عملکرد مدل­، از شاخص­های خطاسنجی MSE، RMSE و MAE استفاده شد. استفاده از سناریوهای جدید SSP برای پیش­بینی پارامتر مهم اقلیمی دما و بارش در شهرهای مطرح در زمینه گردشگری ایران، نوآوری این پژوهش است. نتایج آزمون شیب سن حاکی از روند صعودی دمای حداکثر در همه شهرهای مورد مطالعه در سطح اطمینان 99% به‌جز شهرهای تکاب و سردشت بود. طبق نتایج روندیابی بارش، تکاب و مراغه در سطح اطمینان 99% دارای روند کاهشی معنادار و جلفا و ماکو در سطح اطمینان 95% دارای روند افزایشی معنادار بودند. بر اساس نتایج مدل‌سازی، بارش در همه شهرهای مورد مطالعه در فصل بهار افزایش خواهد داشت که بالاترین درصد افزایش مربوط به جلفا خواهد بود (20%). بارش در تابستان و پاییز کاهش خواهد یافت که بیش‌ترین درصد کاهش برای مراغه پیش­بینی شد (33%). دمای حداکثر در همه شهرهای مورد مطالعه به‌ویژه در ماه­های سرد افزایش خواهند داشت که میزان این افزایش تا ℃2 خواهد بود.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Projection of Temperature and Precipitation Changes in Northwestern Cities of Iran under SSP Scenarios of the NorESM2 Climate Model

نویسندگان [English]

  • Roghayeh Maleki Meresht 1
  • Bromand Salahi 2
1 Ph.D. of Climatology, Department of Physical Geography, University of Mohaghegh Ardabili, Ardabil, Iran.
2 Professor, Department of Physical Geography, University of Mohaghegh Ardabili, Ardabil, Iran.
چکیده [English]

The aim of the current research is to forecast temperature and precipitation changes in northwest of Iran under SSP scenarios of the NorESM2 model. By using Sen's slope estimator, the temperature and precipitation of selected cities were trended during 1985–2014. Then, using SDSM6.1 software, the mentioned parameters were simulated for the base period (1985–2014) and predicted for 2015–2043.To evaluate the performance of the model, the error measurement indices MSE, RMSE and MAE were used. The innovation of this research is the use of new SSP scenarios to predict the important climatic parameters of temperature and precipitation in prominent cities in the field of Iranian tourism. The results of the test of Sen's slope estimator indicated an upward trend of the maximum temperature in all the studied cities at the 99% confidence level except Takab and Sardasht cities. According to the results of precipitation trending, Takab and Maragheh had a significant decreasing trend at the 99% confidence level, and Jolfa and Mako had a significant increasing trend at the 95% confidence level. Based on the modeling results, precipitation will increase in all the studied cities in spring, and the highest percentage of increase will be in Jolfa (20%). Precipitation will decrease in summer and autumn, and the highest percentage of decrease was predicted for Maragheh (33%). The maximum temperature will increase in all the studied cities, especially in the cold months, which will be up to 2 °C.

کلیدواژه‌ها [English]

  • Temperature Changes
  • Precipitation Changes
  • Ssp Scenarios
  • NorESM2 Climate Model
  • Northwest of Iran

مراجع

Ahmadabadi A, & sedighifar Z. (2018). Prediction of Climate Change Induced Hydrogeomorphology by using SDSM in CAN Watershed. Applied Research in Geographical Sciences, 18(51), 114-103. )In Persian) doi: 10.29252/jgs.18.51.103
 Akbarzadeh, M., Nouri, H., Mortazavi, S., & attaeian, B. (2024). Investigation and prediction of climate changes using the approach of General Circulation Models (GCMs) in the western provinces of Iran. Desert Ecosystem Engineering, 12(39), 23-42.) In Persian) doi: ‎10.22052/deej.2024.253424.1019
Bates, B., Kundzewicz, Z.W., Wu, S. & Palutikof, J. (2008). Climate Change and Water. Intergovernmental Panel on Climate Change (IPCC), 200.
Chen, Z., Zhou, T., Zhang, L., Chen, X., Zhang, W., & Jiang, J. (2020). Global land monsoon precipitation changes in CMIP6 Projections. Geophysical Research Letters, 47(14), 1-9. https://doi.org/10.1029/2019GL086902
Chobeh, S., & Kake Mami, A. (2014). Investigating the effectiveness of statistical exponential microscale model (SDSM) in predicting temperature and precipitation parameters (case study: Ardabil Baliqlochai watershed), the second national conference on protection of natural resources and environment, University of Mohaghegh Ardabili. [In Persian]. https://civilica.com/doc/491153
Cui, T., Li, Ch., & Tian, F. (2021). Evaluation of Temperature and Precipitation Simulations in CMIP6. https://doi.org/10.1029/2020EA001620
De Oliveira, V. A., de Mello, C. R., Beskow, S., Viola, M. R. & Srinivasan, R., (2019). Modeling the effects of climate change on hydrology and sediment load in a headwater basin in the Brazilian Cerrado biome, Ecological Engineering, 133, 20–31. https://doi.org/10.1016/j.ecoleng.2019.04.021
Eyring, V., Bony, S., Meehl, G. A., Senior, C., Stevens, B., Stouffer, R. J., & Taylor, K. E. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development Discussions, 8(12), 1937-1958. https://doi.org/10.5194/gmd-9-1937-2016
ghorbani H, vali A A, & zarepour H. (2019). Analysis of the Climatological Drought Trend Variations Using Mann-Kendall, Sen and Pettitt Tests in Isfahan Province. Journal of Spatial Analysis Environmental Hazards; 6 (2):129-146.)In Persian) doi: 10.29252/jsaeh.6.2.129
Greene, A.M., Goddard, L. & Lall, L. (2006). Probabilistic multi-model regional temperature change projections. J. Climate. 19: 4326-4346. doi: 10.1175/JCLI3864.1
Gupta, V., Singh, V., & Jain, M. K. (2020). Assessment of precipitation extremes in India during the 21st century under SSP1-1.9 mitigation scenarios of CMIP6 GCMs. Journal of Hydrology, 590. https://doi.org/10.1016/j.jhydrol.2020.125422
Hennessy, K. J., Whetton, P. H., Walsh, K., Smith, I. N., Bathols, J. M., Hutchinson, M., & Sharples, J. (2007). Climate change effects on snow conditions in mainland Australia and adaptation at ski resorts through snowmaking. Climate Research, 35(3), 255. doi: 10.3354/cr00706
Hoshiar, M., Sobhani, B., & Hosseini, S A. (2017). The perspective of changes in the maximum temperatures of Urmia using the CanESM2 model output statistical fine-tuning, Scientific-Research Journal of Geography and Planning, 22 (63): 325-305. )In Persian) https://geoplanning.tabrizu.ac.ir/article_7598.html
IPCC, (2013). Climate Change 2013. The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovern-mental Panel on Climate Change [Stocker,T.F.,Qin,D.,Plattner,G.K.,Tignor,M.,Allen,S.K.,Boschung,J.,Nauels,A.,Xia,Y.,Bex,V & Midgley,P. M.,(eds.)]. Cambridge University Press, Cambridge, Unitesd Kingdom and New York, NY, USA, 1535.
IPCC, (2021). Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, Lonnoy, K., Matthews, E., J.B.R., Waterfield, T.K., T., Yelekçi, O., Yu, R. & Zhou, B. (eds) Cambridge University Press.
Jiménez-Navarro, I. C., Jimeno-Sáez, P., López-Ballesteros, A., Pérez-Sánchez, J., & Senent-Aparicio, J. (2021). Impact of Climate Change on the Hydrology of the Forested Watershed That Drains to Lake Erken in Sweden: An Analysis Using SWAT+ and CMIP6 Scenarios. Forests, 12 (12), 1803. https://doi.org/10.3390/f12121803
Karamouz, M., & Iraqinejad, Sh. (2006). Advanced Hydrology, Publications of Amir Kabir University of Technology. Tehran. )In Persian)
Kim, B.S., Kim, H.S., Seoh, B.H. & Kim, N.W. (2007). Impact of climate change on water resources in Yongdam Dam Basin, Korea. Stochastic Environmental Research and Risk Assessment, 21, 355–373. https://doi.org/10.1016/j.ejrh.2015.06.023
Kwesi Nooni, I.,  Katchele Ogou, F.,  Saidou Chaibou., A. A.,  Nakoty. F. M.,  Gnitou, G.T., &  Lu, J. (2023). Torical Mean Precipitation over Africa and the Arabian Peninsula against Satellite-Based Observation, Journals Atmosphere,14 (3), 607.  https://doi.org/10.3390/atmos14030607
Lei, X ., Xu, Ch ., Liu, F., Song,  L ., Cao, L ., & Suo, N . (2023) Precipitation over Arid Central Asia, Remote Sens., 15(9), 2376. https://doi.org/10.1016/j.gloplacha.2023.104142
Maleki Nejad, H., Soleimani Mutlaq, M., Jaidari, A., & Shater Abshuri, S. (2012). Analysis of changes in rainfall and drought using Mann-Kendall and age tests in Tehran province, Newar Scientific and Technical Journal, 80, 43-55. )In Persian) https://nivar.irimo.ir/article_13186.html
Mason, S.J. (2004). Simulating climate over western North America using stochastic weather generators. Climate Change. 6, 155-187. https://link.springer.com/article/10.1023/B:CLIM.0000013700.12591.ca.
Menon, A., Levermann, A., Schewe, J., Lehmann, J., & Frieler, K. (2013). Consistent increase in Indian monsoon rainfall and its variability across CMIP-5 models. Earth System Dynamics, 4(2), 287-300. https://doi.org/10.5194/esd-4-287-2013
Mesgari, E., Hosseini, S A., Hemmesy, M S., Houshyar, M., & Golzari Partoo, L. (2022) Assessment of CMIP6 models’ performances and projection of precipitation based on SSP scenarios over the M ENAP region, Journal of Water and Climate Change, 13 (10): 3607–3619. https://doi.org/10.2166/wcc.2022.195
Rasouli, Ali-Akbar; Sari Saraf, Behrouz; Mohammadi, Gholam-Hassan. (2011). Analyzing the occurrence of the climatic phenomenon of dust in the west of the country in the last 55 years by using non-parametric statistical methods, Natural Geography, 11, 1-16.)In Persian) https://doi.org/10.2166/wcc.2022.195
Rezaei Zaman, M; Masah Bowani, A; Javadi, S. (2022). Evaluation of the effects of SSP scenarios from the climatic outputs of the coupled intercomparative models of the sixth phase (CMIP6) on water resources and agriculture in the Hashtgerd region with the approach of applying adaptive strategies, Environmental Science and Technology, 24 (12), 107-93. )In Persian)
Roshani, A., & Hamidi, M. (2022). Forecasting the effects of climate change scenarios on temperature & precipitation based on CMIP6 models (Case study: Sari station). Water and Irrigation Management, 11(4), 781-795. )In Persian) doi: 10.22059/jwim.2022.330603.920
 Safarian, V, Salahi, B, Maleki Meresht, R & Kiyanian, M. (2019). Analysis of standardized precipitation and drought indicators in the city centers of Ardabil province, Urban Ecology Research Quarterly, 1, 121-136. )In Persian) doi: 10.30473/grup.2020.7476
Salmi, T., A. Määttä, P. Anttila, T, Ruoho, & T, Amnell. (2002). Detecting trends of annual values of atmospheric pollutants by the Mann-Kendall test and Sen’s slope estimates –the Excel template application MAKESENS.
Sami, E., & Ebadi, M. (2024). Urban Flood Hazard Zoning Using Fuzzy-Analytic Network Process (ANP), Case study: Maragheh City. Journal of Urban Ecology Researches, 15(Vol 1 , Series 34), 171-186. doi: 10.30473/grup.2024.55316.2554
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. )In Persian) doi: 10.22034/jmas.2021.278862.1129
Scott, D., Dawson, J., & Jones, B. (2008). Climate change vulnerability of the US Northeast winter recreation–tourism sector Mitigation and Adaptation Strategies for Global Change, 13(5-6), 577-596. https://link.springer.com/article/10.1007/s11027-007-9136-z
Serrano, A., Mateos, V.L & Garcia, J.A. (1999). Trend Analysis of Monthly Precipitation over the Iberian Peninsula for the Period 1921-1995. Physics Chem. Earth (B), 24 (1-2) 85- 90. https://doi.org/10.1016/S1464-1909(98)00016-1
Seymohammadi, S., Tavakoli, M., Zarafshani, K., Mahdizadeh, H., & Amiri, F. (2021). Prediction Impact of Climate Change on the Temperature & Precipitation by General Circulation Model, a Strategy for Sustainable Agriculture: (Case of Kermanshah Township). Journal of Environmental Science and Technology, 23(6), 15-31. (In Persian) https://sid.ir/paper/951683/fa
Shoja, F., & Shamsipour, A. (2023). Projection of future Precipitation changes in Tehran's water supply watersheds. Journal of Natural Environmental Hazards, 12(36), 151-180. )In Persian) doi: 10.22111/jneh.2022.42622.1908
Srivastava, A., Grotjahn, R., & Ullrich, P.A. (2020). Evaluation of historical CMIP6 model simulations of extreme precipitation over contiguous US regions, Weather and Climate Extremes, 29, 100-268. https://doi.org/10.1016/j.wace.2020.100268
Turgay, P. & Ercan K. (2005). Trend Analysis in Turkish Precipitation data. Hydrological processes published online in Wiley Interscience. 20 (9), 2011 – 2026. doi/10.1002/hyp.5993
Zareian M. (2022). Effects of Climate Change on Temperature and Precipitation in Yazd Province Based on Combined Output of CMIP6 Models. Journal of Water and soil sciences; 26 (2): 91-105. )In Persian) .doi: 10.47176/jwss.26.2.31501
Zarrin, A., & Dadashi Roudbari, A. A. (2020). Projection the Long-Term Outlook Iran Future Temperature Based on the Output of The coupled model intercomparison project phase 6 (CMIP6). Journal of the Earth and Space Physics, 46(3), 583-602. )In Persian) doi: 20.1001.1.24235474.1400.8.4.14.9

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  • تاریخ دریافت: 20 دی 1402
  • تاریخ بازنگری: 05 آذر 1403
  • تاریخ پذیرش: 07 دی 1403

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