ارزیابی دقت مدل‌های CMIP6 در برآورد دما و بارش ایران بر اساس تحلیل شبکه‌ای

نوع مقاله : مقاله پژوهشی

نویسندگان

1 مسئول، پژوهشکده مطالعات و تحقیقات منابع آب، مؤسسه تحقیقات آب، تهران، ایران.

2 پژوهشکده مطالعات و تحقیقات منابع آب، مؤسسه تحقیقات آب، تهران، ایران.

3 گروه مهندسی آب، دانشکده کشاورزی، دانشگاه صنعتی اصفهان، اصفهان، ایران.

چکیده

تغییر اقلیم یکی از مهم‌ترین عواملی است که بر وضعیت آب‌وهوای جهان اثرگذار بوده است. به‌دلیل اهمیت برآورد این اثرات، ضروری است از ابزار و مدل‌های مناسب برای تخمین اثرات تغییر اقلیم بر متغیرهای اقلیمی (به‌ویژه دما و بارندگی) استفاده شود. بدین منظور استفاده از مدل‌های گردش عمومی جو- اقیانوس (AOGCMs) به‌عنوان رایج‌ترین این ابزارها، کاربرد فراوانی در مطالعات مرتبط با تغییر اقلیم پیدا نموده است. در این راستا، هدف این مطالعه منظور ارزیابی دقت جدیدترین مدل‌های AOGCM مربوط به ششمین گزارش ارزیابی IPCC در نواحی مختلف ایران بوده است. بدین منظور، خروجی تاریخی مربوط به 10 مدل AOGCM در دوره 1980 تا 2014 از پایگاه IPCC استخراج شد و با داده‌های بازتحلیل ERA5 مربوط به مرکز ECMWF مقایسه شد. این مقایسه براساس شاخص‌های RMSE، ضریب همبستگی پیرسون و شاخص ترکیبی کلینگ- گوپتا (KGE) صورت گرفت. نتایج نشان دادند که مدل‌های مختلف در ماه‌های متفاوت سال، دقت‌ یکسانی در برآورد دما و بارش کشور دارا نیستند. تغییرپذیری خطای مدل‌ها در برآورد بارش، بیش‌تر از تغییرپذیری این خطاها در برآورد دما بود. در مقیاس سالانه نیز نتایج نشان دادند که مدل IPSL-CM6A-LR بهترین عملکرد را در برآورد دما و مدل HadGEM3-GC31-LL بهترین عملکرد را در برآورد بارش سالانه دارا بودند. هم‌چنین نتایج نشان داد که میزان خطای این مدل‌ها در نواحی مرکزی و شرقی ایران، کم‌تر از سایر نواحی بود.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Evaluation of the Accuracy of CMIP6 Models in Estimating the Temperature and Precipitation of Iran Based on a Network Analysis

نویسندگان [English]

  • Mohammad Javad Zareian 1
  • Hossein Dehban 2
  • Alireza Gohari 3
1 Department of Water Resources Study and Research, Water Research Institute, Tehran, Iran.
2 Department of Water Resources Study and Research, Water Research Institute, Tehran, Iran.
3 Department of Water Engineering, College of Agriculture, Isfahan University of Technology, Isfahan, Iran.
چکیده [English]

Climate change is one of the most important factors affecting the world's climate. Due to the importance of estimating these effects, it is necessary to use appropriate tools and models to estimate the effects of climate change on climatic variables (especially temperature and precipitation). For this purpose, the use of Atmosphere-Ocean General Circulation Models (AOGCMs) as the most common of these tools, has found a lot of use in studies related to climate change. In this regard, the aim of this study was to evaluate the accuracy of the latest AOGCMs related to the sixth IPCC assessment report of IPCC in in different regions of Iran. For this purpose, the historical outputs of 10 AOGCMs in the period 1980 to 2014 were extracted from the IPCC database and compared with the ERA5 reanalysis data of the ECMWF center. This comparison was based on RMSE, Pearson correlation coefficient and Kling-Gupta combined index (KGE). The results showed that different models do not have the same accuracy in estimating the temperature and precipitation of Iran in different months of the year. The variability of the model errors in precipitation estimation were more than the variability of these errors in temperature estimation. On the annual scale, the results showed that the IPSL-CM6A-LR model had the best performance in estimating temperature and the HadGEM3-GC31-LL model had the best performance in estimating annual precipitation. Also, the results showed that the error rate of these models was lower in central and eastern regions of Iran than the other regions.

کلیدواژه‌ها [English]

  • Climate change
  • General Circulation Models
  • Iran
  • Network analysis
  • Reanalysis

مراجع

  1. Ahmed, K., Sachindra, D.A., Shahid, S., Demirel, M.C., & Chung, E.S. (2019). Selection of multi-model ensemble of GCMs for the simulation of precipitation based on spatial assessment metrics. Hydrology and Earth System Sciences Discussions, 23, 4803-4824.
  2. Ansari, M., Dehban, H., Zareian, M.J., & Farokhnia, A. (2022). Investigation of temperature and precipitation changes in the Iran's basins in the next 20 years based on the output of CMIP6 model. Iranaian Water Research Journal, 16(1), 11-24. (In Persian).
  3. Ashraf, S., Nazemi, A., & AghaKouchak, A. (2021). Anthropogenic drought dominates groundwater depletion in Iran. Scientific Reports, 11(1), 1-10.
  4. Azizi, J., Rasoulzadeh, A., Rahmati, A., Shayeghi, A., & Bakhtar, A. (2021). Evaluating the Performance of Era-5 Re-Analysis Data in Estimating Daily and Monthly Precipitation, Case Study; Ardabil Province. Iranian Journal of Soil and Water Research, 51(11), 2937-2951. (In Persian).
  5. Chen, J., Brissette, F.P., Lucas-Picher, P., & Caya, D. (2017). Impacts of weighting climate models for hydro-meteorological climate change studies. Journal of Hydrology, 549, 534-546.
  6. Dagbegnon, C., Djebou, S., & Singh, V.P. (2016). Impact of climate change on the hydrologic cycle and implications for society. Environment and Social Psychology, 1(1).
  7. Eslamian, S., Safavi, H.R., Gohari, A., Sajjadi, M., Raghibi, V., & Zareian, M.J. (2017). Climate change impacts on some hydrological variables in the Zayandeh-Rud River Basin, Iran. In: Reviving the dying giant, Springer, Cham, pp. 201-217.
  8. Goyal, R. K. (2004). Sensitivity of evapotranspiration to global warming: a case study of arid zone of Rajasthan (India). Agricultural water management, 69(1), 1-11.
  9. Gohari, A., & Zareian, M.J. (2017). Interbasin Transfers of Water: Zayandeh-Rud River Basin, Handbook of Drought and Water Scarcity, Taylor and Francis.
  10. Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horanyi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., & Simmons, A. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999-2049.
  11. Hoseeni, Z.S., Moghaddasi, M., & Paimozd, S. (2022). Accuracy assessment of ECMWF datasets in prediction of climate data and drought monitoring of Garechai basin of Markazi Province. Iranian Journal of Soil and Water Research, 53(4), 715-732. (In Persian).
  12. (2013). Climate Change 2013: The Physical Science Basis. Final Draft Report of Working Group I, Stockholm, Sweden
  13. Kiani Ghalehsard, S., Shahraki, J., Akbari, A., & Sardar Shahraki, A. (2021). Assessment of the impacts of climate change and variability on water resources and use, food security, and economic welfare in Iran. Environment, Development and Sustainability, 23(10), 14666-14682.
  14. Knoben, W.J., Freer, J.E., & Woods, R.A. (2019). Inherent benchmark or not? Comparing Nash- Sutcliffe and Kling- Gupta efficiency scores. Hydrology and Earth System Sciences, 23(10), 4323-4331.
  15. Mal, S., Singh, R.B., Huggel, C., & Grover, A. (2018). Introducing linkages between climate change, extreme events, and disaster risk reduction. In: Climate change, extreme events and disaster risk reduction. Springer, Cham.
  16. Okhravi, S., Eslamian, S., & Dalezios, N.R. (2019). Reducing water shortage crisis through rainwater reuse: lessons learned from ancient toward integrated technology. International Journal of Hydrology Science and Technology, 9(6), 587-602.
  17. Okhravi, S., Eslamian, S., Eslamian, F., & Tarkesh-Esfahani, S. (2014). Indigenous knowledge as a supportive tool for sustainable development and utilisation of rainwater harvesting systems. Journal of Flood Engineering, 5(1), 39-50.
  18. Patil, S.D., & Stieglitz, M. (2015). Comparing spatial and temporal transferability of hydrological model parameters. Journal of Hydrology, 525, 409-417.
  19. Solomon, S., Manning, M., Marquis, M., & Qin, D. (2007). Climate change 2007-the physical science basis: Working group I contribution to the fourth assessment report of the IPCC (Vol. 4). Cambridge university press
  20. Tebaldi, C., Debeire, K., Eyring, V., Fischer, E., Fyfe, J., Friedlingstein, P., Knutti, R., Lowe, J., O'Neill, B., Sanderson, B., & Van Vuuren, D. (2021). Climate model projections from the scenario model intercomparison project (ScenarioMIP) of CMIP6. Earth System Dynamics, 12(1), 253-293.
  21. Uppala, S.M., Kallberg, P.W., Simmons, A.J., Andrae, U., Bechtold, V.D.C., Fiorino, M., Gibson, J.K., Haseler, J., Hernandez, A., Kelly, G.A., & Li, X. (2005). The ERA‐40 re‐analysis. Quarterly Journal of the Royal Meteorological Society: A journal of the atmospheric sciences, Applied Meteorology and Physical Oceanography, 131(612), 2961-3012.
  22. Werndl, C. (2016). On defining climate and climate change. The British Journal for the Philosophy of Science, 67(2), 337-364.
  23. Woolf, D., & Wolf, J. (2013). Impacts of climate change on storms and waves. MCCIP Science Review.
  24. Zareian, M.J. (2021). Optimal water allocation at different levels of climate change to minimize water shortage in arid regions (Case Study: Zayandeh-Rud River Basin, Iran). Journal of Hydro-Environment Research, 35, 13-30.
  25. Zareian, M.J., Eslamian, S., & Safavi, H.R. (2015). A modified regionalization weighting approach for climate change impact assessment at watershed scale. Theoretical and Applied Climatology, 122(3), 497-516.
مدیریت آب و آبیاری
دوره 12، شماره 4
بهمن 1401
صفحه 783-797

فایل ها

هم رسانی

ارجاع به این مقاله

آمار