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چشم‌انداز بارش ایران در قرن 21 با به‌کارگیری مقیاسکاهی آماری برونداد مدل‌های منتخبCMIP6 توسط نرم‌افزار CMHyd | ||
| فیزیک زمین و فضا | ||
| مقاله 10، دوره 49، شماره 2، شهریور 1402، صفحه 431-449 اصل مقاله (3.23 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jesphys.2023.332410.1007436 | ||
| نویسندگان | ||
| ایمان بابائیان* ؛ راهله مدیریان؛ لیلی خزانه‌داری؛ مریم کریمیان؛ سعیده کوزه‌گران؛ منصوره کوهی؛ یاشار فلامرزی؛ شراره ملبوسی | ||
| پژوهشگاه هواشناسی و علوم جو، پژوهشکده اقلیم‌شناسی، مشهد، ایران. | ||
| چکیده | ||
| این پژوهش چشم‌انداز تغییرات محتمل بارش در 43 ایستگاه هواشناسی کشور را تا انتهای قرن حاضر ارائه می‌کند. برای این منظور، از داده‌های چهار مدل از نوع ESM به نام‌های MIROC6، FGOALS_g3، BCC-CSM2-MR و ACCESS-ESM1-5 از مجموعه مدل‌های سری CMIP6 استفاده شد. برونداد خام بارش توسط نرم‌افزار CMHyd مقیاسکاهی شد. دوره مشاهداتی 2014-1985 و دوره‌های آینده به‌صورت آینده نزدیک 2050-2026، آینده میانه 2075-2051 و آینده دور 2100-2076 در نظر گرفته شدند. تغییرات بارش در سطح معنی‌داری 05/0 بر مبنای سه سناریوی SSP1-2.6، SSP2-4.5 و SSP5-8.5 بررسی شد. نتایج نشان دادند که تغییرات بارش آینده در حدود 78 درصد از ایستگاه‌ها معنی‌دار نیستند و در 19 و 3 درصد ایستگاه‌ها به‌ترتیب افزایش و کاهش معنی‌داری دارند. بیشترین افزایش بارش در جنوب-جنوب‌شرق و بیشترین کاهش در زاگرس مرکزی رخ خواهد داد. میانگین بارش کشور در مقیاس سالانه 4/0 درصد (با دامنه عدم‌قطعیت 14 درصد) افزایش می‌‌یابد. پیش‌نگری در مقیاس فصلی نشانگر تغییرات در فصول بهار، تابستان، پاییز و زمستان به‌ترتیب به مقدار 2/15+، 11-، 6- و 5/3+ درصد می‌‌باشد. اگرچه برای فصل بهار افزایش بارش 2/15 درصدی پیش‌نگری شده است، اما دامنه تغییرات 9/81 درصدی نشان از بی اعتمادی به بارش‌های آینده این فصل است. | ||
| کلیدواژه‌ها | ||
| تغییر اقلیم؛ بارش؛ ایران؛ CMIP6؛ CMHyd | ||
| عنوان مقاله [English] | ||
| Projection of Iran’s precipitation in 21st Century using downscaling of selected CMIP6 Models by CMHyd | ||
| نویسندگان [English] | ||
| Iman Babaeian؛ Raheleh Modirian؛ Leili Khazanedari؛ Maryam Karimian؛ Saeedeh Kouzegaran؛ Mansoureh Kouhi؛ Yashar Falamarzi؛ Sharareh Malbusi | ||
| Atmospheric Science and Meteorological Research Center, Climate Research Institute, Mashhad, Iran. | ||
| چکیده [English] | ||
| Geographical location of Iran in arid and semi-arid regions has strongly affected its food security, water resources and weather- and climate-related extreme events due to climate change. Global warming, on average, increases precipitation on Earth by increasing evaporation from the oceanic surface that enters the atmosphere, but the response of the West Asian region to global warming is generally a decrease in precipitation. Some studies have confirmed a decrease in the average precipitatiomn of Iran with an increase of precipitation in the south and southeast of the country. They also confirmed that the largest decrease in the precipitation of Iran occurs in the Zagros region. To this end, in this study, an overview of possible changes in precipitation trends in 43 stations of Iran is presented. To achieve the goals of the paper, the data of four Earth System Models from CMIP6 models, including MIROC6, FGOALS_g3, BCC-CSM2-MR and ACCESS-ESM1-5 were used. The precipitation output of the selected models has been statistically downscaled using CMHyd software for 43 synoptic stations over Iran. Observational period of 1985-2014 and the next three 25-year periods as the near future 2020-2026, the mid future 2075-2051 and the far future 2100-2076 were considered as study periods. Future changes in precipitation under three Shared Socio-economic Pathways (SSP) scenarios of SSP1-2.6, SSP2-4.5 and SSP5-8.5 were estimated. During the downscaling process, the Classius-Clapiron (CC) rate was applied to increase the trend of heavy rainfall. The air holding capacity is controlled by the Clausius-Clapiron relation, which is the water vapour holding capacity of the air at 7% /oC, the so-called Clausius–Clapeyron (CC) rate (bellow equation). Since the maximum temperature increase in the country was considered 10 degrees Celsius based on the worst possible scenario (SSP5-8.5), so all severe precipitation events were projected that were above the threshold of 130% compared to the normal period that were reduced to 130% of its normal amount. The results showed that future rainfall changes were not significant in about 78% of the stations. The increase and decrease of rainfall were significant in 19% and 3% of the stations, respectively. The largest increase in precipitation will occur in the south-southeast and the largest decrease will occur in the central Zagros area. The average rainfall of the country will increase by 0.4% annually (with a uncertainty range of 14%). On a seasonal scale, precipitation changes in spring, summer, autumn and winter were estimated to be + 15.2, -11, -6 and +3.5, respectively. Although a 15.2% increase in precipitation is projected for spring, the range of uncertainty with 81.9% indicates a lack of confidence in future precipitation estimates of spring season. The maximum uncertainty is related to the spring rains, which shows that the rains of this season are becoming more and more distrustful. Under warming conditions, more spring rainfall increases will occur. In summer, in the near future, summer rains tend to increase and then decrease with more level of Global Warming. After spring, most uncertainty is related to summer precipitation. The autumn precipitation tends to be less than normal with lower amount of uncertainty, although its decrease is not significant. The small range of the autumn rainfall uncertainty chart indicates the agreement of different model-scenarios in projection of autumn rainfall. Winter rainfall projection does not have significant uncertainty and almost all model-scenarios agree on the relatively low trend of rainfall increase. In this season, under higher amount of Global Warming, the precipitation increases and the amplitude of uncertainty also increases. Among the next three periods, the lowest and highest range of uncertainty in the annual changes of precipitation in the first and last decade of the present century will occur with the range of changes of 14 and 29.1%, respectively. The lowest and highest amplitude of uncertainty on the seasonal scale with 3.6 and 81.9 are estimated in winter (near future) and spring (far future), respectively. This situation indicates that in the future most precipitation fluctuations will occur in the spring. | ||
| کلیدواژه‌ها [English] | ||
| Climate change, Precipitation, Iran, CMIP6, CMHyd | ||
| مراجع | ||
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بابائیان، ا.؛ نجفی نیک، ز.؛ زابل عباسی، ف.؛ حبیبی نوخندان، م.؛ ادب، ح. و ملبوسی، ش. (1388). ارزیابی تغییر اقلیم کشور در دوره 2010-2039 میلادی با استفاده از ریزمقیاس نمایی داده‌های مدل گردش عمومی جوECHOG . مجله جغرافیا و توسعه، 7(16)، 135-152.
جباری، ا. (1392). روش‌‌های آماری در علوم محیطی و جغرافیایی، انتشارات دانشگاه رازی کرمانشاه، چاپ سوم، 294 صفحه.
کوهی، م. و پاکدامن، م. (1400). ارزیابی عملکرد مدل‌های CMIP5 در تحلیل فراوانی دو متغیره مفصل- مبنای ویژگی‌های خشکسالی در بخش جنوبی حوضه آبریز کارون. مجله فیزیک زمین و فضا، 48(1)، 153-172.
لطفی، ی.؛ مفتاح هلقی، م. و قربانی، خ. (1399). بررسی عدم‌قطعیت پیش‌یابی‌های مدل‌های گردش کلی جو: مطالعه موردی: ایستگاه هاشم‌آباد گرگان. مجله هواشناسی کشاورزی، 8 (1)، 75-79.
Almazroui, M., Saeed, S., Saeed, F., Islam, M. N., & Ismail, M. (2020). Projections of precipitation and temperature over the South Asian countries in CMIP6. J. Earth Systems and Environment, 4(2), 297-320. Babaeian, I., Rahmatinia, A.E., Entezari, A., Baaghideh, M., Aval, M.B., & Habibi, M. (2021). Future Projection of Drought Vulnerability over Northeast Provinces of Iran during 2021–2100. J. Atmosphere, 12, 1704. Baker, N., & Huang, H. (2013). A Comparative Study of Precipitation and Evaporation between CMIP3 and CMIP5 Climate Model Ensembles in Semiarid Regions. J. Climate, 27, 3731-3749. Blázquez, J., & Nuñez, M.N. (2013). Analysis of Uncertainties in Future Climate Projections for South America: Comparison of WCRP-CMIP3 and WCRP-CMIP5 Models. J. Climate Dynamics, 41, 1039-1056. Diaz-Nieto, J., & Wilby, R. L. (2005). A comparison of statistical downscaling and climate change factor methods: Impacts on low flows in the River Thames, United Kingdom. J. Climate Change, 69(2–3), 245–268. Drobinski, P., Alonzo, B., Bastin, S., Da Silva, N., & Muller, C.J. (2016). Scaling of precipitation extremes with temperature in the French Mediterranean region: what explains the hook shape?. J. Geophys Res., 121(7), 3100-3119. Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., & Taylor, K. E. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. J. Geosci. Model Dev., 9, 1937–1958. Grose, M. R., Narsey, S., Delage, F. P., Dowdy, A. J., Bador, M., Boschat, G., & Power, S. (2020). Insights from CMIP6 for Australia's future climate. J. Earth's Future, 8(5), e2019EF001469. Hardwick, J., Westra, R. S., & Sharma, A. (2010). Observed relationships between extreme sub-daily precipitation, surface temperature, and relative humidity, J. Geophys Res Lett, 37, L22805. IPCC. (2014). Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp. IPCC. (2021). 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, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, In press. Jiang, J., Zhou, T., Chen, X., & Zhang, L. (2020). Future changes in precipitation over Central Asia based on CMIP6 projections. J. Environmental Research Letters, 15(5), 054009. Leander, R., & Buishand, T.A. (2007). Resampling of regional climate model output for the simulation of extreme river flows. J. Hydrol., 332, 487–496. Martinkova, M., & Kysely, J. (2020). Overview of Observed Clausius-Clapeyron Scaling of Extreme Precipitation in Midlatitudes. J. Atmosphere, 11, 786. Matthes, K., Funke, B., Andersson, M. E., Barnard, L., Beer, J., Charbonneau, P., & Versick, S. (2017). Solar forcing for CMIP6 (v3.2). J. Geoscientific Model Development, 10, 2247-2302. Mendez, M., Maathuis, B., Hein-Griggs, D., & Alvarado-Gamboa, L.-F. (2020). Performance Evaluation of Bias Correction Methods for Climate Change Monthly Precipitation Projections over Costa Rica. J. Water, 12, 482. Molnar, P., Fatichi, S., Gaál, L., Szolgay, J., & Burlando, P. (2015). Storm type effects on super Clausius–Clapeyron scaling of intense rainstorm properties with air temperature. J. Hydrol. Earth Syst. Sci., 19, 1753–1766. O’Neill, B. C., Timothy R. C., Kristie, E., Paula, A., Harrison, E. K., & Kasper Kok, E. K. (2020). Achievements and needs for the climate change scenario framework. J. Nature climate change, 12, 1074-1084. Piani, C., Weedon, G., Best, M., Gomes, S., Viterbo, P., Hagemann, S., & Haerter, J. (2010). Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models. J. Hydrol., 395, 199–215. Rathjens, H., Bieger, K., Srinivasan, R., Chaubey, I., & Arnold, J. G. (2016). CMhyd User Manual Documentation for preparing simulated climate change data for hydrologic impact studies, User manual, 1-17 Schmidli, J., Frei, C., & Vidale, P.L. (2006). Downscaling from gcm precipitation: A benchmark for dynamical and statistical downscaling methods. Int. J. Climatol., 26, 679–689. Taylor, K. E. (2001). Summarizing multiple aspects of model performance in a single diagram. J. Geophysical Research: Atmospheres, 106(D7), 7183-7192. Zamani, Y., Monfared, S. A. H., & Hamidianpour, M. (2020). A comparison of CMIP6 and CMIP5 projections for precipitation to observational data: the case of Northeastern Iran. J. Theoretical and Applied Climatology, 142(3), 1613-1623. | ||
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