پیش‌نگری بارش‌های فرین در ایران بر اساس رویکرد همادی مدل‌های CMIP6 در آینده نزدیک (2050-2026) با وزن‌دهی مبتنی‌بر رتبه

نوع مقاله : مقاله پژوهشی

نویسندگان

پژوهشگاه هواشناسی و علوم جو، تهران، ایران.

چکیده

در این مطالعه پیش­نگری چهار شاخص بارش فرین (R95p، R95d، AEPI و R95pT) بر روی کشور ایران، با استفاده از دوره مرجع 2014-1990 بر اساس رویکرد مجموعه چندمدلی و روش وزن­دهی مبتنی‌بر رتبه با کاربست پنج مدل از مدل­های CMIP6 انجام شد. وزن هر مدل بسته به مهارت شبیه­سازی تاریخی آن محاسبه و سپس، گروه­های وزن­دار و بدون وزن برای پیش­نگری­های آینده استفاده شدند. نتایج بررسی مهارت نشان می­دهد که مدل MPI-ESM1-2-HR با MR_taylor برابر با 5/0 و با MR_IVS برابر با 6/0 به‌ترتیب دومین و اولین مدل مناسب در بین پنج مدل منتخب برای شبیه­سازی الگوهای فضایی و زمانی شاخص­های بارش فرین است. بنابراین تفکیک افقی مدل تنها عامل تعیین‌کننده مهارت مدل در شبیه­سازی نیست و بهبود در فرایندهای فیزیکی نیز مورد نیاز است. نتایج نشان می‌دهد احتمال این‌که کل بارش فرین (R95p) و شدت مطلق بارش فرین (AEPI) در منطقه مورد مطالعه، در دوره 2050-2026 تحت چهار سناریوی SSP1-2.6، SSP2-4.5، SSP3-7.0 و SSP5-8.5 بیش از صفر باشد، در کل کشور بزرگ‌تر از 5/0 است. با توجه به مقدار میانه نزدیک به صفر و یا حتی منفی شاخص فرین R95d، تقدم افزایش مقدار بارش فرین بر تعداد روزهای رخداد استنباط می­شود و این بارش­های فرین در تعداد روزهای کمتری رخ خواهند داد که خود اعلام خطری برای رخداد بارش­های سیل­آسا می­باشد. مقایسه بین میانگین‌های گروه وزن‌دار و بدون وزن نشان می‌دهد که عدم‌قطعیت پیش‌نگری احتمالی آینده در منطقه مورد مطالعه تقریباً همیشه پس از اعمال حالت وزن‌دهی به مدل­ها برای پیش‌نگری احتمالی آینده کاهش می‌یابد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Projection of extreme precipitation over Iran based on the ensemble approach of CMIP6 models in the near future (2026-2050) with rank-based weighting

نویسندگان [English]

  • Sakineh Khansalari
  • Seyedeh Atefeh Mohammadi
Atmospheric science and Meteorological Research Center, Tehran, Iran.
چکیده [English]

In this study, a future projection of four extreme precipitation indices consists of total extreme precipitation (R95p), extreme precipitation days (R95d), the fraction of total rainfall from events exceeding the extreme precipitation threshold (R95pT) and extreme precipitation intensity (AEPI) over Iran was carried out using the reference period of 1990-2014 based on the multi-model ensemble approach and a rank-based weighting method using five models from the CMIP6 models (MPI-ESM1-2-HR, EC-Earth3, EC-Earth3-Veg, GFDL-ESM4, and MRI-ESM2-0). The weight of each model was calculated depending on its historical simulation skill and then weighted and unweighted groups were used for future projections. In this study, the threshold of heavy precipitation was calculated using percentiles, according to the method of Zhai et al. (2005). For this purpose, at each grid point, annual rainfall data (daily rainfall greater than or equal to 1 mm) were sorted in ascending order to obtain the sequence of annual rainfall for each year from 1990 to 2014. Then, the mean 95th percentile for the 25-year precipitation sequence at each grid point was defined as the extreme precipitation threshold (Rwn95). It should be noted that the heavy rainfall threshold for observations and CMIP6 models was calculated separately from observation data of synoptic stations and from each CMIP6 model. Based on this extreme precipitation threshold, four extreme precipitation indices were calculated. The results of the spatial skill of the simulation show that the EC-Earth3 model with MR_taylor equal to 0.65 and with a horizontal resolution of 0.7 degrees has the best skill in simulating the spatial pattern of extreme precipitation indicators, and the MPI-ESM1-2-HR model with MR_taylor equal to 0.5 and with a horizontal resolution of 0.938 degrees is the second suitable model among the five selected models used for simulating the spatial pattern of extreme precipitation indices. Also, the results of the simulation time skill test show the superiority of MPI-ESM1-2-HR and GFDL-ESM4 models with MR_IVS respectively equal to 0.6 and 0.5 compared to other studied models. It is important to note that the horizontal resolution of the model is not the only factor that determines the skill of the model in simulating extreme precipitation indicators in the study area. Because the model with above-average weight (>0.2) is not a high-resolution model and improvement in physical processes which are also needed. The results show that the probability of increasing the total extreme precipitation (R95p) and extreme precipitation intensity (AEPI) in the study area in the period 2026-2050 under four scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 with values greater than 0.5 is almost certain. According to the median value close to zero or even negative of the extreme index R95d, it is inferred that the priority of the increase of the extreme amount of precipitation is over the number of days of occurrence, and these extreme precipitations will occur in fewer days, which is a warning for the flood risk. A comparison between the weighted and unweighted ensemble means shows that the uncertainty in the study area is almost always reduced after applying the weighted scheme to future probabilistic projections.

کلیدواژه‌ها [English]

  • Extreme precipitation
  • CMIP6 model
  • Scenario
  • Probabilistic projection
  • Rank-based weighting

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فیزیک زمین و فضا
دوره 49، شماره 3
تاریخ انتشار الکترونیکی: 24 آبان 1402
آبان 1402
صفحه 727-746

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