پیش‌نگری دمای ایران در آینده نزدیک (2040-2021) بر اساس رویکرد همادی چند مدلی CMIP6

نوع مقاله : مقاله کامل

نویسندگان

1 استادیار آب‏وهواشناسی، گروه جغرافیا، دانشگاه فردوسی مشهد، مشهد

2 پژوهشگر پسادکتری آب و هواشناسی، گروه جغرافیا، دانشگاه فردوسی مشهد، مشهد

چکیده

هدف از این پژوهش پیش‏نگری دمای ایران بر اساس رویکرد همادی چندمدلی (MME) با کاربست مدل‏های CMIP6 است. برونداد پنج مدل برای دورة تاریخی (1995-2014) و آیندة نزدیک (2012-2040) تحت دو سناریوی بدبینانه (SSP3-7.0) و خیلی بدبینانه (SSP5-8.5) بهکار گرفته شد. برای درستیسنجی مدل‏ها از سنجه‏های آماری MBE و NRMSE و داده‏های دمای روزانة 120 ایستگاه همدید استفاده شد. از روش‏های تغییر عامل دلتا (DCF) و میانگین وزنی مستقل (IWM) به‏ترتیب برای تصحیح اریبی و ایجاد یک مدل همادی استفاده شد. برای آشکارسازی تنش‏های گرمایی از شاخص طول مدت گرما (WSDI) استفاده شد. نتایج نشان داد که تصحیح اریبی و همادیکردن مدل‏ها با روش IWM پیش‏نگری دمای سالانه را بهویژه در مناطق خشک و نیمه‏خشک نسبت به مناطق مرطوب شمالی بهبود می‏بخشد. نتایج کلی نشان داد که بر اساس سناریوهای SSP3-7.0 و SS5-8.5، میانگین دمای سالانة کشور بهترتیب 13/1 و 26/1 درجة سلسیوس افزایش خواهد یافت. کمینة بی‏هنجاری در جنوب شرق و بیشینة آن در مناطق شمال غربی و مرکزی ایران اتفاق می‏افتد. شاخص طول مدت گرما نیز بی‏هنجاری مثبت‏ را برای آینده نشان می‏دهد. بر اساس سناریوی SSP5-8.5، بیشینة این شاخص در سواحل جنوبی ایران بی‏هنجاری مثبت 5/74 روز را نشان می‏دهد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Projected changes in temperature over Iran by 2040 based on CMIP6 multi-model ensemble

نویسندگان [English]

  • Azar zarrin 1
  • Abbasali Dadashi-Roudbari 2
1 Department of Geography, Ferdowsi University of Mashhad, Mashhad, Iran
2 Department of Geography, Ferdowsi University of Mashhad, Mashhad, Iran
چکیده [English]

Introduction
Climate change is one of the major threats of the 21st century. One of the major data sources for studying climate change is the general circulation model (GCM), which is widely used to assess and project past and future climate change and to manage regional risk hazards. GCMs can make significant assessments of temperature and precipitation. The application of individual models has high uncertainty. Therefore, in this study, applying an ensemble approach has been considered to project future temperature changes in Iran. The purpose of this study is to create a multi-model ensemble (MME) with bias-corrected CMIP6 models to project the temperature of Iran and the warm spell duration index (WSDI) in the near future (2021-2040).

Materials and methods
To evaluate the CMIP6 models in simulating the average annual temperature for the period of 1995 to 2014 (these 20 years were considered as the historical period), we used 120 synoptic stations in Iran. In this study, five CMIP6 models (GFDL-ESM4, MPI-ESM1-2-HR, IPSL-CM6A-LR, MRI-ESM2-0, UKESM1-0-LL) with a horizontal resolution of 0.5 degrees were used. Using intermediate (SSP3-7.0) and worst-case (SSP5-8.5) scenarios, annual temperature and heat stress were projected under climate change conditions for the near future (2040-2021).
In this study, normalized root mean square error (NRMSE) and mean bias error (MBE) were used to validate the performance of the models. To correct the bias of CMIP6 models, Delta change Factor (DCF) method with 120 synoptic station was used. Then, independence weighted mean (IWM), were used to ensemble five models.
In this study, in addition to temperature anomalies, warm spell duration index (WSDI) was also projected by 2040. The warm spell duration index is the number of days per year with at least 6 consecutive days when TX > TX90th, where TX is the daily maximum temperature and TX90th is the calendar day 90th percentile.

Results and discussion
Based on the Direct Model Output (DMO) in the climates of Sea of Oman and Caspian Sea coasts, all five CMIP6 models underestimated the average annual temperature (Chabahar and Rasht stations). However, CMIP6 models in other climates of Iran overestimated the average annual temperature. The mean bias of 1.00, 0.962, 0.983, 1.001, 0.936 degrees Celsius is compute for GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0 and UKESM1-0-LL, respectively. Therefore, UKESM1-0-LL and IPSL-CM6A-LR models are efficient models among the studied models for estimating Iran temperature, respectively. The temperature bias values fluctuate from -2.27 to 2.70 degrees Celsius in Iran. The average annual temperature based on MME-CMIP6 output fluctuates between 6.27 and 27.7 degrees Celsius. The coasts of the Persian Gulf and the Sea of Oman showed the maximum temperature and the northwestern regions of Iran showed the minimum temperature. The warm spell duration index varies between 3.48 to 12.5 days during the historical period. The temperature projected for both SSP3-7.0 and SSP5-8.5 scenarios show a further increase in the interior, northwest, north, and northeast of Iran. The average annual anomaly in Iran is estimated to be 1.13 and 1.26 degrees Celsius, based on SSP3-7.0 and SSP5-8.5 scenarios, respectively for the near future during the years 2021-2040.

According to the SSP3-7.0 scenario, the minimum temperature anomaly of the country is 0.765 and the maximum is 1.227 degrees Celsius. Also, for the SSP5-8.5 scenario, temperature anomalies for minimum and maximum annual temperature are estimated to be 0.785 and 1.380 degrees Celsius, respectively. Projecting the time-series changes of the temperature in eight representative stations of Iran under the scenarios of SSP3-7.0 and SSP5-8.5 showed that the amount of warm spell duration varies in different regions of Iran. The anomaly of the warm spell duration index (WSDI) in Iran according to the SSP3-7.0 scenario will increase by at least 13.1 and at most 58.6 days. Also, the results related to the worst-case scenario (SSP5-8.5) have shown a minimum increase of 17.4 days and a maximum of 74.5 days in Iran.

Conclusion
Evaluation of direct output of five models from the series of Coupled Model Intercomparison project (GFDL-ESM4, MPI-ESM1-2-HR, IPSL-CM6A-LR, MRI-ESM2-0, UKESM1-0-LL) In the historical period (1995-2014) showed that although some models performed better in some climate regions of the country, the use of the direct output of individual models will increase the uncertainty in the results. For this purpose, using the independent weighted average (IWM) method, the output of the models was improved. The evaluation of the output of the ensemble model emphasizes this result.
In general, the results of model validation showed that the output of CMIP6 models has higher performance for arid and semi-arid regions of Iran, whether before or after bias-correction. However, using either individual or bias-corrected and ensemble models in very humid climates (The northern parts of Iran) should be used with more caution because even with the correction of bias and doing ensemble, the positive bias is more than 2 degrees Celsius for the mentioned areas.
The projection by using MME-CMIP6 show the annual temperature increase in near future (2021-2040). The average annual temperature anomaly will increase by 1.13 and 1.26 degrees Celsius, under the scenarios of SSP3-7.0 and SSP5-8.5 respectively, during the near future (2021-2040). The results indicate the very important role of unevenness in the heterogeneous distribution of temperature in increasing the average annual temperature of the country in the next two decades. The minimum temperature anomaly is projected in the southeast of Iran and the maximum in the northwestern and central regions.
Projecting the warm spell duration index (WSDI) indicates an increasing anomaly of that in the country. The main hotspot of WSDI is on the southern coast of Iran, especially in the Persian Gulf area, which according to the results of the worst-case scenario will increase by 74.5 days by 2040. Analysis of changes in the average annual temperature of MME-CMIP6 shows that Iran will warm up more rapidly in the near future (2021-2040) than in the historical period (1995-2014). More warming, especially in high and snowy areas, will affect natural ecosystems and limit future access to water resources. Most of the interior, east and south of Iran has arid and semi-arid climate and a sharp rise in temperature under the worst-case scenario (SSP5-8.5) leads to environmental degradation and intensification of drought on the one hand and increased desertification on the other.

Keywords
Temperature projection, CMIP6 models, ensemble model, WSDI index, Iran.

کلیدواژه‌ها [English]

  • Temperature projection
  • CMIP6 Models
  • Ensemble model
  • WSDI index
  • Iran
احمدی، محمود؛ داداشی رودباری، عباسعلی؛ اکبری ازیرانی، طیبه و کرمی، جمال (1398). کارایی مدل HadGEM2-ES در ارزیابی نابهنجاری فصلی دمای ایران تحت سناریوهای واداشت تابشی، فیزیک زمین و فضا، 45(3): 625-644.
آزادی، مجید؛ واشانی، سعید و حجام، سهراب (1391). پیش‏بینی احتمالاتی بارش با استفاده از پسپردازش (post processing) برونداد یک سامانة همادی، مجلة فیزیک زمین و فضا، 38(3): 203-216.
زرین، آذر و داداشی رودباری، عباسعلی (1399). پیش‏نگری چشم‏انداز بلندمدت دمای آیندة ایران مبتنی بر برونداد پروژة مقایسة مدل‏های جفت‏شدة مرحلةششم (CMIP6)، فیزیک زمین و فضا، 46(3): 583-602.
زرین، آذر؛ داداشی رودباری، عباسعلی و صالحآبادی، نرگس (1400). بررسی بیهنجاری و روند دمای ایران در پهنه‏های مختلف اقلیمی با استفاده از پروژة مقایسة مدل‏های جفتشدة مرحلةششم (CMIP6)، مجلة ژئوفیزیک ایران، 15(1): 1-13.
علیزاده چوبری، امید و نجفی، محمدسعید (1396). روند تغییرات دمای هوا و بارش در مناطق مختلف ایران، فیزیک زمین و فضا، 43(3): 569-584.
علیزاده، احمد؛ بابائیان، ایمان؛ نوری، حمید و نجاتیان، محمدعلی (1399). بررسی اثر تغییر اقلیم بر کیفیت انگور بیدانه سفید (مطالعة موردی: ایستگاه هواشناسی کشاورزی گلمکان)، پژوهشهای اقلیمشناسی، 1399(43): 109-126.
مسعودیان، سیدابوالفضل (1390). آب‏وهوای ایران، مشهد: شریعة توس.
نجفی، حسین؛ مساح بوانی، علیرضا؛ ایران‏نژاد، پرویز و رابرتسون، اندرو (1396). کاربست مدل‏های همادی امریکای شمالی در پیش‏بینی فصلی بارش گسترة ایران، تحقیقات منابع آب ایران، 13(4): 28-38.
Bai, H.; Xiao, D., .; Wang, B.; Liu, D. L.; Feng, P. and Tang, J. (2020). Multi‐model ensemble of CMIP6 projections for future extreme climate stress on wheat in the North China Plain, International Journal of Climatology, 40: 21-39.
Becker, E.; Kirtman, B. P. and Pegion, K. (2020). Evolution of the North American Multi‐Model Ensemble, Geophysical Research Letters, 47: 35-53.
Bishop, C. H. and Abramowitz, G. (2013). Climate model dependence and the replicate Earth paradigm, Climate dynamics, 41: 885-900.
Dai, A. (2011). Drought under global warming: a review, Wiley Interdisciplinary Reviews: Climate Change, 2: 45-65.
Fallah-Ghalhari, G., .; Shakeri, F., & and Dadashi-Roudbari, A. (2019). Impacts of climate changes on the maximum and minimum temperature in Iran. Theoretical and Applied Climatology, 138(3-4), ): 1539-1562.
Gidden, M. J., .; Riahi, K., .; Smith, S. J., .; Fujimori, S., .; Luderer, G., .; Kriegler, E., ... & and Takahashi, K. (2019). Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century. Geoscientific model development, 12(4), ): 1443-1475.
Grose, M.R.; Narsey, S.; Delage, F.P.; Dowdy, A.J.; Bador, M.; Boschat, G.; Chung, C.; Kajtar, J.B.; Rauniyar, S.; Freund, M.B. and Lyu, K. (2020). Insights from CMIP6 for Australia's future climate, Earth's Future, 8(5): 12-24.
Gupta, H. V.; Kling, H.; Yilmaz, K. K. and Martinez, G. F. (2009). Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, Journal of hydrology, 377(1-2): 80-91.
Hawkins, E. and Sutton, R. (2009). The potential to narrow uncertainty in regional climate predictions, Bulletin of the American Meteorological Society, 90(8): 1095-1108.
He, S., .; Yang, J., .; Bao, Q., .; Wang, L., & and Wang, B. (2019). Fidelity of the observational/reanalysis datasets and global climate models in representation of extreme precipitation in East China. Journal of Climate, 32(1), ): 195-212.
Im, E. S., .; Pal, J. S., & and Eltahir, E. A. (2017). Deadly heat waves projected in the densely populated agricultural regions of South Asia. Science advances, 3(8), ): e1603322.
IPCC (2013). Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge and New York.
Kang, S., .; Zhang, Q., .; Qian, Y., .; Ji, Z., .; Li, C., .; Cong, Z., ... & and Qin, D. (2019). Linking atmospheric pollution to cryospheric change in the Third Pole region: current progress and future prospects. National Science Review, 6(4), ): 796-809.
Khan, A. J., .; Koch, M., & and Tahir, A. A. (2020). Impacts of Climate Change on the Water Availability, Seasonality and Extremes in the Upper Indus Basin (UIB). Sustainability, 12(4), ): 1283.
Kim, Y. H., .; Min, S. K., .; Zhang, X., .; Sillmann, J., & and Sandstad, M. (2020). Evaluation of the CMIP6 multi-model ensemble for climate extreme indices. Weather and Climate Extremes, 29, : 100269.
Knoben, W. J., Freer, J. E., and 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.
Knoben, W. J.; Freer, J. E. and 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.
Lee, J. Y. and Wang, B. (2014). Future change of global monsoon in the CMIP5. Climate Dynamics, 42(1-2): 101-119.
Lorenz, R.; Herger, N.; Sedláček, J.; Eyring, V.; Fischer, E. M. and Knutti, R. (2018). Prospects and caveats of weighting climate models for summer maximum temperature projections over North America, Journal of Geophysical Research: Atmospheres, 123(9): 4509-4526.
Lu, C., .; Sun, Y., & and Zhang, X. (2018). Multimodel detection and attribution of changes in warm and cold spell durations. Environmental Research Letters, 13(7), ): 074013.
Marengo, J. A.; Rusticucci, M.; Penalba, O. and Renom, M. (2010). An intercomparison of observed and simulated extreme rainfall and temperature events during the last half of the twentieth century: part 2: historical trends, Climatic Change, 98(3-4): 509-529.
Moss, R.H.; Edmonds, J.A.; Hibbard, K.A.; Manning, M.R.; Rose, S.K.; Van Vuuren, D.P.; Carter, T.R.; Emori, S.; Kainuma, M.; Kram, T. and Meehl, G.A. (2010). The next generation of scenarios for climate change research and assessment, Nature, 463(7282): 747-756.
Pathak, R.; Sahany, S.; Mishra, S. K. and Dash, S. K. (2019). Precipitation Biases in CMIP5 Models over the South Asian Region, Scientific reports, 9(1): 1-13.
Rehman, N.; Adnan, M. and Ali, S. (2018). Assessment of CMIP5 climate models over South Asia and climate change projections over Pakistan under representative concentration pathways, International Journal of Global Warming, 16(4): 381-415.
Riahi, K., .; Van Vuuren, D. P., .; Kriegler, E., .; Edmonds, J., .; O’neill, B. C., .; Fujimori, S., ... & and Tavoni, M. (2017). The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Global Environmental Change, 42, : 153-168.
Schär, C. (2016). Climate extremes: The worst heat waves to come. Nature Climate Change, 6(2), ): 128-129.
Swart, N. C., .; Cole, J. N., .; Kharin, V. V., .; Lazare, M., .; Scinocca, J. F., .; Gillett, N. P.,  ... & and Winter, B. (2019). The canadian earth system model version 5 (CanESM5. 0.3). Geoscientific Model Development, 12(11), ): 4823-4873.
Tatebe, H., .; Ogura, T., .; Nitta, T., .; Komuro, Y., .; Ogochi, K., .; Takemura, T., ... & and Kimoto, M. (2019). Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6. Geoscientific Model Development, 12(7), ): 2727-2765.
Vogel, M. M., .; Hauser, M., & and Seneviratne, S. I. (2020). Projected changes in hot, dry and wet extreme events’ clusters in CMIP6 multi-model ensemble. Environmental Research Letters, 15(9),: 094021.
Yan, Y., .; Lu, R. and Li, C. (2019). Relationship between the future projections of Sahel rainfall and the simulation biases of present South Asian and Western North Pacific rainfall in summer, Journal of Climate, 32(4): 1327-1343.
Yang, X., .; Wood, E. F., .; Sheffield, J., .; Ren, L., .; Zhang, M., . & and Wang, Y. (2018). Bias correction of historical and future simulations of precipitation and temperature for China from CMIP5 models. Journal of Hydrometeorology, 19(3), ): 609-623.
Yoo, J. H. and Kang, I. S. (2005). Theoretical examination of a multi‐model composite for seasonal predictionm Geophysical research letters, 32(18): 14-27.
You, Q., .; Wu, F., .; Wang, H., .; Jiang, Z., .; Pepin, N., & and Kang, S. (2020). Projected changes in snow water equivalent over the Tibetan Plateau under global warming of 1.5° and 2° C. Journal of Climate, 33(12), ): 5141-5154.
Yu, H.; Wei, Y.; Zhang, Q.; Liu, X.; Huang, J.; Feng, T. and Zhang, M. (2020). Multi‐model assessment of global temperature variability on different time scales, International Journal of Climatology, 40(1): 273-291.
Zheng, X. T.; Hui, C. and Yeh, S. W. (2018). Response of ENSO amplitude to global warming in CESM large ensemble: uncertainty due to internal variability, Climate Dynamics, 50(11-12): 4019-4035.
Zhu, Y. Y., . and& Yang, S. (2020). Evaluation of CMIP6 for historical temperature and precipitation over the Tibetan Plateau and its comparison with CMIP5. Advances in Climate Change Research, 11(3), ): 239-251.