پیش‌بینی تغییر اقلیم تهران و یزد در آینده تحت سناریوهای RCP و توسط مدل LARS-WG
محورهای موضوعی :
آب و محیط زیست
رضا کاظمی
1
,
محمد رضا خزائی
2
1 - کارشناس ارشد، گروه مدیریت ساخت و آب، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران.
2 - استادیار، گروه مهندسی عمران، دانشگاه پیام نور، ایران. *(مسوول مکاتبات)
تاریخ دریافت : 1398/07/25
تاریخ پذیرش : 1398/12/05
تاریخ انتشار : 1401/06/01
کلید واژه:
تغییر اقلیم,
RCPs,
.LARS-WG,
عدم قطعیت,
ریزمقیاس نمایی,
چکیده مقاله :
زمینه و هدف: تغییر اقلیم موجب تغییر متغیرهای اقلیمی در آینده می شود. برای پیشگیری از اثرات سوء تغییر اقلیم، لازم است متغیرهای اقلیمی برای آینده پیش بینی شوند. هدف این تحقیق شبیه سازی صحیح بارش و دمای روزانه تهران و یزد، با اقلیم خشک، در دوره 2065-2036 تحت اثر تغییر اقلیم و با درنظر گرفتن عدم قطعیت ها است.روش بررسی: خروجی های مدل CanESM2 تحت سناریوهای RCPs توسط LARS-WG ریزمقیاس شد. در ریزمقیاس نمائی، ویژگی های مختلف سناریوهای بزرگ مقیاس به سناریوهای ریزمقیاس شده انتقال یافت. در اغلب مطالعات پیشین تنها تغییرات میانگین ها در نظر گرفته شده است. عدم قطعیت های سناریوهای انتشار و نوسانات اقلیمی با به کارگیری سه سناریوی انتشار و تولید 100 سری 30 ساله ی متغیرها برای هر سناریو بررسی شد. لذا دامنه وسیعی از حالات محتمل آینده پیش بینی شد و نتایج مطمئن تری به دست آمد.یافته ها: در آینده دمای تهران و یزد در اغلب ماه های سال افزایش می یابد، اما در بعضی از ماه ها نیز کاهش می یابد. به‌عنوان نمونه میانگین دمای حداکثر ماه آوریل (فروردین) در تهران بین 1/6 تا 9/6 و در یزد بین 1/7 تا 2/8 درجه سانتیگراد افزایش می یابد، اما در ماه سپتامبر (شهریور) دمای حداکثر در تهران تا 4/2 و در یزد تا 7/0 درجه کاهش می یابد. بارش سالانه در آینده در تهران بین 20% تا 40% و در یزد بین 43% تا 49% افزایش می یابد. اما تغییرات در ماه های مختلف متفاوت است.بحث و نتیجه گیری: آب و هوای تهران و یزد در آینده می تواند به مقدار زیادی تغییر کند. لذا اتخاذ تدابیر سازگاری با تغییر اقلیم ضروری است.
چکیده انگلیسی:
Background and Objective: Climate change will change climate variables in the future. In order to reduce the negative effects of climate, it is required to project future climate variables. The purpose of this study is to simulate most realistic daily rainfall and temperature series for Tehran and Yazd, with arid climatic, in period of 2036-2065 with considering uncertainty.Material and Methodology: the outputs of the CANESM2 under RCP scenarios were downscaled using LARS-WG. In downscaling, various characteristics of the large-scale scenarios were transferred to the downscaled scenarios. In most of the previous studies, only changes in averages have been considered. The uncertainties of emission scenarios and climatic variabilities were investigated using three emission scenarios and generating 100 series of 30-year variables for each scenario. So, a wide range of probable future scenarios is predicted and more reliable results are obtained.Findings: The results show in the future, for both Tehran and Yazd, temperature will rise in most months of the year, while in some months it will decrease. Average maximum temperature in April will increase between 6.1 to 9.9 ºC in Tehran, and between 7.1 to 8.2 in Yazd, while it will decreases in September by 2.4 degrees in Tehran, and by 0.7 degrees in Yazd. Future annual rainfall will increase between 20% to 40% in Tehran and between 43% to 49% in Yazd. However, changes vary in different months.Discussion and Conclusion: Climate regime of both Tehran and Yazd can considerably change in the future. Therefore, adaptation strategies with climate change are necessary.
منابع و مأخذ:
IPCC,2001. Climate change (2001), Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the third assessment report of the Intergovernmental Panel on Climate Change. UK: Cambridge University Press.
Khazaei, M.R., Bayazidi, M., & Sharafati, A.,2017. Climate change impact on annual precipitation and temperature of Zanjan province with uncertainties investigation. Ecohydrology, 4(3) ,pp. 847-860. (In Persian)
Khazaei, M.R. Ahmadi, S. Saghafian, B. and Zahabiyoun, B., 2013. A new daily weather generator to preserve extremes and low-frequency variability. Climatic Change, Vol. 119, pp. 631–645.
Utset, A., Martinez-cob, A., Farre, I., & Cavero, J.2006. Simulating the effects of extreme dry and wet years on the water use of flooding-irrigated maize in a Mediterranean landplane. Agricultural Water Management, Vol. 85 , pp. 77-84.
Dibike, Y.B., & Coulibaly, P.,2005. Hydrologic impact of climate change in the Saguenay watershed: comparison of downscaling methods and hydrologic models. Journal of Hydrology, 307 , pp. 145-163.
Moafi Madani, F., Mousavi Bayagi, , & Ansari, H., 2012. Prediction of drought in the Khorasan Razavi province during 2011-2030 by using statistical downscaling of HADCM3 model output. Geography and environmental hazards, Vol. 1(3) , pp. 21-37. (In Persian)
Agarwal, A., Babel, M.S., Masky, S., Shrestha, S., Kawasaki, A., & Tripathi, N.K., 2015. Analysis of temperature projection in the Koshi River Basin, Nepal. International Journal of Climatology, Vol. 36 (1) , pp. 266-279.
Hejazizadeh, Z., Hosseini, S.M., & Karbalaee Dorei, A., 2015. The Simulation of Climate change in Semnan province with scenarios of atmospheric general circulation model (HADCM3). Geography and environmental hazards, Vol. 4(15) , pp. 1-24. (In Persian)
Goodarzi, E., Massah Bavani, A.R., Dastorani, M.T., & Talebi, A.,2014. Evaluating effect of downscaling methods; change-factor and LARS-WG on surface runoff (A case study of Azam-Harat River basin, Iran). Desert, 19 , pp. 99-109.
Zarghami, , Fotookian, M.R., Safari, N., and Aslanzadeh, A.,2016. Reservior operation using system dynamics under climate change impacts: a case study of Yamchi reservoir, Iran. Saudi Society for Geosciences, 9:678.
Khoorni, A., & Jamali, Z.,2016. Effects of climate change on drought duration and severity in arid and semi-arid stations (Bandarabbass and Shahrekord), based on HADCM3 model. Geography and Planning, 20(57) , pp. 131-115. (In Persian)
Shahabul Alam, M.D., & Elshorbagy, A.,2016. Quantification of the climate change-induced variation Intensity-Duration-Frequency curves in Canadian Paris. Journal of Hydrology, Vol. 527 , pp. 990-1005.
Mohammadlou, M., Haghizadeh, A., Zeinivand, H., & Tahmasebipour, N.,2014. Evaluation of Climate Change Impacts on Runoff Changes Trend in Barandoezchay Watershed in West Azerbaijan Province Using General Circulation Models (GCM). Ecohydrology, Vol. 1(1) , pp. 25-34. (In Persian)
Sobhani, B., & Goldust, A.,2018. Prediction of the Starting and Ending of Freezing Periods of Ardabil Province by Using HADCM3 Climatic Model. Geographical Researches, Vol. 33(2) ,pp.191-205. (In Persian)
Semenov, M.A., & Stratonovitch, P.,2010. Use of multi-model ensembles from global climate model for assessment of climate change impacts. Climate Research, Vol. 41 , pp. 1-14.
Semenov, M.A.,2007. Development of high-resolution UKCIP02-based climate change scenarios in the UK. Agricultural and Forest Meteorology, Vol. 144 , pp. 127-138.
Semenov, M.A., Brooks R.J., Barrow E.M., & Richardson, C.W.,1998. Comparision of the WGEN and LARE-WG stochastic weather generators in diverse climate. Climate Research, 10 , pp. 95-107.
Semenov, M.A.,2008. Simulation of extreme weather events by a stochastic weather generator. Climate Research. Vol. 3 , pp. 203–212.
A Babaei, B., Mirzaei F., Sohrabi T., Assessment of LARS-WG Performance in 12 Coastal Stations of Iran. Iranian Water Researches Journal, Vol. 5 , pp. 217-222. (In Persian)
Khosravanian, J., Onagh M., Goudarzi M., Hejazi S.A.,2015. Predicting of climate parameters using LARS-WG model in Ghare-su basin. Journal of Geography and Planning, Vol. 53 , pp. 93-115. (In Persian)
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IPCC,2001. Climate change (2001), Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the third assessment report of the Intergovernmental Panel on Climate Change. UK: Cambridge University Press.
Khazaei, M.R., Bayazidi, M., & Sharafati, A.,2017. Climate change impact on annual precipitation and temperature of Zanjan province with uncertainties investigation. Ecohydrology, 4(3) ,pp. 847-860. (In Persian)
Khazaei, M.R. Ahmadi, S. Saghafian, B. and Zahabiyoun, B., 2013. A new daily weather generator to preserve extremes and low-frequency variability. Climatic Change, Vol. 119, pp. 631–645.
Utset, A., Martinez-cob, A., Farre, I., & Cavero, J.2006. Simulating the effects of extreme dry and wet years on the water use of flooding-irrigated maize in a Mediterranean landplane. Agricultural Water Management, Vol. 85 , pp. 77-84.
Dibike, Y.B., & Coulibaly, P.,2005. Hydrologic impact of climate change in the Saguenay watershed: comparison of downscaling methods and hydrologic models. Journal of Hydrology, 307 , pp. 145-163.
Moafi Madani, F., Mousavi Bayagi, , & Ansari, H., 2012. Prediction of drought in the Khorasan Razavi province during 2011-2030 by using statistical downscaling of HADCM3 model output. Geography and environmental hazards, Vol. 1(3) , pp. 21-37. (In Persian)
Agarwal, A., Babel, M.S., Masky, S., Shrestha, S., Kawasaki, A., & Tripathi, N.K., 2015. Analysis of temperature projection in the Koshi River Basin, Nepal. International Journal of Climatology, Vol. 36 (1) , pp. 266-279.
Hejazizadeh, Z., Hosseini, S.M., & Karbalaee Dorei, A., 2015. The Simulation of Climate change in Semnan province with scenarios of atmospheric general circulation model (HADCM3). Geography and environmental hazards, Vol. 4(15) , pp. 1-24. (In Persian)
Goodarzi, E., Massah Bavani, A.R., Dastorani, M.T., & Talebi, A.,2014. Evaluating effect of downscaling methods; change-factor and LARS-WG on surface runoff (A case study of Azam-Harat River basin, Iran). Desert, 19 , pp. 99-109.
Zarghami, , Fotookian, M.R., Safari, N., and Aslanzadeh, A.,2016. Reservior operation using system dynamics under climate change impacts: a case study of Yamchi reservoir, Iran. Saudi Society for Geosciences, 9:678.
Khoorni, A., & Jamali, Z.,2016. Effects of climate change on drought duration and severity in arid and semi-arid stations (Bandarabbass and Shahrekord), based on HADCM3 model. Geography and Planning, 20(57) , pp. 131-115. (In Persian)
Shahabul Alam, M.D., & Elshorbagy, A.,2016. Quantification of the climate change-induced variation Intensity-Duration-Frequency curves in Canadian Paris. Journal of Hydrology, Vol. 527 , pp. 990-1005.
Mohammadlou, M., Haghizadeh, A., Zeinivand, H., & Tahmasebipour, N.,2014. Evaluation of Climate Change Impacts on Runoff Changes Trend in Barandoezchay Watershed in West Azerbaijan Province Using General Circulation Models (GCM). Ecohydrology, Vol. 1(1) , pp. 25-34. (In Persian)
Sobhani, B., & Goldust, A.,2018. Prediction of the Starting and Ending of Freezing Periods of Ardabil Province by Using HADCM3 Climatic Model. Geographical Researches, Vol. 33(2) ,pp.191-205. (In Persian)
Semenov, M.A., & Stratonovitch, P.,2010. Use of multi-model ensembles from global climate model for assessment of climate change impacts. Climate Research, Vol. 41 , pp. 1-14.
Semenov, M.A.,2007. Development of high-resolution UKCIP02-based climate change scenarios in the UK. Agricultural and Forest Meteorology, Vol. 144 , pp. 127-138.
Semenov, M.A., Brooks R.J., Barrow E.M., & Richardson, C.W.,1998. Comparision of the WGEN and LARE-WG stochastic weather generators in diverse climate. Climate Research, 10 , pp. 95-107.
Semenov, M.A.,2008. Simulation of extreme weather events by a stochastic weather generator. Climate Research. Vol. 3 , pp. 203–212.
A Babaei, B., Mirzaei F., Sohrabi T., Assessment of LARS-WG Performance in 12 Coastal Stations of Iran. Iranian Water Researches Journal, Vol. 5 , pp. 217-222. (In Persian)
Khosravanian, J., Onagh M., Goudarzi M., Hejazi S.A.,2015. Predicting of climate parameters using LARS-WG model in Ghare-su basin. Journal of Geography and Planning, Vol. 53 , pp. 93-115. (In Persian)
