Simulation of Gorgan Synoptic Station Temperature and Precipitation with RCP Scenarios

Document Type : Full length article

Authors

1 PhD Student of Agro-climatology, Faculty of Geography, University of Tehran, Tehran, Iran

2 Professor of Climatology, Faculty of Geography, University of Tehran, Tehran, Iran

3 Assistant Professor of Agricultural Meteorology, Faculty of Geography, University of Tehran, Tehran, Iran

Abstract

Introduction
The Earth's climate has been constantly changing throughout the planet history. The industrial revolution and human intervention in the environment in the recent decades made special conditions for rising global temperature. Increase in Earth's temperature has modified the climatic balance by which widespread climate changes have been occurred on the Earth's surface. To study the effects of climate change on different systems in future, the climate variables should be initially simulated. There are various methods for simulating climatic variables; the most prudent of them is the use of the outputs of atmosphere-ocean general circulation models (AO- GCMs). Since these models can simulate climatic variables in large spatial and temporal scales, to use these simulated variables in smaller scales, the output of these models should be scaled down by various techniques. The microscopic statistical method, including the SDSM model, has more advantages, especially when it comes to lower costs and quick assessment of the factors affecting climate change.
Method and methods
The purpose of this study is to predict climate change by the SDSM model using the CanESM2 Climate Change Output based on RCP8.5, RCP4.5, RCP2.6 climate change scenarios for the coming periods of 2040-2011, 2070-2041, and 2100- 2071, as well as to study the annual trend of these changes using the Man-Kendall test and the age-related slope estimator. For this purpose, daily data of rainfall and temperature parameters during the statistical period (1981-2010) were collected from the Meteorological Organization. Using Statistical Downscaling Model (SDSM), these climatic parameters were simulated in a monthly scale and compared with the base period (1981-2010). In the SDSM model three types of data are used for the microscopic metering. Working with this model is briefly summarized as follows: 1) Preparing predictive data and large scale predictors, 2) quality control of data and conversion (for precipitation data), 3) selection of the best predictor variables, 4) calibrating the model, 5) production of weather forecasting using observational predictors, 6) statistical analysis 7) graphical output of model 8) production of climate scenarios using model climate predictors.
Results and discussion    
According to the results, it was found that during the 21st century the temperature in the station of Gorgan has increasing trend and precipitation has decreasing trend. In three scenarios RCP8.5, RCP4.5, RCP2.6 there is a decrease in rainfall in the two periods of near future (2040-2011), and the middle (2041-2070) from February to August and in the distant future period (2071-2100) from December to August. The highest precipitation decline occurs in the near future period in June, July and August, with 19.1, 20.9, and 20 mm, and in the middle and the distant future period in May from 28.8 till 47.15 mm. Generally, in all the scenarios, as we move towards the end of the 21st century, the average rainfall will be reduced, and the decrease in the RCP 8.5 scenario is more than the other two scenarios. Given temperature conditions, the general trend of temperature variables in future periods is consistent with the trend of these variables in the base period, with the difference that the temperature will increase slightly in the winter and spring until mid-summer, but from late summer to late fall it will experience a decrease. In the upcoming period, at first the temperatures will be higher in June and in the upcoming mid and in later periods it will be higher in May than that in other months. Moreover, moving from the near future towards the end of the century, the temperature will increase. The augmentation in the RCP 8.5 scenario is more than those of the two other scenarios. However, with the annual precipitation rate, RCP 4.5 and RCP 8.5 scenarios are meaningful and decreasing. In the case of maximum, minimum and mean temperature variations, there is a significant increase. Also, the precipitation drop and temperature rise in the end of the century. The values in the RCP 8.5 scenario are more than those of RCP 2.6 and RCP 4.5 scenarios.  
Conclusion  
In this research, the simulation of climatic parameters of temperature and precipitation was carried out using several linear models of SDSM and general atmospheric circulation models in Gorgan. The output of the CanESM2 model was simulated under RCP8.5, RCP4.5, RCP2.6 scenarios for subsequent periods in 21 steps. The results showed that temperature data show better correlation with observation data (compared with rainfall data). According to the results, it was found that during the 21st century the temperature and the precipitation would have increasing and decreasing trends, respectively. At Gorgan Station, in the three scenarios RCP8.5, RCP4.5, RCP2.6, in the two near future (2040-2011) and mid-term (2041-2070) from February to August and in the distant future period (2071-2100) between December and August, we observe a decline in rainfall. The highest precipitation values is in the period in June, July and August, at 19.1, 20.9, and 20 mm, and in the middle and long distances in each of the three scenarios it is from May 28.8 to 47.15 mm.  In general, in all scenarios the average rainfall will be reduced, as we move towards the end of the 21st century.  This decrease in the scenario RCP 8.5 is more than that of the other two scenarios. Regarding temperature variables, the general trend of the variables in future periods is consistent with the trend of these variables in the base period, with the difference that the temperature increased slightly in the winter and spring until mid-summer but with decrease from late summer to late fall. In the upcoming period, the higher temperatures will be more frequent in June and in the upcoming mid and later periods in May than in other months. Also, in the near future towards the end of the century, the temperature will increase higher. This increase the temperatur in the RCP 8.5 scenario is more than those of two other scenarios. It can also be argued that the increase in temperature and precipitation in the spring and summer and the rising rainfall in the autumn seems to be favorable for planning of water resources, and in particular, the planning for the agricultural sector. The trends and drought conditions should be regarded environmental management in order to minimize the potential negative effects of climate change in the study area.

Keywords

References

آبکار، ع.‏ج.؛ حبیب‏نژاد، م.؛ سلیمانی، ک. و نقوی، ه. (1393). حساسیت مدل ریزمقیاس‏نمایی SDSM به داده‏های بازتحلیل‏شده در مناطق خشک، دو فصل‏نامة علمی- پژوهشی خشکبوم، 4(۲): ۱۱-۲۷.
آروین، ع.؛ قانقرمه، ع.؛ حاجی پور، د.؛ حیدری، م. (1395). بررسی روند تغییرات برخی عناصر اقلیمی در استان چهارمحال و بختیاری، تحقیقات کاربردی علوم جغرافیایی، ۱۶(41): ۱۵۳-176.
آقاخانی افشار، ا.؛ حسن‏زاده، ی.؛ بسالت‏پور، ع. و پوررضا بیلندی، م. (1395). ارزیابی سالیانة مؤلفه‏های اقلیمی حوضة آبخیز کشف‏رود در دوره‏های آتی با استفاده از گزارش پنجم هیئت بین‏الدول تغییر اقلیم، پژوهش‏های حفاظت آب و خاک، ۶: ۲۱۷-233.
احمدوند کهریزی، م. و روحانی، ح. (1395). تأثیرات حفاظتی تغییر اقلیم براساس ریزمقیاس‏سازی دمای پیش‏بینی‏شده در قرن 21 (مطالعۀ موردی: دو ایستگاه ارازکوسه و نوده در استان گلستان)، اکوهیدرولوژی، 3(۴): ۵۹۷-609.
بابائیان، ا. و نجفی نیک، ز. (1386). مدل‏سازی اقلیم ایران در دورة 2010 تا 2039، پروژة خاتمه‏یافتة پژوهشکدة اقلیم‏شناسی، ص ۵-13.
بابائیان، ا.؛ فهیمی‏نژاد، ا.؛ باعقیده، م.؛ کریمیان، م.؛ مدیریان، ر. و بیاتانی، ف. (1396). چشم‏انداز اقلیم حوضة جنوبی دریای خزر تحت شرایط گرمایش جهانی- مطالعة موردی مدل گردش کلی Hadcm3، مخاطرات محیط طبیعی، 4: ۱۷-34.
باهک، ب. (1392). بررسی احتمال تغییر اقلیم در استان کرمان با روش من- کندال (مطالعة موردی ایستگاه کرمان)، فصل‏نامة جغرافیایی سرزمین، ۱۰(۳۹): ۶۵-۷۲.
رضایی، م.؛ نهتانی، م.؛ آبکار، ع.؛ رضایی، م.؛ میرکازهی ریگی، م. (1393). پیش‏بینی‏ پارامترهای (SDSM) بررسی کارایی مدل ریزمقیاس‏نمایی آماری دمایی در دو اقلیم خشک و فراخشک (مطالعة موردی: کرمان و بم)، پژوهش‏نامة مدیریت حوضةآبخیز، ۵(۱۰): 8۳۶-8۴۵.
رضایی زمان، م. و افروزی، ع. (1394). ارزیابی اثرات تغییر اقلیم بر عملکرد محصولات و ارائة راهبرد تغییر الگوی کشت (مطالعة موردی: حوضة سیمینه‏رود)، نشریة حفاظتمنابعآبوخاک، ۴(۴): ۵۱-64.
سلاجقه، ع.؛ رفیعی ساردوئی، ا.؛ مقدم‏نیا، ع.؛ ملکیان، آ.؛ عراقی‏نژاد، ش.؛ خلیقی سیگارودی، ش. و صالح‏پور جم، ا. (1395). پیش‏بینی‏ عنصرهای اقلیمی توسط مدل چندگانة خطی SDSM در دورة آینده بر پایة سناریو A2، مدیریت بیابان، 7: ۱۲-25.
خسروانیان، ج.؛ اونق، م.؛ گودرزی، م. و حجازی، س.ا. (1394). کاربرد مدل LARS WG در پیش‏بینی پارامترهای هواشناسی حوضة قره‏سو استان گلستان، جغرافیا و برنامه‏ریزی، 53: ۹۳-115.
خورشیددوست، ع.‏م.؛ صراف، ب.؛ قرمزچشمه، ب. و جعفرزاده، ف. (1396). برآورد و تحلیل مقادیر آتی بارش‏های نواحی خزری با به‏کارگیری مدل‏های گردش عمومی جو، نشریة تحقیقات کاربردی علوم جغرافیایی، ۱۷(۴۷): ۲۱۳-۲۲۶.
صمدی، ز.؛ مساح بوانی، ع. و مهدوی، م. (1386). بررسی تأثیر روش‏های کوچک‏مقیاس‏کردن رگرسیونی بر رژیم سیلاب رودخانه، کارگاه فنی اثرات تغییر اقلیم در مدیریت منابع آب.
طایی سمیرمی، س.؛ مرادی، ح. و خداقلی، م. (1393). شبیه‏سازی و پیش‏بینی‏ برخی از عنصرهای اقلیمی توسط مدل چندخط خطی SDSMو مدل‏های گردش عمومی جو (مطالعة موردی: حوضة آبخیز بار نیشابور)، فصل‏نامة انسان و محیط زیست، 28: ۱-۱۵.
عزیزی آبادی فراهانی، م.؛ بختیاری، ب.؛ قادری، ک. و رضاپور، م. (1395). بررسی تأثیر تغییر اقلیم بر منحنی‏های سختی- مدت- فراوانی خشک‏سالی حوضة آبریز قره‏سو با استفاده از توابع مفصل، تحقیقات آب و خاک ایران، 47(۴): 754-743.
علیزاده پهلوان، ح. و زهرایی، ب. (1393). ریزمقیاس‏نمایی آماری بارش با هدف ارزیابی اثرات تغییر اقلیم بر رویدادهای حدی در نواحی شهری، نخستینهمایشوجشنوارةتغییراقلیموراهیبهسویآیندةپایدار، سازمان محیط زیست.
علیجانی، ب.؛ محمودی، پ. و چوگان، ع. (1391). بررسی روند تغییرات بارش‏های سالانه و فصلی ایران با استفاده از روش ناپارامتریک برآوردکنندة شیب سن، پژوهش‏های اقلیم‏شناسی، ۳(۹): ۲۳-۴۲.
عجم‏زاده، ع. و ملائی‏نیا، م. (1395). ارزیابی اثرات تغییر اقلیم بر رواناب رودخانة فیروزآباد استان فارس با ریزمقیاس‏نمایی خروجی مدل‏های گردش جوی به‏وسیلة نرم‏افزارهای SDSM وLARS-WG ، تحقیقاتمنابعآبایران، ۱۲(1): ۹۵-102.
قاسمی‏فر، ا.: علیجانی، ب. و سلیقه، م. (1395). بررسی تغییرات دما در سواحل جنوبی دریای خزر با استفاده از سه مدل LARSWG، SDSM، و مدل شبکة عصبی مصنوعی، فصل‏نامة جغرافیای طبیعی، ۹(۴۳): ۲۳-۴۱.
کوهی، م.؛ موسوی بایگی، م.؛ فریدحسینی، ع.؛ ثنایی‏نژاد، س.‏ح. و جباری نوقابی، ه. (1391). ریزمقیاس‏نمایی آماری و ارائة سناریوهای آتی رویدادهای حدی بار در حوضة کشف‏رود، نشریة پژوهش‏هایاقلیم‏شناسی، ۳(۱۲): ۵۳.
Aghakhani Afshar, A.; Hassanzadeh, Y.; Besalatpour, A. and Pourreza Bilondi, M. (2016). Seasonal Changes of Precipitation and Temperature of Mountainous Watersheds in Future Periods with Approach of Fifth Report of Intergovernmental Panel on Climate Change (Case study: Kashafrood Watershed Basin), Journal of Water and Soil, 30(5): 217-233.
Ahmadvand Kahrizi, M. and Rouhani, H. (2016). Assessing the conservation impacts of climate change based on temperature projected on 21 centuery (Case study: Arazkoseh and Nodeh stations), Iranian journal of Ecohydrology, 3(4): 597-609.
Ajmzadeh, A. and Mullaei Niya, M. (2016). Assessment of Impact of Climate Change on Firoozabad River Runoff with Downscaling of Atmospheric Circulation Models Output by SDSM and LARS-WG Softwares, Iran Water Resources Research, 12(1): 95-102.
Alijani, B.; Mahmoudi, P. and Choghane, A. (2012). Investigation of annual and seasonal rainfall variations in Iran using nonparametric method "Sense slope estimator, Climate research, 10(9): 42-23.
Alizadeh Pahlavan, H. and Zohrai, B. (2014). The statistical method of rainfall measurement with the aim of assessing the effects of climate change on extreme events in urban areas, The first conference and festival of climate change and a way towards a sustainable future, The environmental organization.
Arora, V.K.; Scinocca, J.F.; Boer, G.J.; Christian, J.R.; Denman, K.L.; Flato, G.M.; Kharin, V. V.; Lee, W.G. and Merryfield, W.J. (2011). Carbon emission limits required to satisfy future representative concentration pathways of greenhouse gases, Geophysical Research Letters, 38(5): 1-6.
Artlert, K.; Chaleeraktrakoon, Ch. and Nguyen, V. (2013). Modeling and analysis of rainfall processes in the context of climate change for Mekong, Chi, and Mun River Basins (Thailand), Hydro- environment Research, 7(1): 2-17
Arvin, A.; Ghangherme, A.; Hajipour, D. and Hidari, M. (2016). Investigating the Trend of Changes in some Climatic Elements in Chaharmahal and Bakhtiari Province, Researches in Geographical Sciences., 16(41): 153-176.
Azizi Abadi Farahani, M.; Bakhtiari, B.; Ghaderi, K. and Rezapur, M. (2016). The Survey of Climate Change Impact on Drought Severity- Duration- Frequency Curves Using Copulas, Iranian Journal of Water and Soil Research, 47(4): 743-754.
Babaeian, A.; Fahimi Nejad, A.; Baqideh, M.; Karimian, M.; Managers, R. and Bayatani, F. (1396). Climate Perspective of the Southern Caspian Basin under Global Warming Conditions - A Case Study of the Hadcm3 Overall Circulation Model. Natural Environment Hazards. No. 4, pp. 17-17.
Babaian, A. and Najafi Nik, Z. (1386). Climate modeling in Iran during the period from 2010 to 2039, The completed project of the Institute of Climatology, pp. 5-13.
Bahak, B. (1392). Investigating the likelihood of climate change in Kerman province using the Man-Kendall method (A case study of Kerman station), Geographical Quarterly of the Territory, Year 10, No. 39. pp. 72-65.
Fung, F.; Lopez, A. and New, M. (2011). Modeling the impact of climate change on water resources, Wiley-Blackwell, N, ISBN: 9781405196710. PP. 43-62.
Ghasemifar, A.; Alijani, B. and Salighe, M. (2016). Investigation of Climate Change on the Southern Coastal of the Caspian Sea Using SDSM, LARS-WG and Artificial Neural Network, Journal of Physical Geography, 10(43): 23-41.
Intergovernment Panel on Climate Change (2007). Impact, adaption and Vulnerability of Climate Change, Contribution of working group II to the Fourth Assessment report, Cambridge University Press, Cambridge UK.
Intergovernment Panel on Climate Change (2013). The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.
Janabkar, A.; Habibnezad, M.; suleymani, K. and Naghavi, H. (2015). Sensitivity of the Statistical Downscaling Model (SDSM) to reanalysis data in arid areas, Aride Biome, 4(2): 11-27.
Kendall, M.G. (1975). Rank Correlation Methods, Griffin, London.
Khan, M.S.; Coulibaly, P. and Dibike, Y. (2006). Uncertainty analysis of statistical downscaling methods, Journal of Hydrology, 6: 357-382.
Khorshid Dost, M.; Saraf, B.; Redfront, B. and Jafarzadeh, F. (2017). Estimation and Analysis of Caspian Region's Future Rainfalls by Using General Atmospheric Circulation Models, Journal of Applied Geosciences Research, 17(47): 213- 226.
Khosravian, J.; Avagh, M.; Goodarzi, M. and Hejazi, SA. (2015). Application of the LARS WG Model in Forecasting of Meteorological Parameters of Gharehoo Basin of Golestan Province, Geography and Planning, 53: 93-115.
Kohoi, M.; Mousavi Bayga, M.; Farideh Hosseini, A.; Sanaei Nejad, S.H. and Jabari Novuchebi, E.) 2012). Tatistical Downscaling of Extremes of precipitation and construction of their future scenarios in the Kashfroud Basin, Journal of Climate Research, 3(12): 35-53.
Kundu, S.; Khare, D. and Mondal, A. (2017). Individual and combined impacts of future climate and land use changes on the water balance, Ecological Engineering, eBook ISBN: 9780080532233 PP. 42-57.
Liu, L.; Liu, Z.; Ren, X.; Fischer, T. and Xu, Y. (2011). Hydrological impacts of climate change in the Yellow River Basin for the 21st century using hydrological model and statistical downscaling model, Quaternary International, 5: 211-220.
Liu, Z.; Xu, Z.; Charles, S.P.; Fu, G. and Liu, L. (2011). Evaluation of two statistical downscaling models for daily precipitation over an arid basin in China, International Journal of Climatology, 3: 2006-2020.
Lopes, P.M.D.A.G. (2008). Assessment of climate change statistical downscaling methods: Application and comparison of two statistical methods to a single site in Lisbon (Doctoral dissertation, FCT-UNL).‏
Mahmood, R. and Babel, M.S. (2014). Future changes in extreme temperature events using the statistical downscaling model (SDSM) in the trans-boundary region of the Jhelum river basin, Weather and Climate Extremes, 1: 56-66.
Mondal, A.; Khare, D. and Kundu, S. (2016). Change in rainfall erosivity in the past and future due to climate change in the central part of India, International Soil and Water Conservation Research, 4: 186-194.
Moriasi, D.N.; Arnold, J.G.; Van Liew, M.W.; Bingner, R.L.; Harmel, R.D. and Veith, T.L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations, J. Amer. Soc. Agric. Biol. Engin., 50(3): 885-900.
Qian, B.; Gameda, S.; Hayhoe, H.; De Jong, R. and Bootsma, A. (2004). Comparison of LARS-WG and AAFC-WG Stochastic Weather Generators for Diverse Canadian climates, Climate Research, 26(3): 175-191.
Rezaei Zaman, M. and Afroozi, A. (2015). Evaluation of the climate change impacts on the crop yields and proposing the changing cropping pattern strategy (case study: Simineh Rood basin), Journal of Water and Soil Conservation, 4(4): 51-64.
Rezaei, M.; Nectani, M.; Abkar, A.; Rezaei M. and Mirkazei Rigi, M. (2014). SDSM (Parameters Forecasting) An Analysis of the Effectiveness of Exponential Temperature Exponential Scale in Two Dry and Quaternary Climates (Case Study: Kerman and Bam), Journal of Water Management, 28(4): 36-845.
Salajeghe, A.; Rafiei Sardouei, A.; Moghadamnia, A.; Malekian, A.; Araghinejad, S.; Khalighi Sigarodi, S. and Salehpour Jam, A. (2016). Forecasting climatic variables by SDSM multiple linear model in the upcoming period based on scenario A2, Desert management, 7: 12-25.
Samadi, S.; Ehteramian, K. and Sarraf, B.S. (2011). SDSM ability in simulate predictors for climate detecting over Khorasan province, Procedia-Social and Behavioral Sciences, 3: 741-749.
Samadi, Z.; Mas'ban Bouani, A. and Mahdavi, M. (2007). Investigating the Effect of Small Regression Scale Methods on River Flooding, Technical Workshop on Climate Change Impacts on Water Resources Management.
Tao, X.E.; Chen, H.; Xu, C.Y.; Hou, Y.K. and Jie, M.X. (2015). Analysis and prediction of reference evapotranspiration with climate change in Xiangjiang River Basin, China, Water Science and Engineering, 4: 273-281.
Tayyi Semiromi, S.; Moradi, H. and Khodgoli, M. (2014). Simulation and prediction some of climate variable by using multi line SDSM and Global Circulation Models (Case study: Bar Watershed Nayshabour), Journal of Human and Environment, 28: 1-15.
Wilby, R.L.; Dawson, C.W. and Barrow, E.M. (2002). SDSM—a decision support tool for the assessment of regional climate change impacts, Environmental Modelling & Software, 6: 145-157.
Xu, C.H. and Xu, Y. (2012). The Projection of Temperature and Precipitation over China under RCP Scenarios using a CMIP5 Multi-Model Ensemble, Atmospheric and Oceanic Science Letters, 5(6): 527-533.
Physical Geography Research
Volume 51, Issue 4
January 2020
Pages 563-579

Files

History

  • Receive Date: 08 May 2019
  • Revise Date: 02 September 2019
  • Accept Date: 02 September 2019

Share

How to cite

Statistics