ارزیابی تغییرات دما و بارش جنوب‌شرق ایران با استفاده از ریزمقیاس ‌نمایی خروجی مدل‌های مختلف گردش عمومی جو در دورة 2011-2099

نوع مقاله: مقاله کامل

نویسندگان

1 استادیار گروه اقلیم‌شناسی، دانشگاه سیستان و بلوچستان، زاهدان، ایران

2 استادیار گروه اقلیم‌شناسی، دانشگاه حکیم سبزواری، سبزوار، ایران

3 دانشجوی دکتری اقلیم‌شناسی، دانشگاه سیستان و بلوچستان، زاهدان، ایران

چکیده

در این پژوهش، با استفاده از مدل ریزمقیاس ‌نمایی آماری LARS-WG5.1، تغییرات بارش و دمای ماهانه در جنوب شرق کشور بر اساس خروجی چهار مدل با سه سناریوی مشترک (A1B ، A2 و B1 )، پس از بررسی توان‌مندی مدل لارس در شبیه‌سازی اقلیم گذشته، همچنین با درنظرگرفتن عدم قطعیت‌ها طی سه دورة زمانی آینده (2011-2030، 2046-2065 و 2080-2099) بررسی شد. یافته‌های پژوهش نشان از افزایش دما بر اساس تمام مدل- سناریوها، طی دوره‌های آتی دارد، به‌طوری که میزان این افزایش دما در ایستگاه‌های واقع در خشکی از ایستگاه‌های مناطق ساحلی بیشتر است. برعکس رفتار یکنواخت افزایشی در دما، تغییرات فصلی بارش در ایستگاه‌های مختلف بسیار نوسانی است. مقدار بارش طی فصول سرد سال در تمامی ایستگاه‌ها روندی افزایشی دارد، در حالی که بارش‌های بهاره روی ایستگاه‌های واقع در خشکی نسبت به نواحی ساحلی افزایش بیشتری خواهد داشت، به‌طوری که در آینده می‌توان انتظار افزایش وقوع سیلاب‌های بهاره را در این مناطق داشت. نتایج تحلیل عدم قطعیت‌ها نیز نشان داد که بهترین عملکرد در شبیه‌سازی مقدار دمای ماهانه را مدل HADCH3 و ضعیف‌ترین عملکرد در شبیه‌سازی مقدار بارش ماهانه را مدل INCM3 نسبت به سایر مدل- سناریوها دارد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Assessment of the Precipitation and Temperature Changes over South East Iran Using Downscaling Of General Circulation Models Outputs

نویسندگان [English]

  • Mohsen Hamidianpour 1
  • Mohammad Baaghideh 2
  • Mohsen Abbasnia 3
1 Assistant Professor of Physical Geography, Sistn and Baluchestan University, Zahedan, Iran
2 Assistant Professor of Physical Geography, Hakim Sabzevari University, Sabzevar, Iran
3 PhD Student in Physical Geography, Sistn and Baluchestan University, Zahedab, Iran
چکیده [English]

Introduction
Numerous studies have demonstrated the relationship between the amount of CO2 in the atmosphere and climate change. In this respect, developed countries have an undeniable role and cause serious damage to earth environment throughout the world. IPCC’ forth evaluation report implies that adding greenhouse gases to the atmosphere during recent decades prevents the heat rays to emit which, in turn, cause atmospheric temperature to increase. During the past centuries, the temperature has increased by 3 to 6 Degrees Centigrade, with a rapid speed in the recent decades. It is believed that if greenhouse gases continue to increase at the present rate, an average increase in temperature, from 1c to3.5c, is expected by the year 2100. Therefore, it is necessary to study and evaluate climate changes in the future decades so as to plan a proper environmental program corresponding to future climate conditions, consequently reduce its unfavorable effects. With the uncertainty in Atmospheric Circulation Models being taken into account, the present study investigates the temperature and precipitation changes in Southeastern Iran during the following periods: 2011-30, 2044-65, and 2080-99.
Material and Methods
We have used two datum groups, namely, observed data and model data. These are including maximum and minimum temperature, precipitation and solar radiation. The period, 1983-2007, was chosen as the observed period; data from weather synoptic stations were gathered. The required data for General Circulation Models including IPCM4, NCCCM3, HADCH3, and INCM3 with three scenarios of A1B, A2, B1 were gathered from the two Reference Networks, Canada Climate Change Reference and data bank of LARS-WG5.1. The most upgraded version of LARS-WG5.1 was used to evaluate climate change in Eastern South of Iran. This version observes the forth report on IPCC. Therefore, it uses the outputs of 15 General Circulation Models with A1B, A2, and B1 Scenarios. Four climate models with three shared Scenarios were used in this study.
Time series of the observed data from synoptic stations in Eastern South of Iran were compared with those of IPCM4, NCCCM3, HADCH3, and INCM3 in similar periods with A1B, A2, and B1 scenarioes. To do so, first, average time series of each station were computed using temperature and precipitation data from synoptic stations; then, monthly thermal data and those of GCM fall values during the study period were received from CCCSN (Canada). Finally, the mentioned data were compared with the average temperature and precipitation during the study period. To investigate the uncertainty resulted from employing various GCM models, weighting method of averages of the observed temperature and precipitation was used.
Results and Discussions
General circulation models don’t have equal results in estimating long-term temperature and precipitation. This indicates the existing uncertainty in their outputs. Analysis by T-test and Chi square statics results for all stations, revealed no significant difference between the modeled and observed values at P <0.05.
In general, the results show that LARS-WG Model is capable of modeling the climate in previous periods of the studied stations. The average precipitation and temperature of the stations were compared using LARS-WG Model. The results revealed an increasing trend in the temperature of all the studied regional stations in future. The 90 year thermal increases in the following stations are 0.44-3.53 in Bam, 0.52-3.30 in Bandar Abbas, 0 .39-2.64 in Chabahar, 0.85-3.41 in Iranshahr, 0.38-2.27 in Jusk, 0.76-3.82 in Kerman, 0.55-3.47 in Zabol, and 0.54-3.57 in Zahedan. The above values are in Degree Centigrade.
The most distinctive feature of modeling, in regard to precipitation, is lack of harmony in its increase or decrease trends in future. In other words, it cannot be concluded that precipitation, like temperature, has an increasing trend; rather it has fluctuations. As the modeled values revealed, precipitation increases in all the stations during spring. Although it is relatively more in such dry stations as Bam, Kerman, Zahedan, Zabol, and Iranshahr, this, in turn, causes spring floods.
Conclusion
This study investigated the effects of climate change on the two weather parameters, temperature and precipitation. This was carried by the data gathered by Atmospheric General Circulation from the synoptic stations located in Eastern South of Iran. The obtained results showed that LAR-WG Model is capable of modeling precipitation and temperature values. According to the results, it was indicated that NCCCM3, HADCH3, IPSLM4, and INCM3 models have a good performance in simulation of precipitation. Regarding temperature, HADCH3 Model proved a good capability in most months. The obtained weights having been applied on model values, an increasing temperature trend was indicated in all the stations. Furthermore, it was also indicated that thermal increasing amount in coastal stations is higher than that of dry ones. The highest increase in temperature belongs to Kerman, Zahedan, Bam, Zabol, and Iranshahr, in order. Accordingly, all coastal stations would experience a thermal increase less than 3c, while the value for dry stations would exceed 3c. It seems that temperature follows a steady increasing trend, whereas precipitation in various stations is fluctuating during different seasons.

کلیدواژه‌ها [English]

  • climate change
  • downscaling
  • general circulation model
  • southeast Iran
بابائیان، ا.؛ نجفی‌نیک، ز. (1385). معرفی و ارزیابی مدل LARS-WG برای مدل‌سازی پارامترهای هواشناسی استان خراسان در دورة 1961تا 2003، مجلة نیوار، 62 و63، پاییز و زمستان: 49-69.
بذرافشان، ج.؛ خلیلی، ع.؛ هورفر، ع.؛ ترابی، ص.؛ و حجام، س. (1388). بررسی و مقایسه عملکرد دو مدل LARS-WG و ClimGen در شبیه‌سازی متغیرهای هواشناسی در شرایط مختلف اقلیمی ایران، مجلة تحقیقات منابع آب ایران، 13: 44-57.
سادات آشفته، پ.؛ مساح‌بوانی، ع. (1391). بررسی تأثیر عدم قطعیت مدل‌های چرخة عمومی جو و اقیانوس و سناریوهای انتشار گازهای گلخانه‌ای بر رواناب حوضة تحت تأثیر تغییر اقلیم؛ مطالعه موردی حوضة قرنقو، آذربایجان شرقی. مجلة تحقیقات منابع آب ایران، 2: 36-47.
ضرغامی، م.؛ حسن‌زاده، ی.؛ بابائیان، ا.؛ کنعانی، ر. (1389). مطالعة تغییر اقلیم و آثار آن بر خشکسالی استان آذربایجان شرقی، مجموعه مقالات نخستین کنفرانس ملی پژوهش‌های کاربردی منابع آب ایران، هیدرولوژی، هیدرولیک و جنبه‌های مختلف منابع آب ایران، شرکت آب منطقه‌ای کرمانشاه، ص 163-172.
عباسی، ف.؛ اثمری، م. (1390). پیش‌بینی و ارزیابی تغییرات دما و بارش ایران در دهه‌های آینده با الگوی MAGICC-SCENGEN، نشریة آب وخاک، 25(1): 70-83.
مساح بوانی، ع.؛ مرید، س.؛ محمدزاده، م. (1385). بررسی عدم قطعیت در توزیع تجمعی احتمالاتی رواناب تحت تأثیر تغییر اقلیم. دومین کنفرانس منابع آب ایران، 3و4 بهمن، اصفهان.
Abbasi, F.; Asmari, M. (2011). Forecasting and assessment of climate change over Iran during future decades using MAGICC-SCENGEN model. J Water and Soil25: 70-83. )In Persian).
An Assessment of the Intergovernmental Panel on Climate Change IPCC, Climate Change (2007). Synthesis Report.
Ashofteh, P.S.; Massah, A.R. (2012). Investigation of AOGCM Model Uncertainty and Emission Scenarios of Greenhouse Gases Impact on the Basin Runoff under Climate Change, Case study Gharanghu Basin, East Azerbaijan. Iran-Water Resources Research 2 (8): 36-47. )In Persian).
Babaeian, I.; Najafi Nik, Z. (2006). The introduction and evaluation of the LARS model for modeling the meteorological parameters of Khorasan Province in the period 1961–2003. J Nivar 62: 49–65. )In Persian).
Bazrafshan, J.; Khalili, A.; Hoorfar, A.; Torabi, S.; Hajjam, S. (2009). Comparison of the Performance of ClimGen and LARS-WG Models in Simulating the Weather Factors for Diverse Climates of Iran. Iran-Water Resources Research, 5 (1): 12-14. )In Persian).
Babaeian, I.; Kwon, W.T.; Im, E. (2004). Application of Weather Generator technique for climate change assessment over Korea. Korea Meteorological Research Institute, Climate Research lab.  
Bae, D.H.; Jung, W.; Chang, H. (2008). Potential changes in Korean water resources estimated by high-resolution climate simulation. J. Clim Res, Vol. 35: 213-226.
Bardossy, A. (1997). Downscaling from GCMs to local climate through stochastic linkages. J Environ Manage, 49:7–17.
Barrow, E.; Hulme, M.; Semenov, M.A. (1996). Effect of using different methods in the construction of climate change scenarios: examples from Europe. Clim Res, 7:195–211.
Barrow, E.M.; Semenov, M.A. (1995). Climate change scenarios with high spatial and temporal resolution for agricultural applications. Forestry, 68:349–360.
Covey, C.; Achuta, K.M.; Cubasch, U.; Jones, P.; lambert, S.J.; Mann, M.E.; Phillipis, T.J.; Taylor, K.E. (2003). An overview of results from the Couplec Model Intercomparison Project. Global Planet. Change, 37: 103-133. 
Darwin, R.; Kennedy, D. (2000). Economic effects of CO2 fertilization Of crops: transforming changes in yield into changes in supply. Environmental Modelingand Assessment 5, 3: 157–168.
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: 145–163. www.elsevier.com/locate/jhydrol.
Dubrovsky, M. (1996). Met&Roll: the stochastic generator of daily weather series for the crop   growth model. Meteorological Bulletin, 49: 97-105.
Elmahdi, A.; Shahkarami, N.; Morid, S.; Massah Bavani, A.R. (2009). Assessing the impact of AOGCMs uncertainty on the risk of agricultural water demand caused by climate change. 18th World IMACS / MODSIM Congress, Cairns, Australia 13-17 July. http://mssanz.org.au/modsim09
Khan, M.S.; Coulibaly, P.; Dibike, Y. (2006). Uncertainty analysis of statistical downscaling methods. Journal of Hydrology 319: 357–382. www.elsevier.com/locate/jhydrol.
Massah Bevani, A.; Morid, S.; Mohammad Zadeh, M.; Godos, C. (2007). Uncertainty analysis in cumulative distribution probability of runoff under climate change. Isfahan University of Technology, Isfahan. Iran, 23 & 24 January: )In Persian).
Murphy, J. (1999). An evaluation of statistical and dynamical techniques for downscaling local climate. J Clim, 12: 2256–2284.
Nakicenovic, N.; Swart, R. (eds) (2000). Emissions scenarios. Special Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge.
Parrey, M.L.; Rosenzweig, C.; Iglesias, A.; Livermore, M.; Fischer, G. (2004). Effects of climate change on global food production under SRES emissions and socio-economic scenarios. Global Environmental Change 14: 53–67.
Racsko, P.; Szeidl, L.; Semenov, M. (1991). A serial approach to local stochastic weather models. Ecol Model, 57:27–41.
Reaney, S.M.; Fowler, J.H. (2008). Uncertainty estimation of climate change impacts on river flow incorporating stochastic downscaling and hydrological model parameterization error source. BHS 10th National Hydrology Symposium, Exeter.
Richardson, C.W. (1981). Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resour Res, 17:182–190.
Richardson, C.W.; Wright, D.A. (1984). WGEN: a model for generating daily weather variables: Agricultural Research Service ARS-8, US Department of Agriculture, Washington, DC.
Salon, S.; Cossarini, G.; Libralato, S.; Gao, X.; Solidoro, S.; Giorgi, F. (2008). Downscaling experiment for the Venice lagoon. I. Validation of the present-day precipitation climatology. Clim Res, 38: 31–41.
Semenov, M.A. (2009). Impacts of climate change on wheat in England and Wales. J. R. Soc. Interface, 6: 343–350.
Semenov M.A. (2008). Ability of a stochastic weather generator to reproduce extreme weather events, Climate Research, 35:203-212
Semenov, M.A. (2007). Development of high-resolution UKCIP02-based climate change scenarios in the UK. Agric For Meteorol, 144:127–138.
Semenov, M.A.; Brooks, R.J.; Barrow, E.M.; Richardson, C.W. (1998). Comparison of the WGEN and LARS-WG stochastic weather generators in divers climates. Climate Research, 10: 95-107.
Semenov, M.A.; Stratonovitch, P. (2010). Use of multi-model ensembles from global climate models for assessment of climate change impacts. Climate Research, 41(2) 1–14.
Trigo, R.M.; Palutikof, J.P. (2001). Precipitation scenarios over Iberia: a comparison between direct GCM output and different downscaling techniques. J Clim, 14: 4422–4446.
Wilby, R.L.; Wigley, T.M.L.; Conway, D.; Jones, P.D.; Hewiston, B.C.; Main, J.; Wilks, D.S. (1998). Statistical downscaling of general circulation model output: a comparison of methods. Water Resour Res, 34: 2995–3008.
Wilby, R.L.; Charles, S.P.; Zorita, E.; Timbal, B.; Whetton, P.; Mearns, L.O. (2004). Guidelines for Use of Climate Scenarios Developed from Statistical Downscaling Methods, IPCC Task Group on Data and Scenario Support for Impact and Climate Analysis.
Wilby, R.L.; Conway, D.; Jones, P.D (2002). Prospects for downscaling seasonal precipitation variability using conditioned weather generator parameters, hydrological processes: 1215-1234.
Wilby, R.L.; Dawson, C.W.; Barrow, E.M. (2001). SDSM Version 3.1–A decision support tool for the assessment of regional climate change impacts.
Wilks, D.S.; Wilby, R.L. (1999). The weather generation game:  a review of stochastic weather models. Progress in Physical Geography, 23: 329-357.
Wilks, D.S. (1992). Adapting stochastic weather generation algorithms for climate change studies. Climate change, 22: 67-84. 
Zarghami, M.; Hassanzadeh, Y.; Babaian, I.; Kanani, R. (2010). The study of climate change and its effects on the drought in East Azerbaijan province. Proceedings of the 1st Iranian National ConferenceonApplied ResearchesinWater Resources (INCRW), Technical University of Kermanshah, Iran, 11-13 May: 163-172. )In Persian).