پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 158 |
| تعداد شماره‌ها | 6,230 |
| تعداد مقالات | 67,765 |
| تعداد مشاهده مقاله | 115,206,713 |
| تعداد دریافت فایل اصل مقاله | 89,955,110 |
ارزیابی کارایی مدل سری CMIP5 در شبیه سازی و پیش بینی پارامترهای اقلیمی بارندگی، دما و سرعت باد (مطالعۀ موردی: استان یزد) | ||
| پژوهش های جغرافیای طبیعی | ||
| مقاله 13، دوره 50، شماره 3، مهر 1397، صفحه 593-609 اصل مقاله (1.13 M) | ||
| نوع مقاله: مقاله کامل | ||
| شناسه دیجیتال (DOI): 10.22059/jphgr.2018.248177.1007156 | ||
| نویسندگان | ||
| مریم میراکبری1؛ طیبه مصباح زاده* 2؛ محسن محسنی ساروی2؛ حسن خسروی3؛ قاسم مرتضایی فریزهندی4 | ||
| 1دانشجوی دکتری بیابانزدایی، گروه احیای مناطق خشک و کوهستانی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران | ||
| 2استادیار گروه احیای مناطق خشک و کوهستانی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران | ||
| 3استاد گروه احیای مناطق خشک و کوهستانی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران | ||
| 4دانشیار پژوهشکدة مطالعات توسعة جهاد دانشگاهی تهران | ||
| چکیده | ||
| ایران جزو کشورهایی است که ضریب تأثیرپذیری آن از تغییرات اقلیمی بالاست. امروزه، تغییرات پارامترهای اقلیمی به وسیلة مدلهای گردش کلی جو بررسی میشود. نسخههای گوناگونی از این مدلها منتشر شده است؛ آخرین نسخة آن مدلهای سری CMIP5 است. مدلهای CMIP5، که در گزارش پنجم ارزیابی تغییر اقلیم (AR5) استفاده شدهاند، از عدم قطعیت پایینتر و وضوح بیشتری نسبت به مدلهای قبل برخوردارند. در این مطالعه، تغییرات پارامترهای اقلیمی بارندگی، دمای متوسط، و سرعت باد حداکثر به وسیلة مدل CanESM2 بهعنوان یکی از مدلهای مورداستفاده در تهیة گزارش پنجم ارزیابی تحت سه سناریوی RCP و براساس روش ریزمقیاسنمایی SDSM بررسی شد. نتایج نشان داد میانگین بارندگی طبق سناریوهای RCP2.6 و RCP 8.5 بهترتیب 28/62 و 69 میلیمتر خواهد بود که نسبت به دورة مشاهداتی بهترتیب 18/9 و 2/17درصد افزایش و براساس سناریوی RCP 4.5، 39/58 میلیمتر بوده است که نسبت به دورة مشاهداتی 81/0درصد کاهش خواهد داشت. میانگین دمای متوسط طبق سناریوهای RCP2.6، RCP4.5، و RCP8.5 بهترتیب 59/20، 03/21، و 10/22 درجة سانتیگراد خواهد بود که نسبت به دورة مشاهداتی بهترتیب 5/4، 7/6، و 8/12درصد افزایش دارد. همچنین، سرعت حداکثر باد تحت سناریوهای RCP2.6، RCP4.5، و RCP8.5 بهترتیب 9/4، 4/4، و 3/5درصد نسبت به دورة مشاهداتی افزایش خواهد داشت. | ||
| کلیدواژه‌ها | ||
| ارزیابی آماری؛ سری CMIP5؛ روند تغییرات؛ CanESM2؛ SDSM | ||
| عنوان مقاله [English] | ||
| Performance of Series Model CMIP5 in Simulation and Projection of Climatic Variables of Rainfall, Temperature and Wind Speed (Case Study: Yazd) | ||
| نویسندگان [English] | ||
| Maryam Mirakbari1؛ Tayyebeh Mesbahzadeh2؛ Mohsen Mohseni Saravi2؛ Hasan Khosravi3؛ Ghasem Mortezaie Farizhendi4 | ||
| 1PhD Candidate in Combating Desertification, Faculty of Natural Resources, University of Tehran, Iran | ||
| 2Assistant Professor of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Iran | ||
| 3Professor of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Iran | ||
| 4Associate Professor of Development Studies, Iranian Academic Center for Education, Culture and Research (ACECR), Iran | ||
| چکیده [English] | ||
| Introduction According to the IPCC (2014) definition, climate change is a change in the state of the climate in which the average or its modifiable properties varies for a long period. To date various versions of climate change models have been presented for different purposes. These models are including (1) the assessment models of the Intergovernmental Panel on Climate Change (FAR), (2) the models entitled SAR, (3) the report model of TAR, and (4) the model report titled AR4 (CMIP3) and, finally, the 5th Assessment Report models, AR5 (CMIP5). These models use new emission scenarios called "RCP". These scenarios have four key lines called RCP2.6, RCP4.5, RCP6 and RCP8.5. General circulation models are considered as the most reliable tools for simulating climatic variables. These models can simulate the present climate and illustrate the conditions of the future climate under specific scenarios. Although these models are very helpful in the investigation and predictions based on future changes in climate, the outputs of these models are based on a large grid scale (250 to 600 km). Therefore, the application of these models is not suitable on a regional or local scale. The most important tool to create a bridge between a regional/local scale and GCM scales is downscaling. Among statistical downscaling methods, SDSM has been widely used for the downscaling of climate variables in the world. Most of the studies on climate change have been used in AR4 models. The majority of the studies in the word used AR5 models to investigate changes in climatic variables. As mentioned, most of the studies about climate change modeling have been performed using AR4 models. Therefore, studying and updating of that with CMIP5 data is necessary to reduce the uncertainty of modeling climate change in recent decades. Thus, in this study, three variables of rainfall, averaged temperature and maximum wind speed are modeled using AR5 models. These parameters are modeled according to the basic period of 1961-2005 and the future climate change will be simulated in a 95-year period from 2006 to 2100. The investigation on changes of the maximum wind speed in this region, as windy areas, is affected by dust storms every year. This can be of great importance in the studies of dust and wind erosion in the future. Material and method In this study, we use three types of data including daily rainfall, average temperature and maximum wind speed from synoptic station. Second sources of data are NCEP variables and the third are CanESM2 outputs. For analysis of General Circulation Model, we used in this study the second generation of Canadian Earth System Model (CanESM2) developed by Canadian Centre for Climate Modeling and Analysis (CCCma) of Environment Canada. This is the only model that made daily predictor variables available to be directly fed into SDSM. Also this model has three scenarios such as RCP2.6, RCP4.5 and RCP8.5. The model SDSM has four main parts including identification of predictors, model calibration, weather generator and generation of future series of climate variables. Results and discussion After reviewing data quality control, predictive variables were determined by NCEP. Calibration of the model was carried out using 70% of the observational data during the statistical period of 1961-1992 to determine the coefficients of the equation for modeling rainfall data, temperature and wind speed. The coefficients obtained in the calibration phase were used for 30% of the remaining data (1993-2005) for model verification. The performance of the SDSM was evaluated based on comparison of the results of verification and observational data in 1993-2005. The performance of the model on downscaling of temperature is higher than rainfall and wind speed. The reason for this is that the temperature parameter is continuous variable and there are no zero values. The results of evaluation of performance and model uncertainty showed that NSE, RMSE, R2, PBIAS, RSR and Pearson correlation coefficient for mean temperature were 0.96, 1.64, 0.96, 1.63, 0.18, and 0.97 based on downscaled data by the NCEP and 0.88, 1.3, 0.91, 6.8, 0.33, 0.49 based on the downscaled data of CanESM2, which is of a fairly good value. The assessment criteria of rainfall and wind speed are less than the average temperature, which can be explained by the fact that the rainfall data and wind speed are not normal due to the presence of zero values. Based on the results of the Man-Kendall test, observation and modeled rainfall under two scenarios of RCP2.6 and RCP8.5 have no significant trend. While the data generated by the RCP4.5 scenario shows a significant decreasing trend. The results of the Mean-Kendall test on average showed that the series modeled by RCP scenarios, as well as observational data, have a significant incremental trend. Incremental trend of temperature indicates the existence of climate change in the study area. The maximum wind speed showed that the observation and modeled data by RCP2.6 and RCP4.5 scenarios had a significant decreasing trend, while the generated data based on the RCP8.5 scenario had an increasing trend. Conclusion Based on the results of CanESM2 model and SDSM, study area is not excluded from the climate change phenomenon. The trend model predicted an increase in average temperature in future under three RCP scenarios. The average temperature will increase by 1.54 degrees. According to the RCP2.6 scenario, the average temperature is 4.5 degrees, the scenario RCP4.5 is 7.7 degrees and the RCP8.5 scenario is 18.12 degrees higher than the base period. Unlike temperature variable, there is no significant change pattern for rainfall. According to RCP2.6 and RCP8.5 scenarios, rainfall will be increased in the future, while under RCP4.5 scenario, it will be decreased. The maximum wind speed will be increased by an average of 0.53 m/s compared with the base period. The RCP2.6 scenario is 4.9%, the scenario RCP4.5 44.4%, and the scenario RCP8.5 53.5% of increase compared with observational data. | ||
| کلیدواژه‌ها [English] | ||
| Fifth Assessment Report, CanESM2, SDSM, statistical evaluation, trend | ||
| مراجع | ||
|
آقاخانی افشار، ا.؛ حسنزاده، ی.؛ بسالتپور، ع.؛ پوررضا بیلندی، م. (1395). ارزیابی سالیانة مؤلفههای اقلیمی حوضة آبخیز کشفرود در دورههای آتی با استفاده از گزارش پنجم هیئت بینالدول تغییر اقلیم، نشریة پژوهشهای حفاظت آب و خاک، 23(6): 217-233. احمدوند کهریزی م. و روحانی ح. (1395). تأثیرات حفاظتی تیر اقلیم براساس ریزمقیاسسازی دمای پیشبینیشده در قرن 21 (مطالعة موردی: دو ایستگاه اراز کوسه و نوده در استان گلستان)، اکوهیدرولوژی، 3(6): 597-609. برزگری، ف. و ملکینژاد، ح. (1395). بررسی و مقایسة تغییرات اقلیمی مناطق دشتی و کوهستانی در دورة 2010 تا 2030 (مطالعة موردی: حوضة آبخیز دشت یزد اردکان)، فیزیکوزمین، 42(1): 171-182. جهانبخش اصل، س.؛ خورشیددوست، ع.؛ عالینژاد، م. و پوراصغر، ف. (1395). تأثیر تغییر اقلیم بر دما و بارش با درنظرگرفتن عدم قطعیت مدلها و سناریوهای اقلیمی، هیدروژئومورفولوژی، 7: 107-122. صیاحی، ث.؛ شهبازی، ع. و خادمی، خ. (1395). پیشبینی اثر تغییر اقلیم بر رواناب ماهانة حوضة دزآب استفاده از مدل IHACRES، دوفصلنامة علوممهندسیآب، 15(7): 7-18. نگارش، ح.؛ فلاح، ح. و خسروی، م. (1390). تجزیه و تحلیل ناهنجارهای اقلیمی مؤثر بر فرایند بیابانزایی در منطقة خضرآباد یزد، مجلة جغرافیا و برنامهریزی محیطی، ۳: 94-71. Ahmadvand, M. and Rouhani, H. (2016). Climate change protection effects based on downscaling of the predicted temperature in the 21st century (case study: Araz Koseh and Navadeh in Golestan Province), Ecohydrology, 3(4): 597-609. Alves, JMB.; Vasconcelos Junior, FC.; Chaves, RR.; Silva, EM.; Servain, J.; Costa, AA.; Sombra, SS. and Barbosa, ACB. (2016). Evaluation of the AR4 CMIP3 and the AR5 CMIP5 model and projections for precipitation in Northeast Brazil, Frontiers in Earth Science, 4(44): 1-22. Aizen, E.M.; Aizen, V.B.; Melack, J.M.; Nakamura, T. and Ohta, T. (2001). Precipitation and atmospheric circulation patterns at mid-latitudes of Asia, International Journal of Climatology, 21(5): 535-556. Almazroui, M.; Nazrul Islam, M., Saeed, F.; Alkhalaf, A. and Dambul, R. (2017). Assessing the robustness and uncertainties of projected changes in temperature and precipitation in AR5 Global Climate Models over the Arabian Peninsula, Atmospheric Research, 194: 202-213. Aghakhani Afshar, A.; Hassanzadeh, Y.; Besalatpour, A. and Pourreza-Bilondi, M. (2017). Annual assessment of Kashafrood watershed basin climate components in future periods by using fifth report of intergovernmental panel on climate change, Water and Soil Conservation, 6: 217-233. Barzegari, F. and Malekinejad, H. (2016). Prediction and comparison of Climate Changes in Mountainous and Palin Regions During 2010-2030 (Case Study: Yazd- Ardakan Watershed), Earth and Space Physics, 42(1): 171-182. Dastorani, M.T.; Massah Bavani, A.R.; Poormohammadi, S. and Rahimian, M.H. (2011). Assessment of potential climate change impacts on drought indicators (Case study: Yazd station, Central Iran), Desert, 1: 159-167. Dibike, Y.B. and Coulibaly, P. (2005). Hydrologic impact of climate change in the Saguenay watershed: Comparison of downscaling methods and hydrologic models, Hydrology, 307(1-4): 145-163. Frey, K.E. and Smith, L.C. (2003). Recent temperature and precipitation increases in West Siberia and their association with the Arctic Oscillation. Polar Research, 22 (2): 287-300. Fiseha, B.M.; Melesse, A.M.; Romano, E.; Volpi, E. and Fiori, A. (2012). Statistical Downscaling of Precipitation and Temperature for the Upper Tiber Basin in Central Italy, International Journal of Water Sciences, 1(3): 1-14. Feng, S.; Hu, Q.; Huang, W.; Ho, C.H.; Li, R. and Tang, Z. (2014). Projected climate regime shift under future global warming from multi-model, multi-scenario CMIP5 simulations, Global and Planetary Change, 112: 41-52. Gahanbakhsh Asl, S.; Khorshid Dost, A.; Ali Nejad, M. and Poor Asghar, F. (2017). He Impact of Climate Change on Temperature and Precipitation Considering the Uncertainty of Models and Climate Scenario, Hydrogeomorphology, 7: 107-122. Gagnon, S.; Singh, B.; Rousselle, J. and Roy, L. (2005) An application of the statistical downscaling model (SDSM) to simulate climatic data for streamflow modelling in Québec, Canadian Water Resources, 30(4): 297-314. Gebremeskel, S.; Liu, Y.B.; de Smedt, F.; Hoffmann, L. and Pfister, L. (2005). Analysing the effect of climate changes on streamflow using statistically downscaled GCM scenarios, International Journal River Basin Management, 2(4): 271-280. Hassan, Z.; Shamsudin, S. and Harun, S. (2014). Application of SDSM and LARS-WG for simulating and downscaling of rainfall and temperature, Theoretical and Applied Climatology, 116(1-2): 243-257. Huang, J.; Zhang, J.; Zhang, Z.; Xu, C.; Wang, B. and Yao, J. (2011). Estimation of future precipitation change in the Yangtze River basin by using statistical downscaling method, Stochastic Environmental Research Risk Assessment, 25(6):781-792. Hay, LE.; Wilby, RL. and Leavesley, GH. (2000). A comparison of delta change and downscaled GCM scenarios for three mountainous basins in the United StatesAmerican Water Resources Association, 36(2): 387-397. 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, United Kingdom and New York, NY, USA: Cambridge University Press, Cambridge. IPCC (2014). Summary for policymakers, In: Climate Change 2014: Impacts, Adaptation, and Vulnerability, Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., Barros, V.R., Dokken, D.J., Mach, K.J., astrandrea, M.D., Bilir, T.E., Chatterjee, M., Ebi, K.L., Estrada, Y.O., Genova, R.C., Girma, B., Kissel, E.S., Levy, A.N., MacCracken, S., Mastrandrea, P.R., and White, L.L. (Eds.)], Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA p. 1-32. Kumar Bal, P.; Ramachandran, A.; Geetha, R.; Bhaskaran, B.; Thirumurugan, P.; Indumathi, J. and Jayanthi, N. (2016). Climate change projections for Tamil Nadu, India: deriving high-resolution climate data by a ownscaling approach using PRECIS, Theoretical Applied, Climatology, 123: 523-535. Kharin, V.V.; Zwiers, F.W.; Zhang, X. and Wehner, M. (2013). Changes in temperature and precipitation extremes in the CMIP5 ensemble, Climate Change, 119: 345-357. 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, 244(2): 211-220. Liu, Z.; Mehran, A.; Phillips, T.J.; Aghakouchak, A.; Res, C.; Liu, Z.; Mehran, A.; Phillips, T.J. and Aghakouchak, A. (2014). Seasonal and regional biases in CMIP5 precipitation simulations, Climate Research, 60(1): 35-50. Miao, C.Y.; Duan, Q.Y.; Sun, Q.H. and Li, J.D. (2013). Evaluation and application of Bayesian multi-model estimation in temperature simulations, Progress in Physical Geograph, 37: 727-744. Mahmood, R. and Babel, S.M. (2012). Evaluation of SDSM developed by annual and monthly sub-models for downscaling temperature and precipitation in the Jhelum basin, Pakistan and India, Theoretical and Applied Climatology, PP. 1-18. Mehran, A.; Aghakouchak, A. and Phillips, T.J. (2014). Evaluation of CMIP5 continental precipitation simulations relative to satellite-based gauge-adjusted observations, Journal of Geophysical Research: Atmospheres, 119(4): 1695-1707. 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.; Meehl, G.A.; Mitchell, J.F.; Nalicenovic, N.; Riahi, K.; Smith, S.J.; Stouffer, R.J.; Thomson, A.M.; Weyant, J.P. and Wilbanks, T.J. (2010). The next generation of scenarios for climate change research and assessment, Journa of Nature, 463: 747-756. Negaresh, H. and Khosravi, M. (2011). The Analysis of Climatical Abnormalities Influencing on Desertification Process in Khezer Abad Region of Yazd, Geography and Environmental Planning, 3: 71-79. Nourein Mohammed, I.; Beverley, A. and Wemple, B. (2015). The use of CMIP5 data to simulate climate change impacts on flow regime within the Lake Champlain Basin, Journal of Hydrology: Regional Studies, 3: 160-186. Pattnayak, K.C.; Kar, S.C.; Dalal, M. and Pattnayak, R. K. (2017). Projections of annual rainfall and surface temperature from CMIP5 models over the BIMSTEC countries, Global and Planetary Change, 152: 152-166. Rui, Li and Geng, S. (2013). Impacts of climate change on agriculture and adaptive strategies in China, Integrative Agriculture, 12(8): 1402-1408 Sayahi, S.; Shahbazi, A. and Khademi, KH. (2016). Prediction of the effect of climate change on the monthly runoff of the basin Dez using IHACRES, Journal of Water Science Engineering, 15(7): 7-18. Tabari, H.; Marofi, S.; Aeini, A.; HosseinzadehTalaeea, P. and Mohammadi, K. (2011). Trend analysis of reference evapotranspiration in the western half of Iran, Agricultural and Forest Meteorology, 151(2):128-136. Tabari, H.; Abghari, H. and Hosseinzadeh Talaee, P. (2013). Impact of the North Atlantic Oscillation on stream flow in Western Iran, Hydrol, Process. DOI: 10.1002/hyp.9960. Thomson, A.M.; Calvin, K.V.; Smith, S.J.; Kyle, G.P.; Volke, A.; Patel, P.; Delgado-Arias, S.; Bond-Lamberty, B.; Wise, M.A.; Clarke, L.E. and Edmonds, J.A. (2011). RCP4.5: A pathway for stabilization of radiative forcing by 2100, Climatic Change, 109(1): 77-94. Van Vuuren, D.P.; Edmonds, J.; Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., Hurtt, G.C., Kram, T., Krey, V., Lamarque, J.F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S.J. and Rose, S.K. (2011). The representative concentration pathways: An overview, Climatic Change, 109(1): 5-31. 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, 17(2): 147-159. Wetterhall, FA.; Bárdossy, D.; Chen, SH. and Xu, C-Y. (2006). Daily precipitation-downscaling techniques in three Chinese regions, Water Resources Research 42(aa):W11423. Wilby, RL.; Whitehead, PG.; Wade, AJ.; Butterfield, D.; Davis, RJ. and Watts, G. (2006). Integrated modelling of climate change impacts on water resources and quality in a lowland catchment: River Kennet, UK. Hydrology, 330(1-2): 204-220. 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. | ||
|
آمار تعداد مشاهده مقاله: 1,329 تعداد دریافت فایل اصل مقاله: 789 |
||