Assessment and Application of Two General Circulation Models (HadCM3 and MPEH5) for Investigating Climate Change (Case Study: Khorramabad Synoptic Station, Iran) | ||
| Desert Ecosystem Engineering Journal | ||
| مقاله 3، دوره 1، شماره 1، 2018، صفحه 21-36 اصل مقاله (2.14 M) | ||
| نوع مقاله: Original Article | ||
| نویسندگان | ||
| Asieh Baiati1؛ Mohsen Tavakoli* 2؛ Iman Babaeian3؛ Fatemeh Dargahian4 | ||
| 1Department of Natural Resources, Agriculture Faculty, Ilam University, Ilam, Iran | ||
| 2Department of Natural resources, Agriculture faculty, Ilam university, Ilam, Iran | ||
| 3Climate Research Institute, Mashhad, Iran | ||
| 4Research Organization of Agricultural Extension, Tehran, Iran | ||
| چکیده | ||
| A popular method for climate change prediction are General Circulation Models which are at coarse spatial resolution and must be downscaled. In this study, observed data of temperature, precipitation and potential evapotranspiration over a base period under two emission scenarios in three time intervals were used to implement SDSM as a downscaling tool for HadCM3 model output. From another standpoint, MPEH5 model predicts data under three emission scenarios for three future periods. Results indicated that all parameters would increase in comparison to the base period. Predictions for all periods under all emission scenarios indicated an increasing trend for all parameters, although it is predicted almost as constant precipitation trend for the future. According to predictions by both models, the greatest increase has been estimated for 2080s under A2 scenario. In SDSM model, the greatest increases in mean monthly temperature would be respectively 6.9, 4.5, 6.2 °C for July and for potential evapotranspiration would be in June by 1.08 mm per day, which are predicted in the 2080s under A2 scenario. For precipitation, the greatest reduction under the same conditions, would be in May by 0.9 mm per day. In LARS-WG model, the greatest increase in mean monthly temperature in the studied station was predicted respectively by 5.5, 5.5, 5.6 °C for August. The greatest reduction in precipitation, would be in February (by 0.88 mm per day). The future uncertainty results of predicted parameters in both models and various scenarios show that uncertainty of the predictions increase towards the end of the century. | ||
| کلیدواژهها | ||
| Climate change؛ HadCM3؛ SDSM؛ MPEH5؛ LARS-WG؛ Uncertainty؛ Khorramabad | ||
| مراجع | ||
|
Ashraf B, Mousavi Baygi M, Kamali GA and Davari K (2011) Prediction of Seasonal Variations of Climatological Parameters over Next 20 Years by Using Statistical Downscaling Method of HADCM3 Data (Case Study: Khorasan Razavi Province), J Water and Soil, 25 (4): 945-957
Chen H, Xu CY, Guo S (2012) Comparison and evaluation of multiple GCMs, statistical downscaling and hydrological models in the study of climate change impacts on runoff. J Hydrology 434–435: 36–45. http: //dx.doi.org/10.1016/j.jhydrol.2012.02.040
Chen J, Brissette FP, Lucas-Picher P (2015) Assessing the limits of bias-correcting climate model outputs for climate change impact studies. J Geophys Res Atmos 120:1123–1136. doi:10.1002/2014JD022635.
Chmura Daniel J, et al. ( 2011) Forest responses to climate change in the northwestern United States: Ecophysiological foundations for adaptive management; Forest Ecology and Management 261(7): 1121–1142
Dehghanipoor A, Hassanzadeh M, Attari J, Eraghinejad S (2011) Evaluation of empowerment of SDSM model in downscaling of precipitation, temperature and evaporation (Case study: Tabriz station). Eleventh Seminar irrigation and evaporation. 18-20 February.
Elshamy ME, Weather HS, Gedney N, Huntingford C (2005) Evaluation of the rainfall component of weather generator for climate change studies, J Hydrology, 326: 1-24. doi: 10.1016/j.jhydrol.2005.09.017
Hao Z, Ju Q, Jiang W, Zhu, C (2013) Characteristics and Scenarios Projection of Climate Change on the Tibetan Plateau. The Scientific World Journal, http://doi.org/10.1155/2013/129793
Hashemi MZ, Shamseldin AY, Melville BW (2011) Comparison of SDSM and LARS-WG for simulation and downscaling of extreme precipitation events in a watershed. Stochastic Environmental Research and Risk Assessment, 25(4): 475-484. doi: 10.1007/s00477-010-0416-x
IPCC (2013) Summary for Policymakers. In: Climate Change (2013): The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, and G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 4 pp.
Maraun D, Wetterhall F, Ireson AM, Chandler RE, Kendon EJ, Widmann M, Brienen S, Rust HW, Sauter T, Themeßl M, Venema VKC, Chun KP, Goodess CM, Jones RG, Onof C, Vrac M, Thiele‐Eich I (2010) Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Rev Geophys, 48 (3): 1-34. doi: 10.1029/2009RG000314
Meteorological Organization of Lorestan province, Applied Research Center (2010) Climate identity of city of Khorramabad, Iran.
Mirdarvishan M, Najafinejad A, Malekian A, Sadoddin A (2017) Downscaling the contribution to uncertainty in climate-change assessments: representative concentration pathway (RCP) scenarios for the South Alborz Range Iran. Meteorol. Appl., DOI: 10.1002/met.1709
Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE, 50(3): 885–900.
Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual model. J Hydrol, 10: 282-290. doi: 10.1016/0022-1694(70)90255-6
Nasoohyan S, Ghobadiniya M, Tabatabai S, Khaleghi H (2013) The effect of climate change on temperature and precipitation in the plains of Shahrekord & Borujen during the 2049-2020. The first national conference of Iran Climatology. May 21 & 22, 2013. Graduate University of Advanced Technology, Kerman, Iran
Pinto JG, Neuhaus CP, Leckebusch GC, Reyers M, Kerschgens M (2010) Estimation of wind storm impacts over Western Germany under future climate conditions using a statistical–dynamical downscaling approach. Tellus A 62 (2): 188–201. doi: 10.1111/j.1600-0870.2009.00424.x
Rajabi A, Shabanlou S (2010) Climate index changes in future by Using SDSM time periods in Kermanshah Iran. Journal of Environmental Research and Development, 7(1).
Rana A, Foster K, Bosshard T, Bengtsson L, Olsson J (2014) Impact of climate change on rainfall over Mumbai using Distribution-based Scaling of Global Climate Model projections. Journal of Hydrology: Regional Studies, 107–128. http: //dx.doi.org/10.1016/j.ejrh.2014.06.005
Rehan Dastagir M (2014) Modeling recent climate change induced extreme Events in Bangladesh: A review, Weather and Climate Extremes. http: //dx.doi.org/10.1016/j.wace.2014.10.003.
Salajegheh A, Rafiei Sardoii E, Moghaddamnia A, Malekian A, Araghinejad S, Khalighi Sigarodi S, Saleh Pourjam A (2017) Performance assessment of LARS-WG and SDSM downscaling models in simulation of precipitation and temperature. Iranian Journal of Soil and Water Research, 48(2); 253-262.
Samadi S, Ehteramain K, Sarraf BS (2011) SDSM ability in simulate predictors for climate detecting over Khorasan province, Procedia Social and Behavioral Sciences 19: 741–749. doi: 10.1016/j.sbspro.2011.05.193
Samadi SZ, Sagareswar G, Tajiki M (2010) Comparison of General Circulation Models: methodology for selecting the best GCM in Kermanshah Synoptic Station, Iran, Int J Global Warming, 2(4)347–365. http: //dx.doi.org/10.1504/IJGW.2010.03759
Schoof JT, Shin DW, Cocke S, LaRow E, Lim YK, O’Brien JJ (2009) Dynamically and statistically downscaled seasonal temperature and precipitation hind cast ensembles for the southeastern USA. Int. J. Climatol, 29 (2): 243-257. doi: 10.1002/joc.1717
Semenov MA, Barrow EM (2002) LARS-WG a stochastic weather generator for use in climate impact studies. User’s manual, Version3.0
Spickett Jeffery T, Brown Helen L, Katscherian Dianne (2011) Adaptation strategies for health impacts of climate change in Western Australia: Application of a Health Impact Assessment framework; Environmental Impact Assessment Review, 31: 297–300. http: //dx.doi.org/10.1016/j.eiar.2010.07.001
Teutschbein C, Wetterhall F, Seibert J (2011) Evaluation of different downscaling techniques for hydrological climate-change impact studies at the catchment scale. Clim. Dyn, 37 (9): 2087–2105. doi: 10.1007/s00382-010-0979-8
Wilby RL, Dawson CW (2004)Using SDSM Version 3.1- A decision suport tool for the assessment of regional climate change impacts, User Manual.
Wilby RL, Hay LE, Leavesley GH (1999) A comparison of downscaled and raw GCM output: implications for climate change scenarios in the San Juan River basin, Colorado J Hydrol 225 (1–2): 67–91. http: //dx.doi.org/10.1016/S0022-1694(99)00136-5
Willems P, Vrac M (2011) Statistical precipitation downscaling for small-scale hydrological impact investigations of climate change. J Hydrol, 402 (3–4): 193–205. http: //dx.doi.org/10.1016/j.jhydrol.2011.02.030
Zhao Yong WD, Yuan-sheng P (2010) Numerical Simulation and Evaluation of Regional Climate Change in Southwest China by a Regional Climate Model. International Society for Environmental Information Sciences 2010 Annual Conference (ISEIS). Procedia Environmental Sciences, 2: 1540–1554. | ||
|
آمار تعداد مشاهده مقاله: 590 تعداد دریافت فایل اصل مقاله: 397 |
||