نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشجوی دکتری آب و هواشناسی دانشگاه سیستان و بلوچستان، زاهدان، ایران
2 استاد اقلیم شناسی، گروه جغرافیای طبیعی، دانشگاه سیستان و بلوچستان، زاهدان، خیابان دانشگاه، دانشگاه سیستان و بلوچستان، زاهدان، ایران.
3 استادیار اقلیم شناسی، گروه جغرافیای طبیعی، دانشگاه سیستان و بلوچستان، زاهدان، ایران.
چکیده
کلیدواژه‌ها
عنوان مقاله [English]
نویسندگان [English]
Introduction
South East of Iran will yearly receive part of its water requirements from the summer precipitation, which is mostly due to the monsoon expansion. These precipitations are related to the temperature changes in the Indian Ocean level (Aramash et al., 2016: 2). Producing the accurate climate data is one of the main goals of the forecasting and modeling centers. Among the most important advantages of this data can mention the following: better estimation of the climatic variables in the regions which have no station, possibility to study the climate more appropriately and evaluate the fluctuations and changes of climate elements (Forsythe et al., 2015).
Data and Methods
In this research, we used daily precipitation data of 6 synoptic stations and the outputs of various CORDEX models in the South Asia during the statistical period of 30 years (2005-1976) in the studied region.
Results and Discussion
Evaluation of the accuracy of the global models' output on the basis of the downscaling of the studied regional model on a daily basis during the monsoon period (June, July, August and September) in (1976-2005) indicated that according to the different indicators the CanESM2, CSIRO and NorESM1 models have respectively more accurate estimation of precipitation values at most studied stations than other models. The correlation coefficient of none of the models has a good relationship. The observed and estimated precipitation values of the studied models in the form of the average activity months of the monsoon system showed the accuracy of the investigated models at each station is different but, in general, the three models CanESM2, CSIRO and NorESM1 are more appropriate in the estimation at the most stations than other models. A number of macro criteria must be considered to select the proper model; the 500 HPA Geo-potential height and the sea level pressure have been used in this research for this purpose. In fact, investigating the model's ability to simulate the 500 height as well as the earth is preferable over precipitation and temperature.
Conclusion
The results have suggested that the accuracy of the studied models at a variety of stations is different but, totally and at most of the stations, the three models CanESM2, CSIRO and NorESM1 have respectively better estimation of monsoon precipitation values than the other models. We cannot definitely prefer one model over the others according to the error estimation criteria as well as the comparison of different models with each other. We have shown in this research that the large scale variables such as Geo-potential height and pressure are more predictable than the precipitation and CanESM2, CSIRO-Mk, IPSL-CM2A-MR, MIROC2 and MPI ESM-LR models can be introduced as suitable models to simulate and predict climatic parameters of sea level pressure and 500 HPA Geo-potential in the monsoon system activity governing on the South East of Iran.
کلیدواژه‌ها [English]
10. Goswami, B.B.; Mani, N.J.; Mukhopadhyay, P.; Waliser, D.E.; Benedict, J.J.; Maloney, E.D.
11. Hamidian Pour, Mohsen, 2018, overview of Sistan wind and Climate Change Effects on it, 2nd National Conference on Climatology of Iran(in Persian)
12. Hasson, Sh. 2016. Seasonality of Precipitation over Himalayan Watersheds in CORDEX South Asia and their Driving CMIP5 Experiments, Atmosphere, Vol 7(123): pp 1-29.
13. Hu, T.S., Lam, K.C., Ng, S.T.2001. River flow time series prediction with a range dependent neural network. Hydrological Science Journal, Vlo 46:pp 729-745.
14. IPCC. 2013. Summary for Policymakers. In Climate Change: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Stocker, T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P.M., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, pp. 1–32.
15. Kamyar, Asghar, Movahedi. Saeed, Yazdanpanah, Hojjat,2017, Projection of Minimum and Maximum Air Temperatures in Isfahan Province during 2050-2017, Journal of climate research, Vol 1396(29):pp 37-54(in Persian).
16. Kamyar, Asghar, Movahedi. Saeed, Yazdanpanah, Hojjat, 2018, Evaluation of CORDEX-WAS temperature and precipitation output accuracy in Iran, 2nd National Conference on Climatology of Iran (in Persian).
17. Karamoz, Mohammad, Ramezani, Farid, Razavi, Saman. 2006. Long-term prediction Precipitation Using Meteorological Signals: Application of Artificial Neural Networks, 7th International Congress on Civil Engineering, Tehran, Page11 (in Persian).
18. Khansari, Seyed Mahmoud, Mousavi Baygi ,Seyed Mohammad,2018,Heat Waves Projection under Climate Change Scenarios in Khorasan Razavi province, 2nd National Conference on Climatology of Iran(in Persian).
19. Kavakebi,Ghazale, Mousavi Baygi ,Mohammad, Alizade, Amin, Mosaedi ,Abolfazl, Jabbari Noghabi, Mahdi, 2018, Detection of Change of Meteorological Drought Hazard under Climate Change(Case Study: Afin sub-basin), 2nd National Conference on Climatology of Iran(in Persian).
20. Khairoutdinov, M.; Goswami, B.N. 2011.Monsoon intraseasonal oscillations as simulated by the superparameterized Community Atmosphere Model.Journal of geophysical research vol5:pp1-17
21. Lee, J.-Y.; Wang, B. 2014. Future change of global monsoons in the CMIP5. Clim. Dyn. Vol 42: pp 101–119.
22. Lin, J.Y., Cheng, C.T., Chau, K.W. 2006. Using support vector machines for long-term discharge prediction. Hydrological Science Journal, Vol 51: pp 599-612.
23. Masoodian S A, Rayat pishe F, Keikhosravi Kiany M S , Introduction and a Comparison among Gridded Precipitation Database of Asfazari with GPCC, GPCP and CMAP. Geores. 2014; Vol 29 (1): pp 73-88(in Persian).
24. Mianabadi, Ameneh, Alizadeh,Amin, Sanaeinejad ,Seyed Hossein, Ghahraman, Bijan, Davary, Kamran,2016,,Prediction of Annual Evaporation Change in Dry Regions Using the Budyko-type framework (Case Study of Neishaboor-RokhWatershed), Vol 10(3), pp 398-411(in Persian).
25. Moulood, Elham, Ghasemi, Somayeh, Yazdan-Panah, Hojatollah. 2015. The zoning of areas susceptible to apple cultivation in the Khorasan Razavi province in the 2020-2016 horizon using data from Cordex, International Conference on Modern Research in Agricultural and Environmental Sciences, Kuala Lumpur, Malaysia, page13.
26. Öztürk, T., Altınsoy, H., Türkeş, M., & Kurnaz, M. L. (2012). Simulation of temperature and precipitation climatology for the Central Asia CORDEX domain using RegCM 4.0. Climate Research, Vol 52:pp 63-76.
27. Pattnayak, K C., Panda, S. K., Saraswat1, V., Dash1, S. K. 2018. Assessment of two versions of regional climate model in simulating the Indian Summer Monsoon over South Asia CORDEX domain, Clim Dyn, Vol 50:pp 3049–3061.
28. Sedaghat Kerdar, abdolah, Fatahi, ebrahim, 2008, Drought Early Warning Methods over Iran, Vol 6(11):pp 59-76(in Persian)
29. Syed, F.S.; Iqbal, W; Syed, A.A.B.; Rasul, G. 2014. Uncertainties in the regional climate models simulations of South-Asian summer monsoon and climate change. Clim. Dyn, Vol 42: pp 2079–2097.
30. Tong, K., Su, F., Yang, D., Zhang, L., and Hao, Z., 2014. Tibetan Plateau precipitation as depicted by gauge observations reanalyses and satellite retrievals: International J. Climatology, Vol 34: pp 265–285.
31. Turner, A.G.; Annamalai, H. 2011. Climate change and the south Asian summer monsoon. Nat. Clim. Chang. Vol 2: pp 587–595.
32. Webster, P.J.; Toma, V.E.; Kim, H.M.2011. Were the 2010 Pakistan floods predictable? Geophys. Res. Lett.,pp 38.
33. Zolfaqari, Hasan, Rahimi,Hamid Oji, Rohollah,2017, Assessment the Effects of Climate Change on the Degree of Heating and Cooling Days of Iran, Vol 7(22): pp 1-20(in Persian).
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