4عضو هیات علمی دانشگاه گنبد کاووس، دانشجوی دکتری آمار دانشگاه صنعتی شاهرود
چکیده
علیرغم پیشرفت علم و در نتیجه دقیق‌تر شدن مدل‌های اقلیمی در پروژه‌های تغییر اقلیم، منابع مختلفی از عدم قطعیت وجود دارد که ناشی از فعالیت‌های انسانی و واکنش متقابل سیستم اقلیمی در مقیاس‌های بزرگ مکانی و زمانی است. لذا، به‌منظور کاربرد موفقیت‌آمیز شبیه‌سازی پارامترهای هواشناسی در تحقیقات کاربردی منابع آب، تحلیل عدم قطعیت ضروری است. هدف این تحقیق، بررسی عدم قطعیت شبیه‌سازی سری‌زمانی بارش در افق آتی اول (2040-2011) و افق آتی دوم (2070-2040) با دو روش باکس پلات و بوت استرپ است. شبیه‌سازی‌های سری زمانی بارش خروجی مدل HadCM3 با سناریوهای A1B، A2، B2، B1، مدل‌های NCPCM، CNCM3 با سناریوی A1B، مدل GFCM2 با سناریوهای A1B و A2 و مدل CGCM3با سناریوهای A1B و A2 با دو مدل ریز مقایس گردانی آماری و در مجموع 10 سناریوی مختلف برای بررسی عدم قطعیت شبیه‌سازی‌ها در دو افق آینده اول و دوم استفاده شد. در این تحقیق دو روش باکس- ویسکر و روش غیرپارامتری فاصله اطمینان بوت استرپ جهت بررسی و کاهش عدم قطعیت شبیه‌سازی‌ها بکار برده شد. طبق نتایج نمودار باکس- ویسکر در ایستگاه همدیدی بجنورد، شبیه‌سازی‌های ماهانه در بعضی سناریوها با دو مدل CGCM3 و HadCM3 در افق اول و مدل HadCM3 در افق دوم به عنوان داده پرت برای مرحله بعدی آنالیز در نظر گرفته نشد. در ایستگاه همدیدی مشهد نیز اختلاف معنی‌داری در شبیه‌سازی بعضی مدل‌های GCM و سناریوهای انتشار مشاهده شد که مربوط به مدل CGCM3 در دو ماه ژانویه و مارس و مدل GFCM3 در ماههای مربوط به فصل تابستان بود. بعد از شناسایی و حذف سناریوهای پرت با روش باکس-ویسکر، نتایج بیانگر انتظار افزایش بارش در هر دو ایستگاه و در هر دو افق آینده است. در مرحله بعد با روش بوت استرپ عدم قطعیت خروجی برای مجموعه شبیه‌سازی‌ها محاسبه شد. نتایج در ایستگاه همدیدی بجنورد بیانگر ضخامت زیاد باند عدم قطعیت در اکثر ماه‌ها به‌جز در ماههای آگوست و اکتبر است. همچنین مقایسه مقادیر میانگین شبیه‌سازی بارش ماهانه آینده با دوره پایه بیانگر افزایش بارش در شش ماهه دوم میلادی در دو افق آتی نسبت به دوره پایه است. در بیشتر مطالعات قبلی در ایران طیف گسترده‌ای از عدم قطعیت‌ها در بحث پیش‌بینی تغییر اقلیم را در نظر نگرفتند و درنتیجه یافته‌های آنها دقیق‌تر از آنچه که واقعا هستند به نظر می‌رسد. بنابراین نتایج آنها کمتر مورد قبول محققان است و برای سیاستگزاران منابع آب گمراه کننده است. به نظر محققین این مقاله روش ارایه شده در اینجا تا حدودی نقص اساسی در بیشتر مطالعات تغییر اقلیم در کشور را پوشش می‌دهد و عدم در نظر گرفتن عدم قطعیت در مطالعات تغییر اقلیم می‌تواند به کم بها دادن طیف وسیعی از اثرات تغییر اقلیم منجر شود.
Uncertainty analysis of rainfall projections (case study: Bojnourd and Mashhad synoptic gauge station)
نویسندگان [English]
Azam Ghandi2؛ Seyed Morteza Seyedian3؛
2Former M.Sc. Student of Watershed Management Dept. Gonbad Kavuse University
3Assistance Professor ofGonbad Kavuse University
4Lecturer in Gonbad University
چکیده [English]
Despite recent progress in developing reliable climate models, the different uncertainties inherent in climate change projections. Climate can change due to a number of anthropogenic and natural factors in spatial and temporal large scales. Therefore, a successful application of a climate parameters simulation in applied water research strongly depends on uncertainty analysis of model output. Here we present a detailed and quantitative uncertainty assessment of rainfall for first future epoch (2011-2040) and second future epoch (2040-2070), based on the projections of wide range of rainfall projections resulting from the factorial combination of four emission scenarios, five GCMs and two downscaling methods (LARS-WG و SDSM) in Bojnourd and Mashhad synoptic stations. This enabled us to decompose the uncertainty in the ensemble of projections using Box-whisker plot and Bootstrapping method. The uncertainty in precipitation change in response to the general circulation model (GCM) from HadCM3, NCPCM, CNCM3, GFCM2, CGCM3, SRES emission scenarios (A1B, A2, B1, and B2) and two downscaling method (SDSM and LARS-WG) was investigated in two future epochs. In this study, we evaluate the impact of uncertainty in climate change projections on the future precipitation by Box-whisker plots and Bootstrap technique. In the first step, the outliers were excluded by box-and-whisker plots. In the next step the precipitation projected which is reported by ten different scenarios, is then a vector of about 6000 bootstrap replications (500 per model), from which we take the 2.5th and 97.5th percentiles to calculate the range containing 95% of projected estimates. The GCM models show wide variation in their results, particularly for Bojnourd precipitation forecasting. According to Box-whisker graph in Bojnourd synoptic station (BSS), the projected precipitations by CGCM3 and HadCM3 in first and second epoch fall under the 2.5th and 97.5th percentiles. In Mashhad synoptic station (MSS) some scenarios projected precipitation significantly different from other scenarios which were belonging to CGCM3 in January and March and GFCM3 in summer months. On the basis of these results, it is clear that both stations will experience an increase in precipitation for epoch1 and epoch2, with the largest increase found for epoch2. In the next step confidence interval estimation by the bootstrap method is investigated for the uncertainty quantification of precipitation projections using the random sampling method. In BSS the confidence interval band is large in all month except in August and October. It is interesting that for MSS, the range in GCM predictions is relatively small for all seasons except in spring. This means that the uncertainty in climate predictions is considerably smaller for these months. All GCM and downscaling outputs are inherently uncertain because no model can ever fully describe physical systems. Most studies in the literature on the climate change projection do not capture the full range of plausible future climate variation, making their findings seem more precise than they actually are, and as a result making them less credible among climate scientists and potentially misleading for policymakers. We feel that the methodological approach presented here addresses a fundamental shortcoming in the past research. We show that failing to account for climate uncertainty lead to a false sense of confidence about the likely future impacts of climate change, when in fact impacts are actually far less certain
2; word-spacing: 0px; -webkit-text-sizAbasi, F., Babaeyan, A., Malbosi, Sh., Asmari, M., and Goli Mokhtari, L. 2012. Assessment of climate change in the coming decades (2025 to 2100) using General Circulation Model’s downscaling climate data. J. Geograph. Res. 1: 27. 205-230. (In Persian) 2.Abasnia, M., Tavosi, T., Khosravi, M., and Torous, H. 2016. Uncertainty analysis of future changes in daily maximum temperatures over Iran by GIS. Geographical Data. 25: 97. 29-43. (In Persian) 3.Alexander, L., Zhang, X., Peterson, T., Caesar, J., Gleason, B., Klein Tank, A., Haylock, M., Collins, D., Trewin, B., Rahimzadeh, F., Tagipour, A., Rupa Kumar, K., Revadekar, J., Griffiths, G., Vincent, L., Stephenson, D., Burn, J., Aguilar, E., Brunet, M., Taylor, M., New, M., Zhai, P., Rusticucci, M., and Vazquez-Aguirre, J. 2006. Global observed changes in daily climate extremes of temperature and precipitation. J. Geophysic. Res. Atm. 111, D05. 1-22. 1- With of margin 4.Alexandru, A., and Sushama, L. 2015. Current climate and climate change over India as simulated by the Canadian Regional Climate Model. Climate Dynamics. 45: 1059-1084. 5.Ansari, H., Khadivi, M., Saleh Niya, N., and Babaiyan, A. 2014. Evaluation of uncertainty of LARS-WG under scenario A1B, A2 and B1 in predicting precipitation and temperature (Case Study: Mashhad synoptic station). J. Irrig. Drain. 4: 8. 664-672. (In Persian) 6.Arnell, N. 2004. Climate change and global water resources: SRES emissions and socio-economic scenarios. Global Environmental Change. 14: 131-52. 7.Ashofte, P., and Massah, A.R. 2009. Uncertainty of climate change impact on the flood regime. Case study: Aidoghmoush basin, East Azarbaijan. Water Resources Research. 5: 2. 27-39. 8.Ashraf, B., Alizadeh, A., Mousavi Baygi, M., and Bannayan Aval, M. 2013. Verification of temperature and precipitation data simulated by implementing individual and group five AOGCM models for North East Iran. J. Soil Water (Agricultural Science and Technology). 2: 28. 253-266. (In Persian) 9.Babaiyan, A., and Najafi Nik, Z. 2006. Introduction and evaluation of LARS-WG to simulate meteorological parameters Khorasan period (2003-1961). Quarterly maker. 62: 49-65. (In Persian) 10.Chen, J., Brissettea, F.P., Chaumontb, D., and Braunb, M. 2013. Performance and uncertainty evaluation of empirical downscaling methods in quantifying the climate change impacts on hydrology over two North American river basins. J. Hydrol. 479: 4. 200-214. 11.Christensen, J., and Christensen, O. 2007. A summary of the PRUDENCE model projections of changes in European climate by the end of this century. Climatic Change. 81: 7. 7-30. 12.Ebrahim, G.Y., Jonoski, A., Griensven, A., and Baldassarre, G.D. 2013. Downscaling technique uncertainty in assessing hydrological impact of climate change in the Upper Beles River Basin, Ethiopia. J. Hydrol. Res. 44: 2. 37-44. 13.Efron, B., and Tibshirani, V. 1993. An introduction to the bootstrap. Chapman and Hall, New York. 14.Etemadi, E., Samadi, Z., and Sharifikia, M. 2014. Uncertainty analysis of statistical downscaling models using general circulation model over an international wetland. Climate Dynamics. 42: 2899-2920. 15.Fowler, H.J., Blenkinsop, S., and Tebaldi, C. 2007. Linking climate change modeling to impacts studies: Recent advances in downscaling techniques for hydrologic modeling. Inter. J. Climatol. 27: 1547-1578. 16.Gao, Y., Lu, J., and Leung, L.R. 2016. Uncertainties in projection future changes in atmospheric rivers and their impacts on heavy precipitation over Europe. J. Clim. 29: 18. 6711-6726. 17.Ghandi, A. 2015. Evaluation of uncertainty in estimates of climate parameters by different statistical downscaling methods. Master thesis, University of Gonbad. 18.Ghermez Cheshmeh, B., Rasoli, A., Rezayi Banafsheh, M., Mesah Bavani, A., and Khorshid Dost, A. 2015. Evaluation of uncertainty in the simulated neural network handling HADCM3 using bootstrap confidence intervals. J. Engin. Water. Manage. 3: 7. 306-316. (In Persian) 19.Graham, P., Hagemann, S., Juan, S., and Beniston, M. 2007. On interpreting hydrological change from regional climate models. J. Clim. Change. 81: 97-122. 20.Hoshmand, D., and Khordadi, M.J. 2014. Uncertainty Assessment of AOGCMs and Emission Scenarios in Climatic Parameters Estimation (Case Study in Mashhad Synoptic Station). Geography and Environmental Hazards. 3: 11. 77-92. (In Persian) 21.Hughes, D.A., Mantel, S., and Mohobane, T. 2014. An assessment of the skill of downscaled GCM outputs in simulating historical patterns of rainfall variability in South Africa. Hydrology Research. 45: 1. 134-147. 22.Huth, R. 2004. Sensitivity of local daily temperature change estimates to the selection of downscaling models and predictors. J. Clim. 17: 640-652. 23.Kent, C., Chadwick, R., and Rowell, P.D. 2015. Understanding Uncertainties in Future Projections of Seasonal Tropical Precipitation. J. Clim. 28: 4390-4413. 24.Knutti, R. 2008. Should we believe model predictions of future climate change, Philosophical transactions Series A. Mathematical, Physical and Engineering Sciences. 366: 1885. 4647-4664. 25.Knutti, R., Furrer, R., Tebaldi, C., Cermak, J., and Meehl, G. 2010. Challenges in combining projections from multiple climate models. J. Clim. 23: 10. 2739-2758. 26.Kohi, M., and Sanayi Nejad, H. 2013. Climate change scenarios based on the results of the two methods of handling statistical downscaled variable reference evapotranspiration in Orumiyeh. J. Irrig. Drain. 4: 7. 559-574. (In Persian) 27.Kripalanai, R.H., and Kulkarni, A. 2007. South Asian summer monsoon precipitation variability, 2007: coupled climate model simulations and projections under IPCC AR4. Theor. Appl. Climatol. 90: 133-159. 28.Kumar, P., Wiltshire, A., Mathison, C., Asharaf, Sh., Ahrens, B., Lucas-Picher, P., Christensen, H.J., Gobiet, A., Saeed, F., Hagemann, S., and Jacob, D. 2013. Downscaled climate change projections with uncertainty assessment over India using a high resolution multi-model approach. Science of the Total Environment. 468: 18-30. 29.Lavaysse, C., Vrac, M., Drobinski, P., Lengaigne, M., and Vischel, T. 2012. Present and projection in an anthropogenic scenario. Natural Hazards and Earth System Science. 12: 3. 651-670. 30.Meinshausen, M., Raper, S., and Wigley, T. 2008. Emulating IPCC AR4 atmosphere ocean and carbon cycle models for projecting global-mean, hemispheric and land/ocean temperatures: MAGICC 6.0. Atmospheric Chemistry and Physics Discussions. 8: 2. 6153-6272. 31.Mojtahedi, S.M.H., and Oo, B.L. 2014. Coastal buildings and infrastructure flood risk analysis using multi-attribute decision-making. J. Flood Risk Manage. 9: 1. 87-96. 32.Pir moradian, N., Hadinia, H., and Ashrafzadeh, A. 2016. Prediction of Minimum and Maximum Temperature, Radiation and Precipitation in Rasht Synoptic Station under Different Climate Change Scenarios. J. Geograph. Plan. 20: 55. 29-44. (In Persian) 33.Rowell, D.P., Senior, C.A., Vellinga, M., and Graham, R.J. 2016. Can climate projection uncertainty be constrained over Africa using metrics of contemporary performance? Climate Change. 134: 621-633. 34.Samadi, S., Wilson, A.M.E., and Moradkhani, H. 2013. Uncertainty analysis of statistical downscaling models using Hadly Center Coupled Model. Theoretical and Applied Climatology. 113: 3-4. 673-690. 35.Semenov, M., and Stratonovitch, P. 2010. Use of multi-model ensembles from global climate models for assessment of climate change impacts. Climate Research. 41: 1-14. 36.Sheffield, J., and Wood, E. 2008a. Global Trends and Variability in Soil Moisture and Drought Characteristics, 1950-2000, from Observation-Driven Simulations of the Terrestrial Hydrologic Cycle. J. Clim. 21: 3. 432-458. 37.Sheffield, J., and Wood, E. 2008b. Projected changes in drought occurrence under future global warming from multi-model, multi-scenario. IPCC AR4 simulations, Climate Dynamics. 31: 1. 79-105. 38.Stainforth, D., Allen, M., Tredger, E., and Smith, L. 2007. Confidence, uncertainty and decision-support relevance in climate predictions. Philosophical Transactions of the Royal Society A – Mathematical. Physical and Engineering Sciences. 365: 2145-2161. 39.Sunyer, M.A., Hundecha, Y., Lawence, D., Willems, P., Martinkova, M., Vormoor, K., Burger, G., Hanel, M., Kriauciuniene, J., Loukas Osuch, M., and Yucel, I. 2014. Inter-comparison of projection of extreme precipitation in Europe. Hydrology and Earth System Sciences Discussions. 11: 6167-6214. 40.Tao, H., Gemmer, M., Jiang, J., Lai, X., and Zhang, Z. 2012. Assessment of CMIP3 climate models and projected changes of precipitation and temperature in the Yangtze River Basin, China. Climate Change. 111: 737-751. 41.Tebaldi, C., and Knutt, R. 2007. The use of the multi-model ensemble in probabilistic climate projections, Philosophical Transactions of the Royal Society. Series A. Mathematical. Physical and Engineering Sciences. 365: 1857. 2053-2075. 42.Turley, M.C., and Ford, E.D. 2009. Definition and calculation of uncertainty in ecological process models. Ecological Modelling. 220: 1968-1983. 43.van Asselt, M., and Rotmans, J. 2002. Uncertainty in Integrated Assessment Modelling. Climatic Change. 54: 1-2. 75-105. 44.Vasiliades, L., Loukas, A., and Patsonas, G. 2009. Evaluation of a statistical downscaling procedure for the estimation of climate change impacts on droughts. Natural Hazards and Earth System Science. 9: 3. 879-894. 45.Yu, W., Nakakita, E., Kim, S., and Yamaguchi, K. 2016. Impact assessment of uncertainty propagation of ensemble NWP rainfall to flood forecasting with catchment scale. Advances in Meteorology. 2016: 1-17. 46.Zhang, H., Huang, G., Wang, D., and Zhang, X. 2011. Uncertainty assessment of climate change impacts on the hydrology of small prairie wetlands. J. Hydrol. 396:1-2. 94-103. 47.Zhang, X., Zwiers, F.W., Hegerl, G.C., Lambert, F.H, Gillett, N.P, Solomon, S., Stott, P., and Nozawa, T. 2007. Detection of human influence on twentieth century precipitation trends. Nature. 448: 461-465
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