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Korean Meteorological Society (한국기상학회)
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Selecting Climate Change Scenarios Reflecting Uncertainties

불확실성을 고려한 기후변화 시나리오의 선정

  • Lee, Jae-Kyoung (Department of Civil and Environmental Engineering, Seoul National university) ;
  • Kim, Young-Oh (Department of Civil and Environmental Engineering, Seoul National university)
  • 이재경 (서울대학교 건설환경공학부) ;
  • 김영오 (서울대학교 건설환경공학부)
  • Received : 2011.12.05
  • Accepted : 2012.03.14
  • Published : 2012.06.30
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Abstract

Going by the research results of the past, of all the uncertainties resulting from the research on climate change, the uncertainty caused by the climate change scenario has the highest degree of uncertainty. Therefore, depending upon what kind of climate change scenario one adopts, the projection of the water resources in the future will differ significantly. As a matter of principle, it is highly recommended to utilize all the GCM scenarios offered by the IPCC. However, this could be considered to be an impractical alternative if a decision has to be made at an action officer's level. Hence, as an alternative, it is deemed necessary to select several scenarios so as to express the possible number of cases to the maximum extent possible. The objective standards in selecting the climate change scenarios have not been properly established and the scenarios have been selected, either at random or subject to the researcher's discretion. In this research, a new scenario selection process, in which it is possible to have the effect of having utilized all the possible scenarios, with using only a few principal scenarios and maintaining some of the uncertainties, has been suggested. In this research, the use of cluster analysis and the selection of a representative scenario in each cluster have efficiently reduced the number of climate change scenarios. In the cluster analysis method, the K-means clustering method, which takes advantage of the statistical features of scenarios has been employed; in the selection of a representative scenario in each cluster, the selection method was analyzed and reviewed and the PDF method was used to select the best scenarios with the closest simulation accuracy and the principal scenarios that is suggested by this research. In the selection of the best scenarios, it has been shown that the GCM scenario which demonstrated high level of simulation accuracy in the past need not necessarily demonstrate the similarly high level of simulation accuracy in the future and various GCM scenarios were selected for the principal scenarios. Secondly, the "Maximum entropy" which can quantify the uncertainties of the climate change scenario has been used to both quantify and compare the uncertainties associated with all the scenarios, best scenarios and the principal scenarios. Comparison has shown that the principal scenarios do maintain and are able to better explain the uncertainties of all the scenarios than the best scenarios. Therefore, through the scenario selection process, it has been proven that the principal scenarios have the effect of having utilized all the scenarios and retaining the uncertainties associated with the climate change to the maximum extent possible, while reducing the number of scenarios at the same time. Lastly, the climate change scenario most suitable for the climate on the Korean peninsula has been suggested. Through the scenario selection process, of all the scenarios found in the 4th IPCC report, principal climate change scenarios, which are suitable for the Korean peninsula and maintain most of the uncertainties, have been suggested. Therefore, it is assessed that the use of the scenario most suitable for the future projection of water resources on the Korean peninsula will be able to provide the projection of the water resources management that maintains more than 70~80% level of uncertainties of all the scenarios.

Keywords

References

  1. 통일연구원, 2009: 2009 The outline of the North Korea, 통일연구원.
  2. Boer, G. J., 2004: Long time-scale potential predictability in an ensemble of coupled climate models, Climatic Dynamics, 23, 23-44.
  3. Boer, G. J., and S. J. Lambert, 2008: Multi-model decadal potential predicability of precipitation and temperature, Geophysical Research Letters, 35, L05706. https://doi.org/10.1029/2008GL033234
  4. California Environmental Protection Agency, 2006: Climate Action Team Report to Governor Schwarzenegger and the Legislature, Climate Action Team. 16-25.
  5. Chiew, F. H. S., J. Teng, J. Vaze, and D. G. C. Kirono, 2009: Influence of global climate model selection on runoff impact assessment, Journal of Hydrology, 379, 172-180. https://doi.org/10.1016/j.jhydrol.2009.10.004
  6. Cover, T. M. and J. A. Thomas, 1991: Elements of Information Theory, Wiley. 13-54.
  7. Douglass, A. R., M. J. Prather, T. M. Hall, S. E. Strahan, P. J. Rasch, L. C Sparling, L. Coy, and J. M. Rodriguez, 1999: Choosing meteorological input for the global modeling initiative assessment of high-speed aircraft. Journal of Geophysical Research, 104(27), 545-564.
  8. Epstein, E. S., and A. H. Murphy, 1989: Skill scores and correlation coefficients in model verification, Monthly Weather Review, 117, 572-581. https://doi.org/10.1175/1520-0493(1989)117<0572:SSACCI>2.0.CO;2
  9. Gay, C., and F. Estrada, 2010: Objective probabilities about future climate are a matter of opinion, Climatic Change, 99, 27-46. https://doi.org/10.1007/s10584-009-9681-4
  10. Gleckler, P. J., K. E. Taylor, and C. Doutriaux, 2008: Performance metrics for climate models. Journal of Geophysical Research, 113, D06104. https://doi.org/10.1029/2007JD008972
  11. Intergovermental Panel on Climate Change, 2007: Climate change 2007: Synthesis report. IPCC.
  12. Jaynes, E. T., 1957: Information theory and statistical mechanics, The Physical Review, 106(4), 620-630. https://doi.org/10.1103/PhysRev.106.620
  13. Katz, R. W., 2002. Techniques for estimating uncertainty in climate change scenarios and impact studies, Climate Research, 20, 167-185. https://doi.org/10.3354/cr020167
  14. Kay, A. L., H. N. Davies, V. A. Bell, and R. G. Jones, 2009: Comparison of uncertainty sources for climate change impacts: flood frequency in England, Climatic Change, 92, 41-63. https://doi.org/10.1007/s10584-008-9471-4
  15. Kleeman, R., 2002: Measuring dynamical prediction utility using relative entropy, Journal of the Atmospheric Sciences, 59, 2057-2072. https://doi.org/10.1175/1520-0469(2002)059<2057:MDPUUR>2.0.CO;2
  16. Ma, K., and J. Cao, 1999: Climatic noise and potential predictability of monthly mean temperature over China. Meteorology and Atmospheric Physics, 69, 231-237. https://doi.org/10.1007/BF01030424
  17. Murphy, A. H., 1989: Skill scores based on the mean-square error and their relationships to the correlation coefficient, Monthly Weather Review, 116, 2417-2424.
  18. Murphy, A. H., 1996: General decompositions of MSE-based skill scores: Measures of some basic aspects of forecast quality, Monthly Weather Review, 124, 2353-2369. https://doi.org/10.1175/1520-0493(1996)124<2353:GDOMBS>2.0.CO;2
  19. Perkins, S. E., and A. J. Pitman, 2009: Do weak AR4 models bias projections of future climate changes over Australia?, Climatic Change, 93, 527-558. https://doi.org/10.1007/s10584-008-9502-1
  20. Perkins, S. E., A. J. Pitman, N. J. Holbrook, and J. Mcaneney, 2007: Evaluation of the AR4 climate models' simulated daily maximum temperature, minimum temperature, and precipitation over Australia using probability density functions, Journal of Climate, 20, 4356-4376. https://doi.org/10.1175/JCLI4253.1
  21. Pitman, A. J., and S. E. Perkins, 2008: Regional projections of future seasonal and annual changes in rainfall and temperature over Australia based on skill-selected AR4 models, Earth Interactions, vol. 12, DOI: 10.1175/2008EI260.1.
  22. Prudhomme, C., and H. Davies, 2009: Assessing uncertainties in climate change impact analyses on the river flow regimes in the UK. Part 2: future climate, Climatic Change, 93, 197-222. https://doi.org/10.1007/s10584-008-9461-6
  23. Raisanen, J., 2007: How reliable are climate models?. Tellus, 59A, 2-29.
  24. Reichler, T., and J. Kim, 2008: How well do coupled models simulate today's climate?, Bulletin of the American Meteorological Society, 89, 303-311. https://doi.org/10.1175/BAMS-89-3-303
  25. Shannon, C. E., 1948: A Mathematical Theory of Communication, Bell System Technical Journal, 27, 379-423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
  26. Shukla, J., T. Delsole, M. Fennessy, J. Kinter, and D. Paolino, 2006: Climate model fidelity and projections of climate change, Geophysical Research Letters, 33, L07702. https://doi.org/10.1029/2005GL025579
  27. Taylor, K. E., 2001: Summarizing multiple aspects of model performance in a single diagram, Journal of Geophysical Research, 106(D7), 7183-7192. https://doi.org/10.1029/2000JD900719
  28. Wang, X., and K. Smith, 2006: Characteristic-based clustering for time series data, Data Mining and Knowledge Discovery, 13, 335-364. https://doi.org/10.1007/s10618-005-0039-x
  29. Wilby, R. L., and I. Harris, 2006: A framework for assessing uncertainties in climate change impacts: Low-flow scenarios for the River Thames, U.K., Water Resources Research, 42, W02419.
  30. Wilks, D. S., 2005: Statistical methods in the atmospheric sciences, 2nd edition, Academic Press, New York. 648pp.
  31. Winter, C. L., and D. Nychka, 2009: Forecasting skill of model averages, Stochastic Environmental Resources Risk Assessment, DOI 10.1007/s00477-009-0350-y.
  32. Wolock, D. M., and G. J. McCabe, 1999: Estimates of runoff using water-balance and atmospheric general circulation models, Journal of the American Water Resources Association, 35, 1341-1350. https://doi.org/10.1111/j.1752-1688.1999.tb04219.x

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