DOI QR코드

DOI QR Code

Selection framework of representative general circulation models using the selected best bias correction method

최적 편이보정 기법의 선택을 통한 대표 전지구모형의 선정

  • Song, Young Hoon (Department of Civil Engineering, Seoul National University of Science and Technology) ;
  • Chung, Eun-Sung (Department of Civil Engineering, Seoul National University of Science and Technology) ;
  • Sung, Jang Hyun (Han River Flood Control Office, Ministry of Environment)
  • 송영훈 (서울과학기술대학교 건설시스템공학과) ;
  • 정은성 (서울과학기술대학교 건설시스템공학과) ;
  • 성장현 (국토교통부 한강홍수통제소)
  • Received : 2019.02.28
  • Accepted : 2019.03.31
  • Published : 2019.05.31

Abstract

This study proposes the framework to select the representative general circulation model (GCM) for climate change projection. The grid-based results of GCMs were transformed to all considered meteorological stations using inverse distance weighted (IDW) method and its results were compared to the observed precipitation. Six quantile mapping methods and random forest method were used to correct the bias between GCM's and the observation data. Thus, the empirical quantile which belongs to non-parameteric transformation method was selected as a best bias correction method by comparing the measures of performance indicators. Then, one of the multi-criteria decision techniques, TOPSIS (Technique for Order of Preference by Ideal Solution), was used to find the representative GCM using the performances of four GCMs after the bias correction using empirical quantile method. As a result, GISS-E2-R was the best and followed by MIROC5, CSIRO-Mk3-6-0, and CCSM4. Because these results are limited several GCMs, different results will be expected if more GCM data considered.

본 연구에서는 미래 기후예측을 위하여 활용되는 전지구모형(general circulation model, GCM) 중 우리나라에 적합한 대표 GCM을 선정하는 방법을 제시하였다. 이에 격자 기반 GCM 결과를 IDW (Inverse Distance Weighted) 방법을 사용하여 기상 관측소로 지점 규모로 상세화를 하여 관측강수와 비교하였다. GCM과 관측자료 사이의 편이를 보정하기 위하여 6가지 Quantile Mapping 방법과 Random Forest 기법을 사용하였고, 성능 지표를 비교하여 대표 편이보정방법을 선정하였다. 편이보정된 GCM 모의 결과에 대한 성능을 계산하고 다기준의사결정기법 중 하나인 TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) 방법을 이용하여 가장 우수한 GCM을 선정하였다. 그 결과 편이보정방법을 NPT (Non-Parametric Transformation) 방법 중 EQ (Empirical Quantile) 방법이 선정되었고, TOPSIS 성능 평가 결과, GISS-E2-R이 가장 우수하였다. 그 다음으로 우수한 GCM을 순서대로 제시하면 MIROC5, CSIRO-Mk3-6-0, CCSM4 이었다. 향후 더 많은 GCM 자료를 이용한다면 보다 보편적인 결과를 도출할 수 있을 것으로 기대된다.

Keywords

SJOHCI_2019_v52n5_337_f0001.png 이미지

Fig. 1. Locations of meteorological stations used in this study

SJOHCI_2019_v52n5_337_f0002.png 이미지

Fig. 2. Probability density functions of seasonal precipitation by bias correction methods: Case of Seoul

SJOHCI_2019_v52n5_337_f0003.png 이미지

Fig. 3. Probability density functions of seasonal precipitation by four GCMs and the observed data: Case of Seoul

Table 1. Information on four GCMs used in this study

SJOHCI_2019_v52n5_337_t0001.png 이미지

Table 2. Description of performance indices used in this study

SJOHCI_2019_v52n5_337_t0002.png 이미지

Table 3. Performance indices of bias correction methods

SJOHCI_2019_v52n5_337_t0003.png 이미지

Table 4. Results of performance indices of four GCMs for 22 stations

SJOHCI_2019_v52n5_337_t0004.png 이미지

Table 5. Priorities of GCMs by 22 stations based on performance indices

SJOHCI_2019_v52n5_337_t0005.png 이미지

References

  1. Ahmed, K., Shahid, S., Chung, E. S., Wang, X. J., and Harun, S. B. (2019). "Climate change uncertainties in seasonal drought severity-area-frequency curves: Case of arid region of pakistan." Journal of Hydrology, Vol. 570, pp. 473-485. https://doi.org/10.1016/j.jhydrol.2019.01.019
  2. Amit, Y., and Geman, D. (1997). "Shape quantization and recognition with randomized trees." Neural Computation, Vol. 9, No. 7, pp. 1545-1588. https://doi.org/10.1162/neco.1997.9.7.1545
  3. Breiman, L. (2001). "Random forest." Machine Learning, Vol. 45, No. 1, pp. 5-32. https://doi.org/10.1023/A:1010933404324
  4. Cannon, A. J. (2008). "Probabilistic multisite precipitation downscaling by an expanded Bernoulli-gamma density network." Journal of Hydrometeorology, Vol. 9, pp. 1284-1300. https://doi.org/10.1175/2008JHM960.1
  5. Cannon, A. J. (2012). "Neural networks for probabilistic environmental prediction: Conditional Density Estimation Network Creation and Evaluation (CaDENCE) in R." Computers & Geosciences, Vol. 41, pp. 126-135. https://doi.org/10.1016/j.cageo.2011.08.023
  6. Chiew, F. H. S., Teng, J., Vaze, J., and Kirono, D. G. C. (2009). "Influence of global climate model selection on runoff impact assessment." Journal of Hydrology, Vol. 379, No. 1-2, pp. 172-180. https://doi.org/10.1016/j.jhydrol.2009.10.004
  7. Cho, J., Jung, I., Cho, W., and Hwang, S. (2018). "User-centered climate change scenarios technique development and application of Korean peninsula." Journal of Climate Change Research, Vol. 9, No. 1, pp. 13-29. https://doi.org/10.15531/ksccr.2018.9.1.13
  8. Dawson, C. W., Abrahart, R. J., and See, L. M., (2007). "HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts." Environmental Modelling & Software, Vol. 22, No. 7, pp. 1034-1052. https://doi.org/10.1016/j.envsoft.2006.06.008
  9. Dosiom, A., and Paruolo, P. (2011). "Bias correction of the ENSEMBLES high resolution climatechange projections for use by impact models:Evaluation on the present climate." Journal of Geophysical research Atmospheres, Vol. 116, No. D16, pp. 1-22.
  10. Eum, H. I., and Cannon, A. J. (2017). "Intercomparison of projected changes in climate extremes for South Korea: Application of trend preserving statistical downscaling methods to the CMIP5 ensemble." International Journal of Climatology, Vol. 37, No. 8, pp. 3381-3397. https://doi.org/10.1002/joc.4924
  11. Grillakis, M., G., Koutroulis, A. G., and Tsanis, I. K. (2013). "Multisegment statistical bias correction of daily GCM precipitation output." Journal of Geophysical research Atmospheres, Vol. 118, No. 8, pp. 3150-3162. https://doi.org/10.1002/jgrd.50323
  12. Gudmundsson, L., Bremnes, J. B., Haugen, J. E., and Engen-Skaugen, T. (2012). "Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations - a comparison of methods." Hydrology and Earth System Sciences, Vol. 16, pp. 3383-3390. https://doi.org/10.5194/hess-16-3383-2012
  13. Hwang, C. L., and Yoon, K. (1981). Multiple Attributes Decision Making Methods and Applications. Springer-Verlag, New York..
  14. Hwang, S. W. (2014a). "Assessing the performance of CMIP5 GCMs for various climatic elements and indicators over the southeast US." Journal of Korea Water Resources Association, Vol. 47, No. 11, pp. 1039-1050 https://doi.org/10.3741/JKWRA.2014.47.11.1039
  15. Hwang, S. W. (2014b). "Uncertainty of climate model skills estimated using different index." Proceedings of the Korean Society of Agricultural Engineers Conference, p. 42.
  16. Im, E. S., Kwon, W. T., and Bae, D. H. (2006). "A study on the regional climate change scenario for impact assessment on water resources." Journal of Korea Water Resources Association, Vol. 39, No. 12, pp. 1043-1056. https://doi.org/10.3741/JKWRA.2006.39.12.1043
  17. Ines, A. V. M., and Hansen, J. W. (2006). "Bias correction of daily GCM rainfall for crop simulation studies." Agricultural and Forest Meteorology, Vol. 138, No. 1-4, pp. 44-53. https://doi.org/10.1016/j.agrformet.2006.03.009
  18. Intergovernmental Panel on Climate Change (IPCC) (2014). Climate change 2014: synthesis report. contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, p. 151.
  19. Lee, G., Chung, E, S., and Jun, K. S. (2013). "MCDM approach for flood vulnerability assessment using TOPSIS method with a cut level set." Journal of Korea Water Resources Association, Vol. 46, No. 10, pp.977-987. https://doi.org/10.3741/JKWRA.2013.46.10.977
  20. Li, H., Sheffield, J., and Wood, E. F. (2010). "Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching." Journal of Geophysical Research, Vol. 115, No. D10.
  21. Longley, P. A., Goodchild, M. F., Maguire, D. J., and Rhind, D. W. (2005). Geographic information systems and science. Wiley, Grafos S.A.
  22. Masahiro, W., Tatsuo, S., Ryouta, O., Yoshiki, K., Shingo, W., Seita, E., Toshihiko, T., Minoru, C., Tomoo, O., Miho, S., Kumiko, T., Dai, Y., Tokuta, Y., Toru, N., Hiroyasu, H., Hiroaki, T., and Masahide, K. (2010). "Improved climate simulation by MIROC5: Mean states, variability, and climate sensitivity." Journal of Climate, Vol. 23, pp. 6312-6335. https://doi.org/10.1175/2010JCLI3679.1
  23. Mehr, A. D., and Kahya, E. (2017). "Grid-based performance evaluation of GCM-RCM combinations for rainfall reproduction." Theoretical and Applied Climatology, Vol. 129, No. 1-2, pp. 47-57. https://doi.org/10.1007/s00704-016-1758-1
  24. Noor, M., Ismail, T., Chung, E. S., Shahid, S., and Sung, J. H. (2018). "Uncertainty in rainfall intensity duration frequency curves of Peninsular Malaysia under changing climate scenarios." Water, Vol 10, No. 12, pp. 1-25. https://doi.org/10.3390/w10020001
  25. Pang, B., Yue, J., Zhao, G., and Xu, Z. (2017). "Statistical downscaling of temperature with the random forest model." Advances in Meteorology, Vol. 2017, pp. 1-11. https://doi.org/10.1155/2017/7265178
  26. Piani, C., Weedon, G. P., Best, M., Gomes, S. M., Viterbo, P., Hagemann, S., and Haerterd, J. O. (2010). "Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models." Journal of Hydrology, Vol. 395, No. 3-4, pp. 199-215. https://doi.org/10.1016/j.jhydrol.2010.10.024
  27. Pour, S. H., Shahida, S., and Chung, E. S. (2016). "A hybrid model for statistical downscaling of daily rainfall." Procedia Engineering, Vol. 154, pp. 1424-1430. https://doi.org/10.1016/j.proeng.2016.07.514
  28. Sa'adi, Z., Shahid, S., Chung, E. S., and Ismaila, T. (2017). "Projection of spatial and temporal changes of rainfall in Sarawak of Borneo Island using statistical downscaling of CMIP5 models." Atmospheric Research, Vol. 197, pp. 446-460. https://doi.org/10.1016/j.atmosres.2017.08.002
  29. Salman, S. A., Shahid, S., Ismail, T., Al-Abadi, A. M., Wang, X. J., and Chung, E. S. (2018). "Selection of gridded precipitation data for Iraq using compromise programming." Measurement, Vol. 132, pp. 87-98. https://doi.org/10.1016/j.measurement.2018.09.047
  30. Shiha, H. S., Shyurb, H. J., and Lee, E. S. (2007). "An extension of TOPSIS for group decision making." Mathematical and Computer Modelling, Vol. 45, No. 7-8, pp. 801-813. https://doi.org/10.1016/j.mcm.2006.03.023
  31. Shindell, D. T., Faluvegi, G., Unger, N., Aguilar, E., Schmidt, G. A., Koch, D. M., Bauer, S. E., and Miller, R. L. (2006). "Simulations of preindustrial, present-day, and 2100 conditions in the NASA GISS composition and climate model G-PUCCINI." Atmospheric Chemistry and Physics, Vol. 6, pp. 4427-4459. https://doi.org/10.5194/acp-6-4427-2006
  32. Shindell, D. T., Skeie, R. B., Sudo, K., Szopa, S., Takemura, T., and Zeng, G. (2012). "Global air quality and climate." Royal Society of Chemistry, Vol. 41, pp. 6663-6683. https://doi.org/10.1039/c2cs35095e
  33. Song, J. Y., and Chung, E. S. (2017). "Spatial prioritization of climate change vulnerability using uncertainty analysis of multi-criteria decision making method." Journal of Korea Water Resources Association, Vol. 50, pp. 121-128. https://doi.org/10.3741/JKWRA.2017.50.2.121
  34. Sung, J. H., Chung, E. S., and Shahid, S. (2018). "Reliability-Resiliency-Vulnerability approach for drought analysis in South Korea using 28 GCMs." Sustainability, Vol. 10, pp.1-16. https://doi.org/10.3390/su10020001
  35. Temba, N., and Chung, S. O. (2014). "Uncertainty of hydro-meteorological predictions due to climate change in the Republic of Korea." Journal of Korea Water Resources Association, Vol. 47, No. 3, pp. 257-267. https://doi.org/10.3741/JKWRA.2014.47.3.257
  36. Wood, A. W., Leung, L. R., Sridhar, V., and Lettenmaier, D. P. (2004). "Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs." Climatic Change, Vol. 62, No. 1-3, pp. 189-216. https://doi.org/10.1023/B:CLIM.0000013685.99609.9e
  37. Yapo, P. O., Gupta, V. H., and Sorooshian, S. (1995). "Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data." Journal of Hydrology, Vol. 181, pp. 23-48. https://doi.org/10.1016/0022-1694(95)02918-4
  38. Yu, J. S., Waseem M., Shin, J. Y., and Kim T. W. (2015). "Evaluation of extended inverse distance weighting method for constructing a flow duration curve at ungauged basin." Journal of Korean Society of Safety, Vol. 15, No. 3, pp. 329-337.