DOI QR코드

DOI QR Code

변동경향을 보존하는 편의보정기법을 이용한 우리나라의 평균 및 극한기온 모의결과 보정

Correction of Mean and Extreme Temperature Simulation over South Korea Using a Trend-preserving Bias Correction Method

  • Jung, Hyun-Chae (Department of Atmospheric Sciences, Kongju National University) ;
  • Suh, Myoung-Seok (Department of Atmospheric Sciences, Kongju National University)
  • 투고 : 2015.02.03
  • 심사 : 2015.03.10
  • 발행 : 2015.06.30

초록

In this study, the simulation results of temperature by regional climate model (Reg- CM4) over South Korea were corrected by Hempel et al. (2013)'s method (Hempel method), and evaluated with the observation data of 50 stations from Korea Meteorological Administration. Among the 30 years (1981~2010) of simulation data, 20 years (1981~2000) of simulation data were used as a training data, and the remnant 10 years (2001~2010) data were used for the evaluation of correction. In general, the Hempel method and parametric quantile mapping show a reasonable correction both in mean and extreme climate of temperature. As the results, the systematic underestimation of mean temperature was greatly reduced after bias correction by Hempel method. And the overestimation of extreme climate, such as the number of TN5% and freezing day, was significantly recovered. In addition to that, the Hempel method better preserved the temporal trend of simulated temperature than other bias correction methods, such as the quantile mapping. However, the overcorrection of the extreme climate related to the upper quantile, such as TX5% and hot days, resulted in the exaggeration of the simulation errors. In general, the Hempel method can reduce the systematic biases embedded in the simulation results preserving the temporal trend but it tends to overcorrect the non-linear biases, in particular, extreme climate related to the upper percentile.

키워드

참고문헌

  1. Abe, M., H. Shiogama, T. Nozawa, and S. Emori, 2011: Estimation of future surface temperature changes constrained using the future-present correlated modes in inter-model variability of CMIP3 multimodel simulations. J. Geophys. Res. Atmos., 116, D18104. https://doi.org/10.1029/2010JD015111
  2. Baek, H. J., and Coauthors, 2013: Climate change in the 21st century simulated by HadGEM2-AO under representative concentration pathways. Asia-Pac. J. Atmos. Sci., 49, 603-618. https://doi.org/10.1007/s13143-013-0053-7
  3. Brown, J. R., C. Jakob, and J. M. Haynes, 2010: An evaluation of rainfall frequency and intensity over the australian region in a global climate model. J. Climate., 23, 6504-6525. https://doi.org/10.1175/2010JCLI3571.1
  4. Caesar, J., L. Alexander, and R. Vose, 2006: Large-scale changes in observed daily maximum and minimum temperatures: Creation and analysis of a new gridded data set. J. Geophys. Res. Atmos., 111, D05101.
  5. Cao, H. X., J. F. B. Mitchell, and J. R. Lavery, 1992: Simulated diurnal range and variability of surface temperature in a global climate model for present and doubled CO2 climates. J. Climate., 5, 920-943. https://doi.org/10.1175/1520-0442(1992)005<0920:SDRAVO>2.0.CO;2
  6. Charney, J. G., 1975: Dynamics of deserts and drought in Sahel. Quart. J. Roy. Meteor. Soc., 101, 193-202. https://doi.org/10.1002/qj.49710142802
  7. Choi, Y. E., 2004: Trends on temperature and precipitation extreme events in Korea. J. Korean Geogr. Soc., 39, 711-721.
  8. Christensen, J. H., F. Boberg, O. B. Christensen, and P. Lucas-Picher, 2008: On the need for bias correction of regional climate change projections of temperature and precipitation. Geophys. Res. Lett., 35, L20709 doi:10.1029/2008GL035694.
  9. CORDEX website, 2009, http://www.meteo.unican.es/en/projects/CORDEX.
  10. Dosio, A., and P. Paruolo, 2011: Bias correction of the ensembles high-resolution climate change projections for use by impact models: Evaluation on the present climate. J. Geophys. Res., 116, D16106, doi:10.1029/2011JD015934.
  11. Flato, G., and Coauthors, 2013: Evaluation of climate models. 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, 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.
  12. Giorgi. F., and Coauthors, 2012: RegCM4: Model description and preliminary test over multi CORDEX domains. Clim. Res., 52, 7-29. https://doi.org/10.3354/cr01018
  13. Gregersen, I. B., H. J. D. Sorup, H. Madsen, D. Rosbjerg, P. S. Mikkelsen, and K. A. Nielsen, 2013: Assessing future climatic changes of rainfall extremes at small spatio-temporal scales. Climatic Change, 118, 783-797. https://doi.org/10.1007/s10584-012-0669-0
  14. Groisman, P. Y., R. W. Knight, D. R. Easterling, T. R. Karl, G. C. Hegerl, and V. A. N. Razuvaev, 2005: Trends in intense precipitation in the climate record. J. Climate, 18, 1326-1350. https://doi.org/10.1175/JCLI3339.1
  15. Gudmundsson, L., J. B. Bremnes, J. E. Haugen, and T. Engen-Skaugen, 2012: Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations a comparison of methods. Hydrol. Earth Syst. Sci., 16, 3383-3390. https://doi.org/10.5194/hess-16-3383-2012
  16. Halenka, K., J. Kalvova, Z. Chladoca, A. Demeterova, K. Zemankova, and M. Belda, 2006: On the capability of RegCM to capture extremes in long term regional climate simulation comparison with the observation for Czech republic. Theor. Appl. Climatol., 86, 125-145. https://doi.org/10.1007/s00704-005-0205-5
  17. Hay, L. E., L. R. Wilby, and H. G. Leavesley, 2000: A comparison of delta change and downscaled GCM scenarios for three mountainous basins in the United States. J. Amer. Water Resour. Assoc., 36, 387-397. https://doi.org/10.1111/j.1752-1688.2000.tb04276.x
  18. Hempel, S., K. Frieler, L. Warszawski, J. Schewe, and F. Piontek, 2013: A trend-preserving bias correction-the ISI-MIP approach. Earth Syst. Dynam., 4, 219-236.
  19. Hong, S. Y., S. G. Oh, M. S. Suh, D. K. Lee, J. B. Ahn, and H. S. Kang, 2013: Future climate changes over North-East Asian region simulated by RegCM4 based on the RCP scenarios. Clim. Res., 8, 27-44.
  20. ICTP Portal, 2010, RegCM4. http://www.ictp.it/research/esp/models/regcm4.
  21. Im, E. S., and W. T. Kwon, 2007: Characteristics of extreme climate sequences over Korea using regional climate change scenario. Sci. Online Lett. Atmos., 3, doi:10.2151/sola.2007-005.
  22. Im, E. S., M. H. Kim, W. T. Kwon, and D. H. Bae, 2007: Sensitivity of recent and future regional climate simulations to two convection schemes in the RegCM3 nesting system. J. Korean Meteor. Soc., 43, 411-427.
  23. Kharin, V. V., F. W. Zwiers, X. B. Zhang, and G. C. Hegerl, 2007: Changes in temperature and precipitation extremes in the IPCC ensemble of global coupled model simulations. J. Climate, 20, 1419-1444. https://doi.org/10.1175/JCLI4066.1
  24. Kharin, V. V., F. W. Zwiers, X. B. Zhang, and M. Wehner, 2012: Changes in temperature and precipitation extremes in the CMIP5 ensemble. Climatic Change, doi:10.1007/s10584-013-0705-8.
  25. Latif, M., D. Anderson, T. Barnett, M. Care, R. Kleeman, A. Leetmaa, J. Obrien, A. Rosati, and E. Schneider, 1998: A review of the predictability and prediction of ENSO. J. Geophys. Res., 103, 14375-14393. https://doi.org/10.1029/97JC03413
  26. Lee, D. K., and M. S. Suh, 2000: Ten-year East Asian summer monsoon simulation using a Regional Climate Model (RegCM2). J. Geophys. Res., 105, 29565-29577. https://doi.org/10.1029/2000JD900438
  27. Lee, D. K., D. H. Cha, and H. S. Kang, 2004: Regional climate simulation of the 1998 summer flood over East Asia. J. Meteor. Soc. Japan., 82, 1735-1753. https://doi.org/10.2151/jmsj.82.1735
  28. Lee, Y. H., D. H. Cha, and D. K. Lee, 2008: Impact of horizontal resolution of regional climate model on precipitation simulation over the Korean Penisula. Atmosphere, 18, 387-395.
  29. Liu, J. W., B. Li, T. J. Zhou, X. F. Zeng, and L. Feng, 2012: The extreme summer precipitation over East China during 1982-2007 simulated by the LASG/IAP regional climate model. Atmos. Oceanic Sci. Lett., 5, 62-67. https://doi.org/10.1080/16742834.2012.11446966
  30. Myoung, J. S., S. G. Oh, and M. S. Suh, 2012: Improvement of simulated air temperature of regional climate model using linear regression method. Clim. Res., 3, 255-270.
  31. Oh, S. G., and M. S. Suh, 2013: Projection of fine-scale climate changes over South Korea based on the RCP (2.6, 4.5, 6.0, 8.5) scenarios using RegCM4. Clim. Res., 8, 291-307. https://doi.org/10.14383/cri.2013.8.4.291
  32. Oh, S. G., M. S. Suh, and D. H. Cha, 2012: Relationship of simulation performance of RegCM4 and lateral boundary condition for extreme climate of South Korea in the CORDEX East Asia domain. Korean Meteor. Soc., 49-50.
  33. Oh, S. G., J. H. Park, S. H. Lee, and M. S. Suh, 2014: Assessment of the RegCM4 over East Asia and future precipitation change adapted to the RCP scenarios. J. Geophys. Res., 119, 2913-2927.
  34. Park, J., M. S. Kang, and I. Song, 2012: Bias correction of RCP-based future extreme precipitation using a quantile mapping method; for 20-weather stations of South Korea. J. Korean Soc. Agric. Eng., 54, 133-142.
  35. Park, J. H., S. G. Oh, and M. S. Suh, 2013: Impacts of boundary conditions on the precipitation simulation of RegCM4 in the CORDEX East Asia domain. J. Geophys. Res., 118, 1652-1667.
  36. Piani, C., G. P. Weedon, M. Best, S. M. Gomes, P. Viterbo, S. Hagemann, and J. O. Haerter, 2010: Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models. J. Hydrol., 395, 199-215. https://doi.org/10.1016/j.jhydrol.2010.10.024
  37. Randall, D. A., and Coauthors, 2007: Climate models and their evaluation. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor, and H. L. Miller (eds.)] Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 589-662.
  38. Rhein, M., and Coauthors, 2013: Observations: Ocean. 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, 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.
  39. Rojas, R., L. Feyen, A. Dosio, and D. Bavera, 2011: Improving Pan-European hydrological simulation of extreme events through statistical bias correction of RCM-Driven climate simulations. Hydrol. Earth Syst. Sci., 15, 2599-2620. https://doi.org/10.5194/hess-15-2599-2011
  40. Ryoo, S. B., and J. S. Park, 2012: Meteorological Statistics, Chonnam National University Press, 142-143.
  41. Sillmann, J., V. V. Kharin, X. Zhang, and F. W. Zwiers, 2013: Climate extreme indices in the CMIP5 multimodel ensemble. Part 1: Model evaluation in the present climate. J. Geophys. Res., doi:10.1029/2012JD018390.
  42. Suh, M. S., and C. S. Kim, 2014: Comparison of statistical bias correction method for monthly mean temperature in the Korea Penisula simulated by the regional climate model. Proceeding of the Autumn Meeting of Korean Meteorological Society, 2014, 65-66.
  43. Suh, M. S., S. G. Oh, D. K. Lee, D. H. Cha, S. J. Choi, C. S. Jin, and S. Y. Hong, 2012: Development of new ensemble methods based on the performance skills of regional climate models over South Korea. J. Climate, 25, 7067-7082, doi:10.1175/JCLI-D-11-00457.1.
  44. Sung, J. H., H. S. Kang, S. Park, C. H. Cho, D. H. Bae, and Y. H. Kim, 2012: Projection of extreme precipitation at the end of 21st century over South Korea based on Representative Concentration Pathways (RCP). Atmosphere, 22, 221-231. https://doi.org/10.14191/Atmos.2012.22.2.221
  45. Tadross, M., C. Jack, and B. Hewitson, 2005: On RCMbased projection of change in Southern African summer climate. Geophys. Res. Lett., 32, L23713, doi:10.1029/2005GL024460.
  46. Teutschbein, C., and J. Seibert, 2012: Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods. J. Hydrol., 456-457, 12-29. https://doi.org/10.1016/j.jhydrol.2012.05.052
  47. Wilby, R. L., and T. M. Wigley, 1997: Downscaling general circulation model output: A review of methods and limitations. Prog. Phys. Geog., 21, 530-548, doi: 10.1177/030913339702100403.

피인용 문헌

  1. The Use and Abuse of Climate Scenarios in Agriculture vol.18, pp.3, 2016, https://doi.org/10.5532/KJAFM.2016.18.3.170