한국농공학회논문집 (Journal of The Korean Society of Agricultural Engineers)

  • 제60권5호
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  • Pages.149-162
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  • 2018
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  • 1738-3692(pISSN)
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  • 2093-7709(eISSN)
한국농공학회 (The Korean Society of Agricultural Engineers)
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기후변화 시나리오 자료의 불확실성 고려를 위한 대표 GCM 선정기법 개발

Development of Representative GCMs Selection Technique for Uncertainty in Climate Change Scenario

  • Jung, Imgook (Climate Services and Research Department, APEC Climate Center) ;
  • Eum, Hyung-Il (Environment Monitoring and Science Division) ;
  • Lee, Eun-Jeong (Climate Services and Research Department, APEC Climate Center) ;
  • Park, Jihoon (Climate Services and Research Department, APEC Climate Center) ;
  • Cho, Jaepil (Climate Services and Research Department, APEC Climate Center)
  • 투고 : 2017.12.27
  • 심사 : 2018.09.14
  • 발행 : 2018.09.30
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초록

It is necessary to select the appropriate global climate model (GCM) to take into account the impacts of climate change on integrated water management. The objective of this study was to develop the selection technique of representative GCMs for uncertainty in climate change scenario. The selection technique which set priorities of GCMs consisted of two steps. First step was evaluating original GCMs by comparing with grid-based observational data for the past period. Second step was evaluating whether the statistical downscaled data reflect characteristics for the historical period. Spatial Disaggregation Quantile Delta Mapping (SDQDM), one of the statistical downscaling methods, was used for the downscaled data. The way of evaluating was using explanatory power, the stepwise ratio of the entire GCMs by Expert Team on Climate Change Detection and Indices (ETCCDI) basis. We used 26 GCMs based on CMIP5 data. The Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios were selected for this study. The period for evaluating reproducibility of historical period was 30 years from 1976 to 2005. Precipitation, maximum temperature, and minimum temperature were used as collected climate variables. As a result, we suggested representative 13 GCMs among 26 GCMs by using the selection technique developed in this research. Furthermore, this result can be utilized as a basic data for integrated water management.

키워드

참고문헌

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