기후변화 시나리오 자료의 불확실성 고려를 위한 대표 GCM 선정기법 개발
Development of Representative GCMs Selection Technique for Uncertainty in Climate Change Scenario
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참고문헌
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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 selectiontechnique which set priorities of GCMs consisted of two steps. First step was evaluating original GCMs by comparing with grid-based observationaldata for the past period. Second step was evaluating whether the statistical downscaled data reflect characteristics for the historical period. SpatialDisaggregation Quantile Delta Mapping (SDQDM), one of the statistical downscaling methods, was used for the downscaled data. The way of evaluatingwas using explanatory power, the stepwise ratio of the entire GCMs by Expert Team on Climate Change Detection and Indices (ETCCDI) basis. Weused 26 GCMs based on CMIP5 data. The Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios were selected for this study. The periodfor evaluating reproducibility of historical period was 30 years from 1976 to 2005. Precipitation, maximum temperature, and minimum temperature wereused as collected climate variables. As a result, we suggested representative 13 GCMs among 26 GCMs by using the selection technique developedin this research. Furthermore, this result can be utilized as a basic data for integrated water management.
Abstract
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 selectiontechnique which set priorities of GCMs consisted of two steps. First step was evaluating original GCMs by comparing with grid-based observationaldata for the past period. Second step was evaluating whether the statistical downscaled data reflect characteristics for the historical period. SpatialDisaggregation Quantile Delta Mapping (SDQDM), one of the statistical downscaling methods, was used for the downscaled data. The way of evaluatingwas using explanatory power, the stepwise ratio of the entire GCMs by Expert Team on Climate Change Detection and Indices (ETCCDI) basis. Weused 26 GCMs based on CMIP5 data. The Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios were selected for this study. The periodfor evaluating reproducibility of historical period was 30 years from 1976 to 2005. Precipitation, maximum temperature, and minimum temperature wereused as collected climate variables. As a result, we suggested representative 13 GCMs among 26 GCMs by using the selection technique developedin this research. Furthermore, this result can be utilized as a basic data for integrated water management.
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