미래 기상 시나리오에 대한 편의 보정 방법에 따른 지역 기후변화 영향 평가의 불확실성
Uncertainty in Regional Climate Change Impact Assessment using Bias-Correction Technique for Future Climate Scenarios
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초록
It is now generally known that dynamical climate modeling outputs include systematic biases in reproducing the properties ofatmospheric variables such as, preciptation and temerature. There is thus, general consensus among the researchers about the need ofbias-correction process prior to using climate model results especially for hydrologic applications. Among the number of bias-correctionmethods, distribution (e.g., cumulative distribution fuction, CDF) mapping based approach has been evaluated as one of the skillfultechniques. This study investigates the uncertainty of using various CDF mapping-based methods for bias-correciton in assessing regionalclimate change Impacts. Two different dynamicailly-downscaled Global Circulation Model results (CCSM and GFDL under ARES4 A2scenario) using Regional Spectial Model for retrospective peiod (1969-2000) and future period (2039-2069) were collected over the westcentral Florida. Total 12 possible methods (i.e., 3 for developing distribution by each of 4 for estimating biases in future projections)were examined and the variations among the results using different methods were evaluated in various ways. The results for dailytemperature showed that while mean and standard deviation of Tmax and Tmin has relatively small variation among the bias-correctionmethods, monthly maximum values showed as significant variation (～2'C) as the mean differences between the retrospective simulationsand future projections. The accuracy of raw preciptiation predictions was much worse than temerature and bias-corrected resultsappreared to be more significantly influenced by the methodologies. Furthermore the uncertainty of bias-correction was found to berelevant to the performance of climate model (i.e., CCSM results which showed relatively worse accuracy showed larger variationamong the bias-correction methods). Concludingly bias-correction methodology is an important sourse of uncertainty among otherprocesses that may be required for cliamte change impact assessment. This study underscores the need to carefully select a biascorrectionmethod and that the approach for any given analysis should depend on the research question being asked.
Abstract
It is now generally known that dynamical climate modeling outputs include systematic biases in reproducing the properties ofatmospheric variables such as, preciptation and temerature. There is thus, general consensus among the researchers about the need ofbias-correction process prior to using climate model results especially for hydrologic applications. Among the number of bias-correctionmethods, distribution (e.g., cumulative distribution fuction, CDF) mapping based approach has been evaluated as one of the skillfultechniques. This study investigates the uncertainty of using various CDF mapping-based methods for bias-correciton in assessing regionalclimate change Impacts. Two different dynamicailly-downscaled Global Circulation Model results (CCSM and GFDL under ARES4 A2scenario) using Regional Spectial Model for retrospective peiod (1969-2000) and future period (2039-2069) were collected over the westcentral Florida. Total 12 possible methods (i.e., 3 for developing distribution by each of 4 for estimating biases in future projections)were examined and the variations among the results using different methods were evaluated in various ways. The results for dailytemperature showed that while mean and standard deviation of Tmax and Tmin has relatively small variation among the bias-correctionmethods, monthly maximum values showed as significant variation (～2'C) as the mean differences between the retrospective simulationsand future projections. The accuracy of raw preciptiation predictions was much worse than temerature and bias-corrected resultsappreared to be more significantly influenced by the methodologies. Furthermore the uncertainty of bias-correction was found to berelevant to the performance of climate model (i.e., CCSM results which showed relatively worse accuracy showed larger variationamong the bias-correction methods). Concludingly bias-correction methodology is an important sourse of uncertainty among otherprocesses that may be required for cliamte change impact assessment. This study underscores the need to carefully select a biascorrectionmethod and that the approach for any given analysis should depend on the research question being asked.
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