편의보정된 다중지역기후모델 산출물의 불확실성 범위 추정 및 미래 전망
Estimation of Uncertainties and Projection for Bias-corrected Outputs of Multi-regional Climate Models
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초록
In this study, uncertainty ranges for bias-corrected temperature and precipitation in seven metro-cities were estimated using nine GCM-RCM Matrix, and climate changes were predicted based on the corrected temperature and precipitation. During the present climate (1981-2005), both uncertainties for annual temperature and precipitation and differences in regional uncertainties were reduced by bias correction methods. Model’s systematic errors such as cold bias of surface air temperature and underestimated precipitation during the second-Changma period were improved by a bias correction method. Uncertainties of annual variations for bias corrected temperature and precipitation were also decrease. Furthermore, not only mean values but also extreme values were improved by bias correction methods. During the future climate (2021-2050), differences in temperature and precipitation between two RCP scenarios (RCP4.5/8.5) were not quite large. Temperature had an obvious increasing tendency, while future precipitation did not change significantly compared to present one in terms of mean values. Uncertainties for future biascorrected temperature and precipitation were also reduced. In mid-21st centuries, models prospected that mean temperature increased thus lower extremes associated with cold wave decreased and upper extremes associated with heat wave increased. Models also predicted that variations of future precipitation increased thus the frequency and intensity of extreme precipitation increased.
Abstract
In this study, uncertainty ranges for bias-corrected temperature and precipitation in seven metro-cities were estimated using nine GCM-RCM Matrix, and climate changes were predicted based on the corrected temperature and precipitation. During the present climate (1981-2005), both uncertainties for annual temperature and precipitation and differences in regional uncertainties were reduced by bias correction methods. Model’s systematic errors such as cold bias of surface air temperature and underestimated precipitation during the second-Changma period were improved by a bias correction method. Uncertainties of annual variations for bias corrected temperature and precipitation were also decrease. Furthermore, not only mean values but also extreme values were improved by bias correction methods. During the future climate (2021-2050), differences in temperature and precipitation between two RCP scenarios (RCP4.5/8.5) were not quite large. Temperature had an obvious increasing tendency, while future precipitation did not change significantly compared to present one in terms of mean values. Uncertainties for future biascorrected temperature and precipitation were also reduced. In mid-21st centuries, models prospected that mean temperature increased thus lower extremes associated with cold wave decreased and upper extremes associated with heat wave increased. Models also predicted that variations of future precipitation increased thus the frequency and intensity of extreme precipitation increased.
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