考虑气候内部变率影响的偏差校正评价框架及其应用

A Framework to Consider Internal Climate Variability for Bias Correction Methods and Its Application

摘要: 气候内部变率可能引起全球气候模式输出变量(如降雨和气温)偏差非一致性，进而会影响偏差校正方法的结果。因此，本文建立了考虑气候内部变率影响的偏差校正评价框架，采用残留偏差指标定量评价了偏差校正方法在历史和未来时段的偏差校正结果。同时，分别通过4个GCM多成员集合估算了气候内部变率，进而分析气候内部变率模拟值的不确定性对偏差校正评价结果的影响。结果表明，在历史时段，偏差校正方法能够有效地降低GCM输出变量的偏差，使得残留偏差在气候内部变率范围内。在未来时段，偏差校正方法仅仅在一定程度上降低了GCM输出变量的偏差。虽然气候内部变率具有明显的不确定性，但残留偏差依然可能大于气候内部变率。

Abstract: Bias nonstationarity of outputs (precipitation and temperature) of GCMs attributed by internal climate variability could influence the performance of bias correction methods. This study established a framework to consider internal climate variability for bias correction methods. The remaining bias index was introduced to evaluate the performance of tradition bias correction method over historical and future periods. Furthermore, the impacts of uncertainty of internal climate sensitivity estimated by 4 GCMs multi-member ensembles are explored in evaluating the performance of bias correction. The results show that the bias correction method can reduce the bias of raw GCM simulations in the historical period, indicated by the remaining bias which is within internal climate variability. However, the bias correction method can only reduce the bias of raw GCM simulations to some extent in the future period. The remaining bias can be outside the range of internal climate variability, even though the uncertainty of internal climate variability is large.