Decadal prediction skill for Eurasian surface air temperature in CMIP6 models

Abstract

The Eurasian surface air temperature (SAT) is experiencing decadal variations against the background of global warming. The prediction skill for the seasonal mean SAT in CMIP6 Decadal Climate Prediction Project (DCPP) models is investigated in this study. The large decadal variations of winter and autumn Eurasian SAT are barely predicted by the CMIP6 models. IPSL-CM6A-LR and the multimodel ensemble have skill in predicting the variations of spring Eurasian SAT, with significant anomaly correlation coefficients, but not for the amplitude, with negative mean-square skill scores. Significant skill is apparent for the summer SAT over Mongolia and North China, with the CMIP6 models showing their best skill for the summer Eurasian SAT. Compared to external forcing, model skills for Eurasian SAT may derive more from the initialization. It should be noted that there are model system errors in the form of false strong relationships of SAT between winter and other seasons when in fact the variations of other seasons’ SATs are independent of the winter SAT in observations.摘要评估CMIP6年代际预测试验对季节平均SAT的预测技巧的结果表明: 模式不能有效预测冬季和秋季SAT的年代际变率. IPSL-CM6A-LR和多模式集合平均对于春季SAT展现了预测技巧, 其中对于变率的预测技巧好于振幅的结果. 基于蒙古和我国华北地区的显著预测技巧, 模式对于夏季SAT表现出最佳的预测水平. 与外部强迫相比, 模式对于SAT的预测技巧可能来自初始化. 模式中的一个明显系统性误差值得注意, 即模式中冬季SAT的变率可以持续到其他季节, 而在观测中其他季节的SAT变化与冬季SAT相对独立.