Decadal prediction skill for Eurasian surface air temperature in CMIP6 modelsOA
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.
Yanyan Huang;Ni Huang;Qianfei Zhao;
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters,Nanjing University of Information Science&Technology,Nanjing,China Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai),Zhuhai,China Nansen–Zhu International Research Centre,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing,ChinaCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters,Nanjing University of Information Science&Technology,Nanjing,ChinaDalian Meteorological Bureau of Liaoning Province,Dalian,China
大气科学
欧亚地表温度年代际预测CMIP6DCPP
《Atmospheric and Oceanic Science Letters》 2024 (001)
P.9-13 / 5
funded by the National Natural Science Foundation of China[grant number 41991283].
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