Optimizing the key parameter to accelerate the recovery of AMOC under a rapid increase of greenhouse gas forcingOA
Optimizing the key parameter to accelerate the recovery of AMOC under a rapid increase of greenhouse gas forcing
大西洋经向翻转环流(Atlantic Meridional Overturning Circulation,AMOC)通过其经向的热量和水团输送,在气候变化中起着关键作用.然而,气候模式模拟未来AMOC在温室气体强迫下的变化存在较大不确定性.模式参数的不确定性是导致AMOC产生不确定性的主要因素之一.因此,本文采用简化的海气耦合模式首先探寻出模式中AMOC的最敏感参数为淡水通量系数(Freshwater Flux,FWF),再基于集合最优插值(Ensemble Optimal Interpolation,EnOI)探讨通过参数优化减小温室气体强迫下AMOC模拟不确定性的可行方案.理想试验揭示了,北大西洋海表温度和海表盐度在温室气体强迫下的增量可以有效地优化FWF,进而使得AMOC相比默认参数能快速收敛,减小其在未来气候预估中的不确定性.
Haolan Ren;Fei Zheng;Tingwei Cao;Qiang Wang
International Center for Climate and Environment Science(ICCES),Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing,China||University of Chinese Academy of Sciences,Beijing,ChinaInternational Center for Climate and Environment Science(ICCES),Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing,ChinaInternational Center for Climate and Environment Science(ICCES),Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing,China||University of Chinese Academy of Sciences,Beijing,ChinaCollege of Oceanography,Hohai University,Nanjing,China||CAS Key Laboratory of Ocean Circulation and Waves,Institute of Oceanology,Chinese Academy of Sciences and Pilot National Laboratory for Marine Science and Technology(Qingdao),Qingdao,China
大西洋经向翻转环流的重建4×CO2强迫关键参数参数优化资料同化机器学习
Recovery of AMOC4×CO2 forcingKey parameterParameter estimationData assimilationMachine learning
《大气和海洋科学快报（英文版）》 2025 (1)
39-45,7
This work was supported by the National Key R&D Program of China[grant number 2023YFF0805202],the National Natural Science Foun-dation of China[grant number 42175045],and the Strategic Priority Research Program of the Chinese Academy of Sciences[grant number XDB42000000].
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