Atlantic Meridional Overturning Circulation(AMOC)plays a central role in long-term climate variations through its heat and freshwater transports,which can collapse under a rapid increase of greenhouse gas forcing in climate models.Previous studies have suggested that the deviation of model parameters is one of the major factors in inducing inaccurate AMOC simulations.In this work,with a low-resolution earth system model,the authors try to explore whether a reasonable adjustment of the key model parameter can help to re-establish the AMOC after its collapse.Through a new optimization strategy,the extra freshwater flux(FWF)parameter is determined to be the dominant one affecting the AMOC's variability.The traditional ensemble optimal interpolation(EnOI)data assimilation and new machine learning methods are adopted to optimize the FWF parameter in an abrupt 4×CO2 forcing experiment to improve the adaptability of model parameters and accelerate the recovery of AMOC.The results show that,under an abrupt 4×CO2 forcing in millennial simulations,the AMOC will first collapse and then re-establish by the default FWF parameter slowly.However,during the parameter adjustment process,the saltier and colder sea water over the North Atlantic region are the dominant factors in usefully improving the adaptability of the FWF parameter and accelerating the recovery of AMOC,according to their physical relationship with FWF on the interdecadal timescale.
Recovery of AMOC4×CO2 forcingKey parameterParameter estimationData assimilationMachine learning
Haolan Ren、Fei Zheng、Tingwei Cao、Qiang Wang
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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,China
College 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
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