Optimizing the key parameter to accelerate the recovery of AMOC under a rapid increase in greenhouse gas forcing

Abstract The 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 in greenhouse gas forcing in climate models. Previous studies have suggested that the deviation of model parameters is one of the major factors inducing inaccurate AMOC simulations. In this work, with a low-resolution Earth system model, we try to explore whether reasonably adjusting the key model parameter can help to re-estabilish the AMOC after its collapse. Through a new optimization strategy, the freshwater flux (FWF) parameter is determined to be the dominant one on affecting the AMOC’s variability. Traditional ensemble optimal interpolation (EnOI) data assimilation and new machine learning methods are adopted to optimize the FWF parameter in an abrupted 4×CO2 forcing experiment to improve the adaptability of model parameters and accelerate the recovery of AMOC. The results show that under an abrupted 4×CO2 forcing in millennial simulations, the AMOC will first collapse and then be slowly re-established by the default FWF parameter. 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.