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

IF 2.3 4区地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES Atmospheric and Oceanic Science Letters Pub Date : 2025-01-01 DOI:10.1016/j.aosl.2024.100509

Haolan Ren , Fei Zheng , Tingwei Cao , Qiang Wang

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引用次数: 0

Abstract

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.

摘要

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.

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