Southern Ocean Dynamics Under Climate Change: New Knowledge Through Physics-Guided Machine Learning ...

• Main Authors: Yik, William, Sonnewald, Maike, Clare, Mariana C. A., Lguensat, Redouane
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: arXiv 2023
• Subjects: Atmospheric and Oceanic Physics physics.ao-ph; Machine Learning cs.LG; FOS Physical sciences; FOS Computer and information sciences; Antarctic; Southern Ocean; The Antarctic; Antarc*
• Online Access: https://dx.doi.org/10.48550/arxiv.2310.13916; https://arxiv.org/abs/2310.13916

Complex ocean systems such as the Antarctic Circumpolar Current play key roles in the climate, and current models predict shifts in their strength and area under climate change. However, the physical processes underlying these changes are not well understood, in part due to the difficulty of characterizing and tracking changes in ocean physics in complex models. Using the Antarctic Circumpolar Current as a case study, we extend the method Tracking global Heating with Ocean Regimes (THOR) to a mesoscale eddy permitting climate model and identify regions of the ocean characterized by similar physics, called dynamical regimes, using readily accessible fields from climate models. To this end, we cluster grid cells into dynamical regimes and train an ensemble of neural networks, allowing uncertainty quantification, to predict these regimes and track them under climate change. Finally, we leverage this new knowledge to elucidate the dynamical drivers of the identified regime shifts as noted by the neural network ... : 14 pages, 11 figures, NeurIPS 2023 Workshop: Tackling Climate Change with Machine Learning ...