- 1School of Ocean and Earth Science, University of Southampton, Southampton, United Kingdom of Great Britain
- 2School of Mathematical Sciences, University of Southampton, Southampton, United Kingdom of Great Britain
- 3Research School for Earth Sciences and Australian Centre for Excellence in Antarctic Science, The Australian National University, Canberra, ACT, Australia
The transport of Antarctic Bottom Water (AABW) supplies the densest layers of the abyssal ocean circulation, which accounts for up to 40% of the ocean's volume and plays a vital role in Earth's climate. Due to its recently ventilated nature, AABW carries heat and carbon from the surface to the deep ocean, allowing these elements to be isolated for centuries, while also gathering oxygen and delivering it to the ocean's depths. AABW forms when dense, cold waters from the continental shelves descend along the Antarctic slope. The physical conditions necessary for sinking are created by ice formation and freezing winds in this region.
This implies that, as temperatures rise and ice melts due to climate change, the circulation could diminish. Model projections also suggest this, identifying meltwater forcing as a potential primary factor in the reduction of AABW transport. However, the variability of AABW remains poorly constrained by observations. Its origin on the Antarctic continental shelf and slope presents limited opportunities for in situ measurements, and satellite observations are hindered, especially in winter, due to sea ice cover. Further north, AABW spreads approximately 2 km below the surface, making it difficult to monitor directly by satellites, with in situ measurements remaining scarce.
Here, we explore the plausibility of inferring AABW circulation from available satellite measurements of the ocean's surface properties, via machine learning techniques. Our work is focused on implementing a Deep Neural Network (DNN) with high skill and potential for reconstructing the circulation's strength. Different architectures are trained and tested on the ACCESS-OM2-01 model, and a cross-training with other ocean models is investigated, as well as the use of real satellite measurements and change-point detection techniques.
These studies offer a valuable means to overcome current limitations on Southern Ocean and abyssal circulation research, making it more accessible, sustainable, and consistent.
How to cite: Ferrotti, A., Naveira Garabato, A., Silvano, A., Zheng, C., and Morrison, A.: Detecting Rapid Changes and Tipping Points in the Abyssal Ocean Circulation via Deep Learning and Satellite Observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19315, https://doi.org/10.5194/egusphere-egu26-19315, 2026.