The abyssal ocean circulation is key to Earth’s climate. Numerical models suggest that the circulation is slowing down dramatically. However, no approach exists to observe the circulation’s variability. This project will develop and apply the first approach to detect changes in the abyssal circulation from oceanic variables measurable from satellites.
The abyssal ocean circulation plays a fundamental role in Earth’s climate, as it cycles and sequesters vast quantities of carbon, heat, oxygen and nutrients throughout the global ocean for as long as millennia. Recently, it has been proposed, based on a numerical simulation of the ocean (Li et al., 2023) consistent with sparse observations, that the accelerated melting of Antarctica associated with anthropogenic climate change may be disrupting the abyssal ocean circulation https://www.theguardian.com/science/2023/may/25/slowing-ocean-current-caused-by-melting-antarctic-ice-could-have-drastic-climate-impact-study-says). As the pace of Antarctic melting grows, the additional freshwater deposited on the Antarctic margins may be driving a decrease in the salinity and density of the local ocean waters, making them lighter and less prone to sink to the abyss. The ensuing slowdown of the abyssal ocean circulation would be expected to have a very substantial impact on the functioning of the climate system. However, such a slowdown is very challenging to detect from observations, for no direct measurements exist of the abyssal circulation’s rate.
This project will develop and apply an approach to detect rapid changes in the abyssal ocean circulation from oceanic variables measurable from satellites (such as sea level, sea surface temperature, or sea surface salinity). For this purpose, the student will first generate the new approach by working with a state-of-the-art, realistic numerical model of the ocean circulation, using machine learning techniques (Solodoch et al., 2023) to quantify the temporal evolution of the abyssal circulation from a suite of model variables. Subsequently, the student will apply the approach to satellite observations to quantify the variability in the abyssal ocean circulation over recent decades. The student will investigate the use of changepoint detection techniques combined with deep neural networks such as Multilayer Perceptron (MLP) and Long short-term memory network (LSTM) to determine whether and how the abyssal ocean circulation is slowing down.
The MFC CDT programme provides comprehensive training in the theory of climate science, physical sciences, scientific computing, statistics and data analysis to address pressing problems and challenges posed by climate change. The CDT programme also affords extensive opportunities for personal and professional development training, and for students to expand their multi-disciplinary outlook through interactions with a wide network of academic, research and industrial/policy partners. Specific training will include:
* Opportunities to present work at international conferences.
* Opportunities to participate in fieldwork on a scientific cruise in the Southern Ocean.
* Opportunities to perform extended visits to collaborators at the European Space Agency.
* Attendance at relevant summer schools.
To apply for this project please click here (https://student-selfservice.soton.ac.uk/BNNRPROD/bzsksrch.P_Search). Tick programme type - Research, tick Full-time, select Academic year – ‘2024/25', search text – ‘PhD Ocean&EarthSci (Mathematics for our Future Climate CDT)’. In Section 2 of the application form you should insert the name of the project and supervisor(s) you are interested in applying for. If you have any problems please contact acng@soton.ac.uk.
* Li et al., 2023: Abyssal ocean overturning and warming driven by Antarctic meltwater. Nature 615, 841-847.
* Solodoch et al., 2023: Machine-learning derived inference of the meridional overturning circulation from satellite-observable variables in an ocean state estimate.
