In this project you will infer large-scale ocean circulation (>1000 km) in the North Atlantic from fine-scale (~100 m) estimates of the rates of mixing and heat and salt derived from autonomous float observations.
The global ocean’s overturning circulation plays a key regulatory role on Earth’s climate. To a large, but highly uncertain extent, this circulation –and the heat and freshwater fluxes associated with it– are driven by mesoscale (<100 km) and small-scale (<10 m) ocean mixing processes. Historically, the contribution of mixing to the global overturning has been inferred indirectly, as a product of inverse solutions of ocean circulation based on large-scale observations of current velocities and property distributions. In this project, you will tackle this problem by following the complementary approach: solving the inverse problem of ocean circulation starting from fine-scale observational estimates of ocean mixing.
To this end, you will first generate maps of mixing rates by mesoscale and small-scale turbulence based on a combination of classical fine-scale methods and machine learning tools applied to Argo float observations (e.g., Fernández Castro et al., 2023). Secondly, you will design an inverse statistical method to solve the conservation equations of temperature and salinity (e.g., Naveira Garabato et al., 2016), to infer the horizontal and vertical (overturning) ocean circulation. This project will focus specifically in the North Atlantic, a key region of the global overturning where mixing plays a vital role (Evans et al., 2023), with the view of applying the method globally at a later stage.
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:
* Ocean observations and data analysis (with Python)
* Statistics, inverse problems and ocean circulation
* Dynamics of ocean mixing
* Machine learning
* Potential sea-going experience
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.
* Naveira Garabato, A. C., Polzin, K. L., Ferrari, R., Zika, J. D. & Forryan, A. A Microscale View of Mixing and Overturning across the Antarctic Circumpolar Current. J. Phys. Oceanogr. 46, 233–254 (2016).
* Evans, D. G., Holliday, N. P., Bacon, S. & Le Bras, I. Mixing and air–sea buoyancy fluxes set the time-mean overturning circulation in the subpolar North Atlantic and Nordic Seas. Ocean Sci. 19, 745–768 (2023).
* Fernández Castro, B. et al. Isopycnal eddy stirring dominates thermohaline mixing in the upper subpolar North Atlantic. ESS Open Arch. https://doi.org/10.22541/essoar.170196957.75231388/v1 (2023).
