Background and objectives: Oceanic mesoscale variability and eddies (scales from ~10 to 100 km) play a key role in regulating regional and global physical and biogeochemical processes, including heat transport and mixing of nutrients. These mesoscale eddies affect the phytoplankton productivity, hence marine species, and local populations dependent on them. It has been recently found that tropical oceans mesoscale variability is decreasing overall. The tropical north Indian Ocean and the western Bay of Bengal, in particular, is an eddy
rich region which experiences prominent phytoplankton blooms. Previous works on the mechanisms of the productivity in the western Bay of Bengal have mainly focused on tropical cyclones, which impact the supply of nutrients to the surface waters. The presence of eddies and their contribution to biological productivity enhancement in the Bay have been examined for some cyclone events. However, the mesoscale eddies’ variability in the presence of strong stratification and the effect of changes in wind stress forcing on mesoscale processes and primary productivity, remain to be explored in detail. Additionally, eddy presence and influence on the regional productivity has to-date only been investigated using the conventional satellite data or few sparse in-situ data, and no specific eddy detection method was applied, nor improved coastal satellite products used. The aim of this research is to carry out a comprehensive study of the causes and consequences of such variability using Earth Observation (EO) in synergy with numerical model outputs and machine learning. There is considerable flexibility in the direction that the research may ultimately take. The PhD will explore the following key research questions:
1) How has the eddy field varied over the past decades in the western part of the Bay of Bengal?
2) How this variability is affected by changes in wind forcing and how does it affect the regional productivity?
3) How are eddies and their contribution to productivity changing between years and seasons? This will include examining the effects of the El-Niño Southern Oscillation and the Indian Ocean Dipole
Methodology: This PhD will investigate the applicability of unsupervised machine learning techniques to a set of historical and new EO data and numerical model outputs to unravel the impact of mesoscale eddies and wind on the regional ecosystem productivity. The nonlinear interactions of the mesoscale features make unsupervised Machine Learning (ML) methods well suited to objectively determine the eddies spatiotemporal variation. The main ML methods that will be explored are Self Organizing Maps, K-means clustering, and variational autoencoders. Both historical and new satellite datasets will be exploited. These will cover highresolution satellite ocean colour derived chlorophyll-a data and Sea Surface Temperature (SST), winds, altimetry derived Sea Surface Height (SSH) and currents. New satellite SSH altimeter observations can better capture oceanic mesoscale processes, such as data from SWOT which will be launched in December 2022. Additionally, the recent Sentinel 3A&3B satellites, that carry higher along-track resolution synthetic aperture radar (SAR) altimeters, also provide improved data close to the coast. Argo float measurements, which provide physical and biological parameters at different depths, will also be used to understand the biological and
hydrographic properties of the western Bay of Bengal. The new and historical EO data will be compared to output from a high resolution (1/12°) ocean model (NEMO) that includes biogeochemical processes (MEDUSA), covering the satellite data period. Using environmental factors inferred from numerical model outputs, additional physical and biological parameters (e.g., mixed layer depth (MLD), subsurface chlorophyll) will be available which can help further exploration of the variability changes.
Training: This project will enable the PhD student to develop skills in many techniques including statistics, computer programming and artificial neural networks, with the support of a team with a diverse range of expertise. The student will be based at the NOC in Southampton and will work closely with supervisors based at NOC and University of Leeds, where the student will be registered. There may be an opportunity for the student to gain experience of working at sea by participating in a research cruise.
This PhD is part of the NERC and UK Space Agency funded Centre for Doctoral Training "SENSE": the Centre for Satellite Data in Environmental Science. SENSE will train 50 PhD students to tackle cross-disciplinary environmental problems by applying the latest data science techniques to satellite data. All our students will receive extensive training on satellite data and AI/Machine Learning and field training. All students will experience extensive training on professional skills, including spending 3 months on an industry placement. See http://www.eo-cdt.org
More information for applicants can be found on our available Projects, How to Apply and dedicated FAQ webpages. You will also find additionalresources such as How can a PhD help me with my career? and our ‘Demystifying the PhD application process’ webinar'
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