Primary Supervisor: Fatma Jebri (NOC)

Institution: National Oceanography Centre

Academic Supervisors: He Wang, Meric Srokosz (National Oceanography Centre, Marine Physics and Ocean Climate)

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 high-resolution 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 Autumn 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-a, …) 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 network, with the support of a team with a diverse range of expertise. The student will be based at the NOC in Southampton and 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.

The Selection Process

Step 1

Application: Submit an application via the University of Leeds application portal. Step by step guidance is available here. The deadline for applications is 9th January 2022

Step 2 

Selection: Your application will be reviewed by the supervisors of the project that you apply to, and SENSE’s recruitment committee who will read an anonymised application. We encourage you to get in touch with the supervisor of the project you are applying to, to discuss the project. If you are unsure about or would like support in contacting potential supervisors, or would like to contact them anonymously, please contact the centre managers (see ‘Questions’) at the bottom of this page.

Applicants will be invited for interview based on the following criteria:

  • Score for Bachelor’s degree
  • Score for master’s grade, or relevant industry experience
  • References
  • Any scientific outputs (not expected or essential, but let us know if you have any)
  • Research skills / experience
  • Technical experience
  • Earth Observation experience

Please note that it is not necessary for a candidate to be proficient in all these areas: SENSE provides extensive training in Earth Observation, advanced data techniques and programming. We recruit candidates from a broad range of backgrounds and we consider each application individually.

Step 3 

Interview.  We will call the strongest applicants for interview. The interview panel will be Earth Observation academics from the University of Edinburgh, University of Leeds and industry, with a range of specialities, and will not include the project supervisor. Interviews will be held in Leeds in late February / early March 2022. We seek to make offers to students who have the best aptitude for the projects and who perform best at interview.

Step 4 

Offer.  We will make a formal offer to the successful candidate shortly after interviews.

Once you accept the offer of the study place, you will be sent a formal funding letter.

Deadline

The application deadline is Sun 9th January 2022.

For further information on the application procedure, click here, for information on the position, visit this website.

via NOC
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