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OUR CHANGING OCEANS: data science and the marine carbon cycle. PhD in Geography (NERC GW4+ DTP)

  • Full or part time
  • Application Deadline
    Monday, January 06, 2020
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Lead Supervisor
Dr Ute Schuster, Department of Geography, College of Life and Environmental Sciences, University of Exeter

Additional Supervisors
Dr Jacqueline Christmas, Department of Computer Science, College of Engineering, Mathematics and Physical Sciences, University of Exeter
Dr Clare Ostle, Marine Biological Association

Location: University of Exeter, Streatham Campus, Exeter EX4 4QJ

This project is one of a number that are in competition for funding from the NERC GW4+ Doctoral Training Partnership (GW4+ DTP). The GW4+ DTP consists of the GW4 Alliance of research-intensive universities: the University of Bath, University of Bristol, Cardiff University and the University of Exeter plus five unique and prestigious Research Organisation partners: British Antarctic Survey, British Geological Survey, Centre for Ecology & Hydrology, the Natural History Museum and Plymouth Marine Laboratory. The partnership aims to provide a broad training in the Earth, Environmental and Life sciences, designed to train tomorrow’s leaders in scientific research, business, technology and policy-making. For further details about the programme please see

For eligible successful applicants, the studentships comprises:

- A stipend for 3.5 years (currently £15,009 p.a. for 2019/20) in line with UK Research and Innovation rates
- Payment of university tuition fees;
- A research budget of £11,000 for an international conference, lab, field and research expenses;
- A training budget of £3,250 for specialist training courses and expenses.
- Travel and accomodation is covered for all compulsory DTP cohort events
- No course fees for courses run by the DTP

We are currently advertising projects for a total of 10 studentships at the University of Exeter

Project Background

Human activity is changing the environment of our planet, including the carbon cycle. The ocean is the largest absorber of human-generated carbon dioxide (CO2), a key greenhouse gas, but the CO2 content of the atmosphere is still increasing, leading to changes in the terrestrial and marine carbon cycles [1], with long-term consequences.

Knowledge of the ocean uptake of CO2, with a high degree of confidence, is crucial in determining the current state of the carbon cycle in the marine environment, as well as improving the certainty in results from predictive models [2]. We also need to understand what drives the variability in the amount of transfer of CO2 between the atmosphere and the ocean, at different scales in both time and space.

Data/computer science techniques, particularly in machine learning, will help us to incorporate the inherently sparse observations we obtain from ship-borne sensors into predictive models, and estimate the degree of uncertainty in the observed data and hence in the models’ predictions.

Project Aims and Methods

The supervisors are happy to discuss and adapt the project’s details to suit a candidate, but the main elements of the project are as follows:

1) Utilising observations. We have established a time-series of ocean surface CO2 measurements, made from commercial ships travelling between the UK and the Caribbean, as part of an international effort to collect high quality data, which is publically available through the Surface Ocean CO2 Atlas (SOCAT) [3]. This project will be based on the collection of these measurements, the data quality control and the contribution to the global dataset in SOCAT.

2) Develop gap-filled ocean surface carbon maps. Ship-borne observations are, by their nature, very sparse in both time and space; mapping techniques are therefore required to “fill in the gaps”, i.e. to produce spatially complete fields at regular time intervals. Traditional approaches divide the ocean up into smaller areas, and a number of different ocean division methods have been used (e.g. [4-8]), but the resulting divisions are either static in space and time, have unrealistically straight boundaries, or are computationally expensive to produce. We will be using statistical machine learning and data science methods to develop a new, observation-based, time-changing (dynamic) division of ocean biogeochemical regimes, that are biogeochemically realistic and do not require extensive computational power.

3) Improve estimates of ocean CO2 uptake. This will enable us to produce improved regional to global air-sea CO2 transfer models, at monthly time scales and from year to year. We will rigorously test the uncertainties of our models’ estimates, and perform validations and comparisons with published results and model outputs (e.g. CMIP6 results [9]).

Funding Notes

NERC GW4+ funded studentship available for September 2020 entry. For eligible students, the studentship will provide funding of fees and a stipend which is currently £15,009 per annum for 2019-20.


References / Background reading list

[1]Le Quéré et al. (2017) Global Carbon Budget 2017. ESSD, 10, 405-448,

[2]Intergovernmental Panel on Climate Change (2013) The Physical Science Basis.

[3]SOCAT (2018)

[4]Longhurst (2007) Ecological Geography of the Sea. Academic Press, London.

[5]Landschützer et al. (2014) Recent variability of the global ocean carbon sink. GBC, 28, 927-949, doi:10.1002/2014GB004853

[6]Fay and McKinley (2014) Global open-ocean biomes: mean and temporal variability. ESSD, 6, 273-284, doi:10.5194/essd-6-273-2014

[7]Watson et al. (2009) Tracking the variable North Atlantic Sink for atmospheric CO2. Science, 326, 1391, doi:10.1126/science.1177394

[8]Reygondeau et al. (2013) Dynamic biogeochemical provinces in the global ocean. GBC, 27, 1-13, oi:10.1002/gbc.20089

[9]Eyring et al. (2016) Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. GMD, 9, 1937-1958, doi:10.5194/gmd-9-1937-2016

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