University of Exeter funding: NERC GW4+ DTP PhD studentship

OUR CHANGING OCEANS: data science and the marine carbon cycle. PhD in Geography (NERC GW4+ DTP) Ref: 3684

About the award

Supervisors

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 http://nercgw4plus.ac.uk/

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

Eligibility

Students who are resident in EU countries are eligible for the full award on the same basis as UK residents.  Applicants resident outside of the EU (classed as International for tuition fee purposes) are not eligible for DTP funding. Residency rules are complex and if you have not been resident in the UK or EU for the 3 years prior to the start of the studentship, please apply and we will check eligibility upon shortlisting.

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.

We will be using these sea-surface observations, together with data from Earth observation satellites, in statistical machine learning and data science models to improve our understanding of the marine carbon cycle, the biogeochemical (biological, physical, geological and chemical) drivers of its variability, and the exchange of CO2 between the air and the sea.

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]).

map

Map of the northern hemisphere with a long-term mean estimation of the sea-to-air transfer of CO2 in units [mol m-2 year-1]

 

bloom

A phytoplankton bloom off the south west coast of the UK.  These coccolithophores capture CO2 and store it as harmless carbon

Candidate Requirements

The candidate may be (i) a computer or data science or applied mathematics graduate (or equivalent) with interests in, and some knowledge of, climate change and the marine carbon cycle, or (ii) a geography or environmental science or natural science graduate (or equivalent), with knowledge and interests in data/computer science, machine learning and/or mathematics.

Collaborative Partner

The Continuous Plankton Recorder (CPR) dataset is the world’s longest running and geographically extensive biological time-series, providing insight into the current and historic conditions of our oceans. By providing training and direction to the use of the CPR data, the Marine Biological Association (MBA) will support the biological components of the project, focussing on the students’ investigations into the variability of carbon drawdown that is driven by the plankton.  The MBA will provide the necessary facilities to assist this training and support throughout the project.

Training

Due to the interdisciplinary nature of this project, a training plan will be developed according to the background and expertise of the successful candidate.  This will include health & safety for laboratory and field work, applying best practices and standard operating procedure in obtaining high quality observations, effective communication for international, interdisciplinary research, data science methods and techniques, statistical analyses, and high quality scientific writing. There may also be the opportunity to gain fieldwork experience at sea.

References / Background reading list

[1]Le Quéré et al. (2017) Global Carbon Budget 2017. ESSD, 10, 405-448, doi.org/10.5194/essd-10-405-2018

[2]Intergovernmental Panel on Climate Change (2013) The Physical Science Basis. http://www.ipcc.ch/report/ar5/wg1/ 

[3]SOCAT (2018) https://www.socat.info/

[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

 

Entry requirements

Applicants should have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK.   Applicants with a Lower Second Class degree will be considered if they also have Master’s degree.  Applicants with a minimum of Upper Second Class degree and significant relevant non-academic experience are encouraged to apply.

All applicants would need to meet our English language requirements by the start of the  project http://www.exeter.ac.uk/postgraduate/apply/english/.

 

How to apply

In the application process you will be asked to upload several documents.  Please note our preferred format is PDF, each file named with your surname and the name of the document, eg. “Smith – CV.pdf”, “Smith – Cover Letter.pdf”, “Smith – Transcript.pdf”.

  • CV
  • Letter of application outlining your academic interests, prior research experience and reasons for wishing to undertake the project.
  • Transcript(s) giving full details of subjects studied and grades/marks obtained.  This should be an interim transcript if you are still studying.
  • If you are not a national of a majority English-speaking country you will need to submit evidence of your current proficiency in English.

Reference information
You will be asked to name 2 referees as part of the application process, however we will not expect receipt of references until after the shortlisting stage. Your referees should not be from the prospective supervisory team.

If you are shortlisted for interview, please ensure that your two academic referees email their references to the pgr-recruitment@exeter.ac.uk, 7 days prior to the interview dates.  Please note that we will not be contacting referees to request references, you must arrange for them to be submitted to us by the deadline.

References should be submitted by your referees to us directly in the form of a letter. Referees must email their references to us from their institutional email accounts. We cannot accept references from personal/private email accounts, unless it is a scanned document on institutional headed paper and signed by the referee.

All application documents must be submitted in English. Certified translated copies of academic qualifications must also be provided.

The closing date for applications is 1600 hours GMT Monday 6 January 2020.  Interviews will be held between 10 and 21 February 2020.  For more information about the NERC GW4+ DPT please visit https://nercgw4plus.ac.uk

If you have any general enquiries about the application process please email pgrenquiries@exeter.ac.uk.  Project-specific queries should be directed to the lead supervisor.


Data Sharing
During the application process, the University may need to make certain disclosures of your personal data to third parties to be able to administer your application, carry out interviews and select candidates.  These are not limited to, but may include disclosures to:

  • the selection panel and/or management board or equivalent of the relevant programme, which is likely to include staff from one or more other HEIs;
  • administrative staff at one or more other HEIs participating in the relevant programme.

Such disclosures will always be kept to the minimum amount of personal data required for the specific purpose. Your sensitive personal data (relating to disability and race/ethnicity) will not be disclosed without your explicit consent.

Summary

Application deadline:6th January 2020
Value:£15,009 per annum for 2019-20
Duration of award:per year
Contact: PGR Enquiries pgrenquiries@exeter.ac.uk