Reducing Uncertainty in the Response of the Earth System to Anthropogenic CO2 Emissions with Statistical Emulation of Land Surface Models. PhD in Mathematics (NERC GW4+ DTP) Ref: 3697
About the award
Dr Oliver Stoner, Department of Mathematics, College of Engineering, Mathematics and Physical Sciences, University of Exeter
Dr Anna Harper, Department of Mathematics, College of Engineering, Mathematics and Physical Sciences, University of Exeter
Prof Peter Challenor, Department of Mathematics, College of Engineering, Mathematics and Physical Sciences, University of Exeter
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 accommodation 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
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.
The land surface removes about one-third of human emissions of carbon dioxide (CO2) from the atmosphere each year. This carbon is stored in vegetation and soils. The future of the land surface response to climate change is highly uncertain, and some models predict that the land could switch from a sink to a source of CO2 during this century. This project focuses on JULES, a process-based computer model that represents the exchange of CO2, heat, moisture, and momentum between the land surface and the atmosphere in the UK’s climate model. JULES has more than 60 parameters which affect processes such as photosynthesis, respiration, evaporation, and plant growth and mortality. There is an urgent need for rapid improvements and better quantification of uncertainty in models such as JULES because of the importance of climate change and the need for science to support commitments to keep climate change to below 2°C.
Project Aims and Methods
An efficient method for calibrating numerical models like JULES is to employ a statistical (or machine learning) model which emulates the relationship between the input parameters and the mathematical processes, in this case of the land surface. Using this emulator, it is then possible to systematically study the effect of previously untried parameter combinations on the model’s ability to reproduce observed data, without having to run the original model too many times (which would in the case of JULES be computationally expensive). The aims of this project are therefore to:
- Develop an emulator for JULES, to quantify the effect of varying model parameters on the land surface processes.
- Use the emulator to calibrate JULES so that it can better reproduce historical observations. Specifically, the project will focus on seasonal and interannual variations in photosynthesis in dryland ecosystems. Despite having low rainfall and relatively sparse vegetation, drylands could store up to 25% of the land surface carbon. The student will compare JULES to available remote sensing data (for example leaf area index as shown in the image) to improve the model’s representation of the carbon cycle in these ecosystems.
- Use the improved JULES to quantify the change in carbon storage in dryland ecosystems under future climate change. This will involve running the improved JULES with several different climates.
The supervisors are open-minded to adapting the project to suit the candidate’s research interests, e.g. to:
- explore other approaches to emulation (e.g. machine learning);
- place greater emphasis on the methodological development of the emulator or place greater emphasis on the use of the emulator to improve climate predictions.
Tropical rainforests remove 10-15% of annual human emissions CO2 from the atmosphere. This project aims to better understand the annual trend and variations in land carbon uptake.
Dryland ecosystems are not as carbon-dense as forests, but they are the dominant driver of both year-to-year variations and trends in land carbon uptake1,2
Candidates from a wide variety of backgrounds (e.g. Mathematics, Physics, Natural Sciences, Physical Geography, Statistics, Computer Science, Biology) are encouraged to apply. The candidate should have knowledge of basic quantitative analysis methods (e.g. linear regression or equivalent).
CASE or Collaborative Partner
The student will regularly spend time at the Met Office with co-Supervisor McNeall and work with the Earth Systems and Mitigation Science team. They will form close working ties with Met Office Hadley Centre staff and may attend meetings that are held to direct Earth System Science and engage stakeholders, including UK Government departments.
The student will have access to relevant master’s level modules at the University of Exeter (see Useful links for available data science modules). They will be encouraged to attend the Academy for PhD Training in Statistics (APTS), to develop a solid foundation in both applied and methodological statistics, in addition to a relevant earth system summer school (e.g. see Useful links). They could also take part in Met Office training, preparing them for potential careers in research, industry/consultancy or policy.
1Ahlstrom, A. and co-authors, 2015: The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science. DOI: 10.1126/science.aaa1668.
2Poulter, B. and co-authors, 2014: Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature. DOI: 10.1038/nature13376.
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”.
- 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.
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 email@example.com, 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 firstname.lastname@example.org. Project-specific queries should be directed to the lead supervisor.
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.
|Application deadline:||6th January 2020|
|Value:||£15,009 per annum for 2019-20|
|Duration of award:||per year|
|Contact: PGR Enquiriesemail@example.com|