Applications are invited for a fully-funded three-year PhD to commence in October 2023.
The PhD will be based in the Faculty of Science, and will be supervised by Dr Nick Pepin, Dr Harold Lovell and Professor Richard Teeuw.
Candidates applying for this project may be eligible to compete for one of a small number of bursaries available. Successful applicants will receive a bursary to cover tuition fees for three years and a stipend in line with the UKRI rate (£17,668 for 2022/23). Bursary recipients will also receive a contribution of £1,500 per annum towards consumables, conference, project or training costs
The work on this project could involve:
- Using a combination of satellite datasets (e.g. MODIS) and primary field datasets to examine the variation in temperature trends in mountainous regions and at high elevations on a global scale
- Developing new methods of calibrating satellite datasets in mountain regions against real world primary air temperature data collected by the supervisory team. This will involve a variety of statistical analyses and modelling. There is also an option to take part in fieldwork in the locations concerned (e.g. Finnish Lapland, Pyrenees, Kilimanjaro), but this is not required, and would be dependent on obtaining additional funding.
- Understanding the various physical drivers which control the patterns of mountain warming, through comparing the reconstructed satellite temperature trends with estimates of changes in land cover (e.g. deforestation, reduction in snow and ice cover, urbanisation, greening and/or browning)
- Communicating important findings concerning climate change in mountains with a diverse range of stakeholders using both traditional and new/innovative ways of research dissemination.
Project description
Mountain regions are known on average to be warming more rapidly than other lower elevation environments. This phenomenon is called elevation dependent warming and has been of global concern following the publication of an article by the supervisory team in 2015. Mountains provide water supply for billions of the world’s population, and hold much of the cryosphere (snow, ice and glaciers), so accelerated mountain warming has global consequences. There are not many weather stations in high mountains so satellite datasets provide a potential data source to understand mountain temperature trends. Satellites cover all of the globe, and some platforms now extend back to 2000, providing reasonably homogenous data sources for trend analysis. However, there are many concerns including cloud contamination and the fact that land surface temperatures (LSTs) measured by satellites are influenced by many local factors. It is challenging to get a representative spatial signal equivalent to air temperature, the latter being the global standard for understanding climate change (used to define the 1.5C warming threshold for example). This project will validate satellite LST data against unique mountain datasets of air temperature collected by the supervisory team in tropical (Kilimanjaro), mid-latitude (Pyrenees) and Arctic (Finnish Lapland) locations. The team also has contacts in China and the USA and throughout the world should the student wish to expand analysis to other locations. The first supervisor is the lead of the Mountain Research Initiative Elevation Dependent Climate Change Group which is an international network of over 100 scientists from all continents interested in this topic, and this provides opportunity for extensive international collaboration.
General admissions criteria
You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in an appropriate subject. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0. Appropriate subjects include environmental sciences, physical geography, climate science, and other numerate/science disciplines.
Specific candidate requirements
Prior experience with handling large datasets, particularly those obtained though remote sensing is required. Some programming expertise (e.g. Python or other equivalent) and experience in use of GIS (e.g. ArcGIS) and statistical analysis (R, SPSS, Stata or equivalent) is desirable, but there is time during the degree to learn new packages and techniques.
How to Apply
We strongly encourage you to contact Dr Nick Pepin ([Email Address Removed]) to discuss your interest and to discuss the proposed research before you apply, quoting the project code.
When you are ready to apply, you can use our online application form. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process.
If you want to be considered for this funded PhD opportunity you must quote project code SEGG7860423 when applying.
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