About the Project
Applications from UK candidates are invited for a fully funded PhD in the modelling of glacier mass balance, to be based in the Department of Geography, King’s College London. Funded by the Faculty of Social Science and Public Policy, the PhD will improve estimates of glacier extent and water resource security under different scenarios of future climate change.
Background: Runoff from glaciers acts as a reliable source of freshwater for almost two billion people worldwide. The almost universal glacier retreat observed in recent decades therefore represents a major concern to society, and there is an urgent need to improve understanding of their possible future extent in response to climate forcing. At present, though, these projections are characterised by considerable uncertainties: both known, due to differences between the highly idealised empirical models used to simulate future glacier mass balance, and unknown, because the parameters within these models are rarely varied across their plausible bounds when making projections. The contribution of the PhD will be to confront this uncertainty by characterising it more fully than it has been before, and reducing it where possible. The result will be a major improvement in our understanding of plausible glacier extents under possible scenarios of future climate change.
Aims: The ambition outlined above will be pursued by addressing the following aims: (1) to quantify the full range of future glacier extents deemed plausible by existing mass balance models and parameter sets; (2) to understand the drivers of difference across these different projections; (3) to reduce uncertainty by evaluating models and parameter values using observations and improved physical understanding; (4) to provide a more comprehensive and realistic assessment of equilibrium glacier extent under given levels of global warming since preindustrial times, such as the 1.5°C and 2°C limits of the Paris Agreement.
Methodology: The student will begin by using models from the Glacier Model Intercomparison Project (GlacierMIP) to simulate glacier mass balance under future climate scenarios taken from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Relevant parameters in the respective GlacierMIP models will be changed within published bounds to generate a large, multi-model “perturbed parameter ensemble”, which will be used to quantify the sensitivity of projections to both model structure and parameter values. As the student’s understanding of glacier mass balance processes improves, the PhD will then attempt to constrain uncertainty by filtering (or weighting) GlacierMIP model simulations based on their agreement with observations. There is also scope here for the candidate – supported by the supervisory team — to develop a new empirical model. Modelling uncertainty will then be quantified far more comprehensively than ever before, with projections provided as probabilities of different plausible equilibrium glacier extents for different levels of global warming since preindustrial times.
The student will be supervised by Dr Tamsin Edwards and Dr Tom Matthews. The student will join the Physical and Environmental Geography Research Group within the Department of Geography, King’s College London. They will also have opportunities through their supervisors to meet a wider network of collaborators in glaciology, including members of the Centre for Polar Observation and Modelling (CPOM: https://cpom.org.uk) at UCL and elsewhere in the UK, and international researchers working within the EU H2020 project PROTECT (https://protect-slr.eu; 2020-2024) and Glacier Model Intercomparison Project (GlacierMIP: https://climate-cryosphere.org/glaciermip). The project will be designed to complement and inform these other activities.
The studentship will be funded at £18,062 stipend per annum including London allowance (rising in line with annual increments), plus fees (UK students only) paid for 4 years. There will be £4000 support funding for project costs and conference attendance. The studentship is available from September 2022, to allow induction with other PhD students, although a January 2023 start date can be requested if needed.
Applicant Background and Application Process
Applicants should have a 2:1 or higher degree in a science subject, in geography (quantitative), or in allied fields such as computing, engineering, physics or mathematics (or equivalent experience). Some knowledge and past experience of programming is essential. Students working towards or already having a Master’s degree in a relevant field (and expecting a distinction or high merit grade) are particularly welcome to apply. The successful applicant will have a strong academic background, the ability to communicate to high standards, an interest in working with equipment as well as digital data, and enthusiasm regarding this topic. They will join a vibrant and engaging research group, and attend regular group meetings.
Application deadline is 16:00 hrs on 10th July 2022
Interviews will take place on 2nd August, most likely by video conference.
To apply, applicants should themselves prepare a single PDF file titled with their name and containing the following materials:
- Completed application form that can be found here.
- Cover letter detailing qualifications/experience and interest in the PhD (max 2 A4 sides).
- CV (max 2 A4 sides) with names of the two academic referees (see above).
- Sample of written work, ideally demonstrating their scientific and/or technical ability to work with, analyse and display data (could be dissertation, project or coursework or any other relevant material).
This single PDF file should be sent to [email protected]. Please ensure this is done before 16:00 hrs on 10th July 2022.
The studentship will be funded at £18,062 stipend per annum including London allowance (rising in line with annual increments), plus fees (UK students only) paid for 4 years. There will be £4000 support funding for project costs and conference attendance.
Applicants should also request that two academic referees send a reference to [email protected]