Project highlights
- Use cutting edge methods to quantify risks to UK infrastructure from compound weather extremes that drive major social and economic impacts
- Put the above weather extremes into the context of variability in the midlatitude jet, and use these insights to explore potential future risks in a changing climate
- Work across disciplines of meteorology and engineering, to gain experience in infrastructure meteorology and climate dynamics, as well as transferrable skills such as coding, data analysis and machine learning, and project management
Overview
Storm Isha (21st -22nd January 2024) brought gusts of 69-81 mph to the northern UK. Damage to infrastructure resulted in widespread power cuts and travel disruption. Storm Isha was closely followed by Storm Jocelyn (22nd-24th Jan 2024), driving further damage and hindering the response to Storm Isha. The number of extreme extratropical storms is projected to increase throughout the 21st century (Priestley and Catto, 2022) motivating a pressing need to quantify the risks such events pose.
Critical infrastructure such as power and rail are vulnerable to a range of weather hazards, including wind, temperature extremes and heavy rainfall (e.g. Jia et al., 2024). Compound events are combinations of hazards that drive increased risk (e.g. Zscheischler et al. 2020). These include successive hazards that lead to more severe impacts, and events where multiple variables drive damage, e.g. combined extreme rainfall and wind. Such extremes are significantly driven by circulation patterns in the mid- to upper-troposphere (e.g. Röthlisberger et al. 2016); for example Storms Isha & Jocelyn were influenced by a stronger than normal midlatitude westerly jet stream. Methods which can anticipate the impact to infrastructure from these extremes are of great importance for improving infrastructure resilience.
This PhD project will identify the key meteorological drivers of infrastructure failure in the UK, with the aim to estimate current and future risk. We will begin by exploring how cutting-edge statistical methods can facilitate the integration of infrastructure fault and meteorological data, to better identify the weather regimes and jet structures underlying severe impacts to infrastructure. We will then estimate present-day risk, exploring plausible but unseen extreme events (Osinski et al., 2016; Thompson et al., 2017) and future risk via analysis of jet changes and jet-surface weather linkages in climate change projections. Depending on student preferences, there is also scope to explore seasonal forecasting of compound extremes and infrastructure risk via our Met Office collaboration.
This project would best suit a student with a numerical background, for example Meteorology, Physics, or Maths, with experience coding in Python or similar (e.g. R, Matlab).
Figure 1: Storm Arwen (26-27/11/21) left hundreds of thousands of homes without power. Left: Satellite image of Storm Arwen. Right: Photograph of a tree in Wales felled by the storm that damaged phone lines. Image credits: (left) NASA – https://worldview.earthdata.nasa.gov/, Public Domain; (right) Cwmcafit, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=113062329
CENTA Flagship
This is a CENTA Flagship Project
Host
University of BirminghamTheme
- Climate and Environmental Sustainability
Supervisors
Project investigator
- Ruth Geen (UoB, [email protected])
Co-investigators
- Daniel Donaldson (UoB, [email protected])
- Hazel Thornton (UK Met Office, [email protected])
- Gregor Leckebusch (UoB, [email protected])
How to apply
- Each host has a slightly different application process.
Find out how to apply for this studentship. - All applications must include the CENTA application form. Choose your application route
Methodology
Recent studies have pioneered the use of ensemble hindcast data to better estimate current day risk. This approach, commonly known as the UNSEEN approach (Osinski et al., 2016; Thompson et al., 2017) provides sets of weather events that are plausible, but did not occur. We will use infrastructure asset data (e.g. power and rail) and meteorological data to identify weather regimes and compound event types that drive asset damage (cf. Donaldson et al. 2023; Jia et al. 2024), using supervised and unsupervised machine learning and statistical methods as appropriate. Hindcast data will be used to better quantify current return times and historical trends (cf. Kelder et al., 2020), to explore unseen compound events, and to analyse the role of the jet stream in driving these events (e.g. Röthlisberger et al. 2016; Geen et al. 2023). Finally future risk will be estimated by exploring jet stream dynamics within climate change projections.
Training and skills
DRs will be awarded CENTA Training Credits (CTCs) for participation in CENTA-provided and ‘free choice’ external training. One CTC can be earned per 3 hours training, and DRs must accrue 100 CTCs across the three and a half years of their PhD.
The student will build specialist knowledge in impacts and drivers of midlatitude weather extremes, and experience in analysing climate and infrastructure asset datasets. The student will develop computing skills, in particular coding and visualising data with Python, with the opportunity to build further transferrable skills, e.g. machine learning techniques, version control (Git), HPC usage. Idealised climate model simulations could also be used to interrogate processes, if of interest.
This PhD would provide a strong foundation for an academic career in climate dynamics and/or impacts research, or a career in the private sector e.g. climate intelligence industry or energy sectors.
Partners and collaboration
Project co-supervisor Hazel Thornton manages the UK Met Office Monthly to Decadal Prediction and Impacts team. Her research explores the seasonal forecast skill of current coupled modelling systems, and the utility of such forecasts, with a focus on applications for the energy industry. This collaboration provides the student with an excellent opportunity to engage with Met Office scientists and will support translation of results from the PhD project to applications.
Depending on the direction taken, there may be further opportunities for international collaboration with groups in Canada (UBC, Rachel White) and Sweden (Uppsala University, Gabriele Messori).
Further details
Please contact Dr. Ruth Geen, University of Birmingham, [email protected] for more information about the project and application process.
To apply to this project:
- You must include a CENTA studentship application form, downloadable from: CENTA Studentship Application Form 2025.
- You must include a CV with the names of at least two referees (preferably three) who can comment on your academic abilities.
- Please submit your application and complete the host institution application process via: https://sits.bham.ac.uk/lpages/LES068.htm. Please select the PhD Geography and Environmental Science (CENTA) 2025/26 Apply Now button. The CENTA Studentship Application Form 2025 and CV can be uploaded to the Application Information section of the online form. Please quote CENTA 2025-B10 when completing the application form.
Applications must be submitted by 23:59 GMT on Wednesday 8th January 2025.
Possible timeline
Year 1
This interdisciplinary project offers a high level of flexibility for the successful student to choose the angle of most interest to them as the work develops, across large-scale dynamics, seasonal forecasting, statistical downscaling, infrastructure resilience, and engineering. A possible timeline could be:
Year 1: Student uses data on observed weather and infrastructure faults to identify how multiple weather events and variables enhance the risk of damage at regional scales.
Year 2
Year 2: Hindcast data is used to explore the return times, trends, and large-scale setting (jet structures, modes of variability) of key compound event types identified in year 1.
Year 3
Year 3: Student explores trends in weather events, jet location and structure in climate models, and assesses plausible storylines for future infrastructure risk.
Further reading
Donaldson, D. L., Ferranti, E. J., Quinn, A. D., Jayaweera, D., Peasley, T., & Mercer, M. (2023). Enhancing power distribution network operational resilience to extreme wind events. Meteorological Applications, 30(2), e2127.
Geen, R., Thomson, S. I., Screen, J. A., Blackport, R., Lewis, N. T., Mudhar, R., … & Vallis, G. K. (2023). An explanation for the metric dependence of the midlatitude jet‐waviness change in response to polar warming. Geophysical Research Letters, 50(21), e2023GL105132.
Jia, Z., Donaldson, D. L., & Ferranti, E. (2024, January). Weather-related fragility modelling of critical infrastructure: a power and railway case study. In Proceedings of the Institution of Civil Engineers-Civil Engineering (Vol. 177, No. 5, pp. 50-58). Emerald Publishing Limited.
Kelder, T., Müller, M., Slater, L. J., Marjoribanks, T. I., Wilby, R. L., Prudhomme, C., … & Nipen, T. (2020). Using UNSEEN trends to detect decadal changes in 100-year precipitation extremes. npj Climate and Atmospheric Science, 3(1), 47.
Osinski, R., Lorenz, P., Kruschke, T., Voigt, M., Ulbrich, U., Leckebusch, G. C., … & Majewski, D. (2016). An approach to build an event set of European windstorms based on ECMWF EPS. Natural Hazards and Earth System Sciences, 16(1), 255-268.
Priestley, M. D., & Catto, J. L. (2022). Future changes in the extratropical storm tracks and cyclone intensity, wind speed, and structure. Weather and Climate Dynamics, 3(1), 337-360.
Röthlisberger, M., Pfahl, S., & Martius, O. (2016). Regional‐scale jet waviness modulates the occurrence of midlatitude weather extremes. Geophysical Research Letters, 43(20), 10-989.
Thompson, V., Dunstone, N. J., Scaife, A. A., Smith, D. M., Slingo, J. M., Brown, S., & Belcher, S. E. (2017). High risk of unprecedented UK rainfall in the current climate. Nature communications, 8(1), 1-6.
Zscheischler, J., Martius, O., Westra, S., Bevacqua, E., Raymond, C., Horton, R. M., … & Vignotto, E. (2020). A typology of compound weather and climate events. Nature reviews earth & environment, 1(7), 333-347.