Professor Sander Veraverbeke (VU Amsterdam, Faculty of Science (Earth and Climate))
Professor John Abatzoglou (University of California Merced, School of Engineering)
Professor Corinne Le Quéré (School of Environmental Sciences, University of East Anglia)
Record-breaking wildfires in seasonally dry forests of the western US, southeast Australia, and the Mediterranean have significantly impacted livelihoods, economies, ecosystems, and carbon stocks in recent years.
Lightning strikes are implicated as a major ignition source of the largest wildfires in these regions, however incomplete lightning observations have until now restricted the assessment of regional relationships between lightning and wildfire ignition on large scales. Consequently, the potential impacts of climate change on wildfire ignitions by lightning are poorly understood.
Wildfire ignitions occur disproportionately during extreme hot and dry conditions, when vegetation is driest and most flammable. These fire-prone weather conditions are becoming more frequent globally due to climate change. Moreover, warming of the atmosphere intensifies atmospheric convection and can promote increases in lightning frequency. Consequently, climate change presents compound risks of wildfire occurrence by enhancing both forest flammability and ignition opportunities. These compound risks remain understudied.
This project will unravel the contribution of lightning ignitions to modern wildfire patterns in seasonally dry forests and use climate models to predict the impact of climate change on lightning ignitions in future. The project will deliver novel understanding of regional exposure to future wildfire risks and highlight priority locations for risk mitigation.
With the support of an international supervisory team of leading fire and climate scientists, the student will:
- identify lightning-ignited wildfires using observations of lightning and fire from satellites and ground-based sensors.
- study the regional impact of lightning strikes on spatial and temporal patterns of wildfire.
- examine the climatic thresholds that determine whether a lightning strike ignites a wildfire.
- predict future trends in fire-prone weather and lightning using climate model simulations, and use these predictions to study compound impacts on fire risk.
- Expertise in programming with Python/R: data carpentry, machine learning, geospatial analysis.
- NCAS climate modelling summer school (https://ncas.ac.uk/study-with-us/climate-modelling-summer-school/).
- Overseas visits to supervisors in California (2 months) and Amsterdam (1 month), plus visits to the UK Met Office, to learn/develop skills in wildfire detection and modelling.
- Support to present at international conferences and submit findings to academic journals.
- Degree in any natural science or data science.
- Skills: Geospatial and statistical analyses using code (e.g. Python/R).