Climate change impacts on wildfire risk in seasonally dry forests

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Scientific backgroundWildfires in the seasonally dry forests of western North America, Australia, and the Mediterranean have had record-breaking impacts on livelihoods, economies, ecosystems, and even carbon storage in recent years. Lightning strikes have been implicated as a major ignition source in these regions, however limited observations of lightning have until now restricted the assessment of relationships between lightning and wildfire in a changing climate. Consequently, the potential impact of climate change on wildfire ignitions by lightning are poorly understood.Wildfire ignitions occur disproportionately during extremely hot and dry conditions, when vegetation is at its driest and most flammable. Such fire-prone weather conditions are becoming more frequent globally due to climate change. Moreover, global warming might also promote increases in lightning frequency over land. Consequently, climate change presents compound risks of wildfire occurrence by enhancing both forest flammability and ignition opportunities.This project will use observations to unravel the contribution of lightning ignitions to modern wildfire patterns in seasonally dry forests, and subsequently employ climate model projections to evaluate the impact of future climate change on lightning ignitions. The project will improve understanding of how wildfire risks may change across the world's forests in future, with the view to help society prepare and adapt to changing risks and to optimise the planning of climate-smart and fire-smart re/afforestation programmes.Research MethodologyWith the support of an international supervisory team, the student will:identify lightning-ignited wildfires by combining observations of lightning and fire from satellites and ground-based sensors.evaluate the regional impact of lightning strikes on spatial and temporal patterns of wildfire. examine the climatic thresholds that determine whether lightning ignites wildfires.predict future changes in wildfire risk resulting from compound increases in extreme weather and lightning, based on climate model projections.TrainingProgramming with Python/R (advanced level): data analysis, machine learning, geospatial analysis. NCAS climate modelling summer school (https://ncas.ac.uk/study-with-us/climate-modelling-summer-school/).Overseas visits to co-supervisors in California (2 months) and Amsterdam (1 month).Visits to the UK Met Office to interact with fire and lightning modellers. Support to present at international conferences and submit findings to academic journals.