Climate change impacts on wildfire risk in seasonally dry forests
Interested in how climate change could affect the occurrence of lightning and the ignition of dangerous forest wildfires? This is the PhD for you!
Application deadline 12th Jan — Interviews with supervisors 24th Jan-3rd Feb — Supervisors nominate a single candidate 7th Feb — Formal panel interviews with ARIES DTP 23rd-24th Feb
Globally, wildfire disasters have directly killed ~2,500 and displaced 175,000 people from their homes since 1990. Over 90% of wildfire disasters occur in seasonally dry forests of the western US, southeast Australia and Mediterranean. The 2019/20 Australian wildfires caused damages amounting to 6% of the country’s GDP, and the costs of Californian wildfires in 2018 amounted to 1.5% of the state’s GDP. The impact of extreme wildfires on wildlife and ecosystems can also be profound. For example, the population of 21 endemic species was reduced by over 30% during the Australian bushfires of 2019/20.
Wildfires occur disproportionately in these environments during ‘fire weather’ conditions (when temperatures are high, humidity is low, rainfall has been low and winds may be high), when vegetation is driest and most flammable. These fire weather conditions are becoming more frequent and intense globally due to climate change.
Moreover, warming of the atmosphere intensifies atmospheric convection and may 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 fire weather and lightning ignitions in future. The project will deliver novel understanding of regional exposure to future wildfire risks and highlight priority locations for risk mitigation.
State of the Science
Wildfires occur disproportionately in seasonally dry forests during ‘fire weather’ conditions (when temperatures are high, humidity is low, rainfall has been low and winds may be high), when vegetation is driest and most flammable. Co-supervisor Prof. Abatzoglou has shown that fire weather will increase globally under future climate change and supervisor Jones has shown that these increases will be particularly pronounced in seasonally dry forests (article under review, available upon request).
Lightning strikes are implicated as a major ignition source for the largest wildfires in some seasonally dry forests, however observations of lightning ignitions tend to be spatially limited, deriving from regional land managers (e.g. national parks) or from fire services who cover limited areas.
The detection of lightning ignitions using lightning observations from remote sensing has been achieved in very few regions, with one prominent example being high latitude environments by co-supervisor Prof. Veraverbeke. Consequently, the potential impacts of climate change on wildfire ignitions by lightning are poorly understood at the global scales, including in seasonally dry forests where forest wildfires can be particularly destructive.
Until now, a lack of observational data has restricted the assessment of relationships between lightning and wildfire ignition on large scales. The NERC grant awarded to Dr. Jones provides funding to purchase aviation-grade lightning observations and unlocks new potential to study climate-lightning-fire relationships at the global scale.
Climate models indicate that extratropical lightning frequency will increase in the extratropics under future climate change, in addition to the projected increases in the frequency and intensity of fire weather. Nonetheless, the compound impacts of synchronous increases in fire-prone weather and lightning frequency on future wildfire risk are yet to be evaluated.
Project Focus and Aims
This PhD project will focus on using observations of lightning to understand how fire weather and lightning have interacted to influence wildfire ignition in seasonally dry forests during recent decades. The student will then work with fire weather and lightning projections from climate models to quantify synchronous future increases in fire weather and lightning.
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.
Beyond this project, the new insights generated will feed into the improved mechanistic modelling of lightning ignitions in climate models, enabling future changes in wildfire risk to be modelled more accurately under changing fire weather and lightning frequency.
The student will take the following steps to achieve the aims of the project:
- Lightning-ignited wildfires will be identified in the past decade using observations from lightning sensors aboard three NASA satellites (TRMM-LIS, ISS-LIS, GLM), two ground-based lightning sensor networks (Vaisala, WWLLN), and fire ignition hotspots detected by the NASA FIRMS service. Using NumPy arrays and geospatial analysis packages in Python, lightning ignitions will be identified as instances when observed lightning strikes coincide with observed fire hotspots.
- Regional climatic thresholds conducive to the ignition of wildfires by lightning will be assessed using logistic regression in Python or R. Specifically, the presence/absence of fire at lightning strike locations will be modelled as a function of the fire weather index.
- UK Earth System Model (UK-ESM) simulations of lightning will be provided by partners at the UK Met Office. Using similar geospatial analysis skills as in prior phases of the project, the model simulations will be analysed and used to quantify future trends in lightning activity at policy-relevant global warming increments of 1.5°C, 2.0°C and 3.0°C. In addition, climate model simulations of fire weather will be used to quantify future trends in fire weather.
- Using UK-ESM simulations of future change in fire weather and lightning activity, the first analysis of the compound effects climate change on wildfire risk will be performed. Compound risk will be quantified as the number of days on which lightning activity coincides with fire weather exceeding the regional ignition thresholds derived in step 2.
Dr Matthew Jones (School of Environmental Sciences, University of East Anglia)
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)
- 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.
About the Opportunity
This project is advertised as part of a competitive funding competition under the umbrella of the Natural Environment Research Council (NERC) ARIES doctoral training partnership.
- This project has been shortlisted for funding by the ARIES NERC DTP and will start on 1st October 2022. The closing date for applications is 23:59 on 12th January 2022.
- Successful candidates who meet UKRI’s eligibility criteria will be awarded a NERC studentship, which covers fees, stipend (£15,609 p.a. for 2021-22) and research funding. International applicants (EU and non-EU) are eligible for fully-funded UKRI studentships. Please note ARIES funding does not cover visa costs (including immigration health surcharge) or other additional costs associated with relocation to the UK.
- ARIES students benefit from bespoke graduate training and ARIES provides £2,500 to every student for access to external training, travel and conferences. Excellent applicants from quantitative disciplines with limited experience in environmental sciences may be considered for an additional 3-month stipend to take advanced-level courses in the subject area.
- ARIES is committed to equality, diversity, widening participation and inclusion in all areas of its operation. We encourage enquiries and applications from all sections of the community regardless of gender, ethnicity, disability, age, sexual orientation and transgender status. Academic qualifications are considered alongside non-academic experience, and our recruitment process considers potential with the same weighting as past experience.
- All ARIES studentships may be undertaken on a part-time or full-time basis, visa requirements notwithstanding.
- A minimum 2:1 BSc in any natural science or data science is required.
- We particularly welcome applicants with a track record of using code to undertake geospatial and statistical analyses.
- Familiarity with Python or R would be preferable.
- A passion for studying the environment and knowledge surrounding the issue of climate change is an advantage.