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Title: Developing and evaluating a global model for landscape fires
Author: Mangeon, Stephane
ISNI:       0000 0004 6347 2881
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2017
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Fires are integral to the global environment. Changes to that environment have, and will, modify fire behaviour. In turn, fires impact the land surface by burning vegetation, and the atmosphere by emitting large amounts of heat, gases and aerosols. I first studied extreme wildfires in North America, finding satellite observations estimate 79% of the burnt area logged by ground crews during firefighting campaigns, and only 54% during peak burning days. I then describe the development of one such model: the INteractive Fire and Emission algoRithm for Natural envirOnments (INFERNO), a global fire model developed for the UK Met Office’s Unified Model. It innovates by providing a simple parameterisation which uses information on temperature, humidity, soil moisture, and vegetation, to estimate burnt area and fire emissions at the global scale. Live vegetation is used as a proxy for fuel, in reality, litter and ground fuel are predominant. The model performs well against fire danger indices and observations, but shows regional biases and a notable underestimate of emissions during El-Niño years. INFERNO was most sensitive to changes in soil moisture, with regional patterns of fire also strongly influenced by number of ignitions, fuel density, temperature and humidity. Results from multiple fire models participating in the Fire Model Intercomparison Project (FireMIP) show similar performances, with a multi-model mean underestimate of global burnt area, and overestimate of emissions. I identified systematic biases in fuel consumption, which models generally underestimate in tropical forests and peatlands compared to field observations. Models particularly disagreed in areas with strong human-influences. Global fire models are central to our understanding of how fires interact with the land and the atmosphere on large scales, and hold the potential to be an integral part of global models in the future and to catalyse new understandings of feedbacks in the Earth system.
Supervisor: Voulgarakis, Apostolos ; Toumi, Ralf Sponsor: Natural Environment Research Council ; Met Office
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral