Developing a Data-Driven Global Fire Model to Explore the Impacts of Increasing CO2e on the Global Fire Regime and Vegetation Cover

The aim of this PhD is to combine the current body of literature, existing global fire models and new emerging data sets (MODIS Vegetation and Fire datasets, Global Fire Atlas etc.) to build an empirical global fire model. By moving away from process-based models and relying on emergent trends from data to determine parameters relative role, this PhD fits into the current work of the REALM project led by Professor Iain Colin Prentice. In line with the current move towards simpler Earth System Models, one idea would be to incorporate into this fire model a newly developed global equation for GPP (Wang et al., 2017; Stocker et al., 2020). This would allow for a major simplification in terms of GPP inputs and could greatly reduce uncertainties surrounding how different plant-functional types respond to fire (flammability and post-fire regeneration properties for example). If successful in predicting burnt area and fire trends, this simplification could a step forward in reducing uncertainties concerning of global fire controls. In addition, different response variables will be explored (fire radiative power and fire size) to move away from the reliance on Burnt Area. Once the model is robust and functional, the impact of increasing atmospheric CO2e on the dynamical relationship of vegetation-fire will be explored.