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

这个博士学位的目的是结合联合收割机目前的文献，现有的全球火灾模型和新出现的数据集（中分辨率成像分光仪植被和火灾数据集，全球火灾地图集等）。建立一个全球火灾的经验模型。通过远离基于过程的模型，并依靠数据中的新兴趋势来确定参数的相对作用，这个博士学位适合由Iain Colin普伦蒂斯教授领导的REALM项目的当前工作。根据目前向更简单的地球系统模型的发展，一个想法是将新开发的GPP全球方程纳入该火灾模型（Wang等人，2017; Stocker等人，2020年）。这将大大简化全球伙伴关系的投入，并大大减少围绕不同工厂功能类型如何应对火灾的不确定性（例如易燃性和火灾后再生特性）。如果能够成功预测火灾面积和火灾趋势，这种简化可能会在减少全球火灾控制的不确定性方面向前迈出一步。此外，将探讨不同的响应变量（火灾辐射功率和火灾规模），以摆脱对烧伤面积的依赖。一旦该模型是强大的和功能，增加大气CO2 e的植被火灾的动力学关系的影响将进行探讨。

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.