Quantifying the drivers and predictability of seasonal changes in African fire

Yan Yu, Jiafu Mao (), Peter E. Thornton, Michael Notaro, Stan D. Wullschleger, Xiaoying Shi, Forrest M. Hoffman and Yaoping Wang
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Yan Yu: Princeton University
Jiafu Mao: Environmental Sciences Division and Climate Change Science Institute
Peter E. Thornton: Environmental Sciences Division and Climate Change Science Institute
Michael Notaro: University of Wisconsin-Madison
Stan D. Wullschleger: Environmental Sciences Division and Climate Change Science Institute
Xiaoying Shi: Environmental Sciences Division and Climate Change Science Institute
Forrest M. Hoffman: Computational Sciences and Engineering Division and Climate Change Science Institute
Yaoping Wang: University of Tennessee

Nature Communications, 2020, vol. 11, issue 1, 1-8

Abstract: Abstract Africa contains some of the most vulnerable ecosystems to fires. Successful seasonal prediction of fire activity over these fire-prone regions remains a challenge and relies heavily on in-depth understanding of various driving mechanisms underlying fire evolution. Here, we assess the seasonal environmental drivers and predictability of African fire using the analytical framework of Stepwise Generalized Equilibrium Feedback Assessment (SGEFA) and machine learning techniques (MLTs). The impacts of sea-surface temperature, soil moisture, and leaf area index are quantified and found to dominate the fire seasonal variability by regulating regional burning condition and fuel supply. Compared with previously-identified atmospheric and socioeconomic predictors, these slowly evolving oceanic and terrestrial predictors are further identified to determine the seasonal predictability of fire activity in Africa. Our combined SGEFA-MLT approach achieves skillful prediction of African fire one month in advance and can be generalized to provide seasonal estimates of regional and global fire risk.

Date: 2020
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DOI: 10.1038/s41467-020-16692-w

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