Quantifying the drivers and predictability of seasonal changes in African fire

Yan Yu,Xiaoying Shi,Jiafu Mao,Forrest M. Hoffman,Yaoping Wang,Stan D. Wullschleger,Peter E. Thornton,Michael Notaro

doi:10.1038/s41467-020-16692-w

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.

Highlights

Africa contains some of the most vulnerable ecosystems to fires
This study applies the combined Stepwise Generalized Equilibrium Feedback Assessment (SGEFA)-machine learning techniques (MLTs) analytical framework that benefits from the capabilities of both methodologies
MLTs have been widely applied for disentangling the controls and building prediction models of regional and global fire activity[23,29,30], they have been criticized as being black boxes and are seldom considered optimal for examining underlying mechanisms and processes[23]

Summary

Introduction

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. Observed variations in regional climate and hydrology across sub-Saharan Africa have been shown to be sensitive to global seasurface temperature (SST) variability and regional land surface changes on the seasonal timescale[17,18] Such oceanic and terrestrial controls on African climate variability can potentially enhance the ability to predict African fire at relevant timescales, since the oceanic and terrestrial states generally exhibit longer memory than does the atmosphere[19]. While different modes of variability in SSTs presumably co-impact regional fire activity in Africa[22], their synergistic and independent roles lack systematic exploration in either observations or model simulations Vegetation indices, such as leaf area index (LAI), may influence African fire through multiple mechanisms. Expanded vegetation cover provides additional fuel for burning, in semi-arid landscapes[1]

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