A new paper, led by AOS Postdoc Yan Yu, assessed the drivers and predictability of seasonal changes in African fire and was published today in Nature Communications. This study used 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 were 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 were further identified to determine the seasonal predictability of fire activity in Africa. The combined SGEFA-MLT approach achieved skillful prediction of African fire one month in advance and can be generalized to provide seasonal estimates of regional and global fire risk.
Quantifying the Drivers and Predictability of Seasonal Changes in African Fire
Tuesday, Jun 9, 2020