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Title: Quantifying the drivers and predictability of seasonal changes in African fire

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.

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [2];  [3]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [4]; ORCiD logo [5]
+ Show Author Affiliations
  1. Princeton Univ., NJ (United States);
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  3. Univ. of Wisconsin, Madison, WI (United States)
  4. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States)
  5. Univ. of Tennessee, Knoxville, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI Identifier:
1633166
Grant/Contract Number:  
AC05-00OR22725; SC0012534
Resource Type:
Accepted Manuscript
Journal Name:
Nature Communications
Additional Journal Information:
Journal Volume: 11; Journal Issue: 1; Journal ID: ISSN 2041-1723
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Climate sciences; environmental sciences; fire ecology; projection and prediction

Citation Formats

Yu, Yan, Mao, Jiafu, Thornton, Peter E., Notaro, Michael, Wullschleger, Stan D., Shi, Xiaoying, Hoffman, Forrest M., and Wang, Yaoping. Quantifying the drivers and predictability of seasonal changes in African fire. United States: N. p., 2020. Web. doi:10.1038/s41467-020-16692-w.
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Yu, Yan, Mao, Jiafu, Thornton, Peter E., Notaro, Michael, Wullschleger, Stan D., Shi, Xiaoying, Hoffman, Forrest M., & Wang, Yaoping. Quantifying the drivers and predictability of seasonal changes in African fire. United States. https://doi.org/10.1038/s41467-020-16692-w
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Yu, Yan, Mao, Jiafu, Thornton, Peter E., Notaro, Michael, Wullschleger, Stan D., Shi, Xiaoying, Hoffman, Forrest M., and Wang, Yaoping. Tue . "Quantifying the drivers and predictability of seasonal changes in African fire". United States. https://doi.org/10.1038/s41467-020-16692-w. https://www.osti.gov/servlets/purl/1633166.
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@article{osti_1633166,
title = {Quantifying the drivers and predictability of seasonal changes in African fire},
author = {Yu, Yan and Mao, Jiafu and Thornton, Peter E. and Notaro, Michael and Wullschleger, Stan D. and Shi, Xiaoying and Hoffman, Forrest M. and Wang, Yaoping},
abstractNote = {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.},
doi = {10.1038/s41467-020-16692-w},
journal = {Nature Communications},
number = 1,
volume = 11,
place = {United States},
year = {Tue Jun 09 00:00:00 EDT 2020},
month = {Tue Jun 09 00:00:00 EDT 2020}
}
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Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1038/s41467-020-16692-w
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Citation Metrics:
Cited by: 9 works
Citation information provided by
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Figures / Tables:

Fig. 1 Fig. 1: Robustness of controls on African fire by season. The multi-dataset robustness of oceanic and terrestrial controls on African fire carbon emission is assessed by the Stepwise Generalized Equilibrium Feedback Assessment (SGEFA) by season. Color represents the number of dataset combinations [out of the currently examined six dataset combinationsmore » (Supplementary Table 1)] that indicate significant (p < 0.1) response of regional average fire carbon emission to either of the leading two principal components’ (PCs) time series corresponding to the sea-surface temperature (SST) empirical orthogonal functions (EOFs) from eight oceanic basins [North Atlantic (NA), North Pacific (NP), tropical Indian (TI), tropical Atlantic (TA), tropical Pacific (TP), South Indian (SI), South Atlantic (SA), and South Pacific (SP)], and regional average leaf area index (LAI) and 0–10 cm soil moisture (SM), across a northern Africa and b southern Africa. In both a, b, labels on the x-axis stand for 3-month seasons, for example, January–March (JFM) for January, February, and March. The fire-active season is highlighted with red x-axis labels. The forcings are detailed in the Methods section.« less
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