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Machine learning-based observation-constrained projections reveal elevated global socioeconomic risks from wildfire.

Yan Yu1, Jiafu Mao2, Stan D Wullschleger3

  • 1Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China.

Nature Communications
|March 23, 2022
PubMed
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Machine learning refines wildfire projections by using observational data. This reveals lower global fire emissions but increased wildfire risks to populations and economies, especially in Africa.

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Area of Science:

  • Climate Science
  • Earth System Science
  • Machine Learning Applications

Background:

  • Accurate wildfire projections are vital for adaptation and mitigation strategies.
  • Current Earth system models lack sufficient observational constraints, impacting projection credibility.
  • Wildfire risk assessments require integrated analysis of fire behavior and socioeconomic factors.

Purpose of the Study:

  • To develop a machine learning framework for constraining future fire carbon emissions from Earth system models.
  • To assess the impact of observational constraints on projected global fire carbon emissions and wildfire socioeconomic risks.
  • To identify regions with heightened future wildfire socioeconomic risks.

Main Methods:

  • Utilized a machine learning framework to constrain Coupled Model Intercomparison Project phase 6 (CMIP6) Earth system model outputs.
  • Employed historical, observed joint states of fire-relevant variables for model constraint.
  • Compared an observation-constrained ensemble with a default ensemble for future projections.

Main Results:

  • The observation-constrained ensemble projected a weaker increase in global fire carbon emissions compared to the default ensemble.
  • Global wildfire exposure to population, gross domestic production, and agricultural area showed a higher increase in the constrained ensemble.
  • Elevated socioeconomic risks were concentrated in western and central African countries due to combined increases in wildfire activity and socioeconomic development.

Conclusions:

  • Observational constraints significantly alter wildfire projection outcomes, highlighting increased socioeconomic risks.
  • Western and central Africa face emergent, heightened wildfire socioeconomic risks.
  • Strategic preparedness for wildfires in these specific regions is urgently needed.