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

Yan Yu, Jiafu Mao (maoj@ornl.gov), Stan D. Wullschleger, Anping Chen, Xiaoying Shi, Yaoping Wang, Forrest M. Hoffman, Yulong Zhang and Eric Pierce
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Yan Yu: Peking University
Jiafu Mao: Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory
Stan D. Wullschleger: Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory
Anping Chen: Colorado State University
Xiaoying Shi: Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory
Yaoping Wang: University of Tennessee
Forrest M. Hoffman: Computational Sciences and Engineering Division and Climate Change Science Institute, Oak Ridge National Laboratory
Yulong Zhang: University of Tennessee
Eric Pierce: Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory

Nature Communications, 2022, vol. 13, issue 1, 1-11

Abstract: Abstract Reliable projections of wildfire and associated socioeconomic risks are crucial for the development of efficient and effective adaptation and mitigation strategies. The lack of or limited observational constraints for modeling outputs impairs the credibility of wildfire projections. Here, we present a machine learning framework to constrain the future fire carbon emissions simulated by 13 Earth system models from the Coupled Model Intercomparison Project phase 6 (CMIP6), using historical, observed joint states of fire-relevant variables. During the twenty-first century, the observation-constrained ensemble indicates a weaker increase in global fire carbon emissions but higher increase in global wildfire exposure in population, gross domestic production, and agricultural area, compared with the default ensemble. Such elevated socioeconomic risks are primarily caused by the compound regional enhancement of future wildfire activity and socioeconomic development in the western and central African countries, necessitating an emergent strategic preparedness to wildfires in these countries.

Date: 2022
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Citations: View citations in EconPapers (3)

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DOI: 10.1038/s41467-022-28853-0

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