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Improved representation of black carbon mixing structures suggests stronger direct radiative heating

  • Yuanyuan Wang
  • , Zhonghua Zheng
  • , Yuan Sun
  • , Yu Yao
  • , Po Lun Ma
  • , Aoxing Zhang
  • , Shupeng Zhu
  • , Zexuan Zhang
  • , Xiyao Chen
  • , Yuner Pang
  • , Qiyuan Wang
  • , Huizheng Che
  • , Joseph Ching
  • , Weijun Li*
  • *Corresponding author for this work
  • Zhejiang University
  • Department of Earth and Environmental Sciences
  • University of Manchester
  • Cornell University
  • Pacific Northwest National Laboratory
  • Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks
  • Southern University of Science and Technology
  • Department of Atmospheric Sciences
  • Key Laboratory of Aerosol Chemistry and Physics
  • CAS - Institute of Earth Environment
  • Chinese Academy of Meteorological Sciences
  • Department of Science and Environmental Studies
  • The Education University of Hong Kong

Research output: Contribution to journal › Article › peer-review

Abstract

Black carbon significantly influences the Earth system because of its strong solar radiation absorption. However, its direct radiative effect remains poorly understood in current climate models, partly because current climate models oversimplify the diverse structures formed when black carbon mixes with other atmospheric components. Here, we show that incorporating more realistic, multi-mixing structure representations of black carbon increases the direct radiative effect. We find that aged black carbon particles, with thicker coatings and higher embedded fractions, enhance the direct radiative effect more efficiently. Using machine learning alongside the Community Earth System Model, we show that the direct radiative effect at the top of the atmosphere in regions with heavy black carbon pollution is 31.6% greater when multi-mixing structures are considered. These findings highlight the importance of modeling complex mixing structures of particle-resolved black carbon to accurately capture their warming impacts on the global atmosphere, particularly in highly polluted regions.

Original languageEnglish
Article number101311
JournalOne Earth
Volume8
Issue number5
DOIs
StatePublished - 16 May 2025
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 13 - Climate Action
    SDG 13 Climate Action

Keywords

  • Community Earth System Model
  • black carbon
  • climate warming
  • direct radiative effect
  • fractal dimension
  • individual particle analysis
  • machine learning
  • mixing structure
  • optical absorption
  • transmission electron microscope

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