Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 26, issue 12
Articles | Volume 26, issue 12
https://doi.org/10.5194/acp-26-9149-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-26-9149-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 12
Research article
 | 
30 Jun 2026
Research article |  | 30 Jun 2026

Microphysical evolution and column loading drive nonlinear regional contrast in black carbon top-of-atmosphere forcing

Pravash Tiwari, Jason Blake Cohen, Hongrui Gao, Lingxiao Lu, Jun Wang, Oleg Dubovik, and Kai Qin

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Latest update: 10 Aug 2026
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Short summary
Black carbon's climate impact is highly uncertain because its radiative effect depends on particle size, mixing state, and column loading.  This study combines satellite data, physics-based simulations, and machine learning to estimate black carbon forcing across contrasting regions. The same black carbon amount can warm or cool the atmosphere depending on local aerosol properties. The machine learning framework provides a fast, transferable tool for regional climate assessment.
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