Articles | Volume 22, issue 22
https://doi.org/10.5194/acp-22-14825-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-14825-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Nov 2022
Research article |
| 22 Nov 2022
| 22 Nov 2022
Constraining the particle-scale diversity of black carbon light absorption using a unified framework
Center for Aerosol Science and Engineering, Department of Energy,
Environmental and Chemical Engineering, Washington University in St. Louis,
St. Louis, MO 63130, USA
Rajan K. Chakrabarty
CORRESPONDING AUTHOR
Center for Aerosol Science and Engineering, Department of Energy,
Environmental and Chemical Engineering, Washington University in St. Louis,
St. Louis, MO 63130, USA
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We developed a surrogate model to predict how black carbon particles absorb and scatter light based on their size, shape, and mixing with other aerosol species. The model was trained on detailed physics simulations and also estimates uncertainty caused by limited training data and unresolved internal structures. It outperformed common simplified particle assumptions, especially for scattering, and can help target new simulations to improve predictions of black carbon’s radiative effects.
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We developed a surrogate model to predict how black carbon particles absorb and scatter light based on their size, shape, and mixing with other aerosol species. The model was trained on detailed physics simulations and also estimates uncertainty caused by limited training data and unresolved internal structures. It outperformed common simplified particle assumptions, especially for scattering, and can help target new simulations to improve predictions of black carbon’s radiative effects.
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Accurate long-term measurement of aerosol light absorption is vital for assessing direct aerosol radiative forcing. Light absorption by aerosols at the US Department of Energy long-term climate monitoring SGP site is measured using the Particle Soot Absorption Photometer (PSAP), which suffers from artifacts and biases difficult to quantify. Machine learning offers a promising path forward to correct for biases in the long-term absorption dataset at the SGP site and similar Class-I areas.
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We present a comparison of the changes to light absorption behavior and chemical composition of wildfire smoke particles from day- and nighttime oxidation processes and discuss the results within the context of previous laboratory findings.
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Short summary
Understanding and parameterizing the influences of black carbon (BC) particle morphology and compositional heterogeneity on its light absorption represent a fundamental problem. We develop scaling laws using a single unifying parameter that effectively encompasses large-scale diversity observed in BC light absorption on a per-particle basis. The laws help reconcile the disparities between field observations and model predictions. Our framework is packaged in an open-source Python application.
Understanding and parameterizing the influences of black carbon (BC) particle morphology and...
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