Articles | Volume 19, issue 21
https://doi.org/10.5194/gmd-19-10795-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-10795-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
DDA-BNN v1.0: a morphology-aware surrogate model for the optical properties of black carbon-containing particles
Payton Beeler
Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
Sam Donald
Physical and Computational Sciences, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
Related authors
Gavin Cornwell, Alla Zelenyuk-Imre, Gourihar Kulkarni, Nurun Nahar Lata, Kaizad Patel, Jennifer Yao, Zihua Zhu, Laura Fierce, Payton Beeler, Swarup China, Isabelle Steinke, and Susannah Burrows
EGUsphere, https://doi.org/10.5194/egusphere-2026-3882, https://doi.org/10.5194/egusphere-2026-3882, 2026
Short summary
Short summary
Ice in clouds strongly affects rainfall and how much sunlight clouds reflect. We studied which airborne particles can trigger ice formation. We created soil-like mixtures and examined individual particles to see whether biological material and mineral dust were truly combined or stayed separate, and how this affected their ice formation. We found that the total amount of biological material was more important than how the particles were mixed.
Payton Beeler and Rajan K. Chakrabarty
Atmos. Chem. Phys., 22, 14825–14836, https://doi.org/10.5194/acp-22-14825-2022, https://doi.org/10.5194/acp-22-14825-2022, 2022
Short summary
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.
Gavin Cornwell, Alla Zelenyuk-Imre, Gourihar Kulkarni, Nurun Nahar Lata, Kaizad Patel, Jennifer Yao, Zihua Zhu, Laura Fierce, Payton Beeler, Swarup China, Isabelle Steinke, and Susannah Burrows
EGUsphere, https://doi.org/10.5194/egusphere-2026-3882, https://doi.org/10.5194/egusphere-2026-3882, 2026
Short summary
Short summary
Ice in clouds strongly affects rainfall and how much sunlight clouds reflect. We studied which airborne particles can trigger ice formation. We created soil-like mixtures and examined individual particles to see whether biological material and mineral dust were truly combined or stayed separate, and how this affected their ice formation. We found that the total amount of biological material was more important than how the particles were mixed.
Payton Beeler and Rajan K. Chakrabarty
Atmos. Chem. Phys., 22, 14825–14836, https://doi.org/10.5194/acp-22-14825-2022, https://doi.org/10.5194/acp-22-14825-2022, 2022
Short summary
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.
Cited articles
Beeler, P., Donald, S., and Fierce, L.: Training dataset for DDA-BNN v1.0, Zenodo [data set], https://doi.org/10.5281/zenodo.19324185, 2026a. a
Beeler, P., Donald, S., and Fierce, L.: DDA-BNN v1.0: A Morphology-Aware Surrogate Model for the Optical Properties of Black Carbon–Containing Particles, Zenodo [code], https://doi.org/10.5281/zenodo.21478183, 2026b (code also available: https://github.com/pnnl/DDA-BNN, last access: 12 October 2026). a, b
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D.: Weight uncertainty in neural networks, in: International Conference on Machine Learning, PMLR, 1613–1622, https://proceedings.mlr.press/v37/blundell15.html (last access: 12 October 2026), 2015. a
Bonnet, D., Hirtzlin, T., Majumdar, A., Dalgaty, T., Esmanhotto, E., Meli, V., Castellani, N., Martin, S., Nodin, J.-F., and Bourgeois, G.: Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks, Nat. Commun., 14, 7530, https://doi.org/10.1038/s41467-023-43317-9, 2023. a
Cappa, C. D., Onasch, T. B., Massoli, P., Worsnop, D. R., Bates, T. S., Cross, E. S., Davidovits, P., Hakala, J., Hayden, K. L., and Jobson, B. T.: Radiative absorption enhancements due to the mixing state of atmospheric black carbon, Science, 337, 1078–1081, 2012. a
China, S., Mazzoleni, C., Gorkowski, K., Aiken, A. C., and Dubey, M. K.: Morphology and mixing state of individual freshly emitted wildfire carbonaceous particles, Nat. Commun., 4, 2122, https://doi.org/10.1038/ncomms3122, 2013. a
Fei, N., Gao, Y., Lu, Z., and Xiang, T.: Z-score normalization, hubness, and few-shot learning, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 142–151, https://doi.org/10.1109/ICCV48922.2021.00021, 2021. a, b
Fierce, L., Bond, T. C., Bauer, S. E., Mena, F., and Riemer, N.: Black carbon absorption at the global scale is affected by particle-scale diversity in composition, Nat. Commun., 7, 12361, https://doi.org/10.1038/ncomms12361, 2016. a, b
Fierce, L., Onasch, T. B., Cappa, C. D., Mazzoleni, C., China, S., Bhandari, J., Davidovits, P., Fischer, D. A., Helgestad, T., and Lambe, A. T.: Radiative absorption enhancements by black carbon controlled by particle-to-particle heterogeneity in composition, P. Natl. Acad. Sci. USA, 117, 5196–5203, 2020. a, b, c, d
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q.: On calibration of modern neural networks, in: International Conference on Machine Learning, PMLR, 1321–1330, https://proceedings.mlr.press/v70/guo17a.html (last access: 12 October 2026), 2017. a
Hu, K., Liu, D., Tian, P., Wu, Y., Deng, Z., Wu, Y., Zhao, D., Li, R., Sheng, J., Huang, M., Ding, D., Li, W., Wang, Y., and Wu, Y.: Measurements of the diversity of shape and mixing state for ambient black carbon particles, Geophys. Res. Lett., 48, e2021GL094522, https://doi.org/10.1029/2021GL094522, 2021. a
Kalbermatter, D. M., Močnik, G., Drinovec, L., Visser, B., Röhrbein, J., Oscity, M., Weingartner, E., Hyvärinen, A.-P., and Vasilatou, K.: Comparing black-carbon- and aerosol-absorption-measuring instruments – a new system using lab-generated soot coated with controlled amounts of secondary organic matter, Atmos. Meas. Tech., 15, 561–572, https://doi.org/10.5194/amt-15-561-2022, 2022. a
Koeylue, U., Xing, Y., and Rosner, D. E.: Fractal morphology analysis of combustion-generated aggregates using angular light scattering and electron microscope images, Langmuir, 11, 4848–4854, 1995. a
Koshelev, M.: Standard statistical transformations (logarithm and logit) are uniquely determined by the corresponding symmetries, Journal of Uncertain Systems, 9, 103–112, 2015. a
Kuleshov, V., Fenner, N., and Ermon, S.: Accurate uncertainties for deep learning using calibrated regression, in: International Conference on Machine Learning, PMLR, 2796–2804, https://proceedings.mlr.press/v80/kuleshov18a.html (last access: 12 October 2026), 2018. a
Lack, D. A. and Cappa, C. D.: Impact of brown and clear carbon on light absorption enhancement, single scatter albedo and absorption wavelength dependence of black carbon, Atmos. Chem. Phys., 10, 4207–4220, https://doi.org/10.5194/acp-10-4207-2010, 2010. a
Laskin, A., Laskin, J., and Nizkorodov, S. A.: Chemistry of atmospheric brown carbon, Chem. Rev., 115, 4335–4382, 2015. a
Li, H. and May, A. A.: Estimating mass-absorption cross-section of ambient black carbon aerosols: Theoretical, empirical, and machine learning models, Aerosol Sci. Tech., 56, 980–997, 2022. a
Li, J., Zhang, F., Liu, J., Li, W., Wu, K., Hu, S., and Lin, H.: Parameterization of optical properties for liquid cloud droplets containing black carbon based on neural network, Opt. Express, 31, 40124–40141, 2023. a
Liu, C., Li, J., Yin, Y., Zhu, B., and Feng, Q.: Optical properties of black carbon aggregates with non-absorptive coating, J. Quant. Spectrosc. Ra., 187, 443–452, 2017a. a
Liu, D., Whitehead, J., Alfarra, M. R., Reyes-Villegas, E., Spracklen, D. V., Reddington, C. L., Kong, S., Williams, P. I., Ting, Y.-C., and Haslett, S.: Black-carbon absorption enhancement in the atmosphere determined by particle mixing state, Nat. Geosci., 10, 184–188, 2017b. a
Liu, F., Yon, J., Fuentes, A., Lobo, P., Smallwood, G. J., and Corbin, J. C.: Review of recent literature on the light absorption properties of black carbon: Refractive index, mass absorption cross section, and absorption function, Aerosol Sci. Tech., 54, 33–51, 2020. a
Liu, S., Aiken, A. C., Gorkowski, K., Dubey, M. K., Cappa, C. D., Williams, L. R., Herndon, S. C., Massoli, P., Fortner, E. C., and Chhabra, P. S.: Enhanced light absorption by mixed source black and brown carbon particles in UK winter, Nat. Commun., 6, 8435, https://doi.org/10.1038/ncomms9435, 2015. a, b
Liu, X., Ma, P.-L., Wang, H., Tilmes, S., Singh, B., Easter, R. C., Ghan, S. J., and Rasch, P. J.: Description and evaluation of a new four-mode version of the Modal Aerosol Module (MAM4) within version 5.3 of the Community Atmosphere Model, Geosci. Model Dev., 9, 505–522, https://doi.org/10.5194/gmd-9-505-2016, 2016. a, b
Lundberg, S. M. and Lee, S.-I.: A unified approach to interpreting model predictions, Adv. Neur. In., 30, https://proceedings.neurips.cc/paper_files/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf (last access: 12 October 2026), 2017. a
Luo, J., Zhang, Y., Wang, F., Wang, J., and Zhang, Q.: Applying machine learning to estimate the optical properties of black carbon fractal aggregates, J. Quant. Spectrosc. Ra., 215, 1–8, 2018. a
Olivier, A., Shields, M. D., and Graham-Brady, L.: Bayesian neural networks for uncertainty quantification in data-driven materials modeling, Comput. Method. Appl. M., 386, 114079, https://doi.org/10.1016/j.cma.2021.114079, 2021. a
Qin, Z., Wu, J., Wang, H., Zhang, Y., and Zhang, Q.: Using convolutional neural networks to predict the optical properties of coated black carbon, J. Quant. Spectrosc. Ra., 333, 109326, https://doi.org/10.1016/j.jqsrt.2024.109326, 2025. a, b
Ramanathan, V. and Carmichael, G.: Global and regional climate changes due to black carbon, Nat. Geosci., 1, 221–227, 2008. a
Romshoo, B., Patil, J., Michels, T., Müller, T., Kloft, M., and Pöhlker, M.: Improving the predictions of black carbon (BC) optical properties at various aging stages using a machine-learning-based approach, Atmos. Chem. Phys., 24, 8821–8846, https://doi.org/10.5194/acp-24-8821-2024, 2024. a, b
Saleh, R., Cheng, Z., and Atwi, K.: The brown–black continuum of light-absorbing combustion aerosols, Environ. Sci. Tech. Let., 5, 508–513, 2018. a
Samset, B. H., Sand, M., Smith, C. J., Bauer, S. E., Forster, P. M., Fuglestvedt, J. S., Osprey, S., and Schleussner, C.-F.: Climate impacts from a removal of anthropogenic aerosol emissions, Geophys. Res. Lett., 45, 1020–1029, 2018. a
Schwarz, J. P., Spackman, J., Fahey, D., Gao, R., Lohmann, U., Stier, P., Watts, L., Thomson, D., Lack, D., Pfister, L., Mahoney, M. J., Baumgardner, D., Wilson, J. C., and Reeves, J. M.: Coatings and their enhancement of black carbon light absorption in the tropical atmosphere, J. Geophys. Res.-Atmos., 113, https://doi.org/10.1029/2007JD009042, 2008. a
Shamjad, P., Tripathi, S., Aggarwal, S., Mishra, S., Joshi, M., Khan, A., Sapra, B., and Ram, K.: Comparison of experimental and modeled absorption enhancement by black carbon (BC) cored polydisperse aerosols under hygroscopic conditions, Environ. Sci. Technol., 46, 8082–8089, 2012. a
Sumlin, B. J., Heinson, W. R., and Chakrabarty, R. K.: Retrieving the aerosol complex refractive index using PyMieScatt: A Mie computational package with visualization capabilities, J. Quant. Spectrosc. Ra., 205, 127–134, 2018. a
Xu, X., Zhao, W., Qian, X., Wang, S., Fang, B., Zhang, Q., Zhang, W., Venables, D. S., Chen, W., Huang, Y., Deng, X., Wu, B., Lin, X., Zhao, S., and Tong, Y.: The influence of photochemical aging on light absorption of atmospheric black carbon and aerosol single-scattering albedo, Atmos. Chem. Phys., 18, 16829–16844, https://doi.org/10.5194/acp-18-16829-2018, 2018. a
Zhao, G., Zhao, C., Kuang, Y., Bian, Y., Tao, J., Shen, C., and Yu, Y.: Calculating the aerosol asymmetry factor based on measurements from the humidified nephelometer system, Atmos. Chem. Phys., 18, 9049–9060, https://doi.org/10.5194/acp-18-9049-2018, 2018. a
Short summary
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.
We developed a surrogate model to predict how black carbon particles absorb and scatter light...
Altmetrics
Final-revised paper
Preprint