Preprints
https://doi.org/10.5194/egusphere-2023-2400
https://doi.org/10.5194/egusphere-2023-2400
10 Nov 2023
 | 10 Nov 2023

Improving the predictions of black carbon (BC) optical properties at various aging stages using a machine-learning-based approach

Baseerat Romshoo, Jaikrishna Patil, Tobias Michels, Thomas Müller, Marius Kloft, and Mira Pöhlker

Abstract. It is necessary to accurately determine the optical properties of highly absorbing black carbon (BC) aerosols to estimate their climate impact. In the past, there has been hesitation about using realistic fractal morphologies when simulating BC optical properties due to the complexity involved in the simulations and the cost of the computations. In this work, we demonstrate that the predictions of optical properties like single scattering albedo (ω) and mass absorption cross-section (MAC) can be improved compared to the conventional Mie-based predictions using a highly accurate benchmark machine learning algorithm. Unlike the computationally intensive simulations of complex scattering models, the ML-based approach accurately predicts optical properties in a fraction of a second. There has been an extensive evaluation procedure carried out in this study. Based on comparisons with laboratory measurements, it was demonstrated that incorporating realistic morphologies of BC significantly improved their optical properties. The results indicate that it is possible to generate optical properties in the visible spectrum using BC fractal aggregates with any desired physicochemical properties, such as size, morphology, or organic coating. Based on these findings, climate models can improve their radiative forcing estimates using such comprehensive parameterizations for the optical properties of BC based on their aging stages.

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Baseerat Romshoo, Jaikrishna Patil, Tobias Michels, Thomas Müller, Marius Kloft, and Mira Pöhlker

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2400', Anonymous Referee #1, 29 Dec 2023
    • AC1: 'Reply on RC1', baseerat romshoo, 08 Mar 2024
  • RC2: 'Review of egusphere-2023-2400', Anonymous Referee #2, 20 Jan 2024
    • AC2: 'Reply on RC2', baseerat romshoo, 08 Mar 2024
    • AC3: 'Reply on RC2', baseerat romshoo, 08 Mar 2024
Baseerat Romshoo, Jaikrishna Patil, Tobias Michels, Thomas Müller, Marius Kloft, and Mira Pöhlker

Data sets

Database of physicochemical and optical properties of black carbon fractal aggregates B. Romshoo, T. Müller, J. Patil, T. Michels, M. Kloft, and M. Pöhlker https://doi.org/10.5281/zenodo.7523058

Model code and software

Machine-learning-based approach to predict optical properties of black carbon (BC) at various aging stages Baseerat Romshoo, Jaikrishna Patil, Tobias Michels, Thomas Müller, Marius Kloft, & Mira Pöhlker https://doi.org/10.5281/zenodo.8060207

Baseerat Romshoo, Jaikrishna Patil, Tobias Michels, Thomas Müller, Marius Kloft, and Mira Pöhlker

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Short summary
It is demonstrated that the predictions of optical properties like single scattering albedo (ω) and mass absorption cross-section (MAC) can be improved compared to the conventional Mie-based predictions using highly accurate and fast benchmark machine learning methods. Our findings assist the global modeling community in considering realistic BC morphologies depending on the aging stage so that uncertainties can be reduced in climate predictions.