Preprints
https://doi.org/10.5194/egusphere-2024-62
https://doi.org/10.5194/egusphere-2024-62
02 Feb 2024
 | 02 Feb 2024

AeroMix v1.0.1: a Python package for modeling aerosol optical properties and mixing states

Sam P. Raj, Puna Ram Sinha, Rohit Srivastava, Srinivas Bikkina, and D. Bala Subrahamanyam

Abstract. Assessing aerosol mixing states, which mainly depend on aerosol chemical compositions is indispensable to estimate aerosol direct and indirect effects. While the limitations in the measurements of aerosol chemical composition and mixing states persist globally, the Optical Properties of Aerosols and Clouds (OPAC) model has been widely used to construct optically equivalent aerosol chemical compositions from measured aerosol optical properties using Mie inversion. However, the representation of real atmospheric aerosol mixing scenarios in OPAC has perennially been challenged by the exclusive assumption of external mixing. A Python successor to the aerosol module of the OPAC model is developed, named 'AeroMix,' with novel capabilities to 1) model externally and core-shell mixed aerosols, 2) simulate optical properties of aerosol mixtures constituted by any number of aerosol components, 3) and define aerosol composition and relative humidity in up to 6 vertical layers. Designed as a versatile open-source aerosol optical model framework, AeroMix is tailored for sophisticated inversion algorithms aimed at modeling aerosol mixing states and also their physical and chemical properties. AeroMix's performance is demonstrated by modeling the probable aerosol mixing states over Kanpur (urban), India, and the Bay of Bengal (marine). The modeled mixing states are consistent with independent measurements using single-particle soot photometer (SP2) and transmission electron microscopy (TEM), substantiating the potential capability of AeroMix to model complex aerosol mixing scenarios involving multiple internally mixed components in diverse environments. This work contributes a valuable tool for modeling aerosol mixing states to assess their impact on cloud nucleating properties and radiation budget.

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Sam P. Raj, Puna Ram Sinha, Rohit Srivastava, Srinivas Bikkina, and D. Bala Subrahamanyam

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-2024-62', Anonymous Referee #1, 29 Mar 2024
    • AC1: 'Reply on RC1', Puna Ram Sinha, 18 Apr 2024
  • RC2: 'Comment on egusphere-2024-62', Anonymous Referee #2, 04 Apr 2024
    • AC2: 'Reply on RC2', Puna Ram Sinha, 18 Apr 2024
  • RC3: 'Comment on egusphere-2024-62', Simon O'Meara, 26 Apr 2024
    • AC3: 'Reply on RC3', Puna Ram Sinha, 09 May 2024
Sam P. Raj, Puna Ram Sinha, Rohit Srivastava, Srinivas Bikkina, and D. Bala Subrahamanyam

Model code and software

AeroMix Sam P. Raj and Puna Ram Sinha https://doi.org/10.5281/zenodo.10552078

Interactive computing environment

Codes and model output used to generate the figures Sam P. Raj and Puna Ram Sinha https://doi.org/10.5281/zenodo.10552113

Sam P. Raj, Puna Ram Sinha, Rohit Srivastava, Srinivas Bikkina, and D. Bala Subrahamanyam

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Short summary
The aerosol mixing states significantly impact their direct and indirect effects. However, assessing real atmospheric aerosol mixing states has been a challenge, owing to the inadequacies of current aerosol optical models in representing complex mixtures. To address this gap, AeroMix, we developed an open-source Python aerosol optical model framework aiming at the modeling of physical and chemical properties of aerosols, particularly mixing state, using the Mie inversion technique.