Preprints
https://doi.org/10.5194/acp-2021-409
https://doi.org/10.5194/acp-2021-409

  21 Jul 2021

21 Jul 2021

Review status: a revised version of this preprint was accepted for the journal ACP and is expected to appear here in due course.

Quantifying the structural uncertainty of the aerosol mixing state representation in a modal model

Zhonghua Zheng1, Matthew West2, Lei Zhao1,3, Po-Lun Ma4, Xiaohong Liu5, and Nicole Riemer6 Zhonghua Zheng et al.
  • 1Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
  • 2Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
  • 3National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, USA
  • 4Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA
  • 5Department of Atmospheric Sciences, Texas A&M University, College Station, TX, USA
  • 6Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA

Abstract. Aerosol mixing state is an important emergent property that affects aerosol radiative forcing and aerosol-cloud interactions, but it has not been easy to constrain this property globally. This study aims to verify the global distribution of aerosol mixing state represented by modal models. To quantify the aerosol mixing state, we used the aerosol mixing state indices for submicron aerosol based on the mixing of optically absorbing and non-absorbing species (χo), the mixing of primary carbonaceous and non-primary carbonaceous species (χc), and the mixing of hygroscopic and non-hygroscopic species (χh). To achieve a spatiotemporal comparison, we calculated the mixing state indices using output from the Community Earth System Model with the modal MAM4 aerosol module, and compared the results with the mixing state indices from a benchmark machine-learned model trained on high-detail particle-resolved simulations from the particle-resolved stochastic aerosol model PartMC-MOSAIC. The two methods yielded very different spatial patterns of the mixing state indices. In some regions, the yearly-averaged χ value computed by the MAM4 model differed by up to 70 percentage points from the benchmark values. These errors tended to be zonally structured, with the MAM4 model predicting a more internally mixed aerosol at low latitudes, and a more externally mixed aerosol at high latitudes, compared to the benchmark. Our study quantifies potential model bias in simulating mixing state in different regions, and provides insights into potential improvements to model process representation for a more realistic simulation of aerosols.

Zhonghua Zheng et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2021-409', Anonymous Referee #3, 14 Aug 2021
  • RC2: 'Comment on acp-2021-409', Anonymous Referee #1, 30 Aug 2021
  • RC3: 'Comment on acp-2021-409', Anonymous Referee #2, 09 Sep 2021
  • AC1: 'Response to referees', Zhonghua Zheng, 12 Oct 2021

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2021-409', Anonymous Referee #3, 14 Aug 2021
  • RC2: 'Comment on acp-2021-409', Anonymous Referee #1, 30 Aug 2021
  • RC3: 'Comment on acp-2021-409', Anonymous Referee #2, 09 Sep 2021
  • AC1: 'Response to referees', Zhonghua Zheng, 12 Oct 2021

Zhonghua Zheng et al.

Data sets

code_ms_ml_mam4 Zhonghua Zheng https://doi.org/10.5281/zenodo.4731385

Zhonghua Zheng et al.

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Short summary
Aerosol mixing state is an important emergent property that affects aerosol radiative forcing and aerosol-cloud interactions, but it has not been easy to constrain this property globally. We present a framework for evaluating the error in aerosol mixing state induced by aerosol representation assumptions, which is one of the important contributors to structural uncertainty in aerosol models. Our study provides insights into potential improvements to model process representation for aerosols.
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