Preprints
https://doi.org/10.5194/egusphere-2022-1292
© 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/egusphere-2022-1292
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
13 Dec 2022
Parameterization of Size of Organic and Secondary Inorganic Aerosol for Efficient Representation of Global Aerosol Optical Properties
Haihui Zhu
CORRESPONDING AUTHOR
Department of Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
Randall Martin
Department of Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, Canada
Betty Croft
Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, Canada
Department of Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
Shixian Zhai
Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
Department of Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
Liam Bindle
Department of Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
Jeffrey Pierce
Department of Atmospheric Science, Colorado State University, Fort Collins, CO, USA
Rachel Chang
Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, Canada
Bruce Anderson
NASA Langley Research Center, Hampton, VA, USA
Luke Ziemba
NASA Langley Research Center, Hampton, VA, USA
Johnathan Hair
NASA Langley Research Center, Hampton, VA, USA
Richard Ferrare
NASA Langley Research Center, Hampton, VA, USA
Chris Hostetler
NASA Langley Research Center, Hampton, VA, USA
Inderjeet Singh
Department of Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
Deepangsu Chatterjee
Department of Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
Jose Jimenez
Cooperative Institute for Research in Environmental Sciences and Department of Chemistry, University of Colorado, Boulder, CO, USA
Pedro Campuzano-Jost
Cooperative Institute for Research in Environmental Sciences and Department of Chemistry, University of Colorado, Boulder, CO, USA
Benjamin Nault
Center for Aerosol and Cloud Chemistry, Aerodyne Research, Inc., Billerica, MA, USA
Jack Dibb
Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, USA
Joshua Schwarz
National Oceanic and Atmospheric Administration Chemical Sciences Laboratory, Boulder, CO, USA
Andrew Weinheimer
National Center for Atmospheric Research, Boulder, CO, USA
Abstract. Accurate representation of aerosol optical properties is essential for modeling and remote sensing of atmospheric aerosols. Although aerosol optical properties are strongly dependent upon the aerosol size distribution, use of detailed aerosol microphysics schemes in global atmospheric models is inhibited by associated computational demands. Computationally efficient parameterizations for aerosol size are needed. In this study, airborne measurements over the United States (DISCOVER-AQ) and South Korea (KORUS-AQ) are interpreted with a global chemical transport model (GEOS-Chem) to investigate the variation in aerosol size when organic matter (OM) and sulfate-nitrate-ammonium (SNA) are the dominant aerosol components. The airborne measurements exhibit a strong correlation (r = 0.83) between dry aerosol size and the sum of OM and SNA mass concentration (MSNAOM). A global microphysical simulation (GEOS-Chem-TOMAS) indicates that MSNAOM, and the ratio between the two components 
are the major indicators for SNA and OM dry aerosol size. A parameterization of dry effective radius (Reff) for SNA and OM aerosol is proposed, which well represents the airborne measurements (R2 = 0.74, slope = 1.00) and the GEOS-Chem-TOMAS simulation (R2 = 0.72, slope = 0.81). When applied in the GEOS-Chem high-performance model, this parameterization improves the agreement between the simulated aerosol optical depth (AOD) and the ground-measured AOD from the Aerosol Robotic Network (AERONET; R2 from 0.68 to 0.73, slope from 0.75 to 0.96). Thus, this parameterization offers a computationally efficient method to represent aerosol size dynamically.
How to cite. Zhu, H., Martin, R., Croft, B., Zhai, S., Li, C., Bindle, L., Pierce, J., Chang, R., Anderson, B., Ziemba, L., Hair, J., Ferrare, R., Hostetler, C., Singh, I., Chatterjee, D., Jimenez, J., Campuzano-Jost, P., Nault, B., Dibb, J., Schwarz, J., and Weinheimer, A.: Parameterization of Size of Organic and Secondary Inorganic Aerosol for Efficient Representation of Global Aerosol Optical Properties, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1292, 2022.
Download & links
-
Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
-
Preprint
(3427 KB)
Download & links
-
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(3427 KB) - BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
04 May 2023
Parameterization of size of organic and secondary inorganic aerosol for efficient representation of global aerosol optical properties
Haihui Zhu, Randall V. Martin, Betty Croft, Shixian Zhai, Chi Li, Liam Bindle, Jeffrey R. Pierce, Rachel Y.-W. Chang, Bruce E. Anderson, Luke D. Ziemba, Johnathan W. Hair, Richard A. Ferrare, Chris A. Hostetler, Inderjeet Singh, Deepangsu Chatterjee, Jose L. Jimenez, Pedro Campuzano-Jost, Benjamin A. Nault, Jack E. Dibb, Joshua S. Schwarz, and Andrew Weinheimer
Atmos. Chem. Phys., 23, 5023–5042, https://doi.org/10.5194/acp-23-5023-2023, https://doi.org/10.5194/acp-23-5023-2023, 2023
Short summary
Short summary
Particle size of atmospheric aerosol is important for estimating its climate and health effects, but simulating atmospheric aerosol size is computationally demanding. This study derives a simple parameterization of the size of organic and secondary inorganic ambient aerosol that can be applied to atmospheric models. Applying this parameterization allows a better representation of the global spatial pattern of aerosol size, as verified by ground and airborne measurements.
Haihui Zhu et al.
Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
| : Report abuse
-
RC1: 'Comment on egusphere-2022-1292', Anonymous Referee #1, 18 Jan 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-1292/egusphere-2022-1292-RC1-supplement.pdf
-
RC2: 'Comment on egusphere-2022-1292', Anonymous Referee #2, 01 Mar 2023
Here the authors present work on the use of observations and global size-resolved model output to parameterize and improve representation of particle dry effective radius in bulk aerosol simulations. Data from two airborne campaigns is used to fit Reff to two chosen predictive covariates: total mass of SNA and OM particulates, and OM/SNA ratio. On the whole I find this work to be an interesting, well-composed, and valuable contribution to the aerosol modeling literature, and have only a few concerns and questions for the authors before publication.
- While the bulk modeling is done on a relatively high resolution cubed-sphere grid, much of the parameterization relies on output from a much coarser 4x5 TOMAS simulation. While I recognize the computational time limitations inherent to high resolution global simulations, I'm a little concerned that important features may be completely washed out in such coarse output. It would be helpful to see a comparison between key metrics for the 4x5 run and a higher resolution alternative, even if just for a selected month or two.
- I don't see any mention of spinup time preceding analyzed model output. Please include this modeling detail in section 2.2.
- One part of the parameterization process selects for locations dominated by Msnaom. However, if I understand Figure 3 correctly, this subset excludes a very large fraction of the surface from the calculation, and I'm unclear on the intent and consequences of this when the resulting parameterizations are applied over areas that were excluded from fitting. Is the resulting parameterization only subsequently applied to the SNA+OM fraction of particulate mass? How is this combined with other aerosol species in terms of radiative properties in the bulk simulation? The description of this process in 3.3 does not adequately cover some of these details.
- I have some concerns over the presentation of Figure 4. First, the chosen color scheme strikes me as somewhat odd, as it uses a diverging bar centered in very light hues at around 110 nm. This creates some (potentially unintended?) artifacts in the perception of differences between values near the center vs differences at the extremes of the displayed size range. Unless there is a compelling reason to use a diverging scheme centered at 110, I would recommend a more balanced sequential scheme. Based on the text description, it also appears that the colorbar is saturated fairly aggressively, potentially washing out model differences at the extremes. This presentation choice should be more clearly described, and the behavior outside of the saturated bounds should be discussed as needed.
- Finally, while I recognize this parameterization as a potentially valuable addition over current defaults used in bulk aerosol schemes, I couldn't help but wonder about how the chosen parameter covariates relate to the overall mass distribution, rather than just the resulting Reff. While a full examination of this may be unnecessary and out of scope, it would be helpful to have some context regarding the size distributions shifts surrounding the Reff differences associated with SNA+OM and OM/SNA. Are the changes in Reff mostly driven by shifts in the heavy tail? Are the mean changes more proportionally seen across the size distribution? A little bit more info here would be very interesting, and also suggestive of possibilities for future work stemming from this manuscript.
Citation: https://doi.org/10.5194/egusphere-2022-1292-RC2 - AC1: 'Comment on egusphere-2022-1292', Haihui Zhu, 27 Mar 2023
Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
| : Report abuse
-
RC1: 'Comment on egusphere-2022-1292', Anonymous Referee #1, 18 Jan 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-1292/egusphere-2022-1292-RC1-supplement.pdf
-
RC2: 'Comment on egusphere-2022-1292', Anonymous Referee #2, 01 Mar 2023
Here the authors present work on the use of observations and global size-resolved model output to parameterize and improve representation of particle dry effective radius in bulk aerosol simulations. Data from two airborne campaigns is used to fit Reff to two chosen predictive covariates: total mass of SNA and OM particulates, and OM/SNA ratio. On the whole I find this work to be an interesting, well-composed, and valuable contribution to the aerosol modeling literature, and have only a few concerns and questions for the authors before publication.
- While the bulk modeling is done on a relatively high resolution cubed-sphere grid, much of the parameterization relies on output from a much coarser 4x5 TOMAS simulation. While I recognize the computational time limitations inherent to high resolution global simulations, I'm a little concerned that important features may be completely washed out in such coarse output. It would be helpful to see a comparison between key metrics for the 4x5 run and a higher resolution alternative, even if just for a selected month or two.
- I don't see any mention of spinup time preceding analyzed model output. Please include this modeling detail in section 2.2.
- One part of the parameterization process selects for locations dominated by Msnaom. However, if I understand Figure 3 correctly, this subset excludes a very large fraction of the surface from the calculation, and I'm unclear on the intent and consequences of this when the resulting parameterizations are applied over areas that were excluded from fitting. Is the resulting parameterization only subsequently applied to the SNA+OM fraction of particulate mass? How is this combined with other aerosol species in terms of radiative properties in the bulk simulation? The description of this process in 3.3 does not adequately cover some of these details.
- I have some concerns over the presentation of Figure 4. First, the chosen color scheme strikes me as somewhat odd, as it uses a diverging bar centered in very light hues at around 110 nm. This creates some (potentially unintended?) artifacts in the perception of differences between values near the center vs differences at the extremes of the displayed size range. Unless there is a compelling reason to use a diverging scheme centered at 110, I would recommend a more balanced sequential scheme. Based on the text description, it also appears that the colorbar is saturated fairly aggressively, potentially washing out model differences at the extremes. This presentation choice should be more clearly described, and the behavior outside of the saturated bounds should be discussed as needed.
- Finally, while I recognize this parameterization as a potentially valuable addition over current defaults used in bulk aerosol schemes, I couldn't help but wonder about how the chosen parameter covariates relate to the overall mass distribution, rather than just the resulting Reff. While a full examination of this may be unnecessary and out of scope, it would be helpful to have some context regarding the size distributions shifts surrounding the Reff differences associated with SNA+OM and OM/SNA. Are the changes in Reff mostly driven by shifts in the heavy tail? Are the mean changes more proportionally seen across the size distribution? A little bit more info here would be very interesting, and also suggestive of possibilities for future work stemming from this manuscript.
Citation: https://doi.org/10.5194/egusphere-2022-1292-RC2 - AC1: 'Comment on egusphere-2022-1292', Haihui Zhu, 27 Mar 2023
Peer review completion
AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Haihui Zhu on behalf of the Authors (27 Mar 2023)
Author's response
Author's tracked changes
Manuscript
ED: Publish subject to technical corrections (12 Apr 2023) by Fangqun Yu
AR by Haihui Zhu on behalf of the Authors (12 Apr 2023)
Manuscript
Journal article(s) based on this preprint
04 May 2023
Parameterization of size of organic and secondary inorganic aerosol for efficient representation of global aerosol optical properties
Haihui Zhu, Randall V. Martin, Betty Croft, Shixian Zhai, Chi Li, Liam Bindle, Jeffrey R. Pierce, Rachel Y.-W. Chang, Bruce E. Anderson, Luke D. Ziemba, Johnathan W. Hair, Richard A. Ferrare, Chris A. Hostetler, Inderjeet Singh, Deepangsu Chatterjee, Jose L. Jimenez, Pedro Campuzano-Jost, Benjamin A. Nault, Jack E. Dibb, Joshua S. Schwarz, and Andrew Weinheimer
Atmos. Chem. Phys., 23, 5023–5042, https://doi.org/10.5194/acp-23-5023-2023, https://doi.org/10.5194/acp-23-5023-2023, 2023
Short summary
Short summary
Particle size of atmospheric aerosol is important for estimating its climate and health effects, but simulating atmospheric aerosol size is computationally demanding. This study derives a simple parameterization of the size of organic and secondary inorganic ambient aerosol that can be applied to atmospheric models. Applying this parameterization allows a better representation of the global spatial pattern of aerosol size, as verified by ground and airborne measurements.
Haihui Zhu et al.
Haihui Zhu et al.
Viewed
Total article views: 589 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 13 Dec 2022)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 425 | 151 | 13 | 589 | 5 | 9 |
- HTML: 425
- PDF: 151
- XML: 13
- Total: 589
- BibTeX: 5
- EndNote: 9
Viewed (geographical distribution)
Total article views: 569 (including HTML, PDF, and XML)
Thereof 569 with geography defined
and 0 with unknown origin.
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Latest update: 04 May 2023
Download
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(3427 KB) - Metadata XML
Short summary
Particle size of atmospheric aerosol is important for many fields, but simulating atmospheric aerosol size is computationally demanding. This study derives a simple parameterization of the size of organic and secondary inorganic ambient aerosol that can be applied to atmospheric models. Applying this parameterization allows a better representation of the global spatial pattern of aerosol size and improves the agreement between modeled and ground-measured aerosol optical depth.
Particle size of atmospheric aerosol is important for many fields, but simulating atmospheric...