the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Accounting for Black Carbon Aging Process in a Two-way Coupled Meteorology – Air Quality Model
Abstract. Black carbon (BC) exerts significant impacts on both climate and environment. BC aging process alters its hygroscopicity and light absorption properties. Current models, like the Weather Research and Forecasting – Community Multiscale Air Quality (WRF-CMAQ) two-way coupled model, inadequately characterize these alterations. In this study, we accounted for BC aging process in the WRF-CMAQ model (WRF-CMAQ-BCG). We introduced two new species (Bare BC and Coated BC) in the model and implemented a module to simulate the conversion from Bare BC to Coated BC, thereby characterizing the aging process. Furthermore, we improved the cloud chemistry and aerosol optics modules to analyze the effects of BC aging on hydrophobicity and light absorption. The simulated results indicate a spatial distribution pattern with Bare BC prevalent near emission sources and Coated BC more common farther from sources. The average Number Fraction of Coated BC (NFcoated) is approximately 57 %. Temporal variation exhibits a distinct diurnal pattern, with NFcoated increasing during the daytime. The spatial distribution of wet deposition varies significantly between Bare and Coated BC. Bare BC exhibits a point-like deposition pattern, whereas Coated BC displays a zonal distribution. Notably, Coated BC dominates the BC wet deposition process. Additionally, incorporating BC aging process reduces BC wet deposition by 17.7 % and increases BC column concentration by 10.5 %. The simulated Mass Absorption Cross-section (MAC) value improved agreement with observed measurements. Overall, the WRF-CMAQ-BCG model enhances the capability to analyze aging-related variables and BC mixing state, while also improving performance in wet deposition and optical properties.
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Status: open (until 08 Oct 2024)
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RC1: 'Review Comment on egusphere-2024-2372', Anonymous Referee #1, 27 Aug 2024
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The authors implemented a BC aging scheme into the WRF-CMAQ model and found that accounting for BC aging process improves simulated BC optical properties. They also found that adding the aging process significantly affect BC concentration distribution and wet deposition. Overall, the manuscript is well organized and the study fits well into the journal scope. However, there are a few places that require further descriptions and clarifications. I have a few comments and suggestions below for the authors to consider.
Major comments:
- The major concern I have is the insufficient model evaluation. Currently, the authors only evaluated the model simulation at one measurement sites, while all their modeling analysis and conclusions come from regional results. Thus, regional-scale evaluation is needed, such as evaluations using IMPROVE aerosol measurement network, AERONET AOD/AAOD measurement network, EPA AirNow network, and/or MODIS AOD data.
Specific comments:
- Lines 87-88: This “limited impact” is not accurate, since different BC mixing states affect aerosol hygroscopicity and hence wet deposition, which subsequently change the mass concentration.
- Line 103: “… coated with scattering aerosol component”. This is not very accurate since BC can also be coated by some absorbing organics.
- Line 108: Even for two-way coupled WRF-CMAQ model, there is no aerosol indirect effect considered? Did the authors mean the standard EPA-version of WRF-CMAQ? There might be a WRF-CMAQ version from individual research group that may already have this capability. Please double check.
- In Figures 1 and 2, the authors showed that the model includes aerosol-cloud interaction, but in the description of Section 2.1, it seems that the model does not account for the aerosol indirect effect. This needs further clarification.
- Equation (3): Which term is for condensation (fast-aging)? It seems that the first beta*[OH] term represents chemical aging and alpha represents coagulation.
- Section 2.3: the use of “cloud chemistry” is confusing since BC does not undergo any chemical process in the cloud droplets in the model. Also, the description of this part is not very clear. How did the authors set the hydrophobicity of coated BC? What scheme did the authors use to compute CCN from aerosol number concentration and hygroscopicity? What equations did the authors use to compute the impact scavenging of BC aerosol? More details are needed.
- Section 2.4: It is not clear that how the authors compute the number concentrations of bare and coated BC. (1) Are these number concentrations two new prognostic variables tracked by the model? What are the size distributions of bare and coated BC particles used during the mass-to-number conversion? More clarifications are needed. (2) Another key uncertainty factor related to the calculation of BC optics is the particle structure. Many previous studies have shown that using core-shell assumption for coated BC and spherical shape for bare BC cannot realistically represent BC particle optics (e.g., https://doi.org/10.1029/2021GL096437; https://doi-org.cuucar.idm.oclc.org/10.1021/acs.estlett.7b00418; https://doi.org/10.5194/acp-15-11967-2015). It may be challenging to add the particle structure info into the model, but some discussions on this uncertainty factor will be helpful.
- Lines 220-221: How sensitive the model results are to the assumption of equal fraction of bare and coated BC in the initial and boundary conditions?
- Line 223: “the model assimilated data” Did the authors mean they also used data assimilation in their model simulations? Did the authors conduct 3 different simulations by using these 3 datasets (FNL, NAM, and NARR), respectively?
- Figures 4-5: It seems that including BC aging only has negligible benefits on model performance. How to better justify the need to include this aging scheme?
Citation: https://doi.org/10.5194/egusphere-2024-2372-RC1
Data sets
The WRF-CMAQ-BCG model data Yuzhi Jin et al. https://zenodo.org/doi/10.5281/zenodo.12798177
Model code and software
The WRF-CMAQ-BCG model code Yuzhi Jin et al. https://zenodo.org/doi/10.5281/zenodo.12798673
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