Exploring the Aerosol Activation Properties in a Coastal Area Using Cloud and Particle-resolving Models

Yu, Ge; Wang, Yueya; Wang, Zhe; Shi, Xiaoming

doi:https://doi.org/10.5194/egusphere-2024-3581

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https://doi.org/10.5194/egusphere-2024-3581

Preprints

02 Dec 2024

| 02 Dec 2024

Exploring the Aerosol Activation Properties in a Coastal Area Using Cloud and Particle-resolving Models

Ge Yu, Yueya Wang, Zhe Wang, and Xiaoming Shi

Abstract. Atmospheric aerosols significantly impact the global climate by affecting the Earth's radiative balance and cloud formation. However, conducting high-altitude aerosol observations is currently costly and challenging, leading to gaps in accurately assessing aerosol activation properties during cloud formation. In this study, the Cloud Model 1 (CM1) is employed to investigate the movement of air parcels under shallow convection conditions in a coastal area. Subsequently, the evolution of various aerosol populations in the ideal scenarios is simulated by the PartMC-MOSAIC model to investigate their activation properties. It is found that leaving the boundary layer and entering the free atmosphere causes environmental changes in the parcels, which in turn alter the aerosol evolution and the cloud-forming potential. The impact of ascent timing is notably manifested in the concentration of ammonium nitrate rather than other chemical constituents. The rapid formation of ammonium nitrate accelerates the aerosol aging process, thereby modifying the hygroscopicity of the population. The differences between the aerosol populations in the boundary layer and high altitudes highlight the necessity of vertical observations and numerical modeling. In addition, as supersaturation rises from 0.1 % to 1 %, the relative discrepancy in cloud condensation nuclei (CCN) activation ratio between the particle-resolved results and the internal mixing assumption increases from 7 % to 30 %. This emphasizes the potential of appropriate mixing state parameterization in assessing aerosol activation properties. This study advances the understanding of aerosol hygroscopic changes under real weather conditions and offers insights into future modeling of aerosol-cloud microphysics.

Received: 16 Nov 2024 – Discussion started: 02 Dec 2024

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Journal article(s) based on this preprint

18 Jul 2025

Exploring the aerosol activation properties in coastal shallow convection using cloud- and particle-resolving models

Ge Yu, Yueya Wang, Zhe Wang, and Xiaoming Shi

Atmos. Chem. Phys., 25, 7527–7542, https://doi.org/10.5194/acp-25-7527-2025,https://doi.org/10.5194/acp-25-7527-2025, 2025

Short summary

Ge Yu, Yueya Wang, Zhe Wang, and Xiaoming Shi

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2024-3581', Anonymous Referee #1, 24 Dec 2024
This study uses the Cloud Model 1 (CM1) and the PartMC-MOSAIC model to simulate air parcel movement and aerosol evolution under shallow convection in coastal regions. The study concludes that transitioning from the boundary layer to the free atmosphere significantly affects aerosol properties, particularly through the rapid formation of ammonium nitrate, which increases the aerosols’ cloud-forming potential. Additionally, the study highlights the importance of detailed aerosol mixing state representation for calculating CCN activity.
The premise of the paper, namely investigating aerosol aging processes as a function of altitude in the atmosphere, is interesting, as are the combination of tools (a cloud resolving model and a particle-resolved aerosol model). However, I have concerns about the setup of the simulations, about the novelty of the study, and about the generalization of the results. These need to be addressed before the paper can be considered for publication in ACP.
Detailed comments are as follows:
Simulation setup: There seems to be a mismatch between the PartMC-MOSAIC box model simulations and the way these are combined with the CM1 simulations. The authors use the scenario from Riemer et al. (2009), which was designed to represent the well-mixed boundary layer in a polluted urban region during the day (emissions are added to the simulation), and the residual layer during the night (no emissions added). The CM1 simulations on the other hand provide temperature, pressure, etc., of a particular parcel. If the CM1 simulations are used to drive the PartMC simulations, the authors need to think carefully about how emissions are added to the parcel. Specifically, once the parcel leaves the surface, no emissions should be added (i.e., even before it reaches the free troposphere), but mixing with the environment should potentially be considered.

Novelty: Ching et al. (2017, ACP, 17, 7445-7458) have investigated mixing state impacts on CCN activity in great detail. Granted, they did not use CM1 to construct trajectories to drive PartMC. However, at the end of the day, what do we learn from your study that is new and unique compared to Ching et al. (2017)?

Figure S1 shows cloud water/ice mixing ratios. Is it cloud water or ice? Are the aerosols interacting with the existing clouds in any way? I assume they don’t. What does this mean for the realism of the aerosol simulations? This deserves some discussion. I also noticed that the cloud water mixing ratios are quite small. Could the authors comment on this?

Since the paper is about CCN properties in a coastal area, I would assume that there should be sea spray aerosol, however the paper mentions that sodium chloride concentrations are very small. Please explain.

The nitrate concentrations are very high. Please provide some context where (in the world) such high nitrate concentrations could be encountered. How generalizable are these results? It would be helpful to have simulations with lower nitrate levels.

Please add more information about the individual trajectories. What are the temperatures (what range is covered between the different trajectories), what is the RH (range?), and what are the gas phase concentrations?

The first two paragraphs of the introduction are too generic. I recommend that the authors get to the point more quickly – how mixing state impacts CCN properties, what is known about this, and what the novel contribution of this paper is. Additionally, the references for some statements are not well chosen, e.g., Mishra et al., (2023), Curtis et al. (2017), Lolli et al. (2023). Please carefully check to make sure that each reference really supports the statement made.

The authors refer with “kappa” somewhat loosely to both a population-level “bulk” kappa and to a per-particle kappa. It would be helpful if the authors could clearly explain early on (using appropriate notation) that aerosol particles should be described by a distribution of kappa values, just like they are described by a distribution of sizes. From there we can average within size ranges or within the whole population to arrive at an average kappa for the population

Line 142: How was the conversion to high-altitude background aerosol data done?

Figure 1: To help the reader, in the caption please repeat what cases A, B, C, D are

Figure 2: How is kappa for the population derived from the kappa_i (i.e., weighted by the particle mass?)?

How was the composition averaging done – over the whole population, or within certain size ranges, e.g., fine and coarse? A size-resolved treatment could be interesting because the error probably comes from a narrow size range (which also depends on supersaturation)

Figure 5, y-axis, suggest to display error in percent rather than as a fraction.
Citation: https://doi.org/10.5194/egusphere-2024-3581-RC1
- AC1: 'Reply on RC1', Ge Yu, 18 Mar 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3581/egusphere-2024-3581-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2024-3581-AC1
RC2:
'Comment on egusphere-2024-3581', Anonymous Referee #2, 21 Jan 2025

The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3581/egusphere-2024-3581-RC2-supplement.pdf

Citation: https://doi.org/10.5194/egusphere-2024-3581-RC2
- AC2: 'Reply on RC2', Ge Yu, 18 Mar 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3581/egusphere-2024-3581-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2024-3581-AC2

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2024-3581', Anonymous Referee #1, 24 Dec 2024
This study uses the Cloud Model 1 (CM1) and the PartMC-MOSAIC model to simulate air parcel movement and aerosol evolution under shallow convection in coastal regions. The study concludes that transitioning from the boundary layer to the free atmosphere significantly affects aerosol properties, particularly through the rapid formation of ammonium nitrate, which increases the aerosols’ cloud-forming potential. Additionally, the study highlights the importance of detailed aerosol mixing state representation for calculating CCN activity.
The premise of the paper, namely investigating aerosol aging processes as a function of altitude in the atmosphere, is interesting, as are the combination of tools (a cloud resolving model and a particle-resolved aerosol model). However, I have concerns about the setup of the simulations, about the novelty of the study, and about the generalization of the results. These need to be addressed before the paper can be considered for publication in ACP.
Detailed comments are as follows:
Simulation setup: There seems to be a mismatch between the PartMC-MOSAIC box model simulations and the way these are combined with the CM1 simulations. The authors use the scenario from Riemer et al. (2009), which was designed to represent the well-mixed boundary layer in a polluted urban region during the day (emissions are added to the simulation), and the residual layer during the night (no emissions added). The CM1 simulations on the other hand provide temperature, pressure, etc., of a particular parcel. If the CM1 simulations are used to drive the PartMC simulations, the authors need to think carefully about how emissions are added to the parcel. Specifically, once the parcel leaves the surface, no emissions should be added (i.e., even before it reaches the free troposphere), but mixing with the environment should potentially be considered.

Novelty: Ching et al. (2017, ACP, 17, 7445-7458) have investigated mixing state impacts on CCN activity in great detail. Granted, they did not use CM1 to construct trajectories to drive PartMC. However, at the end of the day, what do we learn from your study that is new and unique compared to Ching et al. (2017)?

Figure S1 shows cloud water/ice mixing ratios. Is it cloud water or ice? Are the aerosols interacting with the existing clouds in any way? I assume they don’t. What does this mean for the realism of the aerosol simulations? This deserves some discussion. I also noticed that the cloud water mixing ratios are quite small. Could the authors comment on this?

Since the paper is about CCN properties in a coastal area, I would assume that there should be sea spray aerosol, however the paper mentions that sodium chloride concentrations are very small. Please explain.

The nitrate concentrations are very high. Please provide some context where (in the world) such high nitrate concentrations could be encountered. How generalizable are these results? It would be helpful to have simulations with lower nitrate levels.

Please add more information about the individual trajectories. What are the temperatures (what range is covered between the different trajectories), what is the RH (range?), and what are the gas phase concentrations?

The first two paragraphs of the introduction are too generic. I recommend that the authors get to the point more quickly – how mixing state impacts CCN properties, what is known about this, and what the novel contribution of this paper is. Additionally, the references for some statements are not well chosen, e.g., Mishra et al., (2023), Curtis et al. (2017), Lolli et al. (2023). Please carefully check to make sure that each reference really supports the statement made.

The authors refer with “kappa” somewhat loosely to both a population-level “bulk” kappa and to a per-particle kappa. It would be helpful if the authors could clearly explain early on (using appropriate notation) that aerosol particles should be described by a distribution of kappa values, just like they are described by a distribution of sizes. From there we can average within size ranges or within the whole population to arrive at an average kappa for the population

Line 142: How was the conversion to high-altitude background aerosol data done?

Figure 1: To help the reader, in the caption please repeat what cases A, B, C, D are

Figure 2: How is kappa for the population derived from the kappa_i (i.e., weighted by the particle mass?)?

How was the composition averaging done – over the whole population, or within certain size ranges, e.g., fine and coarse? A size-resolved treatment could be interesting because the error probably comes from a narrow size range (which also depends on supersaturation)

Figure 5, y-axis, suggest to display error in percent rather than as a fraction.
Citation: https://doi.org/10.5194/egusphere-2024-3581-RC1
- AC1: 'Reply on RC1', Ge Yu, 18 Mar 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3581/egusphere-2024-3581-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2024-3581-AC1
RC2:
'Comment on egusphere-2024-3581', Anonymous Referee #2, 21 Jan 2025

The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3581/egusphere-2024-3581-RC2-supplement.pdf

Citation: https://doi.org/10.5194/egusphere-2024-3581-RC2
- AC2: 'Reply on RC2', Ge Yu, 18 Mar 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3581/egusphere-2024-3581-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2024-3581-AC2

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Ge Yu on behalf of the Authors (18 Mar 2025) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (20 Mar 2025) by Zhonghua Zheng

RR by Anonymous Referee #2 (04 Apr 2025)

RR by Anonymous Referee #1 (07 Apr 2025)

Suggestions for revision or reasons for rejection

The authors have addressed the reviewers’ questions well, and the paper is improved in many ways. There are a few issues left that require another round of revisions. They are minor in nature, so I suggest minor revisions.

Motivation of this study: in the introduction, the authors now say: “Currently, large-scale meteorological simulations generally employ relatively simple aerosol parameterization methods when considering the contribution of CCN to cloud microphysical processes (Thompson and Eidhammer, 2014; Morrison and Milbrandt, 2011; Hazra et al., 2020), such as directly prescribing aerosol activation rates. These simplifications may introduce errors in the prediction of cloud behavior and associated atmospheric processes. To this end, this study integrates the meteorological Cloud Model 1 (CM1) (Bryan and Fritsch, 2002) with the aerosol evolution model PartMC-MOSAIC for the first time.”

I agree with you that CCN in large-scale models are represented overly simplified, but this paper does not directly address the impact of these simplifications. Could you add something to the paper that addresses the question how directly prescribing aerosol activation rates would introduce error? (This is even more simplified than using composition-averaged information, which is what the paper does investigate.) This would be very useful for large-scale modelers.

Re: Comment 3:
Figure S1: Average over the entire x-y plane not meaningful. Suggest to only average over the core of the cloud?

In general, it’d be helpful to visualize the parcel trajectories better, i.e. include a 3D plot of a few example trajectories. I’m not sure how feasible this is, but it would illustrate the purpose (and novelty) of this study well.

Re: Comment 9:
Thanks for adding information about the MERRA-2 dataset. However, it is still not clear to me how the mass concentrations of sulfate, black carbon, organic carbon and sea salt from MERRA are converted into the data that PartMC can use. I believe, you needed to apply some assumptions about size distributions and mixing state. What are these assumptions?

Table S1: Instead of “ground fraction”/ “high-altitude fraction”, I suggest using “mixing ratio at ground level” and “mixing ratio at elevated altitude” (I would not consider 880 hPa as “high-altitude”)

Furthermore, the following sentences are unclear (in section S5):
“For elevated atmospheric conditions, we implemented the ideal gas Clapeyron equation with the following boundary conditions”.

There is the ideal gas law, and there is the Clausius-Clapeyron equation. I assume you mean the ideal gas law here, but how did you use it in this context to obtain the numbers in the column for “high-altitude fraction”?

Equation (5): Why is the denominator the total aerosol concentration? Usually when defining the error in a quantity, one would divide by the reference value (N_CCN in this case).

New figure 6:
I suggest labelling the x-axis with the size ranges of the bins that the particles were grouped into.

Nomenclature:
The authors use “hydrophilic” and “hygroscopic” interchangeably. The two terms relate to how substances interact with water, but they’re not exactly interchangeable. When referring to substances like sulfate, ammonium, and nitrate, I suggest calling them hygroscopic species, because this is directly related to water uptake and CCN activity.

Choice of title:
I think the title is too generic. In my opinion, this paper is really about different pathways of aerosol aging, depending on how quickly the air parcel is cut off from the emissions at the ground. Suggest something like: “Modeling Aerosol Aging and Cloud Activation During Air Parcel Ascent in Coastal Shallow Convection”

Last sentence: “Additionally, the mixing state index has the potential to be accounted for in future climate models as it is still challenging to represent some microscale aerosol-cloud interactions and reduce the microphysical uncertainties in large-scale models.” I’m not clear on how this is supposed to work.

Hide

ED: Publish subject to minor revisions (review by editor) (13 Apr 2025) by Zhonghua Zheng

Please find the comments below.

Anonymous referee #1:
The authors have addressed the reviewers’ questions well, and the paper is improved in many ways. There are a few issues left that require another round of revisions. They are minor in nature, so I suggest minor revisions.

Motivation of this study: in the introduction, the authors now say: “Currently, large-scale meteorological simulations generally employ relatively simple aerosol parameterization methods when considering the contribution of CCN to cloud microphysical processes (Thompson and Eidhammer, 2014; Morrison and Milbrandt, 2011; Hazra et al., 2020), such as directly prescribing aerosol activation rates. These simplifications may introduce errors in the prediction of cloud behavior and associated atmospheric processes. To this end, this study integrates the meteorological Cloud Model 1 (CM1) (Bryan and Fritsch, 2002) with the aerosol evolution model PartMC-MOSAIC for the first time.”

I agree with you that CCN in large-scale models are represented overly simplified, but this paper does not directly address the impact of these simplifications. Could you add something to the paper that addresses the question how directly prescribing aerosol activation rates would introduce error? (This is even more simplified than using composition-averaged information, which is what the paper does investigate.) This would be very useful for large-scale modelers.

Re: Comment 3:
Figure S1: Average over the entire x-y plane not meaningful. Suggest to only average over the core of the cloud?

In general, it’d be helpful to visualize the parcel trajectories better, i.e. include a 3D plot of a few example trajectories. I’m not sure how feasible this is, but it would illustrate the purpose (and novelty) of this study well.

Re: Comment 9:
Thanks for adding information about the MERRA-2 dataset. However, it is still not clear to me how the mass concentrations of sulfate, black carbon, organic carbon and sea salt from MERRA are converted into the data that PartMC can use. I believe, you needed to apply some assumptions about size distributions and mixing state. What are these assumptions?

Table S1: Instead of “ground fraction”/ “high-altitude fraction”, I suggest using “mixing ratio at ground level” and “mixing ratio at elevated altitude” (I would not consider 880 hPa as “high-altitude”)

Furthermore, the following sentences are unclear (in section S5):
“For elevated atmospheric conditions, we implemented the ideal gas Clapeyron equation with the following boundary conditions”.

There is the ideal gas law, and there is the Clausius-Clapeyron equation. I assume you mean the ideal gas law here, but how did you use it in this context to obtain the numbers in the column for “high-altitude fraction”?

Equation (5): Why is the denominator the total aerosol concentration? Usually when defining the error in a quantity, one would divide by the reference value (N_CCN in this case).

New figure 6:
I suggest labelling the x-axis with the size ranges of the bins that the particles were grouped into.

Nomenclature:
The authors use “hydrophilic” and “hygroscopic” interchangeably. The two terms relate to how substances interact with water, but they’re not exactly interchangeable. When referring to substances like sulfate, ammonium, and nitrate, I suggest calling them hygroscopic species, because this is directly related to water uptake and CCN activity.

Choice of title:
I think the title is too generic. In my opinion, this paper is really about different pathways of aerosol aging, depending on how quickly the air parcel is cut off from the emissions at the ground. Suggest something like: “Modeling Aerosol Aging and Cloud Activation During Air Parcel Ascent in Coastal Shallow Convection”

Last sentence: “Additionally, the mixing state index has the potential to be accounted for in future climate models as it is still challenging to represent some microscale aerosol-cloud interactions and reduce the microphysical uncertainties in large-scale models.” I’m not clear on how this is supposed to work.

Anonymous referee #2:
I would recommend the authors to provide more details about the exchange (of aerosol species and gases) between the parcel and the environment, i.e. due to diffusion in both vertical and horizontal directions (related to my original comments 1.7 and 1.8), especially the numerical values of dilution coefficients input to PartMC-MOSAIC. Besides, the times series of eddy diffusivity of scenarios A-D would be helpful for readers to understand the impact of parcel motion (driven by meteorology) on aging of aerosol particles.

Hide

AR by Ge Yu on behalf of the Authors (24 Apr 2025) Author's response Author's tracked changes Manuscript

ED: Publish as is (05 May 2025) by Zhonghua Zheng

AR by Ge Yu on behalf of the Authors (10 May 2025)

Journal article(s) based on this preprint

18 Jul 2025

Exploring the aerosol activation properties in coastal shallow convection using cloud- and particle-resolving models

Ge Yu, Yueya Wang, Zhe Wang, and Xiaoming Shi

Atmos. Chem. Phys., 25, 7527–7542, https://doi.org/10.5194/acp-25-7527-2025,https://doi.org/10.5194/acp-25-7527-2025, 2025

Short summary

Ge Yu, Yueya Wang, Zhe Wang, and Xiaoming Shi

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https://doi.org/10.5194/egusphere-2024-3581-supplement

Ge Yu, Yueya Wang, Zhe Wang, and Xiaoming Shi

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Studying the cloud-forming capacity of aerosols is crucial in climate research. The PartMC model can provide detailed particle information and help these studies. This model is integrated with the ideal meteorological Cloud Model 1 (CM1) to simulate the aerosols at cloud-forming locations. Significant changes are revealed in the hygroscopicity distribution of aerosols within ascending air parcels. Additionally, different ascent times also affect aerosol aging processes.


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