the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 02 Dec 2024
Exploring the Aerosol Activation Properties in a Coastal Area Using Cloud and Particle-resolving Models
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.
- Preprint
(1452 KB) - Metadata XML
-
Supplement
(550 KB) - BibTeX
- EndNote
Status: open (until 13 Jan 2025)
-
RC1: 'Comment on egusphere-2024-3581', Anonymous Referee #1, 24 Dec 2024
reply
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
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 128 | 24 | 6 | 158 | 14 | 3 | 2 |
- HTML: 128
- PDF: 24
- XML: 6
- Total: 158
- Supplement: 14
- BibTeX: 3
- EndNote: 2
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1