the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
How meteorological conditions influence aerosol-cloud interactions under different pollution regimes
Abstract. Aerosol-cloud interactions (ACI) involve quite complex physical and dynamical mechanisms, in which meteorological conditions play a crucial role. To investigate how the meteorological conditions impact ACI under different pollution regimes (polluted and clean) for marine liquid-phase clouds, the simulations are conducted using the chemistry version of Weather Research and Forecasting Model coupled with spectral-bin cloud microphysics. Our results indicate that, marine liquid-phase clouds transition from being updraft-driven to cold-advection-driven as lower tropospheric stability (LTS) increases. The enhancement of these clouds by aerosols intensifies with LTS, highlighting the dominant role of cold advection on wintertime clouds. Aerosols prolong cloud lifetime in moist environments and shorten it in dry environments. They generally suppress precipitation but can enhance it during some intense cloud processes by promoting cloud vertical development and collision-coalescence. The influences of meteorological conditions on ACI exhibit distinct differences between the two pollution regimes. Under the clean regime, activation efficiency shows low sensitivity to meteorological conditions, enabling aerosols in clouds to fully activate across most environments, while the aerosol-limited state and the dominance of condensation lead to increases in cloud droplet size, cloud liquid water path, and rainwater path with supersaturation. In contrast, under the polluted regime, ACI are more sensitive to relative humidity than under the clean regime, and clouds respond oppositely to aerosols under different LTS conditions. Additionally, the dominance of collision-coalescence leads to initial cloud intensification followed by weakening with supersaturation.
Competing interests: One of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics, and the authors have no other competing interests to declare.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2025-2555', Anonymous Referee #2, 01 Sep 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-2555/egusphere-2025-2555-RC1-supplement.pdf
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RC2: 'Comment on egusphere-2025-2555', Anonymous Referee #1, 08 Sep 2025
Using WRF-Chem-SBM, the authors conduct a realistic short-term regional simulation and a counterfactual one (same as the realistic one but without continental aerosols). They briefly showcase the model is valid by comparing the control (realistic) simulation with some observations. Then they use the pair of simulations conducted to study ACI. Ultimately, the results are mixed and show that ACI is complex and regime-dependent. I think this is a fine manuscript overall: it is interesting, relevant, and seems logical. I do have some concerns before publication (which I will ultimately support) — for the recored, I concur with Reviewer 1's concerns, though I tried not to repeat them.
General comments:
- An interesting detail in the experiment setup is disabling the radiative effects of aerosols and clouds. That’s smart, but how much does it really impact the simulations here? Could this decision be contextualized with prior studies and potentially by showing results where these effects are not disabled? How about other confounding effects — of course, you didn’t choose to modify other processes (the entropy of the system is different in the simulations). I would like the authors to discuss this decision a little more, and maybe opine on what else could be done to get rid of confounding factors/feedbacks
- This study kind of left me hopeless about the state of ACI research… While I was reading, I was hoping for something in the conclusion section to offer guidance for future research and/or deeper reflection on what all this means for the community at large. I only found something about higher resolution and bigger domains in the final paragraph, but it seems that’s not going to really help, right? Is this just a difficult problem that we are not going to solve well enough?
- Regarding SBM specifically, are you not willing to release the code? Has the code not been released before? Beyond the code, I invite the authors to reflect on whether SBM is the right tool here (this is similar to Reviewers 1’s first general comment, but specifically about SBM). It is not readily clear to me if using SBM is better or worse than using a bulk scheme (MG2, P3, etc.)
Comments I wrote while reading the manuscript:
L10: you can remove “quite” here (or you can keep it)
L14: What’s being driven? The clouds’ existence or some specific property thereof?
F2: I would personally plot na and CLWP on log scale and I would ensure the same axis is used (so that the reader can see how much lower Na and CLWP will be in the clean case)
T1: You might as well also list the microphysics you’re using (SBM)…
L189: I would say this more precisely — you’re trying to avoid feedbacks into the states, right? See my first general comment
L196: Can you say more about this assimilation method?
L208: I don’t really see the “positive impact” — in general, what do you mean by “positive” here? Like improvement compared to observations? Second, I don’t see any significant movement in F3. The results are pretty good anyway, so it seems the assimilation made little difference and it wasn’t needed… But either way, I think discussing the assimilation is confusing because the results look pretty decent without it (so there’s no reason for it)
L217: Define “Control_NoDA” somewhere before you use it; I assume you mean control without data assimilation, right?
L252: That’s good, I like the fact that you did this. Question though: did you also do the experiment with the aerosol/cloud radiation effects enabled? Did you see anything interesting? I guess I am asking if you could tell us precisely what you got out of disabling these effects …
L264: replace “is the containing of continental aerosols” with “is using continental aerosols”
F5: good job on this figure :) just remind the reader the sampling frequency/averaging of the data in the caption
F9: I am not sure what this figure is showing exactly. Can you explain it more in the text? What are you trying to show us?
F10: As Reviewer 1 indicated, be careful with how you define the units.
F11: Consider reworking this figure so that the majority of it is not white space (same for F9)
Citation: https://doi.org/10.5194/egusphere-2025-2555-RC2
Data sets
The namelist file and output of the WRF-Chem-SBM model Jianqi Zhao https://doi.org/10.5281/zenodo.15508465
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