Secondary ice production affects tropical convective clouds under different aerosol conditions

Sun, Mengyu; Connolly, Paul J.; Field, Paul R.; Finney, Declan L.; Blyth, Alan M.

doi:10.5194/egusphere-2025-5665

Preprints

https://doi.org/10.5194/egusphere-2025-5665

Preprints

19 Nov 2025

| 19 Nov 2025

Secondary ice production affects tropical convective clouds under different aerosol conditions

Mengyu Sun, Paul J. Connolly, Paul R. Field, Declan L. Finney, and Alan M. Blyth

Abstract. Secondary ice production (SIP) modulates tropical convection, but its interactions with aerosol loading remain uncertain. Using the UK Met Office Unified Model with detailed SIP parameterizations, this study investigates how multiple SIP mechanisms affect a Hector storm under weak instability and varying cloud condensation nuclei (CCN) concentrations. At moderate aerosol loading (N_d = 400 cm^–3), SIP substantially enhances the ice phase. Ice number increases by over 3 orders of magnitude at temperatures warmer than –10 ℃, and ice water content rises by up to ~120 % in the anvil. SIP also reduces outgoing longwave radiation (OLR) by 6.8 W m^–2 (–2.5 %), increases outgoing shortwave radiation (OSR) by 7.9 W m^–2 (+2 %), over a 110 km × 110 km region in 6 hours. For this case, precipitation becomes more organized near the convective core, with total accumulation increasing by ~25 %. Under polluted (N_d = 800 cm^–3) and clean (N_d = 200 cm^–3) conditions, SIP effects are weaker or spatially fragmented, indicating non-linear aerosol dependence. Sensitivity experiments show that, when combined with rime-splintering (RS), Mode 1 (droplet fragmentation during freezing) weakens at high CCN owing to reduced supercooled raindrop supply in the mixed-phase layer, while Mode 2 (drop–ice collisions with splashing) is inhibited at low CCN due to less encounters with large rimed ice particles. In contrast, RS and its combination with ice–ice collisional breakup show relatively limited aerosol sensitivity in this case.

Received: 14 Nov 2025 – Discussion started: 19 Nov 2025

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02 Nov 2026

Secondary ice production affects tropical convective clouds under different aerosol conditions

Mengyu Sun, Paul J. Connolly, Paul R. Field, Declan L. Finney, and Alan M. Blyth

Atmos. Chem. Phys., 26, 15349–15373, https://doi.org/10.5194/acp-26-15349-2026,https://doi.org/10.5194/acp-26-15349-2026, 2026

Short summary

Mengyu Sun, Paul J. Connolly, Paul R. Field, Declan L. Finney, and Alan M. Blyth

Interactive discussion

Status: closed

RC1: 'Comment on egusphere-2025-5665', Anonymous Referee #1, 30 Dec 2025

This article investigates the influence of contrasted aerosol conditions through variations in cloud condensation nuclei (CCN) concentrations on secondary ice production (SIP) processes and their impacts on cloud microphysics and convection. The authors perform a series of sensitivity experiments by varying CCN concentrations from clean to polluted conditions for the tropical convective cloud system Hector, using the UK Met Office Unified Model coupled with the CASIM microphysics scheme.
The purpose of this study is highly relevant, as only a limited number of studies have investigated the interaction between aerosols and SIP processes. In most previous works, the effects of aerosols and SIP mechanisms are examined separately. The numerical framework, as well as the range of sensitivity experiments and the chosen diagnostics, appear appropriate to address the scientific questions.
However, several aspects of the manuscript could be improved, particularly the clarity and organization of the results. Given the large number of sensitivity experiments, the presentation of the results is sometimes a bit difficult to follow. For example, some figure panels are discussed long after others from the same figure, which complicates the reading. Further efforts to improve the structure and layout of the figures would also greatly improve the readability of the manuscript.
From a scientific perspective, I also note that the study primarily investigates the sensitivity to CCN concentrations rather than to aerosol conditions in a broader sense. This is because the heterogeneous ice nucleation parameterizations used (Cooper, 1986 and Bigg, 1953) do not explicitly account for aerosol number or composition and therefore do not represent polluted environments in terms of INPs. As a result, the interpretation of the simulations as representing “contrasted aerosol conditions” should be treated with caution. Other aspects of the results would also benefit from additional discussion and clarification.
Overall, the manuscript is suitable for publication in ACP, as the scientific concept of the study is relevant. I therefore recommend publication after minor revisions, which should focus on improving the organization and clarity of the results, as well as on providing a clearer discussion of key physical interpretations and some caveats.
Comments:
Lines 30–32: Additional references could be added here to support the statements.
Lines 40–42: The original work of Phillips et al. (2018), which first introduced the partition of drop fragmentation into two modes, could be cited here.
Line 47: The reference to Phillips et al. (2017) is not fully appropriate here, as this publication focuses on the formulation of the ice breakup parameterization. Part II of the study, which presents a modeling application of the new parameterization, would be more suitable (https://doi.org/10.1175/JAS-D-16-0223.1) as it depict its effect on supercooled liquid water.
Lines 66–67: The discussion focuses exclusively on CCN concentrations. However, more polluted conditions may also imply higher INP concentrations, which could lead to more glaciated clouds with less supercooled liquid water. I therefore suggest restricting this statement explicitly to CCN effects, as stated later in lines 68–71.
Lines 92–95: Is the subgrid cloud fraction scheme still applied in the present study, despite the 1.5 km horizontal resolution? While convection is not parameterized, is it still relevant to parameterize cloud fraction and liquid condensation? Is the liquid fraction not directly predicted by CASIM?
Lines 109–110: Since heterogeneous ice nucleation in the model depends only on temperature (Cooper, 1986) and droplet volume (Bigg, 1953), and not on aerosol number or composition, the sensitivity experiments explore variations in CCN concentration only. In reality, contrasted clean and polluted aerosol environments are expected to modify both CCN and INP concentrations (and properties), with a direct impacts on the ice phase through heterogeneous ice nucleation. This coupled CCN-INP effect is not represented in the present framework and therefore constitutes an important limitation that should be clearly acknowledged. As a consequence, references to clean or polluted environments throughout the manuscript should be explicitly restricted to CCN conditions. The title of the paper might also be reconsidered to refer to CCN conditions rather than aerosol conditions more generally.
Line 172: Why is the RS process considered together with M1, M2, and BR, rather than analyzing these SIP processes individually? For instance, interpreting the effect of RSM1 alone may be challenging, as RS effects could dominate and obscure the specific contribution of M1.
Line 178: How is radar reflectivity calculated in the model? A brief description of the radar operator would be helpful here.
Lines 212–213: While the presence and location of the cloud system are captured, it is evident that the minimum OLR values are higher in all simulations compared to observations. This discrepancy is important and should be at least mentioned.
Section 3.1 : The experiment names could be used directly in the text instead of repeatedly describing each configuration. This would significantly improve readability, as the experiment names already depict both the CCN concentration and whether SIP is active.
Overall, the order in which figures and panels are discussed should be made consistent with their layout. For example, in line 224, Figure 3 is introduced starting with panel (b) but it would be clearer to begin with panel (a).
Section 3.1 is overall descriptive and the physical explanations are provided later in Section 3.3. It would be helpful to inform the readers earlier that the underlying mechanisms are discussed in an upcoming section. The same comment applies to Section 3.2 regarding precipitation.
Figures 3 and 5: Panels corresponding to SIP and no-SIP simulations could be arranged side by side, with consistent sizes, to facilitate direct comparison. Figure 8 provides a good example of a clear and effective layout.
Figure 7: The axis labels and legends are difficult to read. I suggest enlarging this figure, for example by using a two-column format with five rows.
Line 335: Panel 7c is mentioned before panel 7b, which focuses on raindrop number and is not discussed here. The order should be revised.
Lines 340–341: This result is somewhat surprising, as SIP typically generates small ice particles. Previous studies (e.g., Dedekind et al., 2021) have shown that SIP tends to produce smaller ice particles with reduced sedimentation. This could be mentioned.
Lines 343–344: SIP increases ice particle number by several orders of magnitude, while ice water mass increases by only a factor of about two. Given this discrepancy, how can the mean particle diameter increase?
Lines 353–356: While enhanced vapor deposition for ice particles has been reported in previous studies (e.g., Dedekind et al., 2021), why is this effect apparent for riming but not for vapor deposition here? Why is the impact stronger on graupel than on snow? If ice mixing ratio and particle size increase, why does snow not respond similarly?
Lines 363–364: What about the role of water vapor availability for deposition?
Line 393: Figure 7b is referenced here, even though Figure 7 began to be discussed much earlier. This is a bit confusing for the readers. The section can be reorganized to group all discussions of Figure 7 together.
Lines 404–406: This explanation is a bit difficult to follow. Referring directly to the experiment names, rather than to “Nd = …” and “SIP/noSIP”, would improve the clarity of this sentence. This approach could also be applied elsewhere in the manuscript (e.g., lines 451–453).
Line 410: Can you specify which figure is discussed ?
Line 423: When multiple SIP processes are active (e.g., RSM1), it is difficult to disentangle the contribution of each process. An alternative approach could be to analyze differences such as RS–RSM1, but I leave this up to the authors consideration.
Section 4: This section provides a clear and useful summary of the results, clarifying the main findings presented in the previous sections.
Lines 526–527: These results should be balanced with other studies that have shown SIP can reduce particle size and precipitation efficiency (e.g., Dedekind et al., 2021).
Lines 560–561: This statement is not universally valid and may hold only for this specific case, which is particularly important to state given that the following sentence mention a study reporting opposite effects.
Lines 588–589: I do not see a clear link between the cited papers and the purpose of this sentence.
Lines 628–631: In my view, this suggests that snow amounts might also increase, as higher ice number, size, and mass should favor conversion to snow.
Lines 638–639: It should be noted that the simulations still differ significantly from observations.
Conclusions: In addition to uncertainties related to the microphysical assumptions of the model, it should be emphasized that the results strongly depend on the SIP parameterizations used. The physical understanding of SIP mechanisms and therefore their parameterizations remains subject to large uncertainties and ongoing debate. This important limitation should be clearly acknowledged in the conclusions.

Citation: https://doi.org/10.5194/egusphere-2025-5665-RC1
RC2: 'Comment on egusphere-2025-5665', Anonymous Referee #2, 02 Jan 2026

My comments are attached.

Citation: https://doi.org/10.5194/egusphere-2025-5665-RC2
AC1: 'Comment on egusphere-2025-5665', Mengyu Sun, 21 Apr 2026

We thank the reviewers for their time and constructive comments. We have addressed each point in detail in the attached PDF file.

Citation: https://doi.org/10.5194/egusphere-2025-5665-AC1

Peer review completion

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

AR by Mengyu Sun on behalf of the Authors (21 Apr 2026) Author's response Author's tracked changes Manuscript

ED: Reconsider after major revisions (21 Apr 2026) by Greg McFarquhar

AR by Mengyu Sun on behalf of the Authors (21 Apr 2026) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (22 Apr 2026) by Greg McFarquhar

RR by Anonymous Referee #1 (04 May 2026)

Suggestions for revision or reasons for rejection

The authors have done a thorough job in addressing each reviewer’s comments. The way the figures are now introduced and presented is much clearer. I would like to give a few more suggestions regarding future perspectives, as well as some technical comments to consider before publication.

Technical comments:
• All figures: The labeling of figure panels (a, b, c, …) is currently placed inside each panel. It would be more visually consistent to put these labels outside each panel (above the top-left corner).
• Figure 1: The text (particularly the axis labels) in panel (b) is too small, especially compared to panel (a).
• Figure 2-3: The label text is also too small and should be increased for better readability.
• Figure 5: In panel (a), the panel title could be written after “a)” for consistency with the other panels.
• Lines 429–430: Do you mean that aggregation and sedimentation are much more dominant than the effect of SIP? This sentence could be reformulated for clarity. I remain somewhat skeptical about the low impact of SIP on snow, given the large increase in ice particle number. Anyway, these are the results produced by the model.
• Figure 8: The names associated with panels (g), (h), and (i) could be added (as suggested for figure 5).
• Lines 663–664: From my perspective, the dominance of each SIP process depends strongly on the microphysical scheme used, not only on the SIP parameterizations or the environmental conditions of each case study. This point partly addressed in next sentences with the example of drop immersion freezing but could be extended for microphysics schemes in general.

Recommendations for future work:
For future work, I strongly recommend that the authors conduct a more detailed validation against observational data to evaluate the ice/snow/graupel number concentrations (or even particle size distributions). Although Figure S1 in supplement provides some comparisons, it is limited to low-temperature regions near the cloud top, where SIP is less likely to significantly enhance ice particle concentrations. The simulated ice particle number appears to peak around 1 L⁻¹ close to 6 km, which is relatively low compared to what in situ aircraft measurements reported for convective clouds.

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RR by Anonymous Referee #2 (15 May 2026)

ED: Reconsider after major revisions (17 May 2026) by Greg McFarquhar

AR by Mengyu Sun on behalf of the Authors (15 Jun 2026) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (16 Jun 2026) by Greg McFarquhar

RR by Anonymous Referee #2 (28 Jun 2026)

Suggestions for revision or reasons for rejection

The authors have responded in detail to the referee comments, but I do not believe that the underlying scientific concern has been adequately addressed in the revised manuscript. My principal concern remains that the manuscript does not establish a clearly defined and observationally evaluated baseline ("control") simulation before interpreting the sensitivity experiments.

At present, comparisons with observations are interwoven with the sensitivity simulations. This makes it difficult to distinguish evaluation of the model from evaluation of the sensitivity tests. A more rigorous approach would be to identify a single control simulation (the Nd = 400 cm⁻³ case appears to be the most appropriate based on the available observations), evaluate that simulation comprehensively against the available measurements, and only then present the sensitivity experiments as perturbations about this validated baseline. Comparisons with observations should not be interwoven with the sensitivity experiments.

This distinction is important because, if the model contains structural biases (for example in aggregation or other aspects of the ice microphysics), one of the sensitivity experiments may appear to agree better with observations simply through compensating errors rather than because it provides the most physically realistic representation of the cloud system. In the present manuscript, the sensitivity experiments span a very wide range of prescribed droplet concentrations, so they effectively provide multiple opportunities for individual experiments to compensate for an underlying model bias and consequently appear to agree better with observations. Without a clearly validated control simulation, the sensitivity experiments themselves begin to act as an implicit calibration, making it more difficult to interpret the physical significance of the simulated SIP responses.

I also remain unconvinced by several of the reasons given for not making greater use of the available observations.

The comparison of upper-tropospheric ice number concentration and ice water content (Figure S1) is, in fact, a useful and convincing validation of the simulated ice field and deserves to be presented in the main manuscript rather than in the Supplement. Although these aircraft observations do not sample the mixed-phase region directly, this is not a major limitation. The upper-tropospheric ice population is the cumulative result of the preceding convective ascent, and the amount of supercooled liquid transported aloft for subsequent homogeneous freezing is itself influenced by SIP processes operating lower in the cloud (e.g. Phillips and Patade, 2022, JAS). Consequently, these observations remain an important validation of the simulated ice microphysics.

Similarly, I do not find the arguments regarding the cloud droplet concentration measurements persuasive. Although the number of available Dornier aircraft samples is small and confined to levels below the freezing level, even a limited number of observations provides a useful constraint on the prescribed cloud droplet concentrations, which are central to the experimental design. The associated uncertainty can readily be quantified using confidence intervals or standard errors. The purpose is not to perform a statistically rigorous validation, but to demonstrate that the selected control simulation is physically consistent with the observed environment.

Likewise, I am not persuaded by the justification for omitting cloud-fraction profiles. The fact that previous studies analysed cloud fraction for different periods does not preclude performing the same diagnostic for the present case. Cloud fraction is a standard model diagnostic and would provide valuable additional information on the realism of the simulated cloud structure.

Overall, I believe the manuscript would be substantially strengthened by introducing a dedicated model evaluation section centred on a single control simulation. This section should include the comparison presently shown in Figure S1 together with the existing radar and satellite evaluations and any additional available observational constraints. The observational evaluation should be clearly separated from the subsequent sensitivity experiments so that the SIP responses are interpreted relative to a validated baseline state rather than using the sensitivity experiments themselves as part of the model evaluation.

Finally, I note that the tracked-changes version indicates that only a very small number of sentences have been modified in the main manuscript during this revision. Consequently, despite the detailed point-by-point response, I do not consider that the manuscript itself has been revised sufficiently to address the substantive concerns raised in the previous review.

For these reasons, I recommend that the manuscript be reconsidered after major revision.

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RR by Anonymous Referee #1 (30 Jun 2026)

ED: Reconsider after major revisions (07 Jul 2026) by Greg McFarquhar

AR by Mengyu Sun on behalf of the Authors (16 Sep 2026) Author's response Author's tracked changes Manuscript

EF by Polina Shvedko (17 Sep 2026) Manuscript Author's tracked changes

ED: Referee Nomination & Report Request started (18 Sep 2026) by Greg McFarquhar

RR by Anonymous Referee #1 (24 Sep 2026)

RR by Anonymous Referee #2 (30 Sep 2026)

Suggestions for revision or reasons for rejection

I believe there is a misunderstanding of my previous comment concerning the “control” simulation, and I would like to clarify the distinction.

I did not suggest that a "noSIP" simulation (a run without any SIP) should be selected as the control, nor did I suggest that noSIP-400 should be used as a common control for the simulations performed at other prescribed droplet concentrations. I agree that, if the objective is to isolate the incremental effect of SIP at a given prescribed Nd, then matched pairs such as noSIP-200/allSIP-200, noSIP-400/allSIP-400, and noSIP-800/allSIP-800 are appropriate comparisons. This is not the issue I was raising.

My concern is instead about establishing the observationally constrained baseline state of the complete model before interpreting the sensitivity experiments. By “control simulation” I meant a single configuration of the model in which all the relevant physical processes, including SIP, are retained, and for which the prescribed N_d is selected and justified using the available observations. I suggested Nd=400 /cm3 as a plausible candidate based on the observations; I did not propose that the corresponding noSIP simulation should be the control.

I see two related but distinct scientific questions. The first is whether the complete model provides a credible representation of the observed cloud system. The second is how the simulated cloud system responds to changes in SIP or other parameters. I agree that matched simulations can be used to quantify the model's incremental SIP response without first establishing which Nd provides the most realistic representation of the case. However, this does not resolve the second aspect of my concern: the physical interpretation of that sensitivity for the observed cloud system requires an observationally constrained reference state.

This is also why I raised the issue of compensating errors. My concern was not that a noSIP simulation should be tuned to observations. Rather, it was that if all of the sensitivity experiments are simultaneously used to determine which configuration agrees best with observations, the sensitivity experiments themselves can effectively become an implicit calibration exercise. For example, a change in prescribed Nd, or in SIP, might compensate for an unrelated structural bias in another part of the microphysics. A simulation that agrees more closely with a particular observation would then not necessarily represent a more physically realistic state of the cloud system.

I do not think that the discussion of the potential problems of selecting or tuning a noSIP simulation addresses the concern I raised. This was never something I advocated. The baseline I am proposing is the full model, including SIP, evaluated against the available observations. Once such a baseline has been established, the matched noSIP/allSIP comparisons can still be used to quantify the incremental SIP effect at different Nd values. These two uses of “control” are conceptually different and need not conflict.

I also do not mean that the observations can establish that the model is “validated” in an absolute sense. Rather, the purpose of the observational evaluation is to establish which complete-model configuration provides the most observationally defensible representation of the particular case being simulated, and to quantify the remaining uncertainties.

Thus, my concern is not with the matched-Nd methodology for isolating the model's SIP response. It is with interpreting that response in the context of the observed cloud without first establishing an observationally constrained reference state of the complete model. Again, I encourage the authors to separate clearly (i) evaluation of the complete model against observations, including justification of the selected or observationally supported Nd, from (ii) the subsequent sensitivity experiments used to diagnose the effects of SIP and Nd.

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ED: Reconsider after major revisions (01 Oct 2026) by Greg McFarquhar

AR by Mengyu Sun on behalf of the Authors (16 Oct 2026) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (20 Oct 2026) by Greg McFarquhar

ED: Publish as is (22 Oct 2026) by Greg McFarquhar

AR by Mengyu Sun on behalf of the Authors (29 Oct 2026) Manuscript

Interactive discussion

Status: closed

RC1: 'Comment on egusphere-2025-5665', Anonymous Referee #1, 30 Dec 2025

This article investigates the influence of contrasted aerosol conditions through variations in cloud condensation nuclei (CCN) concentrations on secondary ice production (SIP) processes and their impacts on cloud microphysics and convection. The authors perform a series of sensitivity experiments by varying CCN concentrations from clean to polluted conditions for the tropical convective cloud system Hector, using the UK Met Office Unified Model coupled with the CASIM microphysics scheme.
The purpose of this study is highly relevant, as only a limited number of studies have investigated the interaction between aerosols and SIP processes. In most previous works, the effects of aerosols and SIP mechanisms are examined separately. The numerical framework, as well as the range of sensitivity experiments and the chosen diagnostics, appear appropriate to address the scientific questions.
However, several aspects of the manuscript could be improved, particularly the clarity and organization of the results. Given the large number of sensitivity experiments, the presentation of the results is sometimes a bit difficult to follow. For example, some figure panels are discussed long after others from the same figure, which complicates the reading. Further efforts to improve the structure and layout of the figures would also greatly improve the readability of the manuscript.
From a scientific perspective, I also note that the study primarily investigates the sensitivity to CCN concentrations rather than to aerosol conditions in a broader sense. This is because the heterogeneous ice nucleation parameterizations used (Cooper, 1986 and Bigg, 1953) do not explicitly account for aerosol number or composition and therefore do not represent polluted environments in terms of INPs. As a result, the interpretation of the simulations as representing “contrasted aerosol conditions” should be treated with caution. Other aspects of the results would also benefit from additional discussion and clarification.
Overall, the manuscript is suitable for publication in ACP, as the scientific concept of the study is relevant. I therefore recommend publication after minor revisions, which should focus on improving the organization and clarity of the results, as well as on providing a clearer discussion of key physical interpretations and some caveats.
Comments:
Lines 30–32: Additional references could be added here to support the statements.
Lines 40–42: The original work of Phillips et al. (2018), which first introduced the partition of drop fragmentation into two modes, could be cited here.
Line 47: The reference to Phillips et al. (2017) is not fully appropriate here, as this publication focuses on the formulation of the ice breakup parameterization. Part II of the study, which presents a modeling application of the new parameterization, would be more suitable (https://doi.org/10.1175/JAS-D-16-0223.1) as it depict its effect on supercooled liquid water.
Lines 66–67: The discussion focuses exclusively on CCN concentrations. However, more polluted conditions may also imply higher INP concentrations, which could lead to more glaciated clouds with less supercooled liquid water. I therefore suggest restricting this statement explicitly to CCN effects, as stated later in lines 68–71.
Lines 92–95: Is the subgrid cloud fraction scheme still applied in the present study, despite the 1.5 km horizontal resolution? While convection is not parameterized, is it still relevant to parameterize cloud fraction and liquid condensation? Is the liquid fraction not directly predicted by CASIM?
Lines 109–110: Since heterogeneous ice nucleation in the model depends only on temperature (Cooper, 1986) and droplet volume (Bigg, 1953), and not on aerosol number or composition, the sensitivity experiments explore variations in CCN concentration only. In reality, contrasted clean and polluted aerosol environments are expected to modify both CCN and INP concentrations (and properties), with a direct impacts on the ice phase through heterogeneous ice nucleation. This coupled CCN-INP effect is not represented in the present framework and therefore constitutes an important limitation that should be clearly acknowledged. As a consequence, references to clean or polluted environments throughout the manuscript should be explicitly restricted to CCN conditions. The title of the paper might also be reconsidered to refer to CCN conditions rather than aerosol conditions more generally.
Line 172: Why is the RS process considered together with M1, M2, and BR, rather than analyzing these SIP processes individually? For instance, interpreting the effect of RSM1 alone may be challenging, as RS effects could dominate and obscure the specific contribution of M1.
Line 178: How is radar reflectivity calculated in the model? A brief description of the radar operator would be helpful here.
Lines 212–213: While the presence and location of the cloud system are captured, it is evident that the minimum OLR values are higher in all simulations compared to observations. This discrepancy is important and should be at least mentioned.
Section 3.1 : The experiment names could be used directly in the text instead of repeatedly describing each configuration. This would significantly improve readability, as the experiment names already depict both the CCN concentration and whether SIP is active.
Overall, the order in which figures and panels are discussed should be made consistent with their layout. For example, in line 224, Figure 3 is introduced starting with panel (b) but it would be clearer to begin with panel (a).
Section 3.1 is overall descriptive and the physical explanations are provided later in Section 3.3. It would be helpful to inform the readers earlier that the underlying mechanisms are discussed in an upcoming section. The same comment applies to Section 3.2 regarding precipitation.
Figures 3 and 5: Panels corresponding to SIP and no-SIP simulations could be arranged side by side, with consistent sizes, to facilitate direct comparison. Figure 8 provides a good example of a clear and effective layout.
Figure 7: The axis labels and legends are difficult to read. I suggest enlarging this figure, for example by using a two-column format with five rows.
Line 335: Panel 7c is mentioned before panel 7b, which focuses on raindrop number and is not discussed here. The order should be revised.
Lines 340–341: This result is somewhat surprising, as SIP typically generates small ice particles. Previous studies (e.g., Dedekind et al., 2021) have shown that SIP tends to produce smaller ice particles with reduced sedimentation. This could be mentioned.
Lines 343–344: SIP increases ice particle number by several orders of magnitude, while ice water mass increases by only a factor of about two. Given this discrepancy, how can the mean particle diameter increase?
Lines 353–356: While enhanced vapor deposition for ice particles has been reported in previous studies (e.g., Dedekind et al., 2021), why is this effect apparent for riming but not for vapor deposition here? Why is the impact stronger on graupel than on snow? If ice mixing ratio and particle size increase, why does snow not respond similarly?
Lines 363–364: What about the role of water vapor availability for deposition?
Line 393: Figure 7b is referenced here, even though Figure 7 began to be discussed much earlier. This is a bit confusing for the readers. The section can be reorganized to group all discussions of Figure 7 together.
Lines 404–406: This explanation is a bit difficult to follow. Referring directly to the experiment names, rather than to “Nd = …” and “SIP/noSIP”, would improve the clarity of this sentence. This approach could also be applied elsewhere in the manuscript (e.g., lines 451–453).
Line 410: Can you specify which figure is discussed ?
Line 423: When multiple SIP processes are active (e.g., RSM1), it is difficult to disentangle the contribution of each process. An alternative approach could be to analyze differences such as RS–RSM1, but I leave this up to the authors consideration.
Section 4: This section provides a clear and useful summary of the results, clarifying the main findings presented in the previous sections.
Lines 526–527: These results should be balanced with other studies that have shown SIP can reduce particle size and precipitation efficiency (e.g., Dedekind et al., 2021).
Lines 560–561: This statement is not universally valid and may hold only for this specific case, which is particularly important to state given that the following sentence mention a study reporting opposite effects.
Lines 588–589: I do not see a clear link between the cited papers and the purpose of this sentence.
Lines 628–631: In my view, this suggests that snow amounts might also increase, as higher ice number, size, and mass should favor conversion to snow.
Lines 638–639: It should be noted that the simulations still differ significantly from observations.
Conclusions: In addition to uncertainties related to the microphysical assumptions of the model, it should be emphasized that the results strongly depend on the SIP parameterizations used. The physical understanding of SIP mechanisms and therefore their parameterizations remains subject to large uncertainties and ongoing debate. This important limitation should be clearly acknowledged in the conclusions.

Citation: https://doi.org/10.5194/egusphere-2025-5665-RC1
RC2: 'Comment on egusphere-2025-5665', Anonymous Referee #2, 02 Jan 2026

My comments are attached.

Citation: https://doi.org/10.5194/egusphere-2025-5665-RC2
AC1: 'Comment on egusphere-2025-5665', Mengyu Sun, 21 Apr 2026

We thank the reviewers for their time and constructive comments. We have addressed each point in detail in the attached PDF file.

Citation: https://doi.org/10.5194/egusphere-2025-5665-AC1

Peer review completion

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

AR by Mengyu Sun on behalf of the Authors (21 Apr 2026) Author's response Author's tracked changes Manuscript

ED: Reconsider after major revisions (21 Apr 2026) by Greg McFarquhar

AR by Mengyu Sun on behalf of the Authors (21 Apr 2026) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (22 Apr 2026) by Greg McFarquhar

RR by Anonymous Referee #1 (04 May 2026)

Suggestions for revision or reasons for rejection

Hide

RR by Anonymous Referee #2 (15 May 2026)

ED: Reconsider after major revisions (17 May 2026) by Greg McFarquhar

AR by Mengyu Sun on behalf of the Authors (15 Jun 2026) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (16 Jun 2026) by Greg McFarquhar

RR by Anonymous Referee #2 (28 Jun 2026)

Suggestions for revision or reasons for rejection

Hide

RR by Anonymous Referee #1 (30 Jun 2026)

ED: Reconsider after major revisions (07 Jul 2026) by Greg McFarquhar

AR by Mengyu Sun on behalf of the Authors (16 Sep 2026) Author's response Author's tracked changes Manuscript

EF by Polina Shvedko (17 Sep 2026) Manuscript Author's tracked changes

ED: Referee Nomination & Report Request started (18 Sep 2026) by Greg McFarquhar

RR by Anonymous Referee #1 (24 Sep 2026)

RR by Anonymous Referee #2 (30 Sep 2026)

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ED: Reconsider after major revisions (01 Oct 2026) by Greg McFarquhar

AR by Mengyu Sun on behalf of the Authors (16 Oct 2026) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (20 Oct 2026) by Greg McFarquhar

ED: Publish as is (22 Oct 2026) by Greg McFarquhar

AR by Mengyu Sun on behalf of the Authors (29 Oct 2026) Manuscript

Journal article(s) based on this preprint

02 Nov 2026

Secondary ice production affects tropical convective clouds under different aerosol conditions

Mengyu Sun, Paul J. Connolly, Paul R. Field, Declan L. Finney, and Alan M. Blyth

Atmos. Chem. Phys., 26, 15349–15373, https://doi.org/10.5194/acp-26-15349-2026,https://doi.org/10.5194/acp-26-15349-2026, 2026

Short summary

Mengyu Sun, Paul J. Connolly, Paul R. Field, Declan L. Finney, and Alan M. Blyth

Supplement

https://doi.org/10.5194/egusphere-2025-5665-supplement

Mengyu Sun, Paul J. Connolly, Paul R. Field, Declan L. Finney, and Alan M. Blyth

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Cumulative views and downloads (calculated since 19 Nov 2025)

Month	HTML	PDF	XML	Total
Nov 2025	705	100	65	870
Dec 2025	345	200	59	604
Jan 2026	260	134	35	429
Feb 2026	179	73	5	257
Mar 2026	192	116	15	323
Apr 2026	88	22	3	113
May 2026	37	51	0	88
Jun 2026	11	2	1	14
Jul 2026	14	10	1	25
Aug 2026	9	4	6	19
Sep 2026	28	23	11	62
Oct 2026	37	28	4	69
Nov 2026	46	16	2	64

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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

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Short summary

We use a high resolution weather model together with satellite and radar data to study how small particles in the air influence ice and rain in a tropical storm near Darwin. We find that when particle levels are moderate, storm clouds form more ice high in the atmosphere, spread a wider cloud cover, and produce stronger rainfall concentrated in certain regions. These results help improve how weather and climate models represent tropical storms and their rainfall.


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