Numerical evidence that the impact of CCN and INP concentrations on mixed-phase clouds is observable with cloud radars

Lee, Junghwa; Seifert, Patric; Hashino, Tempei; Maahn, Maximilian; Senf, Fabian; Knoth, Oswald

doi:https://doi.org/10.5194/egusphere-2023-1887

Preprints

https://doi.org/10.5194/egusphere-2023-1887

Preprints

24 Aug 2023

| 24 Aug 2023

Numerical evidence that the impact of CCN and INP concentrations on mixed-phase clouds is observable with cloud radars

Junghwa Lee, Patric Seifert, Tempei Hashino, Maximilian Maahn, Fabian Senf, and Oswald Knoth

Abstract. In this research, we delve into the influence of cloud condensation nuclei (CCN) and ice-nucleating particles (INP) concentrations on the morphology and abundance of ice particles in mixed-phase clouds, emphasizing the consequential impact of ice particle shape, number, and size on cloud dynamics and microphysics. Leveraging the synergy of the Advanced Microphysics Prediction System (AMPS) and the Kinematic Driver (KiD) model, we conducted simulations to capture cloud microphysics across diverse CCN and INP concentrations. The Passive and Active Microwave radiative TRAnsfer (PAMTRA) radar forward simulator further augmented our study, offering insights into how the concentrations of CCN and INP affect radar reflectivities.

Our experimental framework encompassed CCN concentrations ranging from 10 to 5000 cm⁻³ and INP concentrations from 0.001 to 10 L⁻¹. Central to our findings are the observation that increased INP concentrations yield smaller ice particles, while a surge in CCN concentrations leads to a subtle growth in their dimensions. Consistent with existing literature, our results spotlight plate-like crystals as dominant between temperatures of −20 to −16 °C. Notably, high INP scenarios unveiled a significant prevalence of irregular polycrystals. The Aspect Ratio (AR) of ice particles exhibited a decline with the rise in both CCN and INP concentrations, highlighting the nuanced interrelation between CCN levels and ice particle shape, especially its ramifications on the riming mechanism.

The forward-simulated radar reflectivities, spanning from −11.83 dBZ (low-INP, 0.001 L⁻¹) to 4.65 dBZ (high-INP, 10 L⁻¹), elucidate the complex dynamics between CCN and INP in determining mixed-phase cloud characteristics. Comparable differences in radar reflectivity were also reported from observational studies of stratiform mixed-phase clouds in contrasting aerosol environments. Our meticulous analysis of KiD-AMPS simulation outputs, coupled with insights into aerosol-driven microphysical changes, thus underscores the significance of this study in refining our ability to understand and interpret observations and climate projections.

Received: 18 Aug 2023 – Discussion started: 24 Aug 2023

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Junghwa Lee, Patric Seifert, Tempei Hashino, Maximilian Maahn, Fabian Senf, and Oswald Knoth

Status: closed

RC1:
'Comment on egusphere-2023-1887', Anonymous Referee #1, 07 Nov 2023

Numerical evidence that the impact of CCN and INP concentrations on mixed-phase clouds is observable with cloud radars
By Junghwa Lee et al.
The authors present a model-based analysis of how changes in CCN and INP would affect mixed-phase cloud properties. The most interesting part of the study is the analysis of the impact of CCN and INP on ice particle properties. It is show that changes in CCN and INP could result in detectable changes in particle shapes, for example. Overall, the article is well-written, but there are a few issues that I have summarised below. My major concern is the analysis of how aspect ratio of particle changes as a function of changes in INP and CCN concentrations. I think the analysis would be clearer is a consistent particle classification would be used throughout the study.
Detailed comments:
Page 4. What are particle property variables (PPVs)? It would be helpful to see a list all PPV used in the study.
Page 5. The definition of the sphere volume circumscribing the ice particle is not very clear. Do you have a reference or more detailed explanation of how it is defined? It is no clear what the equation on line 137 implies in terms of an assumed particle shape.
Page 5, line 152 Explanation of the reflectivity factor is not accurate. The reflectivity factor characterises a volume of scatterers, not just one object. One object’s ability to scatter is characterised by a scattering cross section.
To be even more precise Ze is the equivalent reflectivity factor.
Line 177. It is a standard practice to use wavelength appropriate value, not use one computed for cm-wavelengths.
Line 273. “…concentration of INP concentration increases.”
Line 277. “This reduction in droplet size increases the altitude of the cloud base…” I don’t understand why the reduction in droplet size would affect the cloud base.
How is the cloud base defined? Is it defined from the LWC profiles? If yes, how do you separate contributions from cloud droplets and drizzle?
Figure 7. The maximum size of ice particles is just 2 mm for EXP1, while Figure 6 shows significant aggregation. Is there a reason why no larger particles are produced?
Figure 8. In order to interpret the figure, it would be good to know what AR values for typical particle types in your model are. What are AR values for crystals, graupel and aggregates? It is strange to see that graupel in EXP3 does not produce a noticeable change in AR. The statement “…exhibit a plate-like shape with AR (α) < 1,” on line 340 is too general and covers a large fraction of different ice particle types, ranging from pristine dendrites to aggregates. It is expected that aggregates have AR around 0.5 – 0.6 range (Hogan et al. 2012; Li et al. 2018 and Matrosov et al. 2017).
Line 351 “Figure 9(a) provides insights into the predominant shape of ice particles, with plates representing the majority of the particles.”What do you mean when you state that plates represent the majority of the particles? Are aggregates plates? What about graupel? I think you need to use more precise terminology.
Also, in the figure you use “pploy, cploy and irploy”, instead of “ppoly, cpoly and irpoly”.
Hogan, R. J., L. Tian, P. R. A. Brown, C. D. Westbrook, A. J. Heymsfield, and J. D. Eastment, 2012: Radar Scattering from Ice Aggregates Using the Horizontally Aligned Oblate Spheroid Approximation. J. Appl. Meteor. Climatol., 51, 655–671, https://doi.org/10.1175/JAMC-D-11-074.1.
Li, H., Moisseev, D., & von Lerber, A. (2018). How does riming affect dual-polarization radar observations and snowflake shape? Journal of Geophysical Research: Atmospheres, 123, 6070–6081. https://doi.org/10.1029/2017JD028186
Matrosov, S. Y., C. G. Schmitt, M. Maahn, and G. de Boer, 2017: Atmospheric Ice Particle Shape Estimates from Polarimetric Radar Measurements and In Situ Observations. J. Atmos. Oceanic Technol., 34, 2569–2587, https://doi.org/10.1175/JTECH-D-17-0111.1.
Line 364. While it is true that in Figure 9 we can see the difference between AR values for different experiments, I find it difficult to make a connection between this figure and Fig. 6. In Fig. 6 you use rime, aggr. and crystal. But later you introduce a very different classification: namely plate, dendrite, column, and various types of polycrystals in Fig. 9 (a) and in Fig 9 (b) this classification is reduced to plate and ir. poly. It would be better if you would be consistent. In Fig. 10 you go back to riming aggregation and deposition growth, so to which particle in Fig. 9 these processes correspond to?
Figure 12. Looking at EXP1 reflectivity profile in this figure, I find it even more surprising that in Fig. 7 the maximum particle size for EXP1 was just 2 mm. I would have expected to see larger aggregates.

Citation: https://doi.org/10.5194/egusphere-2023-1887-RC1
- AC2: 'Reply on RC1', Junghwa Lee, 13 Feb 2024
  
  Dear Anonymous Reviewer,
  We are sincerely grateful for your insightful comments and suggestions regarding our manuscript.
  
  Attached, please find our detailed responses to Reviews #1 and #2, as well as the diff version of the manuscript reflecting the revisions made in light of your feedback.
  Best regards,
  
  Junghwa Lee
  
  Citation: https://doi.org/10.5194/egusphere-2023-1887-AC2
RC2:
'Comment on egusphere-2023-1887', Anonymous Referee #2, 04 Jan 2024
This study presents modeling the impact of CCN and INP on the mixed-phased clouds, and the application of the model to the radar simulator. Since the CCN and INP are important contributors to cloud formation, the study will help to improve our understanding of formation of mix-phased clouds and the controlling factors. The manuscript is well-written and should be publishable after the following issues are resolved:
The title doesn’t seem to reflect the content of this study which mainly focuses on modeling the impact of CCN and INP concentrations on the mixed-phase clouds. It is suggested to change the title for more precision on the subject.

It would be beneficial to the broader readers if the authors can provide some explanation on why the initial profiles of the potential temperature and specific humidity look like the ones shown in Figure 3.

The effective diameter for EXP2 shown in Figure 4 is not 193 (page 10), judged from the figure.

For EXP1, high INPs lead to not only a spread of smaller particles but also large particles (over 1 mm) as shown in Figure 7. Can the authors give a likely reason for this spread of larger particles?

When accounting for the impact of INPs on the AR, the authors point out that the fluctuations of AR were due to formation of irregular polycrystals after 200 minutes. Two things need to be clarified: 1) why does this occur after 200 minutes? 2) what would happen if the simulation time was extended longer, for example, AR will be more stable?

As shown in Figure 12, the trend of the impact of INPs on the reflectivity is surprisingly reversed from decrease to increase. The explanation for this transition seems not satisfactory. I wonder if more refining experiments were performed, for example, smaller steps of INPs were set, this transition might be observable.
Citation: https://doi.org/10.5194/egusphere-2023-1887-RC2
- AC1: 'Reply on RC2', Junghwa Lee, 13 Feb 2024
  
  Dear Anonymous Reviewer,
  We are sincerely grateful for your insightful comments and suggestions regarding our manuscript.
  
  Attached, please find our detailed responses to Reviews #1 and #2, as well as the diff version of the manuscript reflecting the revisions made in light of your feedback.
  Best regards,
  
  Junghwa Lee
  
  Citation: https://doi.org/10.5194/egusphere-2023-1887-AC1

Status: closed

RC1:
'Comment on egusphere-2023-1887', Anonymous Referee #1, 07 Nov 2023

Numerical evidence that the impact of CCN and INP concentrations on mixed-phase clouds is observable with cloud radars
By Junghwa Lee et al.
The authors present a model-based analysis of how changes in CCN and INP would affect mixed-phase cloud properties. The most interesting part of the study is the analysis of the impact of CCN and INP on ice particle properties. It is show that changes in CCN and INP could result in detectable changes in particle shapes, for example. Overall, the article is well-written, but there are a few issues that I have summarised below. My major concern is the analysis of how aspect ratio of particle changes as a function of changes in INP and CCN concentrations. I think the analysis would be clearer is a consistent particle classification would be used throughout the study.
Detailed comments:
Page 4. What are particle property variables (PPVs)? It would be helpful to see a list all PPV used in the study.
Page 5. The definition of the sphere volume circumscribing the ice particle is not very clear. Do you have a reference or more detailed explanation of how it is defined? It is no clear what the equation on line 137 implies in terms of an assumed particle shape.
Page 5, line 152 Explanation of the reflectivity factor is not accurate. The reflectivity factor characterises a volume of scatterers, not just one object. One object’s ability to scatter is characterised by a scattering cross section.
To be even more precise Ze is the equivalent reflectivity factor.
Line 177. It is a standard practice to use wavelength appropriate value, not use one computed for cm-wavelengths.
Line 273. “…concentration of INP concentration increases.”
Line 277. “This reduction in droplet size increases the altitude of the cloud base…” I don’t understand why the reduction in droplet size would affect the cloud base.
How is the cloud base defined? Is it defined from the LWC profiles? If yes, how do you separate contributions from cloud droplets and drizzle?
Figure 7. The maximum size of ice particles is just 2 mm for EXP1, while Figure 6 shows significant aggregation. Is there a reason why no larger particles are produced?
Figure 8. In order to interpret the figure, it would be good to know what AR values for typical particle types in your model are. What are AR values for crystals, graupel and aggregates? It is strange to see that graupel in EXP3 does not produce a noticeable change in AR. The statement “…exhibit a plate-like shape with AR (α) < 1,” on line 340 is too general and covers a large fraction of different ice particle types, ranging from pristine dendrites to aggregates. It is expected that aggregates have AR around 0.5 – 0.6 range (Hogan et al. 2012; Li et al. 2018 and Matrosov et al. 2017).
Line 351 “Figure 9(a) provides insights into the predominant shape of ice particles, with plates representing the majority of the particles.”What do you mean when you state that plates represent the majority of the particles? Are aggregates plates? What about graupel? I think you need to use more precise terminology.
Also, in the figure you use “pploy, cploy and irploy”, instead of “ppoly, cpoly and irpoly”.
Hogan, R. J., L. Tian, P. R. A. Brown, C. D. Westbrook, A. J. Heymsfield, and J. D. Eastment, 2012: Radar Scattering from Ice Aggregates Using the Horizontally Aligned Oblate Spheroid Approximation. J. Appl. Meteor. Climatol., 51, 655–671, https://doi.org/10.1175/JAMC-D-11-074.1.
Li, H., Moisseev, D., & von Lerber, A. (2018). How does riming affect dual-polarization radar observations and snowflake shape? Journal of Geophysical Research: Atmospheres, 123, 6070–6081. https://doi.org/10.1029/2017JD028186
Matrosov, S. Y., C. G. Schmitt, M. Maahn, and G. de Boer, 2017: Atmospheric Ice Particle Shape Estimates from Polarimetric Radar Measurements and In Situ Observations. J. Atmos. Oceanic Technol., 34, 2569–2587, https://doi.org/10.1175/JTECH-D-17-0111.1.
Line 364. While it is true that in Figure 9 we can see the difference between AR values for different experiments, I find it difficult to make a connection between this figure and Fig. 6. In Fig. 6 you use rime, aggr. and crystal. But later you introduce a very different classification: namely plate, dendrite, column, and various types of polycrystals in Fig. 9 (a) and in Fig 9 (b) this classification is reduced to plate and ir. poly. It would be better if you would be consistent. In Fig. 10 you go back to riming aggregation and deposition growth, so to which particle in Fig. 9 these processes correspond to?
Figure 12. Looking at EXP1 reflectivity profile in this figure, I find it even more surprising that in Fig. 7 the maximum particle size for EXP1 was just 2 mm. I would have expected to see larger aggregates.

Citation: https://doi.org/10.5194/egusphere-2023-1887-RC1
- AC2: 'Reply on RC1', Junghwa Lee, 13 Feb 2024
  
  Dear Anonymous Reviewer,
  We are sincerely grateful for your insightful comments and suggestions regarding our manuscript.
  
  Attached, please find our detailed responses to Reviews #1 and #2, as well as the diff version of the manuscript reflecting the revisions made in light of your feedback.
  Best regards,
  
  Junghwa Lee
  
  Citation: https://doi.org/10.5194/egusphere-2023-1887-AC2
RC2:
'Comment on egusphere-2023-1887', Anonymous Referee #2, 04 Jan 2024
This study presents modeling the impact of CCN and INP on the mixed-phased clouds, and the application of the model to the radar simulator. Since the CCN and INP are important contributors to cloud formation, the study will help to improve our understanding of formation of mix-phased clouds and the controlling factors. The manuscript is well-written and should be publishable after the following issues are resolved:
The title doesn’t seem to reflect the content of this study which mainly focuses on modeling the impact of CCN and INP concentrations on the mixed-phase clouds. It is suggested to change the title for more precision on the subject.

It would be beneficial to the broader readers if the authors can provide some explanation on why the initial profiles of the potential temperature and specific humidity look like the ones shown in Figure 3.

The effective diameter for EXP2 shown in Figure 4 is not 193 (page 10), judged from the figure.

For EXP1, high INPs lead to not only a spread of smaller particles but also large particles (over 1 mm) as shown in Figure 7. Can the authors give a likely reason for this spread of larger particles?

When accounting for the impact of INPs on the AR, the authors point out that the fluctuations of AR were due to formation of irregular polycrystals after 200 minutes. Two things need to be clarified: 1) why does this occur after 200 minutes? 2) what would happen if the simulation time was extended longer, for example, AR will be more stable?

As shown in Figure 12, the trend of the impact of INPs on the reflectivity is surprisingly reversed from decrease to increase. The explanation for this transition seems not satisfactory. I wonder if more refining experiments were performed, for example, smaller steps of INPs were set, this transition might be observable.
Citation: https://doi.org/10.5194/egusphere-2023-1887-RC2
- AC1: 'Reply on RC2', Junghwa Lee, 13 Feb 2024
  
  Dear Anonymous Reviewer,
  We are sincerely grateful for your insightful comments and suggestions regarding our manuscript.
  
  Attached, please find our detailed responses to Reviews #1 and #2, as well as the diff version of the manuscript reflecting the revisions made in light of your feedback.
  Best regards,
  
  Junghwa Lee
  
  Citation: https://doi.org/10.5194/egusphere-2023-1887-AC1

Junghwa Lee, Patric Seifert, Tempei Hashino, Maximilian Maahn, Fabian Senf, and Oswald Knoth

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Short summary

Spectral-bin model simulations of an idealized supercooled stratiform cloud were performed with the AMPS model for variable CCN and INP concentrations. We performed radar forward simulations with PAMTRA to transfer the simulations into radar observational space. The derived radar reflectivity factors were compared to observational studies of stratiform mixed-phase clouds. These studies report a similar response of the radar reflectivity factor to aerosol perturbations as we found in our study.


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