the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Jun 2025
Aerosol effective radius governs the relationship between cloud condensation nuclei (CCN) concentration and aerosol backscatter
Abstract. Understanding the vertical distribution of cloud condensation nuclei (CCN) concentrations is crucial for reducing uncertainty associated with aerosol-cloud interactions (ACI) and their effective radiative forcing (ERFaci). Many studies take advantage of widely available remote sensing observations to develop proxies, parameterizations, and relationships between CCN concentration and aerosol optical properties (AOPs). Such methods generally provide a good constraint for CCN concentration, but many uncertainties and limitations exist, generally related to high relative humidity (RH), environments with internal or external mixtures of several different aerosol types, and differences in parts of the aerosol size distribution relevant for both CCN and AOPs. In this study we use in situ observations of the aerosol size distribution and chemical composition in a recent airborne field campaign to inform theoretical calculations of CCN concentration (CCNtheory) and aerosol backscatter at 532 nm (BSCtheory) with the purpose of understanding the dominant governing factors of the CCNtheory – BSCtheory relationship. Estimates from random forest models indicate that for smoke, marine, and urban aerosols the aerosol size distribution, as parameterized by effective radius (Reff), is the most important predictor of the CCNtheory – BSCtheory relationship. We further investigate how Reff impacts CCNtheory:BSCtheory and find an exponential relationship between the parameters. We find that modelling CCNtheory:BSCtheory using this exponential Reff relationship can explain about 68–79 % of the variance in the CCNtheory – BSCtheory relationship. These findings suggest that including information about aerosol size is critical for future studies in constraining CCN concentration from AOPs.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics.
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: closed
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RC1: 'Comment on egusphere-2025-2422', Anonymous Referee #1, 24 Jun 2025
Please find the comments in the attached file.
- AC1: 'Reply on RC1', Emily Lenhardt, 11 Aug 2025
-
RC2: 'Comment on egusphere-2025-2422', Anonymous Referee #2, 14 Jul 2025
The authors present a systematic observation- and modeling-based investigation of the relationship between the optical properties measured with lidar (particle backscatter coefficient, BSC) and the concentration of cloud condensation nuclei (CCN), i.e., of the number concentration of particles that can be activated to form cloud droplets. The paper is well written and a very good contribution to the lidar literature. The authors combined lidar observations of backscatter profiles with airborne in situ measurements of CCN. As a strong part of the manuscript, they included complex modeling of CCN and lidar backscatter coefficients. In this way, they clearly showed the dependence of the CCN-to-BSC conversion factor on the effective radius of the CCN.
A minor weak point is that they do not discuss and compare their effort with other methods. The use of conversion factors for different aerosol types (characterized by different effective radii) is indirectly the same approach than the one offered in the manuscript. Furthermore, the new approach is applicable to mixtures of hygroscopic particles (urban haze, marine particles, wild fires smoke), only! It is not applicable in the case of mixtures that include hydrophobic particles such as mineral dust. Then the different aerosol fractions have to be separated before a conversion into the dust and non-dust CCN fractions can be done. This point needs to be better addressed in the manuscript.
The manuscript is a methodology paper, and thus appropriate to AMT and should, to my opinion, be published in AMT, and not in ACP.
Minor revisions are needed.
Detailed comments:
Section 1 provides a good overview of CCN retrievals from optical measurements. One could even avoid a too broad discussion by focusing on profiling techniques.
Section 2: Impressive field campaign! ‘Unfortunately’, the observed aerosol mixtures do not represent the full spectrum of relevant aerosol mixtures. The airborne HSRL-2, part of ACTIVATE, conducted many campaigns in the Caribbean and over the United States and detected mixtures of dust and non-dust components. One can find these relevant mixtures almost everywhere in the northern hemisphere, over all continents and adjacent oceans. However, this mixture is not covered in this study. This point needs to be better considered in the concluding discussion later on.
The wavelength of 532 nm is only mentioned in Section 2. It would be good to mention the wavelengths occasionally in the next sections (maybe also in some of the figure captions).
P5, line 127: Please define R2!
P5, line 132: R is not introduced! You write: R2 values ranged from 0.0014 – 0.14 for all RH cases, and 0.0023-0.038 for RH<50%. I am confused! Such numbers indicate no correlation at all! What did I miss?
P 7, line 192: HSRL-2 Aerosol ID product! Is that defined somewhere? Please explain in a bit more detail!
P8, line 195: You define eight, not just well-defined aerosol types! Afterwards you combine marine and polluted marine, aged smoke and fresh smoke, but you still have ice (is that an aerosol type?), dusty mix, and dust, and urban/pollution. What is the dusty mix? Later on, you do not consider DUST at all! Otherwise you would be in trouble with the ‘simple’ link between CCN/BSC and effective radius.
Section 3.3:
P 12, line 292: Any comment on volatile aerosol components? They are lost after drying and measuring/counting dry particles in situ. Humidification will not bring them and their impact back. They are not considered in… bin diameters and refractive index components…. Humidified bin diamters and refractive index components are the final input into the Mie scattering calculation runs in libRadtran.
Section 4.1:
Figure 5: The background values in the figure should be explained in the caption.
Figure 5 shows the correlations for smoke, marine, and urban particles. For these aerosol types, your approach will work. As already mentioned above, in the case of mineral dust occurrence (hydrophobic particles with critical diameters around 200 nm) your approach would not work properly. You would have to use polarization lidar measurements to identify and separate the dust and non-dust contributions before estimating CCNC for the two particle fractions. The modelling part would need to consider the particle shape, which is still a big problem when it comes to BSC modeling (scattering phase function at 179.99 ° to 180°).
This aspect has to be discussed in the paper, may be at the end…
Equation 6: The effective radius of dry particles is given, should probably be mentioned again.
Section 4.3
Figure 8. Without the black exponential curve fit, one would hardly see any correlation. The uncertainty is high.
Figure 9 corroborates the applicability of the CCN-BSC approach when considering aerosol type information, i.e., when considering the effective radius. All this works for a ‘pure’ non-dust aerosol mixtures.
Section 5.2
P 21, line 520: A simple linear approximation with BSCobs will not well constrain CCN obs in most cases. This ‘general’ statement holds for ACTIVATE aerosol mixtures. Other approaches try to find solutions to separate or isolate different aerosol types and then apply conversions. This was for example shown for Barbados dust/pollution mixtures (Haarig et al., 2019, references are given below).
Section 5.3
P 23, lines 565-577: One could add the original paper pointing to non spherical marine particles (Haarig et al., 2017). And regarding dust, the papers of Haarig et al. (2022) and of Saito and Ping paper (2021) provide an impression on the latest modeling approaches with focus on lidar products….
Section 6
P 24, line 601: ..that Reff well captures the strong dependence of the CCN-BSC relationship on the aerosol size distribution….. YES, this is true for the ACTIVATE mixtures. Now, we need to broaden the spectrum towards dust/non-dust aerosol mixtures.
Saito, M., and Yang, P. (2021). Advanced bulk optical models linking the backscattering and microphysical properties of mineral dust aerosol. Geophysical Research Letters, 48, e2021GL095121. https://doi.org/10.1029/2021GL095121
Haarig et al., Dry versus wet marine particle optical properties: RH dependence of depolarization ratio, backscatter, and extinction from multiwavelength lidar measurements during SALTRACE,
Atmos. Chem. Phys., 17, 14199–14217, https://doi.org/10.5194/acp-17-14199-2017, 2017
Haarig et al., Profiles of cloud condensation nuclei, dust mass concentration, and ice-nucleating-particle-relevant aerosol properties in the Saharan Air Layer over Barbados from polarization lidar and airborne in situ measurements, Atmos. Chem. Phys., 19, 13773–13788, https://doi.org/10.5194/acp-19-13773-2019, 2019
Haarig et al., First triple-wavelength lidar observations of depolarization and extinction-to-backscatter ratios of Saharan dust, Atmos. Chem. Phys., 22, 355–369, https://doi.org/10.5194/acp-22-355-2022, 2022
Citation: https://doi.org/10.5194/egusphere-2025-2422-RC2 - AC2: 'Reply on RC2', Emily Lenhardt, 11 Aug 2025
Status: closed
-
RC1: 'Comment on egusphere-2025-2422', Anonymous Referee #1, 24 Jun 2025
Please find the comments in the attached file.
- AC1: 'Reply on RC1', Emily Lenhardt, 11 Aug 2025
-
RC2: 'Comment on egusphere-2025-2422', Anonymous Referee #2, 14 Jul 2025
The authors present a systematic observation- and modeling-based investigation of the relationship between the optical properties measured with lidar (particle backscatter coefficient, BSC) and the concentration of cloud condensation nuclei (CCN), i.e., of the number concentration of particles that can be activated to form cloud droplets. The paper is well written and a very good contribution to the lidar literature. The authors combined lidar observations of backscatter profiles with airborne in situ measurements of CCN. As a strong part of the manuscript, they included complex modeling of CCN and lidar backscatter coefficients. In this way, they clearly showed the dependence of the CCN-to-BSC conversion factor on the effective radius of the CCN.
A minor weak point is that they do not discuss and compare their effort with other methods. The use of conversion factors for different aerosol types (characterized by different effective radii) is indirectly the same approach than the one offered in the manuscript. Furthermore, the new approach is applicable to mixtures of hygroscopic particles (urban haze, marine particles, wild fires smoke), only! It is not applicable in the case of mixtures that include hydrophobic particles such as mineral dust. Then the different aerosol fractions have to be separated before a conversion into the dust and non-dust CCN fractions can be done. This point needs to be better addressed in the manuscript.
The manuscript is a methodology paper, and thus appropriate to AMT and should, to my opinion, be published in AMT, and not in ACP.
Minor revisions are needed.
Detailed comments:
Section 1 provides a good overview of CCN retrievals from optical measurements. One could even avoid a too broad discussion by focusing on profiling techniques.
Section 2: Impressive field campaign! ‘Unfortunately’, the observed aerosol mixtures do not represent the full spectrum of relevant aerosol mixtures. The airborne HSRL-2, part of ACTIVATE, conducted many campaigns in the Caribbean and over the United States and detected mixtures of dust and non-dust components. One can find these relevant mixtures almost everywhere in the northern hemisphere, over all continents and adjacent oceans. However, this mixture is not covered in this study. This point needs to be better considered in the concluding discussion later on.
The wavelength of 532 nm is only mentioned in Section 2. It would be good to mention the wavelengths occasionally in the next sections (maybe also in some of the figure captions).
P5, line 127: Please define R2!
P5, line 132: R is not introduced! You write: R2 values ranged from 0.0014 – 0.14 for all RH cases, and 0.0023-0.038 for RH<50%. I am confused! Such numbers indicate no correlation at all! What did I miss?
P 7, line 192: HSRL-2 Aerosol ID product! Is that defined somewhere? Please explain in a bit more detail!
P8, line 195: You define eight, not just well-defined aerosol types! Afterwards you combine marine and polluted marine, aged smoke and fresh smoke, but you still have ice (is that an aerosol type?), dusty mix, and dust, and urban/pollution. What is the dusty mix? Later on, you do not consider DUST at all! Otherwise you would be in trouble with the ‘simple’ link between CCN/BSC and effective radius.
Section 3.3:
P 12, line 292: Any comment on volatile aerosol components? They are lost after drying and measuring/counting dry particles in situ. Humidification will not bring them and their impact back. They are not considered in… bin diameters and refractive index components…. Humidified bin diamters and refractive index components are the final input into the Mie scattering calculation runs in libRadtran.
Section 4.1:
Figure 5: The background values in the figure should be explained in the caption.
Figure 5 shows the correlations for smoke, marine, and urban particles. For these aerosol types, your approach will work. As already mentioned above, in the case of mineral dust occurrence (hydrophobic particles with critical diameters around 200 nm) your approach would not work properly. You would have to use polarization lidar measurements to identify and separate the dust and non-dust contributions before estimating CCNC for the two particle fractions. The modelling part would need to consider the particle shape, which is still a big problem when it comes to BSC modeling (scattering phase function at 179.99 ° to 180°).
This aspect has to be discussed in the paper, may be at the end…
Equation 6: The effective radius of dry particles is given, should probably be mentioned again.
Section 4.3
Figure 8. Without the black exponential curve fit, one would hardly see any correlation. The uncertainty is high.
Figure 9 corroborates the applicability of the CCN-BSC approach when considering aerosol type information, i.e., when considering the effective radius. All this works for a ‘pure’ non-dust aerosol mixtures.
Section 5.2
P 21, line 520: A simple linear approximation with BSCobs will not well constrain CCN obs in most cases. This ‘general’ statement holds for ACTIVATE aerosol mixtures. Other approaches try to find solutions to separate or isolate different aerosol types and then apply conversions. This was for example shown for Barbados dust/pollution mixtures (Haarig et al., 2019, references are given below).
Section 5.3
P 23, lines 565-577: One could add the original paper pointing to non spherical marine particles (Haarig et al., 2017). And regarding dust, the papers of Haarig et al. (2022) and of Saito and Ping paper (2021) provide an impression on the latest modeling approaches with focus on lidar products….
Section 6
P 24, line 601: ..that Reff well captures the strong dependence of the CCN-BSC relationship on the aerosol size distribution….. YES, this is true for the ACTIVATE mixtures. Now, we need to broaden the spectrum towards dust/non-dust aerosol mixtures.
Saito, M., and Yang, P. (2021). Advanced bulk optical models linking the backscattering and microphysical properties of mineral dust aerosol. Geophysical Research Letters, 48, e2021GL095121. https://doi.org/10.1029/2021GL095121
Haarig et al., Dry versus wet marine particle optical properties: RH dependence of depolarization ratio, backscatter, and extinction from multiwavelength lidar measurements during SALTRACE,
Atmos. Chem. Phys., 17, 14199–14217, https://doi.org/10.5194/acp-17-14199-2017, 2017
Haarig et al., Profiles of cloud condensation nuclei, dust mass concentration, and ice-nucleating-particle-relevant aerosol properties in the Saharan Air Layer over Barbados from polarization lidar and airborne in situ measurements, Atmos. Chem. Phys., 19, 13773–13788, https://doi.org/10.5194/acp-19-13773-2019, 2019
Haarig et al., First triple-wavelength lidar observations of depolarization and extinction-to-backscatter ratios of Saharan dust, Atmos. Chem. Phys., 22, 355–369, https://doi.org/10.5194/acp-22-355-2022, 2022
Citation: https://doi.org/10.5194/egusphere-2025-2422-RC2 - AC2: 'Reply on RC2', Emily Lenhardt, 11 Aug 2025
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