Vertical profiles of cloud condensation nuclei number concentration and its empirical estimate from aerosol optical properties over the North China Plain

Zhang, Rui; Wang, Yuying; Li, Zhanqing; Wang, Zhibin; Dickerson, Russell R.; Ren, Xinrong; He, Hao; Wang, Fei; Gao, Ying; Chen, Xi; Xu, Jialu; Cheng, Yafang; Su, Hang

doi:https://doi.org/10.5194/egusphere-2022-375

Preprints

https://doi.org/10.5194/egusphere-2022-375

Preprints

30 May 2022

| 30 May 2022

Vertical profiles of cloud condensation nuclei number concentration and its empirical estimate from aerosol optical properties over the North China Plain

Rui Zhang, Yuying Wang, Zhanqing Li, Zhibin Wang, Russell R. Dickerson, Xinrong Ren, Hao He, Fei Wang, Ying Gao, Xi Chen, Jialu Xu, Yafang Cheng, and Hang Su

Abstract. To better understand the characteristics of aerosol activation ability and optical properties, a comprehensive airborne campaign was implemented over the North China Plain (NCP) from May 8 to June 11, 2016. Vertical profiles of cloud condensation nuclei (CCN) number concentration (N_CCN) and aerosol optical properties were measured simultaneously. Seventy-two-hour air mass back trajectories show that during the campaign the measurement region is mainly influenced by air masses in northwest and southeast. Air mass sources, temperature structure, anthropogenic emissions, and terrain distribution are factors influencing N_CCN profiles. CCN spectra suggest that the ability of aerosol activation into CCN is stronger in southeast air masses than in northwest air masses and stronger in the free atmosphere than near the surface. Vertical distributions of aerosol scattering Ångström exponent (SAE) indicate that aerosols near the surface mainly originate from primary emissions consisting of more fine particles. The combined effect of aerosol lifting aloft and long-distance transport increase SAE and make it vary more in the free troposphere than near the surface. For parameterizing N_CCN, the equation N_CCN = 10^β ∙ σ^γ is used to fit the relationship between N_CCN and the aerosol scattering coefficient (σ) at 450 nm. The fitting parameters β and γ have linear relationships with the SAE. Empirical estimates of N_CCN at 0.7% water vapor supersaturation (ss) from aerosol optical properties are thus retrieved for the two air masses: N_CCN = 10^{-0.22∙SAE+2.39} ∙ σ^{0.30∙SAE+0.29} for northwest air masses and N_CCN =10^{-0.07∙SAE+2.29}∙ σ^{0.14∙SAE+0.28}for southeast air masses. The estimated N_CCN at 0.7 % ss agrees with that measured, although the performance differs between low and high concentrations in the two air masses. The results highlight the important impact of aerosol sources on the empirical estimate of N_CCN from aerosol optical properties.

Received: 22 May 2022 – Discussion started: 30 May 2022

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Journal article(s) based on this preprint

23 Nov 2022

Vertical profiles of cloud condensation nuclei number concentration and its empirical estimate from aerosol optical properties over the North China Plain

Rui Zhang, Yuying Wang, Zhanqing Li, Zhibin Wang, Russell R. Dickerson, Xinrong Ren, Hao He, Fei Wang, Ying Gao, Xi Chen, Jialu Xu, Yafang Cheng, and Hang Su

Atmos. Chem. Phys., 22, 14879–14891, https://doi.org/10.5194/acp-22-14879-2022,https://doi.org/10.5194/acp-22-14879-2022, 2022

Short summary

Rui Zhang, Yuying Wang, Zhanqing Li, Zhibin Wang, Russell R. Dickerson, Xinrong Ren, Hao He, Fei Wang, Ying Gao, Xi Chen, Jialu Xu, Yafang Cheng, and Hang Su

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2022-375', Anonymous Referee #1, 13 Jun 2022

This manuscript presents the airborne cloud condensation nuclei (CCN) measurements taken during the ARIAs (Air chemistry Research In Asia) campaign. The authors use HYSPLIT trajectories to identify the source regions of different air masses measured during the campaign, and present the results separately for air masses coming from two main directions (northwest and southeast). They show the impact of atmospheric stability on the vertical distribution of CCN. Furthermore, they parametrize the number concentration of CCN (_ccn) in terms of aerosol optical properties. The manuscript presents a novel height resolved in-situ _ccndata and has good potential for publication in ACP only after implementing and addressing the following comments.

Lines 90-92, ‘Tao et al. (2018) proposed … system’. I don’t understand how this is related to the idea of this paragraph. Did they give any empirical relationship between _ccnand optical properties? If yes, then it should be stated.

Lines 92-93, ‘Most of these… in situ _ccnprofiles’. In atmospheric remote sensing, the word “profile” usually refers to a vertical representation. The parametrization schemes are mostly focused on estimating _ccnat ground. So there’s no way one can compare/validate them with _ccn“profiles”. I suggest replacing the word. Overall, I found the fourth paragraph of introduction to be confusing and suggest to modify it. It starts with the in situ _ccn “profile” measurements and the challenges involved in it. Thereafter how researchers have come up with empirical relations to estimate _ccn at “ground” using column integrated aerosol optical properties (AOD, AI, SAE). The ending sentence again discuss the how there’s no validation with in situ _ccn “profile”.

Lines 100-104. The manuscript presents vertical distribution of _ccnfor different regions within the NCP. Currently, we have satellite-based _ccnretrieval algorithms, for instance, Mamouri and Ansmann (2016) and Choudhury and Tesche (2022), to estimate profiles of _ccnfrom CALIPSO measurements. The in-situ measurements presented here will also be beneficial in validating such algorithms. This information is missing in the motivation.

Lines 242-243: The _ccnvalues first increases till the base of the first temperature inversion layer (TIL). It is quite strange as the _ccn in the previous case with one TIL were more or less uniform below the layer, perhaps due to vertical mixing, which is not seen for this case with two inversion layers. Is there a possible reason behind this pattern?

Table 1. As the flights measurements are taken in a spiral path, please mention the maximum horizontal distance covered by individual flight segments chosen in this study. This is important as you consider them as individual profiles later in the paper.

Some important technical information are missing. Did you smooth the flight measurements before the analysis? The pre-processing done to the measurements should be discussed in Section 2. Please also provide the uncertainty or retrieval errors associated with the in-situ measurements.

Lines 346-351: The definition and expression of scattering Ångström exponent should not be included in the “Results and Discussion” section. Please place it either in Section 2 or create a separate section.

Line 371: The section title is misleading. It is not the estimation of _CCN. It is where you parametrize _CCN in terms of aerosol optical properties. Please modify it.

Lines 379-383: Please refer Shinozuka et al. (2015) and correct the statements. Shinozuka et al. (2015) parameterize _CCNin terms of “extinction coefficient (at 500 nm)” and “Angstrom exponent” (calculated from extinction coefficients at 450 and 550 nm) for dry particles. They did not use scattering coefficient or scattering Angstrom exponent for the same. Also for equation 3, it should be stated that in Shinozuka et al. (2015), only the parameter β depends on the Angstrom exponent (computed from extinction coefficients).

Lines 387-388: Coefficient of determination or R² and correlation are synonymously used. R²quantifies the goodness of fit (here linear fit) or performance of the model (here linear model) in simulating the variable of concern (here fitting parameters β and γ). I suggest using either correlation coefficient or slope of the linear fit. Also, is Figure 7 really important to include in the manuscript? I would suggest omitting the figure. If the authors want to retain it, they should justify the significance of the observed relations between SAE and the fitting parameters.

Figure 7 (if retained) and Figure 8 should include the total number of points used in the comparison. I also suggest including two more lines in the figure representing one order of magnitude more and less than the 1:1 line in Figure 8 for better visualization.

Lines 399-404: Qualitative interpretation from a log-log plot can be misleading. What seems to be different by a few millimeters in the plot can be different by orders of magnitude in reality. I suggest using parameters like normalized mean error or bias and root mean square error (normalized by mean) in percentage to get a better quantitative comparison. Such parameters should then be used to quantify the error associated with the proposed parametrization.

Is there any specific reason why the authors use aerosol scattering coefficient instead of extinction coefficient (scattering + absorption). The authors identify anthropogenic emissions as one of the aerosol types in their analysis, which may also include absorbing aerosols. Using scattering coefficient in such scenarios may result in mis-representation of absorbing aerosols in the parametrization, which is perhaps one of the reasons behind the errors in the predicted _ccn.

For identifying the aerosol types in the analyzed samples, HYSPLIT back trajectory analysis is used to track the source regions and the regions through which the air parcels have passes before reaching the target. However, this is based on the assumption that the lifetime of aerosols is long enough to retain its source identity. One of the ways to cross-check the aerosol types is to use CALIPSO aerosol product (CALIPSO, 2018) for the identified cases. If there is no CALIPSO overpass over the region of interest at the desired time, one can also use re-analysis datasets like CAMS (Inness et al., 2019) and/or MERRA-2 (Molod et al., 2015) to identify the aerosol types that are dominant at different height levels. I suggest using either one of these datasets to check if the assumed aerosol signatures are correct.

Please modify Figure 1 caption to include the meaning of RF1, RF2… RF11.

Line 128. Please include the word in bracket. … 182 m above [mean] sea level …

Line 295. Remove the word in the bracket. “…profiles [ss] are influenced…”

Lines 301-302. Rephrase the sentence to “Twomey (1959) first reported an exponential relationship between _ccnand ss.”

In Figure 5, please mention that the y-axis is in log-scale. Please mark at least two (or three, if possible) tick labels in the y-axis of each plot.

Line 345. The acronym “SAE” is previously defined in the paper. There is no need to define it again here.

Lines 349-351. Replace the word “dominated” by “are dominant”.

CALIPSO: Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation Lidar Level 2 Aerosol Profile, V4-20, NASA Langley Atmospheric Science Data Center DAAC [data set], https://doi.org/10.5067/CALIOP/CALIPSO/LID_L2_05KMAPRO-STANDARD-V4-20, 2018.

Choudhury, G. and Tesche, M.: Estimating cloud condensation nuclei concentrations from CALIPSO lidar measurements, Atmos. Meas. Tech., 15, 639–654, https://doi.org/10.5194/amt-15-639-2022, 2022.

Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A.-M., Dominguez, J. J., Engelen, R., Eskes, H., Flemming, J., Huijnen, V., Jones, L., Kipling, Z., Massart, S., Parrington, M., Peuch, V.-H., Razinger, M., Remy, S., Schulz, M., and Suttie, M.: The CAMS reanalysis of atmospheric composition, Atmos. Chem. Phys., 19, 3515–3556, https://doi.org/10.5194/acp-19-3515-2019, 2019.

Mamouri, R.-E. and Ansmann, A.: Potential of polarization lidar to provide profiles of CCN- and INP-relevant aerosol parameters, Atmos. Chem. Phys., 16, 5905–5931, https://doi.org/10.5194/acp-16-5905-2016, 2016.

Molod, A., Takacs, L., Suarez, M., and Bacmeister, J.: Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2, Geosci. Model Dev., 8, 1339–1356, https://doi.org/10.5194/gmd-8-1339-2015, 2015.

Citation: https://doi.org/10.5194/egusphere-2022-375-RC1
- AC1: 'Reply on RC1', Yuying Wang, 24 Sep 2022
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-375/egusphere-2022-375-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2022-375-AC1
RC2:
'Comment on egusphere-2022-375', Anonymous Referee #2, 13 Aug 2022

The manuscript on “Vertical profiles of cloud condensation nuclei number concentration and its empirical estimate from aerosol optical properties over the North China Plain” by R. Zhang and co-authors made airborne measurements of vertical profiles of CCN concentrations and scattering coefficients over the southern plain of Hebei province. Using this data, they have investigated the influence of thermal structure (TIL) and airmass origin on vertical profiles of CCN. The CCN concentration is estimated using the scattering coefficient and its spectral variation.

Considering the limitations and uncertainties associated with the retrieval of vertical profiles of aerosols and CCN using different techniques, the direct measurements of these parameters onboard the aircraft are very important. But I feel disappointed with the way the authors described their experimental details. Details of the sampling inlet are not provided. (i) What is the effect of aircraft propeller on aerosol sampling? (ii) Whether sampling flow was iso-kinetic? (iii) What was the sampling efficiency of the inlet used? (iv) What was the cruising speed of the Y-12 Turboprop? (v) How do authors account for ram heating? (vi) How do authors account for the flow instabilities during ascending and descending phases of spiral flights? (vii) How much is the total sampling time available for each vertical level? (viii) Whether CCN measurements at all the supersaturations were carried out at each altitude? If not, how do authors decouple the change in CCN due to supersaturation change and also due to vertical variation?

The authors mentioned that CCN profiles have a strong dependence on the number and thickness of TIL. This is mostly due to the TIL influence on the vertical transport of aerosols. On the other hand, the influence of airmass trajectory indicates long-range transport. In other words, when long-range transport dominates at higher altitudes, the influence of vertical transport of aerosols from the lower atmosphere is irrelevant. If long-range transport is the prominent mechanism, then how could authors associate TIL with CCN concentration?

How do authors link CCN spectra with activation efficiency? In lines 313-314, the authors mentioned that “A lower value means a stronger aerosol activation ability (i.e., more coarse-mode particles or stronger aerosol hygroscopicity), and vice versa.” This is not always true when hygroscopicity changes with the size of the particles.

How much time CCN counter required for attaining set supersaturation, especially when supersaturation changes from 1.28% to 0.44%? What is the sanctity of 0.7% supersaturation? Why lower supersaturations (<0.4%) are excluded from the sampling? What is the broad range of atmospheric supersaturation observed over the study region?

What kind of drier was used to remove the humidity of the air sampled by the nephelometer? Whether this could maintain a constant RH throughout the campaign?

Line 173: Replace “this” with “integrating”

There are data gaps in Figure 5. For example (i) panel a RF2_c: no CCN data is shown for ss<0.8%. Similar is the case with panel b RF6_b and panel C RF7_c. Explain?

What is the reason for high CCN activation at higher altitudes than lower levels? Normally, fine mode aerosols are transported to higher altitudes and these particles have lower CCN efficiency than coarse mode aerosols.

How does long-range transport increase SAE? Generally, ageing and chemical processing during the long-range transport increases the size of the particles and reduces SAE. Moreover, ultrafine secondary particles have less residence time and they may not get transported to longer distances to increase SAE.

Better association between CCN at high SS and scattering coefficients are expected because both CCN and scattering coefficients depend on the entire size distribution of the aerosol system. On the other hand, predicting CCN concentration for lower ss is challenging, since a small portion of the aerosol NSD (coarse mode) gets activated. Using the high-resolution data (1 sec), the authors should show the CCN vs scattering coefficients for low and high supersaturations.

Figure 7: Standard deviation of the β and γ should be included.

The β and γ showed better association with SAE during the southeast airmass period than the northwest airmass. But the CCN estimated using b and g did not show good association for south-east airmass. Please explain this discrepancy.

Citation: https://doi.org/10.5194/egusphere-2022-375-RC2
- AC2: 'Reply on RC2', Yuying Wang, 24 Sep 2022
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-375/egusphere-2022-375-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2022-375-AC2

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2022-375', Anonymous Referee #1, 13 Jun 2022

This manuscript presents the airborne cloud condensation nuclei (CCN) measurements taken during the ARIAs (Air chemistry Research In Asia) campaign. The authors use HYSPLIT trajectories to identify the source regions of different air masses measured during the campaign, and present the results separately for air masses coming from two main directions (northwest and southeast). They show the impact of atmospheric stability on the vertical distribution of CCN. Furthermore, they parametrize the number concentration of CCN (_ccn) in terms of aerosol optical properties. The manuscript presents a novel height resolved in-situ _ccndata and has good potential for publication in ACP only after implementing and addressing the following comments.

Lines 90-92, ‘Tao et al. (2018) proposed … system’. I don’t understand how this is related to the idea of this paragraph. Did they give any empirical relationship between _ccnand optical properties? If yes, then it should be stated.

Lines 92-93, ‘Most of these… in situ _ccnprofiles’. In atmospheric remote sensing, the word “profile” usually refers to a vertical representation. The parametrization schemes are mostly focused on estimating _ccnat ground. So there’s no way one can compare/validate them with _ccn“profiles”. I suggest replacing the word. Overall, I found the fourth paragraph of introduction to be confusing and suggest to modify it. It starts with the in situ _ccn “profile” measurements and the challenges involved in it. Thereafter how researchers have come up with empirical relations to estimate _ccn at “ground” using column integrated aerosol optical properties (AOD, AI, SAE). The ending sentence again discuss the how there’s no validation with in situ _ccn “profile”.

Lines 100-104. The manuscript presents vertical distribution of _ccnfor different regions within the NCP. Currently, we have satellite-based _ccnretrieval algorithms, for instance, Mamouri and Ansmann (2016) and Choudhury and Tesche (2022), to estimate profiles of _ccnfrom CALIPSO measurements. The in-situ measurements presented here will also be beneficial in validating such algorithms. This information is missing in the motivation.

Lines 242-243: The _ccnvalues first increases till the base of the first temperature inversion layer (TIL). It is quite strange as the _ccn in the previous case with one TIL were more or less uniform below the layer, perhaps due to vertical mixing, which is not seen for this case with two inversion layers. Is there a possible reason behind this pattern?

Table 1. As the flights measurements are taken in a spiral path, please mention the maximum horizontal distance covered by individual flight segments chosen in this study. This is important as you consider them as individual profiles later in the paper.

Some important technical information are missing. Did you smooth the flight measurements before the analysis? The pre-processing done to the measurements should be discussed in Section 2. Please also provide the uncertainty or retrieval errors associated with the in-situ measurements.

Lines 346-351: The definition and expression of scattering Ångström exponent should not be included in the “Results and Discussion” section. Please place it either in Section 2 or create a separate section.

Line 371: The section title is misleading. It is not the estimation of _CCN. It is where you parametrize _CCN in terms of aerosol optical properties. Please modify it.

Lines 379-383: Please refer Shinozuka et al. (2015) and correct the statements. Shinozuka et al. (2015) parameterize _CCNin terms of “extinction coefficient (at 500 nm)” and “Angstrom exponent” (calculated from extinction coefficients at 450 and 550 nm) for dry particles. They did not use scattering coefficient or scattering Angstrom exponent for the same. Also for equation 3, it should be stated that in Shinozuka et al. (2015), only the parameter β depends on the Angstrom exponent (computed from extinction coefficients).

Lines 387-388: Coefficient of determination or R² and correlation are synonymously used. R²quantifies the goodness of fit (here linear fit) or performance of the model (here linear model) in simulating the variable of concern (here fitting parameters β and γ). I suggest using either correlation coefficient or slope of the linear fit. Also, is Figure 7 really important to include in the manuscript? I would suggest omitting the figure. If the authors want to retain it, they should justify the significance of the observed relations between SAE and the fitting parameters.

Figure 7 (if retained) and Figure 8 should include the total number of points used in the comparison. I also suggest including two more lines in the figure representing one order of magnitude more and less than the 1:1 line in Figure 8 for better visualization.

Lines 399-404: Qualitative interpretation from a log-log plot can be misleading. What seems to be different by a few millimeters in the plot can be different by orders of magnitude in reality. I suggest using parameters like normalized mean error or bias and root mean square error (normalized by mean) in percentage to get a better quantitative comparison. Such parameters should then be used to quantify the error associated with the proposed parametrization.

Is there any specific reason why the authors use aerosol scattering coefficient instead of extinction coefficient (scattering + absorption). The authors identify anthropogenic emissions as one of the aerosol types in their analysis, which may also include absorbing aerosols. Using scattering coefficient in such scenarios may result in mis-representation of absorbing aerosols in the parametrization, which is perhaps one of the reasons behind the errors in the predicted _ccn.

For identifying the aerosol types in the analyzed samples, HYSPLIT back trajectory analysis is used to track the source regions and the regions through which the air parcels have passes before reaching the target. However, this is based on the assumption that the lifetime of aerosols is long enough to retain its source identity. One of the ways to cross-check the aerosol types is to use CALIPSO aerosol product (CALIPSO, 2018) for the identified cases. If there is no CALIPSO overpass over the region of interest at the desired time, one can also use re-analysis datasets like CAMS (Inness et al., 2019) and/or MERRA-2 (Molod et al., 2015) to identify the aerosol types that are dominant at different height levels. I suggest using either one of these datasets to check if the assumed aerosol signatures are correct.

Please modify Figure 1 caption to include the meaning of RF1, RF2… RF11.

Line 128. Please include the word in bracket. … 182 m above [mean] sea level …

Line 295. Remove the word in the bracket. “…profiles [ss] are influenced…”

Lines 301-302. Rephrase the sentence to “Twomey (1959) first reported an exponential relationship between _ccnand ss.”

In Figure 5, please mention that the y-axis is in log-scale. Please mark at least two (or three, if possible) tick labels in the y-axis of each plot.

Line 345. The acronym “SAE” is previously defined in the paper. There is no need to define it again here.

Lines 349-351. Replace the word “dominated” by “are dominant”.

CALIPSO: Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation Lidar Level 2 Aerosol Profile, V4-20, NASA Langley Atmospheric Science Data Center DAAC [data set], https://doi.org/10.5067/CALIOP/CALIPSO/LID_L2_05KMAPRO-STANDARD-V4-20, 2018.

Choudhury, G. and Tesche, M.: Estimating cloud condensation nuclei concentrations from CALIPSO lidar measurements, Atmos. Meas. Tech., 15, 639–654, https://doi.org/10.5194/amt-15-639-2022, 2022.

Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A.-M., Dominguez, J. J., Engelen, R., Eskes, H., Flemming, J., Huijnen, V., Jones, L., Kipling, Z., Massart, S., Parrington, M., Peuch, V.-H., Razinger, M., Remy, S., Schulz, M., and Suttie, M.: The CAMS reanalysis of atmospheric composition, Atmos. Chem. Phys., 19, 3515–3556, https://doi.org/10.5194/acp-19-3515-2019, 2019.

Mamouri, R.-E. and Ansmann, A.: Potential of polarization lidar to provide profiles of CCN- and INP-relevant aerosol parameters, Atmos. Chem. Phys., 16, 5905–5931, https://doi.org/10.5194/acp-16-5905-2016, 2016.

Molod, A., Takacs, L., Suarez, M., and Bacmeister, J.: Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2, Geosci. Model Dev., 8, 1339–1356, https://doi.org/10.5194/gmd-8-1339-2015, 2015.

Citation: https://doi.org/10.5194/egusphere-2022-375-RC1
- AC1: 'Reply on RC1', Yuying Wang, 24 Sep 2022
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-375/egusphere-2022-375-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2022-375-AC1
RC2:
'Comment on egusphere-2022-375', Anonymous Referee #2, 13 Aug 2022

The manuscript on “Vertical profiles of cloud condensation nuclei number concentration and its empirical estimate from aerosol optical properties over the North China Plain” by R. Zhang and co-authors made airborne measurements of vertical profiles of CCN concentrations and scattering coefficients over the southern plain of Hebei province. Using this data, they have investigated the influence of thermal structure (TIL) and airmass origin on vertical profiles of CCN. The CCN concentration is estimated using the scattering coefficient and its spectral variation.

Considering the limitations and uncertainties associated with the retrieval of vertical profiles of aerosols and CCN using different techniques, the direct measurements of these parameters onboard the aircraft are very important. But I feel disappointed with the way the authors described their experimental details. Details of the sampling inlet are not provided. (i) What is the effect of aircraft propeller on aerosol sampling? (ii) Whether sampling flow was iso-kinetic? (iii) What was the sampling efficiency of the inlet used? (iv) What was the cruising speed of the Y-12 Turboprop? (v) How do authors account for ram heating? (vi) How do authors account for the flow instabilities during ascending and descending phases of spiral flights? (vii) How much is the total sampling time available for each vertical level? (viii) Whether CCN measurements at all the supersaturations were carried out at each altitude? If not, how do authors decouple the change in CCN due to supersaturation change and also due to vertical variation?

The authors mentioned that CCN profiles have a strong dependence on the number and thickness of TIL. This is mostly due to the TIL influence on the vertical transport of aerosols. On the other hand, the influence of airmass trajectory indicates long-range transport. In other words, when long-range transport dominates at higher altitudes, the influence of vertical transport of aerosols from the lower atmosphere is irrelevant. If long-range transport is the prominent mechanism, then how could authors associate TIL with CCN concentration?

How do authors link CCN spectra with activation efficiency? In lines 313-314, the authors mentioned that “A lower value means a stronger aerosol activation ability (i.e., more coarse-mode particles or stronger aerosol hygroscopicity), and vice versa.” This is not always true when hygroscopicity changes with the size of the particles.

How much time CCN counter required for attaining set supersaturation, especially when supersaturation changes from 1.28% to 0.44%? What is the sanctity of 0.7% supersaturation? Why lower supersaturations (<0.4%) are excluded from the sampling? What is the broad range of atmospheric supersaturation observed over the study region?

What kind of drier was used to remove the humidity of the air sampled by the nephelometer? Whether this could maintain a constant RH throughout the campaign?

Line 173: Replace “this” with “integrating”

There are data gaps in Figure 5. For example (i) panel a RF2_c: no CCN data is shown for ss<0.8%. Similar is the case with panel b RF6_b and panel C RF7_c. Explain?

What is the reason for high CCN activation at higher altitudes than lower levels? Normally, fine mode aerosols are transported to higher altitudes and these particles have lower CCN efficiency than coarse mode aerosols.

How does long-range transport increase SAE? Generally, ageing and chemical processing during the long-range transport increases the size of the particles and reduces SAE. Moreover, ultrafine secondary particles have less residence time and they may not get transported to longer distances to increase SAE.

Better association between CCN at high SS and scattering coefficients are expected because both CCN and scattering coefficients depend on the entire size distribution of the aerosol system. On the other hand, predicting CCN concentration for lower ss is challenging, since a small portion of the aerosol NSD (coarse mode) gets activated. Using the high-resolution data (1 sec), the authors should show the CCN vs scattering coefficients for low and high supersaturations.

Figure 7: Standard deviation of the β and γ should be included.

The β and γ showed better association with SAE during the southeast airmass period than the northwest airmass. But the CCN estimated using b and g did not show good association for south-east airmass. Please explain this discrepancy.

Citation: https://doi.org/10.5194/egusphere-2022-375-RC2
- AC2: 'Reply on RC2', Yuying Wang, 24 Sep 2022
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-375/egusphere-2022-375-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2022-375-AC2

Peer review completion

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

AR by Yuying Wang on behalf of the Authors (24 Sep 2022) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (26 Sep 2022) by Matthias Tesche

RR by Anonymous Referee #2 (29 Oct 2022)

ED: Publish as is (02 Nov 2022) by Matthias Tesche

AR by Yuying Wang on behalf of the Authors (05 Nov 2022) Manuscript

Journal article(s) based on this preprint

23 Nov 2022

Vertical profiles of cloud condensation nuclei number concentration and its empirical estimate from aerosol optical properties over the North China Plain

Rui Zhang, Yuying Wang, Zhanqing Li, Zhibin Wang, Russell R. Dickerson, Xinrong Ren, Hao He, Fei Wang, Ying Gao, Xi Chen, Jialu Xu, Yafang Cheng, and Hang Su

Atmos. Chem. Phys., 22, 14879–14891, https://doi.org/10.5194/acp-22-14879-2022,https://doi.org/10.5194/acp-22-14879-2022, 2022

Short summary

Rui Zhang, Yuying Wang, Zhanqing Li, Zhibin Wang, Russell R. Dickerson, Xinrong Ren, Hao He, Fei Wang, Ying Gao, Xi Chen, Jialu Xu, Yafang Cheng, and Hang Su

Supplement

https://doi.org/10.5194/egusphere-2022-375-supplement

Rui Zhang, Yuying Wang, Zhanqing Li, Zhibin Wang, Russell R. Dickerson, Xinrong Ren, Hao He, Fei Wang, Ying Gao, Xi Chen, Jialu Xu, Yafang Cheng, and Hang Su

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Sep 2022	64	24	5	93
Oct 2022	31	12	2	45
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Cumulative views and downloads (calculated since 30 May 2022)

Month	HTML	PDF	XML	Total
May 2022	97	34	3	134
Jun 2022	125	43	3	171
Jul 2022	41	11	0	52
Aug 2022	58	15	3	76
Sep 2022	64	24	5	93
Oct 2022	31	12	2	45
Nov 2022	24	7	1	32
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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.

Preprint (2277 KB)
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Short summary

In this study, the factors to N_CCN profiles are determined in the NCP, including air mass sources, temperature structure, anthropogenic emissions, and terrain distribution. CCN spectra suggest that the ability of aerosol activation into CCN is stronger in southeast air masses than in northwest air masses and stronger in the free atmosphere than near the surface. A good method to parameterize N_CCN from aerosol optical data is found.


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