the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 06 Aug 2024
Contributions of the synoptic meteorology to the seasonal CCN cycle over the Southern Ocean
Abstract. Cloud Condensation Nuclei (CCN) play a fundamental role in determining the microphysical properties of low-level clouds, crucial for defining the energy budget over the Southern Ocean (SO), a region dominated by low-level clouds. Despite this importance, many aspects of the CCN budget over the SO remains poorly understood including the role of the synoptic meteorology. In this study, we classify the dominant synoptic meteorology over kennaook/Cape Grim Observatory (CGO) and examine its influence on the seasonal variation of the CCN concentration (NCCN).
Our analysis identifies six distinct synoptic regimes: three prevalent in the austral winter, when the subtropical ridge (STR) is strong and centred at lower latitudes, and three in the austral summer, when the STR shifts to higher latitudes. Distinct winter and summer ‘baseline’ regimes contribute to the seasonal cycle in NCCN over the SO with the winter baseline regime characterised by heavier precipitation, a deeper boundary layer and lower NCCN. An analysis of air mass back trajectories, specifically at the free troposphere level, supports this distinction, with wintertime baseline airmasses originating over higher latitudes. Across these two baseline regimes we observe a significant inverse relationship between precipitation and NCCN, underscoring the role of precipitation in reducing NCCN over the SO.
Using forward trajectories within this synoptic framework, we examine the transport of continental airmasses over the SO, finding that frontal air masses more frequently reach high latitudes during winter. We conclude that the location of the STR can moderate the advection of air masses between Antarctica and kennaook/Cape Grim.
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 preprint. The responsibility to include appropriate place names lies with the authors.-
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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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Clouds over the Southern Ocean are crucial to Earth's energy balance, but understanding the factors that control them is complex. Our research examines how weather patterns affect tiny particles called cloud condensation nuclei (CCN), which influence cloud properties. Using data from Kennaook / Cape Grim, we found that winter air from Antarctica brings cleaner conditions with lower CCN, while summer patterns from Australia transport more particles. Precipitation also helps reduce CCN in winter.
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-2397', Anonymous Referee #1, 14 Aug 2024
This paper generally classified 6 synoptic conditions using K-means and looked into each corresponding sonding and pressure field. Local current precipitation is confirmed to be anti-correlated to the population of CCN. The authors have also done a back-and-forward trajectory analysis to check the history and future of tropospheric transportation. This method is also compared to the previous criteria to check each corresponding influence on the clusters.
The most significant conclusion may be that the connections between the focused SO region and the polar area are strong during the winter when the STR is close to the equator, but weakened for the poleward shift of STR during the summer. Implicitly, this summertime poleward transition constrained the precipitation, consistent with the higher CCN population at CGO. Furthermore, the wintertime southern wind is consistent all through the FT so that in general the air mass originates from the south more, even close to the Antarctic. The most unique part of this paper is about using Radon as a reference factor to isolate the influences from the FT transportation and precipitation. Even though the eventual results of this section are inconclusive, the statistic itself is worthwhile to be published and discussed for adding novelties to this paper. However, there are some pivotal gaps in the logic line thus Major revision is suggested to help make the paper flow better.
- Recommend labeling Figure 3 to be Figure 1. The discussion of Figure 1 is primarily based on Figure 3, thus shifting order can help the readers a lot if specific schemes are shown first.
- Section 2 largely lacks a detailed description of precipitation observation. Lines 103-104 are too limited for the interpretation/evaluation of Figure 6. What are the observed precipitation frequency and intensity time resolution; what is precipitation frequency defined; what are the data quality controls and raw data processing processes (acknowledge the mention of the 90th outlier removal in a later section); how large the uncertainties are; what is the precipitation phase; why using the MEAN precipitation to relate to the MEDIAN of CCN; can Figure 6 has variations/ranges shown, e.g. in shading, together with the mean/median value of each month.. Briefly mentioning some of the info above would help validate Figure 6 given using rain gauge data itself can be an advantage compared to the reanalysis data.
- Section 3 serves as the most fundamental part of this paper, which can go further into depth. Recommend adding wind field and front/ridge location on Figure 3 to, 1) help the description of Figure 4 which refers to the consistent surface wind direction and speed at CGO, 2) help the reader see the relative locations of the surface front system and the CGO, which is important for convergence/divergence flow and can be used to explain the Radon concentration as well as the spread extent in history trajectories (related to the wind speed according to Line 181). Recommend the description of inversion from altitude, depth, and strength perspectives, and a more quantitative description can be added in for lines 150-165. For example, it can start from one cluster, mentioning the specific numbers of inversion layer location and depth, and maybe also the estimated inversion strength, boundary layer depth, potential cloud layers locations, where is the referred FT, and how large the Relative Humidity is through the FT. Numbers are vital for intra-comparison in Figure 4 between clusters. Also, in Line 161, “the relative humidity is greatest for W-front suggesting heavier precipitation rate”, the small gap between the two temperature profiles illustrates a more humid condition but does not necessarily suggest a heavier precipitation rate. The mention of open and closed MCC by cross-citing is wonderful and can be shifted here from Section 4.1 which is about precipitation.
- Section 4: reading until now, I recommend combining Figures 1 and 2 in one figure to show the consistency between each other, and adding one new figure showing the correlation plot between Nccn and Radon concentration and that between Nccn and Precipitation, this can be a key figure for the follow-up discussions in Section 4-6. Looks like the correlation has been done already given some of the P-values are provided, plots may better show how related they are.
- How is Radon distributed through the troposphere, do the air mass coming from the boundary layer and FT above the Australian continent have the same Radon concentration? If not, the altitude of air mass back history would be of importance here, before any discussions about staying over terrestrial in history for long is related to the higher Radon concentration.
- The largest concern though comes from the interpretation of Figure 7 and the descriptions in Section 4.2. This needs further clarification. The log10 refers to the log10 scale of the “W-base trajectories subtracted from the S-base”? Does this mean the subtraction results have to be positive to be able to be “logged”? How would the back trajectory starting from 2500m above the GCO be directly useful for surface CCN from the understanding of FT entrainment? I assume the logic is that, if the FT air masses originate more from above the Australian continent, then the FT may contain more aerosols and thus can be a strong source for the surface CCN budget. However, are there any conditions that have to be met so that the 2500m air subsides into the boundary layer? What are the roles of the two below scenarios respectively, 1) an air mass originates from above the Antarctic at, for example, 2500m, and gets transferred into the boundary layer of GCO, and 2) as Figure 7 shows, some air masses from above the Antarctic during the winter travel and arrive at the 2500m right above the GCO. In particular, the 2500m starting level shows more FT continental aerosol information but how can this be used for the surface/below clouds CCN budget discussion without details of discussion of “processes right above the GCO between the surface and 2500m” such as mixing/exchanging and cloud processing? For Line 249, in Kang et al. (2022), FT CCN was quantified using the UHSAS measurement in the FT, and for surface sources, only the wind-oriented primary CCN is quantified in the budget. However, the logic in this paragraph originates from (line 242) whether the less pristine are caused by/related to the biological production in the summer. Could Kang’s (2022) paper be used here to support/debate the conclusion? Instead, Kang’s conclusion about FT entrainment influences the NCCN more can be heavily related to the surface biological production of, e.g., DMS.
- In general, Figure 5 is about the advection history. While Figure 7 is also an advection history (FT though), it is analyzed as a local source for the surface CCN budget at GCO. Then the gap question would be, how efficient are the advections and the local FT entrainment? Without the filling of this gap, Figure 7 only talks about "the potentials" of FT air mass feeding the surface CCN.
- Section 4.3 looks quite independent from the other part of the paper, in particular since the Macquarie Island data are not shown/heavily discussed. Suggest a removal of this section/shift to supporting materials so that the most important scientific question (precipitation/FT transportation) can be focused on using the 11 year data from the GCO.
- Maybe briefly mention the reasons for some methods that are used. Why is 72h chosen for back trajectories instead of 35h or 120h? Why is 2500m chosen as a reference for FT? Why are two-tail-student-t-tests used for precipitation while Whitney-U tests used for CCN and radon?
Minor comments:
- Suggest making the abstract only one paragraph, with more quantitative descriptions. For example, what is the referred “deeper boundary layer” (line 9); specifically how do STR moderate the advection of air masses (line 15).
- Line 49-50, “since new particle formation is rare in the MABL” turns out to be a fundamental basic assumption for this study, which will need citations to support. Zheng et al. 2021 actually states that there are observed new particle formation (NPF) in the remote MABL. Zheng et al. 2018 indeed mention that the NPF events within remote MBLs like the ENA are infrequent, but this is only done through citing other papers, which should be cited instead.
- Figure 2 caption, what are the hollow circles on the plot?
- Line 60 grammar check
- Line 63 grammar check
- Line 40, “.. marine biological sources predominantly govern Nccn during the summer…, ,multiple elements contribute to the CCN throughout the year…” recommend specifically mention what are the multiple elements
- Similarly Line 43, “various other sinks and sources influence the CCN budget” what the “various other”
- Line 43, “coarse mode sea salt”, coarse mode normally refers to aerosols larger than 1um, delete the “coarse-mode”, sea salt plays a crucial role in CCN…
- Line 43-44, recommend adding in related publications about SO aerosols, for summer: Fossum et al. 2018 Scientific Reports, about seasonal variations of CCN. Humphries et al. 2023 ACP, Niu et al. 2024 JGR-A, etc.
Citation: https://doi.org/10.5194/egusphere-2024-2397-RC1 -
AC1: 'Reply on RC1', Tahereh Alinejadtabrizi, 22 Oct 2024
Thank you for your thorough review of our manuscript. We appreciate your understanding of the complexities involved in our study. As you correctly noted, some of our results, particularly those related to the role of free troposphere entrainment, remain inconclusive. Despite these uncertainties, we have endeavored to extract as much insight as possible from the available datasets, aiming to illuminate the potential impact of synoptic meteorology—through both precipitation and free troposphere entrainment—on the CCN population at the Cape Grim Observatory (CGO).
While some conclusions are still open to interpretation, we believe that presenting these findings contributes valuable perspectives to the ongoing discussions in this field. We also appreciate your feedback regarding the logical flow of the manuscript. We have committed to revising the manuscript to enhance clarity and ensure that our findings and their implications are communicated more effectively. Your suggestions have been carefully considered as we worked to improve the structure and coherence of the paper, aiming for a clearer and more straightforward presentation of our results and conclusions.
Please find attached a detailed, point-by-point response to your major and minor comments.
-
RC2: 'Comment on egusphere-2024-2397', Anonymous Referee #2, 28 Aug 2024
Summary
This study identified six main synoptic clusters affecting the Cape Grim Observatory (CGO) and examined the impacts of synoptic meteorology on the observed seasonality of cloud condensation nuclei (CCN). The seasonal cycle of these clusters closely relates to the subtropical ridge's migration, resulting in different wind patterns and precipitation for each cluster. Backward trajectory analysis shows that two baseline clusters predominantly originate from the Southern Ocean, with less terrestrial influence, a finding further supported by radon measurements. For these two baseline clusters, CCN concentrations are inversely related to precipitation intensity and frequency. The study also examined the role of free tropospheric transport using back-and-forward trajectory analysis.
Overall, this paper presents an interesting narrative on the seasonality of CCN from the perspective of synoptic meteorology and enhances our understanding of CCN variability over the Southern Ocean. I recommend accepting this paper with the major revisions suggested below.
Major comments
- Line 227-240, Figure 6: A negative correlation was found between precipitation and CCN. However, the seasonality of CCN also reflects the effect of seasonal variations in sources (e.g., biogenic aerosols are higher in the austral summer and lower in winter). I'm curious if the authors can further isolate the effects of the source and sink by filtering the CCN data based on precipitation rates. For example, what would the CCN in the current Figure 6 look like for non-precipitating and precipitating cases, respectively? For the non-precipitating cases, the seasonality of CCN would mainly be due to sources. On the other hand, the seasonality of precipitating cases would include the effect of both source and sink. I suspect the seasonality of CCN for non-precipitating cases would resemble that in the current Figure 6 (but with higher values). It is also likely that CCN for non-precipitating cases would negatively correlate with precipitation due to coincidental lows and highs. Moreover, the ratio of the CCN values between precipitating and non-precipitating cases might be indicative of the role of precipitation in controlling the seasonality of CCN.
- Section 4.2: This subsection focuses on free tropospheric entrainment and the backward trajectories were run at 2500m. If the focus is on the contribution from the free troposphere to the boundary layer (where CGO CCN measurements were made), why weren't the backward trajectories initialized from the boundary layer instead? In addition, the trajectory analysis in this study mostly focuses on the spatial distribution of the trajectories. How do the trajectories vary vertically?
- Section 3: To improve the flow and readability of this section, it might be helpful to introduce the patterns of the clusters (Figure 3) before discussing their frequency (Figure 1).Minor comments
- Line 64. In this paper, the word “pristine” was used multiple times here and elsewhere. The meaning of pristine needs to be clarified (e.g. in Hamilton et al., 2014). Here, it referred to the Southern Ocean as pristine, while in other places, it seemed to suggest that pristine means low aerosol concentration.
Hamilton, D. S., Lee, L. A., Pringle, K. J., Reddington, C. L., Spracklen, D. V., & Carslaw, K. S. (2014). Occurrence of pristine aerosol environments on a polluted planet. Proceedings of the National Academy of Sciences, 111(52), 18466–18471.- Line 130, Figure 1: Please consider adding the full names for each cluster in the caption.
- Line 133, Figure 2: The circles in the figure are not labeled in the legend and are not mentioned in the main text.
- Line 204 & Line 196. Why was a different test used for mean precipitation (Student's t-test), while the Whitney U test was used for CCN?
- Line 230. What does it mean by “combine the two baseline clusters together”? Please clarify in the text.
- Line 234 & Figure 6: To make understanding the orders of magnitude of rain rate more intuitive, please consider using a base-10 logarithmic scale (log10) for the precipitation rate instead of a natural logarithmic scale.
- Line 259 & Figure 7: It might be helpful to show the back trajectories for S-base and W-base alongside their differences.Citation: https://doi.org/10.5194/egusphere-2024-2397-RC2 -
AC2: 'Reply on RC2', Tahereh Alinejadtabrizi, 22 Oct 2024
We sincerely appreciate your positive feedback on our study and your recognition of its contribution to understanding CCN variability over the Southern Ocean. We have carefully considered all of your suggestions and have made the necessary revisions to improve the clarity of our manuscript. Your insightful comments are invaluable in refining our analysis, and we believe that the revisions have strengthened the overall narrative of the paper.
Please find attached a detailed, point-by-point response to your major and minor comments.
-
AC2: 'Reply on RC2', Tahereh Alinejadtabrizi, 22 Oct 2024
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-2397', Anonymous Referee #1, 14 Aug 2024
This paper generally classified 6 synoptic conditions using K-means and looked into each corresponding sonding and pressure field. Local current precipitation is confirmed to be anti-correlated to the population of CCN. The authors have also done a back-and-forward trajectory analysis to check the history and future of tropospheric transportation. This method is also compared to the previous criteria to check each corresponding influence on the clusters.
The most significant conclusion may be that the connections between the focused SO region and the polar area are strong during the winter when the STR is close to the equator, but weakened for the poleward shift of STR during the summer. Implicitly, this summertime poleward transition constrained the precipitation, consistent with the higher CCN population at CGO. Furthermore, the wintertime southern wind is consistent all through the FT so that in general the air mass originates from the south more, even close to the Antarctic. The most unique part of this paper is about using Radon as a reference factor to isolate the influences from the FT transportation and precipitation. Even though the eventual results of this section are inconclusive, the statistic itself is worthwhile to be published and discussed for adding novelties to this paper. However, there are some pivotal gaps in the logic line thus Major revision is suggested to help make the paper flow better.
- Recommend labeling Figure 3 to be Figure 1. The discussion of Figure 1 is primarily based on Figure 3, thus shifting order can help the readers a lot if specific schemes are shown first.
- Section 2 largely lacks a detailed description of precipitation observation. Lines 103-104 are too limited for the interpretation/evaluation of Figure 6. What are the observed precipitation frequency and intensity time resolution; what is precipitation frequency defined; what are the data quality controls and raw data processing processes (acknowledge the mention of the 90th outlier removal in a later section); how large the uncertainties are; what is the precipitation phase; why using the MEAN precipitation to relate to the MEDIAN of CCN; can Figure 6 has variations/ranges shown, e.g. in shading, together with the mean/median value of each month.. Briefly mentioning some of the info above would help validate Figure 6 given using rain gauge data itself can be an advantage compared to the reanalysis data.
- Section 3 serves as the most fundamental part of this paper, which can go further into depth. Recommend adding wind field and front/ridge location on Figure 3 to, 1) help the description of Figure 4 which refers to the consistent surface wind direction and speed at CGO, 2) help the reader see the relative locations of the surface front system and the CGO, which is important for convergence/divergence flow and can be used to explain the Radon concentration as well as the spread extent in history trajectories (related to the wind speed according to Line 181). Recommend the description of inversion from altitude, depth, and strength perspectives, and a more quantitative description can be added in for lines 150-165. For example, it can start from one cluster, mentioning the specific numbers of inversion layer location and depth, and maybe also the estimated inversion strength, boundary layer depth, potential cloud layers locations, where is the referred FT, and how large the Relative Humidity is through the FT. Numbers are vital for intra-comparison in Figure 4 between clusters. Also, in Line 161, “the relative humidity is greatest for W-front suggesting heavier precipitation rate”, the small gap between the two temperature profiles illustrates a more humid condition but does not necessarily suggest a heavier precipitation rate. The mention of open and closed MCC by cross-citing is wonderful and can be shifted here from Section 4.1 which is about precipitation.
- Section 4: reading until now, I recommend combining Figures 1 and 2 in one figure to show the consistency between each other, and adding one new figure showing the correlation plot between Nccn and Radon concentration and that between Nccn and Precipitation, this can be a key figure for the follow-up discussions in Section 4-6. Looks like the correlation has been done already given some of the P-values are provided, plots may better show how related they are.
- How is Radon distributed through the troposphere, do the air mass coming from the boundary layer and FT above the Australian continent have the same Radon concentration? If not, the altitude of air mass back history would be of importance here, before any discussions about staying over terrestrial in history for long is related to the higher Radon concentration.
- The largest concern though comes from the interpretation of Figure 7 and the descriptions in Section 4.2. This needs further clarification. The log10 refers to the log10 scale of the “W-base trajectories subtracted from the S-base”? Does this mean the subtraction results have to be positive to be able to be “logged”? How would the back trajectory starting from 2500m above the GCO be directly useful for surface CCN from the understanding of FT entrainment? I assume the logic is that, if the FT air masses originate more from above the Australian continent, then the FT may contain more aerosols and thus can be a strong source for the surface CCN budget. However, are there any conditions that have to be met so that the 2500m air subsides into the boundary layer? What are the roles of the two below scenarios respectively, 1) an air mass originates from above the Antarctic at, for example, 2500m, and gets transferred into the boundary layer of GCO, and 2) as Figure 7 shows, some air masses from above the Antarctic during the winter travel and arrive at the 2500m right above the GCO. In particular, the 2500m starting level shows more FT continental aerosol information but how can this be used for the surface/below clouds CCN budget discussion without details of discussion of “processes right above the GCO between the surface and 2500m” such as mixing/exchanging and cloud processing? For Line 249, in Kang et al. (2022), FT CCN was quantified using the UHSAS measurement in the FT, and for surface sources, only the wind-oriented primary CCN is quantified in the budget. However, the logic in this paragraph originates from (line 242) whether the less pristine are caused by/related to the biological production in the summer. Could Kang’s (2022) paper be used here to support/debate the conclusion? Instead, Kang’s conclusion about FT entrainment influences the NCCN more can be heavily related to the surface biological production of, e.g., DMS.
- In general, Figure 5 is about the advection history. While Figure 7 is also an advection history (FT though), it is analyzed as a local source for the surface CCN budget at GCO. Then the gap question would be, how efficient are the advections and the local FT entrainment? Without the filling of this gap, Figure 7 only talks about "the potentials" of FT air mass feeding the surface CCN.
- Section 4.3 looks quite independent from the other part of the paper, in particular since the Macquarie Island data are not shown/heavily discussed. Suggest a removal of this section/shift to supporting materials so that the most important scientific question (precipitation/FT transportation) can be focused on using the 11 year data from the GCO.
- Maybe briefly mention the reasons for some methods that are used. Why is 72h chosen for back trajectories instead of 35h or 120h? Why is 2500m chosen as a reference for FT? Why are two-tail-student-t-tests used for precipitation while Whitney-U tests used for CCN and radon?
Minor comments:
- Suggest making the abstract only one paragraph, with more quantitative descriptions. For example, what is the referred “deeper boundary layer” (line 9); specifically how do STR moderate the advection of air masses (line 15).
- Line 49-50, “since new particle formation is rare in the MABL” turns out to be a fundamental basic assumption for this study, which will need citations to support. Zheng et al. 2021 actually states that there are observed new particle formation (NPF) in the remote MABL. Zheng et al. 2018 indeed mention that the NPF events within remote MBLs like the ENA are infrequent, but this is only done through citing other papers, which should be cited instead.
- Figure 2 caption, what are the hollow circles on the plot?
- Line 60 grammar check
- Line 63 grammar check
- Line 40, “.. marine biological sources predominantly govern Nccn during the summer…, ,multiple elements contribute to the CCN throughout the year…” recommend specifically mention what are the multiple elements
- Similarly Line 43, “various other sinks and sources influence the CCN budget” what the “various other”
- Line 43, “coarse mode sea salt”, coarse mode normally refers to aerosols larger than 1um, delete the “coarse-mode”, sea salt plays a crucial role in CCN…
- Line 43-44, recommend adding in related publications about SO aerosols, for summer: Fossum et al. 2018 Scientific Reports, about seasonal variations of CCN. Humphries et al. 2023 ACP, Niu et al. 2024 JGR-A, etc.
Citation: https://doi.org/10.5194/egusphere-2024-2397-RC1 -
AC1: 'Reply on RC1', Tahereh Alinejadtabrizi, 22 Oct 2024
Thank you for your thorough review of our manuscript. We appreciate your understanding of the complexities involved in our study. As you correctly noted, some of our results, particularly those related to the role of free troposphere entrainment, remain inconclusive. Despite these uncertainties, we have endeavored to extract as much insight as possible from the available datasets, aiming to illuminate the potential impact of synoptic meteorology—through both precipitation and free troposphere entrainment—on the CCN population at the Cape Grim Observatory (CGO).
While some conclusions are still open to interpretation, we believe that presenting these findings contributes valuable perspectives to the ongoing discussions in this field. We also appreciate your feedback regarding the logical flow of the manuscript. We have committed to revising the manuscript to enhance clarity and ensure that our findings and their implications are communicated more effectively. Your suggestions have been carefully considered as we worked to improve the structure and coherence of the paper, aiming for a clearer and more straightforward presentation of our results and conclusions.
Please find attached a detailed, point-by-point response to your major and minor comments.
-
RC2: 'Comment on egusphere-2024-2397', Anonymous Referee #2, 28 Aug 2024
Summary
This study identified six main synoptic clusters affecting the Cape Grim Observatory (CGO) and examined the impacts of synoptic meteorology on the observed seasonality of cloud condensation nuclei (CCN). The seasonal cycle of these clusters closely relates to the subtropical ridge's migration, resulting in different wind patterns and precipitation for each cluster. Backward trajectory analysis shows that two baseline clusters predominantly originate from the Southern Ocean, with less terrestrial influence, a finding further supported by radon measurements. For these two baseline clusters, CCN concentrations are inversely related to precipitation intensity and frequency. The study also examined the role of free tropospheric transport using back-and-forward trajectory analysis.
Overall, this paper presents an interesting narrative on the seasonality of CCN from the perspective of synoptic meteorology and enhances our understanding of CCN variability over the Southern Ocean. I recommend accepting this paper with the major revisions suggested below.
Major comments
- Line 227-240, Figure 6: A negative correlation was found between precipitation and CCN. However, the seasonality of CCN also reflects the effect of seasonal variations in sources (e.g., biogenic aerosols are higher in the austral summer and lower in winter). I'm curious if the authors can further isolate the effects of the source and sink by filtering the CCN data based on precipitation rates. For example, what would the CCN in the current Figure 6 look like for non-precipitating and precipitating cases, respectively? For the non-precipitating cases, the seasonality of CCN would mainly be due to sources. On the other hand, the seasonality of precipitating cases would include the effect of both source and sink. I suspect the seasonality of CCN for non-precipitating cases would resemble that in the current Figure 6 (but with higher values). It is also likely that CCN for non-precipitating cases would negatively correlate with precipitation due to coincidental lows and highs. Moreover, the ratio of the CCN values between precipitating and non-precipitating cases might be indicative of the role of precipitation in controlling the seasonality of CCN.
- Section 4.2: This subsection focuses on free tropospheric entrainment and the backward trajectories were run at 2500m. If the focus is on the contribution from the free troposphere to the boundary layer (where CGO CCN measurements were made), why weren't the backward trajectories initialized from the boundary layer instead? In addition, the trajectory analysis in this study mostly focuses on the spatial distribution of the trajectories. How do the trajectories vary vertically?
- Section 3: To improve the flow and readability of this section, it might be helpful to introduce the patterns of the clusters (Figure 3) before discussing their frequency (Figure 1).Minor comments
- Line 64. In this paper, the word “pristine” was used multiple times here and elsewhere. The meaning of pristine needs to be clarified (e.g. in Hamilton et al., 2014). Here, it referred to the Southern Ocean as pristine, while in other places, it seemed to suggest that pristine means low aerosol concentration.
Hamilton, D. S., Lee, L. A., Pringle, K. J., Reddington, C. L., Spracklen, D. V., & Carslaw, K. S. (2014). Occurrence of pristine aerosol environments on a polluted planet. Proceedings of the National Academy of Sciences, 111(52), 18466–18471.- Line 130, Figure 1: Please consider adding the full names for each cluster in the caption.
- Line 133, Figure 2: The circles in the figure are not labeled in the legend and are not mentioned in the main text.
- Line 204 & Line 196. Why was a different test used for mean precipitation (Student's t-test), while the Whitney U test was used for CCN?
- Line 230. What does it mean by “combine the two baseline clusters together”? Please clarify in the text.
- Line 234 & Figure 6: To make understanding the orders of magnitude of rain rate more intuitive, please consider using a base-10 logarithmic scale (log10) for the precipitation rate instead of a natural logarithmic scale.
- Line 259 & Figure 7: It might be helpful to show the back trajectories for S-base and W-base alongside their differences.Citation: https://doi.org/10.5194/egusphere-2024-2397-RC2 -
AC2: 'Reply on RC2', Tahereh Alinejadtabrizi, 22 Oct 2024
We sincerely appreciate your positive feedback on our study and your recognition of its contribution to understanding CCN variability over the Southern Ocean. We have carefully considered all of your suggestions and have made the necessary revisions to improve the clarity of our manuscript. Your insightful comments are invaluable in refining our analysis, and we believe that the revisions have strengthened the overall narrative of the paper.
Please find attached a detailed, point-by-point response to your major and minor comments.
-
AC2: 'Reply on RC2', Tahereh Alinejadtabrizi, 22 Oct 2024
Peer review completion
Journal article(s) based on this preprint
Clouds over the Southern Ocean are crucial to Earth's energy balance, but understanding the factors that control them is complex. Our research examines how weather patterns affect tiny particles called cloud condensation nuclei (CCN), which influence cloud properties. Using data from Kennaook / Cape Grim, we found that winter air from Antarctica brings cleaner conditions with lower CCN, while summer patterns from Australia transport more particles. Precipitation also helps reduce CCN in winter.
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Tahereh Alinejadtabrizi
Francisco Lang
Steven Siems
Michael Manton
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