On the relationship between mesoscale cellular convection and meteorological forcing: Comparing the Southern Ocean against the North Pacific

Lang, Francisco; Siems, Steven T.; Huang, Yi; Ackermann, Luis

doi:https://doi.org/10.5194/egusphere-2023-518

Preprints

https://doi.org/10.5194/egusphere-2023-518

Preprints

11 Apr 2023

| 11 Apr 2023

On the relationship between mesoscale cellular convection and meteorological forcing: Comparing the Southern Ocean against the North Pacific

Francisco Lang, Steven T. Siems, Yi Huang, and Luis Ackermann

Abstract. Marine atmospheric boundary layer (MABL) clouds cover vast areas over the ocean and have important radiative effects on the Earth’s climate system. These radiative effects are known to be sensitive to the local organization, or structure, of the mesoscale cellular convection (MCC). A convolution neural network model is used to identify the two ideal classes of MCC clouds, namely open and closed, over the Southern Ocean (SO) and Northwest Pacific (NP) from high-frequency geostationary Himawari-8 satellite observations. The results of the climatology show that MCC clouds are roughly distributed over the midlatitude storm tracks for both hemispheres, with peaks poleward of the 40° latitude. Open MCC clouds are more prevalent than closed MCC in both regions. An examination of meteorological forcing associated with open and closed MCC clouds is conducted to illustrate the influence of large-scale meteorological conditions. We establish the importance of the Kuroshio western boundary current in the spatial coverage of open and closed MCC across the NP, presumably through the supply of strong heat and moisture fluxes during marine cold air outbreaks events. For both regions, closed MCC cloud are more frequent at higher static stability than on air-sea temperature difference, opposite to the open MCC cloud behavior. The diurnal cycle reveals a pronounced daily cycle in the frequency of occurrence of closed MCC over the SO, while the NP closed MCC daily cycle is less noticeable.

Received: 21 Mar 2023 – Discussion started: 11 Apr 2023

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31 Jan 2024

On the relationship between mesoscale cellular convection and meteorological forcing: comparing the Southern Ocean against the North Pacific

Francisco Lang, Steven T. Siems, Yi Huang, Tahereh Alinejadtabrizi, and Luis Ackermann

Atmos. Chem. Phys., 24, 1451–1466, https://doi.org/10.5194/acp-24-1451-2024,https://doi.org/10.5194/acp-24-1451-2024, 2024

Short summary

Francisco Lang, Steven T. Siems, Yi Huang, and Luis Ackermann

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2023-518', Anonymous Referee #1, 29 Apr 2023

Review report
The authors investigate the connection between meteorological parameters and the morphology of low-level clouds in two regions: a portion of the Southern Ocean and the North Pacific. To identify three morphological classes (i.e., “closen”, “open”, and “other”) the authors apply a neural network that was prepared in a previous publication (Lang et al., 2022) to radiance fields from the geostationary HIMAWARI satellite. The article documents the cooccurrence of class frequency with average estimated inversion strength (EIS) and a marine cold-air outbreak index, M, mainly finding that closed cells are connected to greater EIS.
The article contributes to a topic that is of great relevance to the climate community. The authors importantly leverage imagery from a geostationary platform to identify cloud morphology on a great temporal resolution – a scientific advancement that was achieved in Lang et al. (2022). With respect to the previous paper, the present article falls short of making a substantial contribution. Below, I’m outlining major issues that lead me to recommend a rejection but encourage a resubmission in improved form. In a nutshell, the authors should (1) include statistics from the class “other”, (2) quantify their results, (3) contextualize the selected meteorology parameters and possibly widen the parameter space, and (4) inspect EIS throughout the text, table, and figures.

Major issues
(1) The classification resulted in peak frequencies of ~20% for “open cells” and ~15% for “closed cells”, leaving at least ~65% of the samples classified as “other”. The authors explain that “other” constitute cases of mid- and high-level clouds, but also low-level clouds that were different enough from the first two classes (ll. 94-96). Figure 1 shows a classification example in which apparent open cellular cloud decks (e.g., ~35degN, ~160degE) were classified neither as “open” nor as “closed” (leading me to believe they were joined “other” which is not shown here). Given that “other” makes up the bulk of the samples and that the nature of these samples is unknown to the reader, the authors need to (1) provide information on them, showing, for example, statistics of cloud-top height, cloud fraction, and cloud optical depth of “other” compared to “open” and “closed” and (2) include “other” where currently only “closed” and “open” is shown. Both aspects will become highly relevant for the diurnal cycle that shows a decrease of “closed” in the afternoon but no increase of “open” at the same time, leading to believe that the class “other” increased in the afternoon and begging the question why that is (e.g., truly a transition from “closed” to “disorganized” or an increase in local cirrus cloud fraction?).
(2) The authors compare class frequency and meteorological fields and often speak of “correlation” (e.g., ll. 161-164, l. 194, ll. 254-255) or even “high correlation” (ll. 267-268) when visually comparing maps. However, there is no actual calculation of correlation. The paper would benefit from a more quantitative rather than qualitative analysis. Given the great temporal resolution, the authors could not only compare seasonally averaged maps but actually compute correlation on a pixel-by-pixel basis using the native 10-min resolution.
(3) The authors selected EIS and the M index as meteorological parameters, and it is unclear why they selected these two and why the analysis excludes other parameters. For example, near-surface wind speed was recently connected to cloud morphological transitions in Eastman et al. (2023). Also, the authors examine the diurnal cycle of class frequency but leave out diurnal cycles of meteorological parameters that were used for the seasonal analysis a few pages before.
(4) The authors equate EIS with static stability in this paper (e.g., l. 11, 164), even though it was introduced as an indicator of which (ll. 110-111). Given that sea-surface temperature can exceed lower-tropospheric air temperature (giving positive M but possibly negative LTS), EIS can become negative, as the authors show in Figure 4. The authors should explain what a negative EIS means and whether EIS still holds as an indicator of static stability in cold-air outbreak conditions. In addition to revising EIS language, there seem to be inconsistencies across Figure 4, Figure 2, and Table 1: Figure 4 shows mostly negative EIS, yet in Figure 2 and Table 1 EIS is shown to be positive. The authors need to explain why that is (e.g., do composites in Figure 2 and statistics in Table 1 cover different time frames compared to Figure 4?).

Minor issues
ll. 10-11 This sentence is hard to understand. Please rephrase.
ll. 23-26 Please also cite a recent paper by McCoy et al., (2023).
ll. 26-27 This sentence seems out of context.
ll. 95 Please clarify whether low-level clouds can be categorized as “other”.
Fig. 1 “Other” is included in the legend, but not shown in the map. Perhaps selected a color different from white.
Fig. 1 Please label the size of each box or add a reference bar to show distance.
Fig. 2 Please change label from “delta T (K)” to “EIS (K)”.
ll. 131ff. Would NP have great M values even when the Kuroshio current was absent? Also please quantify SST gradient.
ll. 141-142 To follow this sentence, please explain how cloud cover is related to both classes.
l. 145 Please substitute “total time” by “total number of observations”.
Fig. 3 Please mark maximum frequency to complement the text.
Fig. 3 Please draw Kuroshio current in here.
ll. 150-151 (and also ll. 154-155 and ll. 174-175) It is unclear which class is referred to in this sentence.
l. 157 Please substitute “to the top” with “near the top”.
l. 160 Please substitute “SST gradient” with “average SST”.
l. 163 Please finish sentence; “as highly … as” is missing an object.
l. 164 Please refer to EIS as an indicator of static stability rather than equating both (see above major points).
ll. 159-175 Please provide correlation maps (see above major points).
l. 167 Please change “44” to “4”.
ll. 171-172 Please check for redundancy is both sentences.
ll. 181-183 Does this mean the portion of “other” has increased?
ll. 196-198 Please elaborate – currently unclear how land masses explain seasonality.
ll. 196-201 Perhaps a sketch or cartoon image would be useful here to illustrate the point.
ll. 200-201 Please rephrase. Currently hard to follow. Substitute “that” with “than”.
ll. 199-200 Are lower SST connected to a greater frequency of open MCCs?
Fig. 7 Please explain how and why these regions were selected. Is the diurnal cycle of “closed” similarly and oppositely seen in “other” (given that “open” remains unchanged)?
Fig. 7 Is there a diurnal cycle in EIS or M?
ll. 231-233 Please rephrase, perhaps to “The geolocation of cloud types matches those in other studies”.
l. 233 With respect to “most prevalent”, isn’t the “other” class even greater in proportion?
l. 235 Please check for redundancy (i.e., a similar content in sentence before).
l. 239 Perhaps is it worth noting in Section 2 how single and multi-layer clouds are handled in this study.
l. 241 Unclear whether a specific type is being referred to or not.
l. 243 Please check position of parenthesis here. Perhaps rephrase to “turbulent heat and moisture (i.e., sensible plus latent heat) fluxes”.
l. 244 Perhaps better “frequency of occurrence” instead of “presence”.
l. 246 Please rephrase sentence for better understanding.
ll. 258-259 Please elaborate on “showing a strong relationship to the SST gradient”. Is SST gradient shown anywhere? How was a “strong relationship” detected?
l. 264 Please elaborate on “well-formed open cells”. Would not-so-well-formed open cells be problematic for the classification?
l. 268 Please quantify “high correlation” (see above major points).
l. 278 Please elaborate why other techniques and other classes are needed.
ll. 280-281 Please briefly explain whether HIMAWARI has a precipitation product.

References
Lang, F., Ackermann, L., Huang, Y., Truong, S. C. H., Siems, S. T., and Manton, M. J.: A climatology of open and closed mesoscale cellular convection over the Southern Ocean derived from Himawari-8 observations, Atmos. Chem. Phys., 22, 2135–2152, https://doi.org/10.5194/acp-22-2135-2022, 2022.
Eastman, R., McCoy, I. L., & Wood, R. (2022). Wind, rain, and the closed to open cell transition in subtropical marine stratocumulus. Journal of Geophysical Research: Atmospheres, 127, e2022JD036795. https://doi.org/10.1029/2022JD036795
McCoy, I. L., McCoy, D. T., Wood, R., Zuidema, P., & Bender, F. A.-M. (2023). The role of mesoscale cloud morphology in the shortwave cloud feedback. Geophysical Research Letters, 50, e2022GL101042. https://doi.org/10.1029/2022GL101042

Citation: https://doi.org/10.5194/egusphere-2023-518-RC1
- AC1: 'Reply on RC1', Francisco Lang, 17 Aug 2023
  
  We thank Reviewer 1 for their helpful comments, which have improved this paper. Please find our responses to your comments in “Ref1_Comments_Answered.pdf”.
  In the next few days, we will submit both the original (old) manuscript and the revised (new) version, with highlighted differences.
  
  Citation: https://doi.org/10.5194/egusphere-2023-518-AC1
RC2:
'Comment on egusphere-2023-518', Anonymous Referee #2, 02 May 2023
Summary:
Lang et al. present an expansion of their earlier study, Lang et al. 2022, where they used a convolutional neural network to identify open and closed mesoscale cellular convective clouds in the Southern Ocean. This study adds clouds in an additional region, the North Pacific, that have been identified with their algorithm (applied to the geostationary Himawari satellite and using brightness temperature, which enables identifications over the full diurnal cycle). Maps of cloud occurrence frequency are contrasted with maps of stability metrics in the two regions annually and seasonally. Visual relationships to regional environmental factors (e.g., Kuroshio current, oceanic polar front, storm track) are documented. The diurnal cycle is also presented for these regions annually and seasonally. Differences in behavior in the North Pacific are qualitatively documented and contrasted with the previously published Southern Ocean behaviors.

General comments:
The premise of this study is exciting, and the data developed as part of this and Lang et al. 2022 is quite valuable. It is especially noteworthy and novel to examine the diurnal cycles of MCC cloud types in these two hemispheres. The figures developed in this analysis are very well done, clear and compelling. However, the analysis is limited to qualitatively documenting the behaviors in these regions along with their visual correspondence to stability metrics and sea surface temperature. This provides some insights about regional differences but without quantitative analysis the conclusions are limited, similar to those presented in previous studies, and ultimately not as substantive as they have the potential to be. However, I think the authors can develop this analysis into a valuable contribution to the field and fully realize the potential of this work.
My main recommendation is to add quantitative comparisons to bolster the qualitative comparisons and help with interpreting/establishing the differences between the NP and SO regions. “Correlations” are currently discussed but they are based on visual comparisons and not calculated/provided. Actual correlations, between occurrence frequency and meteorological variables, could be calculated at many scales (e.g., within spatial map grid boxes, for annual and seasonal relationships, for composite differences, for diurnal cycles, etc.). By quantifying the relationships that you suggest here, you would greatly strengthen your results and better support your conclusions.

The diurnal cycle analysis in this and Lang et al. 2022 is novel and has a lot of potential. However, these results are currently limited to qualitatively documenting the differences between type, season, and region. There is an opportunity here to add more depth to the analysis by quantifying the connections to the meteorological environment (as you do qualitatively in the first part of the paper). This would lead to a deeper understanding of what is contributing to these diurnal cycle differences through understanding how these cloud types are responding to their environmental diurnal cycles. Being able to interpret why you see differences between regions, seasons, etc. in the MCC diurnal development cycle would be a very valuable contribution to the field.

Specific Comments:
Throughout: Please only discuss correlations or variables being “correlated” when you have computed a correlation coefficient and statistically tested whether they are correlated (e.g., p-value ≤ 0.01 for 90% confidence). Visually similar maps and cycle plots are not correlations.
Line 35: I would suggest removing “are most common in” since sub-tropical decks also have a lot of MCC. Agreed that these MCC types dominate the storm tracks (also see Agee et al. 1973, McCoy et al. 2023 for climatology).
Line 37: Fletcher et al. 2016 is for clouds in general, not MCC. Atkinson and Zhang 1997 and Wood 2012 review this MCC-CAO relationship in detail and McCoy et al. 2017 quantified it more recently.
Section 2.2: I see the rational of only identifying open and closed MCC and throwing everything else into a catch all since it gives you a high quality MCC dataset. However, I do think it would be valuable to at least subdivide the “other” into low, middle, and high clouds and clear sky so that you have an idea of what is happening with the other low clouds (besides the MCC types) in these regions. From the small MCC absolute frequencies that you are working with, there is clearly a lot of the low-cloud behavior that you are missing throughout your analysis and analyzing that could give you valuable context for whether the MCC behaviors are unique and whether their differences are statistically significant from the base behavior.
Section 2.3: It might be beneficial to expand beyond stability metrics (EIS and M) and SST. Clouds in this region respond to a variety of factors in opposing ways (e.g., Scott et al. 2020) and MCC are thought to be sensitive to more than just stability and SST in their development (e.g., Eastman et al. 2021, 2022). Temperature advection might be especially useful as it would more accurately characterize the surface forcing contribution in these regions. Expanding your meteorological variable space has the potential to quantify novel relationships between MCC (this is a strong dataset for doing this) and the characteristics of these regional environments and could help to better distinguish your analysis from previous work on MCC behavior in these regions (e.g., Muhlbauer et al. 2014, McCoy et al. 2017, Lang et al. 2022).
Line 134, 158 and Figure 2: Since you use the oceanic polar front as a reference for discussing cloud behavior, it would be useful to have the corresponding annual/seasonal climatological location of the oceanic polar front plotted on these maps. Otherwise, consider citing literature to support these statements and your conclusions.
Line 164: It would be very valuable to spatially correlate these figures, as you imply here, and show the results (e.g., as a map of correlation coefficients with significance indicated). As mentioned above, please do not refer to something as “correlated” unless you are providing a correlation coefficient.
Figure 4 and 5: It might be clearer to see how the regions differ by looking at a difference plot between the NP and SO cases (since you have the data composited to the same space already). You could also consider looking at how the “other-low cloud” category behaves in this space and how it differs from open and closed MCC (e.g., how anomalous the MCC types are from all low clouds).
Line 175: How is the relationship “better”? Please quantify these statements with correlations or other statistics.
Line 188-189: Like with the oceanic polar front, it would be helpful to include a corresponding annual/seasonal climatological storm track to show the relationship with the storm track you suggest here. You could also correlate the location with the occurrence frequency and quantify this. Otherwise, please reference literature supporting this statement about the storm track shift and your conclusions.
Figure 6: Worth noting somewhere in the text what Figure 6 is showing and adding to the story (it is only mentioned in passing, not explained).
Line 192-193: It would help for these types of comparisons if they were quantified. How do you know this is “more relevant”, hard to know that from visually comparing the plots. Consider checking the regressions of frequency on these variables (e.g., in a multiple linear regression that accounts for correlations between predictor variables) and looking at spatial correlation maps.
Line 194 (and throughout): You can say it “corresponds” instead of “correlated”, but it would be much better to calculate the coefficients and quantify this (see above comments).
Line 199-200: Why does the cooler SST explain the higher open MCC frequency? Please explain and support statement.
Line 200-201: What do you mean here? Please explain and support statement.
Line 206, 215-216, 217: These are very exciting results. Would you be able to extend your meteorological factor analysis to this diurnal cycle analysis as well (something like Vial et al. 2021)? This could really help you to begin interpreting what factors might be driving these cycles (and why they are different between regions).
Line 232-233: Also MODIS (Muhlbauer et al. 2014, McCoy et al. 2017, McCoy et al. 2023).
Line 249-251, 263-264, abstract: It seems that you are referring to M as if it is surface forcing or an air-sea temperature difference and contrasting it against static stability as measured by EIS. This is confusing since M is also a stability estimate (essentially a modified form of LTS). This is also inconsistent with your earlier discussions. M can be written as a function of EIS and the air-sea temperature difference (McCoy et al. 2017), is that what you are referencing? Please clarify your meaning and be more accurate in your language.
Line 269: Please explain how you come to this conclusion. Hard to tell from comparing Figure 2 and 5, is this based on a different analysis?
Line 272-276: Great to include the discussion on lines 272-274 of why you have these diurnal cycle differences. It would be very valuable to extend this further to the intriguing regional differences you document (i.e. to add interpretation to Lines 274-276).
Line 277: How are you quantifying “good performance” here? You previously tested and trained on the SO data (and that is presented well in Lang et al. 2022), are you able to similarly check the accuracy for the NP? Or is this from visual inspection?
Line 280-281: Extending this diurnal analysis, either here or in a future paper, would be fantastic. Your results raise so many interesting questions: why are closed MCC NP cycles smaller than SO? Why are they especially small in the summertime? Why are the closed MCC cycles peaking in magnitude in different seasons in the two regions? Why are open MCC cycles much smaller and peaking at different times? What is happening with the remaining contribution of clouds (your “other” type)? You could make a good start at answering these by quantifying the cycle relationships to the meteorological variables you discussed in the first part of the paper.

Technical Corrections:
Line 158: “but is relatively”
Line 167: “Figure 4”
Line 185: “considerably”
Line 246: “current in the Kuroshio region”
Line 248: “MCCs than in the SO”

References:
Agee, E.M., Chen, T.S., Dowell, K.E., 1973. Review of Mesoscale Cellular Convection. Bull. Amer. Meteorol. Soc. 54, 1004–1012. https://doi.org/10.1175/1520-0477(1973)054<1004:aromcc>2.0.co;2
Atkinson, B.W., Zhang, J.W., 1996. Mesoscale shallow convection in the atmosphere. Reviews of Geophysics 34, 403–431. https://doi.org/10.1029/96rg02623
Eastman, R., McCoy, I.L., Wood, R., 2022. Wind, Rain, and the Closed to Open Cell Transition in Subtropical Marine Stratocumulus. JGR Atmospheres 127. https://doi.org/10.1029/2022JD036795
Eastman, R., McCoy, I.L., Wood, R., 2021. Environmental and Internal Controls on Lagrangian Transitions from Closed Cell Mesoscale Cellular Convection over Subtropical Oceans. Journal of the Atmospheric Sciences 78, 2367–2383. https://doi.org/10.1175/Jas-D-20-0277.1
McCoy, I.L., McCoy, D.T., Wood, R., Zuidema, P., Bender, F.A. ‐M., 2023. The Role of Mesoscale Cloud Morphology in the Shortwave Cloud Feedback. Geophysical Research Letters 50. https://doi.org/10.1029/2022GL101042
Scott, R.C., Myers, T.A., Norris, J.R., Zelinka, M.D., Klein, S.A., Sun, M., Doelling, D.R., 2020. Observed Sensitivity of Low-Cloud Radiative Effects to Meteorological Perturbations over the Global Oceans. Journal of Climate 33, 7717–7734. https://doi.org/10.1175/jcli-d-19-1028.1
Vial, J., Vogel, R., Schulz, H., 2021. On the daily cycle of mesoscale cloud organization in the winter trades. Quarterly Journal of the Royal Meteorological Society. https://doi.org/10.1002/qj.4103
Wood, R., 2012. Stratocumulus Clouds. Mon. Weather Rev. 140, 2373–2423. https://doi.org/10.1175/mwr-d-11-00121.1
Citation: https://doi.org/10.5194/egusphere-2023-518-RC2
- AC2: 'Reply on RC2', Francisco Lang, 17 Aug 2023
  
  We thank Reviewer 2 for their helpful comments, which have improved this paper. Please find our responses to your comments in “Ref2_Comments_Answered.pdf”.
  In the next few days, we will submit both the original (old) manuscript and the revised (new) version, with highlighted differences.
  
  Citation: https://doi.org/10.5194/egusphere-2023-518-AC2

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2023-518', Anonymous Referee #1, 29 Apr 2023

Review report
The authors investigate the connection between meteorological parameters and the morphology of low-level clouds in two regions: a portion of the Southern Ocean and the North Pacific. To identify three morphological classes (i.e., “closen”, “open”, and “other”) the authors apply a neural network that was prepared in a previous publication (Lang et al., 2022) to radiance fields from the geostationary HIMAWARI satellite. The article documents the cooccurrence of class frequency with average estimated inversion strength (EIS) and a marine cold-air outbreak index, M, mainly finding that closed cells are connected to greater EIS.
The article contributes to a topic that is of great relevance to the climate community. The authors importantly leverage imagery from a geostationary platform to identify cloud morphology on a great temporal resolution – a scientific advancement that was achieved in Lang et al. (2022). With respect to the previous paper, the present article falls short of making a substantial contribution. Below, I’m outlining major issues that lead me to recommend a rejection but encourage a resubmission in improved form. In a nutshell, the authors should (1) include statistics from the class “other”, (2) quantify their results, (3) contextualize the selected meteorology parameters and possibly widen the parameter space, and (4) inspect EIS throughout the text, table, and figures.

Major issues
(1) The classification resulted in peak frequencies of ~20% for “open cells” and ~15% for “closed cells”, leaving at least ~65% of the samples classified as “other”. The authors explain that “other” constitute cases of mid- and high-level clouds, but also low-level clouds that were different enough from the first two classes (ll. 94-96). Figure 1 shows a classification example in which apparent open cellular cloud decks (e.g., ~35degN, ~160degE) were classified neither as “open” nor as “closed” (leading me to believe they were joined “other” which is not shown here). Given that “other” makes up the bulk of the samples and that the nature of these samples is unknown to the reader, the authors need to (1) provide information on them, showing, for example, statistics of cloud-top height, cloud fraction, and cloud optical depth of “other” compared to “open” and “closed” and (2) include “other” where currently only “closed” and “open” is shown. Both aspects will become highly relevant for the diurnal cycle that shows a decrease of “closed” in the afternoon but no increase of “open” at the same time, leading to believe that the class “other” increased in the afternoon and begging the question why that is (e.g., truly a transition from “closed” to “disorganized” or an increase in local cirrus cloud fraction?).
(2) The authors compare class frequency and meteorological fields and often speak of “correlation” (e.g., ll. 161-164, l. 194, ll. 254-255) or even “high correlation” (ll. 267-268) when visually comparing maps. However, there is no actual calculation of correlation. The paper would benefit from a more quantitative rather than qualitative analysis. Given the great temporal resolution, the authors could not only compare seasonally averaged maps but actually compute correlation on a pixel-by-pixel basis using the native 10-min resolution.
(3) The authors selected EIS and the M index as meteorological parameters, and it is unclear why they selected these two and why the analysis excludes other parameters. For example, near-surface wind speed was recently connected to cloud morphological transitions in Eastman et al. (2023). Also, the authors examine the diurnal cycle of class frequency but leave out diurnal cycles of meteorological parameters that were used for the seasonal analysis a few pages before.
(4) The authors equate EIS with static stability in this paper (e.g., l. 11, 164), even though it was introduced as an indicator of which (ll. 110-111). Given that sea-surface temperature can exceed lower-tropospheric air temperature (giving positive M but possibly negative LTS), EIS can become negative, as the authors show in Figure 4. The authors should explain what a negative EIS means and whether EIS still holds as an indicator of static stability in cold-air outbreak conditions. In addition to revising EIS language, there seem to be inconsistencies across Figure 4, Figure 2, and Table 1: Figure 4 shows mostly negative EIS, yet in Figure 2 and Table 1 EIS is shown to be positive. The authors need to explain why that is (e.g., do composites in Figure 2 and statistics in Table 1 cover different time frames compared to Figure 4?).

Minor issues
ll. 10-11 This sentence is hard to understand. Please rephrase.
ll. 23-26 Please also cite a recent paper by McCoy et al., (2023).
ll. 26-27 This sentence seems out of context.
ll. 95 Please clarify whether low-level clouds can be categorized as “other”.
Fig. 1 “Other” is included in the legend, but not shown in the map. Perhaps selected a color different from white.
Fig. 1 Please label the size of each box or add a reference bar to show distance.
Fig. 2 Please change label from “delta T (K)” to “EIS (K)”.
ll. 131ff. Would NP have great M values even when the Kuroshio current was absent? Also please quantify SST gradient.
ll. 141-142 To follow this sentence, please explain how cloud cover is related to both classes.
l. 145 Please substitute “total time” by “total number of observations”.
Fig. 3 Please mark maximum frequency to complement the text.
Fig. 3 Please draw Kuroshio current in here.
ll. 150-151 (and also ll. 154-155 and ll. 174-175) It is unclear which class is referred to in this sentence.
l. 157 Please substitute “to the top” with “near the top”.
l. 160 Please substitute “SST gradient” with “average SST”.
l. 163 Please finish sentence; “as highly … as” is missing an object.
l. 164 Please refer to EIS as an indicator of static stability rather than equating both (see above major points).
ll. 159-175 Please provide correlation maps (see above major points).
l. 167 Please change “44” to “4”.
ll. 171-172 Please check for redundancy is both sentences.
ll. 181-183 Does this mean the portion of “other” has increased?
ll. 196-198 Please elaborate – currently unclear how land masses explain seasonality.
ll. 196-201 Perhaps a sketch or cartoon image would be useful here to illustrate the point.
ll. 200-201 Please rephrase. Currently hard to follow. Substitute “that” with “than”.
ll. 199-200 Are lower SST connected to a greater frequency of open MCCs?
Fig. 7 Please explain how and why these regions were selected. Is the diurnal cycle of “closed” similarly and oppositely seen in “other” (given that “open” remains unchanged)?
Fig. 7 Is there a diurnal cycle in EIS or M?
ll. 231-233 Please rephrase, perhaps to “The geolocation of cloud types matches those in other studies”.
l. 233 With respect to “most prevalent”, isn’t the “other” class even greater in proportion?
l. 235 Please check for redundancy (i.e., a similar content in sentence before).
l. 239 Perhaps is it worth noting in Section 2 how single and multi-layer clouds are handled in this study.
l. 241 Unclear whether a specific type is being referred to or not.
l. 243 Please check position of parenthesis here. Perhaps rephrase to “turbulent heat and moisture (i.e., sensible plus latent heat) fluxes”.
l. 244 Perhaps better “frequency of occurrence” instead of “presence”.
l. 246 Please rephrase sentence for better understanding.
ll. 258-259 Please elaborate on “showing a strong relationship to the SST gradient”. Is SST gradient shown anywhere? How was a “strong relationship” detected?
l. 264 Please elaborate on “well-formed open cells”. Would not-so-well-formed open cells be problematic for the classification?
l. 268 Please quantify “high correlation” (see above major points).
l. 278 Please elaborate why other techniques and other classes are needed.
ll. 280-281 Please briefly explain whether HIMAWARI has a precipitation product.

References
Lang, F., Ackermann, L., Huang, Y., Truong, S. C. H., Siems, S. T., and Manton, M. J.: A climatology of open and closed mesoscale cellular convection over the Southern Ocean derived from Himawari-8 observations, Atmos. Chem. Phys., 22, 2135–2152, https://doi.org/10.5194/acp-22-2135-2022, 2022.
Eastman, R., McCoy, I. L., & Wood, R. (2022). Wind, rain, and the closed to open cell transition in subtropical marine stratocumulus. Journal of Geophysical Research: Atmospheres, 127, e2022JD036795. https://doi.org/10.1029/2022JD036795
McCoy, I. L., McCoy, D. T., Wood, R., Zuidema, P., & Bender, F. A.-M. (2023). The role of mesoscale cloud morphology in the shortwave cloud feedback. Geophysical Research Letters, 50, e2022GL101042. https://doi.org/10.1029/2022GL101042

Citation: https://doi.org/10.5194/egusphere-2023-518-RC1
- AC1: 'Reply on RC1', Francisco Lang, 17 Aug 2023
  
  We thank Reviewer 1 for their helpful comments, which have improved this paper. Please find our responses to your comments in “Ref1_Comments_Answered.pdf”.
  In the next few days, we will submit both the original (old) manuscript and the revised (new) version, with highlighted differences.
  
  Citation: https://doi.org/10.5194/egusphere-2023-518-AC1
RC2:
'Comment on egusphere-2023-518', Anonymous Referee #2, 02 May 2023
Summary:
Lang et al. present an expansion of their earlier study, Lang et al. 2022, where they used a convolutional neural network to identify open and closed mesoscale cellular convective clouds in the Southern Ocean. This study adds clouds in an additional region, the North Pacific, that have been identified with their algorithm (applied to the geostationary Himawari satellite and using brightness temperature, which enables identifications over the full diurnal cycle). Maps of cloud occurrence frequency are contrasted with maps of stability metrics in the two regions annually and seasonally. Visual relationships to regional environmental factors (e.g., Kuroshio current, oceanic polar front, storm track) are documented. The diurnal cycle is also presented for these regions annually and seasonally. Differences in behavior in the North Pacific are qualitatively documented and contrasted with the previously published Southern Ocean behaviors.

General comments:
The premise of this study is exciting, and the data developed as part of this and Lang et al. 2022 is quite valuable. It is especially noteworthy and novel to examine the diurnal cycles of MCC cloud types in these two hemispheres. The figures developed in this analysis are very well done, clear and compelling. However, the analysis is limited to qualitatively documenting the behaviors in these regions along with their visual correspondence to stability metrics and sea surface temperature. This provides some insights about regional differences but without quantitative analysis the conclusions are limited, similar to those presented in previous studies, and ultimately not as substantive as they have the potential to be. However, I think the authors can develop this analysis into a valuable contribution to the field and fully realize the potential of this work.
My main recommendation is to add quantitative comparisons to bolster the qualitative comparisons and help with interpreting/establishing the differences between the NP and SO regions. “Correlations” are currently discussed but they are based on visual comparisons and not calculated/provided. Actual correlations, between occurrence frequency and meteorological variables, could be calculated at many scales (e.g., within spatial map grid boxes, for annual and seasonal relationships, for composite differences, for diurnal cycles, etc.). By quantifying the relationships that you suggest here, you would greatly strengthen your results and better support your conclusions.

The diurnal cycle analysis in this and Lang et al. 2022 is novel and has a lot of potential. However, these results are currently limited to qualitatively documenting the differences between type, season, and region. There is an opportunity here to add more depth to the analysis by quantifying the connections to the meteorological environment (as you do qualitatively in the first part of the paper). This would lead to a deeper understanding of what is contributing to these diurnal cycle differences through understanding how these cloud types are responding to their environmental diurnal cycles. Being able to interpret why you see differences between regions, seasons, etc. in the MCC diurnal development cycle would be a very valuable contribution to the field.

Specific Comments:
Throughout: Please only discuss correlations or variables being “correlated” when you have computed a correlation coefficient and statistically tested whether they are correlated (e.g., p-value ≤ 0.01 for 90% confidence). Visually similar maps and cycle plots are not correlations.
Line 35: I would suggest removing “are most common in” since sub-tropical decks also have a lot of MCC. Agreed that these MCC types dominate the storm tracks (also see Agee et al. 1973, McCoy et al. 2023 for climatology).
Line 37: Fletcher et al. 2016 is for clouds in general, not MCC. Atkinson and Zhang 1997 and Wood 2012 review this MCC-CAO relationship in detail and McCoy et al. 2017 quantified it more recently.
Section 2.2: I see the rational of only identifying open and closed MCC and throwing everything else into a catch all since it gives you a high quality MCC dataset. However, I do think it would be valuable to at least subdivide the “other” into low, middle, and high clouds and clear sky so that you have an idea of what is happening with the other low clouds (besides the MCC types) in these regions. From the small MCC absolute frequencies that you are working with, there is clearly a lot of the low-cloud behavior that you are missing throughout your analysis and analyzing that could give you valuable context for whether the MCC behaviors are unique and whether their differences are statistically significant from the base behavior.
Section 2.3: It might be beneficial to expand beyond stability metrics (EIS and M) and SST. Clouds in this region respond to a variety of factors in opposing ways (e.g., Scott et al. 2020) and MCC are thought to be sensitive to more than just stability and SST in their development (e.g., Eastman et al. 2021, 2022). Temperature advection might be especially useful as it would more accurately characterize the surface forcing contribution in these regions. Expanding your meteorological variable space has the potential to quantify novel relationships between MCC (this is a strong dataset for doing this) and the characteristics of these regional environments and could help to better distinguish your analysis from previous work on MCC behavior in these regions (e.g., Muhlbauer et al. 2014, McCoy et al. 2017, Lang et al. 2022).
Line 134, 158 and Figure 2: Since you use the oceanic polar front as a reference for discussing cloud behavior, it would be useful to have the corresponding annual/seasonal climatological location of the oceanic polar front plotted on these maps. Otherwise, consider citing literature to support these statements and your conclusions.
Line 164: It would be very valuable to spatially correlate these figures, as you imply here, and show the results (e.g., as a map of correlation coefficients with significance indicated). As mentioned above, please do not refer to something as “correlated” unless you are providing a correlation coefficient.
Figure 4 and 5: It might be clearer to see how the regions differ by looking at a difference plot between the NP and SO cases (since you have the data composited to the same space already). You could also consider looking at how the “other-low cloud” category behaves in this space and how it differs from open and closed MCC (e.g., how anomalous the MCC types are from all low clouds).
Line 175: How is the relationship “better”? Please quantify these statements with correlations or other statistics.
Line 188-189: Like with the oceanic polar front, it would be helpful to include a corresponding annual/seasonal climatological storm track to show the relationship with the storm track you suggest here. You could also correlate the location with the occurrence frequency and quantify this. Otherwise, please reference literature supporting this statement about the storm track shift and your conclusions.
Figure 6: Worth noting somewhere in the text what Figure 6 is showing and adding to the story (it is only mentioned in passing, not explained).
Line 192-193: It would help for these types of comparisons if they were quantified. How do you know this is “more relevant”, hard to know that from visually comparing the plots. Consider checking the regressions of frequency on these variables (e.g., in a multiple linear regression that accounts for correlations between predictor variables) and looking at spatial correlation maps.
Line 194 (and throughout): You can say it “corresponds” instead of “correlated”, but it would be much better to calculate the coefficients and quantify this (see above comments).
Line 199-200: Why does the cooler SST explain the higher open MCC frequency? Please explain and support statement.
Line 200-201: What do you mean here? Please explain and support statement.
Line 206, 215-216, 217: These are very exciting results. Would you be able to extend your meteorological factor analysis to this diurnal cycle analysis as well (something like Vial et al. 2021)? This could really help you to begin interpreting what factors might be driving these cycles (and why they are different between regions).
Line 232-233: Also MODIS (Muhlbauer et al. 2014, McCoy et al. 2017, McCoy et al. 2023).
Line 249-251, 263-264, abstract: It seems that you are referring to M as if it is surface forcing or an air-sea temperature difference and contrasting it against static stability as measured by EIS. This is confusing since M is also a stability estimate (essentially a modified form of LTS). This is also inconsistent with your earlier discussions. M can be written as a function of EIS and the air-sea temperature difference (McCoy et al. 2017), is that what you are referencing? Please clarify your meaning and be more accurate in your language.
Line 269: Please explain how you come to this conclusion. Hard to tell from comparing Figure 2 and 5, is this based on a different analysis?
Line 272-276: Great to include the discussion on lines 272-274 of why you have these diurnal cycle differences. It would be very valuable to extend this further to the intriguing regional differences you document (i.e. to add interpretation to Lines 274-276).
Line 277: How are you quantifying “good performance” here? You previously tested and trained on the SO data (and that is presented well in Lang et al. 2022), are you able to similarly check the accuracy for the NP? Or is this from visual inspection?
Line 280-281: Extending this diurnal analysis, either here or in a future paper, would be fantastic. Your results raise so many interesting questions: why are closed MCC NP cycles smaller than SO? Why are they especially small in the summertime? Why are the closed MCC cycles peaking in magnitude in different seasons in the two regions? Why are open MCC cycles much smaller and peaking at different times? What is happening with the remaining contribution of clouds (your “other” type)? You could make a good start at answering these by quantifying the cycle relationships to the meteorological variables you discussed in the first part of the paper.

Technical Corrections:
Line 158: “but is relatively”
Line 167: “Figure 4”
Line 185: “considerably”
Line 246: “current in the Kuroshio region”
Line 248: “MCCs than in the SO”

References:
Agee, E.M., Chen, T.S., Dowell, K.E., 1973. Review of Mesoscale Cellular Convection. Bull. Amer. Meteorol. Soc. 54, 1004–1012. https://doi.org/10.1175/1520-0477(1973)054<1004:aromcc>2.0.co;2
Atkinson, B.W., Zhang, J.W., 1996. Mesoscale shallow convection in the atmosphere. Reviews of Geophysics 34, 403–431. https://doi.org/10.1029/96rg02623
Eastman, R., McCoy, I.L., Wood, R., 2022. Wind, Rain, and the Closed to Open Cell Transition in Subtropical Marine Stratocumulus. JGR Atmospheres 127. https://doi.org/10.1029/2022JD036795
Eastman, R., McCoy, I.L., Wood, R., 2021. Environmental and Internal Controls on Lagrangian Transitions from Closed Cell Mesoscale Cellular Convection over Subtropical Oceans. Journal of the Atmospheric Sciences 78, 2367–2383. https://doi.org/10.1175/Jas-D-20-0277.1
McCoy, I.L., McCoy, D.T., Wood, R., Zuidema, P., Bender, F.A. ‐M., 2023. The Role of Mesoscale Cloud Morphology in the Shortwave Cloud Feedback. Geophysical Research Letters 50. https://doi.org/10.1029/2022GL101042
Scott, R.C., Myers, T.A., Norris, J.R., Zelinka, M.D., Klein, S.A., Sun, M., Doelling, D.R., 2020. Observed Sensitivity of Low-Cloud Radiative Effects to Meteorological Perturbations over the Global Oceans. Journal of Climate 33, 7717–7734. https://doi.org/10.1175/jcli-d-19-1028.1
Vial, J., Vogel, R., Schulz, H., 2021. On the daily cycle of mesoscale cloud organization in the winter trades. Quarterly Journal of the Royal Meteorological Society. https://doi.org/10.1002/qj.4103
Wood, R., 2012. Stratocumulus Clouds. Mon. Weather Rev. 140, 2373–2423. https://doi.org/10.1175/mwr-d-11-00121.1
Citation: https://doi.org/10.5194/egusphere-2023-518-RC2
- AC2: 'Reply on RC2', Francisco Lang, 17 Aug 2023
  
  We thank Reviewer 2 for their helpful comments, which have improved this paper. Please find our responses to your comments in “Ref2_Comments_Answered.pdf”.
  In the next few days, we will submit both the original (old) manuscript and the revised (new) version, with highlighted differences.
  
  Citation: https://doi.org/10.5194/egusphere-2023-518-AC2

Peer review completion

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

AR by Francisco Lang on behalf of the Authors (29 Aug 2023) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (31 Aug 2023) by Graham Feingold

RR by Anonymous Referee #1 (11 Sep 2023)

Suggestions for revision or reasons for rejection

The authors present an improved version of their paper but need to improve further to make a substantial enough contribution. Please find several major issues below that the author should address before publication.

Major concerns

The authors connect meteorological variables to open and closed cellular structures and there is little to no information about their expected connection provided so far. There are several aspects that come to mind that I am missing in the introduction:
(1) What are the expected mechanisms leading to open versus closed cellular organization? And is “closed” considered to be a stage prior to “open”?
(2) Are these mechanisms expected to be identical in both subtropical and mid-latitude clouds? For example, Scott et al. (2020) lists subsidence as an important parameter in the subtropics – could that be relevant for the North Pacific and Southern Ocean?
To better embed the results into the state-of-the-art research, the author should then reflect on these expectations in their discussion. For example, do the authors cover all parameters connected to the expectations or are there parameters missing (e.g., parameters indicative of cloud microphysics)? And importantly, are the correlations unambiguously pointing at certain mechanisms or are there several parameters with high correlation (i.e., regions where parameters are well-correlated with one another)?

The authors decided to examine monthly statistics when calculating correlations between meteorological parameter and occurrence frequency, and it is unclear what motivated this decision. What are the expected timescales of parameters driving cloud organization (e.g., are monthly means expected to resolve these rather than hourly data)? Regarding Section 3.3, why look at the diurnal cycle to begin with?

The authors sometimes equate correlation with causality (e.g., ll. 184-185, l. 316, and elsewhere). The authors should carefully examine their language throughout the paper.

The authors included some discussion elements into the results section (e.g., ll. 177-183, and elsewhere). To create a crisper results section that is largely free of speculation, I strongly urge the authors to move discussion material into Section 4.

Minor concerns

l. 5 Replace “roughly” with “evenly” if that is meant here.

l. 7 Include “presumed” before “meteorological forcings”.

l. 13 Please rephrase sentence (“cycle … cycle”).

l. 27 McCoy et al. (2023) additionally excluded optical depth effects.

ll. 70-72 This sentence seems better suited elsewhere.

ll. 142-143 Please rephrase “colder SST” with “greater SST”, for example.

ll. 175-177 These sentences do not fit well here. Please check if ordered (and placed) correctly.

l. 136 Please also cite Eastman et al. (2022).

Fig. 4 Please show data points of one selected grid box in a separate scatter plot (perhaps as supporting information) and also plot the interdependence of parameters (windspeed, SST, EIS and M).

Fig. 1, 3, 4, 5, 6, 7, 8: If “closed” is considered the stage prior to “open” (see major points), better order plots showing “closed” on the left and “open” on the right.

Section 1: Perhaps better have one paragraph on identification methods (i.e., uniting ll. 28-33 and ll. 43-49) rather having information scattered throughout.

Spelling

l. 54 Replace “region” with “regions”.

Reference(s)

Eastman, R., McCoy, I. L., & Wood, R. (2022). Wind, rain, and the closed to open cell transition in subtropical marine stratocumulus. Journal of Geophysical Research: Atmospheres, 127, e2022JD036795. https://doi.org/10.1029/2022JD036795

Scott, R. C., Myers, T. A., Norris, J. R., Zelinka, M. D., Klein, S. A., Sun, M., & Doelling, D. R. , 2020, Observed Sensitivity of Low-Cloud Radiative Effects to Meteorological Perturbations over the Global Oceans, Journal of Climate, 33(18), 7717-7734. https://doi.org/10.1175/JCLI-D-19-1028.1

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RR by Anonymous Referee #2 (13 Sep 2023)

Suggestions for revision or reasons for rejection

Overall, I am very satisfied with Lang et al.s detailed and thoughtful responses to my and the other reviewer's comments and the many changes that they implemented to improve the final manuscript. I appreciate how receptive the authors were to expanding their analysis, including robuster statistical evaluation, and modifying their text accordingly. Their efforts were likely time consuming but was a good investment as it has strengthened their points. The new correlation maps for annual and seasonal behaviors are a particularly insightful addition as is their expansion of variables considered (i.e., wind speed). The authors also went so far as to expand their diurnal cycle analysis and include an examination of the "other" low clouds where they were able, both of which have helped to fill out their story and motivate future investigations. Their additional analysis and expanded examinations have also helped to distinguish this study from Lang et al. 2022, making this a more novel contribution to the literature. I have a few minor comments and technical suggestions for the updated sections that would be worth considering for the final version. In general, I found the updated Lang et al. manuscript to be an intriguing and well written study that will generate interest in the field and motivate future research. I recommend its speedy publication.

Minor comments:
Line 172: I think this might be a typo (an extra "mean"), but what are "monthly mean MCC cloud frequencies"? I usually think of frequencies as number of occurrences over a time period, the month in this instance, divided by the total number of observations in that period. How are you computing this as an average?
Line 171-193: There are very apparent, statistically significant anti-correlations between closed MCC and SST, M, and wind speed in the NP (and a little in the SO for SST). It would be worth discussing these briefly and what it means for the cloud behaviors in those regions (like you did for the anti-correlations between open MCC and SST). Also worth discussing the anti-correlations between EIS and open MCC, which tells you that you need reduced stability for those clouds to be successful in the storm track. These anti-correlations are quite interesting and influential for the clouds in these regions, likely also impacting the type of cloud that can evolve in the storm track (which you seem to focus on here), and worth noting.
Line 205-206: What do you mean by "better relation" here? Please clarify your meaning, ideally referencing your statistical analysis.
Line 257-258: Thank you for expanding your diurnal cycle analysis to check these! It might be worth including the climatological diurnal cycles for these boxes as well, as they did in Vial et al. 2021, so that you can make sure the lack of signal is consistent with the general behavior. In the tropics they found that there was a diurnal cycle in the climatology and the separate cloud types but I wonder if the forcing is sufficiently strong in the midlatitudes that there isn't as much of a cycle so you only see the offset in mean metric value between MCCs (quite surprising to see in your results).
Line 277-278: I assume because of anthropogenic aerosol? Please include a citation.
Line 278-279: Please also include a citation here.
Line 280-281: Assuming the LWP is the same between the NP and SO? Please clarify and cite supporting literature. Phase also contributes to the optical properties, shown for the SO MCCs in Danker et al. 2022.
Line 322: Can you clarify what you mean by "most part of the region"?

Technical corrections:
Line 193: Might help to mention in the caption how to identify the fronts, a little hard to tell for the SO but clearer for the NP.
Line 203: suggest "...the most pronounced difference between open and closed MCC correlations with meteorology is found..."
Line 203-204: suggest "...illustrates that more frequent closed MCC clouds are..."
Line 205: suggest "...about 5 to 10 K (Fig. 5)."
Line 225-227: It looks like S2 and S3 are the same figures... I do not expect winter and summer correlations to behave identically so please double check that you have the summer and winter correlation figures included correctly here.
Line 227: "closed MCC" instead of "MCC closed"
Line 231: missing s: "large-scale dynamics,"
Line 233: I think you want "Figures 2f, i"
Line 239-241: This seems like a dangling sentence, suggest linking: " During this season, the frequency of occurrence of open MCC clouds is higher compared to summer while the frequency of occurrences of closed MCCs is higher at lower latitudes than at high latitudes during wintertime."
Line 243-244: Would be helpful to put the boxes on one of the figures for reference (fig 3?), a little hard to understand where you are comparing
Line 266: suggest making clearer this is a category label, something like: " ...subcategory "other LLC" (low-level clouds)..."
Line 272: suggest "...2014). Observations..."
Line 286: suggest "...in the SO while..."
Line 314: suggest "...for open MCCs than in the SO..."
Line 322: suggest ". Open MCC clouds..." instead of ". While open MCC clouds...".
Line 333: suggest "In addition..."

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ED: Reconsider after major revisions (27 Sep 2023) by Graham Feingold

AR by Francisco Lang on behalf of the Authors (14 Nov 2023) Author's response Author's tracked changes Manuscript

ED: Publish subject to minor revisions (review by editor) (24 Nov 2023) by Graham Feingold

Dear Authors,
I have reviewed the changes made to the manuscript and made some comments of my own on the revised version, which I will upload separately.
While you have gone to some length to improve the manuscript, I do believe that further changes are in order.

1) I have some difficulty with the fact that the results are not always clear on differences between how processes play out in the subtropics vs. the mid latitudes in SO and NP. A clear example is that of the SCT, which is a subtropical phenomenon driven by warming SST and may include a role for drizzle (e.g., Yamaguchi, Eastman). The discussion on lines 370-377 mixes subtropical-related work (Eastman) with mid-latitude work (Abel) leading the reader to assume that the mechanisms are the same. Another example of a transition is that of a pocket of open cells which occurs via drizzle, without a clear change in meteorology. A more careful categorization/organization of the role of processes driving transitions is required. I believe this is in line with Reviewer 1's major comments (1) and (2).

2) While I believe there is merit in a monthly data analysis, I am not content with your response to reviewer 1 regarding the choice of monthly rather than daily data. I don't understand the response:
"Calculating hourly frequency of occurrence is not feasible because hourly data represents Open and Closed MCC as integer numbers, lacking physical
meaning, and serving solely to distinguish between cloud types." If the 'integer' indicates that an open or closed MCC occurred, why would it not be useful for calculating a freq of occurrence? I also don't follow the logic in the next sentence:
"Furthermore, larger timescales, such as daily frequencies, do not provide a robust sample size for calculating frequencies and correlating them with the meteorological parameters. Thus, we believe that the most robust correlations are derived from monthly frequency of occurrence."
First, daily frequencies are shorter timescales, and these would increase the sample size, though probably also increase the 'noise'. Please provide a clearer explanation of your choice of monthly cycles in the text.

Sincerely,
Graham Feingold

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AR by Francisco Lang on behalf of the Authors (04 Dec 2023) Author's response Author's tracked changes Manuscript

ED: Publish subject to technical corrections (20 Dec 2023) by Graham Feingold

Dear Colleagues,
First thank you for addressing the suggested changes and for going to the effort to look at daily correlations.

I am happy to recommend publication of your manuscript. I do however request that you make the following quick modifications.
1) In response to my request for a clearer separation between MCC in the subtropics and the midlatitudes you have added in the Introduction and the Summary, (lines 44-49; 340-342) text that suggests that "the absence of significant meteorological differences between open and closed MCC clouds suggests a predominant role of the precipitation mechanism". This is misleading. In the subtropics, pockets of open cells (POCs) do indeed exist under very similar meteorological conditions. But you should differentiate between POCs (constant meteorology) and the stratocumulus-to-cumulus transitions in which the meteorology is changing significantly as the air moves south westward (in the N. Hemisphere; warming SST and weakening subsidence). In both cases precipitation has been shown to play either a major role (POCs) or has been suggested to play a role (the SCT; Yamaguchi et al. 2017, Eastman).

Note too that in the current paper you refer to Kazil et al. 2011 as an SCT simulation but that was a POC simulation. When referring to POCs you should refer to early works such as Savic-Jovcic and Stevens (JAS 2008) or even earlier work by Albrecht's group on 'rifts' (Sharon et al. 2006).

2) I'm also not very keen on the statement: 'Drizzle is transported into the stratocumulus layer by turbulence'. I would suggest: 'The drizzle is produced in the penetrating cumulus and detrained into the stratocumulus'.

Sincerely,
Graham Feingold

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AR by Francisco Lang on behalf of the Authors (23 Dec 2023) Author's response Manuscript

Journal article(s) based on this preprint

31 Jan 2024

On the relationship between mesoscale cellular convection and meteorological forcing: comparing the Southern Ocean against the North Pacific

Francisco Lang, Steven T. Siems, Yi Huang, Tahereh Alinejadtabrizi, and Luis Ackermann

Atmos. Chem. Phys., 24, 1451–1466, https://doi.org/10.5194/acp-24-1451-2024,https://doi.org/10.5194/acp-24-1451-2024, 2024

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Francisco Lang, Steven T. Siems, Yi Huang, and Luis Ackermann

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Marine low-level clouds are essential to the Earth system energy balance as they trap heat from the surface and reflect sunlight back to space. These clouds can be recognized by their large-scale spatial structure. The two primary cloud-type patterns for marine low-level clouds are hexagonal with filled (closed) or empty (open) cells. Using satellite observations the two cloud-type patterns are grouped over the Southern Ocean and North Pacific Ocean using a pattern-recognizing program.


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