the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 28 Mar 2023
Water isotopic characterisation of the cloud-circulation coupling in the North Atlantic trades. Part 2: The imprint of the atmospheric circulation at different scales
Abstract. Water vapour isotopes reflect the history of moist atmospheric processes encountered by the vapour since evaporation from the ocean. This study focuses on water isotope variability in the winter trades near Barbados at cloud base, which has been identified as an important level for understanding the net radiative effects of shallow cumuli. The analyses are based on nested convection resolving COSMOiso simulations during the EUREC4A field campaign. The two main findings are that (i) the contrasting isotope and humidity characteristics in clear-sky versus cloudy cloud base environments emerge due to vertical transport on time scales of 12 hours associated with local, convective circulations and show a clear diel cycle; and (ii) the cloud base isotopes are, in addition, sensitive to variations in the large-scale circulation on time scales of several days, which shows on average a Hadley-type subsidence but occasionally much stronger descent related to extratropical dry intrusions. This investigation, based on high-resolution isotope-enabled simulations in combination with trajectory analyses, reveals how dynamical processes at different scales act in concert to produce the observed humidity variations at the base of trade-wind cumuli.
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Notice on discussion status
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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Preprint
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
- RC1: 'Comment on egusphere-2023-450', Anonymous Referee #1, 17 Apr 2023
-
RC2: 'Comment on egusphere-2023-450', Ann Kristin Naumann, 28 Apr 2023
This study discusses isotope enabled simulation results of winter trade wind clouds near Barbados. Focusing on circulations at different time scales, the authors are able to identify signals of cloud-scale circulations, mostly in relation to the diurnal cycle, and large-scale subsidence in cloud-base humidity and isotope signals. I am not an expert is isotopic data but the results are relevant for a wider scientific community interested in marine boundary layer clouds and circulation and fit well into the scope of the journal. The manuscript is well-structured and concise and as far as I can see the methods are well-described and sound.
I think the paper could benefit from a more detailed discussion in particular of section 3 when it comes to the cloud-scale circulations, where some aspects of the proposed mechanisms remain a bit unclear to me. A couple of the more detailed comments below relate to this point.
Knowing that the authors collected an extensive isotope dataset in the region and time frame that is considered here, I cannot help but wonder how the simulations, e.g., in terms of the diurnal cycle, compare to observed isotope signatures. Some of that might be covered in part 1 (not sure about the diurnal cycle though) but if would be great to see some of these observation (or conclusions from part 1) to be picked up here again.
Detailed comments:
- Introduction: To make the paper more accessible to readers that are not familiar with isotopes (like me) it would be helpful to be more specific or basic in how one expects the different isotope measures to reflect atmospheric processes. E.g., is it correct that heavy isotopes are less likely to evaporate from the ocean and hence that atmospheric vapour is lighter than ocean water, i.e., d2H is negative? Are heavy isotopes more likely to condense on cloud droplets so that condensation in clouds leaves even lighter (more negative d2H) vapor behind? Which physical processes affect the relative variation of d2H and d18O? (Is an O-heavy isotope even more unlikely to evaporate than an H-heavy isotope?) In which way are d-excess values or tendencies affected by thermodynamic conditions? How does vertical transport (physically) affect delta2H or d-excess? Maybe it could be helpful add some of the explanation in the caption of Figure 1 to the main text.
- Figure 1: This is a nice illustration. Is it possible to indicate expected dH2 and d-excess values or tendencies in the figure, e.g., by colouring the background according to values or by adding tendencies to the arrows?
- Figure 1 caption: "can have a depleting or enriching effect" On what does this depend?
- I would be helpful to add a short explanation on why you focus on cloud base properties.
- Section 2.1: Although the simulations are described in Part 1, I would wish for a bit more details here. What are the different domains? Are there limitation due to shallow convection not being resolved at 1 - 10 km grid spacing? Where are trajectories started and which time step do they apply? Can you be more specific in how many trajectories you compute?
- l.68: "Every vertical profile": do you mean vertical profiles for each grid point?
- l. 92: Why are dry-warm points of particular interest?
- l. 94: What is the reasoning to calculate hour-of-the-day deviation from the whole simulation period instead of using day-specific hourly means?
- l. 105: How do you choose starting points? Or do you start from all dry-warm cloud base grid points?
- The naming convention of variables in Fig. 4 is not clear to me.
- l. 126: How do vertical velocities between the 1-km and the 5-km compare? Do cloudy updraft strengths differ?
- l. 137: "largely unaffected": Please explain how you arrive at this interpretation. Because the signal is small?
- l. 140: Can you check how well it is closed by multiplying area fractions and vertical velocities of both branches?
- l. 144: It is not clear to me why "the amount of vapour that returns to the cloud base is less"? Because it is colder higher up and therefore saturated q is less?
- l. 149-153: This is not clear to me. Why does the min-max time shift in dry-warm patch characteristics determine the point in time when subsiding air detrained from the cloud?
- l. 154-159: How do your results compare to literature, e.g. observed circulations from George et al. or the diurnal cycle from Vial et al.?
- Fig. 6 and 8 are barely discussed in the text.
- l.200: Why does a deeper circulation need to be stronger?
- l. 204: Do you refer to the few points with low d-excess around q = 8-9 g/kg here? They don't seem special to me in terms of large-scale subsidence (Fig. 8b).
- Please check figures, their captions and references in the text carefully. I noted a few typos/corrections below as technical comments.Technical:
- l. 76: "761, 914, and 1082m" this is not clear
- l. 107: There is no Fig 4b
- Fig. 2: caption and axis labels do not match
- Fig. 3: add "(red)" after "dry-warm"; (b) -> (d)
- Fig. 5: red and green as contrasting colours are not optimal for colour-blind readers; in c please use dashed line style also to distinguish the thin lines
- Fig. 7: What do the two different dates per plot mean?
- l. 135: delete "d-e"Citation: https://doi.org/10.5194/egusphere-2023-450-RC2 - AC1: 'Comment on egusphere-2023-450', Leonie Villiger, 20 Sep 2023
Interactive discussion
Status: closed
- RC1: 'Comment on egusphere-2023-450', Anonymous Referee #1, 17 Apr 2023
-
RC2: 'Comment on egusphere-2023-450', Ann Kristin Naumann, 28 Apr 2023
This study discusses isotope enabled simulation results of winter trade wind clouds near Barbados. Focusing on circulations at different time scales, the authors are able to identify signals of cloud-scale circulations, mostly in relation to the diurnal cycle, and large-scale subsidence in cloud-base humidity and isotope signals. I am not an expert is isotopic data but the results are relevant for a wider scientific community interested in marine boundary layer clouds and circulation and fit well into the scope of the journal. The manuscript is well-structured and concise and as far as I can see the methods are well-described and sound.
I think the paper could benefit from a more detailed discussion in particular of section 3 when it comes to the cloud-scale circulations, where some aspects of the proposed mechanisms remain a bit unclear to me. A couple of the more detailed comments below relate to this point.
Knowing that the authors collected an extensive isotope dataset in the region and time frame that is considered here, I cannot help but wonder how the simulations, e.g., in terms of the diurnal cycle, compare to observed isotope signatures. Some of that might be covered in part 1 (not sure about the diurnal cycle though) but if would be great to see some of these observation (or conclusions from part 1) to be picked up here again.
Detailed comments:
- Introduction: To make the paper more accessible to readers that are not familiar with isotopes (like me) it would be helpful to be more specific or basic in how one expects the different isotope measures to reflect atmospheric processes. E.g., is it correct that heavy isotopes are less likely to evaporate from the ocean and hence that atmospheric vapour is lighter than ocean water, i.e., d2H is negative? Are heavy isotopes more likely to condense on cloud droplets so that condensation in clouds leaves even lighter (more negative d2H) vapor behind? Which physical processes affect the relative variation of d2H and d18O? (Is an O-heavy isotope even more unlikely to evaporate than an H-heavy isotope?) In which way are d-excess values or tendencies affected by thermodynamic conditions? How does vertical transport (physically) affect delta2H or d-excess? Maybe it could be helpful add some of the explanation in the caption of Figure 1 to the main text.
- Figure 1: This is a nice illustration. Is it possible to indicate expected dH2 and d-excess values or tendencies in the figure, e.g., by colouring the background according to values or by adding tendencies to the arrows?
- Figure 1 caption: "can have a depleting or enriching effect" On what does this depend?
- I would be helpful to add a short explanation on why you focus on cloud base properties.
- Section 2.1: Although the simulations are described in Part 1, I would wish for a bit more details here. What are the different domains? Are there limitation due to shallow convection not being resolved at 1 - 10 km grid spacing? Where are trajectories started and which time step do they apply? Can you be more specific in how many trajectories you compute?
- l.68: "Every vertical profile": do you mean vertical profiles for each grid point?
- l. 92: Why are dry-warm points of particular interest?
- l. 94: What is the reasoning to calculate hour-of-the-day deviation from the whole simulation period instead of using day-specific hourly means?
- l. 105: How do you choose starting points? Or do you start from all dry-warm cloud base grid points?
- The naming convention of variables in Fig. 4 is not clear to me.
- l. 126: How do vertical velocities between the 1-km and the 5-km compare? Do cloudy updraft strengths differ?
- l. 137: "largely unaffected": Please explain how you arrive at this interpretation. Because the signal is small?
- l. 140: Can you check how well it is closed by multiplying area fractions and vertical velocities of both branches?
- l. 144: It is not clear to me why "the amount of vapour that returns to the cloud base is less"? Because it is colder higher up and therefore saturated q is less?
- l. 149-153: This is not clear to me. Why does the min-max time shift in dry-warm patch characteristics determine the point in time when subsiding air detrained from the cloud?
- l. 154-159: How do your results compare to literature, e.g. observed circulations from George et al. or the diurnal cycle from Vial et al.?
- Fig. 6 and 8 are barely discussed in the text.
- l.200: Why does a deeper circulation need to be stronger?
- l. 204: Do you refer to the few points with low d-excess around q = 8-9 g/kg here? They don't seem special to me in terms of large-scale subsidence (Fig. 8b).
- Please check figures, their captions and references in the text carefully. I noted a few typos/corrections below as technical comments.Technical:
- l. 76: "761, 914, and 1082m" this is not clear
- l. 107: There is no Fig 4b
- Fig. 2: caption and axis labels do not match
- Fig. 3: add "(red)" after "dry-warm"; (b) -> (d)
- Fig. 5: red and green as contrasting colours are not optimal for colour-blind readers; in c please use dashed line style also to distinguish the thin lines
- Fig. 7: What do the two different dates per plot mean?
- l. 135: delete "d-e"Citation: https://doi.org/10.5194/egusphere-2023-450-RC2 - AC1: 'Comment on egusphere-2023-450', Leonie Villiger, 20 Sep 2023
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Leonie Villiger
Franziska Aemisegger
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(8190 KB) - Metadata XML