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the Creative Commons Attribution 4.0 License.
| 22 Aug 2024
Co-variability drives the inverted-V sensitivity between liquid water path and droplet concentrations
Abstract. Many studies using climatological data of liquid water path (LWP) and droplet concentration (Nd) find an inverted-V relationship, where LWP increases and then decreases with Nd. Our findings suggest that while these LWP responses to changes in Nd align with proposed causal mechanisms, such as entrainment evaporation feedback and precipitation suppression, the inverted-V is primarily driven by the co-variability between LWP and Nd. This co-variability arises from meteorological conditions and microphysical processes, each independently affecting LWP and Nd in opposite directions. We further demonstrate that the inverted-V relationship reflects the climatological evolution of Stratocumulus clouds (Sc). Therefore, background anthropogenic changes in Nd should, in principle, be manifested in changes across the entire Sc climatology along its evolution. Instantaneous LWP response to Nd derived from ship tracks, or other similar natural experiments, may therefore not accurately represent the climatological LWP response. This is because the local perturbations in Nd may not align with the plausible natural co-variability between LWP and Nd, which varies depending on the cloud state along the Sc evolution.
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RC1: 'Comment on egusphere-2024-2245', Anonymous Referee #1, 31 Aug 2024
Review of: “Co-variability drives the inverted-V sensitivity between liquid water path and droplet concentrations” by Goren et al.
This excellent paper addresses LWP-Nd covariability, a highly relevant scientific question within the scope of ACP. The Authors explain in detail how LWP-Nd covariability shapes the LWP-Nd inverted-V relationship. They demonstrate that the inverted-V relationship reflects the climatological evolution of Stratocumulus (Sc) clouds downwind from the coasts. They focus on low-level marine clouds in Sc regions and use MODIS satellite data, ERA5 reanalysis data, and Sc regime classification based on a neural network algorithm.
In my opinion, the conclusions are very well supported by the analysis, and the methods are fully appropriate. I want to thank the Authors for this important work highly relevant to improving the understanding of aerosol impacts on clouds and assessing aerosol forcing of Earth’s climate. I strongly recommend the paper for publication in ACP and suggest a few minor points for the Authors to consider in revising the paper.
Comments:
I suggest highlighting in the introduction that the inverted-V served as an important line of evidence in Ch7 of the Sixth Assessment Report by the Intergovernmental Panel on Climate Change for positive radiative forcing by decreased LWP (Forster et al., 2021) following Gryspeerdt et al. (2019). Moreover, I suggest more explicitly highlighting in the conclusions (and abstract?) that the inverted-V should no longer be used as a line of evidence for positive radiative forcing through LWP responses to aerosols as it is largely explained by the covariability.
I suggest highlighting in the abstract the specific identified drivers of LWP-Nd covariability. Perhaps something similar to the first paragraph on page 12 under the conclusions?
What do you think about adding a table summarising the drivers of LWP-Nd covariability in Sc clouds (for two regimes, i.e. negative and positive LWP-Nd sensitivities)?
I like Table 1. However, besides Table 1, a map with the region boxes would have been most helpful. Perhaps a map would work best in the supplement? A simple base map or a map with Nd and LWP climatologies?
p3l63 Perhaps the method by Choudhury and Goren (2024) could be summarised by a single sentence.
p12l247 “Such Nd perturbations reflect the causal, instantaneous LWP response” I do not understand how a LWP response could be instantaneous. Do you mean with a short characteristic time scale, during which the steady state is not reached? Please note that “Instantaneous LWP response” is also mentioned in the abstract and p12l250.
p1l9 the local perturbations in Nd may not align with the plausible natural co-variability between LWP and Nd; I am not sure what you mean by “align” here
Technical corrections:
I would suggest adding the depicted parameter with units next to the colour bar in all figures with colour bars.
The titles of the main paper and supplement do not match:
“Co-variability drives the inverted-V sensitivity between liquid water path and droplet concentrations”
“Co-Variability, Not Causality, Drives Inverted-V Sensitivity Between Liquid Water Path and Droplet Concentrations”
p3l64 0.25cire by 0.25cire -> circ symbol missing?
Which geographical region does Fig 4. represent?
p3l63 we use a combination of MODIS-derived R and τc -> we use a combination of MODIS-derived R and τc to identify thin clouds
p4l85 Wood (2012) -> (Wood, 2012)
The caption of Fig4: Nd vs gm−2; please correct the units
Fig S4 Could add the region (SEP) in the title so it would be formatted similarly to other figures
Fig 1: What is depicted by thick black arrows? Meridians?
p12l249 “Glassmeier et al. (2021) who showed that such instantaneous Nd perturbations overestimate LWP adjustments” Please rephrase to be more explicit. E.g. you could say “underestimate the decrease in LWP”.
Abstract p1l2 decreases with Nd -> decreases with increasing Nd
p1l2 LWP responses to changes -> LWP responses to increases
p1l19 to changes in Nd -> to increasing Nd
p1l22 “The inverted-V indicates two opposite sensitivity regimes of the response of LWP to Nd” It might be good to rephrase so no causality would be indicated, e.g. “two opposite regimes for changes in LWP corresponding to increase in Nd?” readers might think of a causal response when you say response?
p2l44 “the response of LWP to Nd”; readers might think of a causal response when you say response?
p2l50 “especially when comparing models to observations” - perhaps it would be better to say something like: “and contribute significantly to the differences between models and observations”
p6l118 consistent increase of re with M -> consistent increase of re with less negative M
p9l154 “regime of LWP with Nd” perhaps this could be rephrased using a more formal wording?
Figure S4. “Correlations derived from the LWP-Nd joint histogram bins of the SEP.” Is “correlations” the best wording here? Relationships?
p2l46 “Here, we address and resolve this ambiguity.” I’m not sure if “resolve” is appropriate here, as the problem is likely not fully resolved. Although you identify important drivers of the co-variability, additional drivers may also play an important role…
Fig S3. Would you please add units for M
References
Forster, P., Storelvmo, T., Armour, K., Collins, W., Dufresne, J.-L., Frame, D., Lunt, D., Mauritsen, T., Palmer, M., Watanabe, M., Wild, M., and Zhang, H.: Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity, 2021.
Gryspeerdt, E., Goren, T., Sourdeval, O., Quaas, J., Mülmenstädt, J., Dipu, S., ... & Christensen, M. (2019). Constraining the aerosol influence on cloud liquid water path. Atmospheric Chemistry and Physics, 19(8), 5331-5347.
Citation: https://doi.org/10.5194/egusphere-2024-2245-RC1 -
RC2: 'Comment on egusphere-2024-2245', Anonymous Referee #2, 07 Sep 2024
General comments
How to reliably assess the sensitivity of warm-cloud liquid water path (LWP) to anthropogenic and natural aerosol changes is a long-standing issue in cloud-aerosol-radiation-climate interactions. This paper analyzes an ‘inverted-V’ relationship between LWP and cloud droplet concentration Nd seen in MODIS retrievals over subtropical stratocumulus regions. The authors argue that this relationship is a byproduct of the downwind deepening and microphysical evolution of the cloud regime rather than a useful indicator of the climatological sensitivity of these clouds to an aerosol perturbation.
As the authors point out, past interpretations of satellite-derived LWP-Nd relationships have been controversial because they rely on inferring causation from clever analyses of correlations, complicated by potential satellite retrieval bias in some cloud regimes. To me, this study runs up against this same challenge. It is an appealing, thoughtful analysis that provides a plausible interpretation of the observed LWP-Nd relationship. It is certainly worthy of publication, but it is mostly a story of meteorological caveat emptor rather than showing a better way to use satellite observations to infer cloud adjustments to anthropogenic aerosol effects on Earth’s radiation budget.
The satellite retrieval methodology may be strongly affecting the results:
- Putting aside possible biases associated with subpixel cloud variability, the ‘filtered’ cloud properties are averaged onto a 1°x1° grid (Line 58-59). Are cloud-free pixels being included in this average? I assume not, but this is important to explicitly mention, because if so, cumuliform and open-cell regimes would be expected to favor the left side of the V, because a population of uniform shallow clouds would produce grid-scale LWP and Nd that both scale with the cloud fraction, and therefore scale linearly with each other. This would also produce a low bias in LWP and Nd, if these are then interpreted as representative in-cloud values.
- The arguments in this paper might be more convincingly made using the 1 km pixel-scale retrievals that suffer much less from issues of averaging over a spatially heterogeneous cloud field.
Specific comments
L46: ‘Here, we address and resolve this ambiguity’ - this study, like others before it, suggest that the observed LWP-Nd relationships in different boundary-layer cloud regimes are strongly tied to macroscale controls like boundary-layer depth. What ambiguity have the authors newly resolved?
L64: $^{circ}$
L85: (Wood 2012)
L100-101: On Lines 37-41, the shallow PBL near the coast was also mentioned as a cause of the high Nd, referencing George and Wood (2010).
L106: typo - should be ‘microphysical’
L106: ‘The concurrent opposing changes…should emerge in any approach that samples clouds through their temporal development’: I don’t get this argument. As we see in pockets of open cells, drizzle microphysics alone would tend to produce lower Nd and lower LWP downstream, if it weren’t for downstream deepening of the boundary layer.
L119-127: This is just the Lagrangian view of downstream cloud-topped boundary layer development, which dates back well before Sandu (e.g. Riehl et al. 1951 QJRMS). But note that air is constantly circulating vertically through the boundary layer during this downstream development, and being modified by surface heat and moisture fluxes as well as entrainment from above - this is very different than an individual cloud element that is vertically developing.
L148: ‘non existing’ -> ‘nonexistent’
L149: Add ‘from’ after ‘downwind’
L150-151 and Fig. 3c-d: I get that the Southern Ocean has a lot of high-LWP stratocumulus clouds which form in a different high-latitude synoptic regime with smaller M and less persistent subsidence. But I am not sure what point you are trying to make here with ‘These findings elucidate…’? Are you trying to say that it is noteworthy that Figs. 3b and 3d look different? Is there a reason to think that they would look the same, given the diverse ways that low clouds form and evolve?
L175-183: This argument is appealing, but it would seem to me to apply better at the 1 km pixel scale than at the 1° grid scale (~100 km, much larger than the ultraclean anvil region of precipitating shallow cumulus clusters), which blends the properties of cumulus clouds and ultraclean layers and gives LWPs that are probably representative of neither of these cloud types.
Line 191: How long is a typical high-Nd or a low-Nd period in your data? In the SE Pacific, it takes several days for PBL air to advect from the coastal region to the stratocumulus edge.
L192-198: Nice sensitivity study!
LIne 208: Clarify: ‘the longitudinal displacement’ of what? Also, in the argument that follows, note that there are surely synoptic differences (e.g. in subsidence or inversion strength) between high-Nd and low-Nd days that also help control the Sc edge in addition to the drizzle threshold.
L221: Should be ‘anthropogenic’
L251: I agree with your argument, but even the climatological Nd distribution is not easily interpretable just an response of the cloud regime to some external aerosol perturbation - instead Nd and the clouds co-evolve, depending on synoptic regime, to produce the observed LWP-Nd relationship, e.g. the simple model of Wood et al. 2012, JGR, doi:10.1029/2012JD018305. We can still get useful observational tests of cloud adjustments to anthropogenic aerosol in regions like the NW Pacific downwind from China, where there have been large changes in anthropogenic aerosol over the satellite record.
Table 1: Degrees W and E are reversed in the table. Also, does the definition of the Southeast Atlantic region really go to 10 N? If so, call it the Tropical Atlantic.
Figures:
(1) What are called ‘joint histograms’ are actually ‘bin-averaged plots’. A joint histogram would instead show the relative frequency of each bin.
(2) In Fig. 1, please include an actual histogram vs. Nd and LWP so the reader can appreciate how much of the cloud distribution lies in the different parts of the V. I think Fig. 1 could also be strengthened by including a map of low cloud fraction, from which I think the reader could infer that the region with Nd < 40 cm-3 has more broken, cumuliform cloud for which the MODIS retrievals might be less well suited.
(3) You might want to note that the color relationships in Figs. 2b and 2d are expected from the functional dependence of cloud optical depth and effective radius on LWP and Nd for a homogeneous warm cloud layer. Related to this, is there any upper bound on the MODIS-retrieved reff?
(4) In the caption of Fig. 3, if the Southern Ocean region is as given in Table 1, that doesn’t seem like a ‘small domain’ since it spans over 100 degrees of longitude.
(5) Fig. 3 would be more self-contained if the lat/lon boundaries of the regions were included in the titles above the two rows, e.g. ‘Eastern South Pacific (10-35 S, 75-85 W)
Citation: https://doi.org/10.5194/egusphere-2024-2245-RC2 - AC1: 'Comment on egusphere-2024-2245', Tom Goren, 14 Nov 2024
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-2245', Anonymous Referee #1, 31 Aug 2024
Review of: “Co-variability drives the inverted-V sensitivity between liquid water path and droplet concentrations” by Goren et al.
This excellent paper addresses LWP-Nd covariability, a highly relevant scientific question within the scope of ACP. The Authors explain in detail how LWP-Nd covariability shapes the LWP-Nd inverted-V relationship. They demonstrate that the inverted-V relationship reflects the climatological evolution of Stratocumulus (Sc) clouds downwind from the coasts. They focus on low-level marine clouds in Sc regions and use MODIS satellite data, ERA5 reanalysis data, and Sc regime classification based on a neural network algorithm.
In my opinion, the conclusions are very well supported by the analysis, and the methods are fully appropriate. I want to thank the Authors for this important work highly relevant to improving the understanding of aerosol impacts on clouds and assessing aerosol forcing of Earth’s climate. I strongly recommend the paper for publication in ACP and suggest a few minor points for the Authors to consider in revising the paper.
Comments:
I suggest highlighting in the introduction that the inverted-V served as an important line of evidence in Ch7 of the Sixth Assessment Report by the Intergovernmental Panel on Climate Change for positive radiative forcing by decreased LWP (Forster et al., 2021) following Gryspeerdt et al. (2019). Moreover, I suggest more explicitly highlighting in the conclusions (and abstract?) that the inverted-V should no longer be used as a line of evidence for positive radiative forcing through LWP responses to aerosols as it is largely explained by the covariability.
I suggest highlighting in the abstract the specific identified drivers of LWP-Nd covariability. Perhaps something similar to the first paragraph on page 12 under the conclusions?
What do you think about adding a table summarising the drivers of LWP-Nd covariability in Sc clouds (for two regimes, i.e. negative and positive LWP-Nd sensitivities)?
I like Table 1. However, besides Table 1, a map with the region boxes would have been most helpful. Perhaps a map would work best in the supplement? A simple base map or a map with Nd and LWP climatologies?
p3l63 Perhaps the method by Choudhury and Goren (2024) could be summarised by a single sentence.
p12l247 “Such Nd perturbations reflect the causal, instantaneous LWP response” I do not understand how a LWP response could be instantaneous. Do you mean with a short characteristic time scale, during which the steady state is not reached? Please note that “Instantaneous LWP response” is also mentioned in the abstract and p12l250.
p1l9 the local perturbations in Nd may not align with the plausible natural co-variability between LWP and Nd; I am not sure what you mean by “align” here
Technical corrections:
I would suggest adding the depicted parameter with units next to the colour bar in all figures with colour bars.
The titles of the main paper and supplement do not match:
“Co-variability drives the inverted-V sensitivity between liquid water path and droplet concentrations”
“Co-Variability, Not Causality, Drives Inverted-V Sensitivity Between Liquid Water Path and Droplet Concentrations”
p3l64 0.25cire by 0.25cire -> circ symbol missing?
Which geographical region does Fig 4. represent?
p3l63 we use a combination of MODIS-derived R and τc -> we use a combination of MODIS-derived R and τc to identify thin clouds
p4l85 Wood (2012) -> (Wood, 2012)
The caption of Fig4: Nd vs gm−2; please correct the units
Fig S4 Could add the region (SEP) in the title so it would be formatted similarly to other figures
Fig 1: What is depicted by thick black arrows? Meridians?
p12l249 “Glassmeier et al. (2021) who showed that such instantaneous Nd perturbations overestimate LWP adjustments” Please rephrase to be more explicit. E.g. you could say “underestimate the decrease in LWP”.
Abstract p1l2 decreases with Nd -> decreases with increasing Nd
p1l2 LWP responses to changes -> LWP responses to increases
p1l19 to changes in Nd -> to increasing Nd
p1l22 “The inverted-V indicates two opposite sensitivity regimes of the response of LWP to Nd” It might be good to rephrase so no causality would be indicated, e.g. “two opposite regimes for changes in LWP corresponding to increase in Nd?” readers might think of a causal response when you say response?
p2l44 “the response of LWP to Nd”; readers might think of a causal response when you say response?
p2l50 “especially when comparing models to observations” - perhaps it would be better to say something like: “and contribute significantly to the differences between models and observations”
p6l118 consistent increase of re with M -> consistent increase of re with less negative M
p9l154 “regime of LWP with Nd” perhaps this could be rephrased using a more formal wording?
Figure S4. “Correlations derived from the LWP-Nd joint histogram bins of the SEP.” Is “correlations” the best wording here? Relationships?
p2l46 “Here, we address and resolve this ambiguity.” I’m not sure if “resolve” is appropriate here, as the problem is likely not fully resolved. Although you identify important drivers of the co-variability, additional drivers may also play an important role…
Fig S3. Would you please add units for M
References
Forster, P., Storelvmo, T., Armour, K., Collins, W., Dufresne, J.-L., Frame, D., Lunt, D., Mauritsen, T., Palmer, M., Watanabe, M., Wild, M., and Zhang, H.: Chapter 7: The Earth’s energy budget, climate feedbacks, and climate sensitivity, 2021.
Gryspeerdt, E., Goren, T., Sourdeval, O., Quaas, J., Mülmenstädt, J., Dipu, S., ... & Christensen, M. (2019). Constraining the aerosol influence on cloud liquid water path. Atmospheric Chemistry and Physics, 19(8), 5331-5347.
Citation: https://doi.org/10.5194/egusphere-2024-2245-RC1 -
RC2: 'Comment on egusphere-2024-2245', Anonymous Referee #2, 07 Sep 2024
General comments
How to reliably assess the sensitivity of warm-cloud liquid water path (LWP) to anthropogenic and natural aerosol changes is a long-standing issue in cloud-aerosol-radiation-climate interactions. This paper analyzes an ‘inverted-V’ relationship between LWP and cloud droplet concentration Nd seen in MODIS retrievals over subtropical stratocumulus regions. The authors argue that this relationship is a byproduct of the downwind deepening and microphysical evolution of the cloud regime rather than a useful indicator of the climatological sensitivity of these clouds to an aerosol perturbation.
As the authors point out, past interpretations of satellite-derived LWP-Nd relationships have been controversial because they rely on inferring causation from clever analyses of correlations, complicated by potential satellite retrieval bias in some cloud regimes. To me, this study runs up against this same challenge. It is an appealing, thoughtful analysis that provides a plausible interpretation of the observed LWP-Nd relationship. It is certainly worthy of publication, but it is mostly a story of meteorological caveat emptor rather than showing a better way to use satellite observations to infer cloud adjustments to anthropogenic aerosol effects on Earth’s radiation budget.
The satellite retrieval methodology may be strongly affecting the results:
- Putting aside possible biases associated with subpixel cloud variability, the ‘filtered’ cloud properties are averaged onto a 1°x1° grid (Line 58-59). Are cloud-free pixels being included in this average? I assume not, but this is important to explicitly mention, because if so, cumuliform and open-cell regimes would be expected to favor the left side of the V, because a population of uniform shallow clouds would produce grid-scale LWP and Nd that both scale with the cloud fraction, and therefore scale linearly with each other. This would also produce a low bias in LWP and Nd, if these are then interpreted as representative in-cloud values.
- The arguments in this paper might be more convincingly made using the 1 km pixel-scale retrievals that suffer much less from issues of averaging over a spatially heterogeneous cloud field.
Specific comments
L46: ‘Here, we address and resolve this ambiguity’ - this study, like others before it, suggest that the observed LWP-Nd relationships in different boundary-layer cloud regimes are strongly tied to macroscale controls like boundary-layer depth. What ambiguity have the authors newly resolved?
L64: $^{circ}$
L85: (Wood 2012)
L100-101: On Lines 37-41, the shallow PBL near the coast was also mentioned as a cause of the high Nd, referencing George and Wood (2010).
L106: typo - should be ‘microphysical’
L106: ‘The concurrent opposing changes…should emerge in any approach that samples clouds through their temporal development’: I don’t get this argument. As we see in pockets of open cells, drizzle microphysics alone would tend to produce lower Nd and lower LWP downstream, if it weren’t for downstream deepening of the boundary layer.
L119-127: This is just the Lagrangian view of downstream cloud-topped boundary layer development, which dates back well before Sandu (e.g. Riehl et al. 1951 QJRMS). But note that air is constantly circulating vertically through the boundary layer during this downstream development, and being modified by surface heat and moisture fluxes as well as entrainment from above - this is very different than an individual cloud element that is vertically developing.
L148: ‘non existing’ -> ‘nonexistent’
L149: Add ‘from’ after ‘downwind’
L150-151 and Fig. 3c-d: I get that the Southern Ocean has a lot of high-LWP stratocumulus clouds which form in a different high-latitude synoptic regime with smaller M and less persistent subsidence. But I am not sure what point you are trying to make here with ‘These findings elucidate…’? Are you trying to say that it is noteworthy that Figs. 3b and 3d look different? Is there a reason to think that they would look the same, given the diverse ways that low clouds form and evolve?
L175-183: This argument is appealing, but it would seem to me to apply better at the 1 km pixel scale than at the 1° grid scale (~100 km, much larger than the ultraclean anvil region of precipitating shallow cumulus clusters), which blends the properties of cumulus clouds and ultraclean layers and gives LWPs that are probably representative of neither of these cloud types.
Line 191: How long is a typical high-Nd or a low-Nd period in your data? In the SE Pacific, it takes several days for PBL air to advect from the coastal region to the stratocumulus edge.
L192-198: Nice sensitivity study!
LIne 208: Clarify: ‘the longitudinal displacement’ of what? Also, in the argument that follows, note that there are surely synoptic differences (e.g. in subsidence or inversion strength) between high-Nd and low-Nd days that also help control the Sc edge in addition to the drizzle threshold.
L221: Should be ‘anthropogenic’
L251: I agree with your argument, but even the climatological Nd distribution is not easily interpretable just an response of the cloud regime to some external aerosol perturbation - instead Nd and the clouds co-evolve, depending on synoptic regime, to produce the observed LWP-Nd relationship, e.g. the simple model of Wood et al. 2012, JGR, doi:10.1029/2012JD018305. We can still get useful observational tests of cloud adjustments to anthropogenic aerosol in regions like the NW Pacific downwind from China, where there have been large changes in anthropogenic aerosol over the satellite record.
Table 1: Degrees W and E are reversed in the table. Also, does the definition of the Southeast Atlantic region really go to 10 N? If so, call it the Tropical Atlantic.
Figures:
(1) What are called ‘joint histograms’ are actually ‘bin-averaged plots’. A joint histogram would instead show the relative frequency of each bin.
(2) In Fig. 1, please include an actual histogram vs. Nd and LWP so the reader can appreciate how much of the cloud distribution lies in the different parts of the V. I think Fig. 1 could also be strengthened by including a map of low cloud fraction, from which I think the reader could infer that the region with Nd < 40 cm-3 has more broken, cumuliform cloud for which the MODIS retrievals might be less well suited.
(3) You might want to note that the color relationships in Figs. 2b and 2d are expected from the functional dependence of cloud optical depth and effective radius on LWP and Nd for a homogeneous warm cloud layer. Related to this, is there any upper bound on the MODIS-retrieved reff?
(4) In the caption of Fig. 3, if the Southern Ocean region is as given in Table 1, that doesn’t seem like a ‘small domain’ since it spans over 100 degrees of longitude.
(5) Fig. 3 would be more self-contained if the lat/lon boundaries of the regions were included in the titles above the two rows, e.g. ‘Eastern South Pacific (10-35 S, 75-85 W)
Citation: https://doi.org/10.5194/egusphere-2024-2245-RC2 - AC1: 'Comment on egusphere-2024-2245', Tom Goren, 14 Nov 2024
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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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