the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 10 Dec 2024
Quantifying cloud masking in a single column
Abstract. We add idealized clouds into a single column model and show that the cloud radiative effects as observed from satellites can be reproduced by a combination of high and either low or mid-level clouds. To quantify all-sky climate sensitivity we define a "fixed-cloud-albedo" null hypothesis, which assumes an understanding of how cloud temperatures change, but assumes no change in cloud albedo. This null-hypothesis depends on how clouds are vertically distributed along the temperature profile and how this changes as the surface warms. Drawing only distributions which match the cloud radiative effects of present day observations yields a mean fixed-albedo (also keeping surface albedo fixed) climate sensitivity of 2.2 K, slightly smaller than its clear-sky value. This small number arises from two compensating effects: the dominance of cloud masking of the radiative response, primarily by mid-level clouds which are assumed not to change with temperature, and a reduction of the radiative forcing due to masking effect by high clouds. Giving more prominence to low-level clouds, which are assumed to change their temperature with warming, reduces estimates of the fixed-albedo climate sensitivity to 2.0 K. This provides a baseline to which changes in surface albedo, and a believed reduction in cloud albedo, would add to.
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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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Status: closed
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RC1: 'Comment on egusphere-2024-3829', Anonymous Referee #1, 27 Dec 2024
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AC1: 'Reply on RC1', Lukas Kluft, 14 Mar 2025
As a common note to all reviewers, we thank them for their constructive comments, and for directing us to relevant parts of the literature that we overlooked. We made considerable revisions to address overarching concerns related to: (i) the use of a (too) simple model; (ii) insufficient specificity in the methodology; (iii) lack of discussion related to apparent differences with the existing literature. We also benefited from, and adopted numerous suggestions for improving the presentation throughout.
> 1. Lack of clarity and coherence in title, abstract and the contents [...]
Section 1 and 2 have been substantially rewritten to address the concerns arising from the interpretation of the admittedly simple approach, and lack of precision in the use of terminology and in the description of the methodology.
We also decided to change the title to "A conceptual framework for understanding longwave cloud effects on climate sensitivity" to both highlight the conceptual methodology of the study and to avoid confusion with existing literature on cloud masking.
> 2. In the methodology part, the method is not well docoumented [...]
The methodoloy section now clearly describes the chosen relative humidity RH profile and how it is computed in different background climates. In addition, the way that (optical) cloud parameters are chosen for the different ensemble members is explained in more detail.
> 3. [...] How does the cloud masking effect connect with the fixed-cloud albedo hypothesis?
The substantial rewriting of Sections 1 and 2, as well as the discussion, aims to better introduce the scope of this study and the terminology used. In this case, for example, the assumption of a fixed cloud albedo is necessary to disentangle effects that arise purely from changes in cloud top temperatures. These changes are implemented according to idealised mechanisms (FAP, FAT, PHAT) that link the cloud top height (temperature) to the thermodynamic profile in a well-defined way.
> 4. The writing and presentation need improvement. [...]
The suggestions related to the presentation have been incorporated in the revisions.
Citation: https://doi.org/10.5194/egusphere-2024-3829-AC1
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AC1: 'Reply on RC1', Lukas Kluft, 14 Mar 2025
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RC2: 'Comment on egusphere-2024-3829', Anonymous Referee #2, 08 Jan 2025
Kluft et al. set out to quantify the all-sky climate sensitivity of an RCE model, extending previous work where this has been done for clear-skies only. Their methodology is to conceptualize clouds as a trimodal system (low,middle,high clouds), which respond to climate change following a physically-plausible null hypothesis. That is, low clouds remain fixed in pressure, mid-level clouds remain at the freezing level, and high clouds move upwards following the level of maximum clear-sky convergence. The albedo of the clouds is assumed to be climate-invariant. This improves upon "cloud-locking" techniques where, high clouds are (unphysically) fixed in pressure coordinates.
For the most part, I found the study to be an interesting and enjoyable read. However, I was confused by some of the terminology and methods, and the final section of the paper introduced a lot of new ideas in quick succession which I found hard to follow. I agree with many of the comments posted by the first anonymous reviewer.
Comments:
Figure 1 is excellent, and it would be helpful to also include a schematic (spectral) representation of how clouds mask the radiative forcing.
Figure 3+4: Use the caption to explain what you mean by "No high cloud" etc
L34: I think this requires fixed relative humidity in temperature coordinates ?
L37: incorrect year of Jeevanjee paper
Table 1: Just write out the numbers again rather than using apostrophes
L87: How do you determine the convective top?
L94: Do you include prescribed ozone? This may affect the high clouds through artificial warming of the anvil layer
L102: This is not what "PHAT" stands for in the literature, please change this abbreviation. Also, although this isn't stated explicitly, it seems like your high clouds don't actually maintain a fixed anvil temperature (alluded to in L200)? The level of maximum clear-sky convergence should remain roughly around the same temperature, no? It would be helpful if you clarified this.
L143: You state that the ensemble mean is within the CERES range, but you don't actually show this anywhere. Could you include this in Figure 3?
L162: suggest amending to something like: "...can no longer be reconciled with the CERES data, and thus we do not plot bold lines in Figure 3 for the 'no high clouds' case."
L194: Why do you use such a big temperature increment? dT=6K feels somewhat arbitrary. I doubt it affects the results, but it was a bit distracting to me at first.
L205: I am missing something. I thought including Planckian clouds (BL ones) would increase the magnitude of \lambda? By allowing emission from wavenumbers where water vapor is optically thick? This is what Fig1 (final row) seems to suggest…
Section 4: This section, up to L223, on the masking of surface albedo changes was confusing to me. I struggled to understand how you got to these numbers and how they relate to the idealized RCE calculations you presented earlier.
L250: This isn’t considered in the final calculations presented in Figure 4 though, right?
Overall, I feel there is a tension between the statements in L250 and L223, in one you state that once cloud masking of surface albedo changes is included, the all-sky and clear-sky ECS are similar (for fixed cloud albedo), but in the other line you seem to say that including cloud masking of surface albedo changes enhances ECS. This issue somewhat spoiled the last section of the paper for me. If the authors could clarify this issue, the paper would be an easier read.
Citation: https://doi.org/10.5194/egusphere-2024-3829-RC2 -
AC2: 'Reply on RC2', Lukas Kluft, 14 Mar 2025
As a common note to all reviewers, we thank them for their constructive comments, and for directing us to relevant parts of the literature that we overlooked. We made considerable revisions to address overarching concerns related to: (i) the use of a (too) simple model; (ii) insufficient specificity in the methodology; (iii) lack of discussion related to apparent differences with the existing literature. We also benefited from, and adopted numerous suggestions for improving the presentation throughout.
> For the most part, I found the study to be an interesting and enjoyable read. However, I was confused by some of the terminology and methods, and the final section of the paper introduced a lot of new ideas in quick succession which I found hard to follow. I agree with many of the comments posted by the first anonymous reviewer.
Thank you for the detailed and positive feedback. Section 1 and 2 have been substantially rewritten to address the concerns arising from the lack of precision in the use of terminology and in the description of the methodology.
> Figure 3+4: Use the caption to explain what you mean by “No high cloud” etc
Done.
> L34: I think this requires fixed relative humidity in temperature coordinates ?
Yes, that's correct. Our simulations prescribed a fixed RH(T) relation and this is now more clearly stated in the methodology.
> L37: incorrect year of Jeevanjee paper
Fixed.
> Table 1: Just write out the numbers again rather than using apostrophes
We think the apostrophes help to emphasise when numbers do not change. We explain this notation in the caption.
> L87: How do you determine the convective top?
It is simply the first level that doesn't need to be convectively adjusted anymore, i.e., the radiative equilibrium profile is stable (or warmer) than the moist adiabat rising from the surface. We have revised the text to make this more clear.
> L94: Do you include prescribed ozone? This may affect the high clouds through artificial warming of the anvil layer
Yes, we do include a fixed ozone profile following the RCEMIP protocol. It is true that this has an impact on the temperature profile in the tropopause region. However, our high-clouds are still located in the "covective" part of the troposphere. Therefore, their temperature change should solely depend on the moist-adiabat and the radiativelt driven divergence.
> L102: This is not what “PHAT” stands for in the literature, please change this abbreviation. Also, although this isn’t stated explicitly, it seems like your high clouds don’t actually maintain a fixed anvil temperature (alluded to in L200)? The level of maximum clear-sky convergence should remain roughly around the same temperature, no? It would be helpful if you clarified this.
We included the proper long name and references for "PHAT". The level of maximum clear-sky convergence warms roughly at a rate of 0.2 K/K.
> L143: You state that the ensemble mean is within the CERES range, but you don’t actually show this anywhere. Could you include this in Figure 3?
Yes, we agree that this would be useful and we added the CERES reference values to the Figure (x-ticks and caption) and the text.
> L162: suggest amending to something like: “…can no longer be reconciled with the CERES data, and thus we do not plot bold lines in Figure 3 for the ‘no high clouds’ case.”
Done.
> L194: Why do you use such a big temperature increment? dT=6K feels somewhat arbitrary. I doubt it affects the results, but it was a bit distracting to me at first.
The somewhat large dT is used to get a clear signal in cloud-top changes. As the cloud-top is always tied to one distinct model level it also can only rise in steps of full model levels. As a consequence, for a small dT some ensemble members might see no change in cloud-top at all. Using a larger dT ensures a clearer signal in cloud-top height while not changing the qualitative results. We also added this explanation to the methods section.
> L205: I am missing something. I thought including Planckian clouds (BL ones) would increase the magnitude of \lambda? By allowing emission from wavenumbers where water vapor is optically thick? This is what Fig1 (final row) seems to suggest…
This effect is visible in Fig 4., where \lambda is decreased in the absence of low-level clouds (Planckian clouds), or vice versa, the presence of low clouds increases the magnitude of \lambda.
> Section 4: This section, up to L223, on the masking of surface albedo changes was confusing to me. I struggled to understand how you got to these numbers and how they relate to the idealized RCE calculations you presented earlier.
The discussion section has been rewritten to address this and other conerns raised in other reviewer comments.
> L250: This isn’t considered in the final calculations presented in Figure 4 though, right?
Correct, this is just a back-of-the-envelope calculation to put the results into perspective.
Citation: https://doi.org/10.5194/egusphere-2024-3829-AC2
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AC2: 'Reply on RC2', Lukas Kluft, 14 Mar 2025
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RC3: 'Comment on egusphere-2024-3829', Anonymous Referee #3, 16 Jan 2025
This study has potentially high intellectual value as it aims to build a conceptual model for understanding clouds' impacts on climate sensitivity. The current paper however is less vigorous than what I would expect of an ACP paper. For now, I will focus my comments on two major issues:
- A general concern is that the model setup is too idealized to be taken seriously for understanding the climate and its sensitivity in reality. The moist-adiabat setup for example can be thought to represent tropics but would fail badly for extratropical climates. The use of a single thermodynamical profile may also decouple cloud and non-cloud (e.g., humidity) fields and doesn’t represent their covariability in real climate. To address this issue, the covariations of the key variables (cloud, water vapor, temperature) should be examined and compared to observations to establish confidence on the model.
Moreover, fitting/constraining a global mean CRE isn't a sufficient validation to me. For the climate sensitivity problem concerned here, it would be more relevant to validate such aspects of the climate as the Ts-OLR and Ts-Net relations (under different prescribed temperatures). This can and should be compared to comprehensive GCM results and/or observations (e.g., CERES). See Feng et al.: DOI 10.1088/1748-9326/acfb98 as an example that can be compared to.
- The current paper offers too little information from the experiments for readers to understand and appreciate the results and conclusion. For example, although the central argument hinges on radiation spectrum, this is merely illustrated by a schematic (Fig 1) and not evidenced by any modelling results. For this matter, note RRTMG has the ability to output band-by-band radiation fields (e.g., Huang & Huang: DOI 10.1088/1748-9326/ad3e17) or even at g-points (Chen et al.: https://doi.org/10.1029/2023GL106433). Moreover, as showed by Huang et al. (DOI 10.1038/s41597-024-03080-y) showed, OLR changes apparently differ dramatically in different regional climates and noticeably deviate from Simpsonian response. Can and need the simple model represent such reality? This ought to be considered and discussed in relevance to these papers.
Other comments:
Eq. 1: is this right? Sum or product?
L153: This doesn’t represent the C-shaped cloud distribution in the reality.
Citation: https://doi.org/10.5194/egusphere-2024-3829-RC3 -
AC3: 'Reply on RC3', Lukas Kluft, 14 Mar 2025
As a common note to all reviewers, we thank them for their constructive comments, and for directing us to relevant parts of the literature that we overlooked. We made considerable revisions to address overarching concerns related to: (i) the use of a (too) simple model; (ii) insufficient specificity in the methodology; (iii) lack of discussion related to apparent differences with the existing literature. We also benefited from, and adopted numerous suggestions for improving the presentation throughout.
> A general concern is that the model setup is too idealized to be taken seriously for understanding the climate and its sensitivity in reality. [..]
> The current paper offers too little information from the experiments for readers to understand and appreciate the results and conclusion.
Sections 1 and 2 have been substantially rewritten to address concerns arising from the interpretation of the admittedly simplistic approach. This rewriting makes a particular effort to motivate the idealised study, and present its limitations. We agree that the model setup is too simple to derive quantitative results directly comparable to reality. However, the goal is to understand how the (vertical) distribution of clouds in temperature space affects the radiative response, which requires a well-defined thermodynamic state.
> [...] Moreover, as showed by Huang et al. (DOI 10.1038/s41597-024-03080-y) showed, OLR changes apparently differ dramatically in different regional climates and noticeably deviate from Simpsonian response.
We agree that the actual changes in OLR with temperature depend on subtelties of atmospheric composition. For example, even small changes in RH will result in a radiative response that is very different from a Simpsonian (non-)response. The strength of an idealised modelling strategy, as in our study, is that it can test specific hypotheses, in our case the role of different (but plausible) changes in cloud in temperature space, while keeping their albedo constant.
> Can and need the simple model represent such reality? This ought to be considered and discussed in relevance to these papers.
We would argue that it cannot represent such reality but that is also does not need to give answer questions in the scope of this study. We hope that the substantial rewriting of the introcution and discussion now better communicates this point.
> Eq. 1: is this right? Sum or product?
The symbols in both equations are correct, we take the product of individual changes in Eq. 1 and sum over the fluxes in Eq. 3
Citation: https://doi.org/10.5194/egusphere-2024-3829-AC3
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RC4: 'Comment on egusphere-2024-3829', Anonymous Referee #4, 20 Jan 2025
Review of “Quantifying cloud masking in a single column”
By Lukas Kluft, Bjorn Stevens, Manfred Brath, and Stefan A. Buehler
Recommendation: Major Revisions
This manuscript addresses an interesting, yet underexplored question: how do clouds modify the Earth’s climate sensitivity, relative to a clear-sky atmosphere? It makes a first attempt at an answer with a single column climate model using cloud parameters that have been selected to reproduce the observed global mean cloud radiative effect, and whose changes with surface warming follow simple, physically based rules. The approach is novel, physically transparent, and complementary to more traditional approaches involving observations or general circulation models. However, I feel this manuscript could do a much better job in citing the relevant literature (only one cited reference was published after 2021 — and it was written by two of the authors of this manuscript). In addition, the observations used to validate the single column model were not explicitly stated or plotted anywhere. The conclusion section could also be improved to put the final result (that the effect of clouds on climate sensitivity is small) into context.
For these reasons, I recommend major revisions for this manuscript. I provide more detailed comments below. If they are addressed, then I would be happy to recommend the revised manuscript for publication.
Major comments
A more thorough assessment of the literature.
- L18-20 A review paper on cloud feedbacks could be put here to illustrate your point that shortwave cloud effects are discussed at more length than longwave cloud effects. (Ceppi et al, 2017 comes to mind.)
- L29 The masking or unmasking of the clear-sky spectral response by clouds, though not well appreciated in the literature, has been mentioned in papers such as McKim et al, 2021 and Jeevanjee et al, 2023.
- L50 This may generally be the case, but some studies have tried (e.g. Raghuraman et al, 2023)
- L56 Has this been noted in the literature? Perhaps in Held and Soden, 2000; or Colman and Soden, 2021? Or Po-Chedley et al, 2019?
- L80 Many more references could be put here, such as Koll et al, 2023; Feng et al, 2023; Jeevanjee et al, 2021; Stevens and Kluft, 2023; Jeevanjee et al, 2023; Roemer et al, 2023.
- L99-101 Citations and/or physical arguments for why low level clouds following a fixed cloud pressure and mid-level clouds following a fixed cloud temperature are reasonable null hypotheses would be appreciated.
- L102 A reference to PHAT would be appreciated (Zelinka and Hartmann, 2010; Bony et al, 2016)
- L107 A reference on the Monte-Carlo Independent Column Approximation (MCICA) would be appreciated.
- L154 A reference showing observed cloud fractions would be appreciated here
- L232 A number of newer papers show a small high cloud area (or amount) feedback more convincingly than in Ceppi et al, 2017. They include Sokol et al, 2024; McKim et al, 2024; Raghuraman et al, 2024; Stouffer et al, 2023.
Single column cloud model validation
- L142, 162, and Figure 3 - Perhaps I missed it, but are the observed CERES values of CRENET, CRESW, and CRELW explicitly stated or plotted anywhere? Doing so would help the reader evaluate the fitness of the simple model.
Incomplete conclusion section
- L251 This is an interesting finding and I would appreciate a bit more discussion on what this tells us about previous studies and what it means for future studies. For instance, can it explain Raghuraman et al 2023 which showed a near-zero trend in the net CRE over the past 20 years? How can this insight help us to better interpret observations? Etc.
Minor comments
- The importance of shortwave effects are established in the first paragraph, so maybe the way to frame the second paragraph is to say that the longwave effects of clouds are less appreciated, yet they may have just as substantial an effect on climate sensitivity.
- It might help to explicitly state your main research question in the introduction, “How do clouds modify the Earth’s climate sensitivity, relative to a clear-sky atmosphere”?
- L36 Should be Jeevanjee et al, 2021; not 2010.
- L99 Perhaps it should be “fixed cloud pressure” rather than “fixed anvil pressure”, since you’re talking about low clouds?
- L101 Perhaps it should be “fixed cloud temperature” rather than “fixed anvil temperature” since you’re talking about mid-level clouds?
- L103 PHAT should be defined
- L142 It looks like it is between -83 W/m^2 and -28 W/m^2. Perhaps you could differentiate the minus sign from the dash more clearly? Or write it as [-83, 28] W/m^2?
- L197 - 200 I would appreciate seeing a plot of the cloud fraction profiles (at Ts=285, 291 K) as a function of temperature and then as a function of pressure. The plot itself could be annotated to point to where clouds follow fixed pressure, fixed temperature, and maximum radiative divergence. This would help to illustrate to the reader this idealized, but elegant approach to modeling clouds in a single column model, which I think could be used in many future studies to great effect.
References
- Bony et al, 2016 - Thermodynamic control of anvil cloud amount
- Ceppi et al, 2017 - Cloud feedback mechanisms and their representation in global climate models\
- Colman and Soden, 2021 - Water vapor and lapse rate feedbacks in the climate system
- Feng et al, 2023 - How a stable greenhouse effect on Earth is maintained under global warming
- Held and Soden, 2000 - Water vapor feedback and global warming
- Jeevanjee et al, 2021 - Simpson's Law and the Spectral Cancellation of Climate Feedbacks
- Jeevanjee et al, 2023 - Climate Sensitivity from Radiative-Convective Equilibrium: a Blackboard Approach
- Koll et al, 2023 - An Analytical Model for the Clear-Sky Longwave Feedback
- McKim et al, 2021 - Joint Dependence of Longwave Feedback on Surface Temperature and Relative Humidity
- McKim et al, 2024 - Weak anvil cloud area feedback suggested by physical and observational constraints
- Po-Chedley et al, 2019 - Climatology Explains Intermodel Spread in Tropical Upper Tropospheric Cloud and Relative Humidity Response to Greenhouse Warming
- Raghuraman et al, 2023 - Forcing, Cloud Feedbacks, Cloud Masking, and Internal Variability in the Cloud Radiative Effect Satellite Record
- Raghuraman et al, 2024 - Observational Quantification of Tropical High Cloud Changes and Feedbacks
- Roemer et al, 2023 - Direct observation of Earth’s spectral long-wave feedback parameter
- Sokol et al, 2024 - Greater climate sensitivity implied by anvil cloud thinning.
- Staufer et al, 2023 - Explicitly Resolved Cloud Feedbacks in the Radiative-Convective Equilibrium Model Intercomparison Project
- Zelinka and Hartmann, 2010 - Why is longwave cloud feedback positive?
Citation: https://doi.org/10.5194/egusphere-2024-3829-RC4 -
AC4: 'Reply on RC4', Lukas Kluft, 14 Mar 2025
As a common note to all reviewers, we thank them for their constructive comments, and for directing us to relevant parts of the literature that we overlooked. We made considerable revisions to address overarching concerns related to: (i) the use of a (too) simple model; (ii) insufficient specificity in the methodology; (iii) lack of discussion related to apparent differences with the existing literature. We also benefited from, and adopted numerous suggestions for improving the presentation throughout.
> However, I feel this manuscript could do a much better job in citing the relevant literature (only one cited reference was published after 2021 [...]
We appreciate the reviewers list of more recent literature, which we happily included to the manuscript. The lack of post-2021 literature was due to the fact that most of the manuscript was written in 2021 and for a vareity of reasons was put aside before being in late 2024. When finishing we made the mistake of only updating our own manuscript in the references. Please forgive this oversight.
> L142, 162, and Figure 3 - Perhaps I missed it, but are the observed CERES values of CRENET, CRESW, and CRELW explicitly stated or plotted anywhere? Doing so would help the reader evaluate the fitness of the simple model.
Thanks for the catch. The numbers are now included in the Figure 3 and the text.
> L251 This is an interesting finding and I would appreciate a bit more discussion on what this tells us about previous studies and what it means for future studies. For instance, can it explain Raghuraman et al 2023 which showed a near-zero trend in the net CRE over the past 20 years? How can this insight help us to better interpret observations? Etc.
Thanks. As noted above, we had mostly finished the manuscript before the Raghuraman study was submitted and failed to catch up with the literature in between this point and the submission some years later. Raghuraman et al 2023 show a cooling trend from changing LW CRE. Our analysis leads us to expect a warming (amplifying) effect from the radiative response to warming, and a cooling effect from the masking of the radiative forcing, with the latter being somewhat stronger. Raghuraman et al also measure a cooling effect consistent in sign, but larger in magnitude than what we would have expected. Because the shortwave effect is opposite, the discrepancy, is likely associated with changes in cloud fraction, wich our model does not consider. We have expanded our discussion to put them in the context of earlier findings by Raghuraman et al.
is very relevant and we have elaborated our disucssion to better address this important study.
> L99 Perhaps it should be “fixed cloud pressure” rather than “fixed anvil pressure”, since you’re talking about low clouds?
> L101 Perhaps it should be “fixed cloud temperature” rather than “fixed anvil temperature” since you’re talking about mid-level clouds?We decided to stick to the term anvil to refer to the cloud top region, as it allows consistency with existing terminology for high clouds. We added a footnote to explain this choice and hopefully avoid confusion.
> L103 PHAT should be defined
Done.
> L142 It looks like it is between -83 W/m^2 and -28 W/m^2. Perhaps you could differentiate the minus sign from the dash more clearly? Or write it as [-83, 28] W/m^2?
Done.
> L197 - 200 I would appreciate seeing a plot of the cloud fraction profiles (at Ts=285, 291 K) as a function of temperature
As the cloud fraction is kept constant between simulations at different Ts, a plot as a function of T (or height) seemed to obscure the relevant numbers. However, we see that the change in cloud top temperature for the different cloud types is of interest, and is therefore shown in the table below:
Surface Low cloud Mid-level cloud High cloud 285 K 278.05 K 269.97 K 215.29 K 291 K 284.84 K 269.61 K 216.35 K Citation: https://doi.org/10.5194/egusphere-2024-3829-AC4
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RC5: 'Comment on egusphere-2024-3829', Anonymous Referee #5, 21 Jan 2025
Review of “Quantifying cloud masking in a single column” by Kluft et al.
The paper uses an idealized framework to make progress toward a simple derivation of all-sky climate sensitivity. I like the idea, but I think the simplicity of the set up may be hampering the realism of the values.
Major Comment
My main concern is that the cloud masking radiative response to warming is amplifying, rather than stabilizing, warming. In Figure 4, \lambda_clear = -1.93 Wm^-2K^-1 and the introduction of clouds makes this less stabilizing with \lambda_all = -1.67 Wm^-2K^-1. Several studies on cloud masking show that the cloud masking feedback is stabilizing (Soden et al., 2004, 2008; Yoshimori et al., 2020; Raghuraman et al., 2023a). How do the authors reconcile their results with the past work? Could you provide the LW and SW breakdown? Additionally, could you provide the masking due to specific terms such as water vapor, surface temperature, surface albedo, etc.?
Minor Comments
L35: This “Simpsonian” response is stated as a fact of matter, when in reality, emission does change with warming in bands outside of the window (Feng et al., 2023; Koll et al., 2023). Altogether, atmospheric emission contributes ~40% to the global-mean clear-sky longwave feedback parameter due to contributions from water vapor’s rotational and rotational-vibrational bands and other greenhouse gas bands such as CO2, CH4, N2O, O3 (Raghuraman et al., 2019, 2023b).
L100: Please provide more details on why mid-clouds follow FAT while high-clouds follow PHAT. The latter is well documented but providing some references and explanations for the former would help the reader.
References
Feng, J., Paynter, D., & Menzel, R. (2023). How a stable greenhouse effect on Earth is maintained under global warming. Journal of Geophysical Research: Atmospheres, 128(9), e2022JD038124.
Koll, D. D., Jeevanjee, N., & Lutsko, N. J. (2023). An analytic model for the clear-sky longwave feedback. Journal of the Atmospheric Sciences, 80(8), 1923-1951.
Raghuraman, SP., Paynter, D., Menzel, R., & Ramaswamy, V. (2023). Forcing, cloud feedbacks, cloud masking, and internal variability in the cloud radiative effect satellite record. Journal of Climate, 36(12), 4151-4167.
Raghuraman, S. P., Paynter, D., Ramaswamy, V., Menzel, R., & Huang, X. (2023). Greenhouse gas forcing and climate feedback signatures identified in hyperspectral infrared satellite observations. Geophysical Research Letters, 50(24), e2023GL103947.
Soden, B. J., Broccoli, A. J., & Hemler, R. S. (2004). On the use of cloud forcing to estimate cloud feedback. Journal of climate, 17(19), 3661-3665.
Soden, B. J., Held, I. M., Colman, R., Shell, K. M., Kiehl, J. T., & Shields, C. A. (2008). Quantifying climate feedbacks using radiative kernels. Journal of Climate, 21(14), 3504-3520.
Yoshimori, M., Lambert, F. H., Webb, M. J., & Andrews, T. (2020). Fixed anvil temperature feedback: Positive, zero, or negative?. Journal of Climate, 33(7), 2719-2739.
Citation: https://doi.org/10.5194/egusphere-2024-3829-RC5 -
AC5: 'Reply on RC5', Lukas Kluft, 14 Mar 2025
As a common note to all reviewers, we thank them for their constructive comments, and for directing us to relevant parts of the literature that we overlooked. We made considerable revisions to address overarching concerns related to: (i) the use of a (too) simple model; (ii) insufficient specificity in the methodology; (iii) lack of discussion related to apparent differences with the existing literature. We also benefited from, and adopted numerous suggestions for improving the presentation throughout
> The paper uses an idealized framework to make progress toward a simple derivation of all-sky climate sensitivity. I like the idea, but I think the simplicity of the set up may be hampering the realism of the values.
Thank for the general intereset in the idea. We agree that the quantitative results of this study are hard to compare to the real Earth. However, the goal of this study is to understand (some of) the underlying physical mechanisms in a clearer way. An idealised model is helpful for this purpose, something we now try to better motivate through a rewriting of sections 1 and 2, as well as the discussion .
> [...] How do the authors reconcile their results with the past work? Could you provide the LW and SW breakdown? Additionally, could you provide the masking due to specific terms such as water vapor, surface temperature, surface albedo, etc.?
The discussion has been revised to address differences to previous studies, especially apparent differences in the sign of important terms. The apparent discrepanceies weree due to different definitions of masking, and some uphysical effects that arise from the assumption of unphysical states as the reference for feedback studies.
> L35: This “Simpsonian” response is stated as a fact of matter, when in reality, emission does change with warming in bands outside of the window (Feng et al., 2023; Koll et al., 2023). Altogether, atmospheric emission contributes ~40% to the global-mean clear-sky longwave feedback parameter due to contributions from water vapor’s rotational and rotational-vibrational bands and other greenhouse gas bands such as CO2, CH4, N2O, O3 (Raghuraman et al., 2019, 2023b).
We agree that the Simpsonian response doesn't hold in many real-world scenarios. However, we argue that the reason for this is that the prerequisites, in particular the constancy of RH, are not met. Even subtle changes in RH will lead to noticeable changes in OLR in supposedly Simpsonian parts of the spectrum. The rewrite of the Discussion section makes it clearer that this imposes limitations on how far our results can (and should) be compared quantitatively to the real world. However, we remain convinced that a well-defined idealised model helps to disentangle contributions from cloud top changes.
> L100: Please provide more details on why mid-clouds follow FAT while high-clouds follow PHAT. The latter is well documented but providing some references and explanations for the former would help the reader.
Two references are now provided for mid-level clouds following FAT, one is the paper by Stevens et al (2017) which associates their accumulation with processes at the melting level (what those authors call the first mover hypothesis), the other is a very interesting and recent paper by Spaulding and Mitchell (AGU Advances 2025) which makes a radiative argument for them appearing at water vapor specific humidities typical of the freezing level.
Citation: https://doi.org/10.5194/egusphere-2024-3829-AC5
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AC5: 'Reply on RC5', Lukas Kluft, 14 Mar 2025
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-3829', Anonymous Referee #1, 27 Dec 2024
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AC1: 'Reply on RC1', Lukas Kluft, 14 Mar 2025
As a common note to all reviewers, we thank them for their constructive comments, and for directing us to relevant parts of the literature that we overlooked. We made considerable revisions to address overarching concerns related to: (i) the use of a (too) simple model; (ii) insufficient specificity in the methodology; (iii) lack of discussion related to apparent differences with the existing literature. We also benefited from, and adopted numerous suggestions for improving the presentation throughout.
> 1. Lack of clarity and coherence in title, abstract and the contents [...]
Section 1 and 2 have been substantially rewritten to address the concerns arising from the interpretation of the admittedly simple approach, and lack of precision in the use of terminology and in the description of the methodology.
We also decided to change the title to "A conceptual framework for understanding longwave cloud effects on climate sensitivity" to both highlight the conceptual methodology of the study and to avoid confusion with existing literature on cloud masking.
> 2. In the methodology part, the method is not well docoumented [...]
The methodoloy section now clearly describes the chosen relative humidity RH profile and how it is computed in different background climates. In addition, the way that (optical) cloud parameters are chosen for the different ensemble members is explained in more detail.
> 3. [...] How does the cloud masking effect connect with the fixed-cloud albedo hypothesis?
The substantial rewriting of Sections 1 and 2, as well as the discussion, aims to better introduce the scope of this study and the terminology used. In this case, for example, the assumption of a fixed cloud albedo is necessary to disentangle effects that arise purely from changes in cloud top temperatures. These changes are implemented according to idealised mechanisms (FAP, FAT, PHAT) that link the cloud top height (temperature) to the thermodynamic profile in a well-defined way.
> 4. The writing and presentation need improvement. [...]
The suggestions related to the presentation have been incorporated in the revisions.
Citation: https://doi.org/10.5194/egusphere-2024-3829-AC1
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AC1: 'Reply on RC1', Lukas Kluft, 14 Mar 2025
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RC2: 'Comment on egusphere-2024-3829', Anonymous Referee #2, 08 Jan 2025
Kluft et al. set out to quantify the all-sky climate sensitivity of an RCE model, extending previous work where this has been done for clear-skies only. Their methodology is to conceptualize clouds as a trimodal system (low,middle,high clouds), which respond to climate change following a physically-plausible null hypothesis. That is, low clouds remain fixed in pressure, mid-level clouds remain at the freezing level, and high clouds move upwards following the level of maximum clear-sky convergence. The albedo of the clouds is assumed to be climate-invariant. This improves upon "cloud-locking" techniques where, high clouds are (unphysically) fixed in pressure coordinates.
For the most part, I found the study to be an interesting and enjoyable read. However, I was confused by some of the terminology and methods, and the final section of the paper introduced a lot of new ideas in quick succession which I found hard to follow. I agree with many of the comments posted by the first anonymous reviewer.
Comments:
Figure 1 is excellent, and it would be helpful to also include a schematic (spectral) representation of how clouds mask the radiative forcing.
Figure 3+4: Use the caption to explain what you mean by "No high cloud" etc
L34: I think this requires fixed relative humidity in temperature coordinates ?
L37: incorrect year of Jeevanjee paper
Table 1: Just write out the numbers again rather than using apostrophes
L87: How do you determine the convective top?
L94: Do you include prescribed ozone? This may affect the high clouds through artificial warming of the anvil layer
L102: This is not what "PHAT" stands for in the literature, please change this abbreviation. Also, although this isn't stated explicitly, it seems like your high clouds don't actually maintain a fixed anvil temperature (alluded to in L200)? The level of maximum clear-sky convergence should remain roughly around the same temperature, no? It would be helpful if you clarified this.
L143: You state that the ensemble mean is within the CERES range, but you don't actually show this anywhere. Could you include this in Figure 3?
L162: suggest amending to something like: "...can no longer be reconciled with the CERES data, and thus we do not plot bold lines in Figure 3 for the 'no high clouds' case."
L194: Why do you use such a big temperature increment? dT=6K feels somewhat arbitrary. I doubt it affects the results, but it was a bit distracting to me at first.
L205: I am missing something. I thought including Planckian clouds (BL ones) would increase the magnitude of \lambda? By allowing emission from wavenumbers where water vapor is optically thick? This is what Fig1 (final row) seems to suggest…
Section 4: This section, up to L223, on the masking of surface albedo changes was confusing to me. I struggled to understand how you got to these numbers and how they relate to the idealized RCE calculations you presented earlier.
L250: This isn’t considered in the final calculations presented in Figure 4 though, right?
Overall, I feel there is a tension between the statements in L250 and L223, in one you state that once cloud masking of surface albedo changes is included, the all-sky and clear-sky ECS are similar (for fixed cloud albedo), but in the other line you seem to say that including cloud masking of surface albedo changes enhances ECS. This issue somewhat spoiled the last section of the paper for me. If the authors could clarify this issue, the paper would be an easier read.
Citation: https://doi.org/10.5194/egusphere-2024-3829-RC2 -
AC2: 'Reply on RC2', Lukas Kluft, 14 Mar 2025
As a common note to all reviewers, we thank them for their constructive comments, and for directing us to relevant parts of the literature that we overlooked. We made considerable revisions to address overarching concerns related to: (i) the use of a (too) simple model; (ii) insufficient specificity in the methodology; (iii) lack of discussion related to apparent differences with the existing literature. We also benefited from, and adopted numerous suggestions for improving the presentation throughout.
> For the most part, I found the study to be an interesting and enjoyable read. However, I was confused by some of the terminology and methods, and the final section of the paper introduced a lot of new ideas in quick succession which I found hard to follow. I agree with many of the comments posted by the first anonymous reviewer.
Thank you for the detailed and positive feedback. Section 1 and 2 have been substantially rewritten to address the concerns arising from the lack of precision in the use of terminology and in the description of the methodology.
> Figure 3+4: Use the caption to explain what you mean by “No high cloud” etc
Done.
> L34: I think this requires fixed relative humidity in temperature coordinates ?
Yes, that's correct. Our simulations prescribed a fixed RH(T) relation and this is now more clearly stated in the methodology.
> L37: incorrect year of Jeevanjee paper
Fixed.
> Table 1: Just write out the numbers again rather than using apostrophes
We think the apostrophes help to emphasise when numbers do not change. We explain this notation in the caption.
> L87: How do you determine the convective top?
It is simply the first level that doesn't need to be convectively adjusted anymore, i.e., the radiative equilibrium profile is stable (or warmer) than the moist adiabat rising from the surface. We have revised the text to make this more clear.
> L94: Do you include prescribed ozone? This may affect the high clouds through artificial warming of the anvil layer
Yes, we do include a fixed ozone profile following the RCEMIP protocol. It is true that this has an impact on the temperature profile in the tropopause region. However, our high-clouds are still located in the "covective" part of the troposphere. Therefore, their temperature change should solely depend on the moist-adiabat and the radiativelt driven divergence.
> L102: This is not what “PHAT” stands for in the literature, please change this abbreviation. Also, although this isn’t stated explicitly, it seems like your high clouds don’t actually maintain a fixed anvil temperature (alluded to in L200)? The level of maximum clear-sky convergence should remain roughly around the same temperature, no? It would be helpful if you clarified this.
We included the proper long name and references for "PHAT". The level of maximum clear-sky convergence warms roughly at a rate of 0.2 K/K.
> L143: You state that the ensemble mean is within the CERES range, but you don’t actually show this anywhere. Could you include this in Figure 3?
Yes, we agree that this would be useful and we added the CERES reference values to the Figure (x-ticks and caption) and the text.
> L162: suggest amending to something like: “…can no longer be reconciled with the CERES data, and thus we do not plot bold lines in Figure 3 for the ‘no high clouds’ case.”
Done.
> L194: Why do you use such a big temperature increment? dT=6K feels somewhat arbitrary. I doubt it affects the results, but it was a bit distracting to me at first.
The somewhat large dT is used to get a clear signal in cloud-top changes. As the cloud-top is always tied to one distinct model level it also can only rise in steps of full model levels. As a consequence, for a small dT some ensemble members might see no change in cloud-top at all. Using a larger dT ensures a clearer signal in cloud-top height while not changing the qualitative results. We also added this explanation to the methods section.
> L205: I am missing something. I thought including Planckian clouds (BL ones) would increase the magnitude of \lambda? By allowing emission from wavenumbers where water vapor is optically thick? This is what Fig1 (final row) seems to suggest…
This effect is visible in Fig 4., where \lambda is decreased in the absence of low-level clouds (Planckian clouds), or vice versa, the presence of low clouds increases the magnitude of \lambda.
> Section 4: This section, up to L223, on the masking of surface albedo changes was confusing to me. I struggled to understand how you got to these numbers and how they relate to the idealized RCE calculations you presented earlier.
The discussion section has been rewritten to address this and other conerns raised in other reviewer comments.
> L250: This isn’t considered in the final calculations presented in Figure 4 though, right?
Correct, this is just a back-of-the-envelope calculation to put the results into perspective.
Citation: https://doi.org/10.5194/egusphere-2024-3829-AC2
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AC2: 'Reply on RC2', Lukas Kluft, 14 Mar 2025
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RC3: 'Comment on egusphere-2024-3829', Anonymous Referee #3, 16 Jan 2025
This study has potentially high intellectual value as it aims to build a conceptual model for understanding clouds' impacts on climate sensitivity. The current paper however is less vigorous than what I would expect of an ACP paper. For now, I will focus my comments on two major issues:
- A general concern is that the model setup is too idealized to be taken seriously for understanding the climate and its sensitivity in reality. The moist-adiabat setup for example can be thought to represent tropics but would fail badly for extratropical climates. The use of a single thermodynamical profile may also decouple cloud and non-cloud (e.g., humidity) fields and doesn’t represent their covariability in real climate. To address this issue, the covariations of the key variables (cloud, water vapor, temperature) should be examined and compared to observations to establish confidence on the model.
Moreover, fitting/constraining a global mean CRE isn't a sufficient validation to me. For the climate sensitivity problem concerned here, it would be more relevant to validate such aspects of the climate as the Ts-OLR and Ts-Net relations (under different prescribed temperatures). This can and should be compared to comprehensive GCM results and/or observations (e.g., CERES). See Feng et al.: DOI 10.1088/1748-9326/acfb98 as an example that can be compared to.
- The current paper offers too little information from the experiments for readers to understand and appreciate the results and conclusion. For example, although the central argument hinges on radiation spectrum, this is merely illustrated by a schematic (Fig 1) and not evidenced by any modelling results. For this matter, note RRTMG has the ability to output band-by-band radiation fields (e.g., Huang & Huang: DOI 10.1088/1748-9326/ad3e17) or even at g-points (Chen et al.: https://doi.org/10.1029/2023GL106433). Moreover, as showed by Huang et al. (DOI 10.1038/s41597-024-03080-y) showed, OLR changes apparently differ dramatically in different regional climates and noticeably deviate from Simpsonian response. Can and need the simple model represent such reality? This ought to be considered and discussed in relevance to these papers.
Other comments:
Eq. 1: is this right? Sum or product?
L153: This doesn’t represent the C-shaped cloud distribution in the reality.
Citation: https://doi.org/10.5194/egusphere-2024-3829-RC3 -
AC3: 'Reply on RC3', Lukas Kluft, 14 Mar 2025
As a common note to all reviewers, we thank them for their constructive comments, and for directing us to relevant parts of the literature that we overlooked. We made considerable revisions to address overarching concerns related to: (i) the use of a (too) simple model; (ii) insufficient specificity in the methodology; (iii) lack of discussion related to apparent differences with the existing literature. We also benefited from, and adopted numerous suggestions for improving the presentation throughout.
> A general concern is that the model setup is too idealized to be taken seriously for understanding the climate and its sensitivity in reality. [..]
> The current paper offers too little information from the experiments for readers to understand and appreciate the results and conclusion.
Sections 1 and 2 have been substantially rewritten to address concerns arising from the interpretation of the admittedly simplistic approach. This rewriting makes a particular effort to motivate the idealised study, and present its limitations. We agree that the model setup is too simple to derive quantitative results directly comparable to reality. However, the goal is to understand how the (vertical) distribution of clouds in temperature space affects the radiative response, which requires a well-defined thermodynamic state.
> [...] Moreover, as showed by Huang et al. (DOI 10.1038/s41597-024-03080-y) showed, OLR changes apparently differ dramatically in different regional climates and noticeably deviate from Simpsonian response.
We agree that the actual changes in OLR with temperature depend on subtelties of atmospheric composition. For example, even small changes in RH will result in a radiative response that is very different from a Simpsonian (non-)response. The strength of an idealised modelling strategy, as in our study, is that it can test specific hypotheses, in our case the role of different (but plausible) changes in cloud in temperature space, while keeping their albedo constant.
> Can and need the simple model represent such reality? This ought to be considered and discussed in relevance to these papers.
We would argue that it cannot represent such reality but that is also does not need to give answer questions in the scope of this study. We hope that the substantial rewriting of the introcution and discussion now better communicates this point.
> Eq. 1: is this right? Sum or product?
The symbols in both equations are correct, we take the product of individual changes in Eq. 1 and sum over the fluxes in Eq. 3
Citation: https://doi.org/10.5194/egusphere-2024-3829-AC3
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RC4: 'Comment on egusphere-2024-3829', Anonymous Referee #4, 20 Jan 2025
Review of “Quantifying cloud masking in a single column”
By Lukas Kluft, Bjorn Stevens, Manfred Brath, and Stefan A. Buehler
Recommendation: Major Revisions
This manuscript addresses an interesting, yet underexplored question: how do clouds modify the Earth’s climate sensitivity, relative to a clear-sky atmosphere? It makes a first attempt at an answer with a single column climate model using cloud parameters that have been selected to reproduce the observed global mean cloud radiative effect, and whose changes with surface warming follow simple, physically based rules. The approach is novel, physically transparent, and complementary to more traditional approaches involving observations or general circulation models. However, I feel this manuscript could do a much better job in citing the relevant literature (only one cited reference was published after 2021 — and it was written by two of the authors of this manuscript). In addition, the observations used to validate the single column model were not explicitly stated or plotted anywhere. The conclusion section could also be improved to put the final result (that the effect of clouds on climate sensitivity is small) into context.
For these reasons, I recommend major revisions for this manuscript. I provide more detailed comments below. If they are addressed, then I would be happy to recommend the revised manuscript for publication.
Major comments
A more thorough assessment of the literature.
- L18-20 A review paper on cloud feedbacks could be put here to illustrate your point that shortwave cloud effects are discussed at more length than longwave cloud effects. (Ceppi et al, 2017 comes to mind.)
- L29 The masking or unmasking of the clear-sky spectral response by clouds, though not well appreciated in the literature, has been mentioned in papers such as McKim et al, 2021 and Jeevanjee et al, 2023.
- L50 This may generally be the case, but some studies have tried (e.g. Raghuraman et al, 2023)
- L56 Has this been noted in the literature? Perhaps in Held and Soden, 2000; or Colman and Soden, 2021? Or Po-Chedley et al, 2019?
- L80 Many more references could be put here, such as Koll et al, 2023; Feng et al, 2023; Jeevanjee et al, 2021; Stevens and Kluft, 2023; Jeevanjee et al, 2023; Roemer et al, 2023.
- L99-101 Citations and/or physical arguments for why low level clouds following a fixed cloud pressure and mid-level clouds following a fixed cloud temperature are reasonable null hypotheses would be appreciated.
- L102 A reference to PHAT would be appreciated (Zelinka and Hartmann, 2010; Bony et al, 2016)
- L107 A reference on the Monte-Carlo Independent Column Approximation (MCICA) would be appreciated.
- L154 A reference showing observed cloud fractions would be appreciated here
- L232 A number of newer papers show a small high cloud area (or amount) feedback more convincingly than in Ceppi et al, 2017. They include Sokol et al, 2024; McKim et al, 2024; Raghuraman et al, 2024; Stouffer et al, 2023.
Single column cloud model validation
- L142, 162, and Figure 3 - Perhaps I missed it, but are the observed CERES values of CRENET, CRESW, and CRELW explicitly stated or plotted anywhere? Doing so would help the reader evaluate the fitness of the simple model.
Incomplete conclusion section
- L251 This is an interesting finding and I would appreciate a bit more discussion on what this tells us about previous studies and what it means for future studies. For instance, can it explain Raghuraman et al 2023 which showed a near-zero trend in the net CRE over the past 20 years? How can this insight help us to better interpret observations? Etc.
Minor comments
- The importance of shortwave effects are established in the first paragraph, so maybe the way to frame the second paragraph is to say that the longwave effects of clouds are less appreciated, yet they may have just as substantial an effect on climate sensitivity.
- It might help to explicitly state your main research question in the introduction, “How do clouds modify the Earth’s climate sensitivity, relative to a clear-sky atmosphere”?
- L36 Should be Jeevanjee et al, 2021; not 2010.
- L99 Perhaps it should be “fixed cloud pressure” rather than “fixed anvil pressure”, since you’re talking about low clouds?
- L101 Perhaps it should be “fixed cloud temperature” rather than “fixed anvil temperature” since you’re talking about mid-level clouds?
- L103 PHAT should be defined
- L142 It looks like it is between -83 W/m^2 and -28 W/m^2. Perhaps you could differentiate the minus sign from the dash more clearly? Or write it as [-83, 28] W/m^2?
- L197 - 200 I would appreciate seeing a plot of the cloud fraction profiles (at Ts=285, 291 K) as a function of temperature and then as a function of pressure. The plot itself could be annotated to point to where clouds follow fixed pressure, fixed temperature, and maximum radiative divergence. This would help to illustrate to the reader this idealized, but elegant approach to modeling clouds in a single column model, which I think could be used in many future studies to great effect.
References
- Bony et al, 2016 - Thermodynamic control of anvil cloud amount
- Ceppi et al, 2017 - Cloud feedback mechanisms and their representation in global climate models\
- Colman and Soden, 2021 - Water vapor and lapse rate feedbacks in the climate system
- Feng et al, 2023 - How a stable greenhouse effect on Earth is maintained under global warming
- Held and Soden, 2000 - Water vapor feedback and global warming
- Jeevanjee et al, 2021 - Simpson's Law and the Spectral Cancellation of Climate Feedbacks
- Jeevanjee et al, 2023 - Climate Sensitivity from Radiative-Convective Equilibrium: a Blackboard Approach
- Koll et al, 2023 - An Analytical Model for the Clear-Sky Longwave Feedback
- McKim et al, 2021 - Joint Dependence of Longwave Feedback on Surface Temperature and Relative Humidity
- McKim et al, 2024 - Weak anvil cloud area feedback suggested by physical and observational constraints
- Po-Chedley et al, 2019 - Climatology Explains Intermodel Spread in Tropical Upper Tropospheric Cloud and Relative Humidity Response to Greenhouse Warming
- Raghuraman et al, 2023 - Forcing, Cloud Feedbacks, Cloud Masking, and Internal Variability in the Cloud Radiative Effect Satellite Record
- Raghuraman et al, 2024 - Observational Quantification of Tropical High Cloud Changes and Feedbacks
- Roemer et al, 2023 - Direct observation of Earth’s spectral long-wave feedback parameter
- Sokol et al, 2024 - Greater climate sensitivity implied by anvil cloud thinning.
- Staufer et al, 2023 - Explicitly Resolved Cloud Feedbacks in the Radiative-Convective Equilibrium Model Intercomparison Project
- Zelinka and Hartmann, 2010 - Why is longwave cloud feedback positive?
Citation: https://doi.org/10.5194/egusphere-2024-3829-RC4 -
AC4: 'Reply on RC4', Lukas Kluft, 14 Mar 2025
As a common note to all reviewers, we thank them for their constructive comments, and for directing us to relevant parts of the literature that we overlooked. We made considerable revisions to address overarching concerns related to: (i) the use of a (too) simple model; (ii) insufficient specificity in the methodology; (iii) lack of discussion related to apparent differences with the existing literature. We also benefited from, and adopted numerous suggestions for improving the presentation throughout.
> However, I feel this manuscript could do a much better job in citing the relevant literature (only one cited reference was published after 2021 [...]
We appreciate the reviewers list of more recent literature, which we happily included to the manuscript. The lack of post-2021 literature was due to the fact that most of the manuscript was written in 2021 and for a vareity of reasons was put aside before being in late 2024. When finishing we made the mistake of only updating our own manuscript in the references. Please forgive this oversight.
> L142, 162, and Figure 3 - Perhaps I missed it, but are the observed CERES values of CRENET, CRESW, and CRELW explicitly stated or plotted anywhere? Doing so would help the reader evaluate the fitness of the simple model.
Thanks for the catch. The numbers are now included in the Figure 3 and the text.
> L251 This is an interesting finding and I would appreciate a bit more discussion on what this tells us about previous studies and what it means for future studies. For instance, can it explain Raghuraman et al 2023 which showed a near-zero trend in the net CRE over the past 20 years? How can this insight help us to better interpret observations? Etc.
Thanks. As noted above, we had mostly finished the manuscript before the Raghuraman study was submitted and failed to catch up with the literature in between this point and the submission some years later. Raghuraman et al 2023 show a cooling trend from changing LW CRE. Our analysis leads us to expect a warming (amplifying) effect from the radiative response to warming, and a cooling effect from the masking of the radiative forcing, with the latter being somewhat stronger. Raghuraman et al also measure a cooling effect consistent in sign, but larger in magnitude than what we would have expected. Because the shortwave effect is opposite, the discrepancy, is likely associated with changes in cloud fraction, wich our model does not consider. We have expanded our discussion to put them in the context of earlier findings by Raghuraman et al.
is very relevant and we have elaborated our disucssion to better address this important study.
> L99 Perhaps it should be “fixed cloud pressure” rather than “fixed anvil pressure”, since you’re talking about low clouds?
> L101 Perhaps it should be “fixed cloud temperature” rather than “fixed anvil temperature” since you’re talking about mid-level clouds?We decided to stick to the term anvil to refer to the cloud top region, as it allows consistency with existing terminology for high clouds. We added a footnote to explain this choice and hopefully avoid confusion.
> L103 PHAT should be defined
Done.
> L142 It looks like it is between -83 W/m^2 and -28 W/m^2. Perhaps you could differentiate the minus sign from the dash more clearly? Or write it as [-83, 28] W/m^2?
Done.
> L197 - 200 I would appreciate seeing a plot of the cloud fraction profiles (at Ts=285, 291 K) as a function of temperature
As the cloud fraction is kept constant between simulations at different Ts, a plot as a function of T (or height) seemed to obscure the relevant numbers. However, we see that the change in cloud top temperature for the different cloud types is of interest, and is therefore shown in the table below:
Surface Low cloud Mid-level cloud High cloud 285 K 278.05 K 269.97 K 215.29 K 291 K 284.84 K 269.61 K 216.35 K Citation: https://doi.org/10.5194/egusphere-2024-3829-AC4
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RC5: 'Comment on egusphere-2024-3829', Anonymous Referee #5, 21 Jan 2025
Review of “Quantifying cloud masking in a single column” by Kluft et al.
The paper uses an idealized framework to make progress toward a simple derivation of all-sky climate sensitivity. I like the idea, but I think the simplicity of the set up may be hampering the realism of the values.
Major Comment
My main concern is that the cloud masking radiative response to warming is amplifying, rather than stabilizing, warming. In Figure 4, \lambda_clear = -1.93 Wm^-2K^-1 and the introduction of clouds makes this less stabilizing with \lambda_all = -1.67 Wm^-2K^-1. Several studies on cloud masking show that the cloud masking feedback is stabilizing (Soden et al., 2004, 2008; Yoshimori et al., 2020; Raghuraman et al., 2023a). How do the authors reconcile their results with the past work? Could you provide the LW and SW breakdown? Additionally, could you provide the masking due to specific terms such as water vapor, surface temperature, surface albedo, etc.?
Minor Comments
L35: This “Simpsonian” response is stated as a fact of matter, when in reality, emission does change with warming in bands outside of the window (Feng et al., 2023; Koll et al., 2023). Altogether, atmospheric emission contributes ~40% to the global-mean clear-sky longwave feedback parameter due to contributions from water vapor’s rotational and rotational-vibrational bands and other greenhouse gas bands such as CO2, CH4, N2O, O3 (Raghuraman et al., 2019, 2023b).
L100: Please provide more details on why mid-clouds follow FAT while high-clouds follow PHAT. The latter is well documented but providing some references and explanations for the former would help the reader.
References
Feng, J., Paynter, D., & Menzel, R. (2023). How a stable greenhouse effect on Earth is maintained under global warming. Journal of Geophysical Research: Atmospheres, 128(9), e2022JD038124.
Koll, D. D., Jeevanjee, N., & Lutsko, N. J. (2023). An analytic model for the clear-sky longwave feedback. Journal of the Atmospheric Sciences, 80(8), 1923-1951.
Raghuraman, SP., Paynter, D., Menzel, R., & Ramaswamy, V. (2023). Forcing, cloud feedbacks, cloud masking, and internal variability in the cloud radiative effect satellite record. Journal of Climate, 36(12), 4151-4167.
Raghuraman, S. P., Paynter, D., Ramaswamy, V., Menzel, R., & Huang, X. (2023). Greenhouse gas forcing and climate feedback signatures identified in hyperspectral infrared satellite observations. Geophysical Research Letters, 50(24), e2023GL103947.
Soden, B. J., Broccoli, A. J., & Hemler, R. S. (2004). On the use of cloud forcing to estimate cloud feedback. Journal of climate, 17(19), 3661-3665.
Soden, B. J., Held, I. M., Colman, R., Shell, K. M., Kiehl, J. T., & Shields, C. A. (2008). Quantifying climate feedbacks using radiative kernels. Journal of Climate, 21(14), 3504-3520.
Yoshimori, M., Lambert, F. H., Webb, M. J., & Andrews, T. (2020). Fixed anvil temperature feedback: Positive, zero, or negative?. Journal of Climate, 33(7), 2719-2739.
Citation: https://doi.org/10.5194/egusphere-2024-3829-RC5 -
AC5: 'Reply on RC5', Lukas Kluft, 14 Mar 2025
As a common note to all reviewers, we thank them for their constructive comments, and for directing us to relevant parts of the literature that we overlooked. We made considerable revisions to address overarching concerns related to: (i) the use of a (too) simple model; (ii) insufficient specificity in the methodology; (iii) lack of discussion related to apparent differences with the existing literature. We also benefited from, and adopted numerous suggestions for improving the presentation throughout
> The paper uses an idealized framework to make progress toward a simple derivation of all-sky climate sensitivity. I like the idea, but I think the simplicity of the set up may be hampering the realism of the values.
Thank for the general intereset in the idea. We agree that the quantitative results of this study are hard to compare to the real Earth. However, the goal of this study is to understand (some of) the underlying physical mechanisms in a clearer way. An idealised model is helpful for this purpose, something we now try to better motivate through a rewriting of sections 1 and 2, as well as the discussion .
> [...] How do the authors reconcile their results with the past work? Could you provide the LW and SW breakdown? Additionally, could you provide the masking due to specific terms such as water vapor, surface temperature, surface albedo, etc.?
The discussion has been revised to address differences to previous studies, especially apparent differences in the sign of important terms. The apparent discrepanceies weree due to different definitions of masking, and some uphysical effects that arise from the assumption of unphysical states as the reference for feedback studies.
> L35: This “Simpsonian” response is stated as a fact of matter, when in reality, emission does change with warming in bands outside of the window (Feng et al., 2023; Koll et al., 2023). Altogether, atmospheric emission contributes ~40% to the global-mean clear-sky longwave feedback parameter due to contributions from water vapor’s rotational and rotational-vibrational bands and other greenhouse gas bands such as CO2, CH4, N2O, O3 (Raghuraman et al., 2019, 2023b).
We agree that the Simpsonian response doesn't hold in many real-world scenarios. However, we argue that the reason for this is that the prerequisites, in particular the constancy of RH, are not met. Even subtle changes in RH will lead to noticeable changes in OLR in supposedly Simpsonian parts of the spectrum. The rewrite of the Discussion section makes it clearer that this imposes limitations on how far our results can (and should) be compared quantitatively to the real world. However, we remain convinced that a well-defined idealised model helps to disentangle contributions from cloud top changes.
> L100: Please provide more details on why mid-clouds follow FAT while high-clouds follow PHAT. The latter is well documented but providing some references and explanations for the former would help the reader.
Two references are now provided for mid-level clouds following FAT, one is the paper by Stevens et al (2017) which associates their accumulation with processes at the melting level (what those authors call the first mover hypothesis), the other is a very interesting and recent paper by Spaulding and Mitchell (AGU Advances 2025) which makes a radiative argument for them appearing at water vapor specific humidities typical of the freezing level.
Citation: https://doi.org/10.5194/egusphere-2024-3829-AC5
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AC5: 'Reply on RC5', Lukas Kluft, 14 Mar 2025
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Bjorn Stevens
Manfred Brath
Stefan A. Buehler
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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