the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 08 Jan 2026
Morphology-Conditioned Susceptibility of Marine Stratocumulus Clouds Suggests Weak Marine Cloud Brightening Potential
Abstract. We introduce a new framework for defining marine stratocumulus cloud morphologies using a ternary diagram. A ternary diagram is a triangular representation of three components, with each vertex corresponding to 100% of one component, and any point within the triangle representing a mixture of all three that sums to 100%. We use cloud optical thickness (τc) as the diagnostic physical variable and accordingly define three corresponding τc classes. Different combinations of the three τc classes define different cloud morphologies, which vary continuously within the ternary space. The method is applied to one year of satellite observations of stratocumulus clouds and reveals the frequency of occurrence of the different morphologies across the ternary space. Large-eddy simulations complement the satellite analysis and show that cloud evolution tends to follow preferred paths across the ternary morphology space, explaining why the observations are concentrated within a limited range of morphologies. We further investigate the susceptibility of cloud liquid water path (LWP), cloud albedo, and cloud fraction to variations in droplet number concentration, conditioned on cloud morphology. We find that for the most frequent observed morphologies, LWP and cloud albedo susceptibilities largely offset each other, resulting in a net in-cloud albedo response close to zero. The cloud fraction susceptibility is found to be positive in precipitating morphologies and negative in non-precipitating morphologies. These findings have important implications for marine cloud brightening, whose effectiveness needs to be evaluated in a morphology-dependent framework to achieve the intended outcomes.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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CC1: 'Comparison with effect of IMO 2020 regulations', Paul Stansell, 30 Jan 2026
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AC1: 'Reply on CC1', Tom Goren, 02 Feb 2026
Thank you very much for your comment. Your understanding is correct. Our approach derives cloud susceptibilities from natural variability in background cloud droplet concentrations.
A key advantage of our framework is that it allows susceptibility estimates conditioned on cloud morphology. The most prominent ship track signatures observed in visible satellite imagery are typically found in thin and relatively clean cloud layers, where enhanced brightening is readily visible against the background. These conditions are consistent with cloud morphologies for which we find positive net cloud albedo susceptibility. If ship tracks with the strongest radiative effect preferentially form in such susceptible morphologies, then our results are not inconsistent with studies that infer a substantial reduction in net radiative forcing after IMO 2020. We will add a discussion in the revised manuscript that places our results in the context of IMO 2020 studies.Citation: https://doi.org/10.5194/egusphere-2026-81-AC1
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AC1: 'Reply on CC1', Tom Goren, 02 Feb 2026
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RC1: 'Comment on egusphere-2026-81', Anonymous Referee #1, 09 Mar 2026
This paper presents a framework for classification of properties of marine stratocumulus clouds, by simply placing the clouds in space defined by three corners of optical thickness classes. The method is demonstrated by application to one year of satellite data, and large-eddy simulation, and thereby gives insight into cloud processes and reveals how cloud susceptibility to aerosol perturbations depends on cloud morphology. Several quite remarkable conclusions can be drawn regarding magnitudes and even sign of radiative effects of aerosol-cloud interaction.
The paper is well written, the methodology overall clearly and reliably described and the results very relevant. It presents a useful perspective for estimating susceptibility, for classifying and determining representativity of means, and for refining classifications based on often used discrete classifications from ambient quantities.
I would recommend for publication in ACP after only minor revisions, according to the following comments.
General comments
1. Scope and focus
The title can be read as having a focus on marine cloud brightening as a climate intervention method, which is actually mentioned only at the very end of the paper as one application of relevance to the results. One way to go would be to discuss the results in relation to MCB literature more but I would rather recommend emphasizing some of the general findings more. Perhaps even remove or replace the “suggests weak cloud brightening potential” in the title to make it more general, to reflect the broad application of the method and results, not limiting it to MCB.
Also, arguably, MCB would be a candidate for the strong local aerosol perturbations that are explicitly said not to be considered (line 95-96), and if the authors want to turn the focus more to MCB that should be discussed too.
There are many noteworthy results and conclusions, such as how the scene mean is not representative of the morphology (line 157-158), that SLWP is negative everywhere (line 212), that SLWP dominates Stwomey (line 253), that Snet near zero is the most common response (line 253). I think these results should be emphasized, and also could be discussed more in relation to previous literature. For instance for the sign of SLWP, what might be the explanation for many earlier estimates of positive SLWP in precipitating cases, and V-shaped sensitivity? There are some hints e.g. in line 225 and 288-289 to sampling biases, but it would be interesting to dig deeper, if possible.
2. Data and methods
a) The method is demonstrated with one year of satellite data (line 52), and this is what the results are based on. Why is not more data used? Do you think it matters? Is there any sensitivity in the results to the year chosen? Does the screening and selection (lines 54-59) affect the results?
b) Is it not possible to reach a certain state (or morphology) in different ways? The LES simulations present one plausible path, but discussing this equifinality could be interesting. Could it matter to the susceptibility-properties ascribed to the categories?
c) Snet is defined as the in-cloud susceptibility (eg line 25), which seems to mean that cloud fraction adjustment is not taken into account, only LWP adjustment. This is also indicated by equation 1, that doesn’t have a term dlnAc/dlnCF*dlnCF/dlnNd. But equation 8 in Bellouin et al (2019), that the nomenclature follows, does on the other hand include a term for cloud fraction adjustment to Nd. It is not clear how the satellite derived S can distinguish the in-cloud and total contributions to albedo-changes and exclude SCF, and hence why the residual between theoretical SAc and satellite observed Snet is attributable to SLWP (as stated in line 107, and then used to estimate SLWP). Might this approach to estimating SLWP as a residual affect the results?
Specific and technical comments
Line 17: Why not introduce re annotation for cloud droplet size, it appears fist on line 57
Line 21, 24: How certain is SAc, compared to SLWP described as "uncertain" (cf. Bellouin et al 2019)?
Line 42 says SLWP greater in small MCCs, with the explanation that the adjustment is active in cloud cores rather than peripheries. But wouldn't we expect smaller horizontal-extent clouds to have more periphery relative to cell core? The Zhou and Feingold (2023) reference given rather states that small MCC cores have higher Nd, and more evenly distributed LWP. Maybe the explanation could be expanded a bit, to clarify the mechanism.
Line 68: Please specify the Ac used, if and how it is based on radiative fluxes for clear sky and all sky, or on cloud properties, or other
Line 66: It wouldn’t hurt to include Fig A1 int the main manuscript
Line 78: Fractions of cloudy pixels, should rather be percentage
Line 125: SCF notation has not been defined before
Line 136-137 repeats line 43-45, and again in line 158-159
Fig 1: Where is this/these scene/s (lat, lon)?
Line 166: “represent -> represents”
Line 179: It is not clear from Fig 2 c/d that nighttime thickening follows the same trajectory as the thickening during the previous night, the two trajectories marked with arrows are rather separated. Please clarify this statement.
Line 188: The phrasing is a bit unclear here. I think you mean that the information from a satellite snapshot placed in the ternary diagram can give information about the state of the cloud scene, as different parts of the diagram represent different processes. Directly from the satellite snapshot, without the help of the ternary this arguably *can’t* be inferred. Perhaps rephrase to clarify. The same formulation appears in line 282.
Line 209: Would anything else be possible, than clouds forming in a thin state, and dissipating via a thin state?
Figure 2: Caption could say what contours (in b) represent. That information is currently only in later figures. Also, Figure A2 and Fig A3 refer to figure 2a (for what the contours represent, presumably), whereas Fig 3 and Fig 4 refer to Figure 1a. Please correct and/or clarify in the figure captions.
Fig A3: What is REF here? This could be made consistent with text (line 240) referring to Ac.
Fig 4b: Diagram says Stwomey but text says SAc, this would be good to make consistent
Line 270: “fig 2c and 2c” -> “fig 2c and 2d”
Line 271: “are found “-> “is found”
Line 296: Is it possible to give an error estimate on that number?
Citation: https://doi.org/10.5194/egusphere-2026-81-RC1 -
RC2: 'Comment on egusphere-2026-81', Anonymous Referee #2, 17 Mar 2026
This paper seeks to better understand cloud adjustments to aerosol perturbations, with a focus on the response of the liquid water path. Noting that the likely adjustment in different cloud types or morphologies will vary, they propose a novel method for constraining for this factor, using the distribution of cloud optical depth. They find that in many cases, the intrinsic cloud responses (Twomey and LWP adjustment) offset each other, leaving the overall cloud field response to be driven by cloud fraction changes, which they find can be negative under some circumstances.
This is a novel and clever decomposition that aims to solve a long-standing problem. It is clearly in scope for Atmospheric Chemistry and Physics. I have a few points, primarily about the framing of some of the results, but following these I think it would be suitable for publication.
Main pointsThe method for calculating S_LWP is supported by theoretical considerations, but I am a little unclear as to how well this decomposition works in practice. Almost all of the terms are determined by the average bin optical depth, other than S_net (which is determined from observations). To what extent is S_net also being determined at a constant optical depth? Given the authors define their cloud type based on the properties that are expected to vary under an aerosol perturbation, is this restricting the magnitude of the response? I appreciate the authors have already made considerable effort to demonstrate this, but perhaps it is possible to demonstrate (potentially using some synthetic data), that this method is able to extract the required susceptibilities?
To what extent is the albedo-Nd relationship due to meteorological covariations? This has been some uncertainty about it in the past. Would we expect it to be free of the same issues that drive the negative Nd-LWP relationship (Arola et al., 2022)? Would the use of a microwave LWP product to determine the Nd-LWP relationship address some of the issues raised in this work (as it doesn't depend directly on the LWP)?
The negative cloud fraction response to aerosol is interesting, being different from almost all previous studies. I would note that the Meskhidze et al (2009) result is likely due to a regression to the mean effect. Analyses that controls for the initial cloud state shows a (very weak) increase in cloud amount during the day (Gryspeerdt et al., 2014a; Pugsley et al, 2025). While a don't think the results presented here are affected by the same bias, it does make them almost unique (and so perhaps worth some additional scrutiny).
Minor comments
L14 - Is this not a bit more broadly relevant? If I understand it correctly, it would suggest that almost all of the net aerosol forcing would have to come from the cloud fraction response too?
L28 - There is some earlier work in this area from Landsat studies, e.g. Davis et al, JAS, 1997 (10.1175/1520-0469(1997)054<0241:TLSBIS>2.0.CO;2).
L37 - To what extent is this different? Presumably cloud morphology controlled by meteorological factors? To me, a strong aspect here is that morphology is an observed parameter, unlike the large scale meteorology (which will have biases and uncertainties). I would note that there have been some studies that investigated susceptibilities as a function of observed cloud properties (which is related to the method here; e.g. Gryspeerdt et al., GRL, 2012; Langton et al., GRL, 2021)
L55 - SPI filters have been shown to help improve retrieval biases (Grosvenor et al, Rev. Geophys., 2018). Are these not used due to the morphology decomposition? How does this affect the distribution of biases in the results?
Fig. 1e - These scenes are relatively important as they have a reader decide what cloud types each part of the diagram refers to. How were they chosen?
L162 - To what extent is this a CF-optical depth relationship, rather than the more complex morphology-dependant relationship?
L183 - I am not quite clear what the LES diagrams are demonstrating. There is a small loop which looks like it is related to the diurnal cycle (daytime breakup), but I am not clear what the transition to/from the bottom left of the diagram represents? Is this the spinup of the simulation? A night time breakup is more unusual.
L192 - I agree that precipitation must play an important role in cloud breakup, but this might not be the whole story, as there is relatively little precipitation during the day (Burleyson et al, JAS, 2013), which might explain the lack of an aerosol impact on cloud development during the daytime breakup period (Pugsley et al, PNAS, 2025).
Fig. 2 - Assuming the authors are using python, the use of a mesh plotting command (e.g. pcolormesh) might help make these plots more triangular
L214 - Some studies have cast doubt on the causality of the Nd-LWP relationship (e.g Gryspeerdt et al., ACP, 2019; Arola et al, 2022). Does this method avoid the retrieval biases that have been hypothesised to drive this?
L219 - Does this relationship depend on cloud top RH? This would add confidence that the negative relationship is due to entrainment, rather than a retrieval bias/effect.
L237/Fig. 4b - S_Ac is called S_Twomey in the figure
Fig. 4b - What are the units for S_Twomey?
L239 - I think S_Ac is defined in Eq. 2 such that it would be at a maximum for the smallest Ac
L242/Fig. 4c - The S_net pattern looks very similar to S_LWP. this appears to be due to the S_Twomey/S_Ac values having very little variability.
L295 - Given the small overall relationship in the most common cases, why is this not consistent with other methods (or is it)?
Fig A2 - This should be in same colorscheme as Fig 4a to make comparison simpler.
Citation: https://doi.org/10.5194/egusphere-2026-81-RC2
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Hello,
Thank you for the fine paper. My reading of it is that, as a function of changes in cloud droplet concentration, it reports a net albedo change close to zero, which implies that MCB would be far less effective than many other papers suggest.
There are a number of papers based on satellite observations that report large positive effects on cloud albelo caused by the IMO 2020 regulations that reduced sulfur in ship fuels. Is it possible to reconcile your results with those in papers that report significant reductions on net radiative forcing resulting from the IMO 2020 regulations? Might the difference lie in the fact that your paper makes inferences about the efficacy of MCB from observations of the natural background levels of cloud droplet concentrations in ranges that are low compared to the ranges one would expect from MCB interventions? For example, an MCB intervention might aim to double the cloud droplet concentration over the natural background level.
Paul