the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 23 Mar 2026
Sensitivity of Marine Cloud Brightening over the Great Barrier Reef to Spatial Variability in Aerosol Forcing: A Case Study using convection-permitting model
Abstract. The Great Barrier Reef (GBR) is increasingly threatened by mass thermal coral bleaching events under climate change. Marine cloud brightening (MCB) has been proposed as a potential adaptation strategy to reduce thermal stress by enhancing cloud reflectivity through aerosol injection. This study evaluates the sensitivity of cloud–aerosol interactions to aerosol emission intensity and spatial configuration over the GBR using convection-permitting Weather Research and Forecasting (WRF) model simulations.
A control simulation representing a non- to weakly-precipitating shallow trade-cumulus regime is compared with three MCB sensitivity experiments: a densely distributed (20 km apart), moderate-intensity emission scenario (EXP20), a sparsely distributed (100 km spacing), high-intensity scenario (EXP100), and an intermediate configuration (EXP40). Results show that enhanced aerosol emissions substantially increase near-surface aerosol concentrations, with dispersion strongly governed by source spacing and prevailing trade winds. The EXP20 configuration produces more homogeneous and widespread aerosol enhancements, whereas EXP100 generates localized peaks that are rapidly scavenged, resulting in smaller domain-mean increases despite identical total emissions.
Over a 24-hour period, domain-averaged cloud droplet number concentration (CDNC), optical depth, and cloud albedo exhibit strong sensitivity to aerosol loading, while cloud water path (CWP) and cloud fraction show limited responses. These findings indicate a dominant Twomey effect in this cloud regime, with only weak evidence of the Albrecht effect. Nonlinear CWP responses are noted under varying conditions of mid-level humidity, wind shear, and lower-tropospheric stability. Overall, the results highlight the importance of aerosol source configuration and background atmospheric state in shaping MCB effectiveness over the GBR.
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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Status: open (until 04 May 2026)
- RC1: 'Comment on egusphere-2026-1251', Anonymous Referee #1, 20 Apr 2026 reply
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RC2: 'Comment on egusphere-2026-1251', Anonymous Referee #2, 27 Apr 2026
reply
General comments:
This paper reports on simulations, using WRF, of a case study in the Australian GBR region, of how cloud microphysical and macrophysical properties are affected by marine cloud brightening sea salt aerosol injections. Three injection cases are simulated, all with the same total mass of injected sea salt, but with different distributions of point sources (EXP20: 75 sources; EXP40: 12 sources; EXP100: 3 sources). The EXP20 case produces the largest cloud albedo changes, and this is shown to be mostly via the Twomey effect. The paper further breaks down the results from the EXP20 study to quantify differences in cloud brightening driven by three metrics for meteorological controls: above-cloud humidity, wind sheer below vs above cloud, and boundary layer estimated inversion strength (EIS).
In particular, the contribution of examining the effect on MCB efficacy of spray system spatial distribution is a valuable addition to the literature, and overall the analysis is technically sound. The paper is generally well-written and well structured, and the figures clear. However, I think the paper includes statements that I think are either too strong or unsubstantiated by the presented analysis, and in a few places where wording needs clarification.
Specific comments:
lines 21-24: It’s not clear what’s meant by “intensity”. Is this just referring to the number of emission points? Or the rate of emission at each emission point? It’s not clear from the text here that all simulations have the same total emissions, just distributed over a different number of emission points, as described in the main text. This text should be edited for better clarity.
lines 134-136: What size aerosol are emitted? This is important, given the strong dependence of aerosol activation on aerosol size.
line 193-197: In reference to the comparison of the simulated (panels a and d) vs observed (panels b-c and e-f) cloud fields, shown in Figure 4: I’m struggling to agree with the authors that the simulations capture the cloud field “reasonably well”. The cloud fraction in the study area is quite different (given later as ~7% in the simulations but what looks more like 50-60% in from Himawari obs), as is the morphology of the cloud fields (very small, broken, evenly distributed Cu in the simulation, vs broader and more variable areas of cloud cover in the obs).
This leads me to think that the boundary layer thermodynamics might be different in the model than in reality. Figure 3 shows good what looks like very agreement between the simulated and observed temperature, dew point and wind profiles, but for these clouds what will most matter is what’s happening in the lowest few km of the atmosphere. In Figure 3a, the simulated and actual winds below ~800hPa are actually pretty different; in Figure 3b it’s hard to see what the sounding winds are doing.
-> I’d suggest adding two additional panels to this figure, reproducing the current panels a) and b) but zoomed in on, e.g., 700mb to the surface.
-> Further, I think this statement needs to be softened. The truth is that it is really challenging to reproduce the specifics of a given cloud field, even with a perfectly initialized simulation. This can be acknowledged in the context of not overstating how well the model is reproducing reality.
Figure 5 vs Figure 6: Comparing these two figures it appears the model is only activating about 15% of the aerosol to CCN – an activation rate that seems very low. This could be due to the resolution of the model (1km in the inner domain; 5km in the outer domain), as the lower resolution could be leading to a low bias in updraft rates. Ultimately this will produce a bias in the mass of aerosol that needs to be emitted to achieve a given forcing. Perhaps in the discussion, the simulated aerosol activation rate should be put in the context of expected values for typical low marine stratocumulus and this potential source of bias acknowledged.
Figure 6 a-c and Figure 7a: Why show the *surface* aerosol concentration, rather than the concentration at cloud base? The variability in concentration at the surface isn’t what matters; it’s the variation in concentration at cloud base that will affect CDNC.
(Note that I’m asking this based on the assumption that “surface” means the value in the model’s lowest layer, but maybe that’s incorrect? If you’re going to stick to showing ‘surface’ concentrations it would be good to at least define what you mean by ‘surface’.)
lines 241-244: The explanation given here of the precip scavenging efficiency at higher aerosol concentrations driving the difference in domain-mean concentrations across the different cases intuitively makes sense -- except that it is noted earlier that these are “non- to weakly-precipitating” clouds. Later, Figure S3 shows that the clouds were indeed only precipitating for a few hours in the middle of the simulation, and at very low rates (<0.01mm/hr). Can the simulated rain rate be used to quantify (even approximately) how much precip scavenging could be accounting for the differences in domain-mean aerosol concentrations across the different cases? And does this align with the simulated differences in concentrations? I think such an analysis should be included, especially given it affects the first main conclusion of the paper (lines 429-430)
More generally, is there a reason for ruling out other causes of the differences? e.g. Could coagulation or other processes be driving some of the difference? (This relates to the question of the injected aerosol size).
If a difference in precip scavenging is truly the main reason why the domain-mean aerosol concentrations differ, then the results shown here are very specific to the precip rates in this case, which brings into question whether the analysis of a single case allows one to reach a conclusion about the most efficient deployment configuration in general.
lines 321-323: I have to object to the assertion that the cloud albedo exhibited “pronounced increases” in the injection cases relative to the CTRL case. The increases are well within the range of variability in the CTRL case (Figure 8e); in Figure 7e, only the EXP20 case has a cloud albedo that is notably and consistently higher with aerosol injection than without.
lines 431-432:
This is an incomplete sentence.
Also, again I think that other than (marginally) in the EXP20 case, the cloud albedo increases were modest to (EXP100) negligible.
line 441, Conclusions: “The findings confirm that radiative forcing can be meaningfully enhanced under suitable conditions through spatially coherent aerosol seeding. As such, strategic deployment of MCB during favorable weather regimes holds promise as a targeted intervention to mitigate extreme heat exposure over sensitive marine ecosystems like the GBR”.
I feel strongly that these statements cannot be made. No radiative forcing calculations are shown. The paper later asserts that the cloud albedo change from EXP20 vs CNTRL could produce a forcing “potentially on the order of several tens of W m⁻²” but I don’t see how this is the case. In EXP20, the cloud albedo increases to 0.51 from 0.48. If there was 100% cloud cover this would, ballpark, result in an increase in reflected solar flux of ~10 W/m2 (downwelling 340 W/m2*(0.51-0.48)=10.2 W/m2). But cloud cover isn’t 100% -- it’s, in this case, 7%.
Further: Being able to say forcing is *meaningfully* enhanced, and that MCB could actually mitigate extreme heat exposure in the GBR, would require showing that the resulting forcing would be sufficient to produce ocean cooling.
All of this is well beyond the scope of the paper – but so is the assertion.
Unless the authors want to add this analysis, they can only assert what the paper has actually shown, which is articulated nicely on lines 446-495, and is of sufficient scientific value to stand on its own.
Technical comments:
- lines 68-69: A classic paper that should probably also be cited here is the Stevens and Feingold (2009; https://doi.org/10.1038/nature08281) paper that’s also cited in another context later in the paper.
- line 184: skilfully -> skillfully
- line 184-185: “The control simulation skilfully simulated the evolution of the synoptic-scale mean surface level pressure and surface winds (not shown).” The evidence showing this should be included in the Supplemental data/file.
- Figure 5: It appears that none of the CDNC values in any of the panels exceed 150/cm3, so why not set the colorbar range from 0-150/cm3 (or maybe even 0-125/cm3)?
- line 231: “In the contrast, …” -> “In contrast, the …”
- line 233: “exhibited” -> “exhibits” (for consistency w/ present tense used elsewhere)
- line 435: “mid-level” is a bit ambiguous; I’d say “above-cloud”
- line 524: this is the first use of LWP; for consistency with earlier usage, suggest changing to CWP
- Figure 9: The caption says that “colors with black dots indicate *significant* differences”. Is this correct? I suspect that this should read *insignificant* differences, given that the back dots correspond to when there is near-zero delta-density.
Citation: https://doi.org/10.5194/egusphere-2026-1251-RC2
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