the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 14 May 2025
Aircraft In-situ Measurements from SOCRATES Constrain the Anthropogenic Perturbations of Cloud Droplet Number
Abstract. Aerosol-cloud interactions (ACI) in warm clouds alter reflected shortwave radiation by influencing cloud microphysical and macrophysical properties. The variable of state controlling ACI is the cloud droplet number concentration (Nd). Here, we examine the perturbations in Nd due to anthropogenic aerosols (∆Nd, PD–PI) using a perturbed parameter ensemble (PPE) hosted in the sixth Community Atmosphere Model (CAM6). Surrogate models are created for the CAM6 PPE outputs and are used to generate 1 million model variants of CAM6 by sampling 45 sources of parameter uncertainty. The range of uncertain physical parameters related to ACI are constrained with observations of aerosol and cloud properties from SOCRATES. The likely range of uncertain parameters and the associated range of ∆Nd, PD–PI are more strongly constrained with observations of Nd relative to observations of cloud condensation nuclei. We conduct sensitivity tests of how constraints on ∆Nd, PD–PI are affected by systematic uncertainties in observations and our limitations in our surrogate models created for CAM6 PPE outputs. Based on this, we provide guidance on the impact of reducing systematic uncertainty in airborne microphysical observations and in surrogate models.
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CC1: 'Comment on egusphere-2025-2009', Marc Daniel Mallet, 10 Jun 2025
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The work the authors have done shows that accurate measurements of Nd (cloud droplet concentration) from SOCRATES over the Southern Ocean can strongly constrain global Nd perturbations due to anthropogenic aerosol. The authors conclude that while observations of CCN (cloud condensation nuclei) and Nd also provide a strong constraint, observations of CCN alone only provide a minimal constraint.
For this analysis, the authors used integrated particle counts above 100 nm from a UHSAS (N100) as a proxy for CCN, rather than direct measurements of CCN that were made during SOCRATES (Sanchez et al. 2021). The authors cite McCoy et al. (2021) stating that there is a 1:1 relationship between N100 and CCN for SOCRATES, but Figure S2b in that paper shows that N100 only explains ~half of the variance (r = 0.719) in CCN (at 0.2 % supersaturation). The last sentence of the current discussion emphasizes that the strong constraint provided by Nd measurements is only possible when these measurements are accurate. I therefore wonder what the impact on this analysis might be if the direct CCN measurements were used instead of N100. This type of study (which is very interesting) could be useful for guiding the planning and logistics for future field campaigns. The authors touch on these issues briefly in the current manuscript, but it would be useful to get clarification on the following:
- Is there a reason why the direct CCN measurements (either from the scanning or constant supersaturation CCN counters; Sanchez et al., 2021) collected during SOCRATES were unsuitable for this analysis?
- Given a correlation coefficient of 0.719 between N100 and CCN0.2 during SOCRATES (McCoy et al. 2021; Figure S2b), are the authors confident that using N100 as a proxy for CCN is sufficient to conclude that CCN observations alone can only provide minimal global constraint on Nd?
References
McCoy, I. L. et al. (2021). Influences of recent particle formation on Southern Ocean aerosol variability and low cloud properties. Journal of Geophysical Research: Atmospheres, 126(8), e2020JD033529.Sanchez, K. J. et al. (2021). Measurement report: Cloud processes and the transport of biological emissions affect southern ocean particle and cloud condensation nuclei concentrations. Atmospheric Chemistry and Physics, 21(5), 3427-3446.
Citation: https://doi.org/10.5194/egusphere-2025-2009-CC1 -
AC1: 'Reply on CC1', Ci Song, 10 Jun 2025
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We thank the reviewer for their comment. Our study builds upon the framework of McCoy et al. (2021), which evaluated the default version of CAM6 using N100 and Nd from SOCRATES. Consistent with their approach, we use N100 as a proxy for CCN to enable comparison across multiple CAM6 simulations with varied parameter combinations. One advantage of using N100 is that it allows direct comparison of our perturbed parameter ensemble (PPE) results with those from previous studies (e.g., Figure 2).
We acknowledge that the correlation between N100 and CCN at 0.2% supersaturation during SOCRATES (r = 0.719; McCoy et al., 2021) indicates that N100 explains only part of the variance in CCN. However, the ratio between N100 and CCN at 0.2% is 1:1 (see Figure S2a,b, McCoy et al. 2021). Thus, the campaign mean N100 will be equivalent to the campaign mean CCN at 0.2% supersaturation. Because the campaign mean N100 is ultimately what is used as the constraint for this study (e.g., Figure 7b), it will be equivalent to using the campaign mean CCN at 0.2%.
Citation: https://doi.org/10.5194/egusphere-2025-2009-AC1
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RC1: 'Comment on egusphere-2025-2009', Anonymous Referee #1, 10 Jun 2025
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The authors use field measurements from the SOCRATES campaign to constrain a CAM6 perturbed parameter ensemble. With a primary focus on CCN and Nd, the authors use an emulator to create surrogate models and constrain plausible parameter combination. Lastly, the authors show how observational uncertainty affects the ability to constrain.
The paper is well written and the figures provide sufficient visual context. There are a few concerns that the authors should address before publication.
Major concerns
The authors stress in several places that the performance of the emulator is crucial for the task at hand. Looking at Fig. 7, the colors of points and shading strongly disagree in many places. I wonder if the authors have an explanation of why the apparent performance is so poor and whether a better emulator is needed (or even possible).
The authors largely leave out cloud macrophysical properties (e.g., cloud fraction, cloud geometric thickness, etc.). Does it go without saying that the nudged PPE runs produce plausible macrophysical properties? The authors should at least provide a brief assessment.
Along with the above concern, I am wondering if this study is limited to stratiform clouds (as no convective scheme is described in Sec. 2). Have the authors ensured that all clouds in CAM6 are stratiform over the SOCRATES domain? How would the results change if a substantial portion was handled by the convective scheme?
The authors use observed surface precipitation rates, but it is unclear where these rates stem from. The authors need to update Section 2 and describe the retrieval.
Minor concerns
l. 16 “possible” rather than “much easier”
ll. 105ff Please briefly describe the synoptic situation encountered during the flights.
ll. 265-266 Could a lower updraft speed also explain this?
Typo(s)
l. 453 “of without”
Citation: https://doi.org/10.5194/egusphere-2025-2009-RC1
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