the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 11 Jun 2026
Investigating aerosol-cloud interactions by fusing DOE ARM observations with ship tracks: A methodology
Abstract. Aerosol-cloud interactions (ACIs) remain one of the largest uncertainties in Earth’s climate system, partly because large-scale meteorology can independently influence both aerosols and clouds, complicating causal attribution. To leverage the rich ground-based measurements from Atmospheric Radiation Measurement (ARM) campaigns and improve aerosol effect attribution, we develop an approach to identify ship-emission-influenced observation cases through source tracing. The method detects local peaks in cloud condensation nuclei number concentrations (NCCN) and applies 24 h backward trajectories to determine whether the sampled air masses intersect ship emissions in the past day. Applying this framework to the ARM Eastern North Atlantic (ENA) observations in 2023 yields several dozen ship influenced cases, including a stratocumulus case in which two ship plumes contribute to a pronounced NCCN enhancement. An increase in cloud fraction and liquid water path, along with a 1 h delay in precipitation, is observed by the comprehensive ARM measurements at the time of the NCCN spike. As large-scale meteorological conditions remain steady, the cloud responses are more likely an aerosol-driven signal rather than meteorology-mediated covariability. Preliminary application to the Marine ARM GPCI Investigation of Clouds (MAGIC) campaign is also discussed. This framework provides a basis for building a multi-year, multi-site library of ship-emission-influenced cloud measurements, offering improved observational constraints for ACI research and model evaluation.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2026-2430', Anonymous Referee #1, 19 Jul 2026
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AC1: 'Reply on RC1', Haipeng Zhang, 02 Sep 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2026/egusphere-2026-2430/egusphere-2026-2430-AC1-supplement.pdf
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AC1: 'Reply on RC1', Haipeng Zhang, 02 Sep 2026
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RC2: 'Comment on egusphere-2026-2430', Anonymous Referee #2, 31 Jul 2026
Review of the article titled “Investigating aerosol-cloud interactions by fusing DOE ARM observations with ship tracks: A methodology” by Zhang and coauthors for publication in the Atmospheric Chemistry and Physics.
The authors have extended their ship-track impact on stratocumulus work based on satellite data to data collected at the ARM sites. The authors have focused on two ARM sites and have presented a method to identify ship-track signatures in the ground-based data, and then tried to understand the impact of high CCN on cloud properties. The article is straightforward to understand. However, the methods employed by the authors are not rigorous, thereby yielding flawed results/conclusions.
Broadly I find 1) the method to identify ship-track influence from the surface aerosol data, and 2) method to understand impact of changes in CCN on clouds/rain to be deeply flawed. In addition, the authors have ignored a lot of aerosol research already done at the site. The article also has several contradictory statements. Below I am listing the major and minor issues.
Major Comments
The authors have cited the Gallo et al. (2020) and Ghate et al. (2023) who have reported the aerosol measurements to be affected by the local sources, especially during southerly wind conditions. Despite this, the chosen case to highlight the ship-track impact on aerosol is of southerly wind conditions. It is possible that this case is an exception and the increased CCN are simply due to ships. To understand this maybe the authors can use other aerosol chemical, and size distribution measurements made at the site. I assume the ship emissions are largely at sub-100nm sizes. As of now, it seems that the local sources that are only ~100 meters away from the site are affecting the measurements rather than a ship that is ~100 km away.
It has been shown by Jensen et al. (2021) and Ghate et al. (2021) that the cloud and precipitation during southerly wind conditions is affected by island heating and topography. So how can the authors be sure that the CCN effects on cloud/rain (Figure 6) are due to aerosol and not due to the topography. This issue needs to be explored further. Especially as the rain shaft during the CCN peak is very similar to the other rain shafts per the radar image (Figure 6). The surface rain gages are well-known to miss light rain, and hence the couple of rain measurements shown in Figure 6 bottom panel cannot truly be trusted.
Since you already have data from geostationary satellite (Figure A2). Maybe for this ship-track case you can show the enhanced cloud reflectivity as it gets advected over the island. This might not be possible as this is a nighttime case, but a similar image for another case might drive home the point.
Since you have identified 79 cases of ship-tracks (Line 163) from the ENA data. I suggest you do a scatter plot between the delta-NCCN, and other microphysical properties. The correlations within these plots will tell us if the changes in cloud/rain properties are caused by ship emitted aerosol. Thank you.
A lot of research has been done to understand the impact of aerosol on clouds using the ARM ENA data. Please see papers by Robert Wood, Jian Wang, Fan Yang, Zheen Zhu etc. Also some other papers by Xue Zheng, Matt Christiensen, and Xiaojian Zheng. It would be unwise not to put your results in the context of results of these papers. Thank you.
There are several instances of contradictory statements in the text. These need to be redone before the article can be published. For example, Line 180: “indicating an anomalously high enhancement for the site and suggesting that local sources are unlikely to dominate in this case.” Followed by “assessment of local aircraft contributions is challenging here due to the lack of flight time schedule data”. The local aircraft times are available from the site operators.
Line 239: “The precipitation appears to be delayed by approximately 1h, as evidenced by stronger reflectivity and a pronounced surface precipitation rate immediately after the NCCN peak. That said, the delayed precipitation may also be coincidental and not necessarily related to aerosol perturbations, and its significance should be evaluated with a larger sample of cases in the future.” So unclear if you do believe that this is delayed precipitation or not, and why?
Minor Comments:
Figure 6 and 7: Please clean the image for noise. I am sure there are no lidar returns above the cloud base. Also Figure 7b is not discussed in the text.
Since you don’t have any results from the MAGIC campaign, I’d remove that figure and related text.
Since all four panels of Figure 3 look exactly the same, I’d remove it and put it in supporting information.
Citation: https://doi.org/10.5194/egusphere-2026-2430-RC2 -
AC1: 'Reply on RC1', Haipeng Zhang, 02 Sep 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2026/egusphere-2026-2430/egusphere-2026-2430-AC1-supplement.pdf
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AC1: 'Reply on RC1', Haipeng Zhang, 02 Sep 2026
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Review for “Investigating aerosol-cloud interactions by fusing DOE ARM observations with ship tracks: A methodology”
The manuscript by Zhang et al. proposes a methodology for detecting clouds affected by ship emissions using ground-based observations. The authors first identify local maxima in CCN number concentration (NCCN) from ARM measurements. They then calculate 24-hour backward trajectories originating from the ARM site at the times corresponding to these NCCN maxima to determine whether they intersect the path of a ship. If an intersection is found, the observed NCCN enhancement is attributed to ship emissions. If clouds are simultaneously observed, the properties of these potentially ship-polluted clouds are subsequently analyzed. Overall, the manuscript is well written, easy to follow, and the proposed methodology is scientifically sound. However, there are several challenges that should be acknowledged and discussed before the manuscript can be considered for publication.
One important question concerns whether the clouds sampled during the CCN peaks have actually interacted with the transported ship-emitted aerosols. It is possible that the clouds simply advected into the field of view of the ground-based sensors from elsewhere, while the enhanced CCN remained confined below the cloud layer. In the example case study, the backward trajectories are initialized at altitudes of only 100, 200, and 300 m above the surface, whereas the cloud base is located at approximately 1 km altitude (Figs. 6 and 7). Furthermore, the reported updraft velocities appear relatively weak, raising questions about whether the transported aerosols could realistically reach cloud base within the required timescale. The authors should discuss this potential limitation and provide justification for assuming that the observed clouds were indeed influenced by the detected ship-emitted aerosols.
In addition, how the authors would use the resulting dataset could be added to the discussion. Given the continuous dispersion and dilution of ship emissions, it is challenging to unambiguously distinguish clouds that are affected by ship emissions from those that are not. Consequently, the conventional approach of comparing polluted and unpolluted cloud populations to quantify cloud susceptibility, and ultimately estimate the effective radiative forcing due to aerosol–cloud interactions (ERFaci), does not appear to be directly applicable using this source-tracing methodology. The authors should comment on the intended applications of the dataset and discuss its limitations in the context of aerosol–cloud interaction studies.
Finally, Section 3.2 appears incomplete, as it lacks a discussion comparable to that presented in Section 3.1. I recommend renaming the section to "Challenges in obtaining ship-emission-affected cases during MAGIC", which would better reflect its current content.
Other minor comments:
1. Lines 25-26: I would particularly recommend refraining from relying on scientific findings from the distant past, as these estimates were based on older models that have since undergone substantial modifications and improvements. For example: “A 4% increase in stratocumulus cloud coverage can offset the warming induced by a doubling of atmospheric carbon dioxide concentrations (Randall et al., 1984).”
2. Lines 57-59: Satellite retrievals of Nd and LWP are also subject to sampling biases arising from retrieval failures when the liquid cloud effective radius is larger than 30 µm, which may introduce artifacts into the derived correlations and potentially lead to misleading physical interpretations (Cho et al., 2015; Choudhury and Goren, 2025).
References:
Choudhury, G., & Goren, T. (2025). Sampling bias from satellite retrieval failures of cloud properties and its implications for aerosol-cloud interactions. Geophysical Research Letters, 52, e2025GL115429. https://doi.org/10.1029/2025GL115429
Cho, H.-M., Zhang, Z., Meyer, K., Lebsock, M., Platnick, S., Ackerman, A. S., et al. (2015). Frequency and causes of failed modis cloud property retrievals for liquid phase clouds over global oceans. Journal of Geophysical Research: Atmospheres, 120(9), 4132–4154. https://doi.org/10.1002/2015JD023161
3. Lines 119-127: Could the authors please clarify in the manuscript why and how the “Virtual Ship Track” dataset was used in the study, when already a high resolution ship emission dataset is available? Virtual ship track data is tied to MODIS overpass time, and may therefore lead to much less number of intersections with the ENA site.
4. Figure 2: I do not fully understand why the ship emission locations marked in the two panels of Fig. 2 are so different. If the emission data is an instantaneous snapshot, for every hour, it should just be one pixel for every ship location. What do these lines represent and why is panel (a) not an extension of panel (b), since (b) is 4 hours behind (a)?
5. Line 201: Could the increase in low cloud cover and liquid water path (LWP) be due to the movement of clouds with high LWP from other regions above the ARM site? Can the authors comment on whether the clouds were stagnant or being transported from elsewhere during the peak in CCN concentrations.
6. Figure 6: I recommend removing the fill color in the pink bar for indicating the Nccn peak. It is interfering with the color keys beneath, confusing its interpretation. I would recommend only highlighting the boundaries of the pink bar.
7. Line 237: On what basis do the authors state that “the boundary layer is topped by stratocumulus”? Please clarify.
8. Line 239: I suggest using “cloud radar reflectivity” instead of cloud reflectivity.
9. Line 288: The term “ship-track” could also mean bright linear cloud features observed in satellite images. I would rather use “ship ground track”.