| 06 Aug 2026
Vertical structure and controlling factors of cloud condensation nuclei activation over eastern China: Insights from aircraft measurements and interpretable machine learning
Abstract. Cloud condensation nuclei (CCN) play a critical role in cloud droplet formation and microphysical processes. Based on aircraft observations, this study investigated the factors controlling CCN number concentrations (NCCN) under different aerosol vertical structures (Decrease, Increase, and Stable) and supersaturation (SS) conditions using generalized additive models (GAM) combined with SHapley Additive exPlanations (SHAP).NCCN reached up to 10³ cm⁻³ near the surface and generally decreased with altitude, while aerosol vertical structures modulated its abundance, with the Increase structure showing higher NCCN than Stable and Decrease structures. CCN activation ratios increased with SS and exhibited a non-monotonic vertical variation, with no consistent ranking among aerosol structures, indicating that supersaturation dominates CCN activation. Activated CCN droplet spectra showed unimodal distributions, with peak diameters increasing from ~2 μm at SS = 0.2 to ~5 μm at SS = 1.0. Although spectral shapes were similar among different structures, higher small-size aerosol concentrations (SA) enhanced CCN peak concentrations. GAM results identified temperature (T), SA, relative humidity (RH), and horizontal wind speed (WS) as important explanatory variables for NCCN variations, with contributions of 20 % – 56 %, 9 % – 45 %, 9 % – 40 %, and 3 % – 19 %, respectively. SHAP analysis revealed that the contributions of T varied among different aerosol vertical structures, showing positive associations under Decrease and Increase structures but negative associations under Stable conditions, whereas SA consistently exhibited positive contributions. RH showed nonlinear relationships with NCCN, with an inflection point near 60 %.
Comment to “Vertical structure and controlling factors of cloud condensation nuclei activation over eastern China: Insights from aircraft measurements and interpretable machine learning”
This manuscript presents a valuable observational study investigating the vertical distribution of cloud condensation nuclei (CCN) and their controlling factors over eastern China, using aircraft measurements combined with interpretable machine learning techniques (Generalized Additive Models, GAM, and SHapley Additive exPlanations, SHAP). The topic is highly relevant to aerosol–cloud–climate interactions, and the integration of aircraft observations with explainable AI methods represents a timely and scientifically sound approach. The writing is generally clear. I would like to recommend its acceptance for publication with necessary modifications.
General comments
Detailed comments
Line 33-34, Regarding the aerosol impacts, recent review studies could serve as important supporting references, such as Zhao et al. (2024, doi: 10.1016/j.scib.2024.03.014) and so on.
Line 37-39, Regarding this point, Negative Aerosol-Cloud re Relationship from Aircraft Observations over Hebei has been found, which is worthy to mention.
Line 39-40, In addition to the decrease of precipitation, aerosol invigoration effect on precipitation should also be briefly introduced for fair.
Line 42, Citation format is not right, which should be corrected (also other citations).
Line 45-50, Actually, different even opposite findings regarding aerosol-cloud interactions have been demonstrated by previous studies, which should be briefly introduced.
Line 56, based on Köhler theory, not only aerosol size, but also aerosol chemical composition affects the CCN activity.
Line 61-62, There are also laboratory experimental characterizations.
Line 62-64, For fair, the limitation of aircraft observations should be also discussed.
Line 74-75, Supporting evidence should be given for this claim.
Line 79-80, What do the authors mean “CCN research”?
Line 81, Ren et al. 2025 is not listed in the reference list.
More background information about the aircraft observations should be given, particularly over east China regions.
Line 108-134, Uncertainties in the instrument measurements should be briefly introduced.
Line 135-140, How do the authors solve the measurement issues in the first or second bins, along with the shattering of ice crystals?
Line 145, How reliable for this cloud identification method? Or how sensitive are the results to threshold values?
Line 205, UTC time or Beijing time?