General comments:

The paper presents a new method that employs the energy budget analysis framework to estimate the amount of supersaturation at warm cloud base by assuming the saturated parcel ascent as reversible adiabatic process with conserved isobaric enthalpy. The authors partition the supersaturation of the cloud (S_c) into vapor-phase (S_v) and liquid-phase (S_l) supersaturations to derive magnitudes of S_c, S_v, and S_l in the context of the energy balance defined by the first law of thermodynamics. This method is complemented by a closure analysis conducted using airborne observations of warm cumulus clouds from the ACRIDICON-CHUVA campaign. The analysis demonstrates how droplet number concentration (N_d) scales with S_v and agrees within the uncertainty range of CCN(S_v) spectra measured below cloud base prior to cloud observations.

Overall, the manuscript is well written and easy to follow. However, some analyses require further clarification. It is recommended that the manuscript be reconsidered after a major revision.

Specific Comments:

1. In section 3.1, please clarify the impact of background buoyant forcing on the estimated values of S_l and S_v using this framework regardless of aerosol regimes. In other words, how do uncertainty in S_l and S_v scales with increasing background buoyant forcing in any given environment?
2. Cloud passes were considered using a threshold criterion of N_d greater than 20 cm^–3 at initial stage of cloud formation (Lines 260-265). Since such in-cloud measurement thresholds are currently not standardized in our community, please perform sensitivity analysis on the resulting supersaturations with different threshold criteria defined using a combination of both minimum N_d and/or LWC. Additionally, please verify whether such definitions have any significant impact on the correlation coefficients discussed with power-law fits in Figure 4, S10, and S11. Include the analysis in the supporting information document.
3. The derived correlation coefficients values exhibit inverse correlation with the sample size. Larger sample sizes are associated with lower correlation coefficients and vice versa. Given the extremely limited sample size for this analysis and considering the prior argument, justify whether Nd is well constrained by S_v (Lines 408-409).
4. The reported uncertainties of 10% for N and LWC at lines 307-309 are applicable only to the ACRIDICON-CHUVA dataset. These uncertainties can be considered as the best-case scenario for measurement uncertainties, given that the Baumgardner et al., 2017 report the uncertainty range for scattering probes for sizing to be 10%-50% and for concentrations to be 10%-30%. Therefore, to extend the energy balance method to be applicable for other datasets from other field campaigns and to account for the worst-case scenario containing the maximum possible range of energy terms and supersaturations, please consider discussing the uncertainty to be defined as both the best-case and worst-case scenarios.
5. Lines 473-474: Please provide the magnitude of overestimation to better highlight the importance of including the energetic cost of vapor expansion during the ascent of the saturated parcel.

Technical corrections:

1. The spelling of ‘modeling’ in the manuscript is inconsistent. At Lines 33,36,237, it is spelled as ‘modeling’, whereas at lines 189, 226, 467 it is spelled as ‘modelling’.
2. Figure 3 and 4: Include a description in the figure captions that indicates that the color choices correspond to different aerosol backgrounds in the Amazon region and list the colors with their corresponding regions.

Reference:

Baumgardner, D., and Coauthors, 2017: Cloud Ice Properties: In Situ Measurement Challenges. Meteor. Monogr., 58, 9.1–9.23, https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0011.1