This commentary is not intended to be a comprehensive review but rather to comment on some aspects that, as a cloud researcher in the Arctic myself, I find interesting or worthy of improvement and explanation. My main points are three: the PHACT dataset, the error treatment and the conclusions drawn from the analysis of SIC and cloud radiative effect.

1. The PHACT dataset
   
   
   
   L43-44: "PHACT, which will be described in a separate paper".
   
   It is peculiar to read the analysis of a dataset without having the ability to read the details of the algorithm used to create the same dataset. In my opinion, the authors could describe the algorithm in more detail, in the absence of a peer-reviewed article supporting the use of the dataset in this publication, to equip the reader with the necessary tools to understand the reach and the limitations of the dataset.
   
   
   
   L45-46: "Cloud phase diagnostics are based on the cloud particle sphericity instead of temperature, in contrast with many passive sensors".
   
   It is possible to differentiate the phases not only by means of temperature profiles but also by differential complex refractive index at those wavelengths where ice and water absorption differ. What accuracy is expected with the proposed approach compared to others? What ranges of sphericity values are assigned to categorize the variety of droplets and crystals and everything in between?
2. Errors
   
   
   
   Within PHACT, the instantaneous profiles are aggregated at a spatial resolution of 2.5 degree. Recent studies (Kotarba, AR, 2022; Kotarba, AMT, 2022) have highlighted some shortcomings in the representation of climatological cloud fields by means of spaceborne lidars as function of grid resolution and cloud regimes.  I think these results are relevant for the purposes of this article because it is clear that the errors introduced by undersampling (revisit and transect) can be considerable. I refer specifically to the figure 3 in the AMT article. The plots 3a-3c show the magnitude of cloud amount error with respect to general cloud regime (3a) and to sampling frequency (3c). I conclude that for high cloud amount CALIPSO sampling would result in absolute error of 5%, whereas for cloud amount of 60-80% the expected error would be 6-8% of cloud amount itself.
   
   Two recent papers noted these results (Winker et al, ESSD, 2023; Bertrand et al, ESSD, 2023) and introduced correction factors to account for CALIPSO undersampling. It would be interesting to know the authors' opinion about this aspect and how this feature of CALIPSO impacts aggregate climatological cloud cover fields, differences with other datasets, and, finally, correlation values with sea ice extent.
   
   This consideration brings me to ask what is the standard deviation of the curves in Figure 2 of this paper. The authors seem satisfied with the agreement among the datasets and it is certainly true that the seasonality is indeed in phase. But the absolute values are not in agreement and sometimes even exceeds the errors reported above in Kotarba AMT 2022. What if not even the standard deviation of the respective datasets overlap?
3. Conclusions
   
   
   
   3-a) The sentence at line 182-183 ("Such a cooling effect is found in all seasons but winter, when the LW CRE warming exceeds the SW CRE cooling.") seems inaccurate to me.
   
   Arctic winter months are characterized by the polar night. Assuming the authors calculate the net CRE at the surface from the difference between the downward and upward flux components of SW and LW for all-sky and clear-sky conditions (is this how you compute CRE?), the absence of sunlight makes the terms SW_down and SW_up zero and only the LW emissive terms are present. Cloud cannot reflect shortwave radiation during the polar night, right?
   
   
   
   3-b) The last paragraph of the conclusion supports the results in Lelli et al. 2023.
   
   A paper comes alive only if it ventures outside its own scientific project vacuum and it is related to previous research. Therefore, I think that the authors of this interesting manuscript could try to answer the following questions: (i) do your results contradict, confirm, strengthen previously established knowledge? (ii) which new research questions arise from the contrast between previous research and results here? (iii) How could further your research help resolve them? A better connection with past results is beneficial.
   
   
   
   For instance, during the preparation of Lelli et al ACP 2023, we created the following figure where we look at CRE SW and LW above sea ice areas. The right panels show that the (negative) SW CRE trend increases for a decreasing sea ice extent, while the LW CRF trends slightly increase. Inspecting the crossing point between trends in SW and LW CRF as function of SIC, we see that already for a SIC change of 0.05%/month suffices for the SW CRF to offset the LW CRF. In these results the CRF changes as a function of cloud property changes are clearly conflated. Note that AMJ and JAS stand for Arctic spring (AMJ) and summer (JAS) and some differences might be due to the different season definition.
   
   
   
   
   
   
   
   We additionally point the authors to Sections 3.2 onward in Lelli et al. 2023 for a detailed description of those cloud property changes and the CRE sensitivities. As a side note, cloud properties and fluxes have been extensively validated. The references of interest are given in Lelli et al. ACP 2023.
   
   
   
   3-c) From what I can understand from the present manuscript, the authors analyzed CRE by relating it to the surface, treating it as if it were binary: no sea ice/sea ice. Where we know that in reality Arctic sea ice is a much more complex surface realm (leads, melt ponds, floes ... ) such that it likely results in distribution of ice concentration (0-100%) within a 2.5 degree side-by-side grid cells of the chosen aggregation. Would it be possible for the authors to add a more refined dimension of analysis and treat Arctic sea ice (and related trends) as a continuum? How would the result change?
   
   
   
   3-d) I would like to ask whether the albedo of the analyzed sea ice surfaces can change the results found by the authors. CRE is by definition a net quantity, so it is to be related to the effectiveness of the surface to reflect SW. And this is a function of albedo, which in turn changes depending on whether - for example - there is fresh snow in the spring or old snow in the summer before the start of the melt season and so on. Can the authors comment on why they do not consider this aspect? 
   
   
   
   3-e) I think that is a bit of a missed opportunity - yet at authors' discretion - not to present also quantitative results on optical thickness of the clouds. In this way, the claims of this paper could be more substantiated and directly linkable to Lelli et al.

REFERENCES

Lelli, L., Vountas, M., Khosravi, N., and Burrows, J. P.: Satellite remote sensing of regional and seasonal Arctic cooling showing a multi-decadal trend towards brighter and more liquid clouds, Atmos. Chem. Phys., 23, 2579–2611, https://doi.org/10.5194/acp-23-2579-2023, 2023. 

Bertrand, W., Kay, J. E., Haynes, J., & de Boer, G. (2023). A Global Gridded Dataset for Cloud Vertical Structure from Combined CloudSat and CALIPSO Observations. Earth System Science Data Discussions, 2023, 1-21.

Winker, D., Cai, X., Vaughan, M., Garnier, A., Magill, B., Avery, M., & Getzewich, B. (2023). A Level 3 Monthly Gridded Ice Cloud Dataset Derived from a Decade of CALIOP Measurements. Earth System Science Data Discussions, 2023, 1-37.

Kotarba, A. Z. (2022). Errors in global cloud climatology due to transect sampling with the CALIPSO satellite lidar mission. Atmospheric Research, 279, 106379.

Kotarba, A. Z.: Impact of the revisit frequency on cloud climatology for CALIPSO, EarthCARE, Aeolus, and ICESat-2 satellite lidar missions, Atmos. Meas. Tech., 15, 4307–4322, https://doi.org/10.5194/amt-15-4307-2022, 2022.