|Review of “Cloud Phase Characteristics Over Southeast Asia from A-Train Satellite Observations” by Y. Hong and L. Di Girolamo|
This is the second round of review.
First I want to remind the authors that as a reviewer, I’m required to give my opinion on certain criteria, including the novelty of a study and whether its length is appropriate or not, which I’m doing here based on my (imperfect) knowledge of the literature and regardless of what other reviewers may say. However, it doesn’t mean that I find the study irrelevant or not good.
That being said, the authors addressed most of the points I rose in the first round of review but some concerns remain and need to be addressed before I recommend the paper for publication. These are listed below with additional comments regarding some author arguments.
Comments that do not need to be further addressed
1) Novelty of the study
I just want to remind the author that previous literature already largely investigated the cloud phase characteristics globally (e.g., Cesana et al., 2015; Hu et al., 2010; Li et al., 2017; Matus and L’Ecuyer, 2017; Yoshida et al., 2012). Some of that literature also studies the link with radiation for different overlap using the very same product as the authors (e.g., Matus and L’Ecuyer, 2017). Focusing on a specific region doesn’t make a study novel, but again it doesn’t mean it’s not worth being published. However, I acknowledge that the spatial heterogeneity component of the study ¬¬–and its link with cloud phase– is quite new and interesting.
2) Length of the study
While I appreciate the author efforts to shorten the manuscript, I still find the study quite long, but it doesn’t bar it from being published.
Concerns that need to be addressed
1) Uncertainties and caveats related to the observational product
- The authors now better mention the caveats and uncertainties of the product, which is good, in particular the reduction in confidence of the diagnostic when the lidar is completely attenuated.
However, they fail to mention how often this happens in their study, a breakdown depending on the category would be helpful (i.e., how often the diagnostic relies on radar only by category). This region is dominated by deep convection and therefore I would expect most of the observations to be radar only, which is why it has to be quantified, it’s essential information for the reader.
- The authors say “the most comprehensive cloud phase and overlap information to date” p4 L37
I disagree with that statement. There is no paper that supports this statement to the best of my knowledge. The authors themselves stated that they are not aware of any kind of validation of the 2B-CLDCLASS-LIDAR product, which makes it difficult to conclude on whether this product is the most comprehensive to date. There are at least 2 other LIDAR-RADAR cloud phase datasets out there using different methods (DARDAR and Kyushu University products) as well as 3 lidar-only cloud phase products (CALIPSO-ST, GOCCP and Kyushu University), some of which have been validated against ground-based or in-situ measurements contrary to 2B-CLDCLASS-LIDAR product. Please, rephrase.
- Finally, the method used by the authors to account for the cloud fraction (i.e., cloudy profile each time the lidar cloud fraction within the cloudsat volume is greater than 0) leads to an overestimate of the cloud fraction in regions of fractionated clouds such as the trade winds and this should be explicitly mentioned in the manuscript (e.g., Cesana et al., 2019; Marchand et al., 2010).
2) Climate model evaluation argument
I appreciate the effort of the authors to clarify how to use their results to inform model simulations. However, I’m still not convinced by their explanation. The 2B-CLDCLASS-LIDAR cloud ice and liquid frequency cannot be used to evaluate climate models. There are no cloud ice or liquid frequency in the models to compare with. Also, if such diagnostic was available, it would still be not consistent to directly compare the observations with the models without using a method that takes into account the inherent biases of the instruments. For example, one should use a forward simulator that reproduce the 2B-CLDCLASS-LIDAR product process and biases to compare with the models (see for example Masunaga et al., 2010 and Hashino et al., 2013 referenced by the authors, and many other not referenced here). Such simulator doesn’t exist.
Additionally, a quick look at Loveridge and Davies –referenced by the authors as an example of how to use their heterogeneity index for model evaluation– also shows that they use a simulator in their study to reproduce MISR and MODIS quantities, then compute their Hindex and evaluate the heterogeneity parametrization. A GCM grid box is typically on the order of hundreds of kilometers with the most recent one being on the tens of kilometers, which is still far larger than the 1km pixel size used in MODIS observations. This is why it can’t be used directly to evaluate a GCM (although not true for finer scale models).
I understand how it could be useful for observations as explained in the paper, but in its actual state, these observations cannot be used for pure model evaluation. Therefore, I’m still recommending to remove these statements of the manuscript.
Cesana, G., Waliser, D. E., Jiang, X., & Li, J. L. F., (2015), Multimodel evaluation of cloud phase transition using satellite and reanalysis data, Journal of Geophysical Research, 120(15), 7871–7892. https://doi.org/10.1002/2014JD022932
Cesana, G., Del Genio, A. D., & Chepfer, H., (2019), The Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD), Earth System Science Data Discussions, 2667637(November), 1–33. https://doi.org/10.5194/essd-2019-73
Li, J., Lv, Q., Zhang, M., Wang, T., Kawamoto, K., Chen, S., and Zhang, B.: Effects of atmospheric dynamics and aerosols on the fraction of supercooled water clouds, Atmos. Chem. Phys., 17, 1847–1863, https://doi.org/10.5194/acp-17-1847-2017, 2017.
Hu, Y., Rodier, S., Xu, K. M., Sun, W., Huang, J., Lin, B., et al., (2010), Occurrence, liquid water content, and fraction of supercooled water clouds from combined CALIOP/IIR/MODIS measurements, Journal of Geophysical Research Atmospheres, 115(19), 1–13. https://doi.org/10.1029/2009JD012384
Marchand, R., Ackerman, T., Smyth, M., & Rossow, W. B., (2010), A review of cloud top height and optical depth histograms from MISR, ISCCP, and MODIS, Journal of Geophysical Research Atmospheres, 115(16), 1–25. https://doi.org/10.1029/2009JD013422
Matus, A. V., & L’Ecuyer, T. S., (2017), The role of cloud phase in Earth’s radiation budget, Journal of Geophysical Research, 122(5), 2559–2578. https://doi.org/10.1002/2016JD025951
Yoshida, R., Okamoto, H., Hagihara, Y., & Ishimoto, H., (2010), Global analysis of cloud phase and ice crystal orientation from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data using attenuated backscattering and depolarization ratio, Journal of Geophysical Research Atmospheres, 115(16), 1–12. https://doi.org/10.1029/2009JD012334