|I thank the authors for the work they have put into revising the paper, it is much improved over the previous version, although there are a number of things that I am still concerned about.|
The linking of aerosol properties to the diurnal variation of aerosol remains a nice result to base this study around, but the correlation between cloud and aerosol properties shown in later parts of the work is subject to a number of meteorological covariations. Just controlling for the humidity has been shown to be insufficient to isolate an aerosol effect (Quaas and Boucher, 2012). With this in mind, Section 5 is useful in enumerating the cloud properties that are correlated to CDNC, but I am not sure it demonstrates a causal role of aerosol. I am not sure that it is vital to explain the rainfall results from the first part of the paper. Perhaps this section could be presented relationships between cloud and aerosol properties, rather than evidence of an aerosol impact.
I like the use of the CDNC as an aerosol proxy, but I think that the authors need to do more to demonstrate that it is applicable in this case. It is not just a drop-in replacement for AOD and has a number of specific biases.
The CDNC calculation is only applicable in adiabatic clouds. In general, convective clouds are not adiabatic and a precipitating cloud is unlikely to be. This may not be a large factor in this study, but it should be considered and factored into the discussion/conclusions. There are a number of other potential biases that may affect this work. Grosvenor et al. (2018) is a good summary.
Additionally, is the CDNC calculation done using the 1 by 1 degree mean data? As a non-linear calculation, this can strongly impact the ``retrieved'' CDNC. The MODIS L3 product includes a cloud optical depth-effective radius joint histogram which can be used to better calculate the CDNC (e.g. Quaas et al., 2008; Grandey and Stier, 2010)
As the CDNC is calculated from the CER and COD, it is not clear to me that the CDNC-CER and CDNC-COD relationships are useful. I have similar concerns about the CDNC-LWP relationship, which is difficult to interpret even in low-level liquid clouds (e.g. Sato
et al, 2018; Gryspeerdt et al., 2018b). In this case, I think that Fig. 7 is just reproducing the assumptions used to calculate the CDNC and LWP (if the CER and CDNC are known, then the LWP is uniquely identified - at least at a pixel level).
Finally, is it clear what biases in the CDNC might be caused by the addition of thin overlying ice cloud? The authors are considering very complex situations where this may be an issue in a way that it is not for studies of liquid clouds.
L188 - What are the advantages of the AH here compared to a more common meteorological value such as specific humidity (easily available from ERA-Interim)?
L218 - Absorbing aerosol index is dependent on the altitude of the aerosol layer. What is assumed in this work?
L406 - How does decreasing the supersaturation reduce the strength of the freezing process? The supersaturation over ice is higher than over liquid. There are some studies which have noted an aerosol relationship to observations of mixed phase and ice cloud that might help explain this result (Chylek et al., 2006; Zhao et al., 2018; Gryspeerdt et al., 2018)
L446 - There have been many studies looking at the impact of BC on precipitation, perhaps they might be helpful in interpreting these results (e.g. Fan et al., 2008; O'Gorman et al., 2011). The role of BC is expected to change with altitude. Is it clear that the BC here is all in the boundary layer?
L462 - This might be due to a change in LWP/cloud depth with changing AOD. However, it is not clear if that change in cloud depth could be attributed to aerosols.
Figures - The text on may of the plots is very small.
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