|Review of Fan et al. (acp-2016-802)|
In this study the authors examined the impact of a new emission inventory on aerosols and their radiative effects in eastern China using a global aerosol-climate model. Many global models suffer a low bias in simulating aerosols over the heavily polluted East Asia, and it is well known that aerosol emissions over there are highly uncertain. With the new technology-based multi-resolution emissions inventory for China, they found that low biases in AOD simulated with the widely used AR5 emissions are reduced and seasonal variations in primary aerosols are also improved, as evaluated against satellite retrievals and surface observations. These improvements in aerosols are found to have a significant impact on the regional aerosol direct radiative forcing. I have also read the comments on a previous version of the manuscript from two reviewers and the authors’ responses. I agree with the reviewers on some of the good points and the “fair” scientific significance. However, I believe the authors have done a good job in addressing the comments and revising the manuscript, which has now become publishable. In particular, the new MEIC emissions files prepared for the CAM5 model, if made available to the community, would be a very useful contribution. Below I offer a few comments for the authors to consider before the final publication.
1) How does the MEIC emission inventory compare to the newly released CEDS (Community Emission Data System; Hoesly et al., 2017) for China? The CEDS dataset has been used in CAM5 by Yang et al. (2017) to study black carbon and its direct radiative forcing in China. I understand that the CEDS dataset probably wasn’t available when this manuscript was first submitted, but since it is now released to the community and intended for use in CMIP6 simulations, the authors should include a discussion on this.
2) It is not very clear about the simulations performed in this study and results shown in some of the figures. Please clarify in section 2.1 as well as all of the relevant figure captions, including the time period of observations used for model evaluation. Why is a linear interpolation between year 2008 and 2010 needed to obtained MEIC emissions in 2009 (lines 163-164)? Please clarify.
3) It is good to be precise, but I don’t think it is really useful to keep so many significant figures (e.g., two digits after the decimal point) in some of the numbers in the results section, especially, for those numbers of percentage and/or with trailing zeros.
4) Supposedly, the AR5 SO2 and primary aerosol emissions in Figure 3 are for anthropogenic sectors only (section 2.2), so there are no seasonal variations in SO2 and BC, but why is there variation in POM? Please clarify.
Line 16: energy-statistics?
Line 67: Is it “multi-scale” or “multi-resolution” for MEIC?
Line 123: Did you actually change the model code to take the MEIC emissions? Or just prepare the emissions as input files for the model?
Line 199: Change “presents” to “presence”
Line 343: Please make sure if R squared in Figure 8 is correlation coefficient.
Figure 16 caption: Remove “change of”.
Hoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G., Pitkanen, T., Seibert, J. J., Vu, L., Andres, R. J., Bolt, R. M., Bond, T. C., Dawidowski, L., Kholod, N., Kurokawa, J.-I., Li, M., Liu, L., Lu, Z., Moura, M. C. P., O'Rourke, P. R., and Zhang, Q.: Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emission Data System (CEDS), Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-43, in review, 2017.
Yang, Y., Wang, H., Smith, S. J., Ma, P.-L., and Rasch, P. J.: Source attribution of black carbon and its direct radiative forcing in China, Atmos. Chem. Phys., 17, 4319-4336, https://doi.org/10.5194/acp-17-4319-2017, 2017.