|Review comments for Zhong et al., Spatiotemporal variability of snow depth across the Eurasian continent from 1966-2012.|
Significance: The study has the most comprehensive Eurasian daily snow depth dataset to date. Previous studies have tended to focus on Russia (e.g. Bulygina et al. 2011) and China (e.g. Ke et al. 2016) separately.
Merits: Apart from the unique dataset, the paper adds little to current understanding of Eurasian snow cover variability.
Weaknesses: Even after extensive revisions, the current state of this paper is a long way from reaching the standards required for a journal paper. Some key issues are documented below:
1. Lack of guiding science questions/hypotheses: A fundamental weakness of the paper is related to the lack of clear science questions guiding the analysis which results in a descriptive level analysis without any particularly interesting or relevant conclusions that help advance understanding of key questions such as: Is Eurasian fall snow cover increasing as shown in the NOAA-CDR dataset (e.g. Cohen et al. 2012) and subsequently disputed by Brown and Derksen (2013) and Mudry et al. (2017)? Is there evidence of an accelerating hydrologic cycle (e.g. Syed et al. 2010) in the snow cover data? Do climate models underestimate snow cover temperature sensitivity (e.g. Mudryk et al. 2017) or is this an artifact of the NOAA-CDR dataset? Are precipitation trends consistent with observed changes in snow depth?
The authors chose to analyse snow depth and snow cover in two separate papers which is a strategic error in my opinion. Understanding snow cover variability requires at least four essential snow cover variables: the start/end date of snow cover, and the date and depth of the annual maximum accumulation, together with information on rainfall, snowfall and temperature. For example, this paper shows increasing snow depths over polar latitudes occurring with a shortened snow cover season (the other paper). The only way this can happen is from more intense snowfall during the shorter accumulation period. Is this hypothesis supported by the precipitation data? Analysis of the melt period (SnowOff Date - SDmax date) could also provide insights into melt dynamics and possibly additional evidence of an accelerating hydrologic cycle. Separating snow depth and snow cover precludes examining these kinds of questions.
2. Paper organization and language: The organization of the paper suffers because the authors do not have a clear storyline (i.e. science questions) to build on. This results in the inclusion of often irrelevant material in the introduction, and overly descriptive material in the results section. I urge the authors to look at examples of published papers in journals such a GRL or JGR to see how the papers are structured. Issues with the English language become relatively minor if the paper has a solid science foundation.
3. Methodological issues: The paper contains a number of methodological issues that may have implications for some of the study conclusions:
- The first is the 20-year minimum years of data requirement for a station to be included in the regional average for 1966-2012. This has the potential to generate a temporally varying network of stations that can have a major impact on the trend analysis results of the regional average. The authors should provide a time series plot of the number of stations included in the Eurasia regional average each year to verify that a relatively even spatial distribution of stations is maintained over the full 47 years. You can test the robustness of the regional average to varying minimum data length for a range of years e.g. 20, 30, 40, 50.
- A related issue is the generation of a regional average from all stations which gives a result that is weighted toward the region with the densest observing network (i.e. west of ~90E). Interpolation of station data to a grid would help avoid this potential bias.
- Ignoring homogeneity and undercatch issues with the precipitation data (page 6, lines 22-23) may also have implications for the study conclusions. For example, with corrected precipitation data Groisman et al. (2014) found no evidence of increasing cold season precipitation over most of the Russian Federation, and significant decreases over the Arctic sector.
- The analysis of elevation influence on snow depth (dSD/dZ) is contaminated by other influences such as climate region (e.g. dry interior regions are likely to have a different elevation response than mountains in maritime locations). One way to isolate dSD/dZ would be to use a moving spatial window.
- The discussion of Liston and Hiemstra snow depth data on page 16-17 incorrectly states that it is an assimilation (it is a reconstruction with no observational input), and that SnowModel was driven with surface observations. The model was driven with downscaled MERRA reanalysis fields that do not incorporate surface observations.
- The wavelet analysis only seems to have served as a low-pass filter for the regional averaged time series plots. What happened to the wavelet spectrum plot showing wavelet coefficients versus time like Figure 6 in De Jongh et al. (2006)?
Recommendation: The authors have a unique dataset for answering important science questions. Simple descriptive analyses of these data are not a sufficient basis for a credible publication.
Brown, R.D. and Derksen, C., 2013. Is Eurasian October snow cover extent increasing?. Environmental Research Letters, 8(2), p.024006.
Cohen, J.L., Furtado, J.C., Barlow, M.A., Alexeev, V.A. and Cherry, J.E., 2012. Arctic warming, increasing snow cover and widespread boreal winter cooling. Environmental Research Letters, 7(1), p.014007.
De Jongh, I.L., Verhoest, N.E. and De Troch, F.P., 2006. Analysis of a 105‐year time series of precipitation observed at Uccle, Belgium. International Journal of Climatology, 26(14), pp.2023-2039.
Groisman, P.Ya., E.G. Bogdanova, V.A. Alexeev, J.E. Cherry and O.N. Bulygina, 2014. Impact of snowfall measurement deficiencies on quantification of precipitation and its trends over Northern Eurasia. Ice and Snow, 2:29-43.
Mudryk, L.R., Kushner, P.J., Derksen, C. and Thackeray, C., 2017. Snow cover response to temperature in observational and climate model ensembles. Geophysical Research Letters, 44(2), pp.919-926.
Syed, T.H., Famiglietti, J.S., Chambers, D.P., Willis, J.K. and Hilburn, K., 2010. Satellite-based global-ocean mass balance estimates of interannual variability and emerging trends in continental freshwater discharge. Proceedings of the National Academy of Sciences, 107(42), pp.17916-17921.