the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 05 Feb 2024
A simple snow temperature index model exposes discrepancies between reanalysis snow water equivalent products
Abstract. Current global reanalyses show marked discrepancies in snow mass and snow cover extent for the Northern Hemisphere. Here, benchmark snow datasets are produced by driving a simple offline snow model, the Brown Temperature Index Model (B-TIM), with temperature and precipitation from each of three reanalyses. B-TIM offline snow performs comparably to or better than online (coupled land-atmosphere) reanalysis snow when evaluated against in situ snow measurements. Sources of discrepancy in snow climatologies, which are difficult to isolate when comparing online reanalysis snow products amongst themselves, are partially elucidated by separately bias-adjusting temperature and precipitation in B-TIM. Interannual variability in snow mass and snow spatial patterns is far more self-consistent amongst offline B-TIM snow products than amongst online reanalysis snow products, and specific artifacts related to temporal inhomogeneity in snow data assimilation are revealed in the analysis. B-TIM, released here as an open-source, self-contained Python package, provides a simple benchmarking tool for future updates to more sophisticated online and offline snow datasets.
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RC1: 'Comment on egusphere-2024-201', Anonymous Referee #1, 14 Mar 2024
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This manuscript presents how a simple off-line snow temperature index model (B-TIM) can be considered to highlight discrepancies between snow water equivalent (SWE) products from three reanalysis (JRA-55, ERA5 and MERRA-2) and an additional product (ERA5Snow) for historical period (1980-2020). The authors used either biased, or adjusted temperature and precipitation from reanalysis as input data for B-TIM. The SWEs produced with B-TIM and various sets of input data were then compared to the SWEs produced by the reanalysis. Climatological characteristics and interannual variability were investigated. To carry out this study, they improved and translated the previous version of B-TIM and made it publicly available. This manuscript opens up the possibility of using a simple off-line model for large-scale snow cover studies.
General comments
The manuscript is well written and structured. The figures are simple, clear and concise, with an appropriate choice of colors. It could nevertheless be improved by considering the following points.
Some modifications in the structure could lighten the text and focus more on the results and the contribution of using a simple off-line model like B-TIM. For example, a large part of section 2.1 would have a more appropriate place in the SI. After all, this is not a paper about improving B-TIM, but more about using it. If this is not the case, please change the title and specify this aspect more clearly in the objectives.
It would be useful to remind the reader of the context in the results. For example, simply add a sentence to remind us that ERA5 and ERA5Snow have the same meteorology, which explains why we don't have BrE5S.
Methodological choices (e.g., bias adjustment) could be justified in greater detail, with more references where possible.
The results focus on very large domains (Northern Hemisphere, Eurasia, North America). It would have been interesting to look at these results more regionally.
Specific comments
Temperature and precipitation bias were corrected with a multiplicative factor. As precipitation is a zero-bound variable, it is generally corrected by a multiplicative method, whereas temperature is often corrected by an additive method. The choice of this method is not sufficiently justified. Can you explain thoroughly why you chose a multiplicative factor and why you apply it this way?
Figure captions contain results, whereas captions should only contain descriptions of the elements present in the figure (colors, symbols, etc.). Please remove the result part in the captions.
L102: Did you perform tests regarding the 20% of precipitation reduction?
L109: Table 2. Please find a more consistent way to present column “Model variable”. For example: « t2m (ID 167) » instead of « Parameter ID 167: "t2m" ». Add the model variable name for SWE in ERA5Snow. Also, this table could go in the SI, as it doesn't provide much relevant information to the text.
Table 2, L215, L268, L428 : Modify MERRA2 to MERRA-2.
L232: To take advantage of the fact that you have an SI, it might be interesting to present the differences in domains used for the different reanalyses (land grid points, mountainous grid points, etc.).
Fig. 3 : Please describe colors used in the scatterplots in the caption; modify ERA5-Snow to ERA5Snow; consider using hatches for ERA5Snow in the right panel and present the legend in a neutral color.
L374: Modify JRA55 to JRA-55.
L469 : Modify ERA5-Snow to ERA5Snow.
Citation: https://doi.org/10.5194/egusphere-2024-201-RC1
Data sets
Replication Data for: "A simple snow temperature index model exposes discrepancies between reanalysis snow water equivalent products" Aleksandra Elias Chereque https://doi.org/10.5683/SP3/IV6SVJ
Model code and software
Brown Temperature Index Model Aleksandra Elias Chereque https://doi.org/10.5281/zenodo.10044951
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