the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 24 Feb 2025
Benchmarking Snow Fields of ERA5-Land in the Northern Regions of North America
Abstract. Reanalysis products provide new opportunities for assessments of historical Earth System states. This is crucial for snow variables, where ground-based observations are sparse and incomplete, and remote sensing measurements still face limitation. However, because reanalysis data are model-based, their accuracy must be evaluated before being applied in impact and attribution studies. In this study, we assess the accuracy of ERA5-Land's snow cover, snow depth, and Snow Water Equivalent (SWE) across monthly, seasonal, and annual scales, within the ecological regions of Canada and Alaska, regions that are characterized by prolonged seasonal snow cover. Using MODIS satellite snow cover observations and the gridded snow depth/SWE analysis data from the Canadian Meteorological Centre, we conduct a consistent benchmarking of ERA5-Land’s snow fields to (1) identify discrepancies at both gridded and regional scales, (2) evaluate the reproducibility of spatial structure of snow variables, and (3) uncover potential spatial patterns of discrepancies in ERA5-Land's snow statistics. Our results highlight significant discrepancies, particularly for snow depth and SWE, where ERA5-Land tends to grossly overestimate long-term mean values and interannual variability, while underestimating trends, i.e., moderating positive trends and exaggerating negative ones. The discrepancies in SWE, however, are primarily driven by biases in snow depth rather than snow density. Therefore, we advise against the direct use of ERA5-Land's snow depth and SWE in Canada and Alaska. While snow cover and snow density may still be useful for impact and attribution studies, they should be applied with caution and potential bias corrections particularly at local and smaller scales.
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CC1: 'Comment on egusphere-2024-4150', Colleen Mortimer, 27 May 2025
This paper seeks to understand the performance of snow estimates from ERA5-Land over Canada. Canada-specific analysis of the performance of snow products is lacking in the broad scientific literature; this work could help fill this knowledge gap.
The authors evaluate 3 snow-related variables from the ERA5-Land reanalysis: snow water equivalent (SWE), snow depth (SD), and snow cover fraction (SCF). For each of these three variables, they use a single reference dataset intended to represent "truth". For SCF, the uncertainty in the MODIS dataset may be low enough that a single reference product is sufficient for the evaluation. However, for snow depth and SWE, there is much more uncertainty in historical estimates. At present some of this uncertainty is irreducible, and so previous work has demonstrated the value in using an ensemble of datasets for evaluating SWE (Mudryk et al. 2015; 2025; Mortimer et al. 2020). Ensembles are helpful in providing a range of reasonable values against which outliers can be screened (especially for climatological snow mass and trends (Mudryk et al 2015, 2025)).
The author’s decision to rely on CMC as the only reference data for SD and SWE is further complicated because ERA5-Land is optimized for SWE whereas CMC is optimized for SD. Discrepancies between it and CMC may stem either from errors in the SWE values, or in the parameterizations, and the analysis presented does not distinguish which source of error is contributing to the discrepancy. Although the CMC product provides monthly SWE it is not really a SWE product. Instead, climatological snow density values from a lookup table, which don't evolve over the time series, are used to go from SD to SWE. Therefore, the CMC SWE product should not be considered as a reference 'truth'. On the other hand, SWE is the prognostic variable directly simulated in ERA5-Land, while SD is estimated using snow density parameterizations so discrepancies between it and CMC are expected. While the CMC product does assimilate ground observations, these are not available over the entire country and therefore the SD values in the CMC product represent a mixture of information from both ground observations the snow model. This means that away from locations with assimilated data, the assessed differences between CMC and ERA5-Land will just represent differences in the snow models used to produce each product.
We encourage the authors to identify a more appropriate set of SWE products to use in their evaluation and to discuss the limitations of ERA5-Land's SD estimations. Additional data could include other reanalysis datasets and/or in situ data (e.g. NorSWE (Mortimer and Vionnet, 2025; https://zenodo.org/records/15263370) for SWE, global SYNOP network or GHCN-D [https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily] for SD) as used in Mortimer et al 2024 and Mudryk et al 2025. The strategies employed to understand the reproducibility of spatial structure are interesting, if expanded, could provide useful insight about the strengths and limitations of ERA5-Land.
Finally, we have a few minor comments about the treatment of the CMC product and the analysis regions.
Minor comments
- How were the limitations listed in Brown and Brasnett 2010 Section 3.2.1 Warnings and Notices addressed?
- How was permanent land ice accounted for?
- Given the snow densities are based on snow classes, did you consider using snow classes instead of ecoregions?
References
Mudryk, L. R., Derksen, C., Kushner, P. J., and Brown, R.: Characterization of Northern Hemisphere Snow Water Equivalent Datasets, 1981–2010, J. Climate, 28, 8037–8051, https://doi.org/10.1175/JCLI-D-15-0229.1, 2015.
Mudryk, L., Mortimer C., Derksen, C., Elias-Chereque, A., Kushner, P.: Benchmarking of SWE products based on outcomes of the SnowPEx+ Intercomparison Project, The Cryosphere, 19, 201–218, https://doi.org/10.5194/tc-19-201-2025, 2025.
Mortimer, C., Mudryk, L., Derksen, C., Luojus, K., Brown, R., Kelly, R., and Tedesco, M.: Evaluation of long-term Northern Hemisphere snow water equivalent products, The Cryosphere, 14, 1579–1594, https://doi.org/10.5194/tc-14-1579-2020, 2020.
Mortimer, C. and Vionnet, V.: Northern Hemisphere in situ snow water equivalent dataset (NorSWE, 1979–2021), Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-602, in review, 2025.
Sincerely,
Colleen Mortimer and Lawrence Mudryk
Citation: https://doi.org/10.5194/egusphere-2024-4150-CC1 -
RC1: 'Comment on egusphere-2024-4150', Steven Fassnacht, 27 Jun 2025
General
This paper assesses thew snow information from the ERA5 Land dataset at a 9 km, hourly resolution, in comparison to Moderate Resolution Imaging Spectroradiometer (MODIS) SC data at a 5 km, monthly resolution and the Canadian Meteorological Centre’s (CMC) SD/SWE product at a 24 km, daily/monthly resolution. The ERA5 dataset is being used more and more, and this is an important assessment. There is a mismatch of spatial and temporal resolutions Between the ERA5L and evaluation datasets, and the authors use data harmonization. The final assessment is 1 March 2000 to 31 December 2020 at 25 km on an average monthly time step for October to June (monthly, seasonal, annual). Twenty-one ecological regions across Canada and Alaska were considered.
Overall, this is a lot of work and is the basis for a reasonable paper. However, the paper needs reorganization (e.g., Methods moved from Results and Discussion, improved figures, better explanation on how to interpret figures). There are issues with the “truth” data that are used. Those are not discussed. The Discussion is lacking and needs to circle back to the Introduction.
While it is understandable that the coarsest spatial and temporal resolutions are used to have consistency, the coarsens the datasets being assessed (ERA5L) by 25 times spatially and 720 times temporally. This removes some of the nuance of the finer resolution ERA5L data. The snowpack does not vary drastically on a daily basis, so coarsening to that resolution is acceptable. However, using a monthly time step and averaging the ERA5L data is a problem. The implications need to be discussed further.
While it is acceptable to use the standard deviation (Std Dev – I don’t have sigma handy on my keyboard) to assess interannual variability, this is biased when a trend is present. Specifically, SD could be large because there is a lot of interannual variability, or because there is a large trend. Consider detrending the time series to better assess the interannual variability. If you don’t address this, at least discuss the implications.
The figures are somewhat understandable but need work. I cannot distinguish some of the colors from one another, i.e., there are sets of 3 regions with almost the same color as overall there are only about 6 colors. Perhaps use a sub-set of the 21 regions Further, the rainbow color ramp is difficult for some people who cannot with visual impairment. The individual panes in each figure tend to be small, especially when 12 panes are present (Figures 2 to 5). Make each pane large, homogenize the x-axes range, and remove some of the white space and repeated axes labels. Probably add a letter to each figure pane.
At the beginning of each section in the Results, some methods are introduced in the first paragraph. These should be moved to the Methods section. There is some explanation on how to read the figures, but this seems incomplete. For example, when I look at Figure 2, is there a good shape, i.e., ERA5L is close to the “truth?” I assume that a perfect correlation between ERA5L and “truth” is a vertical line along RD* = 0. It is difficult to interpret what the different curves mean. Referring to Figure 3, the authors use CV (SD / mean) > 1 (vs. < 1), but this cannot be distinguished in Figure 3.
Figures 6 and 7 seem to be meant to explain the previous results. However, they would be better placed in the actual Results section. Similar to above, there are methods presented at the beginning of several of the Discussion sub-sections.
The Discussion does not explain the work outside of the work itself. There are no citations and the authors do not use the literature to explain their findings. As I state above, there are spatial and temporal issues and potential problems with the “truth” datasets. For example, SD/SWE are derived using the Brown et al. (2003) model, but that model has some large assumptions, such as the rain-snow threshold of +2C (this varies with climate and other factors), and the new snow density (the Hedstrom-Pomeroy equation is wrong based on the data that it is fit to). While not stated in Supplement S2 so it is unclear whether it is used in the paper, Brown 2003 do not account for precipitation undercatch and apply a 20% reduction in precipitation to account for sublimation and blowing snow. This is very important across much of the study domain (less so regions 12, 13, 15, 17, 20? – I can’t tell exactly from the colors). The actual difference between precipitation, undercatch, sublimation and blowing snow varies spatially and temporally.
Specific
- Line 33: While there is no complete agreement in snow variables names, we typically call Snow Cover Snow Covered Area, with the short form of SCA. SD is acceptable for snow depth, but d_subscript_s is often used.
- Line 39: SWE and the others are “variables,” not “parameters,” as they change over both space and time. In the next sentence you use “variables.” I recommend using the term “variables” here.
- Lines 42-55: This paragraph is relevant, but it seems somewhat superfluous, as it gives information that is already known to the cryospheric community. It is short but is somewhat of a “throw away” paragraph – consider being more succinct.
- Line 43: I question if in-situ measurements are the most “accurate?” There are various papers that talk about the point to area problem with field measurements.
- Lines 109-110 and Supplement S1: this is a good addition to explain how the Hydrology-Tiled ECMWF Scheme for Surface Exchanges over Land model works to represent the snow variables in ERA5L. Thank you!
- Lines 119-120 and Supplement S2: this is a good addition to explain the “Brown 2003” model.
- Lines 130-131 and Supplement S3: this is a good addition to explain the monthly MODIS time series data.
- Line 135: here you use the work “altitude,” but in the previous part of the sentence you say “elevation.” Elevation is the correct term. Also, be consistent.
- Lines 140, 141, etc.: Data is a plural word, so it should read “The data “are” publicly …”
- Lines 174-179: put these 21 ecological regions in a table. Also give us the area of each. The general location could be helpful, as several are blended together in Figure 1a (and are thus indistinguishable).
- Figure 1 can be improved. Figures 1c and 1d should have the same sized y-axis as they both go from 0 to 100%.
- Line 196: It is the Theil-Sen slope, not just Sen. Also, the two (self) citations provided do not speak specifically to the Mann-Kendall test or Theil-Sen slope – use appropriate citations here.
- While equations 1 and 2 are very simple, they are acceptable since X is a statistic and not a variable.
- Line 211: do you “consider” or “use” the scaled version. Here and in other locations in the text, the language is tenuous.
- Line 222: good idea to consider “brevity,” but are the result presented at the monthly scale? Figures 2, 3, 5, 6, 7 present annual results.
- Lines 224-235: This paragraph is mostly methods and should be moved to that section. More explanation on how to read Figure 2 would be useful – what shape of lines is good, i.e., ERA5L is close to the “truth?”
- Figure 2: this figure is difficult to understand, partly because the individual figures are small. I assume that the right figure is D and the let 3 are RD*? Consider use the same x-axis scales for all 12 figures so that the reader can visually compare the results (at least the same for all RD and for all D). As per my comment about the colors in Figure 1, I cannot tell regions apart. Consider how you can improve upon this – perhaps 10 representative figures. Since ECDF = 0.5 and RD* of 0 are the centre? Perhaps a horizontal dotted line across ECDF = 0.5 would help.
- Figure 3: The caption should read second moment (Std Dev) “versus” the first moment
- Figure 4: apparently the spatial structure can be some other than the 4 states listed? For example, SC for region 1 is along the dashed line for all four statistics. Does that mean that they are the same, i.e., complete agreement?
I stopped examining specifics at this point, as the general reworking of the paper is necessary before the details can be examined.
Citation: https://doi.org/10.5194/egusphere-2024-4150-RC1 -
RC2: 'Comment on egusphere-2024-4150', Anonymous Referee #2, 25 Jul 2025
This manuscript tackles an important topic: the benchmarking of ERA5-Land snow cover (SC), snow depth (SD), and snow water equivalent (SWE) across northern North America. The study is technically sound in its execution and the dataset analyzed is valuable, but the manuscript in its current form requires substantial revisions to meet publication standards.
While the topic is timely and relevant, the paper does not sufficiently highlight its novelty or unique contributions compared to previous benchmarking studies. The Discussion section, in particular, is weak: it lacks citations and broader context from other similar studies, and the conclusions mostly restate well-known limitations of reanalysis products without offering new insights or practical guidance for end-users. Overall, the manuscript is overly descriptive and at times repetitive, without providing deeper interpretation or actionable recommendations that could advance the field of snow process modelling.
A key shortcoming is the lack of critical evaluation of uncertainties, such as input biases in the Canadian Meteorological Centre (CMC) data or cloud-masking issues in MODIS. The discussion also fails to situate the results within the broader literature, which is necessary for assessing the significance of the findings. There is also little justification for certain methodological choices (e.g., grid harmonization, use of specific non-parametric tests), and the paper would benefit from synthesizing the main findings in a more structured way.\
I recommend major revisions before this manuscript can be considered for acceptance. The paper needs improved readability, a stronger discussion, and clearer presentation of the key results and their practical implications. Below are detailed, line-specific comments.
Please find below some specific comments
Line 8: The opening sentence is too broad. Suggest revising to something more direct:
“We benchmark ERA5-Land’s snow cover (SC), snow depth (SD), and snow water equivalent (SWE) across northern North America.”
Line 9: Change “limitation” to “limitations”.
Line 11: The phrase “regions that are characterized by prolonged seasonal snow cover” is redundant given the focus on Canada and Alaska.
Line 19: Avoid prescriptive language such as “we advise against direct use.” Instead, write:
“ERA5-Land’s SD and SWE require bias correction before being applied directly in hydrological or ecological modeling.”
Lines 23-30: The introduction is a good start, but it should emphasize what sets this study apart from other benchmarking efforts. Clearly state the unique contribution or new perspective.
Line 38: The paragraph about snowpack as a reservoir is informative but verbose. Consider splitting it into two or more concise sentences.
Line 57: The sentence defining reanalysis and describing its uses is too long. Break it into two parts, one for the definition and one for the applications.
Line 62: Add a bridging sentence explaining how reanalysis datasets fill observational gaps left by sparse in-situ networks and cloud-contaminated satellite retrievals.
Line 65: Rephrase “scale mismatch is often overlooked” to something softer like:
“The issue of scale mismatch has not been fully addressed in prior studies.”
Provide one or two references to back this statement.Line 71: The discussion on downscaling vs. upscaling is solid. It would benefit from citing specific studies that examine the uncertainties introduced by these approaches.
Line 85: The Canadian Rockies example could be more quantitative. For instance, provide typical snow depth values eg. “depths often exceeding 100 cm by mid-winter
Line 105: Ensure that “ERA5-Land” or its abbreviation “ERA5L” is used consistently after its first introduction.
Line 149: Briefly explain why 25 km was chosen as the grid resolution for harmonizing datasets.
Line 196: While Mann-Kendall and Sen’s slope tests are valid, add a quick note on why non-parametric approaches are particularly suitable for snow datasets
Line 210: Provide a short explanation of why a logarithmic RD transformation was used, especially for readers less familiar with this technique.
Line 215: The introduction of ECDF analysis is strong but consider adding a simple example of how bias shows up in ECDF curves
Line 223: When discussing grid-scale discrepancies, include numerical ranges of bias e.g., mean over/underestimation values for SC, SD, and SWE.
Also consider adding a summary table highlighting key discrepancy metrics across ecological regions.Line 243: Check Figure 2’s ECDF axes and colour schemes, ensure they remain clear when printed or scaled down.
Line 246: Add more context when describing “extremely large overestimations.” Are these localized such as in specific ecozones or widespread?
Line 273: When referencing seasonal results, make explicit connections to Supplementary Figures S1–S4 to help guide readers.
Line 276: The seasonal discrepancies discussion is repetitive. Consider merging SC, SD, and SWE discussions into a single, comparative paragraph when trends are similar.
Line 307: Add a line explaining why ERA5-Land might underestimate trends, mention limitations in the model physics or the quality of the forcing data.
Line 370: Provide a brief explanation of why spatial structure weakens at seasonal versus annual scales.
Line 377: Summarize the north-south gradient in snow density discrepancies clearly. Introduce a short subsection in the Discussion specifically addressing uncertainty sources, such as MODIS cloud-masking errors, CMC interpolation, and ERA5-Land physics.
Line 430: Discuss how the spatial patterns of discrepancies might inform bias-correction strategies or regional model tuning.
Line 435: The snow density analysis is insightful but would be stronger with quantitative differences like ERA5L overestimates mean snow density by 15–20 kg/m³ compared to CMC.
Line 442: Clarify whether snow density estimates in CMC and ERA5-Land are based on station observations or modeled parameters.
Line 524: Avoid prescriptive statements like “we advise against…..” Instead, rephrase as:
“ERA5L SD and SWE show systematic biases that limit their direct applicability without bias correction.”
Line 570: Search for and cite recent studies that validate ERA5-Land or other snow reanalysis products, to strengthen the Discussion.
Line 580: Rewrite the closing sentence with a practical takeaway for snow modellers or water resource managers.
Line 600: Check references for consistent formatting including italicizing journal names, adding DOIs
Line 680: Ensure all cited references are included in the reference list and properly formatted.
Citation: https://doi.org/10.5194/egusphere-2024-4150-RC2
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