the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Jun 2025
Evaluation of annual maximum snow depth data estimation from the European-wide reanalysis C3S MTMSI (Copernicus Climate Change Service – Mountain Tourism Meteorological and Snow Indicators) against in-situ observations
Abstract. Large snow load events are a major hazard for both human societies, in particular buildings and transport safety, and natural ecosystems. National and European frameworks provide guidelines and standards in order to take into account extreme snow load hazard in infrastructure design. However, there is a lack of reference data for their implementation. This is even more challenging in the context of climate change, which modifies the frequency and intensity of major snow load events. In the context of the Framework Partnership Agreement on Copernicus User Uptake, we have developed a pan-European extreme value analysis of annual snow load maximum based on the Mountain Tourism Meteorological and Snow Indicators (MTMSI) dataset available on the Copernicus Climate Change Service. This dataset includes reanalysis data, based on the UERRA (Uncertainties in Ensembles of Regional Reanalyses) reanalysis and snow cover simulations, and past and future climate projections based on regional climate simulations. Here we describe the evaluation of the MTMSI reanalysis component in terms of annual snow depth maxima against multiple in-situ observation datasets. Results are provided at the NUTS-3 (Nomenclature des unités territoriales statistiques) scale used in MTMSI, for multiple elevations, over a large area stretching from the European Alps to the Scandinavian countries. We highlight satisfactory skills of MTMSI annual snow depth maxima on most NUTS-3, based on the Kling-Gupta Efficiency metric, correlation, and bias scores. We identify some areas where MTMSI does not adequately portray in-situ observation of snow depth maxima, located in the Alps, and coastal areas of the Netherlands, Norway, Sweden, and Croatia. This study thus provides background information for assessing the relevance of this pan-European dataset in terms of annual snow depth maxima, relevant for annual snow mass and snow load maxima based on complementary information based on snow cover model output. The MTMSI annual maximum snow depth reanalysis dataset is available through the following link: https://doi.org/10.5281/zenodo.15181401 (Kamir et al., 2025).
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Status: open (until 13 Aug 2025)
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RC1: 'Comment on essd-2025-225', Michael Matiu, 30 Jun 2025
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Kamir et al. evaluate maximum snow depth from MTMSI, a reanalysis-based snow indicator dataset over NUTS regions, with respect to in-situ snow depth observations. The paper has high quality graphics and is well readable. Their analysis is able to guide extreme snow loads identification and future assessments, which is of societal relevance.
The study has a few strengths:
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The evaluation covers a large geographic area in Europe, from the Mediterranean to Scandinavia.
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The study is interesting and worthy of publication because it looks at extreme values, while most previous studies only look at averages. Since the behavior can be different comparing means or extremes, it is highly relevant, also because impacts are much higher for extremes than means.
However, also a few issues as outlined below. Finally, I’m not sure if the study lies within the scope of ESSD, since the authors do not produce/describe a dataset, but instead just extract values from an existing one. But this is for the editor to decide.
Also please disregard the formal manuscript rating in the editorial system, since the points there refer to a novel dataset, and thus 2) significance and 4) data quality cannot be meaningfully answered for this study.
Major points:
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While the evaluation of extremes is relevant, the analysis performed in the study is at times superficial. I acknowledge the complicated structure of the MTMSI dataset, which makes it challenging.
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The elevational analysis, for example, is challenging to understand in the current form. It is unclear how many stations/regions at which elevations in which locations were used/considered. Maybe it would be useful to distinguish between plain and mountain NUTS. Also the analysis needs to somehow consider the latitudinal gradient.
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Station data are used multiple times for the different NUTS/elevation groups. While I understand the author’s needs to cover as much as possible of the MTMSI dataset, this still feels like inflating the analysis or the number of observation pairs. There is some discussion at the end, but only based on one example. This might deserve some more thinking.
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Negative bias at high elevations (Fig 6) is not discussed in detail.
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Some comparison of extremes versus means would be highly beneficial. Also to put the previous studies in context. While this could be done just discussing the numbers of previous studies with the ones here, alternatively the analysis, or parts thereof, could be repeated for means.
(some of the above might be repeated/further explained below)
Minor points:
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L11 “satisfactory” means? Some number would be helpful
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L29ff, this paragraph is more a description of methods, not introduction
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L53 Monteiro and Morin did not use remotely sensed snow depth, only in-situ snow depth (and remote sensing snow cover fraction)
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L55 again, “satisfying” is vague. … I see you use this phrase a lot, if possible please add a quantitative number for clearness.
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L78 based on what criteria were plain and mountain NUTS3 distinguished?
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Why did you not use the NH-SWE dataset, which is basically ECAD with quality checks. https://essd.copernicus.org/articles/15/2577/2023/
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Related: The study needs some explanation/discussion why there have not been used tools such as the delta_snow model or HS2SWE, which convert daily time series of snow depth to SWE and vice versa without further input. Snow depth is a good proxy for SWE, but still, the sensitivity of results to the chosen approach could use some further analysis and/or discussion.
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L178-181 Unclear, please reformulate or expand.
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Evaluation metrics:
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Why did you not consider a measure for spread, like RMSE or MAE?
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Besides absolute bias, I recommend investigating also the relative bias, which is often a more useful metric for zero-bounded variables.
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Since you are looking at extremes, why did you not consider extreme value theory, or metrics based on GEV distributions, return levels, or similar?
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Fig 5a, better if you switch red and blue colours, for easier visual perception (red drier, blue wetter)
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L227 you mean lower instead of larger?
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For a better understanding from your readership, I suggest adding a map of the mean maxima over the NUTS regions, so the readers can also put the bias values in perspective. In addition to also showing the relative bias (see commment above).
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Fig 6: are there spatial pattern to this? I would recommend to split at least by latitude bands, since 1000m in the Alps is very different to 1000m in north Scandinavia.
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Figure 7 would be better suited further up, even in methods or beginning of results, to explain the approach. Also, it would be interesting to see the time series of the not-so-good NUTS/elevation pairs. … ok, I see, this comes later with Fig 10.
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Sec 3.3 unclear; also why only correlation is shown and not bias. Is this related to regions > 2 stations?
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Sec 4.1: Vague, since MTMSI has already been evaluated on similar but slightly different variables, please put your results in more quantitative comparison.
Citation: https://doi.org/10.5194/essd-2025-225-RC1 -
Data sets
The European-wide Mountain Tourism Meteorological and Snow Indicators (MTMSI) dataset : annual snow depth maxima E. Kamir et al. https://doi.org/10.5281/zenodo.15181402
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