dataesr/scientific-paragraphs-categorization huggingface dataset:
Snow cover duration trends are in line with the mean winter snow depth trends for long-term window length (40 years and above), with a generalized decline over the last decades ranging from -3 to -18 d per decade depending on the year and dataset considered. Nevertheless, if short-term trends calculated on a 10-year window length exhibit a similar behavior to snow depth, with large values and divergence between consecutive periods and between datasets for a given period, the decreasing duration of the snow season has already been widespread since the 1980s on window lengths of 20 and 30 years. For longer window lengths (40 to 60 years), all datasets except ERA5 are in broad agreement with the reference, showing a decline mostly comprised between -3 and -8 d per decade, whereas ERA5 strongly overestimates it, with a range of values more than twice the values of the other datasets for the same period.
This strong overestimation has to be linked to the one also shown concerning the mean winter snow depth and may be in part induced by heterogeneities concerning the assimilation, strongly reducing the amount and duration of snow (closer to the observation) for the most recent decades. The long-term (last 50 years) decline of around -6 d per decade provided by ERA5-Land, ERA5-Crocus, MESCAN-SURFEX and the reference dataset is largely in line with the literature on that topic. Indeed, this rate lies within the likely range of decline reviewed in  Hock et al. (2019)  of 0 to -10 d per decade, also close to the -8. 9 d per decade over the 1970-2015 period found by  Klein et al. (2016)  over 11 stations in the Swiss Alps, the average -7 d per decade simulated over Austria for the 1960-2019 period by  Olefs et al.
Elevation-dependent climate change (EDCC) and spatial variability in the trends over the 1968-2017 period The following analyses are dedicated to the spatial variability in the trends: their elevational gradients and their spatial distribution. Trends are calculated over the 1968-2017 period using the available datasets for this time period: OBS (i. e. , in situ observations of snow depth for the mean winter snow depth and snow cover duration: E-OBS v19. 0HOM for the winter temperature and LAPrec for the winter precipitation), ERA5, ERA5-Land, ERA5-Crocus and MESCAN-SURFEX. The choice of a window length of 50 years relies on the analyses presented in Sect. 3. 3. 2, showing that at least 40 years is the minimum time duration required to get a consistent signal at the scale of the entire Alps.
Winter temperature trends in Figs. 14 and 15 show different spatial patterns depending on the dataset. E-OBS v19. 0HOM, here taken as the reference, displays a rather smooth winter temperature trend field in Fig. 15  with most of the trends detected as being significant (i. e. , non-hatched areas). Not surprisingly, the three ERA5 products show similar patterns with higher warming rates ranging from 0. 5 to 1. 0 • C per decade in the eastern half of the Alps, and all were detected as being significant compared to the lower trends (i. e. , below 0. 4 • C per decade) elsewhere. MESCAN-SURFEX exhibits the highest warming rates, with stronger values over the Alpine ridge, corresponding to higherelevation areas, which can also be noticed in the boxplot in Fig. 14  through the strong elevational gradient.
Given the amplitude of the elevational gradient simulated by MESCAN-SURFEX above 1500 m (i. e. , median shifted by 0. 5 • C per decade per 1000 m), as well as the fact that the other datasets agree more on a slightly decreasing elevational gradient at these elevations similar to what  Rottler et al. (2019)  found over the Swiss Alps for the 1981-2017 period, MESCAN-SURFEX behaviors may be considered spurious artifacts. Winter precipitation trends are weak for all datasets except for MESCAN-SURFEX. For the reference and the three ERA5 products, spatial patterns are similar in Fig. 15 , with a widespread light decrease in precipitation, higher in the western part of the Alps but still lower than 7 % (not detected significant), and no elevational gradient in Fig. 14. The MESCAN-SURFEX trend field in Fig.
MESCAN-SURFEX snow variable trend fields show similar patterns to its precipitation trend field, with a patchy https://doi. org/10. 5194/tc-17-3617-2023 The Cryosphere, 17, 3617-3660, 2023 field displaying a strong horizontal gradient, probably inherited from the precipitation field. Concerning the elevational gradient in Fig. 14 , all datasets are in broad agreement for both variables. They simulate a stronger relative decline of the mean winter snow depth at low elevations, decreasing with the altitude. Snow cover duration trends also present a weak elevational gradient for all the datasets, with a stronger decline at low and intermediate elevations and with a median near -7 d per decade compared to the -5 d per decade at high elevations for ERA5-Land, ERA5-Crocus, MESCAN-SURFEX and the reference.
Discussion In this study, we have compared the results of various modeling systems (global and regional reanalyses and regional climate model simulations driven by a global reanalysis) against observation references (in situ, kriged datasets and satellite observation). The comparisons are performed in terms of monthly and seasonal snow depth values, snow cover duration, snow onset date, and snow melt-out date, addressing multi-annual averages of regional and subregional mean values, their interannual variations, and trends over various timescales. Here we discuss the main findings of the study and draw implications for further use of these datasets in various contexts. Remember that this study inves- tigates mean seasonal to monthly snow characteristics and their main driving variables (temperature and precipitation) at the scale of the European Alps and for four subregions.
Snow cover characteristics as seen by different modeling systems This subsection is dedicated to the discussion of the different behaviors of the datasets concerning the snow conditions, characterized by snow depth, snow cover duration, snow onset date and snow melt-out date. Compared to the observational references, the worst performances (lowest scores and strongest deviations from the reference) concerning the representation of the snow characteristics are obtained with ERA5-Land, CNRM-AROME and CNRM-ALADIN. CNRM-AROME and ERA5-Land both overestimate the snow depth, the snow season lasts too long (about 1 month later than the other datasets), and there are areas with spurious snow accumulation above 2000 m. For CNRM-AROME, these issues have already been documented by  Monteiro et al. (2022)  over the French Alps; the finding also applies at the scale of the European Alps.
, 2020)  and their corresponding data assimilation frameworks (or not), leading to various snow cover datasets generated through numerical modeling. The performance of these datasets vary, and none are found to be totally irrelevant for the variables analyzed in this study. However, no system is found to outperform all of the other ones in all of the dimensions of the evaluation, which can also be traced to the fact that none of the currently available systems combine the "best" of all worlds (high resolution, sufficiently sophisticated snow cover model well adapted to the land surface model, surface data assimilation including precipitation, snow cover data assimilation, etc. ).
On the complexity of assessing differences between modeling systems In addition to the general discussion provided above, here we insist that the comparison between all of the evaluated datasets, and in particular the three ERA5 datasets and CNRM-AROME, is not straightforward. Even if ERA5 and https://doi. org/10. 5194/tc-17-3617-2023 The Cryosphere, 17, 3617-3660, 2023 ERA5-Land have in common their land surface model and snow scheme configuration, ERA5-Land does not simulate the feedback from the surface to the atmosphere at its resolution, while ERA5 is constrained by a large number of observations through its land data assimilation system. ERA-Crocus is run in standalone mode too, but the configuration of the land surface snow scheme is somewhat specific (e. g. , it considers a fully snow-covered surface even if only a thin layer (i.
Representation of the interannual variability The study of the interannual variability in the snow conditions through two variables, the mean winter snow depth and the annual snow cover duration, as well as their main driving variables (near-surface temperature and precipitation), sheds light on multiple aspects of the variability in the snow conditions in the European Alps. Near-surface winter temperature anomalies are rather homogenous among the different subregions with a small subregional variability, meaning that most of the interannual variability affects the whole Alpine region similarly. All datasets reproduce correctly the anomalies compared to the reference, and even if no strong discrepancies are found between them, climate simulations (CNRM-AROME and CNRM-ALADIN), CERRA-Land and ERA5-Land perform better than ERA5, MTMSI and MESCAN-SURFEX.
Winter precipitation shows a strong subregional variability, varying from ±50 % from one year to the next but rather similarly inside a given subregion. This is coherent with past classifications such as HISTALP  (Auer et al. , 2007) , making winter precipitation vary for a significant part indepen-dently from one subregion to the others. On these anomalies, datasets only show little discrepancies with LAPrec taken as reference, and no dataset outperforms the others in every subregion. Mean winter snow depth and snow cover duration show the largest interannual variabilities across and within subregions of the European Alps. Datasets show the lowest correlation scores with the reference value, with a generalized underestimation of the amplitude of the interannual variability. In fact, both indicators are influenced by a large set of components (temperature, precipitation, wind, radiation, etc.
) and result from the integration, over the course of the winter season, of many processes operating at multiple space scales and timescales. These contribute to widening the discrepancies concerning the snowpack characteristics between locations nearby all throughout the winter periods and increase the interannual variability at a very local scale. Additionally, the accuracy of precipitation and temperature fields throughout the snow season, as well as the snowpack modeling system, is widely different across the datasets, and this ultimately determines a large fraction of the quality of the simulated snow depth time series. Overall, these analyses demonstrate that the snowpack interannual variability is still challenging to simulate, and as the interannual variability in precipitation and air temperature is already rather well represented according to our analysis, efforts might be directed towards the improvement of the snow modeling framework.
). Due to the low amplitudes of these oscillations and the large number of superimposed factors affecting the snowpack at different spatiotemporal scales, no clear consensus has been found so far. Overall, our analyses show that below 30-to 40-year time lengths, many of the trends are not detected as being significant and can fluctuate between strong negative or positive values for consecutive time periods. Beyond 40-year window lengths, the signal-to-noise ratio becomes larger and can lead to the detection of meaningful and significant changes in the mean values. This study also brought to light artifacts affecting the trends held by datasets incorporating information from observations. ERA5 (and to a lesser extent ERA5-Land) and MESCAN-SURFEX winter temperature trends are systematically overestimated compared to the reference E-OBS and past studies.
This leads to a shift in the distribution of a variable, therefore inducing artificial trends. ERA5 and MESCAN-SURFEX are based on modeling strategies that favor the maximization of the agreement be-tween model and observations at the expense of temporal consistency, which would better enable the product to be used for trend assessment. Users of these products should therefore be aware of the limits of the quality of a dataset incorporating information from observations. As reminded by  Cornes et al. (2018 ), Kaiser-Weiss et al. (2019 ), and Bandhauer et al. (2022) , their quality highly varies depending on the spatiotemporal scale considered (i. e. , their effective resolutions) and the areas considered, both of which are strongly affected by the density of the observations used in the product.
Conclusions In this study, we investigate the ability of various modeling systems (global and regional reanalyses and regional climate model simulations driven by a global reanalysis) to represent past snow conditions and the main atmospherical drivers over the European Alps, using observational references (in situ, kriged datasets and satellite observation). The comparisons are performed in terms of monthly and seasonal values, addressing multi-annual averages of regional and subregional mean values, their interannual variations, and trends over various timescales. The comparisons of the datasets over a common period of 30 years , in terms of the average of monthly and seasonal values of the snow depth and snow cover duration at a regional scale, shed light on a variety of behaviors that can lead to strong differences between the evaluated and the reference datasets.
ERA5, ERA5-Crocus, CERRA-Land, UERRA MESCAN-SURFEX and MTMSI simulate a monthly evolution of the snow rather close to the reference, at a regional and subregional scale, with no dataset systematically outperforming the others. CNRM-AROME and ERA5-Land are both found to overestimate the amount of snow and its persistence throughout the snow season, a phenomenon that increases with elevation, leading to spurious snow accumulation above 2000 m. CNRM-ALADIN shows an overestimated accumulation of snow at the beginning of the season and a total melt that occurs about 1 month earlier than the reference dataset for elevations above 1500 m. The spatial comparison against MODIS remote sensing data supports what appears to be a common issue of all datasets, namely the overestimation of the snow cover duration above 1000 m, with stronger errors near the snow line (around 1500 m) and concentrated on the melt-out date.
https://doi. org/10. 5194/tc-17-3617-2023 The Cryosphere, 17, 3617-3660, 2023 The discrepancies concerning the past climatology of snow conditions found between the reference and evaluated datasets may have multiple sources, affecting each of the datasets differently, and we can only hypothesize them here. Indeed, setting a clear attribution of the different factors leading to the different behaviors of the modeling systems would required multiple extensive sensitivity studies addressing independently the impact of multiple error sources. Among the various plausible factors, we suspect biases from the atmospherical forcings, limitations of land surface data assimilation procedures for mountainous areas, and the misrepresentation of surface-atmosphere exchange and key snow processes (i. e. , wind-transport, glacier accumulation), along with potentially inadequate configurations of the snow schemes leading to erroneous melt rate.
The study also addresses the representation of the interannual variability by the different evaluated datasets and their simulated trends over various timescales. Temperature and precipitation interannual variabilities are found to be rather well represented by most of the datasets. However, the simulation of the interannual variability in snow cover variables appears to remain difficult for all datasets. Their lowest performance may come from several causes related to the difficulty of representing the strong variance of the interannual fluctuations and the local variability in the snow conditions. This may be due partly to misrepresented processes related to the problems mentioned above and to the specificity and the complexity of the modeling of the snowpack. We mention that snow variables are somehow specific, as they result from a cumulative history all throughout the snow season.
Therefore, errors from atmospherical forcings, snow modeling, and coupling between surface-atmosphere and assimilation also cumulate over time. The computation of trends over various timescales leads us to reiterate some safeguarding principles on the matter. Indeed, computing trends over timescales shorter than 30 years results in the detection of noisy signals, strongly affected by interannual to multi-decadal variability and often not statistically robust. In addition, some datasets (MESCAN-SURFEX and CERRA-Land for precipitation and snow variables and ERA5 for snow variables only) lead to spurious trends (i. e. , strongly overestimated amplitudes and incoherent geographical patterns even for long-term trends), probably induced by spatiotemporal heterogeneities in their assimilation procedures.
These datasets should be used carefully for climate change applications as some of the simulated variables may be affected by artifacts, impacting the reliability of the resulting trends. Overall, no dataset outperforms the other in terms of the multiple aspects investigated here. Upstream modeling choices have indeed great consequences on the downstream uses of these datasets. Reanalyses (and datasets based upon observation sources) hold the potential to partially fulfill the gap of in situ observations in mountainous areas and provide a consistent and reliable baseline of the key variables describ-ing the evolution of climate conditions in mountainous areas. Nonetheless, we find that the quality of these datasets is scale and location dependent. As mentioned by  Isotta et al. (2015) ,  Kaiser-Weiss et al.
(2019), and Bandhauer et al. (2022) , the quality of the datasets incorporating observations (using assimilation procedures or direct kriging method) is strongly dependent upon the density of the observations used (and their spatiotemporal homogeneity), and the effective resolution of these datasets is in fact generally lower than the provided grid resolution. In mountainous regions the scarcity of observations remains a strong obstacle, and standard assimilation techniques can lead to a deterioration in the quality of simulated fields, as the spatial variability is high over mountainous areas, and the influence of an observation should be restrained to its domain of applicability (same elevation, slope, aspect, etc. ). Furthermore, observation-based products (including reanalyses) necessarily result from a trade-off between reproducing a climatically relevant time evolution and using the maximum number of available observations to produce the best possible description of the state of the atmosphere and snow cover at any given time.
Ultimately, this type of dataset continues to rely on the density and quality of the past observational network, which cannot be extended retrospectively. Here we also evaluated the regional climate simulations driven by a robust larger-scale (global) reanalysis. Our results indicate that this can offer an alternative to computationally intensive high-resolution reanalyses for climate studies. Indeed, they can simulate climatically homogenous atmospherical and surface conditions (depending on the quality of the driving, larger-scale reanalysis) and even provide an estimate of the modeling uncertainties if combined with a set of simulations from different regional climate models, although they do not directly incorporate high-resolution data through a data assimilation framework. However, our study shows that for mountain regions several issues with such models need to be tackled.
Figure3adisplays the relative frequency of the number of grid points for a digital elevation model (DEM) at 100 m horizontal resolution by elevation band and region. The DEM at 100 m is derived from the European Digital Elevation Model (EU-DEM), version 1. 1 (EEA, 2016), at 25 m horizontal resolution. Figure3bshows the difference in the relative frequency of the number of grid points for each dataset topography compared to the DEM at 100 m. It highlights that the elevation distribution over the subregions is substantially different for the various datasets investigated. Moreover, mountainous regions are known to feature a large altitudinal gradient concerning the variables of interest for this study, at.
Figure5shows an overview of the snow depth monthly time series for the different datasets over the whole European Alps. The corresponding values for the correlation, mean errors (MEs) and mean absolute errors (MAEs) compared to the reference calculated using the monthly values for the snow season (November to April) are shown in Fig. 6. In these two figures, three elevation bands are presented: 600 m (from 450 to 750 m; low elevations), 1500 m (from 1350 to 1650 m; intermediate elevations) and 2400 m (from 2250 to 2550 m; high elevations). At all elevations in Fig. 5, all datasets present similar interannual variability, reflecting a satisfactory agreement concerning the chronology of events (see Sect. 3. 3 for a more indepth comparison of the interannual variability). This leads to high correlation scores in Fig.
6, ranging from 0. 67 to 0. 96, most of which are above 0. 85. At low elevations in Fig. 6, CNRM-ALADIN and ERA5 present the lower correlation scores of 0. 67 and 0. 87, respectively, mostly explained by a shifted timing of the snow accumulation and melting phase than the reference. Their normalized mean errors are negative (-76 % for CNRM-ALADIN and -31 % for ERA5) due to a snow cover that is too thin (see Fig. 5c), a behavior that is also found for the other datasets with the exception of ERA5-Land that overestimates the amount of snow with a normalized mean error of +29 %. At intermediate and high elevations, climate simulations (CNRM-ALADIN and CNRM-AROME) and ERA5-Land systematically overestimate peak winter monthly snow depth values (see Figs. 5a, b and 6), leading to a normalized mean error between 49 % and 84 % at intermediate elevations (77 % to 184 % at high elevations).
Figure5ashows that at high elevations, snow depth values never drop below a given value for some products (0. 2 and 0. 3 m for CNRM-ALADIN and CNRM-AROME and 1. 8 m for ERA5-Land). MESCAN-SURFEX, MTMSI, CERRA, ERA5 and ERA5-Crocus display the lowest mean errors, with normalized MAE values rarely exceeding 30 %, as well as correlation values always close to 0. 9 for every elevation bands. Figure7a, b and c show, in the form of boxplots for three elevation bands, the distribution of the mean snow depth values from November to April for each dataset. Figure7d, e and f show the corresponding annual cycle. It confirms the previous analysis and allows a more in-depth analysis of the behavior of some datasets. At all elevations, ERA5-Land overestimates the amount of snow. In winter, snow depth values are about twice larger.
Figure9. Snow cover duration differences between the evaluated datasets and MODIS observations in the European Alps. Note that MODIS products initially at 500 m horizontal resolution have been regridded over each dataset grid using a first-order conservative method. (a) Map of the average differences (mean error) in the snow cover duration (SCD) over 15 seasons(2000-2001 to 2014-2015)  for each dataset compared to MODIS SCD. (b) Boxplot representing the spatial distribution of the average differences (mean error) of the SCD over 15 seasons(2000-2001 to 2014-2015)  compared to MODIS SCD for each dataset for multiple elevation bands.