dataesr/scientific-paragraphs-categorization huggingface dataset:
Precipitation Figure  8l  shows that at low elevations, rather small differences can be seen between the datasets and the reference, ranging between ±25 % of the reference precipitation amount over the winter period (November to April). The boxplot in Fig. 8i  confirms that the winter distribution of the mean values at low elevations for the different datasets stays close to the reference, with slightly higher precipitation values for ERA5-derived datasets and climate simulations (CNRM-AROME and CNRM-ALADIN). MESCAN-SURFEX, CERRA and MTMSI values are close to the LAPrec distribution and mean values (Fig. 8i, l ). At intermediate and high elevations, Fig. 8g , h, j and k show that all datasets indicate a higher amount of precipitation than LAPrec, particularly during the winter season. Apart from the climate simulations CNRM-AROME and CNRM-ALADIN, differences in monthly values range between 10 % and 30 % at the beginning of winter (November) and slightly increase throughout the winter to peak in March, ranging from 25 % to 50 % at most, with the strongest discrepancies always found for ERA5-Land and CERRA.
Climate simulations strongly exceed LAPrec values by 50 % and 100 % for CNRM-AROME to 80 % and 150 % for CNRM-ALADIN at intermediate and high elevations, respectively, with the largest differences at the end of winter (February to April). Figure  8g , h and i confirm that the spread in winter precipitation values and difference from the reference dataset increase with elevation. Although winter precipitation differences are consistent among regions (not shown here; see Fig. A3 in Appendix A ) and datasets, summer precipitation values display more contrasting results. Indeed, except for CNRM-AROME, most of the datasets show an overestimation of summer precipitation in the western part of the Alps at mid and high elevations, while low elevations and the eastern part of the Alps only show low and inconclusive differences.
Note that the overestimation of summer precipitation for the HIRLAM model (used to produced MESCAN-SURFEX, CERRA-Land and MTMSI), ERA5 and CNRM-ALADIN has already been reported in the literature  (Isotta et al. , 2015; Bandhauer et al. , 2022; Monteiro et al. , 2022)  and is attributed to their resolutions and the parameterization of deep convection. Overall, most of the datasets simulate winter precipitation rather close to the reference, except for the climate simulations CNRM-AROME and CNRM-ALADIN that strongly overestimate it at intermediate and high elevations. Our results are in line with  Bandhauer et al. (2022) , who compared ERA5, E-OBS and APGD over the European Alps domain. They found an overestimation of 15 %-20 % of ERA5 winter precipitation compared to the LAPrec dataset. However, the origin of the overestimation also identified for the other datasets during the winter period may be in part due to LAPrec deficiencies.
Indeed,  Isotta et al. (2021)  indicate that the LAPrec framework does not account for snowfall gauge undercatch. Bandhauer et al. (2022)  estimated the underestimation to be close to 30 % above 1500 m in winter, and  Vionnet et al. (2019)  showed that a regional reanalysis assimilating unshielded rain gauges can underestimates up to 50 % of snow mass accumulation over glaciers in the French Alps. We therefore expect that, except climate simulations CNRM-AROME and CNRM-ALADIN that lead to overestimated precipitation amounts above 1500 m, the overestimation found for the other datasets could rather be due to a more realistic estimate of the winter precipitation than LAPrec. Such results are also suggested from studies in North Amer- ica  (Wrzesien et al. , 2019). We however mention that if our results give confidence to monthly and seasonal mean winter values of precipitation for most of the datasets, they do not provides information on the temporal distribution of precipitation.
Timing of the snow cover duration against MODIS observations In this section, we investigate further the timing of the snow season by comparing the evaluated datasets against remote sensing data from Terra/MODIS MOD10A1F (MODIS in the following) over the 2000-2015 period. The comparison is carried out using indicators that can be defined using either the snow depth or the normalized difference snow index (NDSI): the snow cover duration (SCD), the snow onset date (SOD) and the snow melt-out date (SMOD) (see Sect. 2. 2 and 2. 4. 3 for further details). Figure  9a  and b  show maps and boxplots of the mean error between the evaluated datasets and MODIS. To this end, MODIS-based indicators were interpolated using a first-order conservative regridding over each dataset grid. This section does not include the MTMSI dataset because of its peculiar geometry (elevation bands over NUTS3 areas).
We also exclude CNRM-ALADIN simulations because daily snow depth values are not available. Figure  9a  and b reveal positive differences across elevation bands, regions and datasets compared to MODIS, indicating a general overestimation of the snow season duration (SCD) in the evaluated datasets. Looking at the boxplot differences for the entire Alps (Fig. 9b ) brings more details about the elevational distribution of differences as well as their magnitude. All datasets display differences from MODIS values, which differ with elevation, peaking at intermediate elevations with median mean error (ME) ranging from 20 to 70 d. The amplitude of the overestimation compared to MODIS is the strongest and the most spatially extended for ERA5-Land, exceeding 100 d in some locations (Fig. 9a ) with median mean error ranging from 40 to 75 d (Fig.
9b ). This confirms the issues with ERA5-Land snow cover data, consistent with the general overestimation of snow depth values and the spurious snow accumulation occurring above 2000 m, shown in Sect. 3. 1 and Figs. 5, 6 and 7. ERA5 and ERA5-Crocus data show lower mean error values than ERA5-Land, with the largest differences in the western part of the Alps for ERA5. The median of the mean error values ranges from 20 to 50 d for ERA5 and 10 to 40 d for ERA5-Crocus. While ERA5-Crocus does not show substantial improvements over ERA5 in terms of snow depth https://doi. org/10. 5194/tc-17-3617-2023 The Cryosphere, 17, 3617-3660, 2023 (see Sect. 3. 1. 1), it provides results closer to the reference in terms of snow cover duration. MESCAN-SURFEX and CERRA snow cover duration data show similar patterns with either over-or underestimations compared to MODIS.
Indeed, Fig. 9a  shows an underestimation of the SCD of 10 to 30 d (beyond 50 d at specific locations) over the inner-Alpine ridge near the northwestern and southwestern boundary and an overestimation elsewhere, ranging from 10 to 50 d. Their distribution of mean error values are overall more centered around 0 than the other datasets. MESCAN-SURFEX shows the lowest differences overall, and we note that CERRA particularly overestimates the SCD in the western Italian Alps. For CNRM-AROME, the map in Fig. 9a  shows an elevational pattern with a slight underestimation of the snow cover duration in valleys and an overestimation elsewhere. The boxplot in Fig. 9b  confirms it, revealing biases centered around 0 at low elevations and a generalized overestimation above. This is in line with its snow depth underestimation at low elevations and overestimation at intermediate and high elevations described in Sect.
3. 1. Figure  10  provides information about the timing of the snow season by displaying the mean values of the snow onset date (SOD) and snow melt-out date (SMOD) as the edges of the bar plot, while the bar plot length corresponds to the snow cover duration (SCD). The error bar represents the spatial variability (using the standard deviation) of the SOD and SMOD for each dataset and MODIS at its native resolution. Combining the information of Figs. 9a and b and 10a, b and c shows that the snow cover duration is overestimated by all datasets, compared to MODIS, for all elevation bands. From Fig. 10a, b  and c , the size of the error bars informs us about the spatial variability in the SOD and the SMOD. From it, we can conclude that it is underestimated in all datasets except for CNRM-AROME, but it may be partly related to the lack of horizontal resolution in most of the datasets.
At low elevations (Fig. 10c ), MESCAN-SURFEX, CERRA and ERA5-Land provide SOD values close to MODIS, but SMOD values are too late by up to 15 d, i. e. , 30 % longer SCD altogether. At this elevation ERA5-Crocus SOD and SMOD are rather close to the MODIS values, while ERA5 SOD occurs 15 d later and CNRM-AROME SOD 7 d earlier, despite a close overall SCD. We note that in situ observations also provide values towards a later beginning and longer lasting snow season compared with MODIS estimates. At intermediate elevations (Fig. 10b ), all datasets overestimate the duration of the snow season from 30 to 100 d at most, with an earlier SOD and a later SMOD compared to the MODIS dataset. ERA5-Land and ERA5 results provide the longest SCD (around 170-200 d), while MESCAN-SURFEX, CERRA, ERA5-Crocus and CNRM-AROME provide SCD values, ranging from 135 to 150 d closer to MODIS values (about 100 d).
At this elevation, the range of values of in situ observations of SOD and SMOD are close to the range of values of MODIS, with an earlier mean of SOD and later mean of SMOD for the in situ observations that may be due to the oversampling of sites with a longer snow cover duration. At high elevations (Fig. 10a ), whereas all datasets indicate a SOD value earlier than MODIS (around 15 d earlier), MESCAN-SURFEX, CERRA, ERA and ERA5-Crocus SMOD values are in agreement with MODIS values. ERA5-Land and CNRM-AROME SMOD values are 15 to 30 d later than MODIS-based estimates. This comparison with MODIS indicators adds another perspective to Sect. 3. 1: despite different behaviors concerning snow depth values at the monthly or seasonal scale, all datasets overestimate the duration of the snow season at all elevations compared to MODIS-based information.
Interannual variability and trends In this section we analyze and compare the time variability and past trends for the winter season (November to April) obtained using our reference and evaluated datasets over multiple time periods for most of the indicators addressed in this study: the snow depth, the snow cover duration, the air temperature at 2 m and total precipitation. For both the anomaly and the trend analyses, we use a subset of in situ snow observation stations with less than 10 % missing values during the 1968-2017 snow period (taking the daily values from November to April) (see Fig. 1d  and Sect. 2. 3. 1 for more details). For the same reason, we use the homogenized version of E-OBS (v19. 0HOM) for air temperature at 2 m. Anomalies are computed using the 1985-2015 climatology as reference (absolute differences for snow cover duration and temperature, relative differences for precipitation and snow depth).
Representation of the interannual variability in the evaluated dataset This subsection compares the representation of the interannual variability between the different products and the reference datasets. Figure  11  shows the time series of winter (November to April) anomalies for temperature, precipitation, snow depth and snow cover duration. For each variable and dataset, Taylor diagrams based on winter anomaly time series for the The Cryosphere, 17, 3617-3660, 2023 https://doi. org/10. 5194/tc-17-3617-2023 Temperature anomalies in Fig. 11a  show high correlation values (always above 0. 8) with respect to the reference dataset, with a standard deviation of anomalies close to the standard deviation of the reference (0. 8 to 1. 0 ± 0. 2 • C, i. e. , less than 20 % difference from the reference) meaning that anomalies are of comparable amplitudes between datasets.
Climate simulations (CNRM-AROME and CNRM-ALADIN) and ERA5-Land show the highest scores of correlations. CNRM-AROME and CNRM-ALADIN slightly underestimate the amplitude of the anomalies, while ERA5-Land is closer to the reference in this respect or with a slight overestimation. ERA5, MESCAN-SURFEX, MTMSI and CERRA datasets show the lowest correlation values and overestimate the amplitude of anomalies. Figure  B1  in Appendix B shows that the anomalies follow comparable fluctuations among subregions, indicating that most of the interannual variability occurs at a larger spatial scale, affecting the whole Alpine region similarly. In a comparison of temperature anomaly errors between multiple datasets including MESCAN-SURFEX, ERA5, E-OBS (v19. 0HOM) and COSMO-REA6 (a regional reanalysis at 6 km horizontal resolution that does not assimilate near-surface temperature) against a set of homogenized in situ observations over the Swiss Alps, Scherrer (2020) came to similar conclu-sions: the lowest scores (higher mean errors in this case) are found in winter and at elevations above 1000 m for MESCAN-SURFEX and ERA5, whereas COSMO-REA6 does not present significant error differences between summer and winter and low and high elevations.
The explanations put forward are twofold: (i) the insufficient resolution of ERA5 that does not allow us to capture the altitudinal gradient of temperature anomalies that can be strong in winter when there are interplays between synoptic flow and local topography and (ii) the assimilation of near-surface temperature for MESCAN-SURFEX that seems to be problematic in high-elevation regions. We note that in the eastern part of the Alps (regions NE and SE in Fig. B1  in Appendix B), CERRA provides better scores than MESCAN-SURFEX, and it may be the result of a more selective assimilation procedure in this region (i. e. , the assimilation of fewer and/or higher-quality observations). Precipitation anomalies (Fig. 11b ) show a good agreement between datasets with all correlation values above 0. 85 and a rather close, albeit slightly underestimated standard deviations of anomalies for all datasets.
A graphical comparison of the time series of anomalies in Fig. B2  in Appendix B shows rather strong differences between subregions. Indeed, winter precipitation anomalies appear to be weakly correlated between subregions compared to the temperature anomalies, implying that interannual variability in winter precipitation https://doi. org/10. 5194/tc-17-3617-2023 The Cryosphere, 17, 3617-3660, 2023 has a strong subregional component within the European Alps. Snow depth anomalies (Fig. 11c ) show lower agreements between datasets than temperature and precipitation anomalies, with correlation values varying from 0. 75 to 0. 95, with an amplitude of anomalies underestimated by all the datasets compared to the in situ observation dataset. At every scale (i. e. , regional to subregional), snow depth exhibits a higher variability than temperature or precipitation.
The amplitudes of anomalies of mean winter snow depth span ±100 %, with peaks over ±200 % for the eastern regions in Fig. B3  in Appendix B. Comparing the amplitude and the correlation of the anomalies with the reference dataset does not allow us to identify a dataset which outperforms all others. However, we note that ERA5 between 1950 and 1980 shows significantly higher anomaly values along with a large increase in their standard deviations specifically for the western region (northwestern and southwestern), a behavior specific to ERA5. It is due to some low-elevation grid points (below 1000 m) showing a strong decrease in their mean winter snow depth values from 1980 onwards that may be in part an artifact induced by the strong increase in the number of assimilated snow depth observations after the 1980s.
Snow cover duration anomalies (Fig. 11d ) also present a lower agreement among the datasets than winter temperature and precipitation with correlation values between 0. 7 and 0. 95. The amplitudes of anomalies are also slightly underestimated by most of the datasets as shown by the ratio of the standard deviation between the datasets and the reference within the Taylor diagram in Fig. 11d. Figure  B4  in Appendix B confirms that no dataset outperforms the others in terms of correlation scores, RMSE and ratio of the standard deviation, just as for snow depth, but ERA5 systematically shows the lowest scores. Correlation scores of ERA5 barely exceed 0. 7 and drop to 0. 6 for the northwestern region, indicating a low agreement concerning the fluctuations in the anomalies. Additionally, ERA5 snow cover duration anomalies also show the strong shift in the mean values of anomalies from before 1980 to after that we relate to the same causes as for the snow depth.
Running window trend analysis This section compares the long-term trends in the snow variables (snow depth and snow cover duration), temperature and precipitation. Trends are calculated using the mean seasonal values for the winter period (November to April) using the Theil-Sen linear regression, and significance levels are assessed using a Mann-Kendall test based on a p value threshold of 0. 05. As for Sect. 3. 2, CNRM-ALADIN is not in-cluded for the SCD indicators because daily fields of snow depth values are not available. Figures  12  and 13  display the trend values for the winter values of temperature, precipitation, snow depth and snow cover duration for each dataset, calculated using different window time durations  (10, 20, 30, 40, 50 and 60 years) , with a moving central year of the window by steps of 5 years.
Winter temperature trends (Fig. 12 ) calculated on window lengths of at least 30 years show a temperature increase over the Alps, varying in their amplitudes from 0. 1 to 0. 8 • C per decade depending on the years and datasets considered, with a generalized increasing warming over the recent decades. The trend values are highest for the ERA5 and ERA5-Crocus datasets regardless of the window length or the central year of the window used for calculation, with 30-year-long trends varying between 0. 1-0. 2 • C per decade centered on 1975 (±15 years) to 0. 8 • C per decade centered on 2000 (±15 years), as well as 60-year-long trends between 0. 2 and 0. 5 • C per decade. Trends obtained from MESCAN-SURFEX, CERRA and MTMSI are close to ERA5 and ERA5-Crocus, and rather similar trend values are found for climate simulations CNRM-AROME and CNRM-ALADIN, as well as ERA5-Land for window lengths between 10 to 30 years.
In a study comparing interannual variability and trends in temperature over Switzerland from multiple datasets against a set of homogenized in situ observations,  Scherrer (2020)  shows that the homogenized version of E-OBS provides the closest estimates of anomalies and trends for the Swiss Alps, with winter trend values around 0. 3 • C per decade. Pepin et al. (2022)  reviewed mountain temperature and precipitation trends worldwide and found values for the temperature trends varying from 0. 2 to 0. 5 • C per decade over the last decades for the European Alps. Winter temperature trends calculated here with E-OBS are in line with these findings, with values close to 0. 2-0. 3 • C per decade on a 50to 60-year-long window length. This implies that the other datasets (MESCAN-SURFEX, ERA5, ERA5-Land, ERA5-Crocus) tend to slightly overestimate this trend.
Winter precipitation trends (Fig. 12 ) display small values with only a few trend values, compared to temperature trends, computed as being significant, considering a window length of 20 to 60 years. Calculated on 10-year-long time periods, trends can exhibit reversed signs from two consecutive time periods with values varying from -25 % per decade to +25 % per decade. Even trends calculated on a 30-year-long window length show periods of slight increases and slight decreases thereafter, with trend values lower than ±10 % per decade. We note that MESCAN-SURFEX, CERRA and MTMSI provide opposite trend values compared to the other datasets regarding the sign of the trend for a 10-, 20-and 30year time period centered on the year 2005 (2000 to 2010 for the 20-year time period). This behavior may be due to the impact of the assimilation through the heterogeneity in the number and quality of the assimilated observation.
Nevertheless, it makes little sense to compute trend values over such short time periods. Concerning the other datasets, they are in broad agreement concerning the signs and amplitudes of trends, in line with LAPrec taken as reference regardless of the window length considered. Overall, long-term (i. e. , on a 40-to 60-year window length) winter precipitation trends are small with changes between +5 % per decade and -5 % per decade at most. Very few of these trend values are detected as being significant to conclude a change in the winter precipitation over the whole European Alps. Long-term subregional trends (see Fig. C2  in Appendix C) show more contrasting results. Whereas the northern part only shows weak and alternating-sign trend values always lower than 3 % per decade, the southern part exhibits a winter drying trend over the last decades, with trend values ranging from -4 % per decade to -12 % per decade, consistent among the different datasets.
This decrease in winter precipitation in the southern part of the European Alps is in line with previous studies, such as  Masson and Frei (2016) , who calculated trends with EURO4M-APGD, a gridded precipitation dataset used to construct the LAPrec dataset, and  Ménégoz et al. (2020) , who used regional climate simulations driven by ERA-20C  (Poli et al. , 2016). Figure  13  displays a generalized decrease in the mean winter (November to April) snow depth over the recent decades ranging from -2 % per decade to -18 % per decade for window lengths of 40 years and above, showing higher discrepancies between datasets than temperature and precipitation trends. Similarly to the time series of anomalies (see Fig. 13 ), fluctuations for trends in consecutive shorter time periods (10, 20 and 30 years) exhibit larger values, with frequent reversed signs of the trend values.
In such cases, trend values for different datasets strongly diverge, with large differences concerning the trend values and even cases of opposed signs. On window lengths longer than 30 years and for the common available years, MESCAN-SURFEX and ERA5 snow depth datasets show stronger trend values than the reference in situ observation dataset, ranging from -5 % per decade to -22 % per decade compared to +7 % per decade to -16 % per decade. For longer window lengths, ERA5-Land and ERA5-Crocus show trend values closer to the trend values calculated using the observational dataset, from -3 % per decade to -10 % per decade. It is interesting that despite similar atmospherical forcings and a broad agreement in trend values for temperature and precipitation, there are such large differences between the three ERA5-based datasets.
Overall, the snow depth negative trend is already widespread over window lengths of 30 years and generalized for all datasets on window lengths from 40 to 60 years. For 40-to 60-year window lengths, trend values range from 0 % per decade to -18 % per decade but mostly around -5 % per decade to -10 % per decade. These values are consistent with the recent literature on this topic using "purely" observational datasets. Indeed, Fontrodona  Bach et al. (2018)  found mean winter snow depth to have decreased from -5 % per decade to -25 % per decade over the northern part of the European Alps between 1951-2017 using the ECA&D observational dataset. Matiu et al. (2021a) , who gathered the observational dataset used as the snow depth reference in this study, have also computed trends and found them to vary on average between -6 % per decade and -10 % per decade over the 1971-2019 period for the European Alps stations below 2000 m elevation.