dataesr/scientific-paragraphs-categorization huggingface dataset:
tional reference datasets. Indeed, while most of the evaluated model outputs perform well at representing the interannual to multi-decadal winter temperature and precipitation variability, they often fail to address the variability in the snow depth and snow cover duration. We discuss several artifacts potentially responsible for incorrect long-term climate trends in several reanalysis products (ERA5 and MESCAN-SURFEX), which we attribute primarily to the heterogeneities of the observation datasets assimilated. Nevertheless, many of the considered datasets in this study exhibit past trends in line with the current state of knowledge. Based on these datasets, over the last 50 years ) at a regional scale, the European Alps have experienced a winter warming of 0. 3 to 0. 4 • C per decade, stronger at lower elevations, and a small reduction in winter precipitation, homogeneous with elevation.
The decline in the winter snow depth and snow cover duration ranges from -7 % to -15 % per decade and from -5 to -7 d per decade, respectively, both showing a larger decrease at low and intermediate elevations. Overall, we show that no modeling strategy outperforms all others within our sample and that upstream choices (horizontal resolution, heterogeneity of the observations used for data assimilation in reanalyses, coupling between surface and atmosphere, level of complexity, configuration of the snow scheme, etc. ) have great consequences on the quality of the datasets and their potential use. Despite their limitations, in many cases they can be used to characterize the main features of the mountain snow cover for a range of applications. Published by Copernicus Publications on behalf of the European Geosciences Union.
, 2020; Pichelli et al. , 2021). These simulations have demonstrated their potential for the study of rare events such as high precipitation events (HPEs)  (Caillaud et al. , 2021) , as well as improved the representation of mountain variables such as temperature, precipitation and snow cover over the Alps  (Lüthi et al. , 2019; Monteiro et al. , 2022). A recent review from  Lundquist et al. (2019)  suggests that high-resolution climate simulation are now able to produce a better estimate of meteorological variables over mountainous areas than gridded datasets based on in situ observations, limited by the scarcity of in situ observations and some of their observation limitations, e. g. , snow precipitation wind-induced undercatch. Several studies have focused on evaluating datasets and reanalyses in various contexts with the aim of outlining their adequate areas of use.
Muñoz-Sabater et al. (2021)  provide an extensive comparison of ERA5 and ERA5-Land against in situ observations for a set of surface variables (nearsurface air temperature, soil moisture, snow depth). Their study highlights that besides a clear added value of ERA5-Land against ERA5 in the western US considering the representation of snow, their comparison over Scandinavia and the northern part of the Alps leads to more nuanced results which they attributed to the quality of ERA5 due to the variable density of the assimilated observation network (too low and/or unevenly distributed) in ERA5 in these regions. Isotta et al. (2015)  and  Bandhauer et al. (2022)  focus on precipitation characteristics over the European Alps from numerous datasets (ERA5, MESCAN, EURO4M-APGD, E-OBS, etc. ). They report on a widespread overestimation of accumulated precipitation and wet-day frequency of ERA5 and UERRA against gridded observational datasets and show that their local-scale performances depends on the density of the rain gauge network.
Li et al. (2022)  provide an intercomparison of snow depth from ERA5, ERA5-Land and Weather Research and Forecasting (WRF) climate simulations against remote sensing and observational datasets over the Tianshan Mountains in China and find contrasting results. There, ERA5-Land is prone to lower errors (RMSE and mean error, ME) compared to ERA5 at low and intermediate elevations but shows larger biases at high elevations. They both perform poorly regarding the annual evolution of snow, with an overestimated accumulation phase for ERA5 and an underestimation of the melting rate for ERA5-Land. Overall, the WRF climate simulation performs well at all elevations and gives the closer estimates of the annual cycle of snow cover. Orsolini et al. (2019)  study the ERA5 abilities to represent snow characteristics (i.
e. , snow depth, snow cover and snow duration) over the Tibetan Plateau (TP) and find that ERA5 strongly overestimates the amount and duration of snow cover over the TP which they relate to the lack of assimilated observations in this region, as well as a strong overestimation of precipitation. Scherrer (2020) compares nearsurface temperature interannual variability and trends for a set of reanalyses and gridded datasets (i. e. , ERA5, MES-CAN, E-OBS and COSMO-REA) against a gridded dataset for Switzerland and finds that they all perform well at low elevations but have increasing errors in terms of trends and internal variability at high elevations. Kaiser-Weiss et al. (2019)  perform a broad evaluation over Europe of multiple reanalyses (those from the UERRA project and COSMO-REA) for wind, solar radiation, precipitation and temperature and find that the quality of the dataset for a given area is for a large part determined by the number of assimilated observations.
The above studies lead to nuanced results concerning the ability of recent reanalyses, gridded observation-based datasets and climate simulations to provide reliable longterm information relevant to characterize mountain meteorological conditions and the snow cover state. None of them outperforms other datasets in every region and analyzed aspects of the climatology (i. e. , mean values, seasonal patterns, spatial patterns, interannual variability, trend), but they hold promising potential to complement in situ observation records. Multiple factors are involved and strongly depend on the study area such as the quality of atmospheric forcing driving the land surface model, the number and quality of the assimilated observations, and the inherited biases from the underlying model used (atmospherical model and land surface model).
Thus, it is clear that extensive studies are needed to qualify the robustness of these emerging tools for their appropriate use in a wide range of downstream scientific applications. The objective of the present study is to compare the performance of different datasets from different modeling strategies in the European Alps in order to better understand their different characteristics and assess how to provide the best possible estimate of the snow cover spatiotemporal variability and trends and its first-order drivers: wintertime nearsurface temperature and precipitation. We investigate and evaluate mean seasonal and annual values, spatial variability and patterns, and interannual variability and trends over the last decades. We take advantage of the recent study by  Matiu et al. (2021a)  providing a consolidated dataset of in situ snow depth observations in the European Alps.
We also exploit two gridded observational reference datasets: LAPrec  (Frei and Isotta, 2019)  for the precipitation (specifically covering the European Alps) and E-OBS for the near-surface temperature  (Cornes et al. , 2018). By doing so, we aim to provide information on the reliability of several commonly used and most recent reanalyses as well as other modeling approaches in the European Alps and provide estimates of climate trends in the variables from these datasets.
Variables of interest and indicators Based on the availability of the variables across the datasets used in this work (model output and observations; see below), we focus on snow depth to evaluate their snow cover component. In order to compare the evaluated datasets against remote sensing data from MODIS Terra (MOD10A1F) processed by the National Snow and Ice Data Center (NSIDC)  (Hall and Riggs, 2020) , three indicators are analyzed. Consecutive snow cover duration (SCD) is defined as the longest consecutive number of days in a hydrological year with a snow depth value above a given threshold value. The snow onset date (SOD) and the snow melt-out date (SMOD) characterize the beginning and the ending dates of the corresponding time period. In this study, the snow depth threshold is set at 1 cm, motivated by the minimization of error metrics, described in Sect.
In situ snow depth observation The reference snow depth dataset is an ensemble of daily in situ observations spanning the 1971-2019 period  (Matiu et al. , 2021a). In this study, we used three different subsets from the overall dataset, depending on the analysis. Their locations and elevation distributions are displayed in Fig. 1. Section 3. 1 focuses on the reference characteristics of the snow cover over the longest common time period from which the evaluated datasets are available. In order to have the largest spatial coverage to compute monthly to seasonal mean snow depth over large regions, we keep all station data that have at least 70 % valid daily values from November to April for the 1985-2015 period (see Fig. 1b ). Section 3. 2 focuses on the snow cover seasonality, using the indicators SCD, SOD and SMOD computed using continuous daily val-ues of snow depth over the winter period, and compares it to satellite observations from  MODIS (record starting in 2000).
Most of the missing values happen in summer (for most of the observation stations, no snow is present during this period). In this section, we keep stations with more than 80 % valid daily values over all years of the 2000-2015 period (see Fig. 1c ). Section 3. 3 focuses on the interannual variability and trends. This requires the most homogeneous possible dataset along with a sufficiently long time period, so we only keep stations with more than 90 % valid daily values from November to April of the 1968-2017 period (see Fig. 1d ).
MODIS remote sensing satellite observations In order to address the spatial variability and the snow cover seasonality, we used the MODIS Terra daily normalized difference snow index (NSDI) field over the 2000-2015 period. These data from the MODIS Terra sensor have been treated by the National Snow and Ice Data Center (NSIDC)  (Hall and Riggs, 2020) , and they correspond to a daily gap-filled product using an algorithm described in  Hall et al. (2010). In this study, MODIS NDSI data are used at their native horizontal resolution (approximately 500 m) and regridded to match different dataset resolutions (from 2. 5 to 30 km) using a first-order conservative method.
Near-surface air temperature The reference dataset of air temperature at 2 m is the E-OBS v23. 1 daily mean air temperature field  (Cornes et al. , 2018). E-OBS is a gridded observational dataset available at 0. 1 • (12 km) horizontal resolution over the 1950-2020 period. It is obtained by interpolating station data gathered from national meteorological services (NMSs) by the ECA&D initiative  (Klein Tank et al. , 2002; Klok and Klein Tank, 2009). Uncertainties (due to climatological standard error values and from the kriging procedure) are estimated using stochastic simulations to produce en ensemble of 100 realizations of each daily field, and then the spread is calculated using the 95th and 5th percentiles. In Sect. 3. 3, focusing on interannual variability and trends, we used the homogenized version v19.
0HOM of E-OBS. It uses a restricted number of observations, quality checked and homogenized following a procedure described in  Squintu et al. (2019). E-OBS temperature values in high-mountain areas present limitations. They are known to feature a high warm bias (that can reach up to 5 • C against the MeteoSwiss gridded dataset in Switzerland), resulting from the scarcity of observations used in this region at high elevations. Furthermore, the uncertainty values may be underestimated, particularly in areas with low observation density  (Cornes et al. , 2018).
Precipitation The reference for precipitation is LAPrec  (Frei and Isotta, 2019) , a gridded dataset of monthly precipitation at 5 km horizontal resolution covering the European Alps and spanning the 1901-2019 period. It relies on a statistical approach (reduced space optimal interpolation -RSOI) combining information from a set of long-term observation stations used in HISTALP  (Auer et al. , 2007)  and from the high-resolution gridded dataset EURO4M-APGD  (Isotta et al. , 2015). It is specifically appropriate for long-term studies that need a high temporal consistency while staying at a relatively large spatial scale, therefore matching the scope of this study. The user guide  (Isotta et al. , 2021)  of this product provides an estimation of the interpolation error (in terms of mean absolute error -MAE and ME) at observation locations.
UERRA: MESCAN-SURFEX UERRA was a European project focused on the development of regional-scale atmospheric and land surface reanalyses. Multiple datasets were produced within the framework of the UERRA project. Here we used the MESCAN-SURFEX reanalysis. It is a regional reanalysis covering Europe and spanning the 1961-2019 period. It provides analyzed near-surface atmospherical and surface fields every 6 h at a horizontal resolution of 5. 5 km and hourly forecast fields. It uses ERA-40  (Uppala et al. , 2005)  prior to 1979 and ERA-interim  (Dee et al. , 2011)  thereafter as lateral boundary conditions (global forcing) to run the HARMONIE (HIRLAM-ALADIN Regional Mesoscale Operational NWP In Europe) NWP system at 11 km resolution  (Ridal et al. , 2016 ). An analysis is done every 6 h using a 3D-Var assimilation for the upper atmosphere and CANARI  (Taillefer, 2002)  for the surface.
The analyzed atmospherical fields at 11 km horizontal resolution are downscaled to 5. 5 km and passed to the MESCAN system  (Bazile et al. , 2017; Soci et al. , 2016) , producing an analysis of the air temperature at 2 m, the humidity at 2 m and the precipitation. These surface fields along with radiation and wind fields from the atmospherical analysis are used to drive the SURFEX LSM in standalone mode to produce surface fields at 5. (2021)  for further details about this dataset.
Regional averaged analyses In order to provide a common basis for the evaluation of these diverse datasets, we aggregate the temperature, precipitation and snow cover data for relatively large areas (full European Alps domain or subregions) and over elevation bands. , 2016) , at 25 m horizontal resolution. Figure  3b  shows 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 least for the snow cover state and near-surface air temperature. Comparing multiple datasets at different resolutions without taking into account this unequal repartition of grid points per elevation band would inevitably induce strong systematic biases in the analysis results.
Elevation-band-based analyses For most of the analyses, we chose to focus on three elevation bands, acting as a representation of three distinct environments: 600 m ± 150 m for the valleys and low-elevation hills, 1500 m ± 150 m for the intermediate elevations near the snow line, and 2400 m ± 150 m for the high-mountain conditions. Note that the results for the intermediate elevation bands (i. e. , between visualized elevation bands) are generally consistent with the main patterns observed across the elevation bands analyzed and visualized. Section 3. 3 includes figures averaged over multiple elevation bands at the scale of the entire Alps. To circumvent biases induced by the large differences in the representation of the hypsometry in the different datasets (Fig. 3b ), the averages over multiple elevation bands have been weighted by the relative fraction of each elevation band using the DEM at 100 m resolution as a common reference.
This method ensures the same representativeness of each elevation band in the average value, regardless of the horizontal resolution of the dataset under consideration. We also exclude data below 450 m since snow has a very limited role at these elevations and above 2550 m as our datasets used are generally not designed to represent the conditions encountered at very high elevations. Overall, this leads to the exclusion of less than 12 % of the total surface area based on the DEM at 100 m resolution (see Fig. 3a ).
org/10. 5194/tc-17-3617-2023 The Cryosphere, 17, 3617-3660, 2023 covered season generally starts with immediately "high" values of snow depth. SMOD and SCD are more sensitive, and error metrics grow continuously with the increase in the threshold. The standard deviation is rather low for the 1 cm threshold (i. e. , less than 5 d for mean error and mean absolute error), meaning that the detection performs rather similarly at all elevations. This short sensitivity study leads us to the choice of 1 cm for setting the snow depth threshold used to determine the absence or presence of snow with an uncertainty that can be large for a given station (i. e. , on average 10 to 20 d) but well centered, as mean error values are close to 0. This threshold was applied to all the datasets compared with MODIS in Sect.
Time periods, statistics and trends analyses The reference period for the analyses in Sect. 3. 1 is the longest common period available for all datasets: 1985-2015 (see Fig. 2 ). The method in Sect. 3. 2 that focuses on snow cover seasonality mainly against MODIS data is performed over the 2000-2015 period. In these two sections, most values of the different variables are presented on average over the time period, spatially averaged for a given elevation band, over a subregion or the entire Alps. The error metrics used are defined as follows: mean error (ME) = N i=1 (x i -y i ) N -mean absolute error (MAE) = N i=1 |x i -y i | N -root mean square error (RMSE) = N i=1 (x i -y i ) 2 N = √ 1 -r 2 σ y -correlation (Pearson linear correlation) r xy = x i y i -N xy ( x 2 i -Nx 2 ) ( y 2 i -Ny 2 ) with x i and y i being data x and y at time step i; σ x and σ y , respectively, the standard deviations of x and y; and N the sample size.
For convenience, ME, MAE and RMSE are normalized by the mean values of the reference dataset for snow depth and precipitation. Section 3. 3 compares trends for the different variables of interest using seasonal mean winter values (November to April) for the reference and the evaluated datasets. Trends are calculated using the robust nonparametric Theil-Sen estimator, insensitive to the changing variance of the residuals  (Sen, 1968) , along with a Mann-Kendall test for significance assessment based on a p value threshold of 0. 05. In order to verify the robustness of the method, we tested the use of the standard ordinary least squares regressions (OLSs) and the generalized least squares (GLSs) with an autoregressive component (AR(1)) to account for the effect of the interannual variability on the calculated trends, known to lead to an increase in the size of the confidence intervals  (Ribes et al.
Reference characteristics of the snow cover in the European Alps This section presents a general evaluation of snow depth characteristics, air temperature at 2 m and precipitation for each of the datasets by comparing them against the reference datasets presented in Sect. 3. 1 for the 1985-2015 period. Comparisons are described at the scale of the entire Alps, and there are not many differences in the results at the subregional scale. Figures for the four subregions are however provided in the Supplement. 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).
Snow depth Figure  7a , 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. Figure  7d , 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 than the reference values at low and high elevations and around 50 % higher at intermediate elevations. It also leads to a snowpack lasting too long until melt, particularly at intermediate and high elevations, reaching its peak winter value and with a beginning of the melting period 1 month later than the reference. CNRM-AROME also overestimates the snow depth and the duration of the snow cover at intermediate and high elevations.
In this case, it is combined with a delayed accumulation phase depicted by an underestimation of the snow depth until it reaches its peak winter value. CNRM-ALADIN also exhibits an overestimation of the snow depth at intermediate and high elevations but with a reversed behavior concerning the accumulation timing. It overestimates snow accumulation at the beginning of the season with an earlier snow onset date but starts melting the snowpack 1 month earlier than the reference, similar to what was found by  Monteiro et al. (2022)  in the French Alps. The amplitude and timing of the beginning, peak and end of the season are close to the reference for MESCAN-SURFEX, CERRA and MTMSI, with discrepancies concerning the snow depth within 20 % during the season. It is not surprising that only subtle differences can be seen between these three datasets, as MTMSI is a selection of MESCAN-SURFEX grid points (although with a different snow cover model), and MESCAN-SURFEX and CERRA reanalyses roughly share the same modeling systems.
5 • C from November to February, peaking in December and January. The strongest discrepancies are found between ERA5-Land and the reference dataset. At this elevation, for MESCAN-SURFEX, MTMSI and CERRA, negative temperature differences of 1 For the intermediate-and high-elevation comparisons, we need to remain cognizant of the questionable reliability of E-OBS at these elevations (see Sect. 2. 3. 1). The negative temperature difference between all datasets and E-OBS could indeed be due to a bias of E-OBS itself. Indeed  Cornes et al. (2018)  show that E-OBS can exhibit higher temperature values, up to 5 • C at high-elevation locations, compared to the MeteoSwiss reference temperature dataset  (Frei, 2014). Nonetheless, part of these differences could also reflect a genuine negative temperature bias in the evaluated dataset as has already been shown in previous studies.