dataesr/scientific-paragraphs-categorization huggingface dataset:
U-Pb Zircon Geochronology A total of 98 zircons from seven volcanic ash beds were analyzed. Zircon U and Pb data are presented in Supporting Information S1 and interpreted U-Pb dates are illustrated in Figure  2. For each volcanic ash bed, the U-Pb weighted mean date is interpreted on the following basis: (a) not rejecting any analyses that are concordant, (b) the youngest cluster of interpreted concordant zircons consists of ≥3 analyses and (c) assuming that all Pb-loss is effectively removed by the chemical abrasion procedure. This data reduction strategy is in line with previous U-Pb ID-TIMS studies concerning volcanic ash beds straddling the PTB and the Early Triassic (e. g. ,  Augland et al. , 2019; Baresel, Bucher, Brosse, et al. , 2017; Burgess et al. , 2014; Widmann et al. , 2020) , making all of these previous ID-TIMS ages directly comparable.
Data Availability Statement The data set associated with the current study is available on Zenodo  (Edward et al. , 2023). Figure  1  was made based on the Panalesis model  (Vérard, 2019)  using ArcGIS®. Figures  2 3 4 5 6 7 8 were made with Matplotlib version 3. 5. 2 (Caswell et al. , 2022) , available under the Matplotlib license at https://matplotlib. org, and seaborn version 0. 11. 2  (Waskom, 2021) , available at https://pypi. org/project/seaborn/. All figures were edited using Adobe Illustrator©.
III. RESULTS The experimental data presented here were obtained at the High-Energy-Density (HED) instrument of the European X-ray Free-Electron Laser Facility (EuXFEL). 50  The setup used was typical for in situ x-ray diagnostics of laser-driven shock waves at XFEL facilities combining spectrally resolved x-ray scattering in both forward and backward geometries with x-ray diffraction, and only the drive laser was not yet available. The details of the setup together with an analysis of the backward scattering data have been described by Voigt et al. 51  For demonstrating the forward scattering capabilities, 10 lm thick diamond samples were probed by x-ray pulses with a photon energy of $6000 eV, focused to <10 lm spot sizes using beryllium compound refractive lenses. A monochromator was used to reduce the energy bandwidth to $1 eV, from originally $20 eV corresponding to the self-amplified spontaneous-emission (SASE) bandwidth.
D:/AIP/Support/XML_Signal_Tmp/AI-APL# 210696 D:/AIP/Support/XML_Signal_Tmp/AI-APL # 210696  supported 342 by the  Helmholtz Association  under Grant No. ERC-RA-0041. M. S. 343 was supported by the  Helmholtz Association  under Grant No. VH-344 NG-1338. K. R. acknowledges funding by the  Center for Advanced 345 Systems Understanding (CASUS) , which is financed by the  German 346 Federal Ministry of Education and Research (BMBF)  and by the  Saxon 347 Ministry for Science, Culture and Tourism (SMWK)  with tax funds on  348  the basis of the budget approved by the  Saxon State Parliament. The 349 work of J. L. has been supported by  GSI Helmholtzzentrum f€ ur Schwerionenforschung, Darmstadt  as part of the R&D project  GSI-URDK2224  with the  University of Rostock. N. J. H. and L. B.
The application of the parametrizations in global simulations shows contrasting results depending on the location because other processes also explain the snow biases. Nevertheless, the snow cover overestimation in mountain areas is reduced by about 5 to 10 % on average when we include a dependency on the subgrid topography in our SCF parameterizations, which in turn allows to decrease the surface cold bias from -1. 8 °C to about -1 °C in the High Mountain Asia (HMA) region. However, persisting snow cover biases remain in these experiments, with a SCF overestimation in HMA, as well as a SCF underestimation in several other regions (e. g. , the Rockies mountains). Further calibration considering other regions and multiple datasets would allow to improve the SCF parameterizations. Assessing SCF parameterizations is challenging as it requires both realistic snowfall and snowpack in model experiments, and combined SCF, SD, and SWE -or snow density -observations, that are generally limited and uncertain in mountainous regions.
, 2022). The HMASR snow reanalysis is used to optimize and evaluate the SCF parameterizations over HMA. The NY07, SL12, and LA23 parameterizations are then implemented in the ORCHIDEE LSM and tested in global land-atmosphere coupled simulations with LMDZ, the atmospheric component of the IPSL GCM. The added value of the SCF parameterizations is investigated considering the worldwide mountainous areas and considering the land-atmosphere feedbacks induced by SCF changes. Two main questions are approached in this study: (1) does the subgrid topography need to be taken into account in SCF parameterizations? (2) What is the benefit of using SCF parameterizations calibrated over HMA in GCM global experiments? The following secondary questions are also addressed: (3) is it relevant to split the snow accumulation and depletion curves as it is done in SL12? (4) Can machine learning reproduce SCF parameterizations? (5) Does the calibration of SCF parameterizations depend on the spatial resolution? And, (  6 ) what are the land-atmosphere feedbacks induced by SCF changes in model experiments? This article is organized as follows: data and methods are described in Sect.
HMASR The High Mountain Asia Snow Reanalysis (HMASR;  Liu et al. , 2021b)  dataset covers the joint Landsat-MODIS era between the water years 2000 and 2017. The water years are defined from October of the previous year to September of the current year; i. e. , the period of the reanalysis is from 1 October 1999 to 30 September 2017. It provides daily estimates of SCF, SWE, SD, and other snow related variables, at 16 arcsec (∼500 m) spatial resolution over HMA  (27-45°N, 60-105°E). SWE estimates are derived by assimilating SCF from Landsat and MODIS sensors using the reanalysis framework of  Margulis et al. (2019). Meteorological forcing inputs are bias-corrected, downscaled to the model grid, and finally used with an ensemble approach for estimating the uncertainty as described in  Durand et al.
In situ snow depth stations SD in situ measurements are provided by the National Tibetan Plateau Data Center (TPDC;  Li et al. , 2021). 102 meteorological stations are available and most of them have been implemented from the 1950s to the 1970s. This dataset is available over the period 1961-2013 but with some missing values. The temporal resolution is daily, the spatial coverage is the TP, and all the data were quality controlled  (National Meteorological Information Center et al. , 2018). Stations having less than 90 % data availability are excluded from our study. The common period with HMASR is used: from 1 October 1999 to 31 December 2013. Stations with less than 1 mm of snow on average during the winter (DJFMA) are not considered in our analyses. Following these criteria, the 62 stations listed in Table  A1  are considered in this study.
A gap-filling is applied here to increase the temporal coverage of this dataset, by linearly interpolating the available data during periods including missing values that do not exceed 10 days. This method increases the data availability from 49. 7 to 85. 5 % for AVHRR and from 50. 2 to 85. 5 % on average over global land areas (excluding water bodies and permanent snow and ice areas). This method is shown to be a good compromise between accuracy and computational efficiency (e. g. ,  Gascoin et al. , 2015). In a second step, the data is monthly averaged and spatially aggregated at 0. 5°and 1°resolution, excluding water bodies and assuming 100 % snow cover over permanent snow and ice-covered areas.
Near-surface air temperature The Climatic Research Unit gridded Time Series (CRU TS) version 4. 00 is a 0. 5°gridded dataset of the monthly temperature (excluding Antarctica) available from 1901 until present, based on local weather stations and provided with an estimation of the data quality  (Harris et al. , 2020). This dataset has been widely used over HMA and TP (e. g. ,  Gu et al. , 2012; Chen et al. , 2017; Krishnan et al. , 2019; Wang et al. , 2021; Yi et al. , 2021) , and shows satisfying skill in these regions (e. g. ,  Wang et al. , 2013b; Chen et al. , 2017). https://doi. org/10. 5194/tc-2023-113 Preprint. Discussion started: 24 July 2023 c Author(s) 2023. CC BY 4. 0 License.
Method HMASR variables are spatially aggregated to 1°× 1°spatial resolution (typical GCM resolution). Only the seasonal snow is considered and the permanent snow areas are masked (see Sect. 2. 1. 1). The same procedure is used to compute the average and standard deviation of the topography (i. e. , considering only the seasonal snow areas). Grid cells containing more than 30 % https://doi. org/10. 5194/tc-2023-113 Preprint. Discussion started: 24 July 2023 c Author(s) 2023. CC BY 4. 0 License. permanent snow are excluded from the analyses. To assess the resolution dependency of the SCF parameterizations calibration, this procedure is repeated at the spatial resolution of 0. 3°× 0. 3°. To calibrate the parameterization, we split HMASR into a "training" period from 1 October 1999 to 30 September 2013 and a "validation" period from 1 October 2013 to 30 September 2017, which represent approximately 80 % and 20 % of the whole dataset.
DNN training For DNN, the Adam optimizer  (Kingma and Ba, 2014)  is used with the default parameters from TensorFlow Core v2. 7. 0 (https://www. tensorflow. org/versions/r2. 7/api_docs/python/tf/keras/optimizers/Adam, last access: 20 April 2022). The DNN is trained with the same loss function defined above (Eq. 9) over 100 epochs (number of times the algorithm repeats the optimization) with a batch size of 10 (number of training samples used at each iteration for the stochastic gradient method). After 100 epochs, the gain becomes negligible. Inputs are normalized before training (subtracting the mean of each observation and then dividing by the standard deviation). The optimized weights lead to a wMSE of 0. 005 for both the training and validation periods (Table  1 ). As the DNN is only trained in the HMA region, it is not expected to provide good worldwide performances (in particular over flat areas).
Atmospheric nudging The evaluation of SCF parameterizations is usually performed using LSMs forced by atmospheric observations or reanalyses. However,  Gao et al. (2020)  show that large uncertainties in forcing datasets over HMA (especially for precipitation) lead to significant biases of snow related variables. In this study, we use the coupled LMDZOR6A configuration, preserving the land-atmosphere feedback. Obviously, land-atmosphere coupled simulations have their own drawbacks too. To reduce these uncertainties, a nudging technique is applied to guide the large-scale atmospheric circulation and reduce the atmospheric biases in LMDZOR  (Coindreau et al. , 2007). To do so, we add to a given field u of the model a relaxation towards a guiding state u g according to the Eq. (  10 ): du = -α • (u -u g ) , (10) where α = dt/τ , and τ is a time of relaxation.
Therefore, we do not expect to have a perfect agreement between the in situ stations and the HMASR nearest grid cells (especially since in situ stations are mostly located in valleys, while HMASR grid cells overwhelm high-elevation areas). Furthermore, additional uncertainties related to the downscaling of the forcing dataset applied in HMASR are expected. To overcome these limitations, we use a similar method as Orsolini et al. (  2019 ) that consists in averaging all the stations together in subregions before performing the evaluation. The aim is to assess the skills of HMASR to represent the main climatological features such as the annual cycles and climatologies in each region. Figure  3a  represents the surface elevation of the HMASR reanalysis and of the 62 in situ stations (mostly located in the eastern part of HMA ranging from 1 583 m to 4 612 m).
4  split the data into two groups differentiated by a threshold on the standard deviation of the topography of 300 m. Most of the grid cells that have a lower SCF with respect to the SD during the melting phase are actually located over mountainous areas (panels j-l). Indeed, temperature vertical gradients, slopes, and aspects induce a strong variability of temperature close to the surface in mountain regions that translates into snow heterogeneities driven by large differences in both accumulation and melting rates between valleys and high mountain areas  (Liston, 2004). This behavior confirms the results from  Swenson and Lawrence (2012) , suggesting that NY07 parameterization is not suitable for mountainous areas, especially during the melting phase. Although, the HMASR SD overestimation shown in Sect.
3 could also explain part of these discrepancies. Nevertheless,  Miao et al. (2022)  obtain similar results with an independent downscaled SD satellite dataset over HMA, supporting the importance of the influence of topography. To assess the ability of all the SCF parameterizations to reproduce the SCF-SD hysteresis effect and the contrast between explained by the non-dependency on the subgrid topography. R01 shows more differences between flat and mountainous areas (h and n), which is consistent with the fact that it includes a dependency on the standard deviation of topography, although its SCF-SD spread is underestimated as compared to HMASR (g and m). R01 SCF parameterization shows poorer results for other seasons, which might be explained by its single dependence on SWE that does not allow to represent the hysteresis effect of the SCF-SD evolution (not shown).
5 % and 3. 1 % respectively and locally reaching more than +40 % (mostly over the TP edges). During the spring season R01, SL12, and LA23 experiments display reduced SCF biases over the mountainous areas compared to the NY07 experiment, with a MB of 8. 8 % for NY07 and MBs of 3. 6 %, 3. 3 %, and 1. 7 % for R01, SL12, and LA23 experiments respectively. These LMDZOR6A simulations show higher biases compared to the SCF parametrizations directly applied to the HMASR dataset (Fig. 6 ). These deficiencies are clearly related to other model biases. On main reason could be the smoothed topography in the model that would not allow -despite the nudging -a realistic simulation of the orographic drag when air masses cross the high mountain ranges  (Lott and Miller, 1997; Beljaars et al. , 2004; Wang et al.
0 and -1. 2 °C respectively compared to the CRU observations (Fig. C1 ). Improving the representation of the snow cover in mountainous areas allows therefore to reduce the cold bias in HMA in the LMDZOR6A simulations. The main limitation of our study is the use of only one snow reanalysis available over only one region, HMA, to assess and calibrate the SCF parameterizations. The lack of realistic worldwide snow datasets over mountainous areas (especially for SWE and SD) does not allow to perform global SCF parameterization calibrations with homogenous observational data  (Dozier et al. , 2016; Bormann et al. , 2018). Additional snow datasets are required to develop SCF parameterizations (e. g. , National Operational Hydrologic Remote Sensing  Center, 2004; Fang et al. , 2022). Adjusting SCF parameterizations within the model itself is another option, but it carries the risk of introducing bias compensations -e.
g. , a miss of solid precipitation could be partly alleviated with an excessive SCF or the opposite. The use of the Bayesian framework of the HMASR reanalyses could also enable to provide a range of possible parameters instead of a single optimized value. Despite significant uncertainties in atmospheric forcing datasets in mountain regions, especially for precipitation (e. g. ,  Immerzeel et al. , 2015; Lundquist et al. , 2019; Gao et al. , 2020) , additional land surface simulations could provide further insights in the SCF parameterization evaluation by using multiple atmospheric forcing datasets to better understand these uncertainties (e. g. ,  Bernus and Ottlé, 2022). This would allow to quantify the added value of new SCF schemes independently from the influence of the atmosphere. Nonetheless, land-atmosphere coupled simulations have the advantage of providing insight into the land-atmosphere feedbacks caused by changes in the SCF parameterizations in the model, which is a crucial aspect to consider in the climate system.
The LMDZOR6A surface biases in HMA are likely amplified by surface-atmosphere coupling. Indeed, the IPSL-CM6A-LR model exhibits a cold bias reaching -4 to -5 °C in the mid-troposphere which coincides with the elevation of the HMA region  (Boucher et al. , 2020, Fig. 3 ). The atmospheric nudging applied in our study allows to reduce this bias to about -1 to -2°C with respect to ERA-Interim  (Dee et al. , 2011, not shown). Stronger nudging on the temperature would allow to cancel this tropospheric bias, but with the risk to disrupt the physics of the model. Nevertheless, the surface bias in HMA is still present with a stronger temperature nudging (test performed with a 1 day time relaxation, not shown), suggesting a relative independence of the surface and the tropospheric biases. Additional uncertainties in the model evaluation could arise from the Snow CCI observational datasets themselves.
Last but not least, the presence of shadows can also affect the NDSI retrievals  (Jasrotia et al. , 2022). All these factors contribute to the uncertainty of SCF retrievals in the Snow CCI datasets, more observations should be used in the future to assess the impact of these uncertainties. The SCF overestimation in our parameterizations compared to HMASR (Fig. 6 ) could also partly be attributed to the HMASR reanalysis (in addition to the influence of the topography). Indeed, this reanalysis tends to produce higher SD estimates than the in situ stations (Fig. 3 ) which could lead the SCF parameterizations to predict overestimated SCF. However, as discussed in Sect. 3, SD varies greatly at the subgrid scales from tens to hundreds of meters  (Liston, 2004) , and snow pillows and other remote meteorological sites in mountains usually lie on nearly flat terrain leading to a poor representation of snow accumulation and melt rates on nearby slopes  (Dozier et al.
, 2016). Thus, it is not obvious to determine whether HMASR does actually overestimate SD relative to the in situ measurements, or if the stations are merely not representative of surrounding areas. Furthermore,  Miao et al. (2022)  found similar results to those shown in our Fig. 6  by using an independent downscaled SD satellite dataset over HMA, supporting the hypothesis that topography plays the most significant role in the SCF overestimation exhibited in these mountain areas. Despite these uncertainties, our results suggest that the NY07 parameterization is inappropriate for simulating the SCF over mountainous areas  (Figs. 5, 6, and 7) , which confirms the  Swenson and Lawrence (2012)  conclusions over the United States. The NY07 parameters could be optimized with HMASR to improve the SCF parameterization over HMA, however, this approach has been tested and leads to large deteriorations of the simulation over flat areas (not shown).
8 °C for NY07 to -1. 0 °C and -1. 2 °C for SL12 and LA23 respectively (Fig. C1 ). The changes in the SCF parameterizations involve actually many other land-atmosphere feedbacks, such as changes in albedo, cloudiness, precipitations, or SWE (Fig. 11 ), showing the complexity of studying and calibrating SCF parameterizations. Performing land-atmosphere coupled simulations -in addition to offline land simulations -is crucial for a better understanding of these feedbacks and to constrain the SCF parameters. Further calibration should be performed over other regions and with multiple datasets to improve the SCF parameterization skills. Adjusting SCF parameterizations within the model itself is another option, but it carries the risk of introducing bias compensations. Designing and tuning SCF parameterizations is challenging as it requires correct estimations of snowfall and snowpack, which turns out to depend on the simulated SCF itself in land-atmosphere coupled configurations (Fig.
11 ). Furthermore, the lack of global snow observational datasets, combining SWE, SD, and/or snow density as well as snowfall over mountainous areas strongly limits the possibility to develop and validate SCF parameterizations. Overall, further efforts should be conducted to better represent the subgrid-scale physical processes that affect snow in mountainous areas. Code and data availability. All scripts to produce the figures and results are available at: https://github. com/mickaellalande/SCF_param_ paper  (Lalande, 2023). Python  (Oliphant, 2007; Millman and Aivazis, 2011)  version 3. 8. 5 and xarray version 0. 16. 0  (Hoyer and Hamman, 2017; Hoyer et al. , 2020)  were used to perform the analyses. Interpolations were performed with xESMF version 0. 3. 0  (Zhuang et al. , 2020). For statistical purposes, Scipy version 1.
5. 2  (Virtanen et al. , 2020a, b)  was used. All graphics were produced using Proplot version 0. 6. 4 based on Matplotlib version 3. 2. 2  (Hunter, 2007; Caswell et al. , 2020)  and Cartopy version 0. 18. 0  (Elson et al. , 2020). The Taylor diagrams were produced thanks to the Python implementation of  Copin (2012). For machine learning purposes, TensorFlow Core v2. 7. 0 was used  (Developers, 2021).
Acknowledgements. We thank the  National Tibetan Plateau Data Center  (https://data. tpdc. ac. cn/, last access: 1 June 2021) for providing in situ SD data. We acknowledge CIMENT/GRICAD and  CLIMERI-France  infrastructures for providing access to their computational resources. This work was granted access to the HPC resources of IDRIS under the allocation  AD010101523R1  and  A0130113816  made by  GENCI. This research has been supported by the  European Space Agency (ESA) Snow Climate Change Initiative (CCI+)  project (grant no. 4000124098/18/I-NB ).
Note that the formulation of SWEmax (Eq. 7) differs from the Eq. (11) in Swenson and Lawrence (2012)  paper. Indeed, the Eq. (11) in their paper is erroneous(Swenson personal communication). The correct version implemented in their model corresponds well to the Eq. (7) of this paper (https://github. com/ESCOMP/CTSM/blob/master/src/biogeophys/SnowCoverFractionSwensonLawrence2012Mod. F90#L229, last access: 18 March.
Irradiance depth merging Depth-merged profiles were derived for all sites by applying a standardized approach. The underlying strategy for depth merging was to provide users with the information they need to do their own analyses, while modifying the original data as little as possible. Processing was therefore limited to procedures that could be applied equally to all COASTlOOC campaigns, whether helicopter-or ship-based. For example, variations of the incoming solar radiation were not considered in the depth merging process, since such observations were not available for the helicopter-based campaigns. However, if measured while operating from research vessels, such information is included in the depth-merged profile data to allow users to do their own subsequent analyses. Typically, several up-and downcast vertical profiles were performed, especially when using the helicopter as a sampling platform, to increase the potential availability of high-quality observations.
While this procedure has successfully identified individual spikes, some obvious outliers extending over several depth bins remain in the merged dataset, and users will need to make their own judgment on how to identify and remove those. Examples of depth-merged irradiance profiles are shown in Fig. 3  for one clear-water site in the NE Atlantic and one CDOM-rich site in the Baltic Sea. Note that extrapolated surface values and depth-merged profiles for a specific site are not necessarily derived from the same observations. This is especially true for helicopter-based operations typically comprising several up-and downcast close to the water surface to increase the likelihood of having sufficient observations for the surface extrapolation, while usually only one or two deep-water casts were made to obtain observations at larger depth.