dataesr/scientific-paragraphs-categorization huggingface dataset:
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 SDs in situ measurements are provided by the National Tibetan Plateau Data Center (TPDC;  Li et al. , 2021). A total of 102 meteorological stations are available, and most of them were implemented from the 1950s to the 1970s. This dataset is available for 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.
Global snow cover fraction validation In order to evaluate global simulations (Sect. 5), we use the snow cover products produced by the Snow project of the ESA Climate Change Initiative program (Snow CCI) based on the Advanced Very High-Resolution Radiometer (AVHRR) and the Moderate-Resolution Imaging Spectroradiometer (MODIS) satellites. The snow cover fraction on ground (SCFG) version 2. 0 is used and indicates the area of snow observed from space over land surfaces and in forested areas corrected for the transmissivity of the forest canopy. The global SCFG product is available at about 5 km pixel size during 1982-2018 for AVHRR  (Naegeli et al. , 2022) , and 1 km during 2000-2020 for MODIS  (Nagler et al. , 2022)  for all land areas, excluding Antarctica and Greenland ice sheets. These products have the advantage of providing a daily global fractional snow cover, but they include missing data during cloudy periods and polar nights and lack of reliable information for surfaces including water bodies and permanent snow and ice.
A gap-filling technique 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 d. 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 are averaged monthly and spatially aggregated at 0. 5 • and 1 • resolution, excluding water bodies and assuming 100 % snow cover over permanent snow-and ice-covered areas. https://doi. org/10. 5194/tc-17-5095-2023 The Cryosphere, 17, 5095-5130, 2023.
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. , X. Wang et al. , 2013; Chen et al. , 2017).
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. Equation (  9 ) shows the weighted mean square error (wMSE) metric used for the minimization. The weights (w i ) were constructed by combining the area of each grid cell (weighted by the cosine of the latitude) multiplied by their fraction of seasonal snow (Fig. 2e  and f ). The optimization is performed over the training period considering flattened arrays over the time and space dimensions. wMSE = 1 n i=1 w i n i=1 w i SCF i -SCF HMASR,i 2 , ( 9 ) where SCF is the estimated SCF, and SCF HMASR is the targeted HMASR SCF.
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, landatmosphere 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 Eq. (  10 ): du = -α • u -u g , (10) where α = dt/τ , and τ is a time of relaxation.
Simulation configurations Two configurations are used: one at low spatial resolution (LR; 2. 5 • × 1. 25 • ), similar to the resolution for the contribution of IPSL to CMIP6  (Boucher et al. , 2020) , and the other at high spatial resolution (HR; 0. 5 • × 0. 5 • ), with both 79 vertical levels (up to about 80 km above the surface). The Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010;  Danielson and Gesch, 2011 ) topographic file at 0. 0625 • is used in both simulations. The topographic parameters are computed by overlapping the model grid with the high-resolution topography file (for more details, see https://github. com/mickaellalande/SCA_parameterization/ blob/lmdz-zstd-to-condveg/modipsl/modeles/LMDZ/libf/ phylmd/grid_noro_m. F90, last access: 20 October 2023). Other forcing files are kept by default and are based on CMIP6 forcing datasets.
HMASR validation Validation of the HMASR SCF with satellite data would not be appropriate, as satellite observations (Landsat and MODIS) have already been used for assimilation in this reanalysis. Therefore, in this section, we compare HMASR SD with the in situ stations from the TPDC as described in Sec. 2. 1. 2. SD varies greatly at the typical scales from about 10 to 100 m because of snow-canopy interactions, snow redistribution by the wind, or small-scale ground asperities to larger scales due to the influence of the topography inducing orographic precipitation and subgrid-temperature gradients, among other phenomena  (Liston, 2004). 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 to that by  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 HMASR and of the 62 in situ stations (mostly located in the eastern part of HMA ranging from 1583 to 4612 m). On average the HMASR nearest grid cells are 66 m higher than the station elevations (differences range from -97 to +999 m). The SD at the station locations does not exceed a few centimeters in winter (DJFMA climatology), while HMASR SD reaches more than 1 m over high mountain areas (Fig.
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 flat and mountainous areas, Fig. 5  displays the distribution of the SCF with respect to the SD for the different parameterizations during the spring (MAM). In general, the SCF parameterizations show contrasting results, with the SL12, LA23, and DNN having larger SCF-SD spreads (df), while R01 and NY07 ones have narrower SCF-SD evolutions (b and c). No noticeable differences are displayed between flat and mountainous areas for the NY07 SCF parameterization (i and o), which can be explained by the nondependency on the subgrid topography.
SL12 and LA23 experiments still overestimate the average SCF over the mountainous areas, with annual mean biases of 3. 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 parameterizations directly applied to the HMASR dataset (Fig. 6 ). These deficiencies are clearly related to other model biases. One main reason could be the smoothed topography in the model that would not allowdespite the nudging -a realistic simulation of the orographic drag when air masses cross the high mountain ranges  (Lott and Miller, 1997; Beljaars et al.
Global analyses at HR (0. 5 • × 0. 5 • ) The influence of spatial resolution on the simulated SCF in LMDZOR6A experiments over mountainous areas is investigated in Fig. 9. The SCF HR model biases computed with https://doi. org/10. 5194/tc-17-5095-2023 The Cryosphere, 17, 5095-5130, 2023 respect to the Snow CCI dataset are shown for the configurations based on NY07, SL12, and LA23 and over HMA, central Europe, and North and South America. The melting period is considered because this is the season that is the most impacted by the choice of the SCF parameterization (MAM for the NH and SON for the SH). The improvements gained over HMA with the SL12 and the LA23 parameterizations are similar for HR and LR: the MB of 8. 2 % over HMA in the NY07 experiment reaches 2. 9 % and 1. 7 % in the SL12 and LA23 experiments respectively (d, h, and l).
LR and HR simulations' annual cycles Figure  10  represents the area-weighted monthly averaged annual cycles for each experiment over the NH flat areas and the NH, US, EU, SA, and HMA mountain areas for the LR and HR simulations with respect to the Snow CCI MODIS (black) and AVHRR (gray) satellite observations. Note that Snow CCI AVHRR exhibits lower SCF than MODIS (by about 10 % in winter) over most mountainous regions (US: b, e; EU: c, f; SA: h, k) except over HMA (i, l). Snow CCI MODIS likely provides a better SCF estimate over complextopography areas because of its higher spatial resolution (1 km) compared to the AVHRR version (5 km). Therefore, the Snow CCI MODIS product is used as a reference in the following paragraphs. Nevertheless, the inconsistency between AVHRR and MODIS highlights the uncertainties inherent to observational datasets for SCF.
Discussion The main limitation of our study is the use of only one type of 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 global SCF parameterization calibrations to be performed 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 comhttps://doi. org/10. 5194/tc-17-5095-2023 The Cryosphere, 17, 5095-5130, 2023 pensations; 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 HMASR could also enable provision of 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 into the SCF parameterization evaluation by using multiple atmospheric forcing datasets to better understand these uncertainties (e. g. ,  Bernus and Ottlé, 2022). This would allow the added value of new SCF schemes to be quantified 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.
Additional uncertainties in the model evaluation could arise from the Snow CCI observational datasets themselves. Indeed, despite the linear interpolation applied on the temporal axis to reduce the number of missing data (mostly due to cloud cover; see Sect. 2. 1. 3), remaining missing values are still present, especially over mountainous areas during the accumulation periods (not shown). Further interpolation methods could be explored, such as the one used in the MODISderived product MOD10CM  (Hall and Riggs. , 2021) , which uses a method to favor the presence of snow when clouds are present, or in  Gascoin et al. (2015) , which uses a machine learning algorithm to fill the remaining gaps after doing the same linear interpolation as in our study. Alternatively,  Jiang et al. (2019)  used a four-step cloud removal approach to generate a cloud-free dataset.
In addition,  Stillinger et al. (2023)  show that the application of the normalized difference snow index (NDSI) -which is used in Snow CCI products -shows certain limitations over mountainous areas, whereas spectral unmixing techniques could provide finer estimates of the SCF. 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 HMASR (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 Fig. 6  of the current paper 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.
Additional uncertainties could arise from the original file used to compute the standard deviation of the topography, especially due to its resolution. Several sensitivity tests were carried out using multiple topographic files at different resolutions (from about 500 m to 5 km) to compute the standard deviation of topography at 1 • resolution. The differences in the resulting standard deviation of the topography could reach about 100 m locally (relative differences ranging between 5 % to 20 %). However, these differences led to SCF variations mostly lower than 1 % on average over HMA on seasonal climatologies for all the parameterizations tested here -although they could reach more than 10 % locally at a given time (not shown). This highlights the limited impact of the topographical dataset resolution on the estimated SCF with the parameterizations for climatological studies.
The reduction of the snow cover biases -by taking into account the subgrid topography -leads in turn to the reduction of the surface cold bias in HMA from -1. 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 actually involve 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 turn 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. Appendix A: In situ stations.
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. We are grateful for the constructive comments from two anonymous reviewers, which helped to improve the paper. Financial support. This research has been supported by the  European Space Agency (ESA) Snow Climate Change Initiative (CCI+)  project (grant no. 4000124098/18/I-NB ). Review statement. This paper was edited by  Nora Helbig  and reviewed by two anonymous referees.
LA23, and R01 parameterizations   are implemented between the lines 877-878 (LMDZOR-STD-NY07) (https://github. com/mickaellalande/SCA_parameterization/ blob/R01/modipsl/modeles/ORCHIDEE/src_sechiba/ condveg. f90#L877, last access: 30 November 2023), 880-881 (LMDZOR-STD-LA23) (https://github. com/ mickaellalande/SCA_parameterization/blob/R01/modipsl/ modeles/ORCHIDEE/src_sechiba/condveg. f90#L880, last access: 30 November 2023), and 889-896 (LMDZOR-STD-R01) (https://github. com/mickaellalande/SCA_ parameterization/blob/R01/modipsl/modeles/ORCHIDEE/ src_sechiba/condveg. f90#L889, last access: 30 November 2023) respectively. The SL12 parameterization is implemented in the same SUBROUTINE (https://github. com/ mickaellalande/SCA_parameterization/blob/SL12/modipsl/ modeles/ORCHIDEE/src_sechiba/condveg.
f90#L912, last access: 30 November 2023) of the SL12 (https://github. com/ mickaellalande/SCA_parameterization/blob/SL12/modipsl/ modeles/ORCHIDEE/src_sechiba/condveg. f90#L912, last access: 30 November 2023) branch between the lines 912-966 (https://github. com/mickaellalande/SCA_ parameterization/blob/SL12/modipsl/modeles/ORCHIDEE/ src_sechiba/condveg. f90#L912, last access: 30 November 2023). Other versions are available and were used for tests not presented in this paper. Only the fraction of snow cover frac snow, veg has been modified because LMDZOR_v6. 1. 11 does not consider any nobio point (mainly corresponding to areas of glaciers and lakes not considered over continental surface, except over Antarctica and Greenland). 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 (https://doi. org/10. 5281/zenodo. 3700105,  Zhuang et al. , 2020). For statistical purposes, Scipy version 1. 5. 2 (https://doi. org/10. 5281/zenodo. 3958354, 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)  (https://doi. org/10. 5281/zenodo. 3898017,  Caswell et al. , 2020)  and Cartopy version 0. 18. 0 (https://doi. org/10. 5281/zenodo. 3783894,  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 (https://doi. org/10. 5281/zenodo. 5593257, TensorFlow Developers, 2021). The LMDZOR code can be accessed at https://lmdz. lmd. jussieu. fr/pub/src_archives/unstable/modipsl. 20200304. trunk. tar. gz (last access: 28 April 2023). Further details on the parameterizations' implementation are presented in.
2020). The correct version implemented in their model corresponds well to Eq. (7) of this paper (https://github. com/ESCOMP/CTSM/blob/master/src/biogeophys/ SnowCoverFractionSwensonLawrence2012Mod. F90#L229, last access: 18 March 2022). The Cryosphere, 17, 5095-5130, 2023 https://doi. org/10. 5194/tc-17-5095-2023.