dataesr/scientific-paragraphs-categorization huggingface dataset:
The temperature and snow cover model biases are more pronounced in winter. Simulated precipitation rates are overestimated by 1. 5 [0. 3 to 2. 9] mm d -1 , corresponding to a relative bias of 143 % [31 % to 281 %], but this might be an apparent bias caused by the undercatch of solid precipitation in the APHRODITE (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources) observational reference. For most models, the cold surface bias is associated with an overestimation of snow cover extent, but this relationship does not hold for all models, suggesting that the processes of the origin of the biases can differ from one model to another. A significant correlation between snow cover bias and surface elevation is found, and to a lesser extent between temperature bias and surface elevation, highlighting the model weaknesses at high elevation.
Our study focuses on the climate variability over HMA as simulated with CMIP6 models. The near-surface air temperature, the snow cover extent and the total precipitation are considered to answer four questions: (1) what are the biases in HMA in this new generation of climate models for these three variables? (2)  What are the links between the model biases in temperature, precipitation and snow cover? (3) Do the model biases impact the simulated climate trends? (4) Which climate projections can be expected in this area over the next century? The datasets and methods used in this study are described in the next section. Section 3 presents a comparison between observations and 26 CMIP6 models over the historical period with a focus on the potential correlations between the biases of the different variables.
Models In this study, we selected 26 GCMs Table  1  in the CMIP6 database  (Eyring et al. , 2016)  focusing on near-surface air temperature (tas), total precipitation (pr) and snow cover extent (snc) over 1979-2014. Only 10 of these models are available for future projections covering the ensemble of the SSP1-2. 6, SSP2-4. 5, SSP3-7. 0 and SSP5-8. 5 Shared Socioeconomic Pathways, which are combining socioeconomic scenarios and radiative forcing levels  (O'Neill et al. , 2016). Considering the model uncertainties, such a limited number of models might be sufficient to explore future climate trends  (Knutti et al. , 2010). The resolution of the models ranges from about 3 to 0. 5 • (∼ 300 to 50 km), while most of them reach a 1 • resolution. All models are regridded on a common 1 • × 1 • grid using a bilinear interpolation before the multimodel analysis.
Near-surface air temperature The CRU TS (Climatic Research Unit gridded Time Series) version 4. 00 (https://doi. org/10/gbr3nj) provides a 0. 5 • gridded dataset of the monthly temperature (excluding Antarctica) available from 1901 until the 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). Correlation with local measurements, including at high elevation  (Wang et al. , 2013b) , is high in this region. This gives confidence for model evaluation  (Chen et al. , 2017).
Snow cover extent In situ snow observations are sparse over HMA and TP, and when they are available, in situ data are often not representative for snow cover analysis at the regional scale  (Gurung et al. , 2017). Alternatively, remote sensing datasets provide large-scale snow information useful for spatiotemporal analyses. The satellite product available over the longest period, but at a coarse spatial resolution, is the NOAA Climate Data Record (CDR)  (Robinson et al. , 1993 (Robinson et al. , , 2012;; Estilow et al. , 2015) , covering the Northern Hemisphere (NH) from 4 October 1966 to present (referred to as NOAA CDR in this article). Data prior to June 1999 are based on weekly satellite-derived maps of snow cover extent, whereas posterior data have been replaced by daily snow cover extent estimated from the Interactive Multisensor Snow and Ice Mapping System (IMS).
Institute (country) Model Resolution Primary All SSPs Reference (long × lat) member available   , 2015; Hori et al. , 2017; Santolaria-Otín and Zolina, 2020)  and more specifically over HMA (e. g. ,  Xu et al. , 2016). This dataset is adapted for continental-scale studies but shows limitations over mountainous regions  (Déry and Brown, 2007) , even if the inclusion of Meteosat-5 data in 2001 significantly improved its quality over the Asian continent  (Helfrich et al. , 2007). Trend analyses based on NOAA CDR data must be taken with caution because of potential temporal heterogeneities related to changes of experimental protocols  (Mudryk et al. , 2020). To obtain monthly fractional values, we simply average the weekly binaries values included in each corresponding month. The Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) snow cover extent time series version 1 derived in the frame of the European Space Agency's Climate Change Initiative (ESA CCI+) Snow project is the most recent long-term global snow cover product available  (Naegeli et al.
3 (monthly) product at 2. 5 •  (Adler et al. , 2016 (Adler et al. , , 2018)). This product combines satellite products with rain gauge stations available from 1979 to the present. However, the scarcity of high-elevation in situ stations, the interference of wind with the sensors and the problems of satellite-based meteorological radars in identifying snow crystals lead to large uncertainties in observational snowfall datasets  (Palazzi et al. , 2013; Sun et al. , 2018). Total precipitation is therefore generally underestimated, especially over snow-rich areas  (Sanjay et al. , 2017).
ERA-Interim ERA-Interim is a global atmospheric reanalysis dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), covering the period from 1979 to 2019 at approximately 80 km on 60 vertical levels  (Dee et al. , 2011). ERA-Interim shows best overall performance for air temperatures compared to other reanalyses over TP  (Wang and Zeng, 2012)  and high correlations (0. 97 to 0. 99) with respect to ground meteorological stations during 1979-2010  (Gao et al. , 2014). Estimates of precipitation associated with the reanalysis are produced by the forecast model, based on the assimilation of temperature and humidity observations  (Palazzi et al. , 2013). Snow depth is assimilated through station observations  (Orsolini et al. , 2019) , and gridded snow cover from IMS has also been assimilated since 2004  (Drusch et al.
Numerical methods and computations Trend computations are based on linear least-squares regression. We consider a 95 % level of significance, corresponding to a p value equal to 0. 05, computed with a two-sided Wald test for which the null hypothesis corresponds to a slope equal to zero (https://docs. scipy. org/doc/ scipy/reference/generated/scipy. stats. linregress. html, last access: 22 July 2021). The linear relationship between two datasets is estimated with the Pearson correlation coefficient. We consider a 95 % level of significance, corresponding to a p value equal to 0. 05, computed as follows: for a given sample with correlation coefficient r, the p value is the probability that |r | of a random sample x and y drawn from the population with zero correlation would be greater than or equal to |r| (https://docs.
The multimodel mean (Fig. 1h ) shows globally higher values of precipitation over HMA in comparison with the observations. Precipitation tends to spread more over the TP in the model compared to the observations, which might be partly due to the smoothing of the topography in the models. However, precipitation rates are also generally underestimated in observational datasets because of snowfall undercatch issues, which could lend credence in the stronger precipitation rates modeled at high elevation.
Temperature, snow cover and precipitation annual cycle The seasonal cycles are shown in Fig. 2  for the models and different observational datasets and reanalyses over HMA and the three subdomains for temperature, snow cover and precipitation. The model biases with respect to observations are stronger in winter than in summer for temperature and snow cover, a feature already noticed in CMIP5 and CMIP6 (e. g. ,  Su et al. , 2013; Zhu and Yang, 2020). Indeed, the multimodel mean temperature is around 2 to 3 • C below the CRU observations in winter over HMA, while models and observations are much closer in summer (Fig. 2a ). These differences are more pronounced in the HK region (Fig. 2b ) with differences noticed both in winter (4 to 5 • C) and summer (∼2 • C). The cold bias appears in the multimodel mean (dark blue line) from October/November onwards, peaks between December and January, and then decreases until April/May, except in the HK area where the bias persists in summer.
Nevertheless, the multimodel spread encompasses the observation and reanalyses datasets, suggesting a certain reliability of the CMIP6 models. This spread, denoted with the confidence intervals at 50 % and 90 % of the multimodel ensemble (dark and light shadings), highlights a higher dispersion between the models in winter than in summer, except for the HK region. It can be assumed that the larger biases in winter may be due to excessive snowfall, leading to larger snow cover which can amplify the phenomenon. Furthermore, due to the poor representation of fine-scale topography, one can assume that most of the moisture fluxes condense on the plateau at higher elevations, favoring snow precipitation, instead of precipitating earlier on the mountainside. The greater difference in the HK region can be supported by this later hypothesis, knowing that this area is particularly subject to winter WDs bringing a large amount of snow precipitation on the reliefs.
ERA-Interim is again very close to the observations for snow cover, likely because of the assimilation of IMS data in this reanalysis, a satellite product also used in the production of the NOAA CDR dataset  (Drusch et al. , 2004; Robinson et al. , 2012). The more recent ECMWF reanalysis of ERA5 shows an over-estimation of snow cover that is comparable to the CMIP6 model ones. This behavior has already been described in  Orsolini et al. (2019) , explaining this difference by the fact that ERA5 does not assimilate IMS data beyond 1500 m a. s. l. , while  Hersbach et al. (2020)  suggests that the single-layer snow scheme does not allow enough melting in mountainous regions. The differences found over the three subdomains are similar to those highlighted over the whole HMA (Fig. 2f-h ). Nevertheless, we note a precocious spring melt in the multimodel mean and ERA-Interim compared to the NOAA CDR observations and ERA5 in HK region (Fig.
g. ,  Jimeno-Sáez et al. , 2020) , whereas models are expected to provide both solid and liquid precipitation. In addition, rain gauge measurements are generally too sparse to estimate the heterogeneous distribution of precipitation over complex topography areas. Over HK, a large part of the precipitation falls as snow in winter, a period when strong differences between satellite/rain gauge products (black curves) and models/reanalyses are also appearing during February to May, whereas the precipitation is closer between models and observations during the summer (Fig. 2j ). Nevertheless, GPCP data (dashed black line) are slightly closer to the models, especially over the HM do- main. ERA5 has an early precipitation peak in June, while it is found in July for the other products. Precipitation datasets should be considered carefully knowing that there is no better product than the other ones in this region, and the effective values of precipitation rates are highly uncertain in this area  (Palazzi et al.
Indeed, the CRU dataset may overestimate temperature in this area due to a lack of observations, while the low resolution of the NOAA CDR simple binary prod- The annual overestimation of the snow cover in most models arises mainly from a overly wide extension in the inner TP in winter (not shown). While the excess of snow melts in summer in most of the models, leading to a moderate bias during this season (Fig. 2 ), some models keep a persistent excess of snow even in summer (e. g. , HadGEM3-GC31-LL, HadGEM3-GC31-MM and IPSL-CM6A-LR), which partly explains the large dispersion between the models in terms of annual biases. All models show higher precipitation rates in comparison with APHRODITE (Fig. B2 ), as seen in the annual cycles (Fig. 2 ). Indeed, the multimodel annual mean bias of precipitation over HMA is 1.
5 [0. 3 to 2. 9] mm d -1 , corresponding to a relative bias of 143 % [31 % to 281 %]. The bias pattern in terms of total precipitation is somehow proportional to the climatological pattern of precipitation, with stronger biases in the southeastern Himalayas, where high precipitation rates are observed (Fig. B2 ). The quantification of the bias should be considered carefully for precipitation, because the APHRODITE dataset strongly underestimates the precipitation rates at high elevations  (Immerzeel et al. , 2015). Anyway, the dry bias found in the southern flank of the Himalayas, coupled with a positive bias of precipitation over TP, suggests an overly coarse resolution to represent the orographic barrier that blocks the northward moisture flux, a limitation especially pronounced during the Asian summer monsoon that induces strong precipitation rates in the south of HMA.
Therefore, these negative correlations are potentially not reliable and have to be considered carefully. The comparison with GPCP shows correlations between the biases of temperature and precipitation that reach positive values for a higher number of models Fig. C1. These correlations would likely be more positive if we used an observational reference dataset that was not affected by snow undercatch issues. Nevertheless, the BCC-ESM1 and CAS-ESM2-0 models show a strong correlation between snow cover and precipitation biases (0. 48 and 0. 41, respectively). This link is particularly striking for CAS-ESM2-0, for which the biases of snow cover and precipitation show similar patterns over the TP and HM (Figs. B1  and B2 ), suggesting that snow cover biases in that case are partly due to an excess of precipitation.
Historical trends analysis Disentangling the trends related to internal variability from the forced signals related to anthropogenic forcing is challenging. At midlatitudes, internal variability can contribute until 50 % to climate trends computed over 50 years  (Deser et al. , 2020). However, this contribution decreases when considering areas closer to the tropics and when integrating climate signals over large domains  (Hawkins et al. , 2016). The climate trends in HMA are explored over the period 1979-2014 by comparing observational datasets and multimodel mean computed with a single member for each model to give the same weighting to each model (Fig. 7 ). This comparison should be considered carefully, since observational datasets reflect the superposition of both the internal variability and the forced signals whereas the internal variability is partly filtered out when averaging the model outputs.
7g ). Meanwhile, the observation show slightly positive trends for the TP. However, the snow cover trends observed in the NOAA CDR dataset should be taken with caution, due to the poor resolution (∼ 200 km), which is problematic for mountain areas  (Bormann et al. , 2018). Observed trends are generally less significant for precipitation than for temperature. The large interannual precipitation variability limits the possibility to detect long-term trends for this variable. This also explains the discrepancy between the observation and the multimodel mean for precipitation (Fig. 7i-l ), with signals that show a larger amplitude in the observation than in the multimodel mean where the internal variability has been filtered out by averaging several model outputs. In the observations, the main precipitation signal is a significant increase in HK during both seasons (more pronounced in summer), which extends southward in the Indo-Gangetic Plain during the summer, ranging from 0.
2. 2. 3). The discrepancy between different observational datasets and reanalysis trends illustrated in Fig. D3  confirms the strong uncertainty typically found in precipitation datasets. Nevertheless, a summer signal is clearly visible in both models and observations, potentially related to monsoon changes, with an increase in precipitation rates over wide areas of the western part of HMA and the Indian subcontinent that is modulated by drying patterns located more on the east, which spatially diverge among the different products.
However, this effect should mostly be reduced by spatial averages. The recently published ESA CCI snow cover product at a better temporal and spatial resolution, even if not exactly on the same period, suggests that snow cover biases in models can even be higher over HMA. Generally, the complex and highly variable snow cover behavior calls for better spatiotemporal resolution than currently available from presentday snow cover products based on both observations, reanalyses or models and thus hampers reliable trend detection. Precipitation is particularly uncertain over HMA. It is well known that most of the observational datasets underestimate solid precipitation  (Palazzi et al. , 2013; Sanjay et al. , 2017; Sun et al. , 2018)  due to gauge undercatch. An additional issue is the scarce observational network in this region.
Using glacier mass balances to infer the high-altitude precipitation in the upper Indus Basin,  Immerzeel et al. (2015)  suggest an underestimation of precipitation reaching a factor of 2 to 10 in observational datasets. Most studies comparing models and reanalyses with observational datasets also account for large differences from about 100 % to 200 % (e. g. ,  Palazzi et al. , 2013; Su et al. , 2013; Salunke et al. , 2019). GPCP is in closer agreement with CMIP6 models, which could be explained by a better representation of solid precipitation. Overall, it is challenging to estimate model biases, even if the coarse GCM resolutions might underestimate the orographic barrier effect of high mountains leading to a potential excess of water vapor transport toward the TP  (Lin et al. , 2018). Hence, all models and analyses regarding precipitation need to be considered with caution.
Conclusions In this study, we assessed the performance of 26 CMIP6 GCMs over HMA for the historical period 1979-2014 and the future projections from 10 of them under the four Shared Socioeconomic Pathways (SSP1-2. 6, SSP2-4. 5, SSP3-7. 0 and SSP5-8. 5) through three variables: near-surface air temperature, snow cover extent and total precipitation. Cold bias over HMA is still present in this latest generation of GCMs with an average annual underestimation of -1. 9 [-8. 2 to 2. 9] • C compared to the CRU dataset, associated with an average snow cover overestimation of 12 % [-13 % to 43 %], corresponding to a relative bias of 52 % [-53 % to 183 %] compared to the NOAA CDR satellite observation. The recently published ESA CCI snow cover product at a better temporal and spatial resolution shows lower snow cover values compared to NOAA CDR, suggesting that model biases may be even larger.
Further work is required to build realistic climate models over HMA domain. This task is challenging because it requires model parameterizations adapted to high-elevation areas that might require high resolution. These models should be able also to simulate the response of the climate system to GHGs and to the regional imprint of the aerosol forcing, an essential feature in this strongly polluted area. Finally, such models should be also global, since the HMA area is a major piece of the climate system with climate teleconnections found all over the world. the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF. We thank the CLIMERI-France infrastructure (http://climeri-france.
fr/, last access: 5 October 2021) for making the CMIP6 data available. The authors thank the two anonymous reviewers for their constructive comments and their contribution to the improvement of this 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 Govindasamy Bala and reviewed by two anonymous referees.
Code availability. All scripts to produce the figures and results are available at https://github. com/mickaellalande/CMIP6_HMA_ paper/tree/v1. 0 (https://doi. org/10. 5281/zenodo. 5500285, Lalande, 2021). We use Python  (Oliphant, 2007;  Millman and Aivazis,  2011) version 3. 8. 5 and xarray (Hoyer and Hamman, 2017)  version 0. 16. 0 to manipulate NetCDF files. Interpolations are performed using xESMF version 0. 3. 0 (https://doi. org/10. 5281/zenodo. 3700105, Zhuang et al. , 2020a). For statistical purposes, Scipy (Virtanen et al. , 2020)  version 1. 5. 2 is used. All graphics are made using Proplot version 0. 6. 4 based on Matplotlib (Hunter,  2007)    version 3. 2. 2 and Cartopy version 0. 18. 0. The Taylor diagrams were made thanks to the Python implementation of Yannick Copin (https://gist.
github. com/ycopin/3342888; https://doi. org/10. 5281/zenodo. 5548061, Copin, 2012). Data availability. The datasets from CMIP6 simulations are available via the CMIP6 search interface: https://esgf-node. llnl. gov/ search/cmip6/ (WCRP, 2021). CRU TS version 4. 00 is available at https://doi. org/10. 5285/edf8febfdaad48abb2cbaf7d7e846a86 (University of East Anglia Climatic Research Unit et al. , 2017). NOAA CDR of NH snow cover extent version 1 is available at https://doi. org/10. 7289/V5N014G9 (Robinson et al. , 2012). The ESA CCI snow product is available at https://doi. org/10. 5285/5484dc1392bc43c1ace73ba38a22ac56 (Naegeli et al. , 2021) , and the cloud gap filter used in this study can be provided upon request. The APHRODITE products are available here: http://aphrodite. st. hirosaki-u.
ac. jp/download/ (APHRODITE's Water Resources, 2021). GPCP CDR version 2. 3 (monthly) is available at https://doi. org/10. 7289/V56971M6 (Adler et al. , 2016). GMTED2010 can be found here: https://www. temis. nl/data/gmted2010/index. php (TEMIS, 2021). The GLDAS Land/Sea Mask Dataset at 1 • can be found here: https://ldas. gsfc. nasa. gov/gldas/vegetation-class-mask (NASA, 2021). We also used the ERA-Interim and ERA5 reanalyses data that are publicly available at https://www. ecmwf. int/en/forecasts/ datasets/reanalysis-datasets/era-interim (ECMWF, 2020b) and https://doi. org/10. 24381/cds. adbb2d47 (Hersbach et al. , 2018).
In the late 1960's and 1970's, it became clear that several observations from the Alpine-Pyrenean, Himalayan and Zagros collisional belts were not compatible with some of the first-order predictions of the Plate Tectonic theory  (Jackson, 1980). While for some scientists this was the proof that Plate Tectonics did not work, others considered that these were only details of little importance. Today we understand that the Pyrenean and Alpine system in Western Europe probably neither reached mature seafloor spreading, nor subduction. This may explain on the one hand why field geologists working in these orogens who were not aware of the new geophysical and kinematic datasets coming from the oceans did neither believe the Plate Tectonics theory, nor were able to predict it  (Trümpy, 2001). Conversely, it may also explain why neither Steinman's trinity  (Steinmann, 1905) , nor  Decandia and Elter (1969)  exhumed mantle theory, nor Ampferer's concept of "subduction"  (Ampferer and Hammer, 1911;  see  McCarthy et al.