dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2009b ) -as the reference observational dataset. Continuous hourly resolution meteorological time series derived from RCM output by the ADAMONT statistical adjustment method are then used as input of the SURFEX/ISBA-Crocus snowpack model  (Vionnet et al. , 2012). Its default configuration and also, for the first time to the best of our knowledge, a recently developed multiphysics ensemble system  (Lafaysse et al. , 2017)  are used, making it possible to quantify snowpack model errors in the context of climate change impact assessment. We define a series of indicators for meteorological and natural snow conditions at the annual scale based on daily temperature, precipitation, snow depth and snow water equivalent data. The multi-ensemble datasets are analyzed using two specific statistical frameworks, addressing either individual annual values or multi-annual averages, which provide complementary information depending on the application.
SAFRAN reanalysis The SAFRAN system is a regional-scale meteorological downscaling and surface analysis system  (Durand et al. , 1993) , providing hourly data of temperature, precipitation amount and phase, specific humidity, wind speed, and shortwave and longwave radiation. SAFRAN refers here to the original mountain region implementation  (Durand et al. , 1993). SAFRAN was later expanded to wider geographical areas in France  (Vidal et al. , 2010)  and Spain  (Quintana-Seguí et al. , 2017). In this study, we use data from 1958 to 2016 for the single-site setup of the Chartreuse massif at 1500 m a. s. l. , on flat terrain.
Climate projections This study uses the EURO-CORDEX dataset  (Jacob et al. , 2014; Kotlarski et al. , 2014)  available in April 2017, consisting of six regional climate models (RCMs) forced by five different global climate models (GCMs) from the CMIP5 ensemble  (Taylor et al. , 2012)  over Europe, for the historical, RCP 2. 6, RCP 4. 5 and RCP 8. 5 scenarios  (Moss et al. , 2010). Only the GCM-RCM pairs for which the geopotential data for the CMIP5 GCMs were available were used. Historical runs generally cover the period 1950-2005 and RCPs cover the period 2006-2100, with some exceptions due to the availability of either the RCM or the GCM. Table 1 provides the different GCM-RCM combinations used in this study. In total, 43 different 0. 11 • resolution (EUR 11, ≈ 12. 5 km) time series of daily minimum and maximum temperature, total precipitation, longwave and shortwave incoming radiation, zonal and meridian near-surface wind speed, and specific humidity were used.
The EURO-CORDEX raw surface fields were adjusted using the ADAMONT method, which is a quantile mapping and disaggregation method taking into account weather regimes to provide multi-variable hourly adjusted climate projections  (Verfaillie et al. , 2017). The method uses a meteorological observational dataset at hourly time resolution (here the SAFRAN meteorological reanalysis from 1980 to 2011), and regional climate model outputs covering the geographical domain of interest (here the EURO-CORDEX dataset). Raw RCM outputs for the grid point closest to the middle of the Chartreuse massif were used (see  Verfaillie et al. , 2017 for details). The altitude values of the RCM grid points used range from 612 to 1085 m, with a mean value across all RCMs of 880 m. Note that  Verfaillie et al. (2017)  have demonstrated that the ADAMONT method provides adequate results under this setting with several hundreds of meters difference between RCM and the target altitude, and that selecting RCM grid points with a larger geographical distance but lower altitude difference does not necessarily improve the outcome of the adjustment procedure.
Changes between reference and future time periods For both methods, results over the historical period are contextualized with temporal median or mean of the annual indicators computed for the SAFRAN-Crocus reanalysis and for observations at CDP. The values of the post-processed indicators were computed using sliding 15-year windows spanning the entire climate dataset available, i. e. , from 1950 to 2100 in the case of EURO-CORDEX data (although some GCM-RCM pairs do not span the full historical period), from 1958 to 2016 in the case the SAFRAN-Crocus reanalysis, and from 1960 to 2016 in the case of CDP observations. In order to compute differences between conditions of the recent past and future changes, the reference period 1986-2005 (Ref) was selected, which contains all (and only) historical EURO-CORDEX model runs and was used as a baseline period of the IPCC AR5.
, 2012). In addition, the global temperature difference was also computed between future periods (BOC, MOC and EOC) and the reference (Ref) period 1986-2005 T g,BOC-Ref , T g,MOC-Ref and T g,EOC-Ref , respectively. Based on these datasets, we computed linear regressions curves (intercept forced to 0) between interannual means of the local meteorological and snow indicators during BOC, MOC and EOC, and the corresponding global annual temperature difference between the corresponding time period and the Ref period. Linear regressions were also computed using all future time periods together (ALL). In addition, the future values of the local meteorological and snow indicators of all future time periods were binned according to the corresponding global temperature by steps of 0. 5 • C (± 0. 25 • C), and the mean and standard deviation of all values within a given bin were computed.
Comparison between results of numerical simulations and observations On the basis of the annual values of the indicators SD, T and P for the time period from 1986 to 2005, statistics of the differences between reanalysis data and Col de Porte observations were computed, in terms of mean bias, root mean square deviation (RMSD) and correlation (only T , P ). This is not meant to represent an evaluation of the SAFRAN-Crocus reanalysis, because the SAFRAN dataset used in this study was not optimized to correspond exactly to the geographical setting of the Col de Porte observation site (appropriate altitude, specific terrain masks impacting solar radiation time distribution). However, the geographical setting of the observations and simulations are sufficiently close to each other that the two can be analyzed concurrently and provide reasonable information pertaining to the ability of the model chain to represent meteorological conditions in such a mountainous area.
Results This study introduces multi-component ensembles of past and future simulations of meteorological and snow conditions in the Chartreuse mountain range in the Northern French Alps at 1500 m altitude. As described previously, simulations encompass multiple RCPs, multiple GCM-RCM pairs from the EURO-CORDEX database adjusted using the ADAMONT method, and multiple Crocus snowpack model runs using the ESCROC ensemble system. This section describes the wealth of information generated through this process, focussing on meteorological and snow indicators described previously and addressing various components of the uncertainty and variability sources affecting the simulations.
Full ensemble configuration and uncertainty apportionment Figures 2-3 provide an overview of all sources of uncertainty and variability accounted for in this study, in terms of snow conditions (using the SD indicator as an example) for the period from 1950 to 2100, for RCP4. 5 and RCP8. 5 climate projection data, respectively. Figures  2a  and 3a  show continuous time series of annual values of mean winter snow depth data (SD), either observed or generated by the default snowpack model configuration fed by meteorological data from a reanalysis or an adjusted RCM for RCP4. 5 and 8. 5. They highlight the significant interannual variability in observed, reanalyzed and climate model datasets. For the time period 1986-2005, the mean observed SD value is 0. 64 m. Using the default Crocus configuration fed by the SAFRAN reanalysis at 1500 m altitude yields bias and RMSD values of annual SD values of 0.
These figures show the large multicomponent ensemble of individual annual data which can be generated when combining all available information, which highlights the need for appropriate data synthesis methods. Indeed, it is not possible to draw conclusions or make decisions on the sole basis of such a raw ensemble of individual scenarios. Figures  2d  and 3d  present the 13 × 35 15-year running average values spanning all simulation members of Figs. 2c  and 3c , respectively. This corresponds to the second statistical post-processing described in Sect. 2. 5. 2, which removes the interannual variability and allows an easier quantification of each source of uncertainty. Figures  2e  and 3e  aim to apportion the uncertainty in the time series of Figs. 2d  and 3d , respectively, between the uncertainty arising from GCM-RCM intermodel variability (including model uncertainty and internal variability of climate at different timescales) and the uncertainty arising from the multiphysics snowpack model.
g. ,  Steger et al. , 2013; Pierce and Cayan, 2013). The datasets underpinning the present study could be used to address in a more quantitative manner the physical processes responsible for the results of the simulations; however, this falls beyond the scope of this study (e. g. ,  Pierce and Cayan, 2013). Beyond the general trends, which provide an unsurprising -yet required -update of previous assessments based on older climate scenarios applied to the French Alps (e. g. ,  Rousselot et al. , 2012; Castebrunet et al. , 2014; Piazza et al. , 2014) , the main added value of the approach developed here lies in its ability to capture high-order moments of possible snow futures. For example, that the year-to-year variability of snow conditions on the ground remains as large as currently, and even increases in relative terms (until the middle of the century for all RCPs, and towards the end of the century for all RCPs except RCP8.
5), may be of equal, if not higher, significance to stakeholders operating in the alpine environment than the long-term trends. Such results can only be attained making use of a sufficiently large number of independent global and regional climate models, the EURO-CORDEX database corresponding to a significant achievement of the climate modeling community enabling such impact studies to take place. Many of the results discussed above indicate a strong consistency between our results and results obtained using delta-change methods, in French mountain regions as well as in Switzerland (e. g. ,  Castebrunet et al. , 2014; Schmucki et al. , 2014). This consistency is shown for multi-annual multi-model trends on snow depth or snow water equivalent mean values, but it cannot be assessed regarding the interannual variability because this is generally not addressed in these studies.
Furthermore, our results indicate that the response of local meteorological and snow conditions is essentially the same regardless of whether data from the beginning or end of the century are sampled. This indicates that the seasonal snowpack at this location and altitude level responds in a linear and reversible way to global-scale climate change, and the near-term and mid-term responses can be used, in addition to the end-of-century information, to infer the relationship between local and global conditions using a larger dataset, thereby providing more robust assessments of the influence of the global air temperature on local snow and meteorological data. This is all the more relevant in that none of the GCMs used for this study predict EOC warming below 1. 5 • C compared to preindustrial levels, so that using less distant future time periods makes it possible to assess the response of the local snow conditions to 1.
, 2009a, b; Maris et al. , 2009). The method can obviously be applied beyond French borders, provided that an adequate long-term observational dataset can be used as a basis for RCM output adjustment using the ADAMONT method  (Verfaillie et al. , 2017). Beyond the geographical scope, which can be extended to address a wider diversity of territorial climate-related challenges, sector-specific further applications can now be considered. For example, the adjusted climate scenarios can be projected on sloping surfaces, making it possible to address the impact of climate change on avalanche hazard using Crocus model runs, thereby upgrading and consolidating the results of  Castebrunet et al. (2014). Also, the adjusted climate scenarios could be employed to simulate snow conditions on ski slopes in French ski resorts, drawing on the method developed by  François et al.
Acknowledgements. We thank  CEN  staff members for contributing to observational data acquisition at the  Col de Porte site  ( Marie Dumont ,  Bernard Lesaffre ,  Philippe Lapalus ,  Jean-Michel Panel ,  Daniel Poncet ,  Pierre David  and  Marcel Sudul ) and the SAFRAN reanalysis ( Yves Durand ,  Gérald Giraud ,  Laurent Mérindol  and  Matthieu Vernay ). Fruitful discussions and interactions with colleagues at  Météo-France (CNRM ,  Samuel Somot ,  Serge Planton  and  Hervé Douville , and DCSC,  Jean-Michel Soubeyroux ) were beneficial for the present study, too. This study benefited from funding from the  French Ministry for Ecology (MTES)  through the  GICC program  and  ONERC , in the framework of the  ADAMONT  project, from the  Interreg  project  POCTEFA/Clim 'Py and from the  IDEX Univ.
Introduction Collisional orogenic systems are usually characterized by early subduction-related high-pressure (HP) metamorphism and deformation, followed by high-temperature (HT) re-equilibration, crustal thickening and melting  (Dewey et al. , 1993). Some collisional orogens even record polycyclic orogenic evolution such as Alpine reworking of the Variscan orogen in southern Europe (e. g. Lardeaux and Spalla, 1991)  and Caledonian reworking of the Proterozoic orogenic belt in Scandinavia (e. g. Austrheim, 1990). These repeated collisional cycles are of a characteristic feature of recurrent amalgamation of continental blocks associated with closure of small oceanic basins typical for the Pangaea plate system  (Collins, 2003). In contrast, the accretionary orogens at the periphery of the Pacific Ocean are characterized by tectonic switching resulting in alternations of HP-LT and LP-HT metamorphic cycles, due to subduction-related compression alternating with lithospheric extension and associated melting of fertile sediments  (Collins, 2002).
U-Pb zircon geochronology In order to determine the source provenance of the metavolcanosedimentary units and to establish a correlation between different structural, metamorphic and magmatic events, zircon was separated from 12 samples (Fig. 2  and Table  2 ). U-Pb isotope composition was analysed on zircons by Laser Ablation -Inductively Coupled Plasma Mass Spectrometry (LA-ICPMS) at the University of Hong Kong and the Czech Geological Survey in Prague (see details of the analytical method in the Appendix A of the Supplementary data). (See Table  3. ) The U-Pb isotopic data are presented according to the division to the northeastern, central and southwestern domains (see Figs  2  and 5 ). The U-Pb ages are summarized in Table  1  and the original data are available in the Appendix B of the Supplementary data (Tables  B1, B2  and B3 ).
Acknowledgement This study was financially supported by the  National Key Research and Development Program of China  ( 2017YFC0601205 ), the  NSF China  ( 41672056 ,  41230207 ), the  International Partnership Program of the Chinese Academy of Sciences  ( 132744KYSB20160005 ), the  Hong Kong RGC Research Projects  ( 17303415  and  17302317 ), the  HKU  grants (201411159173 and  201511159199 ), and by the  Czech Science Foundation  (GACR grant  17-17540S ). The work forms part of a Ph. D. thesis by  A. Broussolle. A  100 Talents Program  of the  Chinese Academy of Sciences  to JYD is also acknowledged. The authors are grateful to  Dr. Cai Keda  of  Xinjiang Institute of Geography and Ecology  for field trips assistance. We are also indebted to  Dr. Jean Wong  from the  University of Hong Kong  for her help with zircon analyses, the LA-ICPMS operating and the data processing during the course of this work.
, 2013). To make up for their limited spatial coverage, the ground-based observations can be combined with remote sensing data as in  Arthern et al. (2006)  or with regional climate models  (Monaghan et al. , 2006; Agosta et al. , 2012). Lately, both satellites and models have been used without the need for surface data : the snowfall measurements from CloudSat  (Palerme et al. , 2014)  and the high resolution calibration-free model of  Lenaerts et al. (2012). 1 The Cryosphere Discuss. , https://doi. org /10. 5194/tc-2018-156 Manuscript under review for journal The Cryosphere Discussion started: 27 August 2018 c Author(s) 2018. CC BY 4. 0 License. Not all the aforementioned methods yield accumulation time series. Among those that do, none present statistically significant trends in accumulation on a continental scale e.
Data and methods We build upon a similar study in the Arctic  (Dufour et al. , 2016)  and the reanalyses presented herewith. NCEP NCAR was not included due to a well known moisture diffusion problem over Antarctica  (Cullather et al. , 1998)  as well as unrealistic evaporation  (Hines et al. , 1999). We left out JRA 25 and MERRA 1 because they were not extended up to 2016. Our period of study starts in 1980 to adjust to the new reanalysis from NASA, MERRA 2. Among other things, the data assimilation system from MERRA 1 was improved to take into account data from satellites launched after  NOAA-18 (2005)  for instance the hyperspectral infrared radiances of EOS Aqua  (Gelaro et al. , 2017). The methods to compute the fluxes are described in  Dufour (2016)  along with the effects of the various approximations (pressure levels, vapour phase only, 200 hPa cutoff), all of which were limited.
Moreover, the default calibration model of the sensor is inaccurate below -20 • C ("temperature-dependence error"). Unfortunately, the suggested corrections apply only to specific radiosonde models and require metadata and high resolution vertical data absent from IGRA. However, as we shall see, in Antarctica most of the transport of humidity occurs near the surface, often in warm storm conditions. Finally, solar heating of the sensor also can generate errors which were documented in the case of Antarctica in  Rowe et al. (2008)  with the help of an Atmospheric Radiance Emitted Interferometer.
Theoretically at least, strong winds erode and sublimate snow which is a genuine cause of moisture divergence but these processes are not represented in most climate models  (Amory et al. , 2015). However, reanalyses do assimilate moisture profiles and these are known to be affected by blowing snow events  (Barral et al. , 2014). Given the dispersion between datasets on a local scale, we cannot expect the datasets to converge to a common estimate of convergence on the scale of East Antarctica (Fig. 4. a, magenta bars). This is all the more true for evaporation and precipitation, which are forecasted variables and thus even less constrained by observations (light and dark blue bars, respectively). One issue with reanalyses is the non-closure of their moisture budget, witnessed by the gap between upper and lower bars in Figure  4 (a).
The small mismatch in NCEP DOE R2 is illusory, a consequence of its excessively high sublimation  (Bromwich et al. , 2011). The 4D-Var reanalyses exhibit the smallest residual. For comparison, we also give the breakdown of the moisture budget in the case of the whole continent (still excluding ice-shelves). West Antarctica and the Peninsula are more exposed to moisture fluxes and indeed the convergence is higher for the whole continent. The ranking between reanalyses remains the same. The interannual variability in reanalyses are doubtful due to the discontinuities introduced by changes in the observation system  (Bengtsson et al. , 2004). This is especially the case in Antarctica where analyses rely heavily on satellite data as opposed to conventional observations  (Nicolas and Bromwich, 2011).
NCEP DOE R2 has a much weaker convergence cycle perhaps due to the unrealistic evaporation in summer. When the reanalyses are interpolated on the stations and masked in time to match the radiosonde launches, the annual cycle is quite different (Fig. 6. b). The transition from summer to winter is no longer as sharp in most datasets and it is altogether absent from NCEP DOE R2 and IGRA. In the vertical, the moisture flux profiles demonstrate two different regimes (Fig. 7, a ). In most datasets including observations, the transport changes sign around 900 hPa : into the continent above and away from it below. The level of the transition is higher in ERA Interim (850 hPa) and higher still in NCEP DOE R2 (750-700 hPa). Both datasets have stronger surface fluxes. The surface fluxes are weaker above stations (solid lines) than in the continental average (dashed lines).
The interdataset spread also increases near the surface in the case of humidity (Fig. 7, b ). The IGRA observations exhibit a shallow inversion only present in MERRA 2 and JRA 55 but at a much higher altitude. There is practically no wind on average in altitude but below 800 hPa the prevailing winds blow out from the ice-sheet. This is the signature of katabatic flows. There is quite a difference between the reanalyses both sampled in time and interpolated in space and the originals. We suggest that the stations are not representative of their surroundings because they were generally built in places sheltered from the winds. Only the high resolution datasets can represent both the katabatic outflow and the relative haven of the station. What's more, the radiosonde launch is more likely to be cancelled or the balloon lost during a strong katabatic event which biases the observations against these conditions.
8, a  and b ). The positive (inward) transport dominates, particularly the last decile but the negative (outgoing) fluxes are nonetheless important : more than a third of the absolute transport. The inter-dataset variability is as strong for the positive and the negative transport, highlighting once more the role of katabatic winds. In the sub-sampled case, the observations fall within the reanalysis enveloppe but the inter-dataset variability is also greater. To sum up, radiosondes and reanalyses diverge in narrow valleys and near the ground especially for low resolutions models. Otherwise, since radiosondes are assimilated into reanalyses, the difference between the two is predictably small. The added value of radiosondes is the study of time series.
5 The Cryosphere Discuss. , https://doi. org/10. 5194/tc-2018-156 Manuscript under review for journal The Cryosphere Discussion started: 27 August 2018 c Author(s) 2018. CC BY 4. 0 License. Time series from reanalyses reflect modifications to the observation system in addition to changes in the climate. The radiosonde record also has its homogeneity issues e. g. when instruments are upgraded. These events are recorded in the IGRA2 metadata and we found no association with discontinuities in the times series. We will therefore interpret sudden divergences between observations and reanalyses as artefacts of the data assimilation. From Figure  9 , the most conspicuous ones are in NCEP DOE R2 followed by JRA 55 but they do not take the form of the shifts we would expect with, say, the introduction of a new satellite.