dataesr/scientific-paragraphs-categorization huggingface dataset:
5%, at 293. 6K. The processed data contain kinematic kerma factors, total damage energy production and gas production cross section whenever such data are contained in the ENDF library. Processing covariance data into ERRORR and BOXER formats is carried out using SCALE-238 group structure. Therefore, at the end of the processing three files are generated: one HENDF, one BOXER (if evaluated file provides covariance data) and one INTER (containing cross section integral data). These three files are then uploaded into JANIS database. The content of all JANIS databases is summarized in Table  1. NEA Data Bank is working on "Nuclear Data Evaluation Cycle" (NDEC) system  [4] , a systematized workflow for the verification, processing, differential validation and integral benchmarking of evaluated nuclear data.
JANIS Web The online nuclear data services are now provided through direct access to the JANIS databases taking advantage of the new Web interface of JANIS, which allows online browsing, searching and displaying nuclear data without requiring a Java Virtual Machine. The online services also include JANIS Books. The JANIS Books  [1]  are compilations of crosssection curves of evaluated and experimental data from a number of libraries, nuclear reactions and associated reaction products. JANIS Books are available for nuclear reactions induced by neutrons, photons and lightcharged particles. Online JANIS Books (JANIS web) are interactive versions that allow the users to zoom, access complementary information and plot additional data, as shown with the screenshot in Fig. 3  (e. g. 238 U total cross section).
ND2016 JANIS can visualize different formats of uncertainties, both ENDF6/MF33 format and processed format, such as BOXER, ERRORR and COVERX. Recent new tools developed at NEA rely on JANIS features to access nuclear data, for example the Nuclear Data Sensitivity Tool (NDaST) which makes use of the BOXER and COVERX formats support and the JANIS database to retrieve covariance data.
Conclusions and outlook JANIS has been developed to provide a user-friendly access to evaluated, experimental and bibliographic data available in ENDF and EXFOR/CINDA libraries. The NEA maintains the JANIS software and its database upto-date, while developing new features and capabilities after collecting information and feedback from a broad community of users: nuclear data experimentalists, EXFOR compilers, evaluators of nuclear data and users of nuclear data in many applications such as fission reactor neutronics, criticality, safety, waste management, fusion applications, material radiation damage. An upcoming challenge will be to adapt to the successor of ENDF format which is currently being defined by the nuclear data community.
, 1999) , which may impose powerful constraints on plant growth. Siberian and Canadian boreal forests have thus been shown to experience water stress, with ratios of surface sensible to latent heat flux of up to ~2  (Jarvis et al. , 1997; Baldocchi et al. , 1997; Schulze et al. , 1999)  causing further heating of the near-surface atmosphere. These large seasonal shifts of the high-latitude water balance -how water input from precipitation is shared between changes of water storage in snow, ice and soil moisture, and balanced against losses from evapotranspiration, sublimation and river discharge -can now be better assessed and evaluated using state-of-the-art observation datasets. In addition, in terms of realistic process representation, land surface models (LSMs) focusing on high-latitude phenomena require the inclusion of the following non-exhaustive series of pivotal hydrological and biogeochemical interactions: 1-A representation of permafrost physics and seasonal freeze-thaw cycles, which determine the soil hydrologic and thermal budgets and the volume and timing of lateral water flows to rivers.
g. Hugelius et al. , 2013; Tarnocai et al. , 2009). 6-Feedbacks between high soil carbon concentrations and profiles of soil temperature, water and permafrost carbon content (e. g. Lawrence and Slater, 2008; Decharme et al. , 2016). We represent the above processes in an updated version of the ORCHIDEE LSM (ORganizing Carbon and Hydrology in Dynamic EcosystEms), known as ORCHIDEE-MICT (aMeliorated Interactions between Carbon and Temperature), which we describe in this study. Since the comprehensive description of the model ORCHIDEE by  Krinner et al. (2005) , the model went through major modifications and improvements; we present here the major ones linked to high-latitude processes. ORCHIDEE-MICT is evaluated over the last two to three decades (depending on the variable) against empirically generated datasets.
, 2012a; Wang et al. , 2013)  that are incorporated already in ORCHIDEE-TRUNK revision 3976. In Section 4, we focus on new features of ORCHIDEE-MICT compared to revision 3976, including SOC vertical discretization and processes leading to SOC burial in frozen horizons (Section 4. 1), and functions describing how the SOC content in each soil layer alters soil thermal and hydraulic properties (Section 4. 2). The simulation protocol, forcing data and different observations used to evaluate ORCHIDEE-MICT are described in Section 5. Model outputs are evaluated against multiple observations for grid-based simulations forced by two climate datasets, CRUNCEP v7 and Global Soil Wetness Project Phase 3 (GSWP3) v0, at 1°resolution over the Northern Hemisphere over periods within the range 1960-2007. In the result sections, Section 6 evaluates snow depth and soil temperature against measurements from in-situ stations, and land skin temperature against satellite-derived dataset.
, 2008; Guimberteau et al. , 2012) , the maximum extent of which is prescribed by the GLWD (Global Lakes and Wetlands Database) map  (Lehner and Döll, 2004). In grid-cells with flooded areas, river discharge from upstream basins is diverted to a floodplain reservoir, which then feeds a delayed return flow back to the stream reservoir of the basin. 4 New processes and parameterizations.
, 2015)  and ISBA  (Decharme et al. , 2016). In ORCHIDEE, SOM thermal insulation was previously investigated by  Koven et al. (2009)  but its parameterization was imbedded in a prior model version which employed bucket-type soil hydrology. This however, is not applicable to ORCHIDEE-MICT, which uses a new vertically discretized hydrology scheme and its coupling with the thermal module. In addition, the  Koven et al. (2009)  study did not include SOM effects on soil hydraulic properties, which are addressed in ORCHIDEE-MICT and described in detail below.
Fires The prognostic fire module SPITFIRE (SPread and InTensity of FIRE), which has been previously integrated to and calibrated for a standard version of ORCHIDEE  (Thonicke et al. , 2010; Yue et al. , 2014) , was merged into ORCHIDEE-MICT. Ignitions were re-calibrated using the GFED4s data set (http://www. globalfiredata. org/data. html) by using region-specific scaling factors (see Table  S3 ) and the exclusion of cropland fires to ensure that simulated mean annual burned area for 1997-2013 was equal to that of the GFED4s data set. Note that this method only calibrated for mean annual regional burned area, and that simulated latitudinal distributions and grid cell spatial patterns of burned area and fire carbon emissions, and their interannual and seasonal variabilities, could be still compared with observation-based data.
Simulation set-up Two separate runs using different climate forcing input data -CRUNCEP v7 (hereafter CRUNCEP) and GSWP3 -were performed with ORCHIDEE-MICT for the terrestrial Northern Hemisphere (> 30°N) at 1°spatial resolution. Both sets of runs encompass the 20 th and beginning of 21 st century, and were preceded by separate spin-ups for each climate dataset, forced by fixed pre-industrial conditions of atmospheric CO 2 (286 ppm) and vegetation map. The dynamic vegetation model is deactivated throughout both runs. In order to accumulate soil organic carbon in the model, which requires substantial computing time before reaching near-equilibrium in the presence of the slow mixing processes described in Section 4. 1, the spin-up procedure comprised three steps: 1) The full ORCHIDEE-MICT model was forced by looped climate fields over the period  for 100 years to reach equilibria for soil temperature, soil moisture, vegetation productivity, soil carbon inputs from dead plants, etc.
Atmospheric forcing datasets The use of two different forcing datasets represents a first step in documenting atmospheric forcing based uncertainty in model output. Runoff has been shown for instance to be particularly affected by differences in precipitation from different datasets  (Fekete et al. , 2004; Biancamaria et al. , 2009) , and by the methods to partition total precipitation volumes between rainfall and snowfall during the cold season  (Haddeland et al. , 2011). The bias of meteorological driver also impacts the carbon budget  (Zhao et al. , 2012). A description of the two datasets used follows.
GSWP3 v0 This 3-hourly 0. 5°global forcing product  was developed for the third phase of GSWP3 (http://hydro. iis. u-tokyo. ac. jp/GSWP3/). It is based on the 20 th Century Reanalysis (20CR) version 2 performed with the NCEP land-atmosphere model  (Compo et al. , 2011). 20CR was dynamically downscaled to T248 (0. 5°) resolution using the Global Spectral Model (GSM) by data assimilation using the spectral nudging technique  (Yoshimura and Kanamitsu, 2008). Bias correction for precipitation, temperature and longwave and shortwave downward radiations were made using GPCC v6 (Global Precipitation Climatology Centre), CRU TS v3. 21 (Climate Research Unit), and SRB (Surface Radiation Budget) datasets, respectively. Precipitation was partitioned into rainfall and snowfall referring to the ratio of the downscaled 20CR, and wind induced undercatch correction  (Motoya et al.
CRUNCEP v7 This 6-hourly 0. 5°global forcing product  is a combination of the annually-updated CRU TS v3. 24 monthly climate dataset  (New et al. , 2000)  and NCEP reanalysis  (Kalnay et al. , 1996). The latter is only used to generate diurnal and daily anomalies added to CRU TS monthly means, after bi-linear interpolation to the 0. 5°resolution of CRU, except for the precipitation which were linearly interpolated. A threshold of 0°C in 2-m temperature were used to partition the precipitation into rainfall and snowfall in the CRUNCEP forcing. Rainfall, cloudiness, relative humidity and temperature are taken from the CRU while the others fields (pressure, longwave radiation, windspeed) were directly derived from NCEP (see more details at: https: //vesg. ipsl. upmc. fr/thredds/fileServer/store/p529viov/cruncep/readme.
Vegetation and soil texture map The ESA CCI Land Cover map  (Bontemps et al. , 2013)  was used to produce the PFT map for ORCHIDEE. The ESA CCI land cover product is given by 3 maps at a 300 m spatial resolution, corresponding to the years 2010, 2005 and 2000. These maps were derived from the interpretation of MERIS full and reduced resolutions and SPOT-Vegetation time series. Land cover was classified according to the 22 classes used in the UN-LCCS (land cover classification system) scheme, which was translated into PFT fractions used in ORCHIDEE, following the cross-walking method presented by  Poulter et al. (2011 Poulter et al. ( , 2015)). Historical land use maps from the Harmonized Global Land Use dataset  (Chini et al. , 2014)  were incorporated to reconstruct PFT fractions since 1860, following Peng et al.
(  2017 ), which will be detailed in the upcoming ORCHIDEE-TRUNK paper for CMIP6. For soil texture, we use the 12 USDA texture classes provided at a global 0. 08°resolution from  Reynolds et al. (2000)  and upscaled these to the resolution of the atmospheric dataset (1°x1°). Only the dominant texture type for a grid cell is used at the 1°resolution for defining soil hydraulic parameters  (Carsel and Parrish, 1988)  in ORCHIDEE-MICT.
Evaluation datasets for the water budget Table  1  gives an overview of the selected datasets to evaluate ORCHIDEE-MICT. For the water budget evaluation, we selected six Arctic river basins (Fig. 2b ) which are important contributors to total Arctic Ocean river inflow: the four largest Eurasian Arctic basins (Ob, Yenisei, Lena and Kolyma), the Mackenzie basin in northwestern Canada and the Yukon basin in Alaska. The four Eurasian basins (with the smaller Pechora and the Severnaya Dvina basins) drain about two-thirds of the Eurasian Arctic landmass  (Peterson et al. , 2002) , while the Mackenzie is the largest North American river bringing freshwater to the Arctic Ocean  (Woo and Thorne, 2003). We also evaluated the Volga basin (Fig. 2b ), which is subject to snowfall events during the year but is not underlain by permafrost, in order to compare results with the high-permafrost Arctic basins (Fig.
Total terrestrial water storage The change of total terrestrial water storage (TWS) is an integrated measure of the ability of a LSM to partition the cold season storage of water as snow and ice, and its decrease from losses to downstream river discharge, plant and soil evapotranspiration, and soil moisture increase. Alkama et al. (2010)  showed with the ISBA-TRIP model that seasonal variations in TWS over high- dataset  (Swenson and Wahr, 2006; Swenson, 2012; Landerer and Swenson, 2012). We use the ensemble mean of these solutions to reduce uncertainty in GRACE data  (Sakumura et al. , 2014). To compare TWS simulated by ORCHIDEE-MICT to GRACE data over the common time period between the two meteorological forcing data sets and GRACE data (July 2003-December 2007), we summed the water stocks simulated by ORCHIDEE-MICT, i.
Snow water mass The GlobSnow v2. 0 SWE dataset is based on a data-assimilation approach combining space-borne passive radiometer data (SMMR, SSM/I and SSMIS) with data from ground-based synoptic weather stations. The record provides SWE information on a daily, weekly and monthly basis, at a spatial resolution of approximately 25 km (in EASE-Grid format) and applies to non-mountainous regions of the northern hemisphere, excluding glaciers and Greenland. The product is based on the SWE retrieval methodology developed by  Pulliainen (2006)  complemented by a time-series melt-detection algorithm  (Takala et al. , 2009). A complete description of its methodology is given in  Takala et al. (2011). SWE estimates are complemented with uncertainty data at the grid cell scale. This work uses monthly-averages of original daily data, interpolated to a 1°grid for appropriate comparison with ORCHIDEE-MICT output.
River discharge We use two river discharge databases, one global and another exclusively covering Siberia: The Global Runoff Data Centre (GRDC) product is a global database which collected river discharge data from nearly 7800 stations in 156 countries for the [maximum] period 1807-2017. We use one gauging station per river, the closest to the mouth of the river. For Siberia, we also use daily river discharge at the 3 gauges in three large Siberian rivers (the Ob, Yenisei and Lena) which have been reconstructed (naturalized discharge) to exclude the human impact from the data. This has been performed by using the Hydrograph Routing Model (HRM) developed at the University of New Hampshire (USA) in collaboration with the Arctic and Antarctic Research Institute (Russia)  (Shiklomanov and Lammers, 2009).
Top-soil moisture The global ESA CCI SM product (v2. 2) provides daily soil moisture (in volumetric units m 3 m -3 ), at a spatial resolution of 25 km with quality flags specifying potential sources of errors linked to the presence of water bodies, dense vegetation, snow or frozen soils in the pixel area. Three datasets are provided based on different combinations of active and passive satellite sensors. In this work, we used the combined active and passive product which covers the whole period from November 1978 to December 2014. The product results from the merging of soil moisture estimations inversed from two types of instruments and two methodologies: passive microwave radiometers (SMMR, SSM/I, TMI, AMSR-E, AMSR2 and WindSat) using the methodology developed by  Owe et al. (2008)  and active microwave instruments (ERS-1&2 and ASCAT) using the algorithm developed by TU-Wien  (Wagner et al.
Root-zone soil moisture The GLEAM root-zone soil moisture v3. 0a database  (Martens et al. , 2017)    of Hydrometeorological Information-World Data Center (RIHMI-WDC)  (Bulygina et al. , 2011) , and the National Meteorological Information Center of the China Meteorological Administration  (Peng et al. , 2010) , respectively. Monthly averages were calculated when more than 2/3 of daily snow depth data were available.
In-situ air and soil temperatures Simultaneous measurements of snow depth, near-surface air temperature and top soil (at 20 cm depth) temperature from Russian meteorological stations  (Bulygina et al. , 2011; Sherstiukov, 2012)  are used to evaluate the thermal insulating effect of snow during winter, by comparing the relationships between snow depth and the air-to-topsoil temperature gradient (∆T)  (Wang et al. , 2016b). This dataset includes monthly mean values at 268 sites for the period 1980-2000.
Active-layer thickness The Circumpolar Active Layer Monitoring (CALM) network's in-situ data set was used to evaluate modeled soil active layer thickness. The CALM network aims to observe the response of near-surface permafrost and the active layer to climate change over long time scales. Thaw depth is measured at the end of the thawing season, so that it should be comparable to the maximum active layer thickness in the model. Before comparing to ORCHIDEE-MICT, the data from 221 sites were averaged from 1990 to 2015. In addition, we used the active layer thickness map derived by  Beer et al. (2013)  over Yakutia at a spatial resolution of 0. 5°. It is based on regional surveys of landscapes and permafrost conditions in Yakutia during the time period 1960-1987. The gridded datasets can be accessed at the PANGAEA repository.
GIMMS The Global Inventory Modeling and Mapping Studies (GIMMS) LAI3g dataset is derived from Advanced Very High Resolution Radiometers (AVHRR) measurements. It is more specifically computed from the GIMMS Normalized Difference Vegetation Index (NDVI) 3g, using a neural network and the International Geosphere Biosphere Programme (IGBP) land cover classes  (Zhu et al. , 2013). The 1/12 degree files cover 30 years from July 1981 to December 2011, at a bi-weekly resolution. They were aggregated at a 1. 0°spatial resolution on the model grid, to enable comparisons of the time-series and maps.
Site-level GPP and NPP observations In-situ GPP and NPP measurements While there are several NPP datasets especially for forest (e. g. Cannel et al. , 1992; Olson et al. , 2001; Michaletz et al. , 2014) , few provide both GPP and NPP at the same locations. We used a recent database of in situ co-located GPP and NPP measurements  (Campioli et al. , 2015) , which is an extension of the  Luyssaert et al. (2007)  database for forests. The database consists of 131 sites, for which annual NPP obtained from biometric measurements and associated uncertainties are provided. The criteria employed by  Campioli et al. (2015)  for selecting the sites was the availability of methodologically independent and site-specific estimates of NPP and GPP. Carbon Use Efficiency (CUE) was calculated at each site by taking the ratio of NPP to GPP.
ISRIC ISRIC is a global soil information database with spatial predictions for a selection of soil properties, at 1 km resolution and six depths, up to a maximum of 2m ( 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm). About 110,000 soil profiles were used to generate the product, using statistical models based on climatic and biomass indices, lithology, and taxonomic mapping units  (Hengl et al. , 2014). The gridded maps of soil organic carbon mass fraction, soil bulk density, and volumetric fraction of coarse fragments was used to calculate SOC density in the unit of kgC m -2 to be comparable with model output (see Eq. 6 in  Hengl et al. , 2014). However, we found in this product a systematic overestimation of bulk density for organic-rich soils, which is similar to the issue found in the HWSD database  (Köchy et al.
Burned area and fire emissions We compared the simulated burned area and fire carbon emission with the Global Fire Emissions Database (GFED4s) data set, which is based on the GFED modeling framework (van der  Werf et al. , 2010). The GFED4s burned area data is derived from the MODIS sensor data that is then complemented by specific algorithms to retrieve "small fires"  (Randerson et al. , 2012). Emissions are computed from a combination of burned area with the revised Carnegie-Ames-Stanford-Approach biogeochemical model (CASA-GFED, described in van der  Werf et al. , 2010) , that provides an estimation of fuel loads and combustion completeness. GFED4s data is provided at a 0. 25°spatial resolution with a monthly time-step for 1997-2015. Cropland fires are not yet implemented in the ORCHIDEE-MICT model, but are included in the GFED4s dataset.
In order to compare the simulation with GFED4s, it was necessary to correct the simulated burned area for the omission of cropland fires. To do so, we computed for each grid cell the fraction of existing natural PFTs (i. e. all non-cropland PFTs), and divided the simulated burned area in each grid cell by this value. For carbon emissions, the GFED4s dataset provides separate emissions data for cropland and natural fires. In this way, we were able to simply remove cropland fire emissions from GFED for comparison with ORCHIDEE-MICT output. 6 Evaluation of the atmosphere-snow-soil continuum In the following, we analyze model performance in replicating the transfer of heat from atmosphere to deep soils. This is done by evaluation against measured temperature gradients, starting from snow depth controls on winter ∆T, followed by evaluation of surface (skin) temperature in summer, and the temperature gradients between the near-surface and deeper soils.
Yet, according to  Wang et al. (2015a) , biases in precipitation and ET datasets, that are used to evaluate the models, are source of errors for the water imbalances found in the northern high-latitude basins. These river discharge results highlight deficiencies in the model representation of water infiltration in frozen soils, which appear to be too drastic in their prevention of snowmelt infiltration under conditions of frozen topsoils. Alternative parameterizations of these dynamics are underway. For example, infiltration of meltwater into frozen soil could be permitted when accounting for sub-grid scale variability of topsoil freezing and drying which would enhance infiltration  (Gray et al. , 2001). Other perspectives are the improvement of the floodplain parameterization in ORCHIDEE-MICT  (Lauerwald et al.
Leaf area index According to the two evaluation products, the seasonal cycle of LAI (Fig. 14 ) is similar across the basins, with values near zero during winter, and a maximum in summer. There is a consistent phasing of seasonal LAI between the two evaluation datasets, however maximum LAI in GIMMS is systematically higher than in GLASS. In all the basins, the LAI simulated by ORCHIDEE-MICT has a phase delay of up to one month compared to both products. This is due to a delay in the start of the growing season, which may be related to excessive persistence of the snow cover (Fig. 10 ). Note here that ORCHIDEE-MICT is prone to overestimate the timing of senescence  (MacBean et al. , 2015). This is true in particular for conifers, for which the model lacks an explicit senescence inception model.