dataesr/scientific-paragraphs-categorization huggingface dataset:
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 drivers 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 20th Century Reanalysis (20CR) version 2 performed with the NCEP landatmosphere 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 corrections for precipitation, temperature and longwave and shortwave downward radiations were made using the 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 precipitations which were linearly interpolated. A threshold of 0.
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 three 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 • × 1 • ). 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 The selected datasets used to evaluate ORCHIDEE-MICT are summarized in Table  1  and described in the Appendix. 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 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.
As mentioned in Sect. 4. 2, the empirical SOC map from NCSCD  (Hugelius et al. , 2013)  is prescribed for permafrost regions in the soil thermal and hydrological modules, which is upscaled by the model to the target spatial resolution (1 • by 1 • in this study). These SOC values thus do not represent the site-level soil conditions, aside from the uncertainty of the NCSCD database itself. To further investigate this impact, we conducted additional simulations for the sites that provide explicit organic layer thicknesses (in total, 69 sites), forced by CRUNCEP. In these runs, we assumed pure organic soil, i. e. , f i,SOC in Eq. (  9 ) equaling one, for the soil layers above the site-specific organic layer thickness, but kept the SOC concentration unchanged below this thickness, i. e. , from NCSCD.
Total terrestrial water storage change A realistic phase and amplitude of TWS are simulated with both forcing datasets, although peak-to-peak amplitude is slightly overestimated in the Volga, Yenisei and Kolyma basins under GSWP3 input and the seasonal amplitude underestimated in the Yukon with both forcings, and in the Mackenzie and Lena with CRUNCEP forcing (Fig. 8 ). The positive temporal trend of TWS in the Ob, Lena and Kolyma basins is captured well by ORCHIDEE-MICT, where it reflects upward precipitation trends (not shown). In general, the GRACE TWS is captured well by ORCHIDEE-MICT with both forcings, at the seasonal scale and for the 5-year trends, except in the Yukon Basin, where observed TWS decreases are not reproduced in our simulations, with no precipitation decrease in the GSWP3-forced model.
However, according to S. Wang et al. (2015) , biases in precipitation and ET datasets, which 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. 13 ) 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 1 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. 9 ). The phenological models in ORCHIDEE (detailed in MacBean et al. , 2015, Appendix A) do not explicitly take into account this influence, unlike what is done in  Van Wijk et al. (2003) , who model the link of the start of the tussock tundra growing season to the soil thaw at 10 cm depth.
2 Gross (GPP) and net (NPP) primary productivity 8. 2. 1 Spatial distribution and seasonal cycle of GPP GPP at the high latitudes is co-limited by cold temperatures, constraining the duration of the growing season, and by summer water stress  (Schulze et al. , 1999)  in northern Canada and Siberian boreal forests, for which the water balance is usually negative at this time. The simulated spatial pattern of GPP in Eurasia and North America is close to the MTE-GPP dataset (Fig. 14 ). Values lower than MTE-GPP were simulated in eastern Siberia under the GSWP3 forcing, mainly due to water stress (see also the underestimated biomass for eastern Siberia in Fig. 20 ). The seasonal GPP cycle in Fig. 14b  is generally accurate with respect to observations at the scale of large boreal regions; however, peak GPP is strongly overestimated for boreal North America (BONA).
Interestingly, comparing GPP forced by the two climate datasets shows higher values by CRUNCEP than by GSWP3 (Fig. 14 ), despite a generally lower precipitation in CRUN-CEP (Fig. S13 ). This could be explained by the higher specific air humidity during summer in CRUNCEP than in GSWP3 (Fig. S16 ). A low air humidity increases the atmospheric vapor pressure deficit (VPD) and the leaf-to-air vapor pressure difference; plants then partially close the stomata to constrain a potentially fast transpiration  (Oren et al. , 1999; McAdam and Brodribb, 2015) , which leads to a reduced photosynthetic rate. The photosynthesis module in OR-CHIDEE largely follows  Yin and Struik (2009) , in which stomata conductance decreases with an increasing VPD, and thus is able to simulate a lower GPP under dry air conditions.
We plot the 95th percentiles of these distributions in Fig. 16 , which arguably defines an upper envelope for temperature-limited GPP and NPP (see Fig. 3  of  Luyssaert et al. , 2007) , and allows us to evaluate the spatial sensitivity of GPP and NPP to mean annual temperature. The behavior of all products is similar to the sensitivities derived from site observations, with a strong positive relationship between GPP, NPP and mean annual temperature over the range -10 to 10 • C. ORCHIDEE-MICT then captures the decrease in GPP (NPP) at warmer sites, but underestimates the values above 5 • C. Global gridded data products generated independently from the  Campioli et al. (2015)  dataset exhibit sensitivities comparable to those derived from  local sites, whereas modeled GPP and NPP saturate for temperatures above 10 • C, indicating water stress dominant controls.
Likewise, the model estimates extremely large biomass in eastern North America, especially by CRUNCEP forcing, which almost doubles the observed amount. For the rest of the study region, simulated carbon density is slightly lower than observations, by around 5-25 Mg C ha -1. The two observation datasets show considerable similarities. Both model output and observation data are subject to the spatial uncertainties introduced by the use of satellitederived land cover maps. We thus used the forest cover map as prescribed in the model for both datasets, and calculated latitudinal averages to compare with model results (Fig. S8 ). GSWP3-forced model output agrees well with observations averaged over the whole study region, while CRUNCEP-forced output overestimates biomass at all latitudes (Fig.
Discussion The model performances against datasets being documented in previous sections, we focus here on key mechanisms expected from a "high-latitude" model: (1) the conversion of winter snow and ice storage into a peak river discharge in spring, (  2 ) the ability to capture the rectification of the seasonal amplitude of air temperature through the insulating snowpack and its attenuation at depth in the soil profile, and (3) the ability to reproduce the large-scale gradients of carbon input to ecosystems from GPP and NPP and its further partitioning into live biomass and SOC pools driving soil heterotrophic respiration.
Conversion of winter water storage to spring and summer river discharge We have shown that the model simulated very different river discharge values according to the atmospheric forcing used. The spatial distribution estimate of winter snowfall at high latitudes in climate datasets is very difficult. The first reason is the low density of meteorological stations, in particular in the CRU data  (Burke et al. , 2013) , which leads to lower total precipitation in winter and spring (November to April) in CRUNCEP compared to GSWP3 forcing (Fig. S13 ). Another well-known reason is the difficulty in catching snowfall in a gauge, because of its lower density; the wind prevents the snowfall from being vertical, leading to systematic undercatch  (Yang et al. , 2005). Then, the way the total precipitation is partitioned into rainfall and snowfall in the atmospheric forcing can also change the SWE in winter and spring.
This indicates an overestimation of the effective thermal conductivity of snow. In the snow module of ORCHIDEE-MICT, snow compaction is parameterized such that snow density increases due to the weight of the overlying snow, while the top layer of snow keeps relatively low density due to fresh snowfall; thermal conductivity of snow then evolves with time and depth, calculated as a nonlinear function of snow density  (Wang et al. , 2013). Recent field measurements for Arctic snow, however, show that the formation of a thin layer of soft depth hoar at the base of the snowpack, with its low thermal conductivity around 0. 025 W m -1 K -1 which is 10 times smaller than the intermediate snow layers, significantly re-shapes the vertical profile of snow thermal conductivity  (Domine et al. , 2016).
Ability of ORCHIDEE-MICT to simulate northern land carbon fluxes and pools Figure  23  shows the land carbon fluxes and pools modeled and derived from observations (datasets used in the text) in the model domain > 30 • N. Modeled estimates encompass the range of observations, except for the deep SOC pool, where the model underestimates SOC. The same figures for each northern region are given in Fig. S10. At the regional level, model-observed agreement still holds. Arguably, the main variables that should be well simulated for a high-latitude land ecosystem model regarding future carbon-cycle climate feedbacks  (Koven et al. , 2013; Burke et al. , 2017a)  are the carbon stocks in biomass, litter and soils. In this respect, ORCHIDEE-MICT performs generally well for biomass with GSWP3 forcing, including the latitudinal profile, showing a peak between 50 and 60 • N, despite the simple constant background mortality used for forest  (Krinner et al.
, 2016). Interestingly, ORCHIDEE-MICT can reproduce the latitudinal gradient of biomass stocks well, even in warmer regions where NPP is lower than observed  (compare Figs. 20 and 15b). This suggests a possible error compensation with too small mortality and NPP in the southern boreal forests. Lack of representation of forest management, and under-representation of other natural disturbances (insect, wind, etc. ), could collectively contribute to an overall underestimation of biomass turnover. Carvalhais et al. (2014)  reported an average turnover of 53. 5 years for boreal forests, using extrapolated global soil database and satellitederived biomass and GPP estimations. Their estimation of the turnover time integrates both biomass and soil C. Given a longer soil carbon turnover than biomass, the biomass turnover of boreal forests should be smaller than 53.
Simulated phenology shows generally a delay in the onset of the growing season. And the snow module underestimates the thermal insulation of snow. Through the use of two different climate forcing datasets across all diagnostic variables, we found that for a large number of these, the variation in output arising from the choice of a forcing was as large as the discrepancy between model and observations. This raises a caution flag against "over-calibrating" the parameters of a LSM to match measured quantities in the presence of large biases in climate input datasets. One critical aspect of forcing data in this respect is the partitioning of precipitation between snowfall and rainfall, which appears to produce a large difference in modeled snow mass and depth. Future improvements of the ORCHIDEE-MICT model include the need for a better floodplain parameterization  (Lauerwald et al.
, 2017)  and a better representation of floodplain areas and wetland effects on river discharge. Including an explicit senescence inception model in ORCHIDEE-MICT may contribute to correct the excessive persistence of LAI during the fall, or even winter in the Yukon and Mackenzie basins. The subgrid-scale representation of permafrost hydrology in discontinuous permafrost areas may be necessary, as might a more realistic description of the slow processes that accumulate carbon in the soil (peat, yedoma, erosion) on very long timescales. Code and data availability. The source code for ORCHIDEE-MICT version 8. 4. 1 is available online, but its access is restricted. Consequently, one is required to communicate with the corresponding author for a username and password. The source code can be found at the following address: https://forge.
ipsl. jussieu. fr/orchidee/browser/branches/ ORCHIDEE-MICT/tags/ORCHIDEE_MICT_8. 4. 1. Primary data and scripts used in the analysis and other supplementary information that may be useful in reproducing the author's work can be obtained by contacting the corresponding author. This software is governed by the CeCILL license under French law and abiding by the rules of distribution of free software. You can use, modify and/or redistribute the software under the terms of the CeCILL license as circulated by CEA, CNRS and INRIA at the following URL: http://www. cecill. info. The change in 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-latitude basins resulted primarily from snow accumulation in the cold season, despite a wintertime underestimation of TWS values. Decreasing soil moisture contributed to a small decrease in springtime TWS, while exports with river flow increased dramatically during the snowmelt period. The GRACE (Gravity Recovery And Climate Experiment) satellite mission permits estimation of monthly TWS variations through measurements of the Earth's gravitational field. Three solutions, at 1 • resolution, based on spherical harmonic coefficients (Release 05), are obtained by different processing centers, CSR (Center for Space Research), JPL (Jet Propulsion Laboratory) and GFZ (GeoForschungsZentrum Potsdam), and provided in the GRACE Tellus 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 datasets and GRACE data  (July 2003 -December 2007) , we summed the water stocks simulated by ORCHIDEE-MICT, i. e. , soil moisture, snowpack, water on the canopy and water stored for the routing reservoirs. In each grid cell, the corresponding 5-year average is removed from the 2003-2007 time series of TWS output from ORCHIDEE-MICT. The comparison of simulated TWS with GRACE data over seven basins in this study (Fig. 2b ) is statistically satisfactory given large surface areas occupied by these basins (> 400 000 km 2 (see Table  S4 ),  Swenson et al. , 2003).
A2 Snow water mass The GlobSnow v2. 0 SWE dataset is based on a dataassimilation 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 timeseries 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.
A4 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 three 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).
A5 Topsoil 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 and 2 and ASCAT) using the algorithm developed by the TU-Wien  (Wagner et al.
A6 Root-zone soil moisture The GLEAM root-zone soil moisture v3. 0a database  (Martens et al. , 2017)  is based on both satellite observations and reanalysis data. It is a mixture of the soil water content of three soil tiles: bare soil (0-10 cm), and herbaceous (0-100 cm) and tall vegetation (0-250 cm). The tile fractions are static and derived from the MODerate resolution Imaging Spectroradiometer (MODIS) Global Vegetation Continuous Fields product (MOD44B,  Hansen et al. , 2005). Obviously, it is hard to compare the ORCHIDEE soil moisture with the GLEAM root-zone soil moisture directly, not only due to the mismatch of defined soil tile depth, but also due to the difference of soil tile fractions. Thus we implement the comparison after normalization of the relative soil moisture values with their respective SD, following  Koster et al.
B3 In situ air and soil temperatures Simultaneous measurements of snow depth, near-surface air temperature and topsoil (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 ) (W. Wang et al. , 2016). This dataset includes monthly mean values at 268 sites for the period 1980-2000.
B4 Active-layer thickness The Circumpolar Active Layer Monitoring (CALM) network's in situ dataset 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 timescales. 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.