dataesr/scientific-paragraphs-categorization huggingface dataset:
, 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. 15 ). Values lower than MTE-GPP were simulated in Eastern Siberia under the GSWP3 forcing, mainly due to water stress (see also underestimated biomass for eastern Siberia in Figs. 21). The seasonal GPP cycle in Fig. 15  (lower panel) 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).
We plot the 95th percentiles of these distributions in Fig. 17 , 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 of GPP (NPP) at warmer sites but underestimate 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.
For the rest of the study region, simulated carbon density is slightly lower than observations, by around 5-25 MgC ha -1. The two observation data sets show considerable similarities. Both model output and observation data are subject to the spatial uncertainties introduced by the use of satellite-derived land cover maps. When latitudinal distributions of total forest biomass are examined by using forest area distribution as prescribed in the model input for all data sets, strong agreement is found between observed data and GSWP3-forced model output (Fig. S5a ) for the whole study region. CRUNCEP-forced model output shows much higher biomass at all latitudes. For BOEU, the model estimated higher biomass observations for regions above 55°N (Fig. S5c ), whereas for BOAS, model output fell between the observation datasets at latitudes of >50°N (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. 10. 1 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 in 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. S11 ). Another well known reason is the difficulty to catch 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. The threshold of 0°C in 2-m temperature, used to partition the precipitation in the CRUNCEP forcing, can lead to very different results compared to the physical partitioning performed within the dynamical downscaling for the GSWP3 forcing.
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   (2016). Blue and red lines indicate the extent of continuous permafrost and all permafrost categories respectively, according to the IPA permafrost map  (Brown et al. , 2002). (b) The seven high-latitude basins selected for this study with the gauge stations (red circles on the map, more information in Table  S4 ). The grey shaded areas corresponds to the standard deviation of the monthly values for each inversion. Note that a negative sign in NEE corresponds to CO 2 uptake from the atmosphere, and a positive sign to release to the atmosphere. Vertical profiles (kgC m -3 ) averaged over the three high-latitude sub-regions (shown in Fig. 2a ).
Evaluation datasets for air-to-soil temperature continuum 5. 3. 1 Snow depth Realistically simulating snow depth is one of the prerequisites for accurately modeling soil thermal dynamics, particularly in winter. Daily snow depth in-situ data (1975-2005) from 524, 72, 528, 128 and 528 stations in Europe, Canada, USA, Russia and China were obtained from European Climate Assessment & Dataset (ECA&D), the National Climate Data and Information Archive of Environment Canada, the United States Historical Climatology Network (USHCN), the Russian Research Institute.
1979-2015 and includes all sites being added to the network though time, combined with the LMDZ INCA atmospheric transport model and prior information about fossil-fuel CO 2 emissions (from EDGAR3. 2 FastTrack 2000 database) as well as 16 Geosci. Model Dev. Discuss. , https://doi. org/10. 5194/gmd-2017-122 Manuscript under review for journal Geosci. Model Dev. Discussion started: 16 June 2017 c Author(s) 2017. CC BY 3. 0 License. land/ocean-atmosphere fluxes, to minimize a cost function in order to calculate NEE. Here we use the latest version (15r4), in which monthly fluxes are calculated at 1. 875°x 3. 75°lat/lon resolution. The Jena CarboScope atmospheric inversion uses the same set of atmospheric CO 2 observation sites available during the temporal observation period (1996-2015) referred to as Jena s96.
Turner et al. (2006)  evaluated MODIS NPP at nine sites representing various biomes, using local meteorological, LAI and above ground NPP measurements as inputs for the BIOME-BGC model to derive NPP over a 25 km² area. They showed globally no bias but NPP was underestimated at the most productive sites (attributed to a negative bias of E) and overestimated at low productivity sites (attributed to a positive bias of MODIS fAPAR). Zhao et al. (2005)  evaluated MODIS NPP against the Ecosystem Model-Data Intercomparison (EMDI) global database of NPP measurements (Olson et al. , 2001)  and found a good agreement except for tropical forests (spatial R 2 = 0. 77). Zhao et al. (2006)  estimated the sensitivity of the MODIS NPP product to the use of different meteorological reanalysis input data, showing differences at global scale up to more than 20 PgC yr -1 , with the largest differences in the tropics.
Total terrestrial water storage changeA realistic phase and amplitude of TWS is 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. 9). 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 well captured 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.
21 and 16b). 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 satellite-derived 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. 5 years. While ORCHIDEE-derived turnover is between 54 (adding together fire and background mortality) and 80 years (assuming only a 1.
Avitabile et al. (2016)sets are used to evaluate simulated forest biomass. The first is that fromAvitabile et al. (2016), which, at a spatial resolution of 0. 01°, merges two tropical aboveground forest biomass (AGB) datasets fromSaatchi et al. (2011)andBaccini et al. (2012)with northern hemisphere volumetric forest stock growth data fromSantoro et al. (2015). The second forest biomass dataset from Thurner et al. (2014) is given at a spatial resolution of 0. 01°for 2010 and confined to the northern hemisphere (30°N-80°N). This is derived from theSantoro et al. (2011)forest standing stock data. The two datasets were regridded to 1°resolution for comparison with model output. TheAvitabile et al. (2016)AGB was converted into total biomass, assuming a constant AGB to total biomass ratio of 0. 8 and divided by two to obtain total biomass carbon, under the assumption that the carbon content of dry biomass is 50%.
Acknowledgements. This work was financially supported by the  European Research Council Synergy  grant  ERC-2013-SyG-610028 IMBALANCE-P. We acknowledge the  ORCHIDEE-PROJET group , which developed and tested the ORCHIDEE model revision 3976. We thank  Natasha MacBean  who contributed to the production of the land cover map. We thank  Arsène Druel  who processed the GLASS LAI data. Simulations with ORCHIDEE-MICT were performed using computational facilities of the  Institut du Développement et des Ressources en Informatique Scientifique (IDRIS, CNRS, France)  and of the  CCRT (CEA). S. Dantec-Nédélec ,  G. Krinner ,  P. Ciais  and  C. Ottlé  were supported by the  CLASSIQUE national project (ANR Grant  No. ANR 2010-CEPL-012-02 ) and the  PAGE 21  project (grant agreement number  282700 , funded by the  EC seventh Framework Programme theme   FP7-ENV-2011 ).
A. Ducharne was supported by the  IGEM  project ' Impact of Groundwater in Earth system Models' (ANR Grant  No. ANR-14-CE01-0018-01 ). Discharge station data is kindly provided by the  Global Runoff Data Centre, 56068 Koblenz, Germany. GRACE land are available at http://grace. jpl. nasa. gov,supported by the  NASA   MEaSUREs Program. The MOD17A3. 005 data product was retrieved from the online Data Pool, courtesy of the  NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC) ,  USGS/Earth Resources Observation and Science (EROS) Center ,  Sioux Falls ,  South Dakota  (https://lpdaac. usgs. gov/data_access/data_pool). Jena CarboScope inversion data were provided by  Christian Rödenbeck  and are available at http://www. bgc-jena. mpg. de/CarboScope/?ID=s96_v3. 8.
5. 3 3 Evaluation datasets for air-to-soil temperature continuum 5. 3. 1 Snow depth Realistically simulating snow depth is one of the prerequisites for accurately modeling soil thermal dynamics, particularly in winter. Daily snow depth in-situ data (1975-2005) from 524, 72, 528, 128 and 528 stations in Europe, Canada, USA, Russia and China were obtained from European Climate Assessment & Dataset (ECA&D), the National Climate Data and Information Archive of Environment Canada, the United States Historical Climatology Network (USHCN), the Russian Research Institute.
) Turner et al. (2006)  evaluated MODIS NPP at nine sites representing various biomes, using local meteorological, LAI and above ground NPP measurements as inputs for the BIOME-BGC model to derive NPP over a 25 km² area. They showed globally no bias but NPP was underestimated at the most productive sites (attributed to a negative bias of E) and overestimated at low productivity sites (attributed to a positive bias of MODIS fAPAR). Zhao et al. (2005)  evaluated MODIS NPP against the Ecosystem Model-Data Intercomparison (EMDI) global database of NPP measurements (Olson et al. , 2001)  and found a good agreement except for tropical forests (spatial R 2 = 0. 77). Zhao et al. (2006)  estimated the sensitivity of the MODIS NPP product to the use of different meteorological reanalysis input data, showing differences at global scale up to more than 20 PgC yr -1 , with the largest differences in the tropics.
7. 1 1 Total terrestrial water storage changeA realistic phase and amplitude of TWS is 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. 9). 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 well captured 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.
We evaluate the performance of H minority heating in the presence of D beam ions using the ICRF modelling code PION  [4]  coupled to the beam deposition code PENCIL  [5]. Our modelling takes into account the synergy between ICRF and NBI heating through the second harmonic cyclotron resonance of deuterium beam ions, which allows us to assess its impact on the fusion performance. We model a number of key discharges with H minority that were designed in order to evaluate key ICRF aspects as the EPJ Web of Conferences 157, 03015 (2017) avoidance of impurity accumulation with ICRF waves and its dependence on the ICRF resonance position, the ICRF fusion performance enhancement and the impact of H concentration on the ICRF damping mechanisms, i. e, the fundamental H resonance, the 2 nd D hearmonic resonance and the direct electron damping.
We focus, as a benchmark scenario, on the case of a W SSM boson, and perform our study on the basis of the automated resummation technology implemented within the MG5aMC+SCET framework. We compare our veto resummed results to those obtained at NLO+PS accuracy. Our work includes the construction of a new general FeynRules  [44]  model for extra gauge boson studies that can be interfaced with NloCT  [45]  and FeynArts  [46]. The associated Universal FeynRules Output (UFO)  [47]  model is publicly available from the FeynRules model database  [48]  and can be used to simulate hadronic and leptonic collisions up to NLO+PS accuracy. The remainder of this study continues in the following order: In Section 2, we present the theoretical framework for extending the SM field content extra gauge boson, summarize current LHC constraints on new vector bosons, and provide details on our computational setup.
Computational Setup For concreteness, we consider as a benchmark scenario a SSM model with five flavors of massless quarks and a diagonal CKM matrix V CKM with unit entries. We fix the EW inputs as in the 2014 Particle Data Group review  [58] , Our phenomenological study relies on automated NLO predictions matched to NNLL jet veto resummation as computed using the MadGraph5 aMC@NLO-SCET framework  [18, 36]. More precisely, within MG5aMC (v2. 5. 1), one-loop virtual contributions are numerically evaluated by the MadLoop package  [59]  and combined with the real contributions using the Frixione-Kunszt-Signer (FKS) subtraction method  [60]  as implemented in MadFKS  [61]. For a jet veto of p Veto T and a hard process scale Q, logarithms of the form α MS (M Z ) = α k s (p Veto T ) log l (Q/p Veto T ) with l ≤ 2k are resumed up to the NNLL following the procedure detailed in Section 3.
1. To generate the necessary UFO model library  [47] , we design a model file based on the above Lagrangians for the FeynRules program  [44]  (v2. 3. 10) that is jointly used with NloCT  [45]  and FeynArts  [46]  (v3. 8) for the computation of the ultraviolet and R 2 counterterms required for numerical one-loop calculations. Associated UFO files are available publicly from the FeynRules model database  [48]. Hard scattering events are showered and hadronized using the Pythia 8 (PY8) infrastructure  [62]  (v8. 212) and passed to MadAnalysis 5  [63]  (v1. 4) for particle-level clustering using the FastJet library  [64]  (v3. 20) and its implementation of the anti-k T algorithm  [65]. Our calculations rely on PDFs and the evaluation of the strong coupling constant α s (µ r ) extracted using the LHAPDF 6 libraries  [66]  (v6.
We have systematically considered both signal and background processes at NLO+PS accuracy, and included, for color-singlet signal and background channels, the resummation of jet veto logarithms up to the NNLL accuracy with matching to NLO fixed-order results. This has necessitated the development of a new FeynRules model in which we have implemented in a generic fashion new W ′ and Z ′ gauge bosons with model-independent chiral couplings. See Section 2 for more details. Associated model files are public available from the FeynRules model database  [48]. We have investigated the impact of several classes of uncertainties that are attached to jet veto resummation calculations. We have probed the dependence on the choice of jet definition, which suggests larger jet radii (R ∼ 1) lead to smaller uncertainties than smaller radii.
Acknowledgements Thomas Becher ,  Lydia Brenner ,  Fabrizio Caola ,  Mihoko Nojiri ,  Cedric Weiland  are thanked for valuable discussions. The work of RR was funded in part by the  UK Science and Technology Facilities Council (STFC) , the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  690575 , and  Grant-in-Aid for Scientific Research  from the  Ministry of Education, Science, Sports, and Culture (MEXT), Japan  (Nos. 16H06492  and  16H03991  for  M. M. Nojiri ). The authors appreciate the hospitality of IHEP where this work was initiated. RR acknowledges the generous hospitality KEK and the  European Union.
Near Surface Stress Correction Near the surface the gauge volume is only partially penetrating into the material. The surface position with respect to the gauge volume was determined with the known intensity profile. When the gauge volume near a surface is only partially penetrating into the material, the effective position of strain information is geometrically displaced from the centre of the gauge volume. In order to correct these effects a computer program was developed using Mathcad based on intensity-weighed centre of gravity calculations. This enabled the determination of the centre of the diffracting volume inside the gauge volume for each step of the scan, taking into account the size and shape of the gauge volume, the exact position of the sample relative to the neutron beam, and different paths of the neutrons within the sample  [29, 30].
Neutron Diffraction The residual stresses measured in three directions at the three positions SS-WT, SS-HAZ and FS-WT (Figure  4 ) as a function of distance from the plate surface are presented in Figures  6 7 8 , respectively. The residual stress profile from surface to 0. 5 mm below the top surface is magnified for each figure and presented in Figures  6b, 7b  and 8b. Materials 2017, 10, 593 6 of 14 of strain information is geometrically displaced from the centre of the gauge volume. In order to correct these effects a computer program was developed using Mathcad based on intensity-weighed centre of gravity calculations. This enabled the determination of the centre of the diffracting volume inside the gauge volume for each step of the scan, taking into account the size and shape of the gauge volume, the exact position of the sample relative to the neutron beam, and different paths of the neutrons within the sample  [29, 30].
X-ray Diffraction Near surface residual stress results obtained 2 mm away from the weld toe (SS-HAZ) using X-ray and neutron diffraction are summarized in Figure  10. As can be seen from the figure, different residual stresses were measured using the two methods. The different results obtained by X-ray and neutron diffraction methods can be related to the fact that the two techniques did not extract data from the same depth. Since no data are available at depths between the X-ray and neutron results only assumptions can be made about the residual stress distribution within 250 µm below the surface. found in the first welded side (Figures  6  and 8 , Table  7 ). X-ray results (Figure  9 ) also present lower tensile residual stresses in the second welded side 0. 5 mm from the weld toe. This is most probably related to the tempering effect on the first welded side when welding the second side.
X-Ray Diffraction Near surface residual stress results obtained 2 mm away from the weld toe (SS-HAZ) using X-ray and neutron diffraction are summarized in Figure  10. As can be seen from the figure, different residual stresses were measured using the two methods. The different results obtained by X-ray and neutron diffraction methods can be related to the fact that the two techniques did not extract data from the same depth. Since no data are available at depths between the X-ray and neutron results only assumptions can be made about the residual stress distribution within 250 µm below the surface. However, although speculative, the stress trend in position SS-WT (see Figure  6b ) suggests that the stress gradient in position SS-HAZ would most probably result in a stress minimum 120 µm or closer below the surface.
Finally, in all the following numerical results, graphs and contour plots of this section, the derived formulas are evaluated in MATLAB computing environment with use of standard built-in functions and in-house developed source code. In figure  6  0, it is always the first wave that can possess infinite wavenumber as indicated by  (3. 4) ; this happens at different q q = 0 for various e ^according to (3. 3). We notice that the two wavenumbers have a highly dissimilar θ-variation especially in the vicinity of q q = 0 as totally anticipated. However, when q goes above a threshold, the real parts of the two wavenumbers are identical which means that nonzero imaginary parts are appeared; such an effect is indicated by  (3. 5). Indeed in figure 6(b), where the (common) absolute value of imaginary part of the two k z is depicted as function of θ, one observes that it has non-trivial variation for the same interval of θ that the couples of curves in figure 5 coincide each other.
In order to consider metamaterial effects on the NLSE breather type of input, the Crank-Nicolson method has been implemented in MATLAB to numerically solve the propagation equation. The developed code is capable of solving the extended NLSE as laid out in  [120]  which takes into account the appropriate effects of self-steepening and magnetooptic parameters. It requires an initial starting condition which then determines how this input condition will evolve as propagation occurs along z. As discussed in section 6. 1 there is a very real choice of the kind of starting conditions that can be selected. Here in order to initiate a specific type of rogue wave condition we initially base our input on the solution to the standard NLSE determined by Peregrine which for the standard NLSE gives rise to a solitary pulse on an almost flat background.