dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2015; Rogelj et al. , 2015; Mengis et al. , 2018; Tokarska et al. , 2018). The requirement for specific information regarding ZEC to assess remaining carbon budgets was identified in the IPCC Special Report on Global Warming of 1. 5 • C  (Rogelj et al. , 2018). An initial paper exploring ZEC in this context, explicitly on timescales of relevance to 21st century carbon budgets, is planned on a timeline that could support an improved assessment of the ZEC and its influence on carbon budgets in the IPCC Sixth Assessment Report. All participating model groups who are able to complete and provide data for simulation A1 in time will be invited to join this analysis. ZECMIP welcomes community engagement in the participation of simulations and their analysis, as well as input to future analysis and experimental design.
We hope that ZECMIP analysis will address the crucial knowledge gap surrounding committed warming following ceasing emissions and will provide valuable support for assessment of carbon budgets to achieve climate targets. Data availability. As with all CMIP6-endorsed MIPs, the model output from the ZECMIP simulations described in this paper will be distributed through the Earth System Grid Federation (ESGF) with version control and digital object identifiers (DOIs) assigned. No additional model forcings are required beyond those already used for piControl and 1pctCO2 simulations apart from the emission inputs for the proposed B experiments, which are described in Appendix A of this paper and are hosted on the C4MIP and CDRMIP websites. Appendix A: CO 2 emissions for bell-shaped curve simulations B1-3 This table lists the global CO 2 emissions (PgC yr -1 ) to be applied for the first 100 years of simulations B1-3.
This period should be followed by at least 100 years of zero emissions for ESMs or 1000 years for EMICs (see Fig. 2 ). These emissions should be prescribed as globally uniform at the surface. The data were calculated from a Gaussian curve according to E = k 1 √ 2π σ 2 e -(x-µ) 2 2σ 2 , where emissions, E, are scaled by a constant, k, so that the cumulative total matches the required amount for each scenario (1000 PgC for B1, 750 PgC for B2, 2000 PgC for B3). The parameters were set as µ = 50 as the centre of the 100year period and σ = 100/6 so that the distribution spans 3 standard deviations about the centre. These data in. csv file format are available from the C4MIP (http://www. c4mip. net/index. php?id=3387, last access: 6 September 2019) and CDRMIP (https://www. kiel-earth-institute. de/CDR_Model_ Intercomparison_Project.
Acknowledgements. This protocol was devised at a Global Carbon Project workshop supported by  H2020   EU  project  CRESCENDO  under grant agreement no. 641816. Chris D. Jones  was supported by the  Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme  ( GA01101 ). Joeri Rogelj ,  Katarzyna Tokarska  and  Roland Séférian  were supported by  H2020   EU  project  CONSTRAIN  under grant agreement no. 820829. Tatiana Ilyina  and  Thomas L. Frölicher  were supported by  H2020   EU  project  CCICC  under grant agreement no. 821003. Thomas L. Frölicher  acknowledges support from the  Swiss National Science Foundation  under grant  PP00P2_170687. GFDL-ESM2M simulations were performed at the  Swiss National Supercomputing Centre (CSCS). Kirsten Zickfeld  and  Andrew H. MacDougall  acknowledge support from the  National Sciences and Engineering Research Council of Canada's Discovery Grants Program.
Charles Koven  acknowledges support from the  US DOE BER  Regional & Global Model Analysis programme through the  Early Career Research Program  and the  RU-BISCO SFA  projects. Katarzyna B. Tokarska  was also supported by the  UK NERC -funded  SMURPHs  project ( NE/N006143/1 ). Financial support. This research has been supported by the  European Commission  (grant no. CRESCENDO (641816) ). Review statement. This paper was edited by  Carlos Sierra  and reviewed by two anonymous referees.
Rainfall and Soil Parameter Measurements Two rain gauges with a tipping bucket (model 3029, 0. 5 mm/tip from Précis Mécanique, Bezons, France) were installed on site, enabling continuous rainfall measurements. On each gas sampling date, composite samples of the 0-15 cm soil layer were collected from each plot for soil analyses. Gravimetric water content (GWC) (24 h at 105 • C) and mineral nitrogen extractions were undertaken at the laboratory (LAMA, IRD) in Cayenne (French Guiana). The extractions of mineral nitrogen were carried out in 50 mL of 1 M KCl solution for 10 g of soil. The Griess-Ilosvay and the Koroleff with indophenol blue methods were used to measure nitrate (NO 3 -) and ammonium (NH 4 + ) contents, respectively  [44]. The temperature was measured in the top 10 cm of the soil of each plot using a Campbell Scientific 108 probe (30-min averages were registered in a data logger (Campbell Scientific CR1000)).
Statistical Analyses Statistical analyses were carried out with the software R version 3. 4. 3. To test the effect of agricultural systems on soil GHG fluxes, we used linear mixed-effects models using agricultural systems as the fixed effects and block and sampling dates as the random effects (package lmerTest,  [45] ). Then, a Tukey post-hoc test was used to compare the agricultural systems. To identify the soil parameters that best explain the variation in GHG fluxes, we used generalized additive models (GAMs) (package mgcv  [46] ), which allow nonlinear and nonmonotonic relationships. We selected the best model, i. e. , the one that explained most of the variation, using the Akaike information criterion. Analyses were carried out on mean GHG data per plot, i. e. , four replications per agricultural system on each sampling date.
Soil Parameters and GHG Fluxes Soil parameters and GHG fluxes are presented as a time-series in Figure  4. Means and ranges for each parameter and GHG were calculated for the dry seasons and wet seasons. All data are shown in Table  3. Data are unavailable for the periods between August and November 2012 and from August to October 2013 as a result of technical issues. Thus, the fifty GHG sampling campaigns were grouped as follows: 19 dry season dates (i. e. , monthly rainfall ≤ 150 mm) and 31 wet season dates. In the forest plot, additional technical problems prevented us from obtaining temperature data from 21 November 2011 to 29 March 2012 and from 22 January to 07 May 2013. Table  3. Soil parameters and GHG fluxes measured in the three agricultural systems and in the adjacent native forest at the Combi experimental site.
Acknowledgments: Our special thanks go to  Max Sarrazin  ( LAMA, IRD ) for the soil analytical work and to  Céline Decuq ,  Vanessa Lecuyer  ( UMR EcoSys, INRA, AgroParisTech, Université Paris-Saclay ) and  Nicolas Smialkowsky  ( Terres Inovia ) for GHG analyses. We are also grateful to  Pierre-Richard Augustin  ( Terres Inovia ) and  Jean-Yves Goret  ( UMR EcoFoG ) for their helpful assistance on the field. Our thanks go to three anonymous reviewers for their useful comments.
Funding: This study was partly funded by the  Fonds Européens de Développement Régional (FEDER) , the  French Guiana Region, Terres Inovia , the  Centre National d'Etudes Spatiales (CNES)  and the  CASDAR IP n • 9049 (NO GAS  -N 2 O: mesure et modélisation en GrAndes cultureS).
Acknowledgements This research was supported by grants by the  Conseil Régional de Bourgogne  and the  European Commission (Career Integration  Grant  FP7-MC-CIG #618010 ) to RB, and the  France-Berkeley Fund  (FBF # 2014-0016 ) to  Mary Firestone  and RB. IE was supported in part by an  INRA   Ph. D. fellowship  ( Département Environnement et Agronomie ). We thank  Catherine Osborne  for partnership in the experimental setup,  Rina Estera  for the soil extractions,  Amy Welty  for technical help with SIP,  Jérôme Fromentin  for the ultracentrifuge,  David Bru  for qPCRs and help with amplicon preparation,  Arnaud Mounier  for help with processing the sequenceing data,  Mary Firestone  for constructive comments on the manuscript.
Modeling leaf appearance rate Our newly developed LAR model (see Results) was implemented in the wheat phenology model described by  He et al. (2012). This new phenology model was developed as an independent executable component in the BioMA software framework (http://www. biomamodelling. org). The BioMA component was integrated in the wheat model SiriusQuality  (Martre et al. , 2006; Martre and Dambreville, 2018). The source code and the documentation of the BioMA component of the LAR model  (Manceau and Martre, 2018)  and the source code binaries of SiriusQuality and associated BioMA components (https://github. com/SiriusQuality/Release) are available under the MIT (X11) free and open-source software license. Our model of LAR (hereafter referred to as model M3) was compared with two other models.
Solar radiation was measured in the field in 2013 and 2014 and correlated very closely with solar radiation measured at an automated weather station located at 65 km from the experimental site, so daily solar radiation data from the latter station were used for subsequent years. Following emergence, five plants were marked per plot and the number of ligules that appeared were recorded at 7-14 d intervals until flag-leaf ligule appearance. Plants produced 13-18 main-stem leaves and had a protracted tillering phase, so markers were moved up the stem to a recorded position following each measurement to keep an accurate record of the number of leaves that appeared.
Estimation of LAR model parameters LAR for models M1 and M2, and LAR min for model M3 were estimated using the January 2009 sowing for the HSC experiment (Supplementary Fig. S1 ) and the second sowing date in 2014 and 2015 for the NZ200 experiment (Supplementary Fig. S2 ). In model M3, LAR min and PTQ hf were estimated using Eqn 3 (below) and the data we obtained from our experiments (see Results). α was estimated using the January 2009 sowing of the HSC experiment and the same value was used for both field experiments (Supplementary Table  S2 ). The values of all parameters for the three models are given in Supplementary Table  S2. Parameter values were estimated by minimizing the relative root-mean-squared relative error (RMSRE; see Supplementary Methods) for Haun stage >1. 0 using a covariance matrix adaptation-evolutionary strategy  (Hansen and Ostermeier, 2001)  implemented in the SiriusQuality software.
Data analysis and statistics All data analyses and graphs were performed using the R statistical software version 3. 4. 1 (www. r-project. org). Differences in LAR i between treatments and genotypes were determined using ANOVA. Genetic differences in the intercept and slope of the linear relationship between LAR i and PTQ were analysed by reduced major-axis regression with the R package smatr3  (Warton et al. , 2012). All statistical differences were judged at P < 0. 05. Depending on the range of PTQ, data for LAR i versus PTQ were fitted using either a linear equation or a three-parameter asymptotic equation given as: LAR i = LAR min + (LAR max -LAR min ) × PTQ PTQ hf + PTQ (3) where, LAR min (leaves °Cd -1 ) is the minimum LAR when PTQ equals zero, LAR max (leaves °Cd -1 ) is the maximum LAR when PTQ tends to infinite, and PTQ hf (mol PAR m -2 °Cd -1 ) is the PTQ at which LAR is half LAR max + LAR min.
018; Fig. 3A ). A similar correlation was found between LAR i and either daily net photosynthesis (r=0. 982, P=0. 0179; Fig. 3B ) or carbohydrate consumption during the night (r=0. 985, P=0. 0147; Fig. 3C ), supporting the hypothesis that LAR is at least partly limited by carbon availability in the plant. The highest PTQ value tested in our experiments was 1. 5 mol m -2 °Cd -1 , while the range of PTQ sensed by plants in field conditions reached up to 4 mol m -2 °Cd -1 in our database of wheat field trials. The relationship between LAR and PTQ was therefore further tested on a larger range of PTQ values using data from the literature where the response of LAR to either temperature  (Cao and Moss, 1989b, 1989c; Bos and Neuteboom, 1998) , photoperiod  (Cao and Moss, 1989a, 1989c) , or irradiance  (Rickman et al.
Acknowledgements We thank  Mr Stephane Berthézène  for his technical help and  Drs Jacques Le Gouis  ( INRA, GDEC, Clermont-Ferrand, France ) and  Matthew Reynolds  ( CIMMYT, Mexico, D. F. , Mexico ) for providing seeds of several cultivars, and  Ms Séverine Rougeol  for developing the Apache and Récital spring isogenic lines. This work was funded by the  French National Research Agency (ANR)  through the  ERANET + project MODCARBOSTRESS  within the  Joint Programming Initiative on Agriculture ,  Food Security  and  Climate Change  ( FACCE-JPI ; grant agreement no. 618105 ).
The access to digital information from remote sensing; geological mapping; and public databases give an opportunity to express the surface of the bedrock as a mathematical estimation problem. We modelled the bedrock topography as a stochastic function in space. The function is given with high precision in areas where the bedrock is exposed to the surface, but unknown in areas covered by sediments except for a limited number of point information (viz boreholes; wells; geotechnical surveys). Two different approaches were evaluated to reveal the local trend of the bedrock surface: Firstly, we applied the statistical relation between the horizontal distance (L) to the nearest bedrock outcrop and the observed sediment depth (D) in boreholes. The relation between D and L was applied in ordinary kriging and cokriging to include the local trend in the estimation.
Secondly, we applied inverse modelling of the Poisson's equation to model the local trend. After minimizing the difference between the point observations and the parabolic surface from the Poisson's equation, we did ordinary kriging of the residuals between the optimal parabolic function and the observations. These approaches were tested against observations from a test site. Estimates derived from the Poisson's equation gave a lowest mean absolute error for cross-validation by leaving one observation out. Ordinary kriging gave a least mean absolute error when an independent dataset was used for cross-validation. The results show that the extreme large soil depths were better reproduced if the local trend was included in the estimation procedure.
The research challenge is to capitalize on the available information to minimize the estimation uncertainties. In the case study presented below, point observations of unconsolidated sediment thickness are taken from the Norwegian well database GRANADA  (NGU, 2017a). The database was established as a result of national legislation  (Lovdata, 1996) , and 1 https://doi. org/10. 5194/esurf-2019-57 Preprint. Discussion started: 24 October 2019 c Author(s) 2019. CC BY 4. 0 License. the Geological Survey of Norway (www. ngu. no) made the data publicly available. Such databases represent a great value to the society, in particular, if the information can be generalized and transferred to locations with no direct measurements. Even though the results of this study are relevant for urban planning, the motivation for the study was a basic question in hydrology: What is the relation between the shallow groundwater in the sediments and the bedrock? Due to an increasing population and global warming, society needs more precise guidelines on problems related to sustainable management of water resources.
In such cases a major fraction of the subsurface flow took place at the interface between the sediments and the less permeable bedrock. Hopp and McDonnell (2009)  used a synthetic dataset to show the importance of surface topography, soil depth and bedrock topography to simulate rainfall runoff response. They concluded that the subsurface connectivity of saturated areas explained the runoff characteristics. Gabrielli et al. (2012)  studied the relation between runoff and groundwater in sediments and bedrock in hillslope catchments in New Zealand and Oregon, US. In both hillslopes, the bedrock influenced the stream flow, but rapid subsurface stormflow was apparently most important if the bedrock had low permeability compared to the soil. Based on a large dataset  Tromp-van Meerveld and McDonnell (2006a)  showed that pipe flow, matrix flow and total flow had a threshold response to precipitation.
The cdf of B(u) or D(u) in location u is denoted F (u). The uncertainties were quantified in terms of percentiles ξ p , where p ∈ [0, 1]. In the case study presented below, we let p = [0. 05, 0. 1, 0. 25, 0. 5, 0. 75, 0. 9, 0. 95]. As indicated above, we used borehole information from mainland Norway (Ω) stored in the GRANADA database  (NGU, 2017a)  as empirical input to the modelling. The global data was used to estimate covariance-and cross-covariance functions. Local boreholes were used for conditional estimation and inverse modelling of the constant parameter in the Poisson's equation. The modelling procedures were evaluated with data from a local study area Øvre Eiker located 60 km west of Oslo (Fig. 2 ). In a previous study  Kitterød (2017)  discussed the challenges of utilizing the GRANADA database for estimation of sediment thickness.
The main problems were related to small-scale variance; preferential sampling; and bias in the empirical data material. The small-scale variance was about half of the total variance. This explains why less than 50% accuracy can be expected. Preferential sampling refers to the clustering of data in some areas, while other areas have a sparse density of measurements. The GRANADA database had a high density of recordings in urban areas, but few observations in sparsely populated regions. To derive statistical moments and variograms from such data, it is necessary to perform some sort of declustering, which means that areas with high sampling density receive less weight than areas with sparse sampling  (Omre, 1984). For the present study, the semivariograms-and cross-semivariogram functions were based on declustered data calculated according to the grid algorithm suggested by  Deutsch and Journel (1998).
To test the two procedures, two different types of cross-validation was performed: First, cross-validation was undertaken on boreholes sampled before 2010. This was done by leaving one observation out. The second cross-validation was done on boreholes and sedimentary wells recorded after 2010. Because the second cross-validation was done on independent data not included in the parameter inference, this procedure was called jackknife cross-validation. The results were evaluated according to: The mean absolute error M AE ; accuracy A C , and precision P C  (Goovaerts et al. , 2005). For the kriging methods we also calculated the percentile score for the observations at the locations of the crossvalidation wells and boreholes. Before presenting the results, we describe the empirical data and show how this material can be used to derive the spatial functions we needed for the geostatistical modelling.
Data material As indicated above, we employed three sources of information for the current study: (i) Point observations of sediment thickness, D(u i ); (ii) Digital Quaternary maps, DQM; and (iii) Digital elevation model of the surface, DEM. In addition, we also had access to geological maps of the bedrock for the study area. Since the modelling target was the bedrock topography, it is pertinent to include a brief description of the most prominent geological structures in the area.
Sediment thickness and horizontal distance to bedrock outcrop Point observations of sediment thickness D(u i ) for mainland Norway was taken from the public well data base GRANADA  (NGU, 2017a). We used DQM to calculate horizontal distance to the nearest bedrock outcrop L(u i ) for GRANADA boreholes  (Kitterød, 2017). This dataset was used to derive global statistical parameters for spatial variance and covariance for D(u i ) and red dots in Fig. 2 , were later used to calculate F (u|A), where A denotes all available information recorded before 2010. These results were compared to the sedimentary wells and boreholes recorded after 2010 as a jackknife cross-validation. L(u i ), i = 1,. , In addition to point observations of D(u i ), we used R(u) to calculate L(u) in a regular grid with spatial resolution of ∆x × ∆y = 25 m × 25 m.
Geostatistical modelling The methods we applied for this study were based on multi-Gaussian statistics. Gaussian theory is documented in textbooks  (Isaaks and Srivastava, 1989; Journel and Huijbregts, 1989; Deutsch and Journel, 1998) , and will not be reproduced here, except for details that are relevant for the present study. Before the modelling took place, the GRANADA data required pre-processing. This was necessary because the GRANADA wells were clustered and the probability density functions (pdfs) of the variables were not Gaussian distributed.
Declustering Boreholes and wells are usually located in populated areas, which means that many of the GRANADA recordings are from urban areas, and few recordings from rural areas. When such data is used to calculate average properties, uneven sampling will affect the statistics because oversampled domains will dominate. To suppress such cluster effects, each observation received a weight calculated as a function of the distance to other boreholes. Boreholes located close to each other was given less weight than boreholes located far from each other. Calculation of declustering weights were done according to the moving grid algorithm suggested by  Deutsch (1989) , and results presented in  Kitterød (2017). It should be noted that declustering weights do not change the value of the observations that we used for conditional modelling.
Semivariograms and cross-semivariogram The normal score semivariograms [γ D , γ L ] and cross-semivariogram [γ DL ] functions used in the current case study were taken from  Kitterød (2017) , and are therefore not reproduced here. Logarithmic semivariograms (Z(u ) = ln[D(u)] and κ(u i ) = ln[D(u i )/L(u i )] ) were used for the bedrock kriging (BK) method (c. f. section 3. 5 below). All variograms used for the present study were calculated with observations from the global GRANADA database. The covariance functions C(h), were used to solve the kriging equations, which means that the semivariograms were subtracted from the total variance (the nugget C 0 and the sill C 1 ): C(h) = C 0 + C 1 -γ(h), ( 4 ) where h is the separation distance and γ(h) is the semivariogram function: γ(h) = C 0 + C 1 (1 -exp(-β(h/a) α ), (5) where a is the correlation length (or range in geostatistical terms), the practical range β denotes the variance at the chosen distance h = a.
(  6 ) and (  8 ). In practice the lognormal percentiles were achieved by using the MATLAB command logninv (MATLAB, 2017). For kriging on residuals P K, we used the Normal pdf to find the percentiles. That is, we assumed that X(u) ∈ N (µ, σ 2 K ), where µ is the ordinary kriging estimate and σ 2 K is the corresponding estimation variance.
Semivariograms The global GRANADA data were used to calculate experimental semivariograms for ordinary kriging (OK), cokriging (CK) and bedrock kriging (BK). For kriging on residuals (P K) we used GRANADA boreholes located within the study area. For OK and CK the semivariograms were derived from normal score transformed data (c. f. Tab. 3 and 4. , case F in Kitterød (2017)), while semivariograms from logarithmic values are given in (Fig. 4  and Tab. 1).