dataesr/scientific-paragraphs-categorization huggingface dataset:
Forty µL of N-methyl-N-(trimethyl-silyl) trifluoroacetamide was added, and the mixture was vortexed and incubated at 37 • C under shaking for 30 min. Before loading into the gas chromatography autosampler, a mix of a series of fourteen alkanes (chain lengths C10-C36) was included. Analyses were performed by injecting 1 µL in splitless or split mode at 280 • C injector temperature. Separation was performed in helium as a carrier gas at 1 mL min -1 in constant flow mode and using a temperature ramp from 70 • C to 320 • C between 4 and 22 min, followed by 5 min at 320 • C. Ionization was achieved by electron impact at 70 eV, and the mass spectrum acquisition rate was 20 spectra s -1 over the 70-500 m/z range. Peak identity was established by comparing the fragmentation pattern with the mass spectra from our homemade metabolite spectrum database and from another available database (National Institute of Standards and Technology), and based on the retention index using the alkane series as retention standards.
e. the quantity to be measured, lies  (JCGM, 2008). Any measurement consists of a series of transformations from the event observed to the final value, and each transformation may introduce and propagate errors  (Merchant et al. , 2017). For instance, sources of uncertainty in satellite LAI products include uncertainties in the input data (e. g. surface reflectance, radiance, albedo, land cover type), the radiative transfer model, the inversion technique, and the prior information  (Fang et al. , 2012). Unfortunately, observational uncertainty is not reported consistently among reference datasets  (Merchant et al. , 2017). To account for observational uncertainty nevertheless, a pragmatic and common approach is to evaluate model output against multiple reference datasets per variable, which may underestimate uncertainty if reference data are not sufficiently independent and overestimate uncertainty if one reference dataset is strongly inferior compared to others  (Covey et al.
, 2002). The ILAMB framework addresses observational uncertainty by using multiple reference datasets that are weighed depending on their estimated quality and spatiotemporal coverage  (Collier et al. , 2018). However, the ILAMB approach does not indicate what score a model should actually yield given how uncertain reference data are. This makes the interpretation of the ILAMB scores challenging, as it remains unclear to what extent low scores are related to observational uncertainty. The purpose of our study is to evaluate how well TBMs reproduce processes that drive the terrestrial carbon sink term S LAN D. As a novel contribution, we will demonstrate how well models should score given that reference data are imperfect.
e. 1901-1920) , and land cover that uses crops and pasture distri-   (Harris et al. , 2014; Kobayashi et al. , 2015). The LULCC forcing was given by the Land-Use Harmonization 2 (LUH2) dataset  (Hurtt et al. , 2020). For the purpose of our study, all S3 model outputs were spatially interpolated to a common resolution of 1.
In situ reference data In situ reference data include the variables gross primary productivity (GPP), ecosystem respiration (RECO), net ecosystem exchange (NEE), vegetation carbon (CVEG), leaf area index (LAI), latent heat flux (HFLS), and streamflow (   (Schepaschenko et al. , 2019). We merged both datasets and replaced  Xue et al. (2017)  with the more recent  Schepaschenko et al. (2019)  data when a site was present in both datasets. We then converted aboveground biomass to total vegetation carbon using an empirical relation between root biomass y and shoot biomass x (y = 0. 489 × x 0. 890 )  (Mokany et al. , 2006) , as well as a carbon-to-biomass ratio of 0. 5. It must be noted that empirical data on root-shoot ratios are likely to be subject to a sampling bias towards smaller rather than larger trees, as the former are easier to excavate  (Huang et al.
, 2021). Since root-shoot ratios tend to be larger for smaller trees, this sampling bias may result in an overestimation of root-shoot ratios. The conversion from aboveground biomass to total vegetation carbon was necessary as the TRENDY dataset provides only total biomass without separation into below and aboveground components. Measurements located within the same model grid cells were averaged, leading to a total of 592 grid cells with at least one in situ measurement (Figure  Appendix  B1c). LAI observations were taken from the Committee on Earth Observation Satellites (CEOS) which consists of 141 sites with monthly measurements during the 1999-2017 period (Figure Appendix B1b)  (Garrigues et al. , 2008). The values are based on a transfer function that upscales ground LAI measurements to a moderate resolution grid cell using high spatial resolution surface reflectances.
Annual stream flow gauge records were obtained from the Global Runoff Data Centre (GRDC) for the world's 50 largest basins (Figure Appendix B1d)  (Dai & Trenberth, 2002). Measurements were made some time between 1980 and 2010, depending on the basin. -   et al. , 2002). The second reference GPP data, referred to as GOSIF, consists of solar-induced chlorophyll fluorescence (SIF) soundings from the global Orbiting Carbon Observatory-2 (OCO-2). The dataset is based on a linear correlation between SIF soundings and GPP measurements from 91 eddy covariance measurements sites from FLUXNET for the period 2000-2017  (Li & Xiao, 2019). The third GPP reference data, referred to as FluxCom, is based on a variety of machinelearning algorithms that upscale eddy covariance data using remote sensing data and meteorological data as global predictors  (Tramontana et al.
Net biome productivity Globally gridded reference NBP was obtained from the three inversion models Copernicus Atmosphere Monitoring Service (CAMS)  (Chevallier, 2013) , the Jena CarboScope  (Rödenbeck et al. , 2018) , and CarbonTracker 2019 (CT2019)  (Jacobson et al. , 2020). The third inversion-based NBP estimate from CT2019 uses 460 CO 2 measurement sites provided by the GLOBALVIEW+ data product version 5. 0  (Masarie et al. , 2014). The transport model employed by CT2019 is the Transport Model 5 (TM5), which is run for the period 1999-2019  (Huijnen et al. , 2010). The apriori land-atmosphere fluxes are taken from the Carnegie-Ames Stanford Approach (CASA) biogeochemical model  (Potter et al. , 1993). Carbon emissions from fires are prescribed from the Global Fire Emissions Database (GFED) (van der Werf et al.
Vegetation carbon We used three globally gridded and time-invariant vegetation carbon reference datasets. Two of the three datasets originally consisted of aboveground biomass. As for our in situ measurements, we converted aboveground biomass to vegetation carbon using the empirical relation between root biomass and shoot biomass provided by  Mokany et al. (2006). Again, this was necessary as most TRENDY models only reported total rather than aboveground biomass. The first reference dataset, here referred to as GEOCARBON-Mokany, integrates local high-quality biomass data with a boreal forest biomass map by  Santoro et al. (2015)  and a pan-tropical biomass map by  Avitabile et al. (2016) , which is based on data from  Saatchi et al. (2011)  and  Baccini et al. (2012). The dataset covers only areas that are dominated by trees in the Global Land Cover 2000 map  (Bartholome & Belward, 2005).
The boreal biomass estimates are based on radar imagery provided by the Envisat Advanced Synthetic Aperture Radar (ASAR). The pan-tropical biomass maps are based on Light Detection and Ranging (LiDAR) observations that were calibrated with in situ measurements of tree allometry. Baccini et al. (2012)  upscaled data using a random forest machine learning algorithm and satellite imagery, including the MODIS Nadir BRDF-Adjusted Reflectance (NBAR), MODIS land surface temperature, and shuttle radar topography mission (SRTM) digital elevation data. The second vegetation carbon reference dataset, here referred to as Zhang-Mokany, was obtained from Zhang and Liang (2020), who integrated ten existing local and global aboveground biomass maps using a data fusion technique. It must be noted that one of the ten maps is the pan-tropical biomass map by  Avitabile et al.
(2016) , which also forms part of the above-mentioned dataset by  Santoro et al. (2015). Zhang and Liang (2020)  evaluated each of the ten datasets against in situ observations and high-resolution airborne lidar data. The third vegetation carbon dataset was obtained by  Huang et al. (2021) , who upscaled in situ measurements of root biomass using a machine learning algorithm (Ran- manuscript submitted to Global Biogeochemical Cycles dom Forest) and globally gridded predictors of shoot biomass, tree height, soil properties, and climatological data. The shoot biomass presented by  Huang et al. (2021)  was derived from the above ground biomass by  Santoro et al. (2021). Adding root and shoot mass, and converting biomass to carbon mass using a carbon-to-biomass ratio of 0. 5, we obtained a globally gridded dataset for vegetation carbon associated with trees.
Soil organic carbon Reference data for soil organic carbon in the top 100 cm were obtained from the Harmonized World Soil Database (HWSD)  (Wieder, 2014)  and from SoilGrids250m (SG250m)  (Hengl et al. , 2017). The HWSD data provided by the Food Agriculture Organization (FAO) combines existing regional and national updates of soil information worldwide with the information contained by the FAO Soil Map of the World  (Wieder, 2014). The values correspond to the top 100 cm soil depth. The SoilGrids250m (SG250m) dataset provides a globally gridded dataset of soil organic carbon at various depths between the surface and 200 cm belowground. The estimates are produced by an ensemble of machine learning methods that used 150,000 soil profiles and 158 remote sensing-based soil covariates. Our study considers only the top 100 cm to ensure that the values are comparable to estimates from the HWSD dataset.
Leaf area index We used three globally gridded reference LAI that are derived from satellite imagery. MODIS LAI (MOD15A2H, collection 6) (R. Myneni et al. , 2015)  is based on the inversion of a three dimensional canopy radiative transfer model that simulates surface reflectance from canopy structural characteristics  (Knyazikhin et al. , 1998). A second LAI reference dataset was provided by  Claverie et al. (2016)  for the period 1982-2010. This dataset is based on an artificial neural network that relates LAI to surface reflectance from the Advanced Very High Resolution Radiometer (AVHRR). The artificial neural network was calibrated with LAI from MODIS (MCD15A2) and in situ data from BELMANIP2 (445 sites)  (Baret et al. , 2006). The performance of the algorithm was assessed against in situ observations from the DIRECT database (113)  (Garrigues et al.
, 2008). A third LAI dataset was provided by the Copernicus Global Land Service for the period 1999-2019  (Verger et al. , 2014). This product uses an artificial neural network that gives instantaneous estimates from reflectances by SPOT/VEGETATION satellite imagery. The data are filtered to reduce the impacts of atmospheric effects and snow cover, temporally smoothed, and gap-filled. For the purpose of this study only non-gap filled grid cell values were used.
Latent heat flux and runoff We used two globally gridded reference latent heat flux datasets. Bowen ratio correction and residual approach). Our second reference dataset was taken from the Conserving Land-Atmosphere Synthesis Suite (CLASSr), which covers the period 2003-2009  (Hobeichi et al. , 2019). The CLASSr provides estimates of simultaneously balanced surface water and energy budget components. Each variable presents a weighted mean computed from multiple data products that are, to some extent, observation-based. The data are observationally constrained with in situ measurements, and each term is adjusted to allow for energy and water balance closure. Latent heat flux provided by CLASSr is based on blending data from remote sensing, reanalysis, and TBMs. The CLASSr dataset described above also provides monthly runoff.
Automated Model Benchmarking R package (AMBER) The Automated Model Benchmarking R package developed by Seiler (  2020 ) quantifies model performance using a skill score system that is based on the ILAMB framework  (Collier et al. , 2018). The method employs five scores that assess the model's annual mean bias (S bias ), monthly centralized root-mean-square-error (S rmse ), the timing of the seasonal peak (S phase ), inter-annual variability (S iav ), and spatial distribution (S dist ). The exact definition of each skill score is provided in Appendix A. The main steps for computing a score usually include (i) computing a dimensionless statistical metric, (ii) scaling this metric onto a unit interval, and (iii) computing a spatial mean. All scores are dimensionless and range from zero to one, where increasing values imply better performance.
These properties allow us to average skill scores across different statistical metrics in order to obtain an overall score for each variable (S overall )  (Collier et al. , 2018) : S overall = S bias + 2S rmse + S phase + S iav + S dist 1 + 2 + 1 + 1 + 1. ( 2 ) To reward models that reproduce a realistic response to changes in the meteorological forcing, we increase the weight of S rmse by a factor of two. In the case of GPP FluxCom we assign S iav a weight of zero, since the reference data are known to underestimate interannual variability  (Jung et al. , 2020). manuscript submitted to Global Biogeochemical Cycles erence, the value of σ ref is given by CAMS, and if we evaluate CAMS using CT2019 as a reference, the value of σ ref is given by CT2019. We can therefore have different benchmark scores for different reference datasets for the same variable in question.
The final benchmarking step in Figure  1  consists of comparing model scores against benchmark scores. If model scores reach benchmark scores, then the degree of similarity between model output and reference data is the same as between two independent reference datasets. Using this criteria, we then judge models to perform sufficiently well, given how uncertain reference data are. Note that model scores may also exceed benchmark scores when, for instance, model values are enclosed by the uncertainty range span by two or more reference data. All AMBER outputs for TRENDY are available at https:// cseiler. shinyapps. io/TRENDY2020/ (last visited on November 22, 2021). 3 Results.
Gross primary productivity and ecosystem respiration Reference data estimate global annual GPP fluxes to range from 108. 9 (FluxCom) to 123. 8 PgC yr -1 (GOSIF; varies among reference data, so do the multi-model mean values. In relative terms, the mean bias across models ranges from -6% when evaluating models against GOSIF and +6% when choosing FluxCom as reference data. The biases of the individual models range between -27% and +25%, with 7/15 models lying within the uncertainty range of the reference data. Note that differences between reference values, listed in Table  3 , may be caused by differences in the observational period and grid. Although all reference data are regridded to a common horizontal resolution of 1 • ×1 • , datasets may still differ with respect to the distribution of grid cells with missing data.
This is because the currently available gridded reference datasets, which rely on spatially upscaled eddy covariance measurements, yield results that are inconsistent with inversion-based estimates in the tropics  (Jung et al. , 2020). Evaluating modeled ecosystem respiration against FLUXNET data shows that annual mean values are reasonably well reproduced with correlation coefficients ranging between 0. 44 (ORCHIDEE-CNP) and 0. 75 (ISBA-CTRIP) (Figure  B3 ). The corresponding overall score values are similar to the GPP scores for FLUXNET data, with a multi-model mean score value of 0. 62 for both ecosystem respiration and GPP (Figure  4 ). Note that we did not compute ecosystem respiration benchmark scores as we lack a second reference dataset. manuscript submitted to Global Biogeochemical Cycles.
CT2019 than with CAMS (Table Appendix B). The zonal mean NBP of CAMS, CarboScope, and CT2019 show very little agreement, with opposing signs in multiple regions (Figure  2b ). TRENDY models do not reproduce the zonal mean values of either reference dataset. The only region with some reasonable agreement between both reference datasets and models is the tendency for a carbon sink between 50 • N and 65 • N. Averaging NBP values across every 30 degrees latitude shows that models and reference data agree on a stronger sink in higher latitudes compared to the tropics (Figure  2c ).
Vegetation carbon The   (Houghton et al. , 2001) , which is larger compared to the range reported in our study. The multi-model mean value (403. 3-429. 2 PgC) lies within the observational uncertainty range (Table  3  and Figure 2c ). The biases of the individual models range between -35% and +109%, with 10/15 models that are within the uncertainty range. The zonal mean values tend to be largest for Zhang-Mokany followed by Huang2021 and GEOCARBON-Mokay (Figure  2d ). The Zhang-Mokany dataset is in stronger agreement with forest inventory data (S overall = 0. 76) than the Huang2021 (S overall = 0. 69) or the Geocarbon-Mokany dataset (S overall = 0. 68). All three tend towards a negative bias, with a larger bias for Geocarbon-Mokany (-57%) than for Huang2021 (-38%), and Zhang-Mokany (-26%), suggesting that the latter is likely to provide more accurate values, at least for regions where forest inventory data are present (Figure  B5 ).
It must be noted that this comparison is limited by the fact that the three data sets Geocarbon-Mokany, Zhang-Mokany, and FOSXue all use the same approach for estimating belowground biomass, which makes them more similar by construction. Multi-model zonal mean values are in closer agreement with data from Zhang-Mokany compared to Huang2021 and Geocarbon-Mokany. All models tend towards a negative bias when assessed against forest inventory. Benchmark scores (0. 62-0. 74) and multi-model mean scores (0. 60-0. 69) are similar, where 6/15 models meet benchmarks when evaluated against in situ measurements (CLM5. 0, ISAM, ISBA-CTRIP, JSBACH, OCN, SDGVM), and 5/15 models reach benchmarks when assessed against Geocarbon-Mokany (Figure  4 ).
Zonal multi-model mean values are in close agreement with HWSD, lacking the large increase of soil organic carbon at higher latitudes present in SG250m (Figure  2e ). The model CLM5. 0 was excluded from Figure  2e , as it produces zonal mean values that exceed 200 kgC m -2 , dwarfing values from all other datasets. The top three models with largest soil organic carbon stocks are CLM5. 0 (3139 PgC), LPX-Bern (1838 PgC), and ISBA-CTRIP (1549 PgC), all of which include processes required for simulating carbon dynamics in permafrost regions (Table  1 ). Due to the large differences between HWSD and SG250m, the benchmarking values are very small (0. 33-0. 42). All models but CLM5. 0 therefore exceed the benchmark when assessed against HWSD. However, this result must be interpreted with caution.
The large discrepancy between HWSD and SG250m suggests that the datasets have fun- manuscript submitted to Global Biogeochemical Cycles damental differences, possibly related to a poor representation of wetlands and permafrost soils in HWSD  (Tifafi et al. , 2018). It is therefore likely that SG250m is more accurate than HWSD, which implies that the difference between HWSD and SG250m overestimates the true observational uncertainty.
Leaf area index Remotely sensed estimates of LAI yield very similar global mean values, ranging from 1. 4 to 1. 5 m 2 m -2 (Table  3  and Figure  2f ). The multi-model mean value exceeds the observational uncertainty range by up to 67%, with biases from individual models between -4% and +220%. Only one model (ORCHIDEE-CNP) is within the uncertainty range, while all other models (13/14) show positive biases for all three global reference data. Zonal mean values of annual mean LAI are very similar among all three reference datasets (Figure  2e ). The multi-model zonal mean values reproduce the general pattern of the reference data, with a positive bias of up to 2 m 2 m -2 across most latitudes. Individual ensemble members can have very large biases of up to 7 m 2 m -2 at the equator. The tendency for a positive LAI bias is evident for all regions and seasons (Figure  7 ).
The seasonal peak of maximum LAI tends to lag behind the reference data by about one month in the boreal and temperate regions. Also, the model IBIS lacks a seasonal LAI cycle in the tropics. Comparing satellite-based LAI against in situ measurements from CEOS suggests that global reference data tend towards a negative bias ranging between -0. 2 m 2 m -2 (-10%) for Copernicus to -0. 4 m 2 m -2 (-19%) for MODIS when evaluated against data from CEOS. This leads to the question whether the positive LAI of TBMs described above is due to an underestimation of LAI in satellite-based reference data. Comparing modelled LAI against the same in situ data yields far greater biases for multiple models, most notably for the models IBIS (+71%), LPX-Bern (+144%), and OCN (85%; Figure  8  g, k, l). Furthermore model biases derived from globally gridded reference data and in situ data are correlated (R = 0.
95) and of similar magnitude. For instance, a model with a large bias with respect to globally gridded reference LAI (LPX-Bern, 154% with respect to Copernicus) also has a large bias when assessed against in situ measurements (144% with respect to CEOS). Conversely, a model with a small bias with respect to globally gridded reference LAI (ORCHIDEE-CNP, -4%) also has a small bias when assessed against in situ data (1%). This suggests that the positive LAI bias present in some models is real, and not just due to an underestimation of LAI in satellite products. However, it must be noted that the evaluation against CEOS data is limited by the fact that sampling size varies substantially among regions, with the largest sampling density located in Europe. While none of the models reaches benchmarks for globally gridded reference LAI (0.
Latent heat flux Global fluxes of annual mean latent heat from CLASSr and FluxCom range from 32. 6 to 45. 2 W m -2 (Table  3  and Figure  2f ). manuscript submitted to Global Biogeochemical Cycles annual mean biases across all sites range from -31% (LPX-Bern) to +20% (JSBACH) (Figure  B7 ). The multi model mean scores (0. 67 and 0. 70 when assessed against FluxCom and CLASSr, respectively) exceed the benchmark scores for globally gridded and site-level reference data (0. 62-0. 67). Most of the individual models reach the benchmark scores, suggesting that most models perform well given how uncertain current reference data are. One exception is JSBACH with a systematic positive bias across all regions and seasons.
9; Figure  B9 ). However, none of the models nor the multi-model mean streamflow score of 0. 71 reach the corresponding benchmark score of 0. 82 (Figure  4 ). One important exception is LAI, with model scores (0. 50) that are much lower than benchmark scores (0. 66 minimum) for globally gridded products. The large difference suggests that models have a great potential for improving their representation of LAI. This also applies when evaluating models against in situ LAI data from CEOS. Another question we want to address here is to what extent model score differences are related to dynamic carbon-nitrogen (CN) interactions, permafrost, and wetlands (Table 1). There is no indication that a representation of CN interactions improves model performance. Comparing the model versions ORCHIDEE (with CN-interactions) against ORCHIDEEv3 (without CN-interactions) shows no statistically significant difference between the mean scores when considering all evaluations combined (two-sided t-test, pvalue = 0.