dataesr/scientific-paragraphs-categorization huggingface dataset:
Biological N fixation A biological N fixation observation-based dataset was derived from Davies-Barnard and Friedlingstein (2020), a global meta-analysis of field measurements of natural biological N fixation (free-living and symbiotic) that scales biome-specific means onto the Collection 5 MODIS Global Land Cover Type International Geosphere-Biosphere Programme (IGBP) product  (Friedl et al. , 2010). To account for agricultural biological N fixation, we assumed that N-fixing crops account for 15. 7% of global cropland area (U. S. Department of Agriculture, 2022) and their biological N fixation rate as 11. 5 g N m -2 yr -1  (Herridge et al. , 2008). We assumed that N-fixing crops are distributed evenly across all cropland. We amended the dataset from Davies-Barnard and Friedlingstein (2020) to include agricultural biological N fixation (DBF-USDA).
The score of LPX-Bern in simulating biological N fixation is not analysed because it implicitly includes rock N sources and is thus not directly comparable to the observation-based dataset. Global Land Cover Type IGBP product  (Friedl et al. , 2010). First, we obtained N content per dry mass for leaves, root, and stem, as well as C content per dry mass for leaves, root, and stem from the TRY plant trait database. We selected entries that reported species. Second, we obtained plant functional type (PFT) for each species from the TRY plant trait database. We categorised each PFT into the IGBP land cover types (Table  A1 ) and then used this to categorise each entry into the IGBP land cover types. We averaged across entries in each IGBP land cover type. Third, we divided mean tissue C content per tissue dry mass by mean tissue N content per tissue dry mass for each tissue and for each IGBP land cover type.
Soil C:N ratio A soil C:N ratio observation-based dataset was derived from soil C and soil N products from SoilGrids  (Poggio et al. , 2021) , which provides globally gridded datasets of soil organic C and total soil N at a 250m x 250m resolution for six layers up to a depth of 200 cm. These estimates are derived using machine learning methods and soil observations from 240 000 locations across the globe and over 400 environmental covariates. We summed soil C over all layers and soil N over all layers (using the bulk density and depth of each layer) then obtained the soil C:N ratio.
C cycling variables In addition to evaluating N cycling variables, we also evaluated the primary C cycling variables: gross primary productivity (GPP), net biome productivity (NBP), vegetation C (CVEG), soil C (CSOIL), and leaf area index (LAI). These variables have been previously evaluated in detail for the terrestrial biosphere models in the TRENDY ensemble (GCP 2021) in  Seiler et al. (2022). Seiler et al. (2022)  gives further details on the observation-based datasets used to evaluate the primary C cycling variables. Briefly, we evaluated GPP against MODIS  (Zhang et al. , 2017) , GOSIF  (Li and Xiao, 2019) , and FLUXCOM  (Jung et al. , 2020)  products. We evaluated NBP against the CAMS (Agustí-Panareda et al. , 2019), CarboScope  (Rödenbeck et al. , 2018) , and CT2019  (Jacobson et al.
However, the latitudinal distributions of the observation-based datasets display weak agreement among themselves, with opposing signs in multiple regions, especially at southern latitudes and at high northern latitudes (Figure  2b ). This is in part due to the smaller land area at these latitudes. The region showing the strongest agreement is mid to high northern latitudes, in which both the TRENDY-N ensemble and observations suggest a terrestrial C sink (Figure  2b ). (Herridge et al. , 2008) ). Simulated N2O emissions ranged between 0. 9 and 11. 0 Tg N yr -1 (Table  2 ) in comparison to the observation-based estimate of 10. 8 Tg N yr -1 (7. 1 -16. 0 Tg N yr - 1 )  (Tian et al. , 2020). Simulated N losses (which include emissions of NH3, N2O, NOx and N2 as well as NO3 -and NH4 + leaching) ranged between 87 and 603 Tg N yr -1 (Table  2 ) in comparison to the observation-based estimate of 293 Tg N yr -1  (Fowler et al.
Overview of N cycling (1/10 models). All models suggest increasing N2O emissions (7/7 models) and increasing N loss (10/10 models). 2 ) and overestimated the vegetation C:N ratio across latitudes while capturing its latitudinal pattern (Figure  7d ). In comparison to the observation-based dataset from SoilGrids, the TRENDY-N ensemble overestimated the global soil C:N ratio, simulating a relatively constant soil C:N ratio across latitudes (11. 1 vs. 8. 8; Figure  7e  and Table  2 ). The TRENDY-N ensemble was thus unable to capture the latitudinal pattern of the soil C:N ratio (Figure  7f ). Seiler et al. , 2022). For biological N fixation, the time-mean bias score averaged across models was 0. 50 and the mean spatial distribution score across models was 0. 41 (Table  A3 ). For the vegetation C:N ratio, the time-mean bias averaged score across models was 0.
They can potentially vary, such as in response to high photosynthesis rates relative to N uptake rates driven by CO2 fertilisation  (Elser et al. , 2010). Overall scores of N cycling variables, which quantify model performance in reproducing an observation-based dataset, are lower than overall scores of corresponding C cycling variables, suggesting that models could be less capable of capturing N cycling processes than C cycling processes. However, this could also be due to the significant uncertainty associated with measurements of N cycling processes as discussed below. Besides models that represent N limitation of decomposition yielding a higher overall NBP score, there were no statistically significant differences between models with different representations of N limitation of vegetation growth, biological N fixation, the response of vegetation to N limitation, and N limitation of decomposition for the overall score, present-day global value, or Kendall's tau.
Some estimates have suggested that rock N weathering could be as high as 11 -18 Tg N yr -1 globally  (Houlton et al. , 2018)  but is not explicitly represented in the TRENDY-N ensemble (with the exception of LPX-Bern which calculates all external N sources post hoc to simulate a closed N cycle thereby implicitly including rock N sources). The discrepancy between modelled and observed biological N fixation could also be due to uncertainty in the observation-based dataset given the difficulties associated with measuring biological N fixation  (Soper et al. , 2021). Ecological theory  (Hedin et al. , 2009)  has suggested that natural biological N fixation should be higher at low latitudes given large N losses, in contrast to the observation-based dataset from Davies-Barnard and Friedlingstein (2020).
, 2020). Not only are these global totals highly uncertain, but they also do not allow for the analysis of spatial patterns. Here, we present an improved framework to evaluate three key N cycling processesbiological N fixation, vegetation C:N ratio, and soil C:N ratioin terrestrial biosphere models. However, these globally-gridded observation-based datasets are also uncertain, given uncertainty in the estimates of tissue C:N ratios for different plant functional types and tissue fraction of total biomass (especially those of roots and wood which had a lower number of measurements in comparison to that of leaves), as well as in the measurements and models used to derive soil N  (Batjes et al. , 2020). Importantly, more observations of additional N cycling processes are necessary to fully evaluate N cycling in terrestrial biosphere models.
Multiple observation-based datasets from different sources of a given N cycling process are necessary to evaluate observational uncertainty  (Seiler et al. , 2021). Observation-based datasets of N cycling processes at intra-annual and inter-annual time scales are necessary to assess temporal patterns. Paleoclimatic observations could also be utilised for evaluation  (Joos et al. , 2020). Leveraging advances in remote sensing  (Knyazikhin et al. , 2013; Townsend et al. , 2013)  as well as incorporating N cycling process measurements into research networks such as FLUXNET  (Vicca et al. , 2018)  is essential. While some of the models in the TRENDY-N ensemble have the capability of representing coupled C, N, and phosphorus (P) cycling  (Goll et al. , 2012; Nakhavali et al. , 2022; Sun et al. , 2021; Wang et al.
https://doi. org/10. 5194/egusphere-2023-167 Preprint. Discussion started: 6 February 2023 c Author(s) 2023. CC BY 4. 0 License. Table 1: Terrestrial biosphere models in the TRENDY-N ensemble and descriptions of their 140 representations of N limitation of vegetation growth, biological N fixation, vegetation response 141 to N limitation (i. e. , strategies in which vegetation invests C to increase N supply in Ndoi. org/10. 5194/egusphere-2023-167 Preprint. Discussion started: 6 February 2023 c Author(s) 2023. CC BY 4. 0 License. We analysed the S3 simulation which includes historical changes in atmospheric CO2, climate, N deposition, N fertilisation, and land use from 1851 to 2021 (see Friedlingstein et al. (2022) for a full description of the simulation protocol). Briefly, models were forced with atmospheric CO2 from Dlugokencky and Tans (2022), the merged monthly Climate Research Unit (CRU) and 6-hourly Japanese 55-year Reanalysis (JRA-55) dataset or the monthly CRU dataset Harris et al.
https://doi. org/10. 5194/egusphere-2023-167 Preprint. Discussion started: 6 February 2023 c Author(s) 2023. CC BY 4. 0 License. A vegetation C:N ratio observation-based dataset was derived by scaling biome-specific means from the TRY plant trait database (Kattge et al. , 2020) onto the Collection 5 MODIS.
https://doi. org/10. 5194/egusphere-2023-167 Preprint. Discussion started: 6 February 2023 c Author(s) 2023. CC BY 4. 0 License. time-mean bias (Sbias), monthly centralised root-mean-square-error (Srmse), seasonality (Sphase), inter-annual variability (Siav), and spatial distribution (Sdist) in comparison to the observationbased dataset. Scores are dimensionless and range from 0 to 1, where higher values indicate better model performance. The overall score for each variable (Soverall) is 𝑆 𝑜𝑣𝑒𝑟𝑎𝑙𝑙 = mean(𝑆 𝑏𝑖𝑎𝑠 , 𝑆 𝑟𝑚𝑠𝑒 , 𝑆 𝑝ℎ𝑎𝑠𝑒 , 𝑆 𝑖𝑎𝑣 , 𝑆 𝑑𝑖𝑠𝑡 ) We calculated the overall score for each C and N cycling variable. Because biological N fixation, vegetation C:N ratio, and soil C:N ratio datasets are representative of the present-day (as a single time point), Srmse, Sphase, and Siav are not defined and thus do not contribute to Soverall.
score and the spatial distribution score, but also the monthly centralised root-mean-square-error score (which assesses the ability of the model to reproduce the time series of the observationbased dataset), the seasonality score (which assess the ability of the model to reproduce the seasonality of the observation-based dataset), and the inter-annual variability score (which assesses the ability of the model to reproduce the inter-annual variability of the observationbased dataset) because observation-based datasets of C fluxes are available over time (whereas observation-based datasets of C pools and all N cycling variables are representative of the present-day (as a single time point)).
Introduction: While crucial to ensuring the production of accurate and highquality data-and to avoid erroneous conclusions-data quality control (QC) in environmental monitoring datasets is still poorly documented. Methods: With a focus on annual inter-laboratory comparison (ILC) exercises performed in the context of the French coastal monitoring SOMLIT network, we share here a pragmatic approach to QC, which allows the calculation of systematic and random errors, measurement uncertainty, and individual performance. After an overview of the different QC actions applied to fulfill requirements for quality and competence, we report equipment, accommodation, design of the ILC exercises, and statistical methodology specially adapted to small environmental networks (<20 laboratories) and multivariate datasets.
Finally, the expanded uncertainty of measurement for 20 environmental variables routinely measured by SOMLIT from discrete samplingincluding Essential Ocean Variables-is provided. Results, Discussion, Conclusion: The examination of the temporal variations  in the repeatability, reproducibility, and trueness of the SOMLIT network over time confirms the essential role of ILC exercises as a tool for the continuous improvement of data quality in environmental monitoring datasets. KEYWORDS environmental monitoring network, data quality control, inter-laboratory comparison exercises, measurement uncertainty, analyst performance, multivariate dataset.
Introduction In the context of global change, the number of studies that include meta-analytical combinations of environmental data increased at a fast pace in the past decades (e. g. ,  Duarte et al. , 2009; Harvey et al. , 2013; Talarmin et al. , 2016; McCrackin et al. , 2017; Carstensen and Duarte, 2019; Lheureux et al. , 2021; Lheureux et al. , 2023). In these studies, it is assumed that the data were of similar quality and reliability, which is not necessarily true: measurement processes are all subject to systematic (i. e. , bias) and random (i. e. , variations) errors of various types, such as the choice of the method, ambient conditions, or the working practices of analysts  (Taverniers et al. , 2004; de Boer, 2016). At a time when high-quality scientific journals require a comprehensive documentation of data quality control (QC) procedures for publication  (de Boer, 2016) , and because erroneous conclusions may arise from studies using inappropriate methods, it is important to increase the confidence levels of data QC, which is often poorly documented in environmental monitoring datasets.
, 2019). Common standard operating protocols are used and all data are continuously routinely quality-controlled since 2006 by means of robust quality assurance procedures guided by the  ISO-17025 standard (ISO-17025, 2005 , then ISO-17025, 2017). After an overview of the different QC actions implemented by the SOMLIT to fulfill requirements for quality and competence, we report equipment, accommodation, design of the ILC exercises and statistical methodology to assess individual performance specifically adapted to small (<20 laboratories) environmental networks and multivariate datasets, based on the  ISO-13528 standard (ISO-13528, 2005) ,  Heberger and Kollaŕ-Hunek (2011) , and procedures for measurement uncertainty assessment. Finally, we provide an expanded uncertainty of measurement for 20 environmental variables routinely measured by SOMLIT from discrete sampling (Table  1 ), including Essential Ocean Variables  (Miloslavich et al.
We provide an example of the report in Figure  4. ILCs are undertaken annually at the end of summer in one of the SOMLIT laboratories (Figure  1 ). To strengthen the robustness of comparisons, we alternate among different hosting laboratories, with sampling stations in the oligo-to ultra-oligotrophic Mediterranean Sea, oligo-to mesotrophic Atlantic waters, and the meso-eutrophic English Channel (Table  S2 ). ILC allows best practices to be shared among participants from different laboratories, to evaluate data reliability (uncertainty calculation), and to assess the performance of individual operators from each laboratory. If issues are identified, ILC provides a forum to help participants to improve their practices and to correct their errors. In the following sections, we describe the methodology used during ILC and show how efforts to fulfill quality requirements have helped to improve the trueness and reproducibility of SOMLIT data over time.
Design of ILC exercises and sample testing preparation Although the SOMLIT program routinely measures 25 environmental variables, only the 22 variables subjected to discrete sampling (Table  1 ) were considered each year. Figure  5  describes the different steps involved in the ILC exercises. Given that stable reference material is lacking for most of the environmental variables measured by the SOMLIT network, performance in total measurement was evaluated by robust analysis, using measurement data from real natural seawater (see below). Location of the sites and stations belonging to the French coastal monitoring network SOMLIT (the logotype is in the center of the map).
Before filling, vials were rinsed three times with sampling seawater. Samples were also successively sub-sampled for particulate matter analysis: pico-and nanoplankton, chlorophyll a (Chla) and phaeopigments, particulate organic carbon (POC), particulate organic nitrogen (PON), suspended particulate matter (SPM), and d 13 C and d 15 N. Processing and storage during transport and back to home laboratory (Figure  5 ) were carried out following the methodology Within-laboratory control: example of control chart based on  Nordtest TR 569 (2007)  for ensuring that the data are of good quality and the measurement process is statistically stable. Black line: the central line; thin red line: the warming limits; and thick red line: the action limits. The data value is considered in control if it is within the warming limits or if the value is between the warming and action limits but the two previous ones are within the warming limits.
Calculation of systematic and random errors and performance The different steps of the statistical methodology used to assess the total measurement accuracy (trueness and precision) for the 22 environmental variables during ILC exercises, as well as the overall performance of the participating laboratory, are described in Figure  6. The methodology has been adapted for small (<20 participating laboratories) networks, using the algorithm A mentioned in the ISO-13528 standard (see the freeware using Microsoft Excel © in Table  1  of the Supplementary Materials) for non-parametric data, and the user-friendly macro created by  Heberger and Kollaŕ-Hunek (2011)  for both non-parametric and multivariate datasets. The application of the latter approach allows a better appraisal of proficiency testing (PT) in the case of multivariate monitoring in comparison with the ISO-13528 standard  (ISO-13528, 2005 , see Medina-Pastor et al.
Estimation of total measurement uncertainty (U) of the SOMLIT network Two approaches exist for the evaluation of measurement uncertainty. The first is based on a physical model relating the measuring process at all steps, using the propagation law of uncertainty  (GUM, 1993). The second relies on a statistical model that uses data from within-laboratory control and/or ILC exercises or PT exercises  (ISO-13528, 2005; Ferretti, 2011; Magnusson et al. , 2012; de Boer, 2016). The statistical model is often favored, however, and was chosen by the SOMLIT network both for its less apparent mathematical complexity and for the lack of knowledge about the processes that need to be measured for the physical model (e. g. , firm statistical background). For that purpose, we used the data from ILC exercises from the last 6 years.
Conclusion In this paper, we presented the "road map" and the statistical methodology for the ILC exercises performed by the SOMLIT network, a framework especially adapted to small (<20 participating laboratories) and multivariate environmental datasets. By applying this framework to data acquired during 21 years, we showed that the SOMLIT program provided reliable data, and that the data quality increased over time thanks to the implementation of ILCs. Given the paucity of uncertainty values of the measurement of environmental variables-such as measured by the SOMLIT network-in the literature, our study stresses the importance of implementing ILC exercises in order to improve the quality of data representative of longterm environmental monitoring. We also highlight general acceptance limits in the measurement of these environmental variables.
Acknowledgments The authors thank all the past participants involved in the interlaboratory comparison exercises, especially  Louise Oriol  and  Nicole Degros  who greatly contributed to the implementation of the first exercises and the continuous improvement of SOMLIT data quality up to their retirement, as well as our enthusiastic and retired colleague  Jean-Paul Lehodey  and the numerous persons who contributed to the smooth running of the SOMLIT network:  C. Arnaud ,  L. Beaugeard ,  G. Beaugrand ,  M. Breret ,  Y. Brizard ,  A. Brouquier ,  H. Derriennic ,  C. Desnos ,  V. Ferrer ,  M. Ferreri ,  L. Giannecchini ,  F. Guyon ,  G. Izabel ,  J-L Jung ,  V. Kempf ,  C. Larvor ,  S. Marro ,  M-L Pedrotti ,  Mireille Pujo-Pay ,  C. Rouzier ,  S. Soriano ,  E. Thiebaut , and  H. Violette. The authors would also like to thank the different laboratories we visited over the last two decades for providing space and facilities, as well as the different captains and crew members of the research vessels of the marine stations and of the  French National Fleet (Flotte Oceánographique Francaise, FOF).
As in models, the observed Arctic warming was shown to be strongest at the surface and primarily consistent with the concomitant sea-ice retreat. No strong evidence was found for a substantial radiative impact of changes in cloud cover (despite a recent increase in Arctic cloudiness  29  ), but an increase in atmospheric water vapor content was reported and may have contributed to enhance the Arctic warming during summer and early autumn. More recently  26  , a much stronger four-fold AA was reported over 1979-2021 from multiple temperature datasets including the state-of-the-art ERA5 reanalysis  30, 31. Such a warming ratio is extremely rare in the CMIP5 and CMIP6 simulations, thereby suggesting that it is either an extremely unlikely event or that AA is systematically underestimated by climate models  26.
This result advocates for a better understanding and consideration of internal climate variability when constraining the Arctic climate projections with the observed trends  32. Our analysis is based on the KCC statistical package  33, 34  and on the combined use of GSAT and PSAT reconstructions (see Methods). This Bayesian technique allows us to derive a posterior distribution of the projected anomalies from the prior distribution derived from the raw model outputs. Doing so, it takes account of both internal variability and observational uncertainties to constrain recent and future simulated climate changes in a consistent way. Besides the HadCRUT5 gridded dataset at a relatively coarse spatial resolution (5°by 5°), the ERA5 reanalysis has been also used to provide alternative regional constraints, but with no estimate of observational errors.
Note that there is no theroretical motivation and not enough fully-independent CMIP6 models to assume a non-Gaussian distribution. Raw model outputs are thus used to construct the prior on x as: π(x) = N(μ x , Σ x ). Then the posterior distribution given observations y can be derived as p(x |y) = N(μ p , Σ p ). More details about the KCC method can be found in the related original studies and the R scripts and related data file of the current application are available from https://doi. org/10. 5281/zenodo. 8004439. Interestingly, KCC can be used exactly in the same way to constrain historical and projected changes, but also historical changes driven by all or individual radiative forcings as concatenated in the x vector (e. g. , anthropogenic greenhouse gases or natural radiative forcings only).