dataesr/scientific-paragraphs-categorization huggingface dataset:
Phylogenetic analyses Homologs of Kirc were identified from GenBank using BLASTp with the truncated version of Kirc from Wa53 that has no CHROMO domain. Nucleotide sequences of hits with e-values < 1e-100 were compiled along with a homolog from P. anserina (Pa_5_10116), and two homologs from Melanconium sp. NRRL 54901 extracted from MycoCosm (see below), and aligned with MACSE v. 2. 03  (Ranwez et al. 2018). We used TrimAl v. 1. 4. 1 (Capella-Gutierrez, Silla-Martinez, and Gabaldon 2009)  to trim the resulting protein alignment with the -gappyout option. We then used IQTree v. 1. 6. 8 (Kalyaanamoorthy et al. 2017; Nguyen et al. 2014)  to produce a Maximum Likelihood phylogeny with extended model selection (-m MFP). To estimate the branch support, we produced 1000 standard bootstrap pseudoreplicates.
Acknowledgements This work was supported by the  European Research Council (ERC)  grant  ERC-2014 - CoG  (project  648143 ,  SpoKiGen ) and  The Swedish Research Council  to  H. J,  and support from the  Lars Hierta Memorial Foundation  and  The Nilsson-Ehle Endowments of the Royal Physiographic Society of Lund  to S. L. A. V. We thank the support given by the  National Genomics Infrastructure (NGI) / Uppsala Genome  center on massive parallel DNA sequencing. The computations were performed on resources provided by  SNIC  through  Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX)  under the projects  SNIC 2017/1-567  and  SNIC 2019/8-371. The sequence data of Melanconium sp. NRRL 54901 was produced by the  US Department of Energy Joint Genome Institute  http://www.
The search for a long-term benchmark for landsurface models (LSMs) has brought tree-ring data to the attention of the land-surface modelling community, as tree-ring data have recorded growth well before human-induced environmental changes became important. We propose and evaluate an improved conceptual framework of when and how tree-ring data may, despite their sampling biases, be used as century-long hindcasting targets for evaluating LSMs. Four complementary benchmarks -size-related diameter growth, diameter increment of mature trees, diameter increment of young trees, and the response of tree growth to extreme events -were simulated using the ORCHIDEE version r5698 LSM and were verified against observations from 11 sites in the independent, unbiased European biomass network datasets. The potential for big-tree selection bias in the International Tree-Ring Data Bank (ITRDB) was investigated by subsampling the 11 sites from European biomass network.
We find that in about 95 % of the test cases, using ITRDB data would result in the same conclusions as using the European biomass network when the LSM is benchmarked against the annual radial growth during extreme climate years. The ITRDB data can be used with 70 % confidence when benchmarked against the annual radial growth of mature trees or the size-related trend in annual radial growth. Care should be taken when using the ITRDB data to benchmark the annual radial growth of young trees, as only 50 % of the test cases were consistent with the results from the European biomass network. The proposed maximum tree diameter and annual growth increment benchmarks may enable the use of ITRDB data for large-scale validation of the LSM-simulated response of forest ecosystems to the transition from pre-industrial to present-day environmental conditions over the past century.
S1b ; See Sect. 5. 3 for more details)  (Babst et al. , 2014a (Babst et al. , , b, 2017 (Babst et al. , , 2018;; Zuidema et al. , 2018). Until now, tree-ring records have often been collected to reconstruct past climate and hydrological variability from sites where trees grow near the colder or drier fringes of their distribution  (Briffa et al. , 2004; D'Arrigo et al. , 2008). The most comprehensive archive of publicly shared treering data is the International Tree-ring Data Bank (ITRDB), with more than 4000 locations from 226 species across most forested biomes (Fig. S2 )  (Grissino-Mayer and Fritts, 1997; Zhao et al. , 2019). However, a shortage of site metadata and the prevailing geographical, species, and tree selection sampling biases resulting from targeting climate-sensitive trees has limited the use of the ITRDB archive to infer longterm changes in forest growth  (Bowman et al.
, 2013; Briffa and Melvin, 2011; Klesse et al. , 2018; Zhao et al. , 2019). Compared with tree-ring records that were collected for the purpose of benchmarking LSMs, such as the European tree-ring network of biomass plots (hereafter called "European biomass network";  Klesse et al. , 2018)  that is available through the database of the BACI project (BACI, 2020), the aforementioned issues may limit the information content of the ITRDB records. This incomplete information content should, however, be balanced against the associated benefits in terms of time gain and resource savings when reusing the large ITRDB dataset. If tree rings are to be used as benchmarks for LSMs, the models must demonstrate skilful simulations of treering width (TRW). Over the past few decades, the major physiological and ecological processes that are responsible for annual tree-ring growth have become sufficiently wellunderstood to be formalized in mathematical models with different levels of detail.
This study articulates an improved conceptual framework for benchmarking simulated radial growth against ITRDB tree-ring data, addressing limitations in the models, the data, and the methods to compare models and data. The aims are to (1) use current understanding of tree-ring growth to derive the minimal requirements for benchmarking LSMs against tree-ring records archived in the ITRDB, (2) review potential issues of using the ITRDB to benchmark LSMs, (3) propose solutions for a meaningful comparison of LSMs against ITRDB records, and (4) verify the proposed solutions by benchmarking a LSM using data from a European biomass network (BACI, 2020) that is not prone to sampling biases related to dendroclimatic research. The organization of this paper follows these aims, and dependencies between these aims and the workflow of this study are detailed in Fig.
Given that local climate effects may have contributed to the TRW, it might be desirable to correct the bias in reanalysis with present-day site-specific climate observations where they exist  (Ols et al. , 2018). When LSMs are forced by actual climate observations, reproducing the observed climate sensitivity in tree rings would add credibility to the land-surface simulation -if the forcing data and the LSM and TRW models are all realistic and unbiased. Given the above, LSMs that intend to use TRWs as a benchmark should at the minimum simulate the following: (1) size-dependent growth, (2) dynamic plant phenology, (3) differently sized trees within a stand, and (4) responses to chronic exogenous environmental changes (Fig. 2 ). Whereas responses to chronic exogenous environmental changes are the reason LSMs exist and are therefore to some extent accounted for by all current LSMs, size-dependent growth and size differentiation within a stand are, at present, only accounted for in a few LSMs, for example, CLM (ED)  (Fisher et al.
, 2015) , ORCHIDEE  (Naudts et al. , 2015) , and LPJ-GUESS  (Smith, 2001). The ORCHIDEE model (revision 5698) meets the aforementioned minimum requirements and, therefore, will be used in this study. Each dataset in ITRDB consists of incremental cores from multiple trees (tens to several hundred, depending on the dataset) with varying ages and growth rates. In this figure, observations are from a pine forest in Germany, archived as germ214  (Neuwirth et al. , 2007)  (Table  S3 ). The diameter of individual trees was reconstructed by summing the annual tree ring from the dataset. Note the presence of both fast-growing relatively young trees (dark grey lines) and slowgrowing older trees (light grey lines).
Challenges of using ITRDB data as a long-term benchmark A typical record in the ITRDB consists of TRW measurements of incremental cores from tens of individual trees from the same site and species. Each record may have different starting and ending dates and, thus, a different length (Fig. 3 ). If a core reaches the pith of the trunk, the annual tree diameter can be reconstructed  (Bakker, 2005) ; however, even then, diameter reconstruction may come with some uncertainty because trunks are not perfectly round. If the core does not contain the pith, which is often the case for large trees, the lack of information about the rings near the pith adds uncertainty to the diameter and age reconstruction  (Briffa and Melvin, 2011). In this case, the diameter increment could still be reconstructed (by subtracting the measured TRW from the diameter of the tree) if the trunk diameter at the time of sampling is known, but these metadata are rarely recorded in dendroclimatic collections and are not stored in the ITRDB.
Tree-ring datasets often contain cores of individuals from different cohorts. The presence of slow-and fast-growing trees within the same cohort (illustrated by the grey lines in Fig. 3 ) is another source of bias  (Melvin, 2004; Zuidema et al. , 2011). Slow-growing trees tend to live longer than fast-growing trees in the same cohort  (Mencuccini et al. , 2005; Schulman, 1954). Owing to survivorship being biased towards slow-growing individuals  (Bowman et al. , 2013) , TRW records are likely to underestimate the mean tree growth of a stand in long-passed centuries, as fast-growing trees would have died off before the samples were taken  (Brienen et al. , 2012).
2. 4, point ii). -Tree-age-aligned largest virtual treethe largest virtual tree of a stand is calculated after aligning individual trees by their age (Fig. 4c ). The recommendation to remove the age trend from tree-ring records  (Cook et al. , 1995)  confirms the assumption underlying the alignment by age, i. e. that size-dependent age exceeds the growth trends due to long-term environmental changes. Subsequently, the age-aligned TRWs can be used to compile a virtual fast-growing tree that has the maxi-mum observed diameter of all trees for a given tree age (note the difference in the weight of the dark grey lines from virtual trees in Fig. 4b  and c ). Thus, the virtual fast-growing tree gives a better idea of the true mean tree growth in old stands. (see Sect. 2. 4, point iii. ) The proposed data-model comparison largely relies on the concept of virtual trees, as these virtual trees can better account for known sampling biases of the ITRDB datasets and different aspects of TRW, and they facilitate the comparison of simulations and observations at the stand level.
An even-aged forest, for example, is simulated with a small diameter range between the smallest and largest classes. All classes will then effectively belong to the same stratum. An uneven-aged forest is simulated by applying a large range between the diameter classes. Therefore, different diameter classes will effectively represent different strata. The limitations of this approach become apparent when the TRW data and simulations are compared by calendar year, as the model does not track individual trees. Although the dimensions of each model tree itself are well https://doi. org/10. 5194/gmd-14-5891-2021 Geosci. Model Dev. , 14, 5891-5913, 2021 Figure  9. Schematic representation of the verification process for the RMSE metric. Before the verification, two types of datasets were prepared: big-tree data (limited to the 15 % biggest trees) and all-tree data.
Simulations: forcing and model parameterization In this study, offline ORCHIDEE simulations for the 20th century are forced with the merged and homogenized gridded CRU-NCEP climate dataset  (Viovy, 2016) ; the gridded nitrogen deposition product from the Chemistry-Climate Model Initiative (CCMI)  (Eyring et al. , 2013) ; a gridded nitrogen fertilization product for N 2 O (Lu and Tian, 2017); an observation-based time series of global atmospheric CO 2 concentrations  (Keeling et al. , 1996) ; and forest management followed the reported management status for each of 11 sites simulated (Sect. S3) for comparison, via the virtualtree benchmarks, with observations (Sect. 3. 3). Simulations were started from a 300-year-long spin-up, which was required for equilibrium with respect to the slow carbon and nitrogen pools in the soil.
The spin-up was concluded with a forest clear-cut, such that the start year and the length of each simulation matched the observed stand age for the validation dataset  (Sect. 3. 3). During model development, two global (number of diameter classes and ratio for number of trees per each diameter class; see Sect. 3. 1) and six PFT-specific parameters (f power , f σ , k α_r , k α_s , k β_r , and k β_s ; Table  S1 ) were manually adjusted to jointly reproduce the TRW data of 11 ITRDB sites: aust112  (Strumia, 2005) , cana106  (Archambault and Bergeron, 2002), chin037 (PAGES 2k Consortium, 2013) , finl055  (Melvin, 2005) , fran4  (Tessier, 1996) , id007  (Briffa and Schweingruber, 2002) , japa011  (Davi et al. , 2011) , mo009  (Bell et al. , 2002) , nepa003  (Krusic and Cook, 2005) , spai055  (Briongos and Cerro-Barja, 2007) , and turk027  (Griggs et al.
Verification The European biomass network contains TRW samples from "fixed-plot sampling". The database was established within multiple research projects and made publicly available through the EU Horizon 2020 project BACI (BACI, 2020). It currently archives 48 datasets covering temperate and semiboreal climates (Fig. S2 ) that have been collected from a variety of research efforts in Eurasia  (Klesse et al. , 2018). All trees larger than 5. 6 cm in diameter at breast height had to be sampled in a 10-40 m radius plot, depending on stand density, in order to be archived in the European biomass network database  (Babst et al. , 2014b). The European biomass network is, therefore, considered to be free of the big-tree selection bias that has plagued the ITRDB, although other known biases (e. g.
CIM, a site dominated by Fagus sylvatica, was removed from the selection (decreasing the final number of sites to 11) because only one tree out of 61 trees was aged over 100 years, resulting in a diameter distribution that was not at all compatible with the default diameter distribution of the model. The details of the selected sites are given in Table  S3  in the Supplement. The European biomass network data were additionally used to verify whether the big-tree selection bias that is present in the ITRDB data invalidates its use for benchmarking LSMs. The verification checked whether changes in parameter values or model process representation that would be required to make the model output better match the ITRDB data would also result in a better match between the model output and the all-tree data from the European biomass network.
If this were the case, benchmarking LSMs against ITRDB data would result in model changes that would enhance the model's capability to simulate tree growth, thereby justifying the conclusion that ITRDB data can be used for LSM benchmarking despite the known biases. Therefore, the verification used the data from the European biomass network in two different ways. First, all trees in the European biomass network dataset were used (hereafter called "all-tree data") to calculate the four proposed benchmarks at the site level. The results of these benchmarks were used as the reference in the verification. Second, only big trees were subsampled from the data (hereafter called "bigtree data"), and all four benchmarks were calculated against this subsample of data. Big trees were defined as the top 15 % of the trees based on their diameter, and the 15 % threshold was taken to match the diameter distribution in ORCHIDEE, where by definition the largest diameter class contains 15 % of the trees.
The "big-tree" verification required three additional steps (Fig. 9 ). In the first step, the simulated TRW values from the largest diameter class were transformed by modifiers to minimize the two metrics of each benchmark (Sect. 2. 4; Table  S2 ). The different benchmarks may use different metrics, i. e. the RMSE and slope of the residuals were used as the metrics for benchmarking size-related growth trend, growth of mature trees, and growth of young trees, whereas extreme growth and TRW amplitude were used as the metrics for benchmarking extreme growths (Table  S2 ). In the second step, the same modifiers were then applied to all simulated diameter classes, and all four benchmarks. Hence, two metrics were calculated using all-tree data for each benchmark. In the third and final step, the actual verification tested whether -for a given metric and a given benchmark -the modifier improved simulations for the big-tree sample and for the all-tree data.
Improvement of a specific metric of a benchmark was quantified by subtracting the original value for that metric from its modified value for all-tree data. Thus, a negative value indicated an improvement. If all three conditions were satisfied, the benchmarks of the big-tree and all-tree data were said to be consistent, implying that using this benchmark in combination with the ITRDB data would reveal the same model shortcomings as benchmarking OR-CHIDEE against TRW data from all-tree networks. Across the 11 sites and for each of the four proposed benchmarks and both benchmark metrics, sites where the test improved for both datasets were counted to estimate the confidence in using ITRDB in benchmarking LSMs. As this study aims to propose benchmarks making use of the ITRDB study rather than improving the ORCHIDEE model, the modifiers were applied to the model output directly.
Verification by benchmark When benchmarking size-related growth, conclusions would be similar in 16 out of 22 cases (73 %), regardless of whether ORCHIDEE was benchmarked against the big-tree data or the all-tree data. Some sites such as DEO and DVN showed positive differences close to zero, suggesting that simulations with ORCHIDEE r5698 matched the observed sizerelated growth trend reasonably well, leaving limited room for improvement. One site (SCH) showed a positive difference because it contained two slow-growing trees which lived roughly 40 years longer than the rest of trees but whose diameters were too small to be contained in the big-tree sample (Fig. S4 ). Except for this site, the other sites showed marginal inconsistencies or showed improved simulated output against the two datasets.
Thus, the size-related trend in tree growth can be derived from either the big-tree or the alltree data. For the mature-tree benchmark, big-tree data can be used with 68 % (15 out of the 22) confidence for benchmarking against LSMs. At 5 out of 11 sites, the all-tree data and the big-tree data yield different results. Two sites (HD2 and TIC) for which inconsistencies between the big-tree and all-tree data were observed for both metrics have 36 %-44 % of small trees in their size distribution, compared with an average of 28 % at the other nine sites. The proportion of small trees in the observation was estimated by counting trees in the smallest bin when trees were divided into five size classes similar to the model. The site labelled as ZOF has a bimodal size distribution with the biggest number of trees in the 1st and 4th diameter classes (35 % and 32 % respectively).
The default size distribution in ORCHIDEE has 15 % of its trees in the smallest size class and 21 % of its trees in the 4th size class. At two other sites, DEO and SOB, the growth rate for big trees was higher in the observations (0. 95 and 0. 50 for the slope of residuals respectively), as the difference in big trees and small trees was bigger in the observations (Fig. S5b ); despite this, the average simulation at these sites matched well with the average diameter trend as shown by the calculated slope of residuals: 0. 08 and 0. 09 respectively (Fig. S5a ). These results suggest that the mature-tree benchmark is sensitive to the stand structure. With 50 % (11 out of 22) confidence in using the big-tree data in benchmarking LSMs, the young-tree benchmark appears to be the most demanding in terms of its data.
At the DEO, HD2, and SOB sites, inconsistencies between benchmarking the big-tree data and the all-tree data stemmed from (1) the similarity between simulations and observations, with a RMSE of around 10 mm; and (2) the fact that the difference between big trees' and small trees' growth was larger in the observations (Fig. S6 ). The site labelled as SCH contained two extremely fast-growing young trees resulting in a very fast-growing virtual tree in the optimized model output (Fig. S7 ). For SOR, the difficulties may have come from the model itself, more specifically from difficulties in simulating the carbon allocation (Fig. S8 ). These results suggest that a variety of issues decreases the confidence in using big-tree data for young-tree benchmarking. For the extreme-growth benchmark, big-tree data can be used with 95 % (21 out of the 22) confidence for benchmarking LSMs.
The observed consistency between benchmarking the big-tree data and the all-tree data suggests that extreme growth happens in the same years, irrespective of which dataset is being used. The DVN site showed the smallest RMSE for amplitude when it was calculated with the all-tree data (0. 02), but the site has the biggest ratio of bigtrees to all-trees for amplitudes compared with the simulation (1. 30, Fig. S9 ). In other words, if the simulation is adjusted to the big trees in the observation, as the difference between subsampled big-tree and all-tree is larger in the observations, the average simulation becomes bigger than the average observation (see  Figs. S5 and S6). This result suggests that the extreme-growth benchmark is the least demanding benchmark in terms of sampling design.
Our verification approach estimated the level of confidence for each benchmark from the fraction of cases for which scaling simulations to observational benchmarks for big-tree data would result in improved model performance for observational benchmarks for all-tree data. In other words, big-tree-biased verification data should not degrade model performance relative to all-tree verification data, and such tests were performed using the European biomass network dataset (BACI, 2020). The results, then, might inform the use of the ITRDB, which is suspected to contain largetree bias, for large-scale verification of LSMs on decadal to centennial timescales (Fig. S1b ). Verification results (Table  1 ) show that if the output of OR-CHIDEE is benchmarked against data with imposed big-tree bias, there is 70 % confidence that the benchmark will produce similar conclusions as reached from the use of all-tree data (Table  1 , columns 1 and 2).
The same finding suggests, however, that forest inventory data for which the diameter distribution is known but only a few big trees were cored would be a reliable data source for benchmarking LSM. The higher fraction of modifier-improved ORCHIDEE simulations for beech (87 %) relative to spruce (64 %) (Table  1 , compare the bold values in rows 1-7 to the bold values in rows 8-11) suggests that the validity of the assumptions underlying the use of ITRDB data partly depends on tree species. Unfortunately, in this study, the variety of species was too limited to generalize this result in terms of plant functional types. Even when benchmarks calculated from observations and simulations are not in agreement, they may nevertheless be used to identify ways in which to improve the observational database or the simulation model.