dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This study was supported by  MOST   104-2923-M-002-002-MY4  to the  National Taiwan University ; by the  IGEM  project " Impact of Groundwater in Earth system Models ", cofunded by the  French Agence Nationale de la Recherche  ( ANR Grant  no. ANR-14-CE01-0018-01 ) and the  Taiwanese Ministry of Science and Technology ; by the  NASA   GRACE Science Team ; and by the  Research and Technology Development program  of the  NASA Jet Propulsion Laboratory at the California Institute of Technology , under contract with  NASA. M. -H. L. is also supported by  MOST   106-2111-M-002-010-MY4.
Data availability CESM Large Ensemble Community Project generated by NCAR is available at http:// www. cesm. ucar. edu/projects/community-projects/LENS/. GRACE/GRACE-FO Mascon data are available at http://grace. jpl. nasa. gov. The GLDAS data used in this study were acquired as part of the mission of NASA's Earth Science Division and archived and distributed by the Goddard Earth Sciences (GES) Data and Information Services Center (DISC).
Then, a second cross-validation method is performed for assessing the ability of the MBC methods to account for the multidimensional evolutions of the climate model. Additionally, two reference datasets are used to assess the influence of their spatial resolution on (M)BC results. Most of the methods reasonably correct inter-variable and intersite correlations. However, none of them adjust correctly the temporal structure as they generate bias-corrected data with usually weak temporal dependencies compared to observations. Major differences are found concerning the applicability and stability of the methods in high-dimensional contexts and in their capability to reproduce the multidimensional changes in the model. Based on these conclusions, perspectives for MBC developments are suggested, such as methods to adjust not only multivariate correlations but also temporal structures and allowing multidimensional evolutions of the model to be accounted for in the correction.
It provides a more extensive intercomparison framework in which quality of BC outputs can be assessed and compared by evaluating univariate, inter-variable, spatial and temporal properties, as well as multidimensional changes. In addition, each BC method is applied to correct climate model outputs over France and three subregions according to two distinct reference datasets with different spatial resolutions. This permits one to assess the potential influence of the reference spatial resolution on bias correction results and to delineate guidance on relevant good practices for end users concerning this aspect. This paper is organized as follows: Sect. 2 describes the model and reference data used, and Sect. 3 presents the BC methods intercompared. Then, Sect. 4 presents the experimental setup used in this study, while Sect.
Model and reference data Institut Pierre-Simon Laplace (IPSL) coupled model  (Marti et al. , 2010; Dufresne et al. , 2013)  daily data with a 1. 25 • by 2. 5 • spatial resolution are used in this study as model data to be corrected. Simulations of the scenario of atmospheric CO 2 concentration pathway associated with a radiative forcing of +8. 5 W m -2 (RCP 8. 5 scenario, i. e. , the scenario with highest CO 2 concentration) are selected. Daily temperature (T2) and precipitation (PR) time series from 1 January 1979 to 31 December 2016 are extracted over the geographical area of France  ([42, 51  • N] × [-5, 10 • E]), which corresponds to 321 continental grid cells. As BC methods require a reference dataset to adjust the simulations, daily temperature and precipitation time series with a 0.
5 • by 0. 5 • spatial resolution are first used from the "WATCH Forcing Data methodology applied to ERA-Interim data" (WFDEI) from the EU WATCH project  (Weedon et al. , 2014)  over the same geographical area of France. Note that, as spatial resolution between WFDEI and IPSL-CM5 are different, IPSL model data are regridded by a nearestneighbor technique to associate each IPSL grid cell with its nearest WFDEI grid cell center. Hence, in the following, the IPSL data will be used at the 0. 5 • spatial resolution corresponding to that of the WFDEI reference dataset. To assess the influence of the reference spatial resolution on BC results, we use another reference gridded dataset for France with finer resolution: the "Systeme d'Analyze Fournissant des Renseignements Atmosphériques à la Neige" (SAFRAN) reanalysis dataset  (Vidal et al.
, 2010). Daily T2 and PR time series from SAFRAN have a 8 km × 8 km spatial resolution and divide France into 8981 continental grid cells. IPSL data are regridded to the 8 km × 8 km SAFRAN resolution using the nearest-neighbor technique. Once regridded IPSL simulations are obtained, each MBC method can be applied. However, as some MBC algorithms have difficulties in practice in very high-dimensional contexts (here for 8981 grid cells), we restrict the application of MBCs with SAFRAN reference dataset over the Brittany region, located in the northwest part of France  ([47, 49  • N] × [-5, 2 • E]), which corresponds to 345 continental grid cells. Note that we selected this subregion of Brittany for SAFRAN, i. e. , at fine resolution, in order to have a similar number of grid cells as for France selected with the WFDEI reference dataset, i.
Including different dimensional versions in the study will permit one to better highlight the potential losses and benefits associated with them. Therefore, in the following each of the four MBC methods is applied according to the three following configurations: a 2-dimensional (hereinafter referred to as "2d-") version, for which the MBC method is applied independently at each grid cell but jointly corrects both temperature and precipitation time series. For example, to correct a climate dataset of 321 grid cells, the MBC method is performed 321 times, i. e. , for each grid cell across the whole grid. By doing so, 2d-versions are aiming to correct inter-variable correlations within each grid cell. a spatial-dimensional (hereinafter referred to as "Spatial-") version, where all time series for a particular physical variable are corrected jointly but independently from the other physical variable.
Results The correction outputs are evaluated according to different characteristics designed to focus on (i) marginal, (ii) intervariable, (iii) spatial, (iv) temporal and (v) nonstationary properties. Characteristics (i)-(iv) are evaluated on the 1979-2016 period for the adjusted outputs obtained according to the 2-fold protocol and are compared to those from the reference dataset. However, regarding nonstationary properties, corrected outputs from the second protocol are used, and results are compared to the simulations to highlight the performances of the MBC methods regarding their capability to reproduce (or not) the multidimensional changes in the model between the  1979-1997 and 1998-2016 periods. In the following, evaluation is presented for the winter season (December-January-February) only, as conclusions remain generally the same for the other seasons.
Spatial correlations To assess the quality of the corrections in terms of spatial correlations, mean correlograms, i. e. , mean Spearman correlation in function of distance, are computed for temperature and precipitation separately after removing daily areal mean. Indeed, climate variables can present a high day-to-day variability that can affect the evaluation of spatial criteria if not removed (e. g. ,  Vrac, 2018). Figure  3  and S3 show the results obtained for, respectively, precipitation and temperature for the different climate datasets. Note that the choice of the reference dimension for R 2 D 2 -versions modifies results for temporal criteria and, consequently, for some of the spatial criteria. Hence, in the rest of this work, results from R 2 D 2 -versions are presented with the reference dimension corresponding to the variable under interest.
For the sake of brevity, results for precipitation are mainly discussed in this subsection, and nuances are made when different results are obtained for temperature. For France, the IPSL precipitation correlogram is fairly distinct from the WFDEI one. The univariate method CDF-t, by simply adjusting univariate distributions, gets closer to the reference dataset (Fig. 3a1 ), which may be here confusing. Indeed, although CDF-t adjusts the univariate distributions, it is supposed to preserve the rank sequence of the simulations, and therefore spatial correlations are disregarded during the BC procedure. But, as the Singularity Stochastic Removal version of CDF-t  (Vrac et al. , 2016)  is explicitly designed to improve dry days frequency, the method consequently modifies rank correlations, which results here in an improvement of spatial statistics for precipitation.
Also, an additional reason is that the correction of the univariate distributions provided by CDF-t associated with the removing of daily areal means modifies ranks of the data, resulting in getting a correlogram closer to that from the reference dataset, and so improves intersite variability. Correlograms of 2d-versions (dotted) for the four MBC methods (Fig. 3b1 , c1, d1 and e1) show results equivalent to CDF-t. Indeed, 2d-configuration MBCs adjust univariate distributions and inter-variable correlations without modifying spatial correlations. The improvements of correlograms for 2d-versions thereby illustrate again that the correction of univariate distributions improves spatial statistics for France. Particularly, 2d-R 2 D 2 results (Fig. 3b1 ) are, by construction, exactly the same as those from CDF-t  (Vrac, 2018).
Indeed, by construction, 2d-R 2 D 2 driven by precipitation preserves Spearman spatial correlations from CDF-t for the precipitation variable. Note that, however, it is definitely not the case for temperature spatial structure (not shown) when 2d-R 2 D 2 is driven by precipitation. Indeed, for 2d-R 2 D 2 outputs driven by a specific physical variable, spatial structures of the "other" variable are strongly degraded by the reordering step. Correlograms associated with outputs of Spatial-and Fullversions for R 2 D 2 (Fig. 3b1 ) nicely fit the one from the reference dataset -even at long distances -and provide major improvements in adjusting the spatial properties of the simulations. However, for similar reasons as those explained for 2d-R 2 D 2 , undesirable degradation effects on spatial crosscorrelation between temperature and precipitation are obtained for Spatial-R 2 D 2 outputs (not shown).
Therefore, it indicates that practitioners must favor the use of Full-R 2 D 2 for their applications. With regard to Spatial-and Full-dOTC (Fig. 3c1 ) and Spatial-MRec (Fig. 3e1 ), although correlograms are very close to those from the reference dataset, they provide slightly less pronounced improvements compared to the 2d-versions, suggesting a slight degrading effect on results for these methods by considering more variables in the correction. As expected, the correlogram associated with https://doi. org/10. 5194/esd-11-537-2020 Earth Syst. Dynam. , 11, 537-562, 2020 Full-MRec outputs is away from reference data, indicating once again the dysfunction of the MRec method for France. For Spatial-and Full-MBCn (Fig. 3d1 ), at long distances, similar improvement of spatial correlations are provided as those from dOTC.
Temporal structure The different MBC methods implemented here are not intended to adjust temporal structures. Indeed, these multivariate procedures adjust multivariate distributions without accounting for any temporal information. However, although the temporal structures are not adjusted according to the ref-Earth Syst. Dynam. , 11, 537-562, 2020 https://doi. org/10. 5194/esd-11-537-2020 erence, MBCs necessarily modify the rank sequences of the simulations  (Vrac, 2018). This modification is not performed in the same way depending on the MBC or the dimensional configuration used and remains therefore to evaluate. To do so, 1 d lag Pearson autocorrelations are computed at each grid cell for temperature and precipitation. The resulting maps of differences with respect to the reference for the different climate datasets are displayed in Fig.
4  (resp. Fig. S4 ) for temperature (resp. precipitation). For France, IPSL temperature autocorrelations differences (Fig. 4b1 ) are small, indicating a relative agreement of IPSL with the WFDEI reference dataset (Fig. 4a1 ), showing equivalent high values. A similar differences map is provided by CDF-t outputs (Fig. 4c1 ). It is however not the case for precipitation (Fig. S4c1 ), for which a decrease of autocorrelation values is observed over France with respect to the reference and to the model. Although not observed for temperature, it highlights that the univariate correction could have a non-negligible effect on Pearson autocorrelation. Interestingly, 2d-versions (Fig. 4d1 , g1, j1 and m1) do not lead to a strong modification of temporal properties with respect to CDF-t. However, from one method to another, temporal structure modifications are not equivalent for Spatial-and Full-versions.
In a general way, the same conclusions can be drawn for Brittany, sometimes even better illustrated due to a narrower color scale. The results for Full-MRec are easier to interpret. They present results similar to those from 2d-and Spatial-MRec (Fig. 4o2 ). In particular, this indicates that, contrary to dOTC and MBCn, MRec does not present an increasing modification of temperature autocorrelations from 2d-to Full-versions. To better understand the results obtained from Fig. 4 , further explanations are required. The relative agreement of Pearson autocorrelation values between the reference and IPSL dataset shown in Fig. 4  might lead one to believe that temporal properties of the model are quite correct for temperature, which is in reality misleading for two main reasons. First, 1 d lag Pearson autocorrelation permits one to assess only a particular feature of the temporal properties, which is obviously insufficient to draw any general conclusions about the quality of simulations concerning these aspects.
In particular, computing Eq. (  1 ) between a distribution characterizing a sample during the calibration period and another distribution characterizing a sample during the projection period, permits one to provide information on its change across time, whether it represents a univariate, multivariable or multi-site (or both) distribution. More details on how to compute in practice this distance are provided in Appendix C. The resulting metric, denoted Wd, is calculated using the R package "transport"  (Schuhmacher et al. , 2019)  over the region of interest according to three different multivariate distributions: on ranks of temperature only over the whole region to assess change in the spatial dependence structure of temperature; on ranks for precipitation only over the whole region to assess change in the spatial dependence structure of precipitation; on ranks for both temperature and precipitation over the whole region to assess change in the inter-variable and spatial dependence structures of the two variables.
In particular, computing Wd using ranks instead of raw values allows the removal of the change in the univariate distributions from that in spatial and inter-variable relationships. However, comparing Wd values of climate datasets must be made with caution. Indeed, similar values of Wd for different climate datasets do not necessarily imply that their changes in spatial structure are similar. Results for the three Wasserstein distances on ranks are displayed in Fig. 7  for both France and Brittany. Additional results for Wd on raw values are displayed in Fig. S7  for information purposes only. For France (Fig. 7a ), the three Wd are slightly higher for the reference than for the model data (represented by straight lines). Although the differences are quite small, it cannot be concluded directly that changes in spatial structure are identical, as there is no particular reason for this.
Dynam. , 11, 537-562, 2020 https://doi. org/10. 5194/esd-11-537-2020 GCM simulations -and not only one as in this study -in order to provide an ensemble of multivariate BC simulations. The obtained datasets would also be useful to carry out scientific studies on other aspects of climate change, such as climate change attribution studies aimed to identify which mechanisms are responsible for changes in the Earth's climate (e. g. ,  Stott et al. , 2016; Yiou et al. , 2017; Ribes et al. , 2020). Indeed, most of these studies use plain simulations, and consequently do not take into account their statistical biases. Conducting attribution studies using plain and biascorrected simulations will permit one to increase the understanding of the influence of these biases on results, which is essential to provide valuable information to the society concerning the ongoing climate change.
Acknowledgements. Bastien François  and  Mathieu Vrac  acknowledge financial support from the  EUPHEME  project. Mathieu Vrac also acknowledges support from the  CoCliServ project and French "Convention de Service Climatique". Both EUPHEME  and CoCliServ are part of  ERA4CS , an  ERA-NET  initiated by  JPI Climate  and cofunded by the  European Union. This work was supported by the  metaprogram Adaptation of Agriculture and Forest to Climate Change (AAFCC)  of the  French National Research Institute for Agriculture, Food & Environment (INRAE). Financial support. This research has been supported by the  ERA4CS  (grant no. 690462 ). Review statement. This paper was edited by  Ben Kravitz  and reviewed by  Jakob Zscheischler  and one anonymous referee.
B. François et al. : Multivariate bias corrections of climate simulations Code and data availability. The R package for MBCn is available at https://CRAN. R-project. org/package=MBC (Cannon, 2018b). R 2 D 2 is not publicly available yet but only on demand from Mathieu Vrac. dOTC and MRec are publicly available at https://github. com/yrobink/SBCK (Robin, 2019). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/esd-11-537-2020-supplement.
Sampling sites We used data from 91 streams in the Corumbataí River Basin, state of São Paulo, Brazil (Fig. 1 ). The basin has an area of ~170,000 ha and deforestation was the primary impact in the beginning of the 1900s. After that, the region has been intensively used for sugarcane production and cattle ranching  (Valente 2001). Sampled streams were distributed along a gradient of forest cover, which is a major predictor of variation in stream insect communities in Brazil  (Siqueira et al. 2012a (Siqueira et al. ,b, 2015;; Saito et al. 2015a ). We calculated the proportion of different land uses in the catchment area upstream the sampling sites. We calculated the area covered by pasture, sugar cane, and native forest using the software QGIS (Quantum GIS We measured the following in situ stream variables to characterize local environmental variation: % of shading at the stream stretch (visually estimated), average stream width and depth (cm), flow velocity (m/s), water temperature ( o C), pH, conductivity (µS/cm), salinity (mS cm -¹), turbidity (NTU), dissolved oxygen (mg/L) and substrate composition (sand, gravels, pebbles, cobbles, and boulders; %).
Mayfly data Between May and August (dry season) of 2015 we sampled mayflies using a two minutes kick-net procedure in each sampling site. We counted and identified all nymphs to the lowest possible taxonomic level (morphotype and species level). Based on previous studies and the natural history of Neotropical mayflies, we selected six key traits: feeding strategy, gills characteristics, body shape, locomotion strategy (all fuzzy coded trait), number of gills (quantitative discrete trait), and wing size (quantitative continuous traits). We compiled trait values from previous studies and databases  (Tomanova, Goitia and Helešic 2006; Colzani et al. 2013; Saito et al. 2015a ). Additionally, for each species, we measured the mean body size (quantitative continuous trait) of nymphs (last instar, minimum of five individuals) under a stereomicroscope (Table  S1 ).
Taxon-and trait-environment relationships To describe trait syndromes in mayfly traits (i. e. , trait combinations that are consistent among different species), we applied a PCA to the trait data after standardizing fuzzy and quantitative traits by zero mean and unit variance. We then compared taxon-and trait-environment relationships using the following multivariate methods  (Kleyer et al. 2012 ): 1) Redundancy analysis (RDA) estimates the relationship between a multivariate explanatory matrix and a multivariate response matrix  (Legendre and Legendre 2008). We used the environmental variables (described above) as the explanatory matrix and the community composition dataset (Hellinger transformed) as the response matrix in the taxon-based approach (RDA-Composition). For the trait-based approach we used the community-weighted mean (CWM) values of traits as the response matrix (RDA-CWM).
db-RDA finds linear relationships between all the Principal Coordinate Analysis (PCoA) axes (summarizing a dissimilarity matrix between sites) and environmental variables. We used the Bray-Curtis dissimilarity as response matrix in the taxon-based approach (db-RDA-Composition). For the trait-based approach, we used the trait dataset to calculate a dissimilarity matrix among sites that provides maximum dissimilarity between completely dissimilar communities (D β coefficient -equation 12 in Pavoine and Ricotta, 2014) (hereafter the db-RDA-Traits approach). This coefficient relies on differences in traits between species, which were calculated using the modified Gower coefficient  (Pavoine et al. 2009 ). We also used estimated P-values through an ANOVA-like procedure with 999 permutations  (Legendre, Oksanen and ter Braak et al.
We thank many colleagues who stimulated interesting discussions during the elaboration of this study at the  Brazilian Symposium of Limnology  in 2017, as well as colleagues from the  Conference of the Iberian Association of Limnology  in 2018. We also thank two anonymous reviewers that helped us during the preparation of this manuscript. The study was partly funded by grants # 13/50424-1 , # 18/02074-5  and # 19/04033-7 ,  São Paulo Research Foundation (FAPESP). Work by LMB has been supported by  CNPq  ( 304314/2014-5 ) and the  National Institute for Science and Technology (INCT) in Ecology, Evolution and Biodiversity Conservation  (proc. 465610/2014-5  MCTIC/CNPq and FAPEG).
910, t = -10. 316, N = 24, df = 22, p < 0. 001). Indeed, we calculated a maximum difference of 35 dB between the smallest (SL pp = 132 dB re 1 µPa²; CL = 2. 6 cm) and the largest (167 dB re 1µPa²; CL = 13. 5 cm) spiny lobsters (Fig. 1 ). The maximum calculated SPL pp was 172 dB re 1 µPa 2 and was produced by the largest individual (CL = 13. 5 cm) at 0. 5 m. The TL models estimated as a×log 10 (r) from the dataset of SPL rms vs. distance did not significantly vary with body size (Pearson: r = -0. 175, t = -0. 733, N = 19, df = 17, p = 0. 474), as expected. The estimated TL parameter a ranged between 16. 1 and 19. 5 (Fig. 2 ). By fitting the results amongst all individuals (except the very small ones), the global TL parameter a was 17. 6 which is between the theoretical models of practical (a = 15) and spherical (a = 20) TLs (Fig.
The following discussion will thus be qualitative, rather than quantitative. The spectral content of the recorded antennal rasps was different according to the distance from the spiny lobsters. Indeed, while low frequencies (<1 kHz) dominated close to the sound source (<10 m), only high frequencies (>10 kHz) remained at higher distances (>10 m; Figs. 3  and 4 ). Since the spiny lobsters may be sensitive to low frequencies only (<1 kHz), the long-distance high-frequency antennal rasps are likely out of the hearing range of the animals. Thus, we can conclude that the communication distances are much shorter than the detection ranges. On the specific dataset studied here, assuming that the animals are sensitive to frequencies below 1 kHz, the communication distances would be no more than 10 m (see Fig.