dataesr/scientific-paragraphs-categorization huggingface dataset:
The very good agreement among these reference models, while they differ in numerical methods, coding, and spectroscopic databases, is an important factor in the confidence that we can have in their results. To go further and improve our understanding of the remaining discrepancies between them, we plan to compare 4A/OP outputs using either the GEISA or HITRAN spectroscopic databases and thus be able to identify the differences in results and discriminate between those that are due to the spectroscopic properties of gases and those that are due to the radiative transfer modeling. Appendix A: Complementary information on the evaluation of 4A-Flux over the RFMIP database This Appendix presents further information on the differences between 4A-Flux and the other benchmark models of RFMIP-IRF. Table  A1  lists the 18 experiments used in RFMIP; more detailed information is provided in the protocol paper of RFMIP  (Pincus et al.
, 2016) ; PMIP stands for Paleoclimate Modelling Intercomparison Project  (Kageyama et al. , 2016). respond to "future" gas concentrations. The OLR computed with 4A/OP is significantly higher due to the limited gas list that has been accounted for in the simulation (16 out of the 43 specified in the RFMIP database). These minor gases have a small impact on the pre-industrial and present-day radiative forcing but play a greater part in the future radiative forcing. The concentration of some of these gases increases by orders of magnitude in the future scenario compared to the present-day situation. The difference completely disappears for forcing variants f2 and f3 for which the same ensemble of gases has been considered for all models. Gases that are neglected in the present-day forcing can play an important part in the future forcing as their concentrations increase and should thus be accounted for in such cases.
Code and data availability. The distributed version of the 4A/OP radiative transfer model version 1. 5 is available at https://4aop. aeris-data. fr/ (last access: 12 June 2022). This version of 4A/OP does not include the 4A-Flux module version 1. 0 yet. However, the development version of 4A/OP v1. 5 including 4A-Flux v1. 0 -described in this paper -is available on the Zenodo platform (https://doi. org/10. 5281/zenodo. 5667737; Tellier et al. ,.
3 Evaluation of 4A-Flux over the RFMIP database The Radiative Forcing Model Intercomparison Project (RFMIP 4 ; protocol paper: Pincus et al. , 2016) is one of the 23 intercomparison projects supported by the World Climate Research Program (WRCP) in the context of the 6th phase of the Coupled Model Intercomparison Project (CMIP6, see Eyring et al. , 2016). The objective of the experiment RFMIP-IRF (RFMIP Instantaneous Radiative Forcing from greenhouse gases) is to characterize the accuracy of the parameterization of the IRF used in climate models under clear-sky and aerosol-free conditions. This characterization is performed in comparison to line-by-line radiative transfer models that are recognized for their very high accuracy. Thanks to the new implementation of the 4A-Flux module, the 4A/OP radiative transfer model contributed to RFMIP-IRF by providing longwave downwelling and upwelling fluxes.
| Phylogenetic analyses DNA sequences were edited using MEGA v. 7. 0  (Kumar et al. , 2016)  and complemented with previously published  (Jørgensen et al. , 2008; Schultheiß et al. , 2009)  sequences available from NCBI GenBank. The 43 ingroup specimens were supplemented with two ampullariid outgroups, namely Afropomus balanoideus and Pila polita (Table  S1 ). The 16S rRNA and 28S rRNA fragments were aligned using the MAFFT web server  (Katoh & Standley, 2013). We used PartitionFinder v. 2  (Lanfear et al. , 2016)  with the greedy algorithm and codon-position data blocks to identify the best-fit substitution model for subsequent phylogenetic analyses based on the corrected Akaike Information Criterion (AICc). Bayesian inference (BI) was performed using MrBayes v. 3. 2. 6  (Ronquist et al. , 2012) , with two independent Markov Chain Monte Carlo (MCMC) searches (each with four chains) for 10 million generations and sampling every 500 th tree and applying a burn-in of 50%.
Convergence of the two independent runs was examined a posteriori in Tracer v. 1. 5 (Rambaut & Drummond, 2007). A maximum likelihood (ML) analysis was conducted using the RAxML-HPC BlackBox v. 8. 2. 10 (  Stamatakis, 2014)  with the standard GTR+Γ substitution model for all partitions and a stop rule for the bootstrapping. For comparative purposes, RAxML analyses were also conducted on the mitochondrial and nuclear datasets separately. All phylogenetic and molecular-clock analyses (see below) were performed on the CIPRES Science Gateway  (Miller et al. , 2010).
| Species delimitation We delimited molecular operational taxonomic units (OTUs) with the Poisson tree processes (PTP) model  (Zhang et al. , 2013)  using the BI and the ML approaches and default settings as implemented on the bPTP web server with the MrBayes phylogram. In addition, BEAST was run with the full dataset of specimens and a single calibration point (see below). The resulting chronogram was analysed on the GMYC web server using the GMYC species delimitation approach  (Fujisawa & Barraclough, 2013)  with a single threshold. In parallel, specimens were identified based on shell morphology using the taxonomic literature (e. g. Brown, 1994; Cowie, 2015)  to allow matching OTUs to species names. If a name existed for an OTU, we assigned it, otherwise the OTU was indicated as Lanistes sp.
| Estimation of ancestral habitat types We defined three habitat types: lake, river and swamp. Flood-plain habitats have been coded as 'river' because these form a continuous although dynamic habitat for snails; the category 'swamp' included all other types of wetlands. For OTUs that have an assigned species name, habitats were coded based on our empirical data and habitat descriptions from the literature as summarized by  Brown (1994). For other OTUs, habitats were coded only based on our sampling. Ancestral habitat types were estimated using stochastic character mapping  (Huelsenbeck et al. , 2003)  as implemented in phytools. Three models of discrete character evolution available in phytools were tested, namely ARD (all rates different), SYM (symmetric rates) and ER (equal rates) using 1000 stochastic character maps.
| Phylogenetic relationships and biodiversity patterns According to the PartitionFinder analysis, the best-fit partition scheme and substitution models were as follows: GTR+I+Γ for 16S rRNA, GTR+I+Γ for 28S rRNA+H3 2nd , GTR+Γ for COI 1st , GTR+I+Γ for COI 2nd , GTR+I+Γ for COI 3rd , GTR+Γ for H3 1st and JC for H3 3rd. For the MrBayes analysis, effective sample size (ESS) values of >200 indicated adequate sampling of posterior distributions. Phylogenetic reconstruction with RAxML and MrBayes resulted in highly congruent topologies and an overall strongly supported phylogeny (Figure  S1 ) comprising five major clades (Figure  3 ). The analysis of this total dataset was robust, given that it was compatible to analyses based on only mitochondrial or nuclear (Figures  S2, S3 ). Clade C1 is represented by Lanistes libycus from the Donger River (Cameroon), L.
Cladogenetic events River (Botswana), the Zambezi River (Zambia), the Congo River (DR Congo) and the Luapula River (Zambia). Our species delimitation methods both identified 22 OTUs for Lanistes (Figure  2 , Figure  S1 ), of which, however, we treated the two species endemic to Lake Malawi (L. nyassanus and L. solidus) as separate OTUs a posteriori. Including the two outgroup taxa, our final dataset used for subsequent analyses thus comprised 25 OTUs.
Data To study the variability of the density profiles, the CORA V4. 3 dataset is used (Coriolis Ocean Dataset for Reanalysis  (Szekely et al. , 2016)  provided by Copernicus monitoring service 1  and SEANOE (SEA scieNtific Open data Edition 2 ). This dataset gathers all kinds of measurements in the ocean sorted by date and instrument. Because density is targeted, only the instruments that measure profiles of temperature and salinity at the same time are selected: Argo float, CTD, XCTD and moorings. The areas of interest are defined as follows: for the western equatorial Atlantic, from 5 • S to 15 • N and from 60 to 35 • W; for the Bay of Biscay, from 43 to 48. 5 • N and from 10 to 0 • W (Fig. 1b  and d ). These individual profiles are used for the cluster analysis. The typical density profiles derived from the clusters are compared with seasonal means (based on the mean of 3 months; hereafter called 3-month means), averaged over the two areas of interest and processed from the same dataset.
In addition the clusters are compared with existing climatologies, also averaged over the two areas. For the Bay of Biscay, BOBYCLIM is used (Bay Of BiscaY's CLIMatology;  Charraudeau and Vandermeirsch, 2006 ; produced by the Ifremer 3 ). This seasonal climatology uses the profiles in this area from 1862 to 2006 classified into four seasons (3-month means), using a grid of 1/5 •. For both the western equatorial Atlantic and the Bay of Biscay, the ISAS13 climatology is used (In Situ Analysis System;  Gaillard, 2015; provided by Copernicus and SEANOE). This monthly climatology is based on the CORA database and averaged from 2004 to 2014, using a grid of 1/5 •. The seasonal climatology of ISAS13 is built using 3-month means. The potential density and buoyancy frequencies are calculated from TEOS-10 definitions  (Millero et al.
, 2008)  that use the Gibbs Sea Water (GSW) equations of  Feistel (2003 Feistel ( , 2008) )  (Python GSW package 4 ). Figure  1   distribution of the dataset for the two areas of interest. In the western equatorial Atlantic area, the main stratification occurs around 100 m (Fig. 1a ). Most of the variability of the profiles is around this depth but also at the surface. Then, stratification remains constant down to 1500 m and slightly decreases close to the bottom. Note that the spatial distribution of the profiles is not homogeneous, less profiles being available along the shelf break. In the Bay of Biscay area, the stratification is weaker than for the western equatorial Atlantic (Fig. 1f ). The strongest stratification and the largest variability occur at the surface. This pattern expresses the dominance of the radiative forcing in this region.
Methodology of the classification Traditionally, the background stratification is classified using either 3-month means or monthly means. Such a method can mix very diverse profiles, leading to vertically smoothed mean profiles. Multiplying spatial boxes and reducing the time interval helps to detail the variability but increase the number of typical profiles considered. The clustering methods are based on the similarity of the profiles with each other to calculate an optimal classification, without being affected by the spatiotemporal variability of the profiles. For that purpose, a matrix of distance between every profile is built based on the principal component analysis (PCA; Python SciKitLearn decomposition package;  Pedregosa et al. , 2011)  of each profile along two components. The first axis (PC1) is mainly controlled by deep pycnoclines, whereas the second axis (PC2) is mainly controlled by the surface ones.
Stratification of the Bay of Biscay The clustering method is also performed on the Bay of Biscay profiles. The six clusters are sorted by their number of profiles. The density profiles are measured from 1991 to 2015. The distribution of the 2447 profiles into the six clusters can be found in Table  1. In this dataset, no suspicious profiles are detected with the clustering method, so all of the clusters will be used for the density study. Note that BB-1 has the most profiles, BB-6 has the least and the other clusters are almost equally represented. Figure  3d  shows the six typical profiles processed from the six clusters. In this area, the main variability of the profiles is dominated by the upper surface density. BB-2 and BB-6 are the only clusters to have almost the same surface density.
Discussion The stratification in the two areas of interest is driven by very different forcing: the Amazon plume and the circulation for the western equatorial Atlantic and the radiative forcing for the Bay of Biscay. The stratification variability due to the circulation is spatially bound, whereas the one due to radiative forcing affects the area homogeneously. The methodology proposed enables us to distinguish the specificity of each sub-region at once. In this section, some typical profiles from the clusters will be compared with seasonal climatologies (ISAS13 and BOBYCLIM) and to the 3-month means made from the CORA V4. 3 dataset. For the western equatorial Atlantic, the two extreme clusters are chosen. WEA-1 is compared with the spring mean because this is the season with the shallower pycnocline and because this cluster is highly represented in spring.
In summary, for the elevation amplitude in the Bay of Biscay, mode 1 is controlled by the maximum of N at 750 m, mode 2 is controlled by the value of N between 20 and 40 m, and mode 3 might be controlled by the value of N between https://doi. org/10. 5194/os-17-1563-2021 Ocean Sci. , 17, 1563-1583, 2021 the surface and 20 m. So the depth of the pycnoclines is not the only parameter representing the IT variability in this area. As no adequate proxy has been actually found, the IT elevation amplitudes, energy fluxes and horizontal surface wavelengths are processed seasonally. A climatology has been developed with a time step of 3 d. The values of each mode are calculated from the weighted mean of the cluster distribution in the dataset over a window of 3 d along the year. The weighted standard deviation is also calculated to evaluate the variability inside each time step.
(2021)  through two seasons: from March to July (MAMJJ), when the NBC does not show eddies, and from August to December (ASOND), when strong NBC rings influence the area. Two major generation sites are highlighted near 1 • N-45. 8 • W (Aa and Ab). The stratification near these generation sites is similar to WEA-128 m during MAMJJ and WEA-128 m during ASOND (Fig. C1 ). The stratification of NEMO simulation is weaker than the one observed with the CORA database, but the variability of the depth of the unique pycnocline seems comparable. Table  3  shows the elevation amplitudes and the vertically integrated energy fluxes near the generation zone for the two seasons. The values from the NEMO simulation show the same pattern highlighted in this study with stronger ITs when the unique pycnocline is deeper (ASOND).
Acknowledgements. The authors are grateful to the  CNES, CLS ,  CNRS  and  UPS  for the funding of this study as well as the projects COCTO (PIs:  Nadia Ayoub  and  Pierre De Mey-Frémaux ) and  High-Freq  ( PI: Florent Lyard ) for the additional funding of diverse missions for the communication of this study. Damien Allain  is also thanked for his help with the simulations and the development of POCViP. The developers of the Scikit-learn Python package (Pedregosa et al. , 2011) are thanked for providing a well-documented package on machine learning algorithms. Rosemary Morrow  is thanked for her helpful comments on how to make the article easily understandable and on the writing of this article. Financial support. This study have been supported by  CNES  (grant no. 2884 ),  CLS  and  CNRS  (grant no.
Code and data availability. The hydrodynamic code T-UGOm (CECILL licence) is available at https://hg. legos. obs-mip. fr/tugo (last access: 4 October 2021) (Mercurial repository, 2021). The climatology of IT properties in the Bay of Biscay is available upon request to the authors. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/os-17-1563-2021-supplement.
Febuary 2021. 2 https://www. seanoe. org/, last access: 26 Febuary 2021. 3 http://www. ifremer. fr/climatologie-gascogne/climatologie/ index. php, last access: 26 February 2021. 4 https://github. com/TEOS-10/python-gsw, last access: 26 February 2021. Ocean Sci. , 17, 1563-1583, 2021 https://doi. org/10. 5194/os-17-1563-2021.
, 2013; Burke et al. , 2019)  in the STAiG laboratory at the University of St Andrews. Samples and standards were measured twice during a single analytical session, along with total procedural blanks. The reproducibility of the Switzer Falls standard over the course of this study was δ 34 S = 4. 15±0. 15 ‰ and 33 S = -0. 02±0. 17 ‰ (n = 25, 2 SD), in agreement with ratios from previous studies of δ 34 S = 4. 17 ± 0. 11 ‰ and 33 S = 0. 01 ± 0. 10 ‰  (Burke et al. , 2018 (Burke et al. , , 2019)). Procedural blanks had 0. 24 ± 0. 12 nmol S with a δ 34 S = 4. 32 ± 2. 67 ‰ (n = 18, 2 SD) and were used to blank correct all measured ratios. Background sample(s) from immediately before the peak in volcanic sulfate were used in conjunction with concentration data to determine the fraction of volcanic sourced sulfate for each sample and the isotopic compositions of the volcanic fraction (δ 34 S volc and δ 33 S volc ) following  Baroni et al.
We take the approach that given the age estimates of Toba, we can investigate all possible candidates within the age uncertainty and rule out candidate eruptions if they have a muted or weak MIF signal. Although we cannot rule out the possibility that other eruptions deposited these sulfate peaks, providing that the dates of the YTT are accurate and that it emitted substantial sulfur, then at least one of the candidates we investigated is very likely to be Toba. Using the VOGRIPA database  (Crosweller et al. , 2012; Brown et al. , 2014)  we have identified other volcanic events over the age range of T1-T9 (when considered on the AICC2012 age model). These volcanic events and their associated dates are detailed in Table  S4  in the Supplement. There are nine events with VEI ≥ 6 at around 74 ka in VOGRIPA; however, they often have large age uncertainties associated with the eruption dates (over 10 ka).
However the SO 2 dispersion height is not as well constrained for past eruptions as the plume top height  (Aubry et al. , 2021). For the Agung and Pinatubo eruptions, we have used the SO 2 dispersion altitudes from the literature, compiled in the IVESPA database  (Aubry et al. , 2021 ; http://ivespa. co. uk/, last access: 20 August 2021). For the Tambora and Samalas eruptions we have calculated the SO 2 dispersion height using the ratio of SO 2 height to plume top height for the Pinatubo eruption (0. 64). With this data we place the SO 2 dispersion height for Toba at over 30 km (Fig. 6 ). Although this tentative relationship between 33 S and plume height supports the conclusion of an altitude dependence of S-MIF by  Lin et al. (2018) , there are some important caveats to note. For instance, the maximum magnitude of the 33 S measured in the ice will depend on the sampling resolution and preservation in the core.
Toba tephra has yet to be identified in either Greenland or Antarctic ice cores  (Svensson et al. , 2013)  despite tephra being identified in Antarctica for other similar or smaller magnitude tropical volcanic eruptions such as Samalas  (Lavigne et al. , 2013). However, with method improvements in tephra extraction  (Iverson et al. , 2017; Narcisi et al. , 2019)  it may be possible to identify the volcanic peak(s) corresponding to Toba and further constrain the timing of the largest volcanic event of the Quaternary. Data availability. The sulfur isotope composition data for EDC an EDML ice core samples are available on the PANGAEA data repository: https://doi. org/10. 1594/PANGAEA. 933271 (Crick et al. , 2021). Supplement. The supplement related to this article is available online at: https://doi.
org/10. 5194/cp-17-2119-2021-supplement. Author contributions. LC, EAD, and SM sampled the EDC ice cores, and MK and SK sampled the EDML ice cores. AB and KAM measured S isotopes from EDML, and LC measured S isotopes from EDC with supervision from AB, RCJS, and JWBR. LC and AB analysed isotope data, and LC analysed sulfate concentration data with help from EWW. LC and AB developed the initial interpretation, and LC wrote the initial draft manuscript, with early contributions from AB, WH, JS, RSJS, and EWW. AB, RSJS, and MK funded the project through grants (see the acknowledgements). AB supervised the project. All co-authors contributed to the writing and editing of the final submitted manuscript. Competing interests. The authors declare that they have no conflict of interest. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
support. Laura Crick is funded by the University of St Andrews St Leonards 7th Century Scholarship (-117THCENT01) in partnership with the IAPETUS Natural Environment Research Council Doctoral Training Partnership. Andrea Burke is funded by a European Research Council (ERC) Marie Curie Career Integration Grant (CIG14-631752) and NERC Strategic Environmental Science Capital Call (CC082). William Hutchison is funded by a UKRI Future Leaders Fellowship (MR/S033505/1). Mika Kohno was funded through DFG-Project Wo 362/32-1 and Wo 362/46-1,2 to Gerhard Wörner at GZG, University of Göttingen. R. Stephen J. Sparks is funded by a Leverhulme Grant (RPG-2015-246) and Leverhulme Emeritus Fellowship (EM-2018-050), and Eric W. Wolff is funded by a Royal Society Professorship. Review statement. This paper was edited by Michael Sigl and reviewed by Anders Svensson and Jihong Cole-Dai.
2-2. 3). Sea ice core parameters (e. g. , snow thickness, air temperature and photosynthetically active radiation (PAR) estimated at the bottom of sea ice) were collected during both 2015 and 2016 campaigns and have already been published elsewhere  (Amiraux et al. , 2019; Massicotte et al. , 2019; Oziel et al. , 2019). Briefly, for each sampling date, 5-8 snow depth measurements were made with a ruler. Air temperature records were made with a meteorological station: an automated Meteo Mat equipped with temperature sensor (HC2S3; Campbell Scientific) sensor positioned near (< 100 m) the sea ice station. Photosynthetically active radiation (PAR) below the sea ice was estimated at 1. 3 m using the multispectral data collected with a Compact -Optical Profiling System (C-OPS; version Ice-PRO; Biospherical instruments Inc.
Sea ice Principal Component analysis (PCA) biplots of the environmental variables was used to reduce the dimensionality of the dataset using the "FactoMineR" package  (Pagès, 2004). Environmental variables used into the PCA were total fatty acid (TFA), polyunsaturated fatty acid (PUFA), air temperature, PAR, snow thickness and Chl a, to describe the 28 and 23 sea ice sampling dates from 2015 and 2016, respectively. Wilcoxon tests were performed to test the effect of year (fixed with two levels: 2015 and 2016) on sea ice fatty acid (TFA, monounsaturated fatty acid (MUFA), PUFA, saturated fatty acid (SFA)) and HBI (IP 25 , HBI IIa, IIb, III and IV) contents. Spearman's rank order correlation (r) was used to infer the strength of associations between HBI variables and correlation significance was determined at p-value < 0.
Acknowledgements We especially thank  E. Brossier  for the sampling of bivalves,  C. Guilmette  and  F. Dufour  for providing preliminary lipid results. This project was made possible by the support of the hamlet of Qikiqtarjuaq and the members of the community together with the Inuksuit School and its Principal,  Jacqueline Arsenault. It was conducted under the scientific coordination of the  Canada Excellence Research Chair on Remote Sensing of Canada 's new Arctic frontier and the  CNRS & Université Laval Takuvik Joint International Laboratory  ( UMI3376 ). The field campaigns (Scientific Research License #  01 010 15N-M  and #  01 001 16R-M ) owed its success to the contribution of  J. Ferland ,  G. Bécu ,  C. Marec ,  J. Lagunas ,  F. Bruyant ,  J. Larivière ,  E. Rehm ,  S. Lambert-Girard ,  C.