dataesr/scientific-paragraphs-categorization huggingface dataset:
10 Previously unpublished Pb and Pb isotope data from cruises from 1989 (Atlantis II cruise 123) and 1999 (cruise Endeavor EN328) are included here for evaluation of the decadal evolution of Pb in the eastern North Atlantic. We supplement our 1989 data with two published JGOFS stations  (Martin et al. , 1993). Our 1989 samples were collected using "vane bulb" samplers  (Boyle et al. , 1986)  and the 1999 samples were collected using the MITESS mooring sampler  (Bell et al. , 2002). Samples were stored in acid-cleaned 250ml LPE bottles. 15.
Pb Concentrations GEOVIDE samples were analysed at least 1 month after acidification during more than 36 analytical sessions using the isotope-dilution ICP-MS method described in  Lee et al. 2011 , which includes pre-concentration on nitrilotriacetate (NTA) resin and analysis on a quadrupole ICP-MS (Fisons PQ2+). Method details including all 20 cleaning protocols are available in the metadata file, along with the data, in the BCO-DMO repository (see 2. 4). Briefly, triplicate subsamples (1. 3mL) were spiked with a known 204 Pb spike and the pH was raised to 5. 3 using a trace metal clean ammonium acetate buffer, prepared at a pH of between 7. 95 and 7. 98. ~2400 beads of cleaned NTA Superflow resin (Qiagen Inc. , Valencia, CA) were added to the mixture and equilibrated. After equilibration, the resin was rinsed with distilled water and then Pb was eluted with a 0.
2 ± 1. 7 pmol kg -1. Pb concentration analysis for 1989 samples (Atlantis II 123) was achieved by 204 Pb isotope dilution with Mg(OH) 2 coprecipitation followed by VG PQ2+ quadrupole ICPMS  (Wu & Boyle, 1997 ) (analysed in 1996)  and 1999 (Endeavor 328) stations 4,  5, 7, 9, 10 and 11 (analysed between 1999-2003). Endeavor 328 stations 2, 3, 8, and 10 were determined using NTA-extraction ID ICPMS  (Lee et al. , 2011 (Lee et al. , ) (determined in 2010)). Long-term quality control seawater samples were included in each run, and overlapped with new QC samples when the previous 5 samples were depleted. Endeavor 328 station 10 was determined twice by two analysts 8 years apart (in 2002 by Mg(OH) 2 coprecipitation ID-ICPMS, and in 2010 by NTA extraction ID-ICPMS). A regression of the 2010 vs 2002 data forced through the origin had a slope of 0.
As shown in Figure  S2 , the replicate analysis usually agreed within better than 1000ppm for 206 Pb/ 207 Pb and 208 Pb/ 207 Pb, and 5000ppm for 206 Pb/ 204 Pb. We suggest that these provide a reasonable upper limit for the replicability of our isotope measurements. 10 Pb isotope data from the 1999 samples were obtained by IsoProbe Multicollector ICPMS after Mg(OH) 2 preconcentration and anion exchange purification as described by  Reuer et al. (2003). As for the GEOVIDE samples, the mass spectrometer was calibrated using NBS981. (The GEOTRACES  Group, 2015). All other data is available in table  1. 20.
Outliers In this data set, we did not encounter any samples that did not yield acceptably reproducible results upon repeated analysis, so we believe that the data truly represents the concentration of Pb in the sample collection bottle. However, there were a few samples with elevated Pb based on adjacent samples and for which no obvious 25 hydrographic argument could be made for the anomaly. We observed that the samples taken from GOFlo in rosette position 1 (usually the near-bottom sample) were always higher in [Pb] than the samples taken immediately above that, and that the excess decreased as the cruise proceeded (Figure  S1 ). The Pb isotope ratio of these samples were higher than the comparison bottles as well. At two stations where our near-bottom sample was taken from rosette position 2 rather than 1, there was no Pb excess over the samples immediately above.
We believe that this evidence 30 points to GoFLO bottle-induced contamination that was being slowly washed out during the cruise, but never completely. A similar pattern was observed for the samples taken from rosette positions 5, 20 and 21 when compared to the depth-interpolated [Pb] from the samples immediately above and below. We do not believe that these samples should be trusted as reflecting true ocean  [Pb] , so all of the samples from these GOFlos are excluded in our discussion of this work, although they are included and flagged as unreliable within the data repositories. 35 Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2018-29 Manuscript under review for journal Biogeosciences Discussion started: 2 February 2018 c Author(s) 2018. CC BY 4. 0 License. In addition, we observed high  [Pb]  in most of the samples from Station 1 and very scattered Pb isotope ratios.
's estimate that ~half of the Pb in the surface waters is of natural origin, assuming that the Icelandic dust has a similar isotopic composition as pre-anthropogenic North African dust. 30 The mixed sources of Pb throughout the ocean interior are similarly clear. In Figure  7b , seawater was compared to the possible sources: Pre-Holocene sediments, atmospheric Pb measured in Europe and North America, and North African dust. The GEOVIDE Pb samples cluster tightly together and fall along the mixing line between natural Pb and modern anthropogenic sources (i. e. , industrial emissions from North America and Europe). The Pb signatures of 35 European and North American atmospheric samples are difficult to differentiate using Pb isotopes alone, so Pb emissions estimates were evaluated using the EMEP (European Monitoring Evaluation Program) database.
Acknowledgements 30 The GEOVIDE project was funded by the French National Research Agency (ANR-13-BS06-0014, ANR-12-PDOC-0025-01), the French National Center for Scientific Research (CNRS-LEFE-CYBER), the LabexMER (ANR-10-LABX-19), and Ifremer. We also thank the shipboard technical team: Pierre Branellec, Floriane Desprez.
de Gésincourt, Michel Hamon, Catherine Kermabon, Philippe Le Bot, Stéphane Leizour, Olivier Ménage, Fabien 35 Pérault, and Emmanuel de Saint-Léger. GEOVIDE nutrient data was obtained by Manon Le Goff, Emilie Grossteffan, Morgane Gallinari, and Paul Tréguer. We also thank the officers and crews of the RV Atlantis II (1989) Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2018-29 Manuscript under review for journal Biogeosciences Discussion started: 2 February 2018 c Author(s) 2018. CC BY 4. 0 License. and RV Endeavor (1999) for their efforts on our behalf. Our GEOVIDE sample analyses were funded by the US National Science Foundation by grant OCE-1357224. Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2018-29 Manuscript under review for journal Biogeosciences Discussion started: 2 February 2018 c Author(s) 2018.
They are obtained from multiple time series of precipitation and temperature that are obtained by perturbing the observed weather time series following the change factor method. Although all possible combinations between solar PV and RoR hydropower might be considered, we mainly focus on the 25% solar 75% hydro mix, which is the most likely future energy mix in the studied area. The study is organized as follows: Section 2 describes the study area. The database and the models are presented in Section 3. Section 4 show the results obtained for the control climate. The results obtained through the Decision Scaling approach are presented in Section 5. The Section 6 concludes and gives some outlooks for future works. Climate change projections from the ex-post GCM projections from the Climate Model Intercomparison Project phase 5 (CMIP5) are further used for assessing these change factors for the Representative Concentration Pathway 8.
5 and for different climate models and different future prediction lead times. Future system performance/vulnerability to climate change is then discussed by putting in perspective the CRF and the change factor as obtained from GCM projections. As mentioned above, this also allows inferring probabilities of future climate states and thus probabilities of system performance changes. Although all possible combinations between solar PV and RoR hydropower might be considered, we mainly focus on the 25% solar 75% hydro mix, which is the most likely future energy mix in the studied area. The study is organized as follows: Section 2 describes the study area. The database and the models are presented in Section 3. Section 4 show the results obtained for the control climate. The results obtained through the Decision Scaling approach are presented in Section 5.
Data and Methods The flow diagram on Figure  2  summarizes the analysis framework. Each component of the analysis framework is described below, including the database, the hydrological model, the energy and load modeling and the different climate scenarios used to build the CRFs. By carrying out simulations at an hourly time step, we may account for the three key cycles of the load and generation fluctuation-daily, weekly and seasonal. The time step series are obtained over the 1992-2010 period.
Regional Energy Load No time series of electricity consumption is available for the Veneto, Trento and Bolzano-Southern Tyrol provinces. We therefore estimate the regional electricity consumption by weighting the time series of the national consumption according to the Gross Domestic Product (GDP) of these three provinces, which roughly represented 8% of the Italian GDP in 2013. The electricity consumption data for Italy were provided by the European Network of Transmission System Operators of electricity (https://www. entsoe. eu/home/). The hourly database is available from 2006 to 2013. The Italian average hourly consumption equals 38 GWh, leading to an estimate of 3 GWh for the three provinces. As illustrated by François et al. [30] , economic and societal features affect daily, weekly and annual patterns of electricity demand with high consumption peaks around noon and during the evening and low consumption sequences during weekends, holiday periods and civil or religious celebrations.
For each GCM projection, we first fit a trend model to the raw climate projections following Hingray and Said  [57]. A piece-wise linear function of time is considered for precipitation and a third order polynomial trend is used for temperature. We then compute the change factors from the trends (Figure  2 ). This approach was applied to estimate the change factors for 25 climate models, one representative concentration pathway (i. e. , the Representative Concentration Pathways 8. 5), and three prediction lead times, namely 2040-2059, 2060-2079 and 2080-2099 (Table  1 ). Data and detailed description of the models are available at the CMIP5 archive (https://esgf-node. llnl. gov/search/cmip5/). These GCMs were chosen according to their data availability at the time this study was conducted.
3. 2. The generation of initial conditions on a mesh covering the fundamental domain of the T 3 spatial section is presented in Sect. 3. 3. This is done using mpgrafic (this package and the others used in this work are free-licensed 1 software). In Sect. 3. 4 we explain how to numerically measure the invariants of what in Newtonian terms is the peculiar velocity gradient tensor. This is only done in the initial conditions for our main model. However, these invariants can also be measured at later time steps for full N-body calculations. To the degree that the peculiar gradient tensor approximates the (general-relativistic) extrinsic curvature tensor, our approach should not deviate by much from a relativistic approach, even though the initial conditions are defined in Newtonian terms. The library form of the Delaunay Tessellation Field Estimator dtfe, slightly extended by the addition of a small number of functions and corrected for minor bugs, is used to estimate these invariants.
Initial conditions We use the free-licensed (GNU General Public License, version 2 or later; GPL-2+) package mpgrafic-0. H eff,0 = L box L DTFE L DTFE Mpc/h eff H EdS 1 H eff,0 , (4) where h eff := H eff,0 /100 km/s/Mpc. We match the  Ade et al. (2016)  power spectrum normalisation of σ ΛCDM 8 = 0. 8159 to our EdS reference model. We evolve the effective Planck-parametrised ΛCDM model backwards to z = 1000, that is, to t = 547 Myr, and then forwards using our EdS model, obtaining σ 8 = 1. 0395 when a EdS = 1, which we use for mpgrafic initial conditions. The value of L N is set in mpgrafic, and later read in automatically by ramses.
Initial invariants and other initial conditions The inhomog software allows the reference model scale factor a(t) to be normalised at unity either at the initial time or at the present, setting (cf  Buchert et al. 2000  C. 1 last paragraph) a i := a FL (t i ) (17) so that ̺ FL (t) = ̺ FL (t i ) a i 3 a(t) 3 , (18) where the subscript 'FL' indicates the FLRW reference model. In this paper, the reference model is the EdS model described in Sect. 3. 1, and a FL (t i ) is set as described in Sect. 3. 3. In  Buchert et al. (2013) , the reference model is termed a background. Buchert et al. (2000)  internally alternates between defining the invariants of the extrinsic cuvature tensor and the (normalised and zeroed) growth function ξ [(  34 ) below] to have a time dimension ( [I] = T -1 , [II] = T -2 , [III] = T -3 , [ξ] = T, where [x] denotes the dimension of x and T is the time dimension) or to be dimensionless.
Virialisation After turnaround of a given domain D, that is, when ȧD becomes negative, we continue integration towards numerical collapse (a D ≪ 1) at foliation time t coll. The initial solution {a D (t), ȧD (t) : t < t coll } is used as an approximation that is improved iteratively. We assume virialisation -we set a D (t > t coll ) := a EdS (t coll ) /(18π 2 ) 1/3 , ( 43 ) where 18π 2 is the usual EdS stable clustering  (Peebles 1980 ) virialisation overdensity  (Lacey & Cole 1993, eq. (  A16 )). For completeness, the inhomog package provides the corresponding value for the flat-Λ case if that is chosen, using the fitting formula of  Bryan & Norman (1998, (6) ) to  Eke et al. (1996) 's calculation. As motivated below in Sect. 4, we do not introduce any behaviour in other domains that compensates for virialisation events.
g. ,  Bagla 2005; Dehnen & Read 2011). In this work, we provide a patch to the adaptive mesh refinement (AMR) ramses code 6  (Teyssier 2002; Guillet & Teyssier 2011) , available under CeCILL (GNU GPL compatible) as Fortran 90 code, that we tentatively refer to as ramses-scalav 7 This serves as a front end to read in the initial conditions files (Sect. 3. 3) and call the dtfe and inhomog libraries to estimate the per-domain initial invariants I D i , II D i , and III D i , and to carry out VQZA evolution and volume averaging as described in Sections 3. 5 and 3. 6. In an initial exploration of more numerical approaches, the ramses-scalav front end also allows Q D to be calculated numerically from the invariants at each major time step using Eq. (  16 ) instead of using the QZA analytical calculation of (  30 ) and (  31 ), which is based on the initial values of the invariants and the growth rate of the reference model.
VQZA super-EdS expansion u 1 n x = 1 u 1 n x = 8 u 1 n x =64 u 2 n x = 1 u 2 n x = 8 u 2 n x =64 10 -18  in the bottom panel. (See Table  1  for software versions. ) The scale factor growth is very close to EdS for L D = 8 Mpc/h eff and L D = 16 Mpc/h eff. This is reasonable, because typical overdensities on these scales do not have time to collapse by the present. On smaller averaging scales L D , silent virialisation (the VQZA, Definition 1, Sect. 4. 2) starts to play a role in volume expansion. This can be seen in Figure  7 , where the scale factor growth is stronger as L D decreases. The ΛCDM proxy scale factor evolution (× symbols) mostly lies between the L D = 2 and 4 Mpc/h eff curves. In other words, these curves provide the required super-EdS expansion required to reach a unity effective scale factor a eff (t) by t = 13.
N-body measurement of Q D What happens when Q D is calculated from the Lagrangian domains in the evolving N-body simulation, as detailed in Sect. 3. 7 above, where numerical noise plays a stronger role? Figures  13  and 14  show the equivalent of Figs 7 (middle) and 9 (top), respectively. (See Table  1  for software versions. ) Figures 9 (top) and 14, in which Q D is artificially set to zero, appear, by inspection, to be almost identical. This close correspondence provides a check on the numerical integration and coordination between software modules, since the former integration is carried out by calls from the inhomog library to the GSL routine gsl_odeiv_step_rkf45, while the latter is carried out by the ramses-scalav routine update_av_scalars_global_module with a hardwired version of the classical (non-adaptive) Runge-Kutta algorithm.
Nevertheless, it seems reasonable to consider this work to be more accurate than the standard model: sudden, acausal switches in curvature and expansion rates in expanding regions that contain no singular spacetime events would be relativistically surprising. A&A proofs: manuscript no. nbody-bbl referee for useful comments. A part of this project was funded by the National Science Centre, Poland, under grant 2014/13/B/ST9/00845. Part of this work consists of research conducted within the scope of the HECOLS International Associated Laboratory, supported in part by the Polish NCN grant DEC-2013/08/M/ST9/00664. A part of this project has made use of computations made under grant 314 of the Poznań Supercomputing and Networking Center (PSNC). This work has used the free-licensed GNU Octave package  (Eaton et al.
Acknowledgments Thank you to  Jérémy Blaizot ,  Krzysztof Bolejko ,  Justyna Borkowska ,  Thomas Buchert ,  Mikołaj Korzyński ,  Bartosz Lew ,  Jan Ostrowski ,  Pierre Mourier ,  Yann Rasera ,  Joakim Rosdahl ,  Quentin Vigneron ,  David Wiltshire , and an anonymous.
, 2011). In such cases the inertial, hydrodynamic, and seabed contact loads can govern the extreme and cyclical tension regimes on the lines. Dynamic effects can dominate the tension variance especially in domains where high energy and high compliance coexist. This typically applies to conventional deep-water offshore platforms  (Mavrakos et al. , 1996)  and to highly dynamic applications such as marine renewable energy installations  (Johanning et al. , 2007). As a consequence, the current state-of-the-art time-domain software for the design and analysis of offshore mooring systems (e. g. OrcaFlex, aNySIM, Flexcom, FASTlink, 1  etc. ) typically include dynamic simulation capabilities. The above guidelines are readily transposed to the floating wind context and dictate the use of dynamic simulation tools, especially when focussing on the mechanical response of the mooring system.
Code_Aster Code_Aster is EDF R&D's all-purpose open-source FE solver for the thermo-mechanical study of structures  (EDF, 2014). After over 20 years of development, this software offers in the order of 400 finite element typologies for the discretisation of solids and a broad range of solvers, all features which are thoroughly tested and validated. It enables the static, dynamic, and vibrational analysis of mechanically loaded structures as well as modal analysis. The current study looks to employ the nonlinear capabilities of this software to represent the dynamic behaviour of floating wind turbine mooring lines, which are intrinsically characterised by nonlinear kinematics due to the presence of large displacements and intermittent seabed contact. The features peculiar of mooring system modelling which are not readily available in the classic Code_Aster workflow have been implemented by manually augmenting the command file with the necessary scripts, and by developing practical methodologies based on appropriate sequences of simulations.
Although Code_Aster is written in French language, the interested anglophone reader can acquire a grip on this software by consulting specialised wiki pages which provide examples and tutorials (see for instance  CAELinux, 2015). Two related manuscripts have also been recently published which are rich in hands-on examples  (Thakore, 2014a, b). The version of the software employed for this study is 12. 2. 10 (development release).
Mooring line hydrodynamics The Morison approach is employed in the form detailed next to compute the hydrodynamic forces on the lines by taking into account both the structure's motion and the wave kinematics. First, a simplification is made by disregarding the inertial wave forces: due to the small diameter of the mooring lines compared to the length of ocean waves, these are negligible with respect to viscous forces. The same modelling hypothesis is used by commercial mooring simulation software such as aNySIM  (MARIN, 2011). The Morison equation term associated to the fluid's added mass force is accounted for in reactive form with the procedure outlined in 2. 4. 2 and removed from the external loads. This leaves only drag in the Morison equation, which is computed as follows. The mooring line is assimilated to a circular cylinder of equivalent volume whose diameter is D, the mooring line's volumetric diameter.
Floating platform geometry and dynamics Studying the mechanics of a complete FWT mooring system requires to link the fairlead points to simulate the presence of the floater. A rigid platform model is presently utilised for this sake, which consists in a set of massless, undeformable beam elements (see Fl ejou, 2015; for a description of beam elements in Code_Aster) each connecting a fairlead to the platform pivoting point. First, a motion-driven approach is proposed: the platform hydromechanics are solved with EDF R&D's time-domain simulator CALHYPSO (see  Antonutti et al. , 2016;  for a description of this software), which provides the six-DoF motion response signals. These are applied within Code_Aster as a time-dependent boundary condition located on the platform reference node. In this case the feedback loop between platform dynamics and the dynamic component of the mooring system's response is not represented (see for instance  Ormberg and Larsen, 1998).
Results and discussion A comparison of the outputs of the simulations carried out with Code_Aster and the MARIN experiments is presented next. The modelling strategies are denoted as follows: CALHYPSO. The rigid-body hydromechanical simulation is carried out with the CALHYPSO software, where the mooring forces are calculated with the quasi-static method. Code_Aster (1). The coupled dynamics of the system are calculated with the dynamic moorings model. Code_Aster (2A). The platform motions are derived with CALHYPSO as above and subsequently imposed to the dynamic moorings model (motion-driven simulation). Code_Aster (2B). Equivalent to Code_Aster (2A), neglecting the incident wave kinematics in the calculation of mooring drag forces. For regular waves, both the motion and the fairlead tension outputs are given in the response amplitude operator form (RAO), using the maxima and minima of the steady-state response signal.
Conclusion and further work For the first time Code_Aster, the open-source mechanical analysis software developed by EDF R&D, has been used to simulate offshore mooring dynamics. A successful application to the floating wind technology is included in the present work. After the presentation of the methodological choices, hinging on the 1D finite-element representation of slender and compliant structures and unilateral contact, it has been shown that appropriate model set-up and initialisation permit to handle the hydromechanics of catenary moorings with this general-purpose tool. A range of dynamic simulations has been performed and compared with experimental results available in the literature. The model has been shown to satisfactorily predict the coupled platform-moorings dynamics of a FWT in regular waves.