dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , 1 year or shorter) focusing on the skill of daily to weekly operational predictions [e. g. ,  Teruzzi et al. , 2014; Shulman et al. , 2013]  or on the seasonal cycle of the ecosystems [e. g. ,  Triantafyllou et al. , 2007; Fontana et al. , 2010; Ciavatta et al. , 2011 Ciavatta et al. , , 2014;; Mattern et al. , 2013; Hu et al. , 2012; Xiao and Friedrichs, 2014a] , leaving the reanalysis of the interannual variability of shelf-sea biogeochemistry unexplored. The overall aim of this work was to provide the first decadal reanalysis of the biogeochemistry of the North West European shelf-sea. The specific objectives of this paper are: (i) to evaluate the skill and confidence of the reanalysis and (ii) to exemplify the value of the reanalysis data set to assess the status and interannual changes of the shelf ecosystem.
The reanalysis output data set was first skill-evaluated using ocean color data and in situ observations of 10 physical and biogeochemical variables. The data set was then postprocessed to extract information relevant to the case studies, including the confidence level of the reanalysis estimates. Estimate of confidence is a major gap in most of the current modeling applications for ecosystem assessment  [Hyder et al. , 2015; Piroddi et al. , 2015] , thus we suggest that it represents an added value of our reanalysis data set for its possible application in marine policy. The paper is structured as follows. Section 2 describes the ecosystem model, the setup of the assimilation algorithm, the data, and the metrics applied for skill evaluation. In section 3, the results are presented and discussed.
Boundary Conditions and Atmospheric Forcing The oceanic conditions at the open boundaries of the ecosystem model (temperature, salinity, currents, and sea surface elevation) were extracted for the years 1998-2009 from the GLORYS reanalysis product provided within the EC FP7 project MyOcean  [Ferry et al. , 2012]. The corresponding conditions for dissolved nutrients and oxygen were extracted from the 2005 World Ocean Atlas climatology  [Garcia et al. , 2006a [Garcia et al. , , 2006b]] , and for dissolved inorganic carbon (DIC) from the database GLODAP  [Key et al. , 2004]. The model was forced by daily climatological discharges of freshwater and dissolved nutrients from 250 rivers. Data of water discharge were taken from the Global River Discharge Data Base  [V€ or€ osmarty et al. , 1996] , and from data prepared by the UK Centre for Ecology and Hydrology.
Data The data of remotely sensed concentration of surface chlorophyll used in the assimilation (Figure  3 ) were provided by the Ocean Colour-Climate Change Initiative project of the European Space Agency (ESA's OC_CCI product, Version 2. 0)  [Brewin et al. , 2015; Grant et al. , 2015; Sathyendranath et al. , Creating an ocean-colour time series for use in climate studies: the experience of the ocean-colour climate change initiative, submitted to Remote Sensing of  Environment, 2016]. This product was created by merging satellite data from sensors MERIS, MODIS, and SeaWiFS, after shifting the wavelength bands and correcting the bias between the sensors. It consists of a global daily level 3 binned data set provided on a sinusoidal grid at 4 km resolution. It was downloaded via FTP from http://www.
1 and 3. 2 (see Appendix B for a complete list of the reanalysis output). The reanalysis data set can be applied to estimate the interannual variability of the shelf uptake of atmospheric CO 2 , and to evaluate the confidence levels for such estimates, as shown in this case study. The North East Atlantic is a net sink of atmospheric CO 2 at a high confidence level (Figure  12 ). The ocean uptake increases from south to the northern colder waters (from $5 to 15 mol C m 22 yr 21 ) and from the coast toward the open ocean. On the shelf, the uptake is typically lower than 5 mol C m 22 yr 21 (region delimited by the 200 m isobath in Figure  12 ). Weak sources of CO 2 to the atmosphere (<1 mol C m 22 yr 21 ) were found in the English Channel, Irish Sea, and near estuaries. The interannual variability of the fluxes was more homogeneous and smaller than the interannual means, in general (Figure  12b ).
, 2010; Skogen et al. , 2014; Anav et al. , 2013]. The use of our reanalysis in such a type of Finally, to our knowledge, this is the first reanalysis that is taking advantage of the by-pixel estimates of the errors of the assimilated ocean color product, decreasing the level of subjectivity often applied in biogeochemical data assimilation  [Ciavatta et al. , 2014]. However, the product we used was derived primarily for case-I waters. We expect that further advantages for biogeochemical reanalysis in shelf-seas will derive from the availability of long-term, integrated products for case-I and II waters, e. g. , from the current efforts of the Ocean Colour Climate Change Initiative of the European Space Agency. Calcite dissolution in benthic layer a Data of grid cell dimensions are also provided to convert the units of fluxes.
Discussion The permeability curves presented in this manuscript are simulated for strain rates at which continuous permeability measurements are not available. The explicit time stepping method implemented in ABA-QUS-Explicit does not allow simulations for applied strain rates as low as 10 À5 s À1 at which permeability evolution is typically measured  Mitchell and Faulkner (2008). For the sake of simplicity, we ignored any permeability anisotropy in the model that could arise from preferred cracks orientation. We have also simplified the treatment of pore fluid evolution by only modifying the constitutive relationship according to linear poroelasticity. A more careful derivation of the stress-strain relationship would incorporate in the Gibbs free energy density the role of the fluid pressure in the system.
5 Â 10 3 cells per ml was used and growth was monitored over 8 days. Discarded coccoliths of C. braarudii were also counted for selected experiments. As coccolith morphology can be difficult to determine accurately by light microscopy, we did not discriminate between intact and aberrant liths in these analyses. To assess the performance of the photosynthetic apparatus, the maximum quantum yield of photosystem II was determined using a Z985 AquaPen chlorophyll fluorimeter (Qubit Systems, Kingston, Canada). Statistical analyses of these data were performed in SigmaPlot v12. 0 software (Systat Software Inc, London, UK). Fluorescence microscopy of P. neolepis silica scales. One millilitre of P. neolepis cells was incubated with the fluorescent dye LysoTracker yellow HCK-123 or LysoSensor Yellow/Blue DND-160 (Invitrogen; 1 mM, 10 h).
doe. gov/). Further searches were performed at NCBI (http://blast. ncbi. nlm. nih. gov/Blast. cgi), including Transcriptome Shotgun Assembly (TSA) and Expressed Sequence Tag (EST) databases. Databases were searched using BLASTP and TBLASTN. Positionspecific iterative BLAST (PSI-BLAST) was used to identify highly conserved motifs in proteins that exhibit low levels of sequence identity (for example, lipocalins). Each potential hit was manually inspected using a multiple sequence alignment to identify conserved residues and then phylogenetic analyses were performed using both neighbour-joining and maximum likelihood methods within the MEGA5 software package to assess the relationship with known proteins  68. For detailed phylogenetic analysis of SITs and SITLs, multiple sequence alignments were generated using MUSCLE and manually inspected for alignment quality.
After manual refinement, GBLOCKS 0. 91b was used to remove poorly aligned residues  69  and then ProtTest was used to determine the best substitution model (WAG with gamma and invariant). Maximum likelihood phylogenetic trees were generated using PhyML3. 0 software with 100 bootstraps. Bayesian posterior probabilities were calculated using BEAST v1. 8, running for 10,000,000 generations  70. The identification of potential transmembrane domains in SITLs was performed using Phobius and TMHMM. Electron microscopy. SEM images of P. neolepis scales were acquired with a JEOL 5000 and JEOL 7001 F microscopes (Jeol, Japan) at 15 keV accelerating voltage. Scales were collected using lysis buffer (2% SDS, 100 mM EDTA, 0. 1 M Tris pH 8. 00) as described above, but were additionally cleaned by heating at 95 °C for 10 min in the lysis buffer.
5 kDa are long-chain polyamines (LCPA). M, molecular-weight markers. (d) Domain organization of the lipocalin-like protein (LPCL1) identified from both protein bands in NH 4 F extracted silica scales. The approximate positions of the proline/lysine-rich regions and the calycin domain (IPR012674) are shown. Also shown are the positions of six highly conserved cysteines (asterisk) that may be involved in the formation of disulphide bridges. (e) Long-chain polyamines (LCPAs) from P. neolepis silica scales. Electrospray ionization mass spectrometry (ESI-MS) of the low-molecular-weight NH 4 F-soluble fraction of silica scales revealed a series of mass peaks separated by 71 Da (highlighted in red), characteristic of N-methyl propyleneimine units. The additional mass peaks ± 14 Da may indicate different methylation states, as is commonly observed in LCPAs.
Acknowledgements We thank  Roy Moate ,  Glenn Harper  and  Peter Bond  ( University of Plymouth, UK ) for help with electron microscopy imaging,  Malcolm Woodward  ( Plymouth Marine Laboratory, UK ) for help with silicate analyses,  Nicholas Smirnoff  and  Hannah Florance  ( University of Exeter, UK ) for help with protein sequencing and  Nils Kroger  and  Nicole Poulsen  ( TU Dresden, Germany ) for their guidance on analysing the organic component of the silica scales. This study was funded by  EU Interreg IV Marinexus  grant. GW and CB acknowledge support from  NERC grant  ( NE/J021954/1 ). ART acknowledges  NSF  grant  IOS 0949744  and the  University of North Carolina Wilmington Microscopy Facility.
Sites and methods At the coastal site of DDU (66 • 40 S, 140 • 01 E, 40 m a. s. l. , Fig. 1 ), ozone measurements started in 2004, the inlet of the sampling line being located at 2 m above the ground level. Ozone monitoring was also initiated in 2007 at the inland site of Concordia (75 • 06 S, 123 • 20 E, 3233 m a. s. l. , sometimes denoted DC) located 1100 km away from the nearest coast of East Antarctica (Fig. 1 ). At both sites, measurements have been made continuously with a UV absorption monitor (Thermo Electron Corporation, Franklin, Massachusetts, USA), model 49i at Concordia and 49c at DDU. Zero determinations and gas standard calibrations were made once a month. The data collected at 15 s intervals are here used as hourly, daily, and monthly averages. Note that, in the following, times refer to local times.
s. l. ), as well as the coastal sites of Neumayer (NM, 70 • 39 S, 8 • 15 W, 42 m a. s. l. ) and Halley (HA, 75 • 33 S, 26 • 32 W, 30 m a. s. l. ), both located at sea level in the Weddell Sea sector (West Antarctica, Fig. 1 ). Data from SP, SY, NM, and HA were retrieved from the WMO World Data Centre for Greenhouse Gases (http://ds. data. jma. go. jp/gmd/wdcgg/ cgi-bin/wdcgg) and updated with data from individual investigators. More details on data from the South Pole can be found in  McClure-Begley et al. (2014). Details on ozone data from Syowa can also be found at http://ds. data. jma. go. jp/gmd/wdcgg/wdcgg. html. At Concordia, surface ozone data were complemented by investigation of the vertical ozone distribution in summer by using tethered balloons. Electrochemical concentration cell (ECC) sondes (ZECC, EN-SCI Corporation) and PTU sondes (RS92-SGPG, Vaisala) interfaced to RSA921-OIF92 (Vaisala) for remote data transfer were used to document the vertical ozone distribution and meteorological conditions.
Prior to each flight, ECC sondes were calibrated with the Ozoniser/Test Unit TSC-1 (Vaisala). Helium-filled (2 m 3 ) tethered balloons (latex Totex, 1000 g) were deployed. Ascent rates typically ranged from 0. 2 to 0. 8 m s -1 , depending on the operator. Given the 25 s response time of the ECC sonde, delivered ozone data were delayed, causing a upward shift of the ozone profile by up to 20 m. Vertical profiles were obtained from the end of December 2009 to 15 January 2010 and from 11 December 2011 to 9 January 2012. Under low wind conditions, balloons were able to reach heights up to 1010 m above ground level (a. g. l. ) (i. e. , 4225 m a. s. l. ). This allowed the documentation of the vertical ozone change from the ground to above the planetary boundary layer (PBL), which grows to several hundreds of meters in the afternoon.
Data availability The surface ozone data from the South Pole, Syowa, Neumayer, and Halley used in this study are available at the WMO World Data Centre for Greenhouse Gases (http://ds. data. jma. go. jp/gmd/wdcgg/cgi-bin/wdcgg). Surface ozone data from Concordia and Dumont d'Urville can be made available for scientific purposes upon request to the authors. Total ozone column data from the South Pole are available from Earth System Research Laboratory (Global monitoring division, http://www. esrl. noaa. gov/ gmd/dv/data/) (Petropavlovskikh, I. ,  Oltmans, S. J. (2012 ), Dobson Total Ozone measurements, version: 2013-02-25, path: ftp://aftp. cmdl. noaa. gov/data/ozwv/Dobson).
Acknowledgements. The  OPALE  project was funded by the  ANR (Agence National de Recherche)  through contract  ANR-09-BLAN-0226. National financial support and field logistic supplies for the summer campaign were provided by the  Institut Polaire Français-Paul Emile Victor (IPEV)  through program nos. 414 and 903. This work was initiated in the framework of the  French environmental observation service CESOA (Etude du cycle atmosphérique du Soufre en relation avec le climat aux moyennes et hautes latitudes Sud , http://www-lgge. obs. ujf-grenoble. fr/CESOA/spip. php?rubrique2) with the financial support of  INSU (CNRS). Detlev Helmig 's contribution was funded by the  USA National Science Foundation  grant  PLR 1142145. We also thank the researchers and agencies that conducted the ozone measurements at Syowa, Neumayer, and the  South Pole  and making them available for their dissemination in the  World Data Centre for Greenhouse Gases.
Receiver function studies  22  have identified increased crustal thickness, up to 35-38 km, beneath Toba. Here we present an additional local-scale tomography model, which was calculated based on the combination of seismic travel time data obtained from the two above-mentioned temporary seismic networks in the Toba area. The first data set was recorded by a seismic network deployed around Toba during the period from January to May 1995. The network consisted of 10 broadband and 30 short-period seismic stations (white triangles in Fig. 1 ). The second data set was recorded by another seismic network deployed in the same approximate area by GeoForschungsZentrum-Potsdam for 5 months from May to October 2008. This network consisted of 42 short-period threecomponent seismometers (red triangles in Fig.
1 ). For the second data set, we manually identified the local events and picked the arrival times using SEISAN software  23. To select the data for tomography, for both data sets we used only events that had a number of P-and S-picks per event equal to or larger than 8. We rejected the picks with absolute values of the residuals larger than 1 and 1. 5 s for the P-and S-data, respectively. From the first data set we selected 505 events with 7,058 corresponding arrival times (4,122 P-and 2,936 S-waves) and for the second data set we obtained 4,826 arrival times (2,522 P-and 2,304 S-waves) from 149 local events. Although the number of events in the second case was smaller relative to the older data set, the quality of these data was considerably higher. The average number of picks per event in this case was B32, whereas for the older data set it was o14.
For the regional tomography, we use all available data with ray paths propagating, at least partly, through the study volume. This includes the data from worldwide seismicity recorded by stations located in the study region, as well as data from events in the study region recorded by worldwide stations. The inversions are performed independently in a series of overlapping circular windows and subsequently combined into one model. Such an approach allows the identification of finer structures than in the case of global inversion, but it risks losing larger-scale anomalies with sizes comparable to the window diameter  34. This algorithm has been thoroughly tested in many different regions  34  and, in particular, it appeared to be efficient for studying several subduction zones  35. For the case shown here, the inversion was performed for the entire Sunda Arc in a volume down to 1,000 km depth.
The anomalies are shown only in areas with sufficient ray coverage. This model is slightly different to that presented by Luhr et al. 14  , because it incorporates additional data corresponding to the 5 years from 2008 to 2012. In addition, we tuned the values of the inversion parameters to better reveal the slab-related anomaly. The derived result appears to be consistent with another model obtained independently  16. Data for the local tomography inversion. In this study, we used the arrival times of P-and S-seismic body waves from earthquakes located inside or slightly outside the study area. Here we combine two data sets corresponding to two seismic networks that were operated in the Toba Caldera region during different time periods. The older data set was recorded by a seismic network deployed around Toba by the Incorporated Research Institute for Seismology for the Program for Array Seismic Studies and the Indonesian Seismological Agency.
The network, consisting of 10 broadband and 30 short-period seismic stations, was operated during the period from January to May 1995. During the observation period, 1,500 local events were recorded  13. For this study, we used only events that had a number of P-and S-picks per event Z8. We rejected the picks with absolute values of the residuals 41 and 1. 5 s for the P-and S-data, respectively, estimated after source locations in the 1D starting model. These selection criteria reduced the number of events to 505, which had 7,058 corresponding arrival times consisting of 4,122 P-and 2,936 S-waves. An approximately similar data set was used in the previous tomography studies  18, 19. The second data set was recorded by another seismic network deployed in approximately the same area by GeoForschungsZentrum-Potsdam for 5 months from May to October 2008.
The network consisted of 42 short-period threecomponent seismometers and L4-3D stations with natural frequency of 1 Hz. We downloaded the full continuous data from this experiment through the GEOFON system of GFZ. We manually identified the local events and picked the arrival times using SEISAN software  23. To select the data for tomography, we used the same criteria applied to the older data set (8 picks per event and 1-1. 5 s of residual threshold). In total, we obtained 4,826 arrival times (2,522 P-and 2,304 S-waves) from 149 local events. Although the number of events in this case was smaller relative to the older data set, the quality of these data was considerably higher. In particular, the average number of picks per event in this case was B32, whereas for the older data set it was o14.
In addition, the use of such data considerably enhances the variety of ray propagation directions and allows for greater depths to be reached compared with the use of events that only occur inside the network. All of these factors are favourable for improving the quality of tomographic inversions. Local earthquake tomography algorithm. For the tomographic inversion, we used the LOTOS code  24. The main input data for the inversion contained station coordinates and arrival times of P-and S-waves. In addition, the initial 1D velocity model and the parameters for source locations and inversion should be defined. During the preliminary stage, we located the sources in the initial 1D velocity model using a grid-search method. To speed up the calculations in this stage, the travel times were calculated along straight ray paths.
Acknowledgements All of the data used in this study were acquired and distributed by the  GFZ data centre. I. K. and E. K. are supported by the RNSF grant number  14-17-00430. We appreciate the  Deanship of Scientific Research at King Saud University  for funding this work through the research group project  IRG-14-21. The work of N. S. was supported by the  French project  ' Labex UnivEarth ' and by the  UniversiteSorbonne Paris Citevia project 'VolcanoDynamics'.
The data were all from global broadband seismic stations accessed through the Incorporated Research Institutions for Seismology (IRIS) Data Management Center (DMC). The data were selected from hundreds of available seismograms to have good azimuthal coverage (figs. S9 and S10) and high signal-to-noise ratios at epicentral distances from 30°to 90°. The instrument responses were removed from the raw data to recover ground displacement records. A causal bandpass filter with corner frequencies at 0. 005 Hz and 1. 9 Hz was applied to the data. The teleseismic Green's functions were generated with a reflectivity method that accounted for interactions in one-dimensional layered structures on both the source and the receiver sides  (38). The Jeffreys-Bullen mantle velocity structure was used in the modeling.
This paper describes the recommended solar forcing dataset for CMIP6 and highlights changes with respect to CMIP5. The solar forcing is provided for radiative properties, namely total solar irradiance (TSI), solar spectral irradiance (SSI), and the F10. 7 index as well as particle forcing, including geomagnetic indices Ap and Kp, and ionization rates to account for effects of solar protons, electrons, and galactic cosmic rays. This is the first time that a recommendation for solar-driven particle forcing has been provided for a CMIP exercise. The solar forcing datasets are provided at daily and monthly resolution separately for the CMIP6 preindustrial control, historical (1850-2014), and future (2015-2300) simulations. For the preindustrial control simulation, both constant and time-varying solar forcing components are provided, with the latter including variability on 11-year and shorter timescales but no long-term changes.
(NRLTSI2-NRLSSI2) and a semi-empirical one (SATIRE). A new and lower TSI value is recommended: the contemporary solar-cycle average is now 1361. 0 W m -2. The slight negative trend in TSI over the three most recent solar cycles in the CMIP6 dataset leads to only a small global radiative forcing of -0. 04 W m -2. In the 200-400 nm wavelength range, which is important for ozone photochemistry, the CMIP6 solar forcing dataset shows a larger solar-cycle variability contribution to TSI than in CMIP5 (50 % compared to 35 %). We compare the climatic effects of the CMIP6 solar forcing dataset to its CMIP5 predecessor by using timeslice experiments of two chemistry-climate models and a reference radiative transfer model. The differences in the long-term mean SSI in the CMIP6 dataset, compared to CMIP5, impact on climatological stratospheric conditions (lower shortwave heating rates of -0.