dataesr/scientific-paragraphs-categorization huggingface dataset:
Solar ozone forcing CMIP6 models with interactive chemistry are recommended to include a consistent prescription of the CMIP6-SSI variations in their radiation and photolysis schemes, so that they include an internally consistent representation of the solarozone response. Climate models that do not calculate ozone interactively are recommended to use the SPARC/CCMI ozone database for CMIP6, which has been constructed from existing CCM simulations (https://esgf-node. llnl. gov/ projects/input4mips/). This differs from the representation of the solar-ozone response in the CMIP5 ozone database, which was based on satellite ozone measurements  (Cionni et al. , 2011). Multiple linear regression analysis of the CMIP6 ozone database over the period 1960-2011 reveals that an 11-year solar-cycle ozone response is implicitly included in the dataset and resembles previous results from CCM studies.
An analysis of the representation of particleinduced ozone anomalies in the CMIP6 ozone database has not been performed. CMIP6 models that include both CMIP6-SSI and solar induced-ozone variations are expected to show a better representation of solar climate variability compared to models that exclude the solar-ozone response. Appendix D: NO x production by particle-induced ionization Following  Porter et al. (1976)  it is assumed that 1. 25 N atoms are produced per ion pair. This study also further divided the proton impact of N atom production between the ground state N( 4 S) (45 % or 0. 55 per ion pair) and the excited state N( 2 D) (55 % or 0. 7 per ion pair). Ground state nitrogen atoms, N( 4 S), can create other NO y constituents, such as NO, through N( 4 S) + O 2 → NO + O, (DR1) N( 4 S) + O 3 → NO + O 2 (DR2) or can lead to NO y destruction through N( 4 S) + NO → N 2 + O.
Acknowledgements. We are very grateful to the SATIRE and NRL teams for providing their respective solar irradiance reconstructions. Without these datasets the CMIP6 solar forcing recommendation as well as this paper would not have been possible. We also thank the researchers and engineers of  NOAA's Space Environment Center  for the provision of the data and the operation of the SEM-2 instrument carried onboard the POES spacecraft. This work has been conducted in the frame of the WCRP/SPARC SOLARIS-HEPPA activity and an early part inside the  EU COST Action   ES1005  (TOSCA), which some authors were involved in. This work contributes to the ROSMIC activity within the  SCOSTEP VarSITI programme. Part of the work described in this article emanates from two teams at the  International Space Science Institute (ISSI) in Bern , i.
e. , Quantifying Hemispheric Differences in Particle Forcing Effects on Stratospheric Ozone (Leader:  D. R. Marsh ) and  Scenarios of Future Solar Activity for Climate modeling  (Leader:  T. Dudok de Wit ), which we gratefully acknowledge for hospitality. M. Kunze ,  T. Dudok de Wit ,  M. Haberreiter ,  K. Tourpali , and  S. Misios  acknowledge that the research leading to the results has received funding from the  European Commission 's  Seventh Framework Programme  ( FP7 2012)  under grant agreement no. 313188  (SOLID, http://projects. pmodwrc. ch/solid). P. T. Verronen ,  M. E. Anderson ,  A. Seppälä , and M. van de Kamp were supported by the  Academy of Finland  projects no. 276926  ( SECTIC: Sun-Earth Connection Through Ion Chemistry ) and nos. 258165  and  265005  ( CLASP: Climate and Solar Particle Forcing ).
K. Matthes ,  T. Kruschke ,  M. Kunze ,  U. Langematz ,  S. Versick , and  M. Sinnhuber  gratefully acknowledge funding by the  German Ministry of Research (BMBF)  within the nationally funded project  ROMIC-SOLIC  (grant number  01LG1219 ). K. Matthes also acknowledges support from the  Helmholtz University Young Investigators Group NATHAN , funded by the  Helmholtz-Association  through the  President's Initiative and Networking Fund  and the  GE-OMAR Helmholtz Centre for Ocean Research Kiel. M. Shangguan  gratefully acknowledges funding by the  Helmholtz Association of German Research Centres (HGF) , grant  608 VH-NG-624. B. Funke was supported by the  Spanish MCINN  under grant  ESP2014-54362-P  and  EC FEDER  funds. L. Barnard thanks the  Science and Technology Facilities Council  ( STFC ) for support under grant  ST/M000885/1.
Data availability. The CMIP6 solar forcing dataset described in this paper and the metadata description have been published at http://solarisheppa. geomar. de/cmip6 and linked to the Earth System Grid Federation (ESGF), https://esgf-node. llnl. gov/projects/ input4mips/, with version control and digital objective identifiers (DOIs) assigned. An overview of the CMIP6 Special Issue can be found in Eyring et al. (2016).
Biogeochemical models have been used in previous studies in Humboldt Current Large Marine Ecosystem (HCLME) in order to understand the climatological processes affecting the Oxygen Minimum Zone (OMZ)  (Montes et al. 2014 ) and the climatological  (Echevin et al. 2008 , Albert et al. 2010 ) and interannual  (Echevin et al. 2014 ) variability of phytoplankton and nutrients. In the present study, we used statistical metrics to assess the skill of the coupled hydrodynamic and biogeochemical ROMS-PISCES model using data from surveys, remote sensing and international databases. We analyzed the climatological and interannual  (1992 -2008)  biogeochemical variability, including the impact of El Niño 1997 -1998 event on the chlorophyll concentration and depth of the OMZ.
Model Validation Data to validate the model. -To verify that the PISCES model represents the main spatial patterns of biogeochemical variables in the HCLME (i. e. depth of the OMZ, nutrients and surface chlorophyll), the following climatologies and interannual time series were used (Table  1 ): Chemical. Oxygen and nutrients climatologies were obtained from CSIRO Atlas of Regional Seas "CARS 2009"  (Ridgway et al. 2002 ) at a spatial resolution of 1/2º and a vertical resolution of every 5 m depth (from 0 -200 m) and every 100 m depth (from 200 -1000 m). Monthly series from 1992 to 2008 of the depth of the upper limit of the OMZ were obtained by merging the database of the Instituto del Mar del Peru (IMARPE) and the international database of WOD09 (García et al. 2010), following the methodology developed by  Musial et al.
(2011) ; the reader is referred to  Bertrand et al. (2011)  for more details about oxygen data processing. Biological. Surface chlorophyll climatological and interannual time series was obtained from the database of the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) (Behrenfeld & Falkowski 1997) at a spatial resolution of 1/12º and a monthly temporal resolution from 1997 to 2008. All observed time series were interpolated on the model temporal resolution using the Laplace transformation method implemented in the FERRET program (http://ferret. pmel. noaa. gov/Ferret) to make comparisons between model and observations. Data analysis. -Goodness of fit between observed and simulated data was measured using the following statistical indicators  (Vichi and Masina 2009, Lehmann et al. 2009 ): Bias (S), which reflects the overestimation (positive) or underestimation (negative) of a simulation: S = 1/n ∑(M -O) ……………(Eq.
Acknowledgements We acknowledge  Christophe Hourdin  for providing model configurations and running the simulation performed within the  PEPS  project,  Carlos Quispe  from  LMOECC (IMARPE)  for their valuable comments on the statistical analysis. This work was published with the support of  IRD  and the  JEAI EMACEP (Ecología Marina Cuantitativa del Ecosistema de Afloramiento Peruano)  and the  LMI DISCOH (Dinamica de Sistema de Corriente de Humboldt ).
INTRODUCTION 1. 1 Some needs for urban materials maps During last decade, needs for high resolution land cover data have been growing. Indeed, such knowledge is necessary to answer several societal, regulatory and scientific needs, to produce environmental indicators to manage ecosystems and territories, to monitor environmental or human phenomena, or to be able to have a picture of an initial situation and to evaluate the impacts of public policies. Thus, to answer these needs, national mapping or environment agencies, in many countries, have undertaken the production of such large scale land cover databases. Nevertheless, these databases provide a general classification and may not suit some specific (often new) applications requiring a finer semantic or geometric level of details. That is to say that, on one hand, additional land cover classes should sometimes be specified, whereas, on the other hand, some existing classes should be delineated at a finer level.
DATA SET Spectral optimisation was performed from a library of reference spectra of urban materials. These spectra were collected from several available existing spectral libraries listed in section 3. 1. Such data offers several advantages, compared to another alternative such as the use of aerial hyperspectral scenes over several urban landscapes. On one hand, these spectra were captured through field or laboratory measurements, and are thus pure and "clean" reflectance measures. On the other hand, they are generally well described, without ambiguity about their class. Besides, it is a way to have spectra of rare (but thematically important) materials.
Spectral libraries The reference spectra used in this study were collected from several available existing spectral libraries. The number of spectra per original library is shown by table 1. • ASTER Spectral Library 1  : The ASTER spectral library  (Baldridge et al. , 2009)  is made available by the Jet Propulsion Laboratory. It contains more than 2400 spectra of natural and artificial materials from 3 other spectral libraries: the Johns Hopkins University (JHU) Spectral Library, the Jet Propulsion Laboratory (JPL) Spectral Library and the USGS Spectral Library. • SLUM 2  : The Spectral Library of impervious Urban Materials (SLUM)  (Kotthaus et al. , 2014)  is produced within the London Urban Micromet data Archive (LUMA). It contains reflectance spectral measures of 74 impervious materials collected in London.
• DESIREX Spectra from field measurements campaign DE-SIREX 08 (ESA)  (Sobrino, 2008)  in Madrid were also available. First, all collected spectra were integrated into a common data base. This required to define a common legend, to be able to have a homogeneous spectral collection. Interesting taxonomies for urban materials have been proposed in previous works such as  (Heiden et al. , 2007)  or  (Herold et al. , 2004a). These taxonomies are often hierarchical ones, with a last level of detail corresponding to fine information about materials such as colour or condition. However, at this step, it is intended to keep as many information as possible to describe the collected spectra rather than to have a frozen nomenclature. Thus, it was decided to store collected spectra in our database, associating several attributes to each of them: • Material class • Variety (e.
• Slate • Asphalt • Cement/concrete • Gravel • Metal • Stone pavement bricks • Shingle • Bare ground • Tile In order to perform spectral optimisation, a data set was generated from the data base according to this legend. It contained 100 training spectra and 500 test spectra, resampled at a 10 nm spectral resolution ranging from 420 to 2400 nm. Band selection was first performed within the spectral domain ranging from 420 to 2400 nm. First, the optimal number of spectra was defined, owing to SFFS algorithm. Figure  4  shows the evolution of the FS score and of several classification quality rates reached by a RBF SVM classifier depending on the number of selected bands. It can be seen that up to a band subset size, selecting new bands has very few impact on results. Thus, in next experiments 10 bands were selected.
Although 10 bands were selected, a pattern of 4-5 important blobs appears along the spectrum, corresponding approximately to usual multispectral bands (blue, green, red and near infrared). The classification performance of the band subsets previously optimised was evaluated for a RBF SVM classifier. The classifier was applied to a test data set containing 1000 samples per class. Two scenarios were considered to train the classifier using either 100 or 50 samples per class, so as to have an easy case and a more difficult one. This quantitative evaluation was first performed both for the whole Nevertheless, obtained quantitative evaluations are really optimistic and must be considered carefully. Indeed, it must be kept in mind that some classes were represented by very few spectra in the spectral data base, and thus, their variability is not completely considered.
Even when plotting the data from the Frognerkilen Formation (where minor differences are observed in the palynomorph assemblages), we observe an excellent correlation between the lithologies, which is consistent with our interpretation that the cyclic signal in the abundances of a single species, A. bornholmensis, probably is not significant. The outlier XRF results we removed, from TC 12-203 (limestone) and TC 12-226  (marl) in Nakkholmen and WSA-12-090 (limestone) and WSA -12-074A (marl) in Bygdøy, represent a mix of both lithologies, so this does not influence the results. In summary, in the dataset we collected, there is no evidence suggesting a primary rhythm. Nevertheless, as indicated in the introduction, this does not prove that these rhythmites are entirely of diagenetic origin. Differences may have existed that have been destroyed during diagenesis, or the proxies we measured may have been insensitive to the environmental cycles at work.
48″ E10°41′ 29. 17″ ) (Fig. 1 ). (3) The Solvang Formation  47  (Fig. 2 ) has a thickness of 12 m at "Rødlokken Shore" and of 14. 5 m on Nakkholmen Island, and belongs to the Rakvere Baltic Stage 8. The Solvang Formation is developed as irregularly bedded and nodular limestone up to 20 centimeter thick with intervening calcareous marl layers that can be up to 60 cm in thickness. This third interval was mainly measured and sampled on the Bygdøy Peninsula, in a locality known as 'Rodeløkken Shore' (N59°54′ 54. 07″ E10°41′ 28. 92″ ) (Fig. 1. 3a). Because the base of the formation is not well exposed at this location, a second section of the Solvang Formation was measured and sampled on the south-western part of Nakkholmen Island (N59°53′ 19. 03″ E10°41′ 31. 52″ ) (Fig. 1. 3b). Two samples from the base of the formation from Nakkholmen Island complete the dataset from the 'Rodeløkken Shore'.
The chitinozoan taxonomic composition was investigated using multivariate classification and ordination methods, as well as statistical tests. The dataset consists of abundance data (counts of chitinozoan specimens) in each sampled bed. First, a hierarchical cluster analysis (HCA) based on the Bray-Curtis similarity matrix and the UPGMA (Unweighted Pair Group Method with Arithmetic mean) linkage algorithm was performed to initially evaluate if the two lithologies cluster in two different groups or are mixed together. In the former case, this would indicate that the two lithologies have distinct taxonomic compositions. Alongside the HCA, a detrended correspondence analysis (DCA) has been applied. The purpose of this ordination method is to evaluate if some taxa are restricted to a specific lithology and consequently ordered along a gradient.
Acknowledgements Constructive reviews by two anonymous reviewers helped us to improve our paper. TRAV and CEAA acknowledge financial support from the  CNRS (INSU, action SYSTER) , the  French ' Agence Nationale de la Recherche ' through grant  ANR-12-BS06-0014  ' SeqStrat-Ice ' , and the  SMRE doctoral school of the University of Lille. Sabine Van Cauwenberghe (UGent) ,  Laurence Debeauvais  and  Philippe Recourt  (both  CNRS ,  Univ. Lille)  are thanked for assistance with palynology lab protocols and SEM imaging. Discussions with  Emilia Jarochowska  ( Univ. Erlangen ) and  Jacques Verniers (UGent)  improved this manuscript. This is a contribution to IGCP 591 'The Early to  Middle Palaeozoic Revolution'. Miljøvernavdelingen, Fylkesmannen i Oslo og Akershus , is thanked for permission to sample the protected sections at  Hovedøya, Nakholmen and Elnestangen.
Acknowledgments The  Indo-French program  research, "Siwaliks", is under the scientific patronage of  Professor Yves Coppens ,  College de France  and  Academy of Sciences, Institute of France , since 2012 and supported by the  French Ministry of Foreign Affairs  for 3 years (2012, 2013, 2014), in by the  National Museum of Natural History , with the ATM grant of Department of Earth Sciences in 2011, and the  Department of Prehistory  in 2006Prehistory in , 2007Prehistory in , 2010Prehistory in and 2011. We are thankful to the  Archaeological Survey of India  and to the  Department of Tourism, Cultural Affairs, Archaeology and Museums of Punjab Government  for survey permit. We are also grateful to the reviewers  Piotr Tucholka  and  Yan Chen  for their constructive remarks.
Recently, a set of six new brGDGT isomers that differ in the position of the methyl groups were identified and described  (De Jonge et al. , 2013). The relative abundance of these novel, 6-methyl brGDGTs is strongly dependent on pH, and so by excluding them from the MBT' index (newly defined as MBT' 5ME ) the correlation with MAT is improved  (De Jonge et al. , 2014a). The CBT index was also redefined in this study, as CBT', to include all of the pH-dependent 6-methyl brGDGTs and consequently yielded a higher correlation with soil pH as a result  (De Jonge et al. , 2014a). De Jonge et al. (2014a) also developed, based on a dataset of globally distributed soils, a new pH calibration taking into account the new CBT' as well as new MAT calibrations, defined as MAT mr and MAT mrs. In a global soil set they were shown to improve the accuracy of reconstructions, especially in arid regions.
Tagus River SPM The SPM was obtained from the Tagus Estuary near the mouth of the Tagus River once a month over the course of a year (excluding the month of August). Data from the Tagus River SPM showed that the summed brGDGT and crenarchaeol concentrations in the river SPM varied throughout the year and were on average 45 ± 23 and 9. 8 ± 6. 8 µg g OC -1 (Fig. 3a-b ; Table  3 ), respectively, resulting in only small variations in the BIT index (i. e. , 0. 8 ± 0. 1; Fig. 3c ; Table  3 ). The distribution of brGDGTs (Fig. 5c ) was relatively constant throughout the year, as is evident from the values for MBT' 5me (0. 5 ± 0. 0), DC' (0. 3 ± 0. 0), and IR (0. 6 ± 0. 0) for the river SPM (Fig. 3d -f; Table  3 ).
PCA In order to determine the variation in the distribution of brGDGTs, we performed PCA on the distributions of brGDGTs of all the samples examined. Most variation is explained by principal component 1 (PC1; 29. 8 %) and is clearly related to the fractional abundance of the 5-methyl vs. 6-methyl brGDGTs (Fig. 6a ). With the exception of IIIc (which is typically a minor brGDGT with a fractional abundance of < 1 %; Fig. 5 ), all of the 5-methyl brGDGTs score positively on PC1 and the 6-methyl brGDGTs score negatively. For the overall dataset, PC1 is highly negatively correlated with the IR ratio (Fig. 7a , R 2 = 0. 78). PC2 explains 25. 6 % of the variance of the PCA. Branched GDGTs that score positively on PC2 are generally comprised of cyclized and more methylated brGDGTs (Fig. 6a ). With the exception of IIIc (which is typically a minor brGDGT with a fractional abundance of < 1 %; Fig.
, 2012; Dirghangi et al. , 2013; Menges et al. , 2014). Our reanalysis of the soils taking into account the novel 6-methyl brGDGTs now provides the possibility to reevaluate these data. It is clear that the fractional abundances of the novel 6-methyl brGDGTs vary to a large extent. The IR II and IR III vary from 0. 1 to 0. 9 (Fig. 4 ) and some of the soils score very negatively on PC1 (Fig. 6b ), which is predominantly determined by the fractional abundance of the 6methyl brGDGTs. From the global soil brGDGT dataset  (De Jonge et al. , 2014a)  it was evident that the main factor influencing the fractional abundance of the 6-methyl brGDGTs is soil pH, with an increased abundance in high pH soils. In the Tagus River basin soil pH indeed shows a large variation, i. e. , from 5. 5 to 8. 6, and this likely explains the large variation in IR.
When we calculate the pH from the brGDGT distribution using the new Eq. (  9 ) of De  Jonge et al. (2014a) , which is based predominantly on the fractional abundances of 6methyl brGDGTs, we find a highly significant correlation between measured and reconstructed pH (R 2 = 0. 89) following the 1 : 1 line (Fig. 8a ). Differences in soil pH also affect the degree of cyclization of brGDGTs  (Weijers et al. , 2007a; De Jonge et al. , 2014b) , and indeed we find a significant positive correlation between DC' and soil pH (R 2 = 0. 74). The effect of MAT is not clearly revealed in the dataset. For the global soil brGDGT dataset a strong relationship exists between MAT and MBT' 5Me  (De Jonge et al. , 2014a). Although we observe substantial variation for MBT' 5Me in soils (i. e. , 0. 3-0. 7; Fig. 3f ) for this dataset, we do not observe a statistically significant relationship of MAT with MBT' 5Me.
6b ). The Tagus Mudbelt sediments, Lisbon Canyon Head sediments, the Tagus River Floodplain sediments, and the lower-altitude soils (< 350 m) have similar abundances of the 5-and 6-methyl brGDGTs or higher abundances of the 5-methyl brGDGTs and plot mostly positively on PC1. Since the Tagus River Floodplain sediments, the Tagus Mudbelt sediments, and the Lisbon Canyon Head sediments do not have a predominance of 6-methyl brGDGTs, this indicates either that they received an equal contribution of soil-derived organic matter from the lower-altitude soils in the region (< 350 m) as from the higher-altitude region (> 350 m) or, more likely, that in situ production of brGDGTs is a large source of brGDGTs in these sample sets. PC2 also explains a substantial part of the variance in the dataset (25.
Data availability The data presented in this paper can be found in Table  3  and S2. The Supplement related to this article is available online at doi:10. 5194/bg-13-5719-2016-supplement. Author contributions. Jung-Hyun Kim and Jaap S. Sinninghe Damsté designed the study, which was carried out by Lisa Warden, who completed bulk carbon isotope and brGDGT analysis on samples along with Claudia Zell. Claudia Zell, Henko Stigter, Geert-Jan Vis, and Jung-Hyun Kim collected samples for this study. Jérôme Bonnin picked forams for dating. Lisa Warden and Jaap S. Sinninghe Damsté prepared the manuscript with contributions from all co-authors.
Acknowledgements. The research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement no. 226600. Jaap S. Sinninghe Damsté  is supported by funding from the  Netherlands Earth System Science Centre (NESSC)  though a Gravitation grant from the  Dutch Ministry for Education, Culture and Science. We thank the captain and crew of the  RV Pelagia  for their support with sampling. We thank  J. Ossebaar  and  E. C. Hopmans  for their help with the HPLC-APCI-MS analysis,  J. Ossebaar  for his help with the element analyzer, and  L. A. C. Rosales  and  A. Mets  for help with sample preparation. Edited by: S. W. A. Naqvi Reviewed by:  R. Sparkes  and one anonymous referee.