dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. This work benefited from helpful comments by  E. Wolff ,  T. Heaton , and two other anonymous reviewers. We also thank  Florent Molliex ,  Delphine Tardif-Becquet ,  M. Sacchettini ,  M. Nodet  and  E. Blayo  for helpful exchanges. This work was funded by the  INSU/LEFE  project  IceChrono  and by the  ANR  project  ADAGe. Edited by:  D. Goldberg.
Code availability IceChrono1 is an open source model available under the General Public License v3. It is hosted on the github facility (https://github. com/parrenin/IceChrono). A mailing list exists for general support or discussions (https://groups. google. com/forum/?hl=_en#!forum/icechrono). The current code (version 1. 4) is also available as a Supplement to this manuscript. The Supplement related to this article is available online at doi:10. 5194/gmd-8-1473-2015-supplement.
g. , Lyme Bay, Bay of Seine) (Figure  3 ). These simplified investigations were followed by off-line computing approaches combining outputs from bi-dimensional horizontal (2DH) hydrodynamic models with empirical sand transport formulae  [4] [5] [6]. Numerical results refined the quantifications of tidal sediment transport pathways through the Wight-Cotentin section and the Dover Strait. More complex fully-three-dimensional (3D) computations were finally adopted to assess the consequences of local and advection-dispersion-controlled remote resuspensions on total SSC in tide-dominated conditions  [7, 8]  and stormy wave regimes  [9, 10]. Validated against in situ observations, these numerical studies remained, however, restricted to the vicinity of measurement sites. In spite of strong dependency with cloud coverage and low time resolutions, remote-sensing images of near-surface SSC constitute an interesting dataset to assess model performances at the scale of the English Channel  [1].
Whereas satellite data are evidence of clear-sky conditions, the observed signal integrates seasonal variations induced by major climatological conditions  [11] , as well as finer temporal modulations, such as spring-neap tidal signatures  [12]. Nevertheless, whereas advanced modeling systems successfully approach major observed SSC patterns at the scale of the northwestern European shelf  [13, 14] , no further refined investigations have specifically focused on the English Channel. The purpose of the present study is to extend these comparisons relying on a fully-3D multicomponent modeling system integrating the major hydrodynamic forcings of tide and waves, associated non-linear interactions in the bottom boundary layer and the consequences in terms of suspended sediment transport with a realistic heterogeneous distribution of bottom sediments (Section 2).
Setup The geographical domain for full computation of hydrodynamic and sediment transport extends in longitude from 3. 300 o W-3. 000 o E and in latitude from 48. 410 o N-51. 000 o N (Figure  1 ). It is discretized with a spacing of 3 km and 11 uniform vertical σ-grid cells. Time steps for the external and internal modes are 30 and 300 s, respectively. This inner computational domain is extended to a larger outer domain from 4. 000 o W-4. 700 o E and 48. 400 o N-52. 850 o N in order to achieve more suitable open boundary conditions  [8]. The model is driven by the tidal free-surface elevation recomposed from the dominant ten harmonic constituents of the TPXO. 6 (TOPEX/Poseidon) database  [39]. Wind speeds at 10 m above the free-surface are provided by the meteorological model ALADIN (Aire Limité, Adaptation Dynamique, Développement International, Météo-France).
Acknowledgments The authors are particularly grateful to the  MyOcean (European Commission ) project [17] and the space agencies for providing ocean color data from  MODIS (NASA)  and  MERIS (ESA) , as well as to the  IOWAGA (Integrated Ocean WAves for Geophysical and other Applications ) project ( European Commission ) [52] for supplying wave data. Wind field data of the meteorological model ALADIN were provided by  Météo-France. Numerical simulations were performed at the computer facilities CAPARMOR ( CAlcul PARallèlle Mutualisé pour l'Océanographie et la Recherche ). The present paper is a contribution to the research program  FLUSED (FLUx SEDimentaires) of the Cerema (Centre d 'études et d'expertise sur les risques, l'environnement, la mobilité et l'aménagement , Laboratory of Coastal Engineering and Environment ) [53].
Acknowledgments The authors gratefully acknowledge support from the  International Space Science Institute (ISSI)  for our team ''The  Methane Balance-Formation and Destruction Processes on Planets , their Satellites and in the  Interstellar Medium, '' Team ID 193. This work has been carried out thanks to the support of the A* MIDEX  project (n°ANR- 11-IDEX-0001-02 ) funded by the '' Investissements d'Avenir'' French Government program , managed by the  French National Research Agency (ANR). Partial support for this work comes from  NASA Jet Propulsion Laboratory ( JPL ) through subcontract to  Southwest Research Institute  for the  Cassini Ion Neutral Mass Spectrometer , and from the  Stockholm University Astrobiology Centre (SU-ABC ).
, 2003). Lake size was also taken into account at the selection of study sites to enable reconstructions of local-scale land use based on fossil pollen records. The lakes are situated in the parish of Slätthög, established around AD 1000, and the first local population data are available from AD 1571, revealing 301 inhabitants  (Andersson Palm, 2000). During the 1700s the population started to increase rapidly and a population peak was reached in the end of the 1800s, followed by a decrease in rural population due to industrialization. Åbodasjön (Table  1 , Fig. 1 ) is an oligotrophic mesohumic lake fed by two inlet streams, situated in the south and northeast, and with an outlet in the south-west. The village of Åboda (40 residents in 2004) is situated west of the lake, and the area around the lake margin is semi-open with mainly deciduous trees, grassland and cropland.
7+). At least 400 diatom valves per sample were counted under a light microscope at 1000 × magnification, using phase-contrast optics and identification keys  (Krammer and Lange-Bertalot, 1986 , 1988 , 1991a , 1991b; Lange-Bertalot and Krammer 1989; Krammer , 1992). The diatom counts were expressed as relative abundances of each taxon. Diatoms were grouped into planktonic and benthic taxa for calculation of planktonic / benthic (P / B) ratios, indicative of light availability, as decreased light penetration reduces benthic growth. Changes in pH were inferred from sedimentary assemblages (Di-pH) using a transfer function set, the online combined pH training set in the European Diatom Database (http://craticula. ncl. ac. uk/Eddi/jsp/). The calibration set for the model consists of 627 lakes with a pH range of 4.
The temporal resolution of this ordination analysis was determined by the resolution of the inferred VNIRS-TOC reconstruction (see Fig. 3 ). Land cover was considered constant for each 20-year interval. For the other sediment parameters and for atmospheric deposition, linear interpolation was used to derive an estimated value for the year corresponding to each VNIRS-TOC sample. For the climatic variables (annual mean temperature and total annual precipitation), the value measured in the sample year and the mean value for the 10 years up to and including the sample year were both included in the analysis. Ordinations were carried out using CANOCO v4. 51. For all analyses, preliminary detrended canonical correspondence analysis showed the response data set had a gradient length < 1 standard deviation units, implying that linear based ordination techniques such as redundance analysis (RDA) were most suitable for these data sets  (ter Braak and Smilauer, 1998).
After around AD 1540 grassland and cropland cover increases and reaches maxima of ca. 60 and 12 %, respectively, between AD 1820 and 1900. During the 1900s coniferous woodland, dominated by spruce, increases from 10 to 30 %. Coniferous and deciduous woodland covers ca. 60 % of the lake catchment today. RDA was used to describe the major gradients in the limnological data set and relate these patterns to the land-use variables during the last 800 years. Total woodland cover was identified as the most significant land-cover factor, explaining a statistically significant 13 %. Other land-cover variables that were significant when analysed on their own were spruce and cropland cover, each explaining 12 %; wetland and coniferous woodland, 10 %; and deciduous woodland, 6 %. When total woodland cover was included in the RDA analysis, deciduous woodland cover could still explain an additional 6 % of the variation, while no other land-cover parameters were statistically significant at the P < 0.
This suggests a recovery from the phase of maximum sulfur emissions, which resulted in exceptionally low TOC concentrations in the lakes at ca. AD 1930-90. The RDA data obtained from the palaeolimnological records over the period since AD 1880 also indicate a recovery. At both sites, the temporal development of the RDA scores during this period (Fig. 4b  and d ) show that the youngest samples fall near the oldest, indicating a return to pre-industrial conditions, following a time of highly anomalous conditions. At Åbodasjön, there was first a period of low inferred lake-water TOC concentration and high pH in the 1930s-1960s, followed by decreasing pH but high Zr / Ti ratios (perhaps indicating enhanced erosion) in the 1980s and early 1990s, and then increasing lake-water TOC concentration and low Zr / Ti ratios towards the present.
Acknowledgements. This work was funded by the  Swedish Research Council Formas  (grant to  W. Granéli ). The authors are grateful to  Shinya Sugita  for input on the quantitative vegetation reconstructions, and  Sofia Holmgren ,  Linda Randsalu-Wendrup  and  Christian Bigler  for helpful support with diatom preparation and categorization. We are very grateful to all members of the  NordForsk network LANDCLIM  (coordinated by  M. J. Gaillard ,  Linnaeus University, Sweden ) for useful and inspiring discussions during the numerous workshops (2009)(2010)(2011). We acknowledge the  Swedish Meteorological Institute (SMHI)  for precipitation and temperature data, and the  Swedish Environmental Research Institute and MAGIC  for sulfur and nitrogen deposition data. Constructive comments by the reviewers improved the final presentation.
Coastal ocean and shelf seas forecasting GOV aims to advance and promote science in support of coastal ocean and shelf sea (COSS) forecasting. The strategic goal is to achieve a seamless ocean forecasting framework, from the global to the coastal/near-shore scale. After three successful international coordination workshops, the current state of COSS forecasting has been reviewed, and a picture of future scientific challenges is emerging  (Kourafalou et al. 2015). First, despite efforts in integrated monitoring (e. g. IOOS, 3 JERICO 4 ), there are still issues associated with routine observations in many coastal regions:. The SWOT 5 wide-swathe altimeter mission is expected to provide high-resolution (HR) seasurface height starting in 2020. This will facilitate a fundamental change in coastal-to-shelf scale modelling, parameterizations (previously unresolved processes now resolved) and data assimilation.
Amazonia holds the largest continuous area of tropical forests with intense land use change dynamics inducing water, carbon, and energy feedbacks with regional and global impacts. Much of our knowledge of land use change in Amazonia comes from studies of the Brazilian Amazon, which accounts for two thirds of the region. Amazonia outside of Brazil has received less attention because of the difficulty of acquiring consistent data across countries. We present here an agricultural statistics database of the entire Amazonia region, with a harmonized description of crops and pastures in geospatial format, based on administrative boundary data at the municipality level. The spatial coverage includes countries within Amazonia and spans censuses and surveys from 1950 to 2012. Harmonized crop and pasture types are explored by grouping annual and perennial cropping systems, C3 and C4 photosynthetic pathways, planted and natural pastures, and main crops.
Our analysis examined the spatial pattern of ratios between classes of the groups and their correlation with the agricultural extent of crops and pastures within administrative units of the Amazon, by country, and census/survey dates. Significant correlations were found between all ratios and the fraction of agricultural lands of each administrative unit, with the exception of planted to natural pastures ratio and pasture lands extent. Brazil and Peru in most cases have significant correlations for all ratios analyzed even for specific census and survey dates. Results suggested improvements, and potential applications of the database for carbon, water, climate, and land use change studies are discussed. The database presented here provides an Amazon-wide improved data set on agricultural dynamics with expanded temporal and spatial coverage.
Agricultural Census and Survey Data and Maps of Administrative Units We systematized historical crops and pastures data from agricultural census for countries within Amazonia. The study area corresponds to boundaries defined by other important Amazon modeling initiatives  [Soares-Filho et al. , 2006] , except for Guyana and French Guiana, whose share of agricultural lands within the basin is less than 1. 5% of total nonforest lands (for 2008, Table  1 ). For each available census or survey, we collected total area for each crop, pastures, fallow, and abandoned lands, forest areas (within the productive units), livestock, and number of farms. Only main crops types were available from Brazil, although we could not find the definition used by IBGE (Brazilian Institute of Geography and Statistics) to select those on each census.
The adjustments account for a reconstruction of municipality boundaries, for example, based on historical political maps or relational keys depicting boundary evolution as municipalities become subdivided into smaller sized units (details on methods and data sources in Table  3 ). The 1995 data set for Colombia has aggregated data for all Amazonian departments from agricultural surveys, and department values were derived proportional to the area of each administrative unit. For Brazil data from  1995, 2000, and 2006 , this study is based on the work of  Barona et al. [2010]  which relied on systematized surveys, censuses, and databases from the Brazilian Institute of Statistics and Geography [  IBGE-SIDRA, 2006]. In order to obtain consistent data, they aggregated units to match boundaries of the coarsest unit across these three dates.
Systematization of Crop Data Census data collected information for a total of 407 crop types for which we identified 216 here with their scientific names and family in order to harmonize all censuses data. We did not identify in the literature 191 crops from Peru 2012 and 1994 (172 and 19 crops, respectively). All crops not identified were aggregated to the "others" census category which covers <6. 8% of any of the country's total agricultural areas. Peru 2012 reports 2334 associated crops that were not identified by scientific name representing 8% of total croplands. Bolivia reports 13. 4% of total cropland areas with crop associations whose crop types are not described in the census. Crops across countries and census dates were aggregated into three groups (details in the supporting information Table  S2 ).
Discussion We compiled and harmonized a new agricultural land use data set that characterizes crops and pastures in Amazonia according to cropping system (annual or perennial), photosynthetic pathway, and crop physiognomy based on successive agricultural censuses and surveys in countries of the Amazon basin since 1950. Our data set incorporates seven countries within Amazonia, providing a new information resource that treats the entire region. The legal Amazon in Brazil has been the main focus in most of the literature, even though it only covers around two thirds of the region. Although similar studies have covered larger spatial domains [i. e. ,  Ramankutty et al. , 2008] , specific dates [i. e. ,  Morton et al. , 2006; Macedo et al. , 2012] , or disaggregated spatial distribution of census data using land cover maps from remote sensing  [Cardille and Foley, 2003] , we present here a unique data set in terms of its temporal and spatial coverage.
, 2012]. However, the data presented here cannot capture gross gains or losses in areas between crops, planted/natural pasture lands needed to explain deforestation drivers, and further refinement using remote sensing data is required. Accounting for fallow lands could be also helpful in this sense and is a potential improvement of the database presented here. Crops type data have been also useful to understand land use change drivers, for example, the increase in soybean, cotton, and corn fraction in Mato Grosso (Figures S8, S9, and S12 in the supporting information) resulted from an increase in agricultural intensification where areas with double cropping systems (soybean-corn or soybean-cotton on each year) increased from 6% to 30% between 2000 and 2007  [Arvor et al. , 2012]. Productivity data (not presented here) are also needed since it has been the driver for increased soybean production (i.
Conclusions We presented a harmonized database of agricultural censuses and surveys for Amazonia covering countries within Amazonia since 1950 at the municipality level. We described the spatial patterns of agricultural lands, crops, and pastures distribution, annual to perennial cropping systems, planted to natural pastures, and main Global Biogeochemical Cycles 10. 1002/2014GB004999 crop types. Significant correlations were found between these patterns and the fraction of crops and pasture lands. Our database should encourage improved studies on land use change dynamics, water, and carbon cycles at regional and global scales.
Acknowledgments We would like to thank  Instituto Brasileiro de Geografia e Estatística (IBGE, Brazil) ,  Instituto de Pesquisa Econômica Aplicada (IPEA, Brazil) ,  Ministerio de Agricultura y Desarrollo Rural  ( MADR, Colombia ),  Instituto Geográfico Agustín Codazzi (IGAC, Colombia) ,  Instituto Nacional de Estadística y Censos (INEC, Ecuador) ,  Dirección de Información Cartográfica y Estadística (DICE, Ecuador) ,  Instituto Geográfico Militar (IGN, Ecuador) ,  Ministerio de Hacienda y Comercio (MHC, Peru) ,  Instituto Geográfico Militar (IGM, Peru) ,  Comisión Multisectorial para el Seguimiento y Evaluación del Plan de Desarrollo de la Sociedad de la Información del Perú (CODESI) ,  Insituto Nacional de Estadística e Informática (INEI, Peru) ,  Insituto Nacional de Estadística (INE, Bolivia) ,  Instituto Geográfico Militar (IGM, Bolivia) ,  Ministerio de Planeamiento y Coordinación  ( MPC, Bolivia ),  Unidad de Productividad y Competitividad (UPC, Bolivia) ,  Ministeri van Landbow-Suriname (Suriname) , and  Ministerio Popular para la Agricultura y Tierras  ( MAT, Venezuela ) for providing documents, data, guidance on data use, and contacts.
Special thanks to the  Insitutional Library of the Insituto Nacional de Estadística e Informática (INEI, Peru). We are particularly thankful to  Mireya Isidro ,  Allan Guerrero ,  Claudia Bouroncle ,  Marcela Osorio ,  Andrea Tapia , and  Raúl García Palacios  (from the  Insitutional Library at Instituto Nacional de Estadística e Informática in Peru)  for helping with logistics on their countries. Thanks to  Paula Blanco  for kindly providing land cover data for Latin America. This work was financially supported by  EU   FP7 Amazalert  (Raising the alert about feedbacks between climate and long-term land use change in the Amazon) and GEOCARBON projects. Also, has been partially financed by the  CGIAR Research Program on Forestry, Trees and Agroforestry. Finally Philippe Ciais acknowledges support from the  ERC Syg P-Imbalance Grant.
Harmonized tabular and geographic information systems data are available upon request to the corresponding author and at https://www. researchgate. net/publication/276513233_Spatial_and_temporal_contra-sts_in_the_distribution_of_crops_and_pa-stures_across_Amazonia_A_new_agricult-ural_land-use_dataset_from_census_data_ since_1950. Global Biogeochemical Cycles 10. 1002/2014GB004999.
We present 5 years of GOSAT XCH4 retrieved using the "proxy" approach. The Proxy XCH4 data are validated against ground-based TCCON observations and are found to be of high quality with a small bias of 4. 8 ppb (∼ 0. 27 %) and a single-sounding precision of 13. 4 ppb (∼ 0. 74 %). The station-to-station bias (ameasure of the relative accuracy) is found to be 4. 2 ppb. For the first time the XCH4 / XCO2 ratio component of the Proxy retrieval is validated (bias of 0. 014 ppb ppm-1 (∼ 0. 30 %), single-sounding precision of 0. 033 ppb ppm-1 (∼ 0. 72 %)). The uncertainty relating to the model XCO2 component of the Proxy XCH4 is assessed through the use of an ensemble of XCO2 models. While each individual XCO2 model is found to agree well with the TCCON validation data (r = 0. 94-0. 97), it is not possible to select one model as the best from our comparisons.
In a previous work  (Parker et al. , 2011)  we presented the first year of our global short-wave infrared (SWIR) measurements of the dry-air column-averaged mole fraction of CH 4 (XCH 4 ) from the GOSAT mission using the "proxy" retrieval approach. This data product has subsequently been developed  (Buchwitz et al. , 2013)  and validated  (Dils et al. , 2014)  as part of the ESA Climate Change Initiative Greenhouse Gas project and we now report an assessment of the full 5-year data set for version 5. 0 of the University of Leicester GOSAT Proxy XCH 4 data product. This work is motivated by the desire to better understand the uncertainty characteristics of the Proxy XCH 4 data for use within flux inversion systems, especially relating to uncertainties introduced by the model XCO 2. In Sect.
The main disadvantage with the proxy approach is that it is reliant on an accurate, unbiased model XCO 2 data set to convert the XCH 4 / XCO 2 ratio back into an XCH 4 quantity; otherwise errors relating to the model XCO 2 may be folded into the final XCH 4 result. Here we present assessments of the different uncertainties to determine the importance of this aspect of the Proxy XCH 4 data. We process the latest versions of the GOSAT Level 1B files (version 161. 160) acquired directly from the NIES Large Volume Data Server and apply the recommended radiometric calibration and radiometric degradation correction as per  Kuze et al. (2014). For the spectroscopic inputs we use v4. 2. 0 of the OCO line lists with CH 4 taken from the Total Carbon Column Observing Network (TCCON) line lists  (version "20120409").
The a priori pressure, temperature, and water vapour is taken from the ECMWF ERA-Interim data  (Dee et al. , 2011). For the CO 2 a priori we use the MACC-II CO 2 inversion (v13r1) and for the CH 4 we use the MACC-II CH 4 inversion (v10-S1NOAA, using 2012 data for 2013) but here we adjust the stratospheric methane using a specialised full chemistry run (run ID 563) of the TOMCAT stratospheric chemistry model from the University of Leeds  (Chipperfield, 1999). This TOMCAT model run has been validated against ACE-FTS observations and was found to provide a more accurate representation of the stratosphere. The spectral noise is estimated from the standard deviation of the out-of-band signal. Spectra over ocean or with a signal-to-noise ratio (SNR) below 50 are removed. Cloudcontaminated scenes are removed by the comparison of a clear-sky surface pressure retrieval from the O 2 A-band to the ECMWF surface pressure for the relevant measurement time and location.