dataesr/scientific-paragraphs-categorization huggingface dataset:
Lake Consuelo, Rice Lake and Wonderkrater have multiple cores (Consuelo: CON1 and CON2; Rice Lake: Rice Lake 79 and Rice Lake 81) but composite depth records were lacking. Given the focus of this study, age models were created only for Lake Consuelo CON1 and Wonderkrater borehole 4 (CON2 and borehole 3 only cover the Holocene). Age models are available for the two cores from Rice Lake, with a hiatus between the two records of about 2000 years (oldest sample at 16 000 cal yr BP in core 79 and youngest one at 18 000 cal yr BP in core 81). These two cores have to be merged prior to future data analysis. The well-known site of La Grande Pile (de Beaulieu and  Reille, 1992)  is not included in the ACER database because the high-resolution data are not publicly available, and the poor quality of the.
, 2008)  or through shifts in the mean latitudinal position of the Intertropical Convergence Zone  (Tzedakis et al. , 2009). Data from Hulu cave  (Wang et al. , 2001)  and the western Mediterranean region (MD95-2042 and SU81-18 twin pollen sequences) show that during warming events occurring at minima in precession, such as D-O 8, monsoon intensification is stronger and associated with a marked seasonality in the Mediterranean region (strong summer dryness) and, therefore, a strong expansion of the Mediterranean forest and decrease in the summer dry-intolerant Ericaceae (Fig. 6 )  (Sánchez Goñi et al. , 1999 , 2000). Actually, we observe parallel strong and weak increases in the East Asian monsoon and Mediterranean forest during GI 8 and GI 19, respectively. However, here again there is a discrepancy between the harmonized Toushe pollen sequence and that from the Hulu cave and the western Mediterranean region: the Mediterranean forest and monsoon during D-O 8 strongly increased, while the subtropical forest cover weakly expanded.
The different latitudinal position of the Toushe Basin (23 • N) in the tropical region and that of the Hulu Cave (32 • N) and the southern Iberian margin sequence (37 • N), both in the subtropical region, could explain such a discrepancy. A comprehensive analysis of differences in the magnitude of monsoon expansion between D-O 8 and D-O 19 is now possible because of the creation of robust and standardized age models for the ACER records. Besides the study of the monsoon variability, the ACER database also has the potential to reconstruct land cover changes through time to evaluate changes in vegetationalbedo feedback, to recover the expansion and contraction of different taxa during the D-O cycles (i. e. , using isolines), to perform spatial climatic reconstructions, and to evaluate climate and vegetation models dealing with the rapid climatic variability.
Conclusions The ACER pollen and charcoal database (ACER 1. 0) comprises all available pollen and charcoal records covering part or all of the last glacial period, as of July 2015. We foresee future updates of the ACER database by the research community with newly published pollen and charcoal records. For consistency, age models for new sites should be constructed using the strategy described here. www. earth-syst-sci-data. net/9/679/2017/ Earth Syst. Sci. Data, 9, 679-695, 2017   (Huber et al. , 2006; Landais et al. , 2005; Sanchez Goñi et al. , 2008) , the atmospheric CH 4 concentration (blue) record  (Chappellaz et al. , 1997; Flückiger et al. , 2004) , compiled Hulu Cave δ 18 O speleothem records (PD in purple, MSD in green and MSL in blue)  (Wang et al. , 2001)  and the precession index  (Laskar et al.
, 2004). Note the mismatch in the timing of GI 19 between the Greenland and pollen harmonized age models and the chronology of Hulu Cave. The harmonization of the ACER age models in the ACER 1. 0 database increases the consistency between records by (a) calibrating all the radiocarbon dates using the recommended IntCal13 and Marine13 calibration curves, (b) using the same ages for non-radiometric control points and basing these on the most recent Greenland ice core chronology (GICC05), and (c) using the CLAM software to build the age models and taking account of dating uncertainties. While these harmonized age models may not be better than the original models, they have the great advantage of ensuring comparability between pollen and charcoal records from different regions of the world. As we have shown in the preliminary analyses of monsoon-related vegetation changes during D-O 8 and D-O 19, this will facilitate regional comparisons of the response to rapid climate changes.
The same strategy for age-model harmonization is now being applied to the sea-surface temperature records from the last glacial period that have been compiled by the ACER-INTIMATE group (http://www. ephe-paleoclimat. com/acer/ ACER%20INTIMATE. htm). This will ensure that the terrestrial and marine databases share a common chronological framework, a considerable step towards improving our knowledge of the interactions between oceans and land that underlie the nature and timing of abrupt climatic changes. The Supplement related to this article is available online at https://doi. org/10. 5194/essd-9-679-2017-supplement. Author contributions. MFSG, SD and ALD developed the harmonized age models, ALD developed the ACER database in AC-CESS, FB participated in the construction of age models, and JMPM extracted the pollen percentage of the dominant biomes from the European sequences compiled in the ACER database.
Biomes for which the pollen percentages data are included in the ACER database. Bo forest: boreal forest; Te mountain forest: temperate mountain forest; Te forest: temperate forest; WTe forest: warm temperate forest; Tr forest: tropical forest; Subtr forest: subtropical forest; SE Pine forest: southeastern Pine forest; Gr: grasslands and dry shrublands; Sav: savanah. In Europe, Te forest refers to Mediterranean and Atlantic forests.
The marine reservoir age (and associated error) and the radiocarbon calibration curve used in the construction of the original age model, and the original calibrated age, are also given. https://doi. org/10. 1594/PANGAEA. 880501. 4. Charcoal data. The table records the identification number of each sample (sample ID). The charcoal data are recorded by depth (in centimeters from the surface), and information is given on the quantity and unit of meawww. earth-syst-sci-data. net/9/679/2017/ Earth Syst. Sci. Data, 9, 679-695, 2017.
Satellite stereo images Two pairs of Pléiades (French Space Agency, CNES) stereo images were acquired over Drangajökull: the first on 14 October 2014 (beginning of the winter) and the second on 22 May 2015 (end of the winter; Table  1  and Fig. 2 ). An additional dataset of stereo images was acquired from WV2 (Dig-italGlobe Inc via the US National Geospatial Intelligence Agency) on 13 February 2015, covering ∼ 92 % of the ice cap (Table  1  and Fig. 2 ). Pléiades and WV2 images have a spatial resolution of 0. 7 and 0. 5 m at nadir, respectively. The images are encoded in 12 bits (Pléiades) and 11 bits (WV2). The base to height (B / H) ratio from the stereo pairs ranges between 0. 4 and 0. 5 (Table  1 ), providing excellent stereo geometry while minimizing occlusions due to steep topography.
Lidar A lidar DEM was produced from an airborne survey in July 2011 (Fig. 1 ) as part of larger effort to survey all Icelandic glaciers and ice caps from 2008-2012  (Jóhannesson et al. , 2013). For Drangajökull, this survey covered an extensive ice-free area outside of the ice cap, up to ∼ 10 km from the ice margin at some locations. The survey was carried out with an Optech ALTM 3100 lidar, with a typical point cloud density of 0. 33 pts m -2. A DEM with 2 m posting was produced from the point cloud  (Magnússon et al. , 2016a ). An uncertainty assessment performed on another lidar dataset from the same sensor acquired in similar conditions revealed an absolute vertical accuracy well within 0. 5 m  (Jóhannesson et al. , 2011).
g. Guðmundsson, 2000; Thorsteinsson et al. , 2002; Ágústsson et al. , 2013). The position was measured using a handheld GPS at each core location. We used the in situ data collected at eight of these locations in spring 2015 for data calibration and validation. These measurements were carried on 19 June 2015, which is 1 month later than usual due to an unusually cold spring. All available in situ records of snow density from 2005-2014 were also included in this study. Additionally, a manually interpolated map of in situ net mass balance for the glaciological year 2013-2014 was obtained using measurements at the 12 mass balance survey sites and a 110 km profile of snow depth from ground penetrating radar (GPR) traversing through all the survey sites (unpublished data, IMO and IES). The locations of survey sites and the GPR profiles were chosen to represent the spatial variation and elevation dependence of the snow cover.
The interpolation method is described for a similar dataset by  Pálsson et al. (2012). A map of the Drangajökull bedrock topography  (Magnússon et al. , 2016b)  was also used in this study, and daily precipitation and temperature measurements for 2014-2015 from the meteorological station Litla Ávík (LÁ, station #293, 40 km SE of Drangajökull, 15 m a. s. l. , Fig. 1 ) were obtained from IMO (public data, www. vedur. is).
Processing of satellite data Two different schemes (Fig. 3 ) were used to obtain the DEMs and the difference of DEMs (dDEM), spatially co-registered (e. g. ,  Nuth and Kääb, 2011). Spatial calculations are done in the conformal conic Lambert projection, ISN93 (details at www. lmi. is). Scheme A involves lidar-derived ground control points (GCPs) as a reference, whereas scheme B involves common snow-and ice-free areas in the datasets. From each scheme, statistics of elevation difference in snow-and icefree areas were calculated to verify that the dDEM is unbiased and to quantify its relative accuracy.
Scheme B: processing of Pléiades images with DEM co-registration In this approach, the DEMs were produced from the pair of stereo images with the original RPCs. This work was carried out with the open source software Ames Stereo Pipeline (ASP, version 2. 5. 3) developed by NASA  (Shean et al. , 2016). The processing chain uses the routine stereo, producing a point cloud from each pair of stereo images, followed by the routine point2dem, which produces a gridded DEM (4 × 4 m grid size) and an orthoimage (0. 5 × 0. 5 m pixel size) for each pair of stereo images. Areas with thin semitransparent clouds covering the ice cap in the May 2015 Pléiades images (Fig. 2 ) produced data gaps in the DEM. These image fragments were processed separately and then mosaicked and superimposed over the initial May 2015 Pléiades DEM and orthoimage.
The correlation performed in these areas was based directly on the fullresolution images, instead of a pyramidal correlation from subsample images. This improved the correlation  (Shean et al. , 2016) , resulting in full coverage of these areas (Fig. 2 ). The snow-and ice-free areas were delineated from the May 2015 Pléiades orthoimage using a binary mask obtained by setting up a cutoff value of < 0. 2 for the top of atmo-sphere absolute reflectance. These images show clear contrast between snow and bare ground (Fig. 2 ), making image segmentation an efficient approach for the identification of bare ground. The DEMs were co-registered using the routine pc_align in ASP software, based on the iterative closest point (ICP) algorithm for co-registration of two point clouds  (Shean et al. , 2016).
February 2015 WV2 DEM The WV2 data was collected and processed as part of the ongoing US National Science Foundation ArcticDEM project. A gridded DEM with 2 × 2 m grid size was produced with the Surface Extraction with TIN-based Search-space Minimization (SETSM) software  (Noh and Howat, 2015) , using the RPC sensor model and no GCPs. The 13 February 2015 orthoimage acquired from WV2 was also provided in 2 m pixel size. Since the raw WV2 images were not available in this study, the February 2015 WV2 DEM was co-registered to the October 2014 Pléiades DEM using the ICP algorithm as described in the previous section (scheme B). First, the WV2 DEM, originally in polar stereographic projection, was reprojected and bilinearly resampled to 4 × 4 m. Then, the ICP algorithm was applied to the ice-free areas from the May 2015 Pléiades orthoimage after manually aligning it to the February 2015 WV2 orthoimage and verifying a similar distribution of snow-free areas between the orthoimages of February and May 2015.
Computation of glacier-wide mass balance Three dDEMs were produced from the different combinations: dDEM t2 t1 , dDEM t3 t2 and dDEM t3 t1 , where t 1 = 14 October 2014, t 2 = 13 February 2015 and t 3 = 22 May 2015 (Fig. 4 ). The glacier-wide geodetic winter mass balance was calculated as Bw t f t 1 = ρ Snowt f h dDEM t f t 1 + C t f t 1 {h Firn } + C t f t 1 h Snowt 1 , (1) where t f denotes the date of the last DEMs used and h dDEM is the average elevation change over the ice cap observed from the remote sensing data (dDEMs). The term ρ Snowt f is the bulk snow density at the time of the latter DEM, and C t f t 1 represents the spatially averaged densification of the firn layer, h Firn , and the fresh snow, h Snowt 1 , existing on the glacier surface at t 1. The density and firn densification terms are quantified from field measurements (Sect.
Average elevation change The average elevation change over the ice cap, h dDEM , is extracted from the dDEMs. The extent of the ice cap was digitized from the October 2014 Pléiades orthoimage, following the criteria defined in previous studies  (Jóhannesson et al. , 2013; Magnússon et al. , 2016a)  for glacier digitation, which excludes snowfields located at the western and southern sides of the ice cap. We assume that uncertainties in geodetic mass balance caused by digitization of the ice cap outlines are negligible due to the high image resolution. The data gaps in the dDEMs within the ice cap occur in large shadows north of nunataks in October 2014 and February 2015 and in the south-easternmost part of the ice cap in February 2015 (Fig. 2 ). These shadows led to < 1 % data gaps for dDEM t3 t1 and ∼ 8 % gaps for dDEM t 2 t 1 and dDEM.
Firn densification Densification of the firn layer leads to a continuous lowering of the bottom of the annual snow pack and an underestimate of snow volume changes estimated from the dDEM  (Sold et al. , 2013). The total area covered by firn at the end of the 2014 ablation season was 91 km 2 , or about 64 % of the ice cap, based on the extent of snow in a Landsat 8 image acquired on 16 September 2014 (data available from the US Geological Survey, http://earthexplorer. usgs. gov/). Similar spatial distribution of firn areas are inferred from the map of net annual mass balance of the year 2013-2014, showing 58 % of the ice cap with positive mass balance at the end of the summer. The 2013-2014 net mass balance distribution was used to correct for firn densification, assuming this was a typical year of mass balance for Drangajökull.
Uncertainty on elevation difference derived from satellite data The statistics obtained from the dDEMs in snow-and icefree areas (Table  2 ) allow for a quantitative comparison of the different methods and datasets used in the study. The statistics show smaller SDs and NMADs outside of the areas of high slopes and shadows due to the dependency of the DEM accuracy on the steepness of the terrain  (Toutin, 2002; Müller et al. , 2014; Lacroix, 2016; Shean et al. , 2016)  and the presence of shadows  (Shean et al. , 2016 ; Table  2 ). The vertical bias obtained after DEM co-registration ranges from 0 to 0. 1 m based on the median, and the NMAD reveals random errors < 0. 5 m in both schemes A and B as well as in the co-registered WV2 DEM. Both schemes yield a similar result for elevation difference, h dDEM , on the ice cap.
Pléiades and WorldView DEMs for measuring snow accumulation We measure the glacier-wide geodetic mass balance during the winter of 2014-2015, as well as two sub-periods of the same winter, by differencing DEMs obtained from satellite data. In our calculations, we incorporate corrections for snow density and densification of firn and fresh snow, based on in situ measurements. This technique can be applied in small and medium size glaciers (typically ∼ 1000 km 2 can be stereoscopically covered at once based on the capabilities of Pléiades and WorldView), with sufficiently high mass balance amplitude (∼ 0. 5-1 m w. e. or higher). The main advantages of using stereoscopic satellite images are repeatability and coverage of remote glaciated areas. The use of external reference data for bundle adjustment prior to stereo correlation, such as lidar-based or GPS-based GCPs, does not improve the relative accuracy of the Pléiades and WorldView DEMs used here (Table  2 ).
Combining data from Pléiades and WorldView allows for high spatial resolution within a short (3-4 month) interval. The availability of these data and the presented processing strategy allow, to our knowledge, for the first optical satellite-based measurement of winter accumulation on a glacier. Both sensors result in a similar level of accuracy (Table  2 ) and their combination enables more detailed studies of glacier changes. The ArcticDEM project (data available at http://arcticdemapp. s3-website-us-west-2. amazonaws. com/explorer/) freely offers multi-temporal DEMs of the Arctic region collected since ∼ 2010 with dense temporal repetition (more than 30 DEMs during the last 6 years in certain regions of Greenland, e. g. ,  Willis et al. , 2015) , therefore providing a high potential for similar studies of geodetic mass balance on seasonal timescales.
Acknowledgements. We thank the editor  Valentina Radić  and the reviewers  David Shean  and  Ruzica Dadic  for their valuable comments, which greatly improved the quality of the initial manuscript. We also thank  Sydney Gunnarson  for proof reading the revised manuscript. This study was funded by the  University of Iceland (UI) Research Fund. Pléiades images were acquired at research price thanks to the  CNES ISIS program  (http://www. isis-cnes. fr). The WV2 DEM was obtained through the  ArcticDEM  project. This work is a contribution to the  Rannís  grant of excellence project, ANATILS. Collaboration and travels between IES and LEGOS were funded by the  Jules Verne research fund  and the  TOSCA program  from the  French Space Agency, CNES. This study used the recent lidar mapping of the glaciers in Iceland that was funded by the  Icelandic Research Fund , the  Landsvirkjun research fund , the  Icelandic Road Administration , the  Reykjavík Energy Environmental and Energy Research Fund , the  Klima-og Luftgruppen (KoL)  research fund of the  Nordic Council of Ministers , the  Vatnajökull National Park , the organization Friends of Vatnajökull, the  National Land Survey of Iceland , the  Icelandic Meteorological Office  and the UI research fund.
At the end of the study, the results revealed that both Mediterranean diet groups had a 30% lower incidence of the primary endpoint than the control group. Remarkably, incident stroke was reduced by nearly 50% in participants following the Mediterranean diet enriched with nuts (15 g walnuts, 7. 5 g almonds, and 7. 5 g hazelnuts)  [103]. In an observational study within the PREDIMED trial, total polyphenol intake and polyphenol subclasses were calculated from the FFQ and the Phenol-Explorer database. Total polyphenols were associated with a 46% reduction in CVD risk on comparing the fifth versus the first quintiles. The intake of lignans, flavanols, and hydroxybenzoic acids was also inversely and significantly associated with cardiovascular events  [104]. Besides this study, only a few more have directly evaluated the association between polyphenol intake and stroke, and only 2 found a significant inverse association: the Zutphen Elderly Study and the Finnish Mobile Clinic Health Examination Survey.
Acknowledgments This work was supported by  ERAB: The European Foundation for Alcohol Research  ( EA 1324 ,  EA 1514 ,  EA 1515 ,  EA1517 , and  CICYT ( AGL2016-79113-R) ) and the  Instituto de Salud Carlos III (ISCIII) (CIBEROBN)  from the  Ministerio de Economía y Competitividad (MEC)  ( AEI/FEDER, UE)  and  Generalitat de Catalunya (GC)  ( 2014 SGR 773 ).
Observations We used the 1 • resolution World Ocean Atlas 2013 (WOA13;  Boyer et al. , 2013)  for evaluating the model accuracy at representing the large-scale climatological temperature and oxygen. The sea level is a good proxy for vertical movements of the thermocline in tropical regions (e. g. ,  Fukumori et al. , 1998) , and hence we used the sea-level data from satellite altimetry produced by Ssalto/Duacs and distributed by AVISO (http://www. aviso. altimetry. fr/duacs/) as a proxy for the thermocline interannual variability along the WCI. In addition, we used the monthly level-3 Ocean Color Climate Change Initiative (OC-CCI) product, available at http://www. oceancolour. org/, that merges data from the Sea- WiFs, MERIS, and MODIS ocean color missions to evaluate the model surface chlorophyll climatological seasonal cycle.
This dataset consists of a total of 142 vertical profiles during the 1997-2010 period and has been extensively used by previous studies (e. g. ,  Naqvi et al. , 2006 Naqvi et al. , , 2009 Naqvi et al. , , 2010a)). Figure  1a  shows the percentage of years sampled month-wise in this dataset. It indicates that in situ measurements have been performed at this station ∼ 50 % of the years over the 1997-2010 period for most calendar months, including September-October (the time of peaking of anoxic events), except for June (< 10 %) and July (no data), when summer monsoon rough weather conditions prevent observations  (Naqvi et al. , 2009). This temporal coverage allows us to build a reliable monthly climatology of near-coastal oxygen variations, except for the months of June and July. Though CaTS offers the best available dataset to study the oxygen variations along the WCI, a thorough description of oxygen interannual variability is limited for two reasons.
First, during our period of interest, i. e. , September-October, the temporal distribution of the data indicates that only ∼ 50 % of the years were sampled over the 1997-2010 period. Second, the months for which a large number of records are available reveal very different vertical oxygen profile over the same month (like in September 1998 or 2000; Fig. 1c  and 1e ), highlighting the existence of a large variability on a sub-monthly timescale. For some other months, the number of available profiles is very limited, like in September 1999, when only one profile is available (Fig. 1d ). Given the existing high-frequency variability, averaging this very limited number of profiles may not provide a value representative of the actual monthly average. As a result, the uneven temporal distribution and the sub-monthly variability do not allow us to build a reliable monthly pluriannual time series from this dataset.
The oceanic portions of the eastern, northern, and southern boundaries use radiative open boundaries  (Treguier et al. , 2001) , constrained with a 150-day timescale relaxation to physical and biogeochemical inputs from a global simulation  (Dussin et al. , 2009). The circulation and thermodynamics of this regional configuration have been extensively evaluated and found to perform well in several Indian Ocean regions (Praveen  Kumar et al. , 2014) , including the Arabian Sea  (Nisha et al. , 2013; Vialard et al. , 2013; Keerthi et al. , 2015 Keerthi et al. , , 2016) )  and the Bay of Bengal  (Akhil et al. , 2014 (Akhil et al. , , 2016)). The simulation starts from the rest and the WOA13 climatology temperature and salinity  (Boyer et al. , 2013). PISCES biogeochemical tracers are initialized from the WOA13 database for nutrients and a global simulation climatology for other tracers  (Aumont and Bopp, 2006).