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Abstract: Data derived from Core SG-1b (coordinates: 38°21'9.46'' N, 92°16'24.72'' E) which was drilled within the framework of a Sino-German cooperation project in 2008. Attached Excel spreadsheet contains to data sets: 1. Data: Depth, Age, Sedimentation Rate and ln(Rb/Sr) ratio 2. Age model: Depth, Age Abstract: To contribute to a better understanding of Neogene climate evolution in Central Asia, we here present the first orbitally tuned time scale for a drillcore record from the Qaidam Basin (NE Tibetan Plateau) that consists of lacustrine sediments and spans the late Pliocene to early Pleistocene (~3.3 to 2.1 Ma). Our tuning of Core SG-1b is based on the ln(Rb/Sr) ratio derived from XRF core scanning and grain-size distribution data that trace wet-dry climate alternations predominantly paced by orbital obliquity. Based on our ln(Rb/Sr) record, obliquity-precession interferences persisted during the mid-Pliocene warm period, but disappeared during the early Pleistocene. This could indicate that over the course of the late Pliocene a low-latitude-derived climate modulator gained an increasingly prominent role in shaping the environment of the Qaidam paleolake. At the same time, the consistent presence of the precession signal during the early Pleistocene hampers a refinement of the entire tuning on precession time scales. The inferred changes in sedimentation rate from the late Pliocene to the early Pleistocene indicate a long-term decrease in sediment supply into the Qaidam paleolake. This finding is in line with the previously proposed notion of a long-term aridification trend across the Plio-Pleistocene transition in Central Asia. Category: geoscientificInformation Source: Not Available Supplemental Information: Not Availble Coverage: EVENT LABEL: (Qaidam paleolake) * LATITUDE: 38.352628 * LONGITUDE: 92.273533 * ELEVATION: 2900.0 m * LOCATION: Tibetan Plateau * METHOD/DEVICE: Drilling/drill rig
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Abstract: The seafloor lithology of deep-sea sediments of the global ocean was spatially predicted. Seven lithology classes were predicted: Calcareous sediment, Clay, Diatom ooze, Lithogenous sediment, Mixed calcareous-siliceous ooze, Radiolarian ooze and Siliceous mud. The dataset contains probability surfaces of the seven seafloor lithologies, the probability of the most probable class (maximum probability) and the predicted seafloor lithology. The results are presented as geo-referenced floating-point TIFF-files with a spatial resolution of 10 km and Wagner IV equal-area projection as spatial reference. Seafloor lithologies were mapped by building a predictive spatial model. This entails a two-step approach: Initially, the relationship between a set of predictor variables and a response variable is modelled from observations (samples). The established model is then employed to predict the response variable at unsampled locations for which values of the predictor variables are known. The response variable is seafloor lithology, a qualitative multinomial variable. Seafloor lithology data were sourced from Dutkiewicz et al. (2015) and pre-processed in the following way: Only samples deeper than 500 m were used and a minimum distance between sample locations of 14.5 km (the diagonal of a 10 km predictor variable pixel) was enforced to limit the number of samples per pixel to one. This had also the effect of removing duplicates from the original sample dataset and reduced the number of records from 14,400 to 10,190. The choice of predictor variables was initially informed by the current understanding of the controls on the distribution of deep-sea sediments and the availability of data with full coverage of the deep sea at a reasonable resolution. The predictor variable raster layers from Bio-ORACLE (Assis et al., 2018; Tyberghein et al., 2012) and MARSPEC (Sbrocco and Barber, 2013) were utilised. Whenever available, statistics of the variable other than mean were downloaded. These included the minimum, maximum and the range (maximum - minimum). The raster layers were stacked, limited to water depths below 500 m and projected to Wagner IV global equal-area projection with a pixel resolution of 10 km by 10 km. A variable selection wrapper algorithm (Kursa and Rudnicki 2010) was used to identify important predictor variables. Subsequently the set of variables was reduced to those that were uncorrelated (|r| < 0.5). The selected predictor variables, in decreasing order of importance, were sea-surface maximum salinity, sea-floor maximum temperature, bathymetry, sea-surface minimum silicate, sea-surface temperature range, sea-surface maximum primary productivity, distance to shore and sea-surface salinity range. Two thirds of the samples were used to train a Random Forest (Breiman 2001) classification model. The number of trees in the forest and the number of variables to consider at any given split were tuned in a grid search using a 10-fold cross-validation scheme with three repeats on the training dataset. Model validation statistics, derived from the remaining one third of the samples not used for model training, showed that the final model had an overall accuracy of 69.5 %, with 95 % confidence limits of 67.9 % and 71.1 %. Category: geoscientificInformation Source: Not Available Supplemental Information: Not Availble Coverage: Not Available
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Abstract: Production-Diffusion modeling results using a Monte Carlo approach to simulate carbon isotope excursions in organic matter and paleosol carbonate during the Paleocene-Eocene Thermal Maximum. These modeling experiments were designed to test the effects soil methane oxidation and increased soil respiration rates can have on the carbon isotope values of pedogenic carbonate. The results from four modeling experiments are included. Three methane oxidation experiments that simulate a large methane release over 100, 1000, and 10000 years, respectively, and one soil respiration experiment. Category: geoscientificInformation Source: Not Available Supplemental Information: Not Availble Coverage: Not Available
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Abstract: Not Available Category: geoscientificInformation Source: Not Available Supplemental Information: Not Availble Coverage: EVENT LABEL: * LATITUDE: 66.000000 * LONGITUDE: -43.000000 * METHOD/DEVICE: derived from Quick Scatterometer (QuikSCAT)
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Abstract: This megafauna presence/absence matrix was obtained by analysing seabed images collected during successive environmental surveys conducting with remotely operated vehicles. The surveys took place around six oil and gas installations in the Faroe-Shetland Channel, an area known to harbour deep-sea sponge grounds. The matrix is constituted by multiple Operational Taxonomic Units (OTUs) as well as environmental parameters such as temperature, bathymetry and slope. Eigenvectors (Listed as MEM1 to MEM15) resulting from a distance-based Moran's Eigenvector Mapping (dbMEM) applied on the data have also been included. Further details regarding the dataset can be found in the publication listed below. Category: geoscientificInformation Source: Not Available Supplemental Information: Not Availble Coverage: EVENT LABEL: * LATITUDE: 60.896007 * LONGITUDE: -3.010254 * LOCATION: Faroe-Shetland Channel * METHOD/DEVICE: Multiple investigations
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Abstract: Daily precipitation samples were collected at 4 sites in Mount Yulong and Mount Meili regions of Southeastern Tibetan Plateau and analyzed for isotopic composition. We share here the deltaD observations, and some ancillary data useful to interpret the isotopic observations (meteorological observations, simulation with the isotope-enabled general circulation model, remote-sensing water vapor deltaD observations by IASI). Category: geoscientificInformation Source: Not Available Supplemental Information: Not Availble Coverage: Not Available
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Abstract: Overcoming the obstacle of frequent cloud coverage in optical remote sensing data is essential for monitoring dynamic land surface processes from space. APiC, a novel adaptable pixel-based compositing and classification approach, is especially designed to use high resolution spatio-temporal space-borne data. Here, pixel-based compositing is used separately for training data and prediction data. First, cloud-free pixels covered by reference data are used within adapted composite periods to compile a training dataset. The compiled training dataset contains samples of spectral reflectances for respective land cover classes at each composite period. For land cover prediction, pixel-based compositing is then applied region-wide. Multiple prediction models are used based on temporal subsets of the compiled training dataset to dynamically account for cloud coverage at pixel level. Thus we present a data-driven classification approach which is applicable in regions with different weather conditions, species composition and phenology. The capability of our method is demonstrated by mapping 19 land cover classes across Germany for the year 2016 based on Sentinel-2A data. Since climatic conditions and thus plant phenology change on a large scale, the classification was carried out separately in six landscape regions of different biogeographical characteristics. The study drew on extensive ground validation data provided by the federal states of Germany. For each landscape region, composite periods of different lengths have been established, which differ regionally in their temporal arrangement as well as in their total number, emphasising the advantage of a flexible regionalised classification procedure. Using a random forest classifier and evaluating outcomes with independent reference data, an overall accuracy of 88% was achieved, with particularly high classification accuracy of around 90% for the major land cover types. We found that class imbalances have significant influence on classification accuracy. Based on multiple temporal subsets of the compiled training dataset, over 10,000 random forest models were calculated and their performance varied considerably across and within landscape regions. The calculated importance of composite periods show that a high temporal resolution of the compiled training dataset is necessary to better capture the different phenology of land cover types. In this study we demonstrate that APiC, due to its data-driven nature, is a very flexible compositing and classification approach making efficient use of dense satellite time series in areas with frequent cloud coverage. Hence, regionalisation can be given greater focus in future broad-scale classifications in order to facilitate better integration of small-scale biophysical conditions and achieve even better results in detailed land cover mapping. Category: geoscientificInformation Source: Not Available Supplemental Information: Please note: The land cover classes "Forest", "Other Vegetation", "Waters" and "Urban Area" were taken from Geobasisdaten: © GeoBasis-DE / BKG (2015) and are subject to the terms of use listed here: https://sg.geodatenzentrum.de/web_public/nutzungsbedingungen.pdf Only the results of the agricultural area classification (19 land cover types) are subject to the Creative Commons copyright license. File format: GeoTIFF with class names and color table Spatial resolution: 20m Map projection: Lambert Azimuthal Equal Area Coverage: EVENT LABEL: * LATITUDE START: 53.600000 * LONGITUDE START: 7.200000 * LATITUDE END: 47.500000 * LONGITUDE END: 13.000000 * LOCATION: Germany * METHOD/DEVICE: Multiple investigations
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Abstract: Present dataset compiles all the vascular plant taxa existing in Sierra Nevada, together with relevant features including taxonomical, morphological-ecological traits, distribution, habitats, conservation status and abundance. Data were compiled according to all the available information sources on taxonomy, ecology and plant-species distribution. The resulting dataset includes 2,348 taxa belonging to 1,937 species, 377 subspecies and 34 hybrids, and with a total of 756 genera and 146 families represented in the collection. For each taxa, together with taxonomical information (Phylum, Class, Family, Genus, Taxa), we compiled morphological or ecological traits (life-form, spinescence, flower symmetry, flower sexuality, plant gender, ratio androecium/gynoecium, flower color, perianth type, pollinator type, flowering, seed dispersal, vegetative reproduction), distribution (origin, endemic character, general distribution) and habitat/abundance related traits (substrate, altitude, habitat, local abundance, hygrophilous behavior, conservation status). Category: geoscientificInformation Source: Not Available Supplemental Information: Not Availble Coverage: Not Available
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Abstract: The dataset comprises 10 automatic weather stations in Antarctica, deployed and maintained by the Institute for Marine and Atmospheric Research Utrecht (IMAU, https://www.projects.science.uu.nl/iceclimate/aws/index.php, imau@science.uu.nl), the Netherlands, and data from Neumayer Station (also directly available on PANGAEA from AWI). The dataset is published in Jakobs et al. (in review). The dataset contains observed variables (temperature, wind speed, humidity, pressure, radiation) as well as derived variables (turbulent fluxes, ground heat flux, snow temperature and density, melt rates). The derived variables are calculated with a Surface Energy Balance model, see e.g. Reijmer et al. (1999, doi:10.3189/172756499781821166), Van den Broeke et al. (2005, doi:10.3189/172756405781813168) and Jakobs et al. (2019, doi:10.5194/tc-13-1473-2019). Category: geoscientificInformation Source: Not Available Supplemental Information: Not Availble Coverage: Not Available
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Abstract: An integrated radio-astrochronological framework of the Agrio Formation in the Andean Neuquén Basin of west-central Argentina provides new constraints on the age and the duration of the late Valanginian through Hauterivian stratigraphic interval. A CA-ID TIMS U-Pb age of 126.97 ± 0.04(0.07)[0.15] Ma is presented here from the upper Hauterivian Agua de la Mula Member of the Agrio Formation. Biostratigraphic data from ammonoids and calcareous nannofossils and this high precision new radioisotopic age, together with three former ones from the same Agrio Formation are combined with new astrochronological data in the Andes. These are correlated with modern cyclostratigraphic studies in the classical sections of the Mediterranean Province of the Tethys, supporting detailed interhemispheric correlations for the Early Cretaceous. We also provide new δ13C data from the Agrio Formation which are compared with records from the classic Tethyan sections. According to our calibration, the minimum in the values in the mid-Hauterivian appears to be synchronous and, thus, another important stratigraphic marker for global correlation. A new duration of 5.21 ± 0.08 myr is calculated for the Hauterivian Stage, starting at 131.29 ± 0.19 Ma and ending at 126.08 ± 0.19 Ma. The difference between the duration of the Hauterivian in GTS2016 and in this study is 1.32 myr while the base and top of the GTS2016 Hauterivian differ respectively by 3.40 and 4.69 myr. Category: geoscientificInformation Source: Supplement to: Aguirre-Urreta, Beatriz; Martinez, Mathieu; Schmitz, Mark; Lescano, Marina; Omarini, Julieta; Tunik, Maisa; Kuhnert, Henning; Concheyro, Andrea; Rawson, Peter F; Ramos, Victor A; Reboulet, Stéphane; Noclin, Nicolas; Frederichs, Thomas; Nickl, Anna-Leah; Pälike, Heiko (2019): Interhemispheric radio-astrochronological calibration of the time scales from the Andean and the Tethyan areas in the Valanginian–Hauterivian (Early Cretaceous). Gondwana Research, 70, 104-132, https://doi.org/10.1016/j.gr.2019.01.006 Supplemental Information: Not Availble Coverage: Not Available
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