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5 results for “Miocene to present”
Text-fig. 5. Vegetation zones in P. R. China (Editorial Committee of Vegetation Map of China, The Chinese Academy of Sciences 2007), and assumed location of extant reference vegetation type of Wiesa fossil assemblage (rectangle), as revealed from qualitative floristic analysis. Extant reference vegetation type present in southern belt of zone of subtropical evergreen broadleaved forest, with minor overlap into zone of tropical forest. in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 5. Vegetation zones in P. R. China (Editorial Committee of Vegetation Map of China, The Chinese Academy of Sciences 2007), and assumed location of extant reference vegetation type of Wiesa fossil assemblage (rectangle), as revealed from qualitative floristic analysis. Extant reference vegetation type present in southern belt of zone of subtropical evergreen broadleaved forest, with minor overlap into zone of tropical forest.
Text-fig. 5. Distribution of Taxodioxylon gypsaceum in the Miocene (solid circles) and distribution (open circles) of its nearest representatives at present (Sequoia sempervirens, Sequoiadendron giganteum and Metasequoia glyptostroboides) (Eckenwalder 2009, Farjon 2010). in The First Glyptostroboxylon And Taxodioxylon Descriptions From The Late Miocene Of Turkey And Palaeoclimatological Evaluation
Text-fig. 5. Distribution of Taxodioxylon gypsaceum in the Miocene (solid circles) and distribution (open circles) of its nearest representatives at present (Sequoia sempervirens, Sequoiadendron giganteum and Metasequoia glyptostroboides) (Eckenwalder 2009, Farjon 2010).
Text-fig. 4. Distribution of Glyptostroboxylon rudolphii in the Miocene (solid circles) and Glyptostrobus pensilis at present (open circles). It grows in the subtropical swamps of Vietnamese and China (Eckenwalder 2009, Farjon 2010). in The First Glyptostroboxylon And Taxodioxylon Descriptions From The Late Miocene Of Turkey And Palaeoclimatological Evaluation
Text-fig. 4. Distribution of Glyptostroboxylon rudolphii in the Miocene (solid circles) and Glyptostrobus pensilis at present (open circles). It grows in the subtropical swamps of Vietnamese and China (Eckenwalder 2009, Farjon 2010).
Data files associated with the k-nearest neighbor global prediction of isopachs for present to middle Miocene
<p>This compressed dataset (Dataset 1) includes five folders. Each folder contains the observed data, final predictors and final predictions used for global predictions of isopachs from present to mid-Miocene aged sediments. The name of each folder corresponds to the name of the isopach for which the data is related to. For example, the folder named “Isopach0.0_1.8” contains all the data for the 0-1.8-million-year-old isopach.</p> <p> </p> <p>All grids contained within this supplemental material are netCDF4 file format. The grid pitch for all “.nc” files is uniformly at 5-arc minute denoted by “.5m”. Grids are cell-centered sized 4320 x 2160. Within each folder, there contains observed data used in the prediction. This observed data is in centimeters logarithmic base 10 units. Additionally, within each folder there is a final k-NN prediction file and standard deviation (i.e. uncertainty) file. The units for both the prediction file and uncertainty file are in meters.</p> <p> </p> <p>Collectively, there are 85 unique predictor grids. Predictor grid file names adhere to the naming conventions outlined below. The naming structure is partioned by underscores and periods in the following order: interface to which the gridded values refer to, quantity of values contained within the grid, units and reference values/units (e.g. meters below sea level), data source, statistic calculated (if applicable), grid pitch, and file extension (.nc).</p> <p> </p> <p><strong>Possible interfaces from the top – down:</strong></p> <p>SS – Sea surface – atmosphere interface (may also be average of the entire water column)</p> <p>SF – Seafloor – water interface (may also be denoted by GL)</p> <p>GL – Ground level (e.g. bottom of pure liquid, top of dirt)</p> <p>SC – Sediment – crust interface (e.g. sediment above, igneous/metamorphic below)</p> <p>CM – Crust – mantle interface (e.g. Mohorovicic discontinuity)</p> <p> </p> <p>Other grids which have been generated by empirical means are latitude (and derivatives), and longitude (and derivatives).</p> <p> </p> <p>Units referenced are as follows:</p> <p> </p> <p>M - meters</p> <p>MS - meters per second</p> <p>DEG – degree</p> <p>DD – decimal degrees</p> <p>M_ASL - meters above sea level (i.e. meters referenced to sea level)</p> <p>TGC_YR-1 - terragram of carbon per year</p> <p>TGYR - terragram per year</p> <p>PSU – percent salinity units</p> <p>MLL – milliliters per liter</p> <p>PCTSAT – percent saturation</p> <p>C – degree centigrade</p> <p>PDW – percent dry weight</p> <p>MCML -micromole per milliliter</p> <p>KG_M-3 – kilogram per cubic meter</p> <p>MG_M-3 – milligram per cubic meter</p> <p>MG_CM-2 - milligram of carbon per square meter</p> <p>MOL_M3-1 – moles per cubic meter</p> <p> </p> <p> </p> <p> </p> <p>Predictor statistics grids are calculated within a given radius (e.g. 10km, 50km, 100km, 125km, 200km, 250km, 500km) of the respective cell-centered value. The statistics grids include mean (.men), the absolute value of the common logarithm (.alg) and the mean of the common logarithm (.mlg). Additionally, some grids are a weighted count (.wct) for given radii (e.g. seamounts) where weight is a cosine taper from the center of the grid cell. </p> <p> </p> <p>Some grids are denoted by “DECADAL_AVERAGE” or “MISSION_MEAN”. These respective markings denote the values within the grid represent the average of values within a decade (10 year) or over the entire mission of the instrument (e.g. reflectance values from satellite mission). Further, an “s” or “x” in addition to the source name indicates additional conditioning on the final grid was required. The “x” simply indicates upsampling or extension to polar regions was performed via various interpolation techniques (e.g. bilinear or machine learning). The “s” indicates there was smoothing (i.e. averaging over set radii) required to produce a qualitatively geologically reasonable global dataset.</p> <p> </p> <p>Appropriate reference naming marker (bold) listed below including example file name (italics), original data source, and date of last access.</p> <p> </p> <p><strong>CRUST1 </strong></p> <p><em>e.g. CM_MANTLE_DEN_KGM3_CRUST1s.5m.nc</em></p> <p>Pasyanos, M.E., Masters, G., Laske, G. & Ma, Z. (2012). LITHO1.0 - An Updated Crust and Lithospheric Model of the Earth Developed Using Multiple Data Constraints, Abstract T11D-09 presented at 2012 Fall Meeting, AGU, San Francisco, California, U.S.A. Last access: 07/01/2014.</p> <p> </p> <p><strong>GVP</strong></p> <p><em>e.g. GL_VOLCANO_GVP.r10km.wct.5m.nc</em></p> <p>Global Volcanism Program (2013) Volcanoes of the World. In E. Venzke (ed.). (Vol. 4.7.3). Smithsonian Institution. https://doi.org/10.5479/si.GVP.VOTW4-2013. Last access: 09/22/2014.</p> <p> </p> <p><strong>PLATES</strong></p> <p><em>e.g. GL_DIST_TO_PLATE_BOUNDARY_KM_PLATES.5m.nc</em></p> <p>Coffin, M.F., Gahagan, L.M., & Lawver, L.A. (1998). Present-day Plate Boundary Digital Data Compilation. University of Texas Institute for Geophysics Technical Report (No. 174, pp. 5). Last access: 09/15/2014.</p> <p> </p> <p><strong>ORNL</strong></p> <p><em>e.g. GL_RIVERMOUTH_TSS_TGYR-1_ORNL.5m.nc</em></p> <p>Ludwig,W., Amiotte-Suchet, P., & Probst, J. L. (2011). ISLSCP II Global River Fluxes of Carbon and Sediments to the Oceans. In F. G. Hall, G. Collatz, B. Meeson, S. Los, E. Brown de Colstoun, and D. Landis (Eds.), ISLSCP Initiative II Collection. Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A. http://dx.doi.org/10.3334/ORNLDAAC/1028. Last Access: 02/15/2015.</p> <p> </p> <p><strong>Woa13x</strong></p> <p><em>e.g. SF_AVG_SEA_DENSITY_KGM3_DECADAL_MEAN_woa13x.5m.nc</em></p> <p>Boyer, T.P., Antonov, J. I., Baranova, O. K., Coleman, C., Garcia, H. E., Grodsky, A., et al. (2013) World Ocean Database 2013. In S. Levitus, A. Mishonov (Ed.), NOAA Atlas NESDIS 72, Technical Ed. Silver Spring, MD. http://doi.org/10.7289/V5NZ85MT. Last Access: 09/18/2014.</p> <p> </p> <p><strong>KIM </strong></p> <p><em>e.g. SF_SEAMOUNTS_KIM.r10km.wct.5m.nc</em></p> <p>Kim, S.S. & Wessel, P. (2011). New global seamount census from the altimetry-derived gravity data, Geophysical Journal International, 186, 615-631. https://doi.org/10.1111/j.1365-246X.2011.05076.x. Last access: 09/22/2014.</p> <p> </p> <p><strong>HYCOM</strong></p> <p><em>e.g. SF_CURRENT_EAST_MS_2012_12_HYCOMx.5m.nc</em></p> <p>The 1/12 deg global HYCOM+NCODA Ocean Reanalysis was funded by the U.S. Navy and the Modeling and Simulation Coordination Office. Computer time was made available by the DoD High Performance Computing Modernization Program. The output is publicly available at https://hycom.org/publications/acknowledgements/ocean-reanalysis-data.Last access: 03/19/2014.</p> <p> </p> <p><strong>NCEDC</strong></p> <p><em>e.g. SF_SHALLOW_QUAKES_NCEDC.r10km.wct.5m.nc</em></p> <p>NCEDC (2016). Northern California Earthquake Data Center. UC Berkeley Seismological Laboratory. Dataset. doi:10.7932/NCEDC. Last access: 09/21/2014.</p> <p> </p> <p><strong>Wei2010x</strong></p> <p><em>e.g. SS_BIOMASS_BACTERIA_LOG10_MGCM2_WEI2010x.5m.nc</em></p> <p>Wei, C.-L., Rowe, G. T., Escobar-Briones, E., Boetius, A., Soltwedel, T., Caley, M. J., et al.(2010). Global patterns and predictions of seafloor biomass using random forests. PLoS ONE,5(12), e15323. https://doi.org/10.1371/journal.pone.0015323 Last access: 06/20/2016.</p> <p> </p> <p><strong>NGA_egm2008</strong></p> <p><em>e.g. SS_GEOID_M_ABOVE_WGS84_NGA_egm2008.5m.nc</em></p> <p>Pavlis, N.K., Holmes, S. A., Kenyon, S. C., & Factor, J. K. (2008). The EGM2008 Global Gravitational Model, Abstract 2008AGUFM.G22A..01P presented at the 2008 General Assembly of the European Geosciences Union, Vienna, Austria. Last access: 07/10/2014.</p> <p> </p> <p><strong>WAVEWATCH3x</strong></p> <p><em>e.g. SS_WAVE_DIRECTION_DEG_2012_12_WAVEWATCH3x.5m.nc</em></p> <p>The 1/12 deg global HYCOM+NCODA Ocean Reanalysis was funded by the U.S. Navy and the Modeling and Simulation Coordination Office. Computer time was made available by the DoD High Performance Computing Modernization Program. The output is publicly available at https://hycom.org/publications/acknowledgements/ocean-reanalysis-data. Last access: 03/19/2014.</p> <p> </p> <p><strong>Lee</strong></p> <p><em>e.g. SF_TOC_PDW_LEE.5m.nc</em></p> <p>Lee, T. R., Wood, W. T., & Phrampus, B. J. (2019). A machine learning (kNN) approach to predicting global seafloor total organic carbon. Global Biogeochemical Cycles, 33(1), 37-46. https://doi.org/10.1029/2018GB005992 Last access: 12/2018.</p> <p> </p> <p><strong>SRTM15+V2</strong></p> <p><em>e.g. GL_ELEVATION_M_ASL_SRTM15+V2.5m.nc</em></p> <p>Tozer, B. , D. T. Sandwell, W. H. F. Smith, C. Olson, J. R. Beale, and P. Wessel, Global bathymetry and topography at 15 arc seconds: SRTM15+, Accepted Earth and Space Science, August 3, 2019. Last Access: 08/2019.</p> <p> </p> <p><strong>GLOBSED_Straume</strong></p> <p><em>e.g. GL_TOT_SED_THICK_M_GLOBSED_Straume.5m.nc</em></p> <p>Straume, E. O., Gaina, C., Medvedev, S., Hochmuth, K., Gohl, K., Whittaker, J. M., … Hopper, J. R. (2019). GlobSed: updated total sediment thickness in the world’s oceans. Geochemistry, Geophysics, Geosystems, 20(4), 1756–1772. https://doi.org/10.1029/2018GC008115. Last Access: 10/2019.</p> <p> </p> <p><strong>Goyetx</strong></p> <p><em>e.g. SS_MIXED_LAYER_DEPTH_MAX_M_Goyetx.5m.nc</em></p> <p>Goyet, C., Healy, R., Ryan, J., and Kozyr, A. Global Distribution of Total Inorganic Carbon and Total Alkalinity below the Deepest Winter Mixed Layer Depths. United States: N. p., 2000. Web. doi:10.2172/760546. Last Access: 10/2013.</p> <p> </p> <p><strong>MODIS_Aqua</strong></p> <p><em>e.g. SS_CHLOROPHYLL_LOG_MG_M3_MODIS_Aqua_MISSION_MEANx.5m.nc</em></p> <p>Savtchenko, A., Ouzounov, D., Ahmad, S., Acker, J., Leptoukh, G., Koziana, J., & Nickless, D. (2004). Terra and Aqua MODIS products available from NASA GES DAAC. Advances in Space Research, 34( 4), 710– 714. Last Access: 10/2017.</p> <p> </p> <p><strong>SACD_Aquarius</strong></p> <p><em>e.g. SS_WINDSPEED_MS-1_SACD_Aquarius_MISSION_MEANx.5m.nc</em></p> <p>Fore, A. G., Yueh, S. H., Tang, W., Hayashi, A. K., & Lagerloef, G. S. (2013). Aquarius wind speed products: Algorithms and validation. IEEE Transactions on Geoscience and Remote Sensing, 52( 5), 2920– 2927. Last Access: 10/2017.</p> <p> </p> <p> </p>
Dataset used in "The present-day Yangtze River became established in the Late Miocene: evidence from detrital zircon ages"
<p>A large number of detrial zircon U-Pb ages from the offshore basins of China are presented in this dataset. You may find details of this dataset from the original paper "The present-day Yangtze River became established in the Late Miocene: evidence from detrital zircon ages".</p>
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