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81 results for “present day”
A Global Data Set of Present-Day Oceanic Crustal Age and Seafloor Spreading Parameters
<p>Datasets of present-day oceanic crustal age and seafloor spreading parameters from Seton et al. (2020).</p> <p>This dataset contains:</p> <ul> <li>Animations: animations of the present-day age grid and seafloor spreading parameters in both low and high resolution</li> <li>Feature Data: GPlates compatible files (*.gpml and *.rot) consistent with and used to create this dataset. Preferred magnetic anomaly picks are also included.</li> <li>Grids: Gridded datasets (netCDF-4 and netCDF-3) of present-day age, rate, asymmetry, direction, obliquity, confidence, and age misfit (in v1.1 only) in 6 minute resolution. Age grids are also provided in 1 and 2 minute resolution as netCDFs, and as 6 minute xyz files.</li> <li>Images: Images of the present-day age grid and seafloor spreading parameters</li> <li>Workflows: the latest workflow to create the present-day age grid can be found on GitHub: https://github.com/EarthByte/presentday-agegridding </li> </ul> <p>These files can also be downloaded from the EarthByte website <a href="https://earthbyte.org/webdav/ftp/earthbyte/agegrid/2020/">here</a>, and the global plate motion model can be found online <a href="https://www.earthbyte.org/webdav/ftp/Data_Collections/Muller_etal_ 2019_Tectonics">here</a>.</p> <p><strong>Please cite the dataset as:</strong><br> Seton, M., Müller, R. D., Zahirovic, S., Williams, S., Wright, N. M., Cannon, J., et al. (2020). A global data set of present‐day oceanic crustal age and seafloor spreading parameters. <em>Geochemistry, Geophysics, Geosystems</em>, 21, e2020GC009214. https://doi.org/10.1029/2020GC009214</p>
Ensemble of NEMO present-day (1989-2009) and future (2080-2100 under RCP8.5) ocean properties and ice shelf melt rates in the Amundsen Sea
<p>Model outputs used in <a href="https://www.essoar.org/doi/10.1002/essoar.10511482.3">Jourdain et al. (GRL, 2022)</a></p> <p>The output files consist of monthly climatologies over either 1989-2009 or 2080-2100. The file names have the form:</p> <p><strong>climato_monthly_AMUXL12-GNJ002_<simu>_<group>_1989_2009.nc</strong>, where :</p> <ul> <li><simu> is either : <ul> <li>"BM02MAR" (ensemble member A, present-day),</li> <li>"BM03MAR" (ensemble member B, present-day),</li> <li>"BM04MAR" (ensemble member C, present-day),</li> <li>"BM02MARrcp85" (ensemble member A, future for both surface and lateral boundaries),</li> <li>"BM03MARrcp85" (ensemble member B, future for surface BUT NOT for lateral boundaries),</li> <li>"BM03MARrcBDY" (ensemble member B, future for both surface and lateral boundaries),</li> <li>"BM04MARrcp85" (ensemble member C, future for both surface and lateral boundaries),</li> </ul> </li> <li><group> is either : <ul> <li>"SBC" (surface boundary conditions),</li> <li>"icemod" (sea ice variables),</li> <li>"gridT" (temperature, salinity),</li> <li>"gridU" (zonal velocities),</li> <li>"gridV" (meridional velocities).</li> </ul> </li> </ul> <p>Grid information in:</p> <ul> <li>mesh_mask_AMUXL12_BedMachineAntarctica-2019-05-24.nc (ensemble member A),</li> <li>mesh_mask_AMUXL12_BedMachineAntarctica-2020-07-15_v02_ICB380.nc (ensemble members B & C).</li> </ul> <p>where:</p> <ul> <li>glamt : longitude</li> <li>gphit: latitude</li> <li>e1t, e2t, e3t_0 : mesh size (in meters) along x, y, z</li> <li>tmask = 1 for ocean mesh, = 0 otherwise (land, continental ice).</li> </ul> <p> </p> <p><strong>Acknowledgments:</strong> This work was granted access to the HPC resources of CINES (occigen) under the allocation A0100106035 attributed by GENCI.</p>
Present day human hand grasping the same artifact by hand and hafted
<p><em>Examples of a present day human hand demonstrating a precision grip (top left) when grasping an artifact by hand and a power "squeeze" grip (top right) when grasping a hafted artifact (both palmar view). In turquoise (first metacarpal) and purple (trapezium) are the present day human and Neanderthal bones forming the trapeziometacarpal complex at the base of the thumb and responsible for its movements. </em></p>
Repository: The Distribution of Frosts on Mars: Links to Present-Day Gully Activity
<p>This repository contains:</p> <p>1. Global calculated CO2 frost point temperatures (Kelvin) calculated at 1 ppd every 10 Ls using surface pressure from the online version of the Mars Climate Database<br> (http://www-mars.lmd.jussieu.fr/mcd_python/)<br> CO2 Frost Points</p> <p><br> 2. Local Solar Time and Season of THEMIS CO2 Frost Detections at gully locations<br> corr_gully_detections_filenames_meta</p> <p>3. Calculated CO2 frost amounts (kg/m^2) at 30S, 40S, 50S and 60S on pole-facing slopes<br> Frost Amounts</p> <p>4. Calculated CO2 frost amounts (kg/m^2) varying with lower material thermal inertia, slope azimuth, top material thermal inertia, top material thickness and surface albedo<br> Frost Sensitivity</p> <p>5. Predicted H2O frost lifetimes (hours)<br> H2OFrost_Stability</p> <p>6. Global THEMIS CO2 Frost Detections from Mars Year (MY) 26<br> MY26_THEMIS_CO2_Frost_Detections</p> <p>7. THEMIS CO2 Frost Detections at gully locations (Harrison et al. 2015) from MYs 26 - 35<br> MY26_35_THEMIS_GULLY_CO2_Frost_Detections</p> <p>8. H2O frost temperatures (Kelvin) at the Opportunity rover site<br> Opportunity_H2OFrost</p> <p>9. CO2 Frost detections made by Piqueux et al. (2016) using Mars Climate Sounder data<br> Piqueux et al (2016) MCS CO2 Frost Detections</p> <p>10. TES-derived data<br> a) TES_MY26_H2OFrost_Temp_Map<br> b) TES_MY26_H2OFrost_Temp_Seasonal</p> <p>11. The seasonal variation of the CO2 frost point (Kelvin) at the Viking Lander sites<br> Viking_Lander_Data</p>
Climatological global-mean Sea Surface Temperature (SST) in AWI-CM-1-1-MR simulations for CMIP6, in preindustrial, present-day, +2°C, +3°C, and +4°C climates
<p>Daily climatologies of global-mean sea surface temperature (SST, parameter 'tos') free-running simulations performed using the coupled climate models AWI-CM-1-1-MR. The unstructured grid-ocean component FESOM was conservatively remapped to the ERA5 grid. Data was averaged across the 5 ensemble members and temporally averaged over 10-year long time periods: 1850-1859 for preindustrial climate, 2015-2024 for present-day, 2034-2043 for +2°C climate, 2061-2079 for +3°C climate, and 2091-2100 for +4°C climate. </p><p>Data is provided in .nc files, one for each climate.</p>
Global present-day air-conditioning adoption rate
<p>This dataset contains the present-day, global, survey-based, and spatially explicit air-conditioning adoption rate dataset developed in Li et al. (2024), “Enhancing Urban Climate-Energy Modeling in the Community Earth System Model (CESM) through Explicit Representation of Urban Air-conditioning Adoption”, published in <em>Journal of Advances in Modeling Earth Systems</em>. It also contains the simulation results analyzed in the article. Details about this dataset (data sources, data collection and processing methods, simulation setup, etc.) are described in the article. The air-conditioning adoption rate dataset is publicly available in tabular, vector, and gridded formats. It is compatible with CESM, and can also be leveraged in other climate and energy modeling applications and socioeconomic or integrated assessment analyses. This dataset may be useful for multiple scientific communities regarding urban climate and energy, impacts, vulnerability, risks, and adaptation applications. </p> <p>For more detailed description, please refer to the README file (<em>global_AC_adoption_rate_README.txt</em>) included in the dataset.</p>
Monthly averaged lightning and LCC lightning data extracted from present-day (2009-2011) and projected (2090-2095) EMAC simulations (T42L90MA resolution)
<pre>About Dataset Monthly averaged lightning and LCC lightning data extracted from present-day (2009-2011) and projected (2090-2095) EMAC simulations (T42L90MA resolution). Authors: Francisco J. Perez-Invernon, Francisco J. Gordillo-Vazquez, Patrick Joeckel and Heidi Huntrieser Description of the data PaR_T: Lightning parameterization based on cloud top height. PaR_L: Lightning parameterization based on cloud top height and modified over the oceans. Grewe: Lightning parameterization based on updraft velocity. AaP_P: Lightning parameterization based on convective precipitation. A AaP_M: Lightning parameterization based on Updraft strength at 440~hPa. PRaAP: Lightning parameterization based on cloud top height and updraft velocity. FinIF: Lightning parameterization based on updraft mass flux of ice at 440 hPa. extIF: Lightning parameterization based on updraft mass flux of ice at 440 hPa an isotherm. File format: netcdf Example: netcdf </pre> <p>2009_10h_______20090701_0000_mmlb_PRaAP.nc<br> netcdf \2009_10h_______20090701_0000_mmlb_PRaAP {<br> dimensions:<br> time = UNLIMITED ; // (1 currently)<br> lon = 128 ;<br> lat = 64 ;<br> tbnds = 2 ;<br> variables:<br> double time(time) ;<br> time:long_name = "time" ;<br> time:bounds = "time_bnds" ;<br> time:units = "day since 2009-01-01 00:00:00" ;<br> time:calendar = "gregorian" ;<br> double YYYYMMDD(time) ;<br> YYYYMMDD:long_name = "time" ;<br> YYYYMMDD:units = "days as %Y%m%d.%f" ;<br> YYYYMMDD:calendar = "gregorian" ;<br> double dt(time) ;<br> dt:long_name = "delta_time" ;<br> dt:units = "s" ;<br> double nstep(time) ;<br> nstep:long_name = "current time step" ;<br> float lon(lon) ;<br> lon:long_name = "longitude" ;<br> lon:units = "degrees_east" ;<br> float lat(lat) ;<br> lat:long_name = "latitude" ;<br> lat:units = "degrees_north" ;<br> float aps(time, lat, lon) ;<br> aps:long_name = "surface pressure" ;<br> aps:units = "Pa" ;<br> aps:representation = "GP_2D_HORIZONTAL" ;<br> aps:grid_type = "gaussian" ;<br> aps:table = 128 ;<br> aps:code = 134 ;<br> aps:REFERENCE_TO = "g3b: aps" ;<br> aps:coordinates = "lon lat" ;<br> aps:cell_methods = "time: point" ;<br> float aps_ave(time, lat, lon) ;<br> aps_ave:long_name = "surface pressure" ;<br> aps_ave:units = "Pa" ;<br> aps_ave:representation = "GP_2D_HORIZONTAL" ;<br> aps_ave:grid_type = "gaussian" ;<br> aps_ave:table = 128 ;<br> aps_ave:code = 134 ;<br> aps_ave:REFERENCE_TO = "g3b: aps" ;<br> aps_ave:coordinates = "lon lat" ;<br> aps_ave:cell_methods = "time: mean" ;<br> float fpscg(time, lat, lon) ;<br> fpscg:long_name = "CG flash frequency" ;<br> fpscg:units = "1/s" ;<br> fpscg:REFERENCE_TO = "lnox_PRaAP_gp: fpscg" ;<br> fpscg:coordinates = "lon lat" ;<br> fpscg:cell_methods = "time: point" ;<br> float fpscg_ave(time, lat, lon) ;<br> fpscg_ave:long_name = "CG flash frequency" ;<br> fpscg_ave:units = "1/s" ;<br> fpscg_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpscg" ;<br> fpscg_ave:coordinates = "lon lat" ;<br> fpscg_ave:cell_methods = "time: mean" ;<br> float fpsic(time, lat, lon) ;<br> fpsic:long_name = "IC flash frequency" ;<br> fpsic:units = "1/s" ;<br> fpsic:REFERENCE_TO = "lnox_PRaAP_gp: fpsic" ;<br> fpsic:coordinates = "lon lat" ;<br> fpsic:cell_methods = "time: point" ;<br> float fpsic_ave(time, lat, lon) ;<br> fpsic_ave:long_name = "IC flash frequency" ;<br> fpsic_ave:units = "1/s" ;<br> fpsic_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpsic" ;<br> fpsic_ave:coordinates = "lon lat" ;<br> fpsic_ave:cell_methods = "time: mean" ;<br> float fpsm2cg(time, lat, lon) ;<br> fpsm2cg:long_name = "CG flash density" ;<br> fpsm2cg:units = "1/s/m2" ;<br> fpsm2cg:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2cg" ;<br> fpsm2cg:coordinates = "lon lat" ;<br> fpsm2cg:cell_methods = "time: point" ;<br> float fpsm2cg_ave(time, lat, lon) ;<br> fpsm2cg_ave:long_name = "CG flash density" ;<br> fpsm2cg_ave:units = "1/s/m2" ;<br> fpsm2cg_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2cg" ;<br> fpsm2cg_ave:coordinates = "lon lat" ;<br> fpsm2cg_ave:cell_methods = "time: mean" ;<br> float fpsm2ic(time, lat, lon) ;<br> fpsm2ic:long_name = "IC flash density" ;<br> fpsm2ic:units = "1/s/m2" ;<br> fpsm2ic:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2ic" ;<br> fpsm2ic:coordinates = "lon lat" ;<br> fpsm2ic:cell_methods = "time: point" ;<br> float fpsm2ic_ave(time, lat, lon) ;<br> fpsm2ic_ave:long_name = "IC flash density" ;<br> fpsm2ic_ave:units = "1/s/m2" ;<br> fpsm2ic_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2ic" ;<br> fpsm2ic_ave:coordinates = "lon lat" ;<br> fpsm2ic_ave:cell_methods = "time: mean" ;<br> float fpslcc10(time, lat, lon) ;<br> fpslcc10:long_name = "LCC(>10 ms) flash frequency" ;<br> fpslcc10:units = "1/s" ;<br> fpslcc10:REFERENCE_TO = "lnox_PRaAP_gp: fpslcc10" ;<br> fpslcc10:coordinates = "lon lat" ;<br> fpslcc10:cell_methods = "time: point" ;<br> float fpslcc10_ave(time, lat, lon) ;<br> fpslcc10_ave:long_name = "LCC(>10 ms) flash frequency" ;<br> fpslcc10_ave:units = "1/s" ;<br> fpslcc10_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpslcc10" ;<br> fpslcc10_ave:coordinates = "lon lat" ;<br> fpslcc10_ave:cell_methods = "time: mean" ;<br> float fpslcc20(time, lat, lon) ;<br> fpslcc20:long_name = "LCC(>20 ms) flash frequency" ;<br> fpslcc20:units = "1/s" ;<br> fpslcc20:REFERENCE_TO = "lnox_PRaAP_gp: fpslcc20" ;<br> fpslcc20:coordinates = "lon lat" ;<br> fpslcc20:cell_methods = "time: point" ;<br> float fpslcc20_ave(time, lat, lon) ;<br> fpslcc20_ave:long_name = "LCC(>20 ms) flash frequency" ;<br> fpslcc20_ave:units = "1/s" ;<br> fpslcc20_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpslcc20" ;<br> fpslcc20_ave:coordinates = "lon lat" ;<br> fpslcc20_ave:cell_methods = "time: mean" ;<br> float fpsm2lcc10(time, lat, lon) ;<br> fpsm2lcc10:long_name = "LCC(>10 ms) flash density" ;<br> fpsm2lcc10:units = "1/s/m2" ;<br> fpsm2lcc10:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2lcc10" ;<br> fpsm2lcc10:coordinates = "lon lat" ;<br> fpsm2lcc10:cell_methods = "time: point" ;<br> float fpsm2lcc10_ave(time, lat, lon) ;<br> fpsm2lcc10_ave:long_name = "LCC(>10 ms) flash density" ;<br> fpsm2lcc10_ave:units = "1/s/m2" ;<br> fpsm2lcc10_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2lcc10" ;<br> fpsm2lcc10_ave:coordinates = "lon lat" ;<br> fpsm2lcc10_ave:cell_methods = "time: mean" ;<br> float fpsm2lcc20(time, lat, lon) ;<br> fpsm2lcc20:long_name = "LCC(>20 ms) flash density" ;<br> fpsm2lcc20:units = "1/s/m2" ;<br> fpsm2lcc20:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2lcc20" ;<br> fpsm2lcc20:coordinates = "lon lat" ;<br> fpsm2lcc20:cell_methods = "time: point" ;<br> float fpsm2lcc20_ave(time, lat, lon) ;<br> fpsm2lcc20_ave:long_name = "LCC(>20 ms) flash density" ;<br> fpsm2lcc20_ave:units = "1/s/m2" ;<br> fpsm2lcc20_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2lcc20" ;<br> fpsm2lcc20_ave:coordinates = "lon lat" ;<br> fpsm2lcc20_ave:cell_methods = "time: mean" ;<br> float fpssprite(time, lat, lon) ;<br> fpssprite:long_name = "Sprites flash frequency" ;<br> fpssprite:units = "1/s" ;<br> fpssprite:REFERENCE_TO = "lnox_PRaAP_gp: fpssprite" ;<br> fpssprite:coordinates = "lon lat" ;<br> fpssprite:cell_methods = "time: point" ;<br> float fpssprite_ave(time, lat, lon) ;<br> fpssprite_ave:long_name = "Sprites flash frequency" ;<br> fpssprite_ave:units = "1/s" ;<br> fpssprite_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpssprite" ;<br> fpssprite_ave:coordinates = "lon lat" ;<br> fpssprite_ave:cell_methods = "time: mean" ;<br> float fpsm2sprite(time, lat, lon) ;<br> fpsm2sprite:long_name = "Sprites flash density" ;<br> fpsm2sprite:units = "1/s/m2" ;<br> fpsm2sprite:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2sprite" ;<br> fpsm2sprite:coordinates = "lon lat" ;<br> fpsm2sprite:cell_methods = "time: point" ;<br> float fpsm2sprite_ave(time, lat, lon) ;<br> fpsm2sprite_ave:long_name = "Sprites flash density" ;<br> fpsm2sprite_ave:units = "1/s/m2" ;<br> fpsm2sprite_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2sprite" ;<br> fpsm2sprite_ave:coordinates = "lon lat" ;<br> fpsm2sprite_ave:cell_methods = "time: mean" ;<br> float bps(time, lat, lon) ;<br> bps:long_name = "BJ flash frequency" ;<br> bps:units = "1/s" ;<br> bps:REFERENCE_TO = "bluejetbPRaAP_gp: bps" ;<br> bps:coordinates = "lon lat" ;<br> bps:cell_methods = "time: point" ;<br> float bps_ave(time, lat, lon) ;<br> bps_ave:long_name = "BJ flash frequency" ;<br> bps_ave:units = "1/s" ;<br> bps_ave:REFERENCE_TO = "bluejetbPRaAP_gp: bps" ;<br> bps_ave:coordinates = "lon lat" ;<br> bps_ave:cell_methods = "time: mean" ;<br> float bpsm2(time, lat, lon) ;<br> bpsm2:long_name = "BJ flash density" ;<br> bpsm2:units = "1/s/m2" ;<br> bpsm2:REFERENCE_TO = "bluejetbPRaAP_gp: bpsm2" ;<br> bpsm2:coordinates = "lon lat" ;<br> bpsm2:cell_methods = "time: point" ;<br> float bpsm2_ave(time, lat, lon) ;<br> bpsm2_ave:long_name = "BJ flash density" ;<br> bpsm2_ave:units = "1/s/m2" ;<br> bpsm2_ave:REFERENCE_TO = "bluejetbPRaAP_gp: bpsm2" ;<br> bpsm2_ave:coordinates = "lon lat" ;<br> bpsm2_ave:cell_methods = "time: mean" ;<br> double time_bnds(time, tbnds) ;<br> time_bnds:long_name = "time bounds" ;<br> time_bnds:units = "days since 2009-01-01T00:00:00Z" ;<br> time_bnds:cell_methods = "time: point" ;</p> <p>// global attributes:<br> :MESSy = "MESSy version d2.55.1-62-g7ea171fc6-dirty_7ea171fc682744f70976123b863cca166dd2023b_2021-05-19T16:21:08+00:00_2021-06-04T13:28:34+0200, http://www.messy-interface.org" ;<br> :MESSy_switch = "version 1.0" ;<br> :MESSy_channel = "version 2.4.3" ;<br> :MESSy_tracer = "version 2.6" ;<br> :MESSy_timer = "version 0.1" ;<br> :MESSy_qtimer = "version 3.0" ;<br> :MESSy_import = "version 1.0" ;<br> :MESSy_grid = "version v1.5" ;<br> :MESSy_rnd = "version 1.1" ;<br> :MESSy_aeropt = "version 2.0.2" ;<br> :MESSy_cloud = "version 2.2" ;<br> :MESSy_cloudopt = "version 2.1b" ;<br> :MESSy_convect = "version 2.0" ;<br> :MESSy_gwave = "version 1.0" ;<br> :MESSy_lnox = "version 3.0" ;<br> :MESSy_orbit = "version 0.9" ;<br> :MESSy_orogw = "version 1.1" ;<br> :MESSy_rad = "version 2.2" ;<br> :MESSy_e5vdiff = "version 1.2" ;<br> :MESSy_surface = "version 1.2" ;<br> :MESSy_tropop = "version 2.1" ;<br> :MESSy_viso = "version 2.3" ;<br> :MESSy_experiment = "2009_10h" ;<br> :EXEC_CHECKSUM = "45e5ddd17ae5992f931a9ba4bab4a921 bin/echam5.exe (md5sum)" ;<br> :GCM = "ECHAM5 version 5.3.02, Max-Planck Institute for Meteorology, Hamburg" ;<br> :GCM_spherical_trunc_n = 42 ;<br> :GCM_spherical_trunc_m = 42 ;<br> :GCM_spherical_trunc_k = 42 ;<br> :GCM_vertical_mode = "middle atmosphere (MA)" ;<br> :GCM_horizontal_mode = "global" ;<br> :GCM_advection = "Lin&Rood" ;<br> :GCM_start_date_time = "20090101 000000" ;<br> :GCM_timestep = 900.f ;<br> :F95_COMPILER_VERSION = "ifort (IFORT) 17.0.2 20170213" ;<br> :F95_COMPILER_CALL = "/opt/mpi/bullxmpi_mlx/1.2.9.2/bin/mpif90" ;<br> :F95_COMPILER_FLAGS = "-sox -fpp -g -O2 -xCORE-AVX2 -fp-model strict -align all -save-temps -DBULL -I/sw/rhel6-x64/sys/bullxlib-1.0.0/include -L/sw/rhel6-x64/sys/bullxlib-1.0.0/lib -Wl,-rpath,/sw/rhel6-x64/sys/bullxlib-1.0.0/lib -lbullxMATH -no-wrap-margin" ;<br> :F95_PREPROC_DEFINITIONS = "-DMESSY -DLITTLE_ENDIAN -D_LINUX64 -DHAVE_PNETCDF -DPNCREGRID -DMPIOM_13B -D_VCSREV_=\'d2.55.1-62-g7ea171fc6-dirty_7ea171fc682744f70976123b863cca166dd2023b_2021-05-19T16:21:08+00:00_2021-06-04T13:28:34+0200\'" ;<br> :F95_COMPILER_INCLUDES = "-I/sw/rhel6-x64/netcdf/netcdf_fortran-4.4.2-intel14/include -I/sw/rhel6-x64/netcdf/netcdf_fortran-4.4.2-intel14/include -I/sw/rhel6-x64/netcdf/parallel_netcdf-1.6.0-bullxmpi-intel14/include" ;<br> :operating_date_time = "20210615 081833" ;<br> :operating_system = "Linux 2.6.32-754.33.1.el6.x86_64 on x86_64" ;<br> :operating_host = "mlogin102" ;<br> :operating_user = "Francisco-Javier Perez-Invernon (b309171)" ;<br> :channel_io_pe = 172 ;<br> :channel_time_slo = 5182200.f ;<br> :channel_name = "mmlb_PRaAP" ;<br> :channel_file_type = "output" ;<br> :channel_file_name = "2009_10h_______20090701_0000_mmlb_PRaAP.nc" ;<br> :channel_netcdf_lib = "4.3.2 of May 5 2015 13:21:25 $" ;</p>
Present-day and future changes in the hydrology of the Bhagirathi Basin
<p>This repository contains the daily outputs (Jan 1, 1991 to Dec 31 2020) produced in the project SDC project. The folder 'Final_full_30yrs_baseline.rar' contains all the historical outputs generated from the SPHY model. The folder contains data in the different formats (spatial and non spatial) '.map','.csv' and '.tss'</p> <p>The folder 'Climate_change.rar' contains climate runs from (Jan 1, 2021 to Dec 31 2100) for 4 GCM-RCM and ssp combinations.</p>
Fig. 4 in Distributions and phylogeographic data of rheophilic freshwater fishes provide evidences on the geographic extension of a central-brazilian amazonian palaeoplateau in the area of the present day Pantanal Wetland
Fig. 4. Haplotype network showing the occurrence of three groups (upper rio Xingu, upper rio Paraguay and upper rio Tapajós). Traces show the number of mutational steps from two adjacent haplotypes. Circle diameters are proportional to the number of individuals, which each haplotype and the colors represent the locality were those haplotypes were found. Upper rio Xingu= Pink (1: dark pink); upper rio Paraguay = Blue (2: light blue; 3: navy blue; 4: dark blue; 5: light pink; 6: orange; 7: light purple; 8: dark purple; 9: white; 10: yellow; 11: light green; 12: dark green); and upper rio Tapajós= Gray (13: light gray and 14: dark gray).
Fig. 3 in Distributions and phylogeographic data of rheophilic freshwater fishes provide evidences on the geographic extension of a central-brazilian amazonian palaeoplateau in the area of the present day Pantanal Wetland
Fig. 3. Phylogenetic tree showing relationships among major lineages of Jupiaba acanthogaster from the upper rio Paraguay, upper rio Tapajós and upper rio Xingu, obtained by a maximum likelihood partitioned analysis. Numbers at each of the main nodes represents percentage of bootstrap support obtained by maximum parsimony analysis (1000 bootstrap pseudoreplicates).
Fig. 2 in Distributions and phylogeographic data of rheophilic freshwater fishes provide evidences on the geographic extension of a central-brazilian amazonian palaeoplateau in the area of the present day Pantanal Wetland
Fig. 2. Distribution of sampled localities for Jupiaba acanthogaster in the upper rio Paraguay, rio Tapajós and rio Xingú basins. The drainages of the rio Tocantins, rio Araguaia and upper rio Paraná are also illustrated. Drainage boundaries delimited by a continuous black line.
Fig. 1 in Distributions and phylogeographic data of rheophilic freshwater fishes provide evidences on the geographic extension of a central-brazilian amazonian palaeoplateau in the area of the present day Pantanal Wetland
Fig. 1. Map of the upper rio Paraguay basin and adjoining areas showing the distribution of Leporinus octomatulatus, Jubiaba acanthogaster, Oligosarcus perdido, Moenkhausia cosmops, and Hypostomus cochliodon, exemplifying distributional pattern discussed in this paper.
Annual minima and maxima of present day and future discharge, derived from PCR-GLOBWB
<p>Global dataset of annual minima and maxima for major river system in the world. Dataset provides the present day and 2C warming simulation derived from a combination of the Global Hydrological Model, PCR-GLOBWB (https://doi.org/10.5194/gmd-11-2429-2018) and the EC-EARTH Global Climate Model (https://doi.org/10.1007/s00382-011-1228-5). The large-ensemble dataset is part of the HiWAVES3 project (https://www.knmi.nl/research/weather-climate-models/projects/hiwaves3)</p>
Gaia BH1 and BH2 - Evolutionary Models with Overshooting of the Black Hole Progenitors within the Present-Day Binary Separation
<p>Input files and simulation results for stellar evolution tracks computed for the letter "Gaia BH1 and BH2 - Evolutionary Models with Overshooting of the Black Hole Progenitors within the Present-Day Binary Separation". Version 15140 of MESA was used for the simulations. More details in the README.txt file and in the letter.</p>
Datasets used in van den Akker et al (2024) 'Present day mass loss rates are a precursor precursor for West Antarctic Ice Sheet Collapse
<p>This repository contains the default initialization and the continuation runs shown in the paper. </p>
Compiled eruption chronostratigraphy including eruption styles for Santorini volcano, Greece, from ~360 ka to present day
<p>This dataset is a compiled chronostratigraphy for the volcanic island of Santorini, Greece. It comprises information on eruption dates, dating methods and numbers of eruptions of each type (Plinian, interplinian and lava), compiled from existing published sources (all references provided) and some supplementary fieldwork. The record is detailed and quantitative from the present day back to 224 ka, and less detailed and qualitative from 224 ka back to ~360 ka. Two maps are provided to give location context for the dataset, as well as a reference list, and other associated reading. This dataset forms part of the supplementary information for the publication 'Eruptive Activity of the Santorini Volcano Controlled by Sea Level Rise and Fall' by Satow et al. (2021) in Nature Geoscience- <a href="https://doi.org/10.1038/s41561-021-00783-4">https://doi.org/10.1038/s41561-021-00783-4</a></p>
Elucidating the present-day chemical composition, seasonality and source regions of climate-relevant aerosols across the Arctic land surface
<p>Data presented in the figures of the journal article "Elucidating the present-day chemical composition, seasonality and source regions of climate-relevant aerosols across the Arctic land surface" by Moschos et al.</p>
Text-fig. 2. Latest Albian – Late Cretaceous palaeobotanical-palaeogeographical subregions of the North Pacific Region (a); modern outline of North-eastern Asia is shown for the Coniacian (after Smith et al. 1981): 1 – the Verkhoyansk-Chukotka Subregion, 2 – the Okhotsk-Chukotka Subregion, 3 – the Anadyr-Koryak Subregion (modified from Herman 2013) and geographical and geological position of the Turonian – Coniacian floras (b) (present-day map, modified from Shczepetov and Herman 2013). in On The Likely Palaeoelevation Of The Turonian - Coniacian Arman Flora Site (North-Eastern Asia)
Text-fig. 2. Latest Albian – Late Cretaceous palaeobotanical-palaeogeographical subregions of the North Pacific Region (a); modern outline of North-eastern Asia is shown for the Coniacian (after Smith et al. 1981): 1 – the Verkhoyansk-Chukotka Subregion, 2 – the Okhotsk-Chukotka Subregion, 3 – the Anadyr-Koryak Subregion (modified from Herman 2013) and geographical and geological position of the Turonian – Coniacian floras (b) (present-day map, modified from Shczepetov and Herman 2013).
Regional distribution of annual sea-to-air OCS fluxes for the present day atmosphere with 500 ppt OCS and the two OCS geoengineering scenarios with 4.8 ppb and 35.5 ppb.
<p>This dataset was prepared for a publication by von Hobe et al. (2023):</p> <p><strong>Comment on “An approach to sulfate geoengineering with surface emissions of carbonyl sulfide” by Quaglia et al. (2022)</strong></p> <p>In that publication, the data are displayed in Figure 3.</p> <p>Average annual sea-to-air OCS fluxes on a 2.8 ° latitude x 2.8 ° longitude grid for the present day atmosphere and for the two OCS emission scenarios considered by Quaglia et al. (2022) were obtained from a 2003 - 2019 simulation, using a model described in Lennartz et al. (2021).</p> <p>- - - - - - - - - - -</p> <p>File format:</p> <p> netCDF</p> <p>Index Variables:</p> <p> latitude</p> <p> longitude</p> <p>Parameters:</p> <p> ocsem500: mean annual sea-to-air OCS flux calculated for an atmospheric OCS mole fraction of 500 ppt</p> <p> ocsem4800: mean annual sea-to-air OCS flux calculated for an atmospheric OCS mole fraction of 4.8 ppb</p> <p> ocsem35500: mean annual sea-to-air OCS flux calculated for an atmospheric OCS mole fraction of 35.5 ppb</p> <p>- - - - - - - - - - -</p> <p><strong>References:</strong></p> <p>Lennartz, S. T., Gauss, M., von Hobe, M., and Marandino, C. A.: Monthly resolved modelled oceanic emissions of carbonyl<br> sulphide and carbon disulphide for the period 2000–2019, Earth Syst. Sci. Data, 13, 2095-2110, 10.5194/essd-13-2095-2021, 2021.</p> <p>Quaglia, I., Visioni, D., Pitari, G., and Kravitz, B.: An approach to sulfate geoengineering with surface emissions of carbonyl<br> sulfide, Atmos. Chem. Phys., 22, 5757-5773, 10.5194/acp-22-5757-2022, 2022.</p> <p>von Hobe, M., Brühl, C., Lennartz, S. T., Whelan, M. E., and Kaushik, A.: Comment on “An approach to sulfate geoengineering with surface emissions of carbonyl sulfide” by Quaglia et al. (2022) , EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-268, 2023.</p>
Simple Biosphere model version 4.2 (SiB4) simulations for the present day atmosphere with 500 ppt OCS and the two OCS geoengineering scenarios with 4.8 ppb and 35.5 ppb OCS.
<p>This dataset was prepared for a publication by von Hobe et al. (2023):</p> <p><strong>Comment on “An approach to sulfate geoengineering with surface emissions of carbonyl sulfide” by Quaglia et al. (2022)</strong></p> <p>In that publication, the data are displayed in Figures 1 and 2.</p> <p>Simple Biosphere model version 4.2 (SiB4, Haynes et al., 2019; Sellers et al., 1986) was used to calculate (i) the average increase in evapotranspiration anticipated under an elevated OCS scenario for the years 2000-2021 on a 0.5 ° latitude x 0.5 ° longitude grid and (ii) OCS uptake by plants and soils, per month, at baseline (500 ppt) and elevated (4.8 and 35.5 ppb) OCS levels averaged over the years 2000-2021.</p> <p>- - - - - - - - - - -</p> <p><em>File 1: vonHobe_et_al_2023_CarbonylSulfideGeoengineeringScenarios_DeltaEvapotranspiration_GloballyGridded_SiB4.nc</em></p> <p>File Format:</p> <p> netCDF</p> <p>Index Variables:</p> <p> latitude</p> <p> longitude</p> <p>Parameters:</p> <p> percent_diff_et: relative increase in % of evapotranspiration in a scenario where 20% of terrestrial plants exhibit a 50% increase in stomatal conductance under high OCS</p> <p>- - - - - -</p> <p><em>File 2: vonHobe_et_al_2023_CarbonylSulfideGeoengineeringScenarios_BiosphereUptake_MonthlyIntegrated_SiB4.csv</em></p> <p>File Format:</p> <p> comma delimited text file (.csv)</p> <p>Index Variable:</p> <p> time: monthly, format m/dd/yy</p> <p>Parameters:</p> <p> ocs_veg_base: simulated monthly OCS uptake by terrestrial vegetation at an atmospheric OCS mole fraction of 500 ppt</p> <p> ocs_soil_base: simulated monthly OCS uptake by soils at an atmospheric OCS mole fraction of 500 ppt</p> <p> ocs_veg_4.8ppb: simulated monthly OCS uptake by terrestrial vegetation at an atmospheric OCS mole fraction of 4.8 ppb</p> <p> ocs_soil_4.8ppb: simulated monthly OCS uptake by soils at an atmospheric OCS mole fraction of 4.8 ppb</p> <p> ocs_veg_35.5ppb: simulated monthly OCS uptake by terrestrial vegetation at an atmospheric OCS mole fraction of 35.5 ppb</p> <p> ocs_soil_35.5ppb: simulated monthly OCS uptake by soils at an atmospheric OCS mole fraction of 35.5 ppb</p> <p>- - - - - - - - - - -</p> <p><strong>References:</strong></p> <p>Haynes, K. D., Baker, I. T., Denning, A. S., Stöckli, R., Schaefer, K., Lokupitiya, E. Y., and Haynes, J. M.: Representing<br> Grasslands Using Dynamic Prognostic Phenology Based on Biological Growth Stages: 1. Implementation in the Simple<br> Biosphere Model (SiB4), Journal of Advances in Modeling Earth Systems, 11, 4423-4439, 10.1029/2018ms001540, 2019.</p> <p>Quaglia, I., Visioni, D., Pitari, G., and Kravitz, B.: An approach to sulfate geoengineering with surface emissions of carbonyl sulfide, Atmos. Chem. Phys., 22, 5757-5773, 10.5194/acp-22-5757-2022, 2022.</p> <p>Sellers, P. J., Mintz, Y., Sud, Y. C., and Salcher, A.: A Simple Biosphere Model (SiB) for Use within General Circulation Models, Journal of the Atmospheric Sciences, 43, 505-531, 1986.</p> <p>von Hobe, M., Brühl, C., Lennartz, S. T., Whelan, M. E., and Kaushik, A.: Comment on “An approach to sulfate geoengineering with surface emissions of carbonyl sulfide” by Quaglia et al. (2022) ,</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.