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52 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>
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>
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>
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).
Effects of past and present-day landscape structure on forest soil microorganisms
<p><span><span><span><span><span><span><span><span><span><span><span>Principles of landscape ecology have been built on birds and plant species distribution, but the number of clues is now growing on below-ground organisms, whose dispersal may also be affected by above-ground landscape structure. For communities of microorganisms, the question remains if and how they answer to landscape structure, with or without time lag, and if some groups of microorganisms may react more than others. Here, we investigated if fungi or bacteria diversity is driven by the amount of forest cover in the current or the past landscape. We tested the Habitat Amount Hypothesis (HAH) on ancient forests of Cevennes national park, that were particularly fragmented 150 years ago, and are today surrounded by recent forests. As ancient forests are often more diverse in plant species, we hypothesized that the higher quantity of ancient forests in the landscape, the richer fungal and bacterial communities would be locally. More precisely, we expected that ectomycorrhizal fungi, and pathotrophic fungi, often indicators of mature forests, would be also more sensitive to forest history and therefore to the quantity of ancient forests than bacteria and saprotrophic fungi. We sampled 40 soil cores per 0.5 ha, pooled in 8 composite samples per plot in 27 landscapes and sequenced ITS and 16S marker by Illumina-Mi seq. To identify functional groups of fungi, we relied on their taxonomy and the use of public databases. Our results partly follow the HAH, as fungi richness was positively related with the quantity of ancient forests in the landscape and not by the focal patch size. Ectomycorrhizal and pathotrophic fungi were positively affected by the ancient forest cover, and so were saprotrophic ones, but not bacteria. Local factors also shaped the communities such as soil composition and elevation, confirming classical patterns in soil ecology. Interestingly, past landscape structure better explained fungi communities richness than contemporary landscape, suggesting a time lag in the response of communities to landscape modification and a potential extinction debt. Our results invite to consider below-ground communities in landscape studies and historical ecology, as their structure and functions might be intimately linked with soil and landscape history.</span></span></span></span></span></span></span></span></span></span></span></p>
Causes and importance of new particle formation in the present-day and pre-industrial atmospheres: supporting data
<p>Data presented in the manuscript "Causes and importance of new particle formation in the present-day and pre-industrial atmospheres" currently in review.</p> <p>Particle number concentrations (files with initial word "CCN" or "N3") have units of particles per cubic centimetre, calculated at ambient temperature and pressure. Files with initial word "solar" have units of percent. Ion production rates have units ion pairs per cubic centimetre per second.</p> <p>The simulation data presented here was generated with the GLOMAP aerosol model, https://www.see.leeds.ac.uk/research/icas/research-themes/atmospheric-chemistry-and-aerosols/groups/aerosols-and-climate/the-glomap-model/ running on a T42 grid.</p> <p>The manuscript associated with this data was written using results from the CLOUD experiment at CERN, and the author list is a subset of the CLOUD collaboration.</p> <p> </p> <p> </p>
Vertical land motion due to present-day ice loss from Greenland's and Canada's peripheral glaciers
<p>Greenland's bedrock responds to the ongoing loss of ice mass with an elastic vertical land motion (VLM) that is measured by Greenland's GNSS Network (GNET). The measured VLM also contains other contributions, including the long-term viscoelastic response of the Earth to previous deglaciation.</p> <p>Greenland's ice sheet (GrIS) is producing the most significant contribution to the total VLM. The contribution of peripheral glaciers (PGs) from both Greenland (GrPGs) and Arctic Canada (CanPGs) has not been carefully accounted for in the GNSS time series analysis. This is a significant concern, since GNET stations are often closer to PGs than to the ice sheet. </p> <p>We find that PGs produce significant elastic rebound, especially in North and East Greenland. Across these regions, the PGs result in up to 37% of the elastic rebound. For a few stations in the North, the VLM from PGs is larger than the GrIS one.</p>
Present-day surface deformation of Sicily: Insights from Sentinel-1 data processed by a PS-InSAR approach
<p>The directory DATASET.zip provides PS-InSAR data used in Henriquet et al., (2022). The data set contains for each Sentinel-1 track (44, 117, 22, 124) the mean PS velocities along the LOS, before (ps_mean_v.xy.v-dos) and after (ps_mean_v-dos_adjusted2GPS.xy) their adjustment to the 3D-GNSS velocity field, as well as the disparities of the PS velocities (ps_mean_disp.xy). The data set also includes the East- and Up-component of the reconstructed mean PS velocity field (East.grd and Up.grd) used in the Figures 7 to 12 in the paper.</p>
The INMCM-4.8 Earth system model data used in the paper by Guryanov V.V. et al. entitled ''The present-day and future lightning frequency as simulated by four CMIP6 models'
<p>The INMCM-4.8 Earth system model data used in the paper by Guryanov V.V. et al. entitled ''The present-day and future lightning frequency as simulated by four CMIP6 models'</p>
The relationship between the present-day seasonal cycles of low clouds in the mid-latitudes and cloud-radiative feedback
<p>Supporting data for "The relationship between the present-day seasonal cycles of low clouds in the mid-latitudes and cloud-radiative feedback", by K. Furtado, Y. Tsushima and P. R. Field.</p>
Data and code for publication "The stability of present-day Antarctic grounding lines - Part B"
<p>Data and code for the publication <a href="https://tc.copernicus.org/preprints/tc-2022-105/">"The stability of present-day Antarctic grounding lines – Part B: Onset of irreversible retreat of Amundsen Sea glaciers under current climate on centennial timescales cannot be excluded"</a> in The Cryosphere.</p> <p>Zip files contain data, python notebooks for analysis and PISM code. Please contact ronja.reese@northumbria.ac.uk if you have any further questions.</p>
TC25/Chicago_COMPASS: Present-day heat-related estimates for Chicago community areas [COMPASS-GLM]
<p>Chicago_COMPASS</p> <p>Community area summaries for Chicago and related scripts. Specifics below:</p> <p>Data</p> <p>Each CSV file provides summaries for 77 community areas in Chicago from WRF simulations, satellites, and socioeconomic surveys.</p> <p>The spatial polygons for the community areas and the socioeconomic data were accessed through the Chicago Data Portal: <a href="https://data.cityofchicago.org/">https://data.cityofchicago.org/</a></p> <p>The WRF code is open source and can be found at: <a href="https://github.com/wrf-model/WRF">https://github.com/wrf-model/WRF</a></p> <p>Chicago_control, Chicago_no_urb, and Chicago_no_lake have the maximum and minimum average variables of interest for the control, no urban, and no lake simulations. These are for the BEM/BEP runs with the YSU boundary layer scheme and are used for the main results of the paper.</p> <p>The WRF_BEM_MYJ files are for the BEM/BEP control runs with the MYJ boundary layer scheme. The WRF_Noah files are the control runs using just the Unified Noah land surface model (no urban canopy). The WRF_nested file is for a control run using 3-way nested domains, with the inner domain over Chicago at 1.333 km using BEM/BEP and the YSU boundary layer scheme.</p> <p>Chicago_perc_control, Chicago_perc_no_urb, and Chicago_perc_no_lake have the 95th and 98th percentiles of hourly variables of interest for the control, no urban, and no lake simulations.</p> <p>Chicago_MODIStime_control has the daytime and nighttime variables of interest (corresponding to MODIS Aqua overpass) for the control simulations.</p> <p>en01, en02, en03, and so on represent the ensembles for each model configuration.</p> <p>Chicago_geo_socioeconomic includes the socioeconomic variables (median income per capita and Hardship Index), spatial metrics (area and distance from the coast), and satellite-derived estimates (daytime and nighttime land surface temperature (LST), and normalized different vegetation index (NDVI).</p> <p>Scripts</p> <p>WRF_to_tabular.R converts the WRF simulations into tabular data to be injested into Google Earth Engine.<br> Rasterize.js converts the tabular WRF results into a raster with separate bands for each variable on Google Earth Engine.<br> Summarize.js processeses satellite observations and summarizes the satellite and WRF outputs into regions of interest on Google Earth Engine.</p>
Vertical land motion due to present-day ice loss from Greenland’s and Canada’s peripheral glaciers
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Effects of past and present-day landscape structure on forest soil microorganisms
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