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223 results for “model ice”
Results of the ice sheet model initialisation experiments initMIP-Greenland: an ISMIP6 intercomparison
<p>This archive provides the forcing data and ice sheet model output produced as part of the publication "Design and results of the ice sheet model initialisation experiments initMIP-Greenland: an ISMIP6 intercomparison", published in The Cryosphere, https://www.the-cryosphere.net/12/1433/2018/</p> <p>Goelzer, H., Nowicki, S., Edwards, T., Beckley, M., Abe-Ouchi, A., Aschwanden, A., Calov, R., Gagliardini, O., Gillet-Chaulet, F., Golledge, N. R., Gregory, J., Greve, R., Humbert, A., Huybrechts, P., Kennedy, J. H., Larour, E., Lipscomb, W. H., Le clec´h, S., Lee, V., Morlighem, M., Pattyn, F., Payne, A. J., Rodehacke, C., Rückamp, M., Saito, F., Schlegel, N., Seroussi, H., Shepherd, A., Sun, S., van de Wal, R., and Ziemen, F. A.: Design and results of the ice sheet model initialisation experiments initMIP-Greenland: an ISMIP6 intercomparison, The Cryosphere, 12, 1433-1460, 2018, doi:10.5194/tc-12-1433-2018.</p> <p>Contact: Heiko Goelzer, h.goelzer@uu.nl</p> <p>Further information on ISMIP6 and initMIP-Greenland can be found here:<br> http://www.climate-cryosphere.org/activities/targeted/ismip6<br> http://www.climate-cryosphere.org/wiki/index.php?title=InitMIP-Greenland</p> <p>Users should cite the original publication when using all or part of the data. <br> In order to document CMIP6’s scientific impact and enable ongoing support of CMIP, users are also obligated to acknowledge CMIP6, ISMIP6 and the participating modelling groups.</p> <p><br> *** Important note ***<br> For consistency with future ISMIP6 intercomparison exercises and some observational data sets, we have re-gridded all output to a diagnostic grid following the EPSG:3413 specifications, which differs from the grid originally used to distribute the forcing data. We also provide the forcing data conservatively interpolated to the new grid. </p> <p><br> Archive overview<br> ----------------<br> README.txt - this information</p> <p>dSMB.zip - The original surface mass balance anomaly forcing data and description<br> dSMB/<br> dsmb_01B13_ISMIP6_v2.nc<br> dsmb_05B13_ISMIP6_v2.nc<br> dsmb_10B13_ISMIP6_v2.nc<br> dsmb_20B13_ISMIP6_v2.nc<br> README_dSMB_v2.txt</p> <p>dSMB_epsg3413.zip - The surface mass balance anomaly forcing data and description, interpolated to the new grid on EPSG:3413<br> dSMB_epsg3413/<br> dsmb_01e3413_ISMIP6_v2.nc<br> dsmb_05e3413_ISMIP6_v2.nc<br> dsmb_10e3413_ISMIP6_v2.nc<br> dsmb_20e3413_ISMIP6_v2.nc<br> README_dSMB_v2_epsg3413.txt</p> <p><group>_<model>_<experiment>.zip - The model output per group, model and experiment (init, ctrl, asmb)<br> <group1>_<model1>_init/<br> acabf_GIS_<group1>_<model1>_init.nc<br> ...<br> <group1>_<model1>_ctrl/<br> acabf_GIS_<group1>_<model1>_ctrl.nc<br> ...<br> <group1>_<model1>_asmb/<br> acabf_GIS_<group1>_<model1>_asmb.nc<br> ...</p> <p><group1>_<model2>_init/<br> ...<br> <group1>_<model2>_ctrl/<br> ...<br> <group1>_<model2>_asmb/<br> ...</p> <p><group2>_<model1>_init/<br> ...<br> <group2>_<model1>_ctrl/<br> ... <br> <group2>_<model1>_asmb/</p> <p>...</p> <p> </p> <p>The following script may be used to download the content of the archive.</p> <p>#!/bin/bash<br> wget https://zenodo.org/record/1173088/files/README.txt<br> wget https://zenodo.org/record/1173088/files/dSMB_epsg3413.zip<br> wget https://zenodo.org/record/1173088/files/dSMB.zip<br> <br> for amodel in ARC_PISM AWI_ISSM1 AWI_ISSM2 BGC_BISICLES1 BGC_BISICLES2 BGC_BISICLES3 DMI_PISM1 DMI_PISM2 DMI_PISM3 DMI_PISM4 DMI_PISM5 IGE_ELMER1 IGE_ELMER2 ILTS_SICOPOLIS ILTSPIK_SICOPOLIS IMAU_IMAUICE1 IMAU_IMAUICE2 IMAU_IMAUICE3 JPL_ISSM LANL_CISM LSCE_GRISLI MIROC_ICIES1 MIROC_ICIES2 MPIM_PISM UAF_PISM1 UAF_PISM2 UAF_PISM3 UAF_PISM4 UAF_PISM5 UAF_PISM6 UCIJPL_ISSM ULB_FETISH1 ULB_FETISH2 VUB_GISM1 VUB_GISM2; do</p> <p>wget https://zenodo.org/record/1173088/files/${amodel}_init.zip<br> wget https://zenodo.org/record/1173088/files/${amodel}_ctrl.zip<br> wget https://zenodo.org/record/1173088/files/${amodel}_asmb.zip</p> <p>done</p> <p> </p>
Outputs of the next generation sea ice model (neXtSIM) for winter 2006 - 2007 saved for comparison with RGPS.
<p>NeXtSIM was run from 1 December 2006 to 15 April 2007 with the following parameters:</p> <p><code>[mesh]</code><br><code>filename=small_arctic_10km.msh</code></p> <p><code>[simul]</code><br><code>duration=150</code><br><code>time_init=2006-11-15</code><br><code>timestep=900</code></p> <p><code>[dynamics]</code><br><code>compression_factor=13800</code><br><code>C_lab=2675000</code><br><code>nu0=0.301</code><br><code>tan_phi=0.624</code><br><code>substeps=90</code><br><code>time_relaxation_damage=15</code><br><code>use_temperature_dependent_healing=true</code></p> <p><code>[output]</code><br><code>exporter_path=/cluster/work/users/akorosov/music/sa10free_mat00</code><br><code>output_per_day=4</code><br><code>variables=M_VT</code><br><code>variables=Concentration</code><br><code>variables=Thickness</code></p> <p><code>[setup]</code><br><code>atmosphere-type=era5</code><br><code>ice-type=topaz_osisaf_icesat</code><br><code>ocean-type=topaz</code><br><code>bathymetry-type=etopo</code><br><code>dynamics-type=bbm</code></p> <p><code>[thermo]</code><br><code>diffusivity_sss=0</code><br><code>diffusivity_sst=0</code><br><code>h_young_max=0.3</code><br><code>newice_type=1</code><br><code>hnull=0.5</code></p> <p><code>[debugging]</code><br><code>check_fields_fast=false</code></p> <p>The outputs (binary snapshots at every 3 hours) were then merged with RGPS data from the same period using this notebook:</p> <p>https://github.com/nansencenter/music_nextsim_tuning_paper/blob/main/02_process_nextsim.ipynb</p> <p> </p>
Data supporting 'Ice loss in the European Alps until 2050 using a fully assimilated, deep-learning-aided 3D ice-flow model'
<p>The dataset supporting our publication '<strong>Ice loss in the European Alps until 2050 using a fully assimilated, deep-learning-aided 3D ice-flow model</strong>' in <em>Geophysical Research Letters.</em></p> <p>The main .zip archive contains a set of NetCDF files detailing:</p> <ul> <li>Initial optimised glacier states (geology-optimized...)</li> <li>Simulation results (Prog20...)</li> </ul> <p>Initial states and results are given by cluster (see Figure 1 in the paper), as shown in all filenames (C1 through to C12). Prognostic simulation filenames additionally distinguish between runs between 1999 and 2019 (Prog2020) and between 2020 and 2050 (Prog2050). 'NV'/'NoVel' and 'NT'/'NoThk' refer to simulations using the partial optimisation (optimisation without including velocity/thickness observations) as detailed in the paper. 'AV' at the end of the filename denotes the integrated area/volume results file, as opposed to the 2D raster results file. A 'V' before the cluster designation shows that the simulation used the variable SMB as opposed to the fixed SMB (see the paper for details). 'ID' before the cluster designation shows that the simulation was using extrapolated SMB based on the trend in SMB since 2000, instead of assuming the continuation of the current SMB. 'ID' on its own denotes linear extrapolation and 'IDQ' denotes quadratic extrapolation (not used in the published paper). 'SMBF' in the filename shows that the simulation used the SMB-elevation feedback.</p> <p>The additional .zip archive contains the code of IGM v1.0 used to produce the model results. For details on installing and using IGM, please see the Github page at <a href="https://github.com/jouvetg/igm.The">https://github.com/jouvetg/igm</a>.</p> <p>A further .zip archive (in version 3 - Sims2010-2022.zip) contains the simulations based on linear extrapolation of the observed trend in SMB between 2010 and 2022, following the same nomenclature as in the principal archive (see above).</p> <p>Version 4 contains an additional mosaicked DEM of the results for the whole Alps with the ice removed to give the complete basal topography (kindly processed by T. Léger at UNIL) using the Japan Aerospace Exploration Agency (2021) ALOS World 3D 30 meter DEM. V3.2, Jan 2021. Distributed by OpenTopography. <a title="https://doi.org/10.5069/G94M92HB" href="https://doi.org/10.5069/G94M92HB" target="_blank" rel="noreferrer noopener">https://doi.org/10.5069/G94M92HB</a>. Accessed: 2024-09-09.</p>
Using variable-resolution grids to model precipitation from atmospheric rivers around the Greenland ice sheet
<p>This dataset can be used to reproduce the figures created in Waling et al. 2024, "Using variable-resolution grids to model precipitation from atmospheric rivers around the Greenland ice sheet." Each figure has its own script which can be executed.<br><br></p>
NASA Eulerian Snow On Sea Ice Model Version 1.1 (NESOSIMv1.1) data: 1980 - 2024
<p><strong>Repository updates</strong></p> <p><em>Update on Sep 12th 2024: The repository now includes NESOSIM v1.1 output from September 1st 2022 to April 30th 2023 and September 1st 2023 to April 30th 2024 </em></p> <p><em>Update on Sep 5th 2022: </em>The repository now includes NESOSIM v1.1 output from September 1st 2021 to April 30th 2022</p> <p><em>Update on June 7th 2022: </em>The repository now includes NESOSIM v1.1 output from September 1st 2021 to March 31st 2022</p> <p><em>Update on March 8th 2021: </em>The gridded forcing files are now available in the gridded_forcings.zip file. Data are stored as Python pickles which can be easily read in by the core NESOSIM source code. </p> <p><em>Update on March 8th 2021: </em>The repository now includes zip files of gridded forcing (snowfall, winds, ice drift, ice concentration, initial conditions) as well as gridded Operation IceBridge snow depths. </p> <p><em>Update on January 30th 2021: </em>The repository now also includes a NESOSIM v1.1. daily gridded snow climatology using the mean (np.nanmean) of all data available between September 1 2010 and April 30 2020.</p> <p><strong>Overview</strong></p> <p>NESOSIM is a three-dimensional, two-layer (vertical), Eulerian snow on sea ice budget model developed with the primary aim of producing daily estimates of the depth and density of snow on sea ice across the polar oceans through the winter accumulation season, generally September through April (Petty et al., 2018).</p> <p>This repository contains model output from September 1st 1980 to April 30th 2021 [and September 1st 2021 to March 31st 2022 as of June 7th 2022] based on the NESOSIM v1.1 code release which is available on GitHub (https://github.com/akpetty/NESOSIM/tree/v1.1) and archived through Zenodo (10.5281/zenodo.4448355). More information about changes between the v1.0 and v1.1 model framework can be found in those links.</p> <p>A preprint is now available in <em>The Cryosphere Discuss</em> explaining these upgrades and their impacts on ICESat-2 winter Arctic sea ice thickness estimates (Petty et al., 2022). </p> <p><strong>Data production:</strong></p> <p>Data are re-initialized at the end of summer each year (September 1st) using summer near-surface air temperature-scaled initial snow depths and run through until the end of April of the following year. The 1987-1988 winter is missing due to the lack of passive-microwave derived ice concentration data available during this period. Daily data are generated on a 100 km x 100 km North Polar Stereographic grid (EPSG: 3413) across the entire Arctic Ocean including the peripheral seas.</p> <p><strong>Forcings:</strong></p> <p><em>NB: Recent year runs often require the use of near-real-time data products, so the underlying forcings used in this v1.1 release can change in time, as noted below:</em></p> <ul> <li>Snowfall: European Center for Medium Range Weather Forecasts (ECMWF) ERA5 (https://cds. climate.copernicus.eu, September 1 1980 onwards2) + CloudSat scaling (Cabaj et al., 2020).</li> <li>Near-surface winds: ECMWF ERA5 (https://cds. climate.copernicus.eu, September 1 1980 onwards).</li> <li>Sea ice drift: NSIDC Polar Pathfinder v4 (https://nsidc.org/data/nsidc-0116, September 1 1980 to April 30 2019), OSI SAF merged (https://osi-saf.eumetsat.int/products/osi-405-c, September 1 2019 onwards).</li> <li>Sea ice concentration: Final v3 NSIDC Climate Data Record (https://nsidc.org/data/g02202/versions/3/, September 1 1980 to December 31 2020), and near-real-time v2 NSIDC Climate Data Record (https://nsidc.org/data/g10016, January 1 2021 onwards).</li> <li>Near-surface air temperature (to derive temperature-scaled initial conditions): ECMWF ERA5 (https://cds. climate.copernicus.eu, September 1 1980 onwards).</li> </ul> <p><em>The forcings used to generate each winter dataset are described in a new 'forcings' variable in each NetCDF file. </em></p> <p><strong>Operation IceBridge snow depths:</strong></p> <p>The repository now also includes the gridded Operation IceBridge snow depths we used for calibration purposes, as described in Petty et al., (2022). The data contained within <em>gridded_oib_snowdepths.zip</em> includes the daily gridded data on the NESOSIM v1.1 100 km domain, ordered by day of collection. Data are stored as Python pickles and text files and include estimates derived from the following snow depth algorithms: SRLD (2009-2015): snow radar layer detection, JPL (2009-2015): Jet Propulsion Laboratory, GSFC (2009-2015): Goddard Space Flight Center, NSIDC (2009-2012): archived NASA GSFC data on the NSIDC, QL (2013-2019): NSIDC quick-look data based on the GSFC algorithm. MEDIAN (2010-2015): consensus snow depth from median of GSFC, JPL and SRLD. </p> <p><strong>References:</strong></p> <p>Cabaj, A., P. J. Kushner, C. G. Fletcher, S. Howell, A. Petty (2020), Constraining reanalysis snowfall over the Arctic Ocean using CloudSat observations, Geophysical Research Letters, 47, doi:10.1029/2019GL086426.</p> <p>Petty, A. A., M. Webster, L. N. Boisvert, T. Markus (2018), The NASA Eulerian Snow on Sea Ice Model (NESOSIM) v1.0: Initial model development and analysis, Geosci. Model Dev., doi: 10.5194/gmd-11-4577-2018.</p> <p>Petty A. A., N. Keeney, A. Cabaj, P. Kushner, M. Bagnardi (2023), Winter Arctic sea ice thickness from ICESat-2: upgrades to freeboard and snow loading estimates and an assessment of the first three winters of data collection, The Cryosphere, 17, 127–156, doi: 10.5194/tc-17-127-2023.</p>
Model output from Inverting ice surface elevation and velocity for bed topography and slipperiness beneath Thwaites Glacier
<p>This model output dataset accompanies the draft paper 'Inverting ice surface elevation and velocity for bed topography and slipperiness beneath Thwaites Glacier'.</p>
Model Output and Figure Scripts for: "Uncertainty in reconstructing paleo-elevation of the Antarctic Ice Sheet from temperature-sensitive ice core records"
<p>New climate model output and figure scripts for the paper "Uncertainty in reconstructing paleo-elevation of the Antarctic Ice Sheet from temperature-sensitive ice core records".</p>
Data accompanying the article "Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework"
<p><em>Amonthly_files.tar.gz</em> contains the gridded monthly averaged quantities used in the manuscript Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework" for each year between 2000 and 2018.</p> <p>Files containing "simba" in their name contain quantities related to the sea ice mass balance (volume of melt/growth...)</p> <p>Files containing "icemod" in their name contain other quantities related to sea ice properties (thickness, concentration...)</p> <p>In case information is missing, do not hesitate to contact guillaume.boutin@nersc.no , heather.regan@nersc.no or einar.olason@nersc.no</p> <p>This research has been funded by the Norwegian Research Council (Nansen Legacy: grant no. 27673, FRASIL: grant no. 263044, and ARIA: grant no. 302934), JPI Climate and JPI Oceans (MEDLEY project, under agreement with the Norwegian Research Council, grant no 316730), and by Copernicus Marine Environment Monitoring Service (CMEMS) WIzARd project. CMEMS is implemented by Mercator Ocean in the framework of a delegation agreement with the European Union<br> Copernicus Marine Environment Monitoring Services (contract no.<br> 69), and the European Space Agency through the Cryosphere Virtual Laboratory (CVL, grant no. 4000128808/19/I-NS).</p>
Modeled dynamic and thermodynamic sea ice growth in the Arctic 1980-2019 from NAOSIM
<p>This data set is related to the paper "Evidence for an Increasing Role of Ocean Heat in Arctic Winter Sea Ice Growth" by Ricker et al. (2021). Please refer to this study for further details.</p> <p>Ricker, R., Kauker, F., Schweiger, A., Hendricks, S., Zhang, J., & Paul, S. (2021). Evidence for an Increasing Role of Ocean Heat in Arctic Winter Sea Ice Growth, Journal of Climate, 34(13), 5215-5227. Retrieved Nov 24, 2022, from https://journals.ametsoc.org/view/journals/clim/34/13/JCLI-D-20-0848.1.xml</p>
3-D model data used to investigate the role of K-feldspar and quartz in global ice nucleation by mineral dust in mixed-phase clouds
<p>These simulations were run by Chemical Transport Model TM4-ECPL covering the years 2009-01 to 2016-12 and are used for the bellow publication:</p> <p>Chatziparaschos, M., Daskalakis, N., Myriokefalitakis, S., Kalivitis, N., Nenes, A.,<br> Gonçalves Ageitos, M., Costa-Surós, M., Pérez García-Pando, C., Zanoli, M., Vrekoussis,<br> M., and Kanakidou, M.: Role of K-feldspar and quartz in global ice nucleation by mineral dust in mixed-phase clouds,<br> Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2022-551, in press 2023.</p> <p>Laboratory: Environmental Chemical Processes Laboratory (EPCL), Department of Chemistry, University of Crete, Heraklion.<br> contact: Kanakidou Maria <mariak@uoc.gr></p> <p>Model resolution: 2x3<br> Model Levels: 25</p> <p>Data info:</p> <p>DU_m2m(time, lev, lat, lon)<br> short_name :DU_m2m<br> long_name : Dust mode 2 mass accumulation</p> <p>DU_m3m(time, lev, lat, lon)<br> short_name : DU_m3m<br> long_name : Dust mode 3 mass coarse</p> <p>qua2_acc(time, lev, lat, lon)<br> short_name :qua2_acc<br> long_name :Quartz – accumulation mode</p> <p>qua2_coa(time, lev, lat, lon)<br> short_name :qua2_coa<br> long_name :Quartz – coarse mode</p> <p>FEL_acc(time, lev, lat, lon)<br> short_name :FEL_acc<br> long_name : K-Feldspar – accumulation mode</p> <p>FEL_coa(time, lev, lat, lon)<br> short_name :FEL_coa<br> long_name : K-Feldspar – coarse mode</p> <p>INP_QUA(time, lev, lat, lon)<br> short_name :INP_QUA<br> long_name :Ice Nucleating Particles derived form Quartz</p> <p>INP_FELD(time, lev, lat, lon)<br> short_name :INP_FELD<br> long_name :Ice Nucleating Particles derived form K-Feldpsar</p> <p> </p>
Greenland Ice Sheet modeled firn properties from SNOWPACK and the Community Firn Model (1980-2020)
<p>This dataset contains model output from the physics-based SNOWPACK firn model and the semi-empirical Community Firn Model (CFM) over the Greenland Ice Sheet from 1980 through 2020. Included are individual density profiles for locations with firn density observations as well as firn air content (FAC) calculated over different depth intervals. Data for both models are supplied. These data are used in a manuscript to be submitted to The Cryosphere journal (see Thompson-Munson et al., in review).</p>
Model outputs for the article "Modelling the evolution of Arctic multiyear sea ice over 2000–2018"
<p><em>icemod_monthly.tar.gz </em>contains the gridded monthly averaged quantities used in the manuscript "Modelling the evolution of Arctic multiyear sea ice over 2000-2018" for each year between 2000 and 2018.</p> <p>Multiyear ice variables are conc_myi (concentration of multiyear ice in a grid cell) and thick_myi (cell average thickness of multiyear ice in a grid cell, in metres), along with source and sink terms (units per day) for multiyear concentration (dci_mlt_myi, dci_ridge_myi and dci_rplnt_myi, for melt, ridging and replenishment) and volume (dvi_mlt_myi and dvi_rplnt_myi, for melt and replenishment).</p> <p><em>transports_monthly_sections.zip </em>contains the transports of multiyear ice through the sections defining each region in Figure 8 of the paper. MYIsiaXport indicates multiyear ice area transport, while myiXport indicates multiyear ice volume transport.</p> <p>In case information is missing, do not hesitate to contact heather.regan@nersc.no, guillaume.boutin@nersc.no, or einar.olason@nersc.no.</p>
Data: Contrasting current and future surface melt rates on the ice sheets of Greenland and Antarctica: lessons from in situ observations and climate models
<p>These data accompany the publication "Contrasting current and future surface melt rates on the ice sheets of Greenland and Antarctica: lessons from in situ observations and climate models". The data are organized as follows:</p> <p>- two files with time series (csv) of hourly near-surface climate and surface energy balance values for Neumayer station (ice shelf, East Antarctic ice sheet) and automatic weather station S5 (southwest Greenland ice sheet)</p> <p>- two files (nc) with monthly melt fields from the regional climate model RACMO2.3p2 forced by ERA5 over Greenland (0.05-degree resolution) and Antarctica (0.25-degree resolution)</p> <p>- two files (nc) with annual melt fields from the regional climate model RACMO2.3p2 forced by CESM2 over Greenland (0.1-degree resolution) and Antarctica (0.25-degree resolution) or the historical period (1950-2014)</p> <p>- two files (nc) with annual melt fields from the regional climate model RACMO2.3p2 forced by CESM2 over Greenland (0.1-degree resolution) and Antarctica (0.25-degree resolution) for the future emission scenario SSP5-8.5 (2015-2099)</p>
Simulated water models with Apoferritin for use in cryo-EM image simulations with amorphous ice
<p>This dataset contains the atomic coordinates of several water models produced using the NAMD molecular dynamics software. The contents of each file is listed below.</p> <ul> <li><strong><em>water_81_coords.pdb</em></strong> - water only in a cubic box with side length 81A</li> <li><strong><em>water_243_coords.pdb</em></strong> - water only in a cubic box with side length 243A</li> <li><strong><em>water_486_coords.pdb</em></strong> - water only in a cubic box with side length 486A</li> <li><strong>water_567_coords.pdb</strong> - water only in a cubic box with side length 567A</li> <li><strong><em>water_645_coords.pdb</em></strong> - water only in a cubic box with side length 645A</li> <li><strong><em>water_735_coords.pdb</em></strong> - water only in a cubic box with side length 735A</li> <li><strong><em>apo_water_723_coords.pdb</em></strong> - water and apoferritin in a cubic box with side length 723A where apoferritin atoms are constrained</li> </ul>
BISICLES ice-sheet model for the Amundsen Sea Embayment, Antarctica : ensemble simulations to 2050
<p>BISICLES ice-sheet model simulations for the Amundsen Sea Embayment. Full details of the model set-up and ensemble design are described in the attached manuscript which has been accepted for publication in Journal of Glaciology.<br> In brief, a 213-member ensemble of simulations was created by varying four different model parameters. The parameters are the u0 value in a regularised Coulomb friction law, the rate of imposed thinning of floating ice (∂h/∂t(Ωf)), and scaling factors for sliding and viscosity coefficients (<em>C</em> and ϕ) between 0.9 and 1.1. We attach a summary text file of results, as well as NetCDF files of simulated variables land ice thickness and u and v components of velocity.<br> <strong>ASE2050_bisicles.csv </strong>contains annual (2007 to 2050, columns 5 to 48) sea level equivalent (mm) mass losses of ice from the Pine Island and Thwaites Glacier catchment basins. The parameters, given in columns 1 to 4, respectively, are the u0 (m/a), the rate of imposed thinning of floating ice (m/a), and the scaling factors for sliding and viscosity coefficients.<br> The NetCDF files in <strong>ASE_BISICLES.tar.gz</strong> contain annual (2007 to 2050) simulated output variables for the Amundsen Sea region at a spatial resolution of 1 km, with one file per ensemble member. The variables follow the ISMIP6 naming protocol:<br> (https://www.climate-cryosphere.org/wiki/index.php?title=ISMIP6-Projections-Antarctica#A2.3_Model_output_variables_and_README_file).<br> We include state variables lithk, uvelmean, and vvelmean. Each file is named according to the variable, the simulation parameters, and the resultant 2050 SLE value of ice loss (mm). For example, <strong>lithk_ASE_BISICLES.uj_20.dhfdt_5.C_0.90.phi_0.90.slr_43.06.nc </strong>is the land ice thickness data for simulation u0=20 m/a, ∂h/∂t(Ωf) = 5 m/a, C scaled by 0.9, ϕ scaled by 0.9, and a final SLE of 43.06 mm.</p> <p> </p>
The coupled ice sheet-Earth system model Bern3D v3.0: Model output
<p>This dataset contains model output of climate and ice sheet variables for the simulations performed in the study:</p> <p>Pöppelmeier, F., Joos, F., Stocker, T. F. (2023). The coupled ice sheet-Earth system model Bern3D v3.0. Journal of Climate.</p> <p>2D and 3D output variables are available for the preindustrial (PI) and Last Glacial Maximum (LGM) control simulations. Timeseries output is provided for CO<sub>2</sub> experiments for which CO<sub>2</sub> concentrations were increased to 2 and 4 times PI concentrations with rates of 0.5, 1, and 2% per year. Timeseries output is also provided for the simulation of the entire last glacial cycle in the standard setup and with logarithmically scaled dust for the aerosol radiative forcing. More details are provided in the above mentioned manuscript.</p>
PROTECT-SLR BISICLES ice-sheet model simulations for Amundsen Sea Embayment to 2050
<p>BISICLES ice-sheet model results. The NetCDF files in ASE_BISICLES.tar.gz contain simulated output variables for the Amundsen Sea Embayment sector of the West Antarctic Ice Sheet at a spatial resolution of 1 km. The model start date is 2007 and the outputs are yearly to 2052. Each of the 30 simulations is a result of a different combination of model parameters. The parameters are the u<sub>0</sub> value in a regularized Coulomb friction law, the rate of imposed thinning of floating ice, and scaling factors for sliding and viscosity coefficients between 0.9 and 1.1. The final part of each dataset name gives the sea-level equivalent (SLE) of loss of ice above floatation within Pine Island and Thwaites Glacier catchment basins. Each output was randomly selected from a 2 cm 2050 SLE band of a histogram of a large ensemble of simulations.</p> <p>See the pdf report included for further details.</p>
Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3
<p>This is the underlying dataset of the publication "Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3" by Gürses et al. (in press), Geoscientific Model Development. In addition to unstructured mesh information, it contains the results of ocean biogeochemistry in the Regulated Ecosystem Model version 3 (REcoM3) coupled to the ocean and sea ice model FESOM2.1. The model simulations cover the period 1958 to 2021 and are forced with observed atmospheric CO<sub>2</sub> and JRA55-do atmospheric reanalyses. Three simulations are provided:</p> <p><strong>simulation A:</strong> with varying climate forcing conditions and varying atmospheric CO<sub>2</sub></p> <p><strong>simulation B:</strong> with constant climate forcing conditions and constant atmospheric CO<sub>2</sub></p> <p><strong>simulation D:</strong> with varying climate forcing conditions and constant atmospheric CO<sub>2</sub></p> <p>The following 2D/3D monthly-averaged fields (data period is given in parentheses) are provided on the native model grid:</p> <ul> <li><strong>Alk:</strong> Alkalinity (2012-2021)</li> <li><strong>CO2f:</strong> Air-Sea CO<sub>2</sub> flux (1800-2021)</li> <li><strong>DFe: </strong>Dissolved Iron concentration (2012-2021)</li> <li><strong>DIN:</strong> Dissolved Inorganic Nitrogen concentration (2012-2021)</li> <li><strong>DIC:</strong> Dissolved Inorganic Carbon concentration (1800, 1994-2021)</li> <li><strong>DSi:</strong> Dissolved Inorganic Silicon concentration (2012-2021)</li> <li><strong>DiaChl:</strong> Chlorophyll a concentration of diatoms (2012-2021)</li> <li><strong>DetC:</strong> Carbon concentration in slow-sinking detritus (2012-2021)</li> <li><strong>DetCalc: </strong>Calcite concentration in slow-sinking detritus (2012-2021)</li> <li><strong>DetSi: </strong>Silicon concentration in slow-sinking detritus (2012-2021)</li> <li><strong>idetz2c:</strong> Carbon concentration in fast-sinking detritus (2012-2021)</li> <li><strong>idetz2calc:</strong> Calcite concentration in fast-sinking detritus (2012-2021)</li> <li><strong>idetz2si:</strong> Silicon concentration in fast-sinking detritus (2012-2021)</li> <li><strong>HetC:</strong> Small zooplankton carbon biomass (2012-2021)</li> <li><strong>MLD:</strong> Mixed Layer Depth (2012-2021)</li> <li><strong>NPPn:</strong> Net Primary Production of small pyhtoplankton (2012-2021)</li> <li><strong>NPPd:</strong> Net Primary Production of diatoms (2012-2021)</li> <li><strong>O2:</strong> Dissolved Oxygen concentration (2012-2021)</li> <li><strong>PhyChl:</strong> Chlorophyll a concentration of small phytoplankton (2012-2021)</li> <li><strong>Zoo2C:</strong> Macrozooplankton carbon biomass (2012-2021)</li> <li><strong>pCO2s:</strong> Partial pressure of carbon dioxide of the surface ocean (1970-2021)</li> <li><strong>salt:</strong> Salinity (2012-2021)</li> <li><strong>temp:</strong> Temperature (2012-2021)</li> <li><strong>w:</strong> Vertical velocity (2012-2021)</li> </ul> <p>Please contact the corresponding author (ozgur.gurses@awi.de) for further information.</p>
Ice Throw from Wind Turbines: Experimental Data, 6DOF Model, CFD results, 3D Scans
<p>Compiled data and code from the Eisball Project (funded by the Austrian Research Promotion Agency FFG, project number 865060)</p> <p> </p> <p> </p> <p>6DOF_model_octave.zip - reference implementation of the six-degree-of-freedom model in MathML (Octave or MATLAB)</p> <p>experimental_data.csv - Experimental Data from dropping artificial ice fragments from wind turbines, recording drop distance and direction, details in experimental_data_column_description.txt</p> <p>???_forces_and_moments.csv - forces and moments tables for the use in the 6DOF model, specific per specimen type</p> <p> </p> <p>Data was first published in Nov 2021 at https://boku.ac.at/wau/risk/abgeschlossene-projekte/eisball-1 (may not persist)</p>
Dataset for "Brief communication: On calculating the sea-level contribution in marine ice-sheet models"
<p>This archive provides the data in Figures 3 and S1 of the following publication:</p> <p>Goelzer, H., Coulon, V., Pattyn, F., de Boer, B., and van de Wal, R.: Brief communication: On calculating the sea-level contribution in marine ice-sheet models , The Cryosphere, 14, 833–840, https://doi.org/10.5194/tc-14-833-2020, 2020.</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.