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223 results for “model ice”
Multi-scale sea ice kinematics modeling dataset with a tripolar grid hierarchy (TS grids) in CESM
<p>1. We design a new tripolar grid generation method for global ocean-sea ice modeling. The generated grid is orthogonal and compatible with ocean and sea ice models. A hierarchy of ocean-sea ice model grid is constructed and incorporated in CESM by using the grid generation method. The generated tripolar grids are regarded as TS045, TS015 and TS005, with the nominal resolution of 0.45<sup>o </sup>(800x560), 0.15<sup>o</sup> (2400x1680) and 0.05<sup>o </sup>(7200x5040), respectively. The resolution range for the grid hierarchy covers climate modeling to sub-mesoscale capable for ocean modeling.</p> <p>2. Atmosphere forced simulations based on CESM D-type experiments are carried out for TS grid. Both TS045 and TS015 experiments start with no sea ice, integrate for 42 years. TS005 starts from the equilibrium state of TS015 result (36 year), runs for another 7 years.</p> <p>3. TS0*.grid files include necessary grid information, such as grid latitude, longitude, etc. TS0*.kmt files denote the deepest level at each grid location. Bilinear interpolators from TS grid to atmosphere T62 grid are provided with map*.nc files. The model outputs at year 42 are provided with T*.nc files. The last two kinds of netcdf files are compressed with Linux command "gzip".</p>
Dataset for "The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6"
<p>This data set provides processed model output of ISMIP6 Greenland projections as documented and analysed in the following publication:</p> <p>Heiko Goelzer, Sophie Nowicki, Anthony Payne, Eric Larour, Helene Seroussi, William H. Lipscomb, Jonathan Gregory, Ayako Abe-Ouchi, Andy Shepherd, Erika Simon, Cecile Agosta, Patrick Alexander, Andy Aschwanden, Alice Barthel, Reinhard Calov, Christopher Chambers, Youngmin Choi, Joshua Cuzzone, Christophe Dumas, Tamsin Edwards, Denis Felikson, Xavier Fettweis, Nicholas R. Golledge, Ralf Greve, Angelika Humbert, Philippe Huybrechts, Sebastien Le clec'h, Victoria Lee, Gunter Leguy, Chris Little, Daniel P. Lowry, Mathieu Morlighem, Isabel Nias, Aurelien Quiquet, Martin Rückamp, Nicole-Jeanne Schlegel, Donald Slater, Robin Smith, Fiamma Straneo, Lev Tarasov, Roderik van de Wal, and Michiel van den Broeke: The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6 , The Cryosphere, 2020. doi:10.5194/tc-2019-319</p> <p>About the data:<br> - The results are based on model output regridded conservatively to a 5x5 km regular ISMIP6 grid unless this is already the native grid. <br> - The results are calculated over the ice-covered area of Greenland, map projection error corrected, ice sheet model specific densities taken into account.<br> - The contribution of peripheral glaciers and ice caps has been removed, by considering their area-coverage in each grid cell.<br> - The results for the projections 'exp*' are all calculated as differences to the control experiment ctrl_proj (suffix cr in filename for control removed).<br> - Results for ctrl_proj and historical are un-corrected (no suffix cr in filename).</p> <p><br> Directory structure:<br> versionid<br> groupname1<br> modelname1<br> expid<br> scalars_mm_cr_GIS_groupname1_modelname1_expid.nc<br> scalars_rm_cr_GIS_groupname1_modelname1_expid.nc<br> scalars_zm_cr_GIS_groupname1_modelname1_expid.nc<br> ...</p> <p>Variables per file:</p> <p>scalars_mm_cr_GIS ----------------- Greenland wide numbers </p> <p>oarea - assumed ocean area [m2]<br> rhof - model specific freshwater density [kg m-3]<br> rhoi - model specific ice density [kg m-3]<br> rhow - model specific ocean water density [kg m-3]</p> <p>time - time, typically in days since X<br> iarea - Fraction of grid cell covered by land ice [1]<br> iareagr - Fraction of grid cell covered by grounded ice sheet<br> iareafl - Fraction of grid cell covered by ice sheet flowing over seawater</p> <p>ivol - ice volume [m3]<br> ivolgr - grounded ice volume [m3]<br> ivolfl - floating ice volume [m3]<br> ivaf - ice volume above flotation [m3]</p> <p>lim - ice mass [kg]<br> limgr - grounded ice mass [kg]<br> limfl - floating ice mass [kg]<br> limaf - ice mass above flotation [kg]</p> <p>sle - sea-level equivalent mass [m] !! decreases with mass loss !! <br> smb - spatially integrated surface mass balance anomaly [kg s-1]</p> <p><br> scalars_rm_cr_GIS ----------------- IMBIE2-Rignot basins xx=[no,ne,se,sw,cw,nw]</p> <p>oarea - assumed ocean area [m2]<br> rhof - model specific freshwater density [kg m-3]<br> rhoi - model specific ice density [kg m-3]<br> rhow - model specific ocean water density [kg m-3]</p> <p>time - time, typically in days since X<br> ivaf_xx - ice volume above flotation [m3]<br> smb_xx - spatially integrated surface mass balance anomaly [kg s-1]<br> limaf_xx - ice mass above flotation [kg]<br> sle_xx - sea-level equivalent mass [m] !! decreases with mass loss !! </p> <p><br> scalars_zm_cr_GIS ----------------- IMBIE2-Zwally basins xx=[z11,z12,z13,z14,z21,z22,z31,z32,z33,z41,z42,z43,z50,z61,z62,z71,z72,z81,z82]</p> <p>oarea - assumed ocean area [m2]<br> rhof - model specific freshwater density [kg m-3]<br> rhoi - model specific ice density [kg m-3]<br> rhow - model specific ocean water density [kg m-3]</p> <p>time - time, typically in days since X<br> ivaf_xx - ice volume above flotation [m3]<br> smb_xx - spatially integrated surface mass balance anomaly [kg s-1]<br> limaf_xx - ice mass above flotation [kg]<br> sle_xx - sea-level equivalent mass [m] !! decreases with mass loss !! </p> <p> </p> <p>Data usage notice:<br> If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgements should have language similar to the below.</p> <p>“We thank the Climate and Cryosphere (CliC) effort, which provided support for ISMIP6 through sponsoring of workshops, hosting the ISMIP6 website and wiki, and promoted ISMIP6. We acknowledge the World Climate Research Programme, which, through it's Working Group on Coupled Modelling, coordinated and promoted CMIP5 and CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the CMIP data and providing access, the University at Buffalo for ISMIP6 data distribution and upload, and the multiple funding agencies who support CMIP5 and CMIP6 and ESGF. We thank the ISMIP6 steering committee, the ISMIP6 model selection group and ISMIP6 dataset preparation group for their continuous engagement in defining ISMIP6."</p> <p>You should also refer to and cite the following papers:</p> <p>Heiko Goelzer, Sophie Nowicki, Anthony Payne, Eric Larour, Helene Seroussi, William H. Lipscomb, Jonathan Gregory, Ayako Abe-Ouchi, Andy Shepherd, Erika Simon, Cecile Agosta, Patrick Alexander, Andy Aschwanden, Alice Barthel, Reinhard Calov, Christopher Chambers, Youngmin Choi, Joshua Cuzzone, Christophe Dumas, Tamsin Edwards, Denis Felikson, Xavier Fettweis, Nicholas R. Golledge, Ralf Greve, Angelika Humbert, Philippe Huybrechts, Sebastien Le clec'h, Victoria Lee, Gunter Leguy, Chris Little, Daniel P. Lowry, Mathieu Morlighem, Isabel Nias, Aurelien Quiquet, Martin Rückamp, Nicole-Jeanne Schlegel, Donald Slater, Robin Smith, Fiamma Straneo, Lev Tarasov, Roderik van de Wal, and Michiel van den Broeke: The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6 , The Cryosphere, 2020. doi:10.5194/tc-2019-319</p> <p>Sophie Nowicki, Antony Payne, Heiko Goelzer, Helene Seroussi, William Lipscomb, Ayako Abe-Ouchi, Cecile Agosta, Patrick Alexander, Xylar Asay-Davis, Alice Barthel, Thomas Bracegirdle, Richard Cullather, Denis Felikson, Xavier Fettweis, Jonathan Gregory, Tore Hatterman, Nicolas Jourdain, Peter Kuipers Munneke, Eric Larour, Christopher Little, Mathieu Morlinghem, Isabel Nias, Andrew Shepherd, Erika Simon, Donald Slater, Robin Smith, Fiammetta Straneo, Luke Trusel, Michiel van den Broeke, and Roderik van de Wal: Experimental protocol for sea level projections from ISMIP6 standalone ice sheet models, The Cryosphere, doi:10.5194/tc-2019-322, 2020.</p>
Model outputs to "Langtang Ice Cliff Modelling Study"
<p>This dataset contains all the model results of our study "Supraglacial ice cliffs can substantially increase the mass loss of debris-covered glaciers".</p> <p>The zip-file contains one main folder: "Cliffmodel_Results" (produced using the 3D-backwasting ice cliff model from Buri et al., 2016b, Buri & Pellicciotti, 2018).</p> <p>More raw data upon request.</p> <p> </p> <p><strong>Folder structure of "Langtang_IceCliffModellingStudy.7z":</strong></p> <p>───Cliffmodel_Results<br> ├───LangshishaGlacier<br> │ ├───Output_Cliffs_Langshisha2014_A_3m<br> │ ├───Output_Cliffs_Langshisha2014_B_3m<br> │ ├───Output_Cliffs_Langshisha2014_C_3m<br> │ ├───Output_Cliffs_Langshisha2014_D_3m<br> │ ├───Output_Cliffs_Langshisha2014_E_3m<br> │ ├───Output_Cliffs_Langshisha2014_F_3m<br> │ ├───Output_Cliffs_Langshisha2014_G_3m<br> │ └───Output_Cliffs_Langshisha2014_H_3m<br> ├───LangtangGlacier<br> │ ├───Output_Cliffs_Langtang2014_A_3m<br> │ ├───Output_Cliffs_Langtang2014_B_3m<br> │ ├───Output_Cliffs_Langtang2014_C_3m<br> │ ├───Output_Cliffs_Langtang2014_D_3m<br> │ ├───Output_Cliffs_Langtang2014_E_3m<br> │ ├───Output_Cliffs_Langtang2014_F_3m<br> │ ├───Output_Cliffs_Langtang2014_G_3m<br> │ ├───Output_Cliffs_Langtang2014_H_3m<br> │ ├───Output_Cliffs_Langtang2014_I_3m<br> │ ├───Output_Cliffs_Langtang2014_J_3m<br> │ ├───Output_Cliffs_Langtang2014_K_3m<br> │ ├───Output_Cliffs_Langtang2014_L_3m<br> │ ├───Output_Cliffs_Langtang2014_M_3m<br> │ ├───Output_Cliffs_Langtang2014_N_3m<br> │ ├───Output_Cliffs_Langtang2014_O_3m<br> │ ├───Output_Cliffs_Langtang2014_P_3m<br> │ ├───Output_Cliffs_Langtang2014_Q_3m<br> │ ├───Output_Cliffs_Langtang2014_R_3m<br> │ ├───Output_Cliffs_Langtang2014_S_3m<br> │ └───Output_Cliffs_Langtang2014_T_3m<br> ├───LirungGlacier<br> │ ├───Output_Cliffs_Lirung2014_A_3m<br> │ │ └───intermediateOutput<br> │ └───Output_Cliffs_Lirung2014_B_3m<br> │ └───intermediateOutput<br> └───ShalbachumGlacier<br> ├───Output_Cliffs_Shalbachum2014_A_3m<br> │ └───intermediateOutput<br> ├───Output_Cliffs_Shalbachum2014_B_3m<br> │ └───intermediateOutput<br> ├───Output_Cliffs_Shalbachum2014_C_3m<br> │ └───intermediateOutput<br> ├───Output_Cliffs_Shalbachum2014_D_3m<br> │ └───intermediateOutput<br> └───Output_Cliffs_Shalbachum2014_E_3m<br> └───intermediateOutput<br> </p> <p> </p> <p><strong>File structure within Cliffmodel_Results \ <GLACIERNAME> \ Output_Cliffs_<GLACIERNAME>2014_A_3m:</strong></p> <p><em>DC_Fluxes_ts<TIMESTEP1>to<TIMESTEP2>_Cliffs_<GLACIERNAME>2014_3m_ls.rda</em></p> <p>→List of 10 matrices for each energy balance flux with average diurnal cycle (rows) and cliff (columns) for specific model period </p> <p><em>geom_ts<TIMESTEP1>_Cliffs_Lirung2014_A_3m.txt</em></p> <p>→Characteristics (Elevation,Slope,Aspect,x,y,ID,VsI,VsL,Vd) for each pixel of all cliffs (specific ID) for specific model period </p> <p><em>MODELSETTINGS_GSM_<GLACIERNAME>_<GLACIERSECTOR>_22Aug_Cliffs_<GLACIER>2014.txt</em></p> <p>→Details on all modelsettings</p> <p><em>PC_Melt_ts<TIMESTEP1>to<TIMESTEP2>_Cliffs_<GLACIERNAME>2014_3m.txt</em></p> <p>→Melt values for each cliff (columns) and timestep (rows) for specific model period </p> <p><em>PC_Stats_ts<TIMESTEP1>to<TIMESTEP2>_Cliffs_<GLACIERNAME>2014_3m.txt</em></p> <p>→Summary statistics for each cliff (rows) for specific model period </p> <p><em>perPx_DC_Fluxes_ts<TIMESTEP1>to<TIMESTEP2>_Cliffs_<GLACIERNAME>2014_3m_ls.rda</em></p> <p>→List of list with per pixel diurnal cycle and standard deviation of each energy flux for specific model period </p> <p><em>SD_Fluxes_ts<TIMESTEP1>to<TIMESTEP2>_Cliffs_<GLACIERNAME>2014_3m_ls.rda</em></p> <p>→List of 10 matrices for each energy balance flux with standard deviation for each hour of the day (rows) and cliff (columns) for specific model period </p> <p><em>Stats_MeanFluxes_tts<TIMESTEP1>to<TIMESTEP2>_Cliffs_<GLACIERNAME>2014_3m.txt</em></p> <p>→Mean energy fluxes per timestep (rows) averaged over all ice cliffs</p> <p><em>Stats_NonGriddedData_ts<TIMESTEP1>to<TIMESTEP2>_Cliffs_<GLACIERNAME>2014_3m.txt</em></p> <p>→Statistics of non-distributed parameters averaged over all cliffs per timestep (rows)</p>
Model output for "Geometric amplification and suppression of ice-shelf basal melt in West Antarctica"
<h2><strong>Content description<br></strong></h2> <p>This data repository contains Úa-MITgcm model output in support of the manuscript 'Geometric amplification and suppression of ice-shelf basal melt in West Antarctica' (De Rydt and Naughten, 2024).</p> <p>In the paper, results from 4 experiments with the coupled ice-ocean model Úa-MITgcm are presented. The experiments are <em>ref_melt</em>, <em>hi_melt</em>, <em>av_melt</em> and <em>var_melt</em> (see Table 1 in De Rydt and Naughten, 2024). Original MITgcm and Úa output files at the start, middle and end of each experiment are available in the .zip-files with corresponding experiment name. Data for other timestamps are available upon request from the authors.</p> <p>The structure of each file is as follows:</p> <table> <tbody> <tr> <td>exp_name.zip/</td> <td>yyyymm/</td> <td> MITgcm/</td> <td> bathymetry.shice</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>data.diagnostics</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>output.nc</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>pload.mdjwf</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>shelfice_topo.bin</td> </tr> <tr> <td> </td> <td> </td> <td>Ua/</td> <td>DataForMIT.mat</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>NewMeltrate.mat</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>UaDefaultRun_yyyymm_dd.mat</td> </tr> </tbody> </table> <p><br><code>yyyymm</code> refers to the start month of the coupling timestep. Note that while the initial state of the ice sheet has been optimized to represent its present-day configuration, the future evolution of the ocean and ice sheet is driven by idealized forcings. The results should not be treated as projections, and the timestamps correspond to model time (in years), not real time.</p> <p><strong>MITgcm diagnostics</strong> (temperature, salinity, velocities and ice-shelf freshwater flux) are stored in <code>output.nc</code> and provided as an average value over the 1-month coupling time interval. The <code>bathymetry.shice</code>, <code>data</code>, <code>data.diagnostics</code>, <code>pload.mdjwf </code>and <code>shelfice_topo.bin</code> files are MITgcm input files for ocean bathymetry, general model parameters, diagnostic model parameters, intial pressure loading under the ice shelf and ice-shelf draft for the corresponding coupling timestep, respectively.</p> <p><strong>Úa data</strong> is provided in <code>UaDefaultRun_yyyymm_dd.mat</code> as a snapshot at the end of the coupling interval (<code>yyyymm_dd</code>). The other files, <code>DataForMIT.mat </code>and <code>NewMeltrate.mat</code> are ice-shelf geometry and MITgcm melt rates at the start of the coupling interval, respectively.</p> <h2><strong>Other resources</strong></h2> <p>The Úa-MITgcm source code and custom Úa-MITgcm configuration files for the experiments are available <a href="https://github.com/knaughten/UaMITgcm/tree/archer2">here</a> and <a href="https://github.com/knaughten/UaMITgcm/tree/archer2/example/ASE_999">here.</a> Scripts to analyse and plot the data are available <a href="https://github.com/janderydt/uamitgcm_ASE">here</a>.</p> <h2><strong>References</strong></h2> <p>De Rydt, J. and Naughten, K.: Geometric amplification and suppression of ice-shelf basal melt in West Antarctica, The Cryosphere, 18, 1863–1888, https://doi.org/10.5194/tc-18-1863-2024, 2024.</p>
Disentangling the drivers of future Antarctic ice loss with a historically-calibrated ice-sheet model
<p>=========================================================================<br>Disentangling the drivers of future Antarctic ice loss with a historically-calibrated ice-sheet model<br>=========================================================================</p><p>-----------------------<br>INTRODUCTION<br>-----------------------</p><p>This dataset contains the data and scripts required to reproduce the figures and tables presented in the study:<br>"Disentangling the drivers of future Antarctic ice loss with a historically-calibrated ice-sheet model" in <i>The Cryosphere</i>.</p><p>We perform an ensemble of simulations of the Antarctic ice sheet between 1950 and 3014, forced by a panel of CMIP6 climate models, starting from present-day geometry with the Kori-ULB ice-sheet model v0.9. We calibrate our ensemble in a Bayesian framework to produce observationally-calibrated Antarctic projections used to investigate the future trajectory of the Antarctic ice sheet related to uncertainties in the future balance between sub-shelf melting and ice discharge on the one hand, and the surface mass balance on the other. All simulations are performed at a spatial resolution of 16 km.</p><p>Hindcasts of the behaviour of the AIS over the period 1950-2014 CE are reproduced using changes in oceanic and atmospheric boundary conditions derived from the CMIP5 climate model NorESM1-M. As of the year 2015 CE, climate projections derived from a subset of CMIP6 climate models (MRI-ESM2-0, IPSL-CM6A-LR, CESM2-WACCM and UKESM1-0-LL) are used as forcing until the year 2300 CE. Afterwards, no climate trend is applied. The forcing applied is derived from both the Shared Socioeconomic Pathways (SSP) 5-8.5 and 1-2.6 scenarios. </p><p>------------------------------<br>PROVIDED SCRIPTS: <br>------------------------------</p><p> - 'KoriModelAll.m' and 'KoriInputParams.m': Kori-ULB ice flow model (more info at https://github.com/FrankPat/Kori-ULB)<br> - 'Compute_Bayesian_Weight.m': calculation of the ensemble likelihood weights used in the Bayesian calibration.<br> - 'Plot_parameter_space_distributions.m': calculation and plots of prior and posterior parameter probability distributions.<br> - 'Plot_sea_level_distributions.m': calculation and plots of prior and posterior sea-level distributions.<br> - 'Plot_mass_balance_components_distributions.m': calculation and plots of mass balance components distributions.<br> - 'Plot_mean_thickness_change.m': calculation and plots of calibrated mean thickness change.<br> - 'Plot_ungrounded_probability.m': calculation and plots of the marginal probability of being ungrounded.<br> - 'Plot_SMB_sensitivity.m': Calculation and plots of surface mass balance sensitivity.<br> - 'run_MISMIPplus.m' and 'MISMIPplus.m': run and compare MISMIP+ experiment</p><p>-------------------------<br>PROVIDED DATA: <br>-------------------------</p><ul><li>'LHSensemble.mat': 100x9 matrices containing the values of the 100-member ensemble sampled (using maximin Latin Hypercube) within the parameter space in Table 1.<ul><li>1rst column ((:,1)) contains values of atmospheric present-day climatology (CLIMatm): MARv3.11 (1) - RACMOv2.3p2 (2)</li><li>2nd column ((:,2)) contains values of oceanic present-day climatology (CLIMocn): Jourdain2020 (1) - Schmidtko2014 (2)</li><li>3rd column ((:,3)) contains values of the atmospheric lapse rate (°C/km)</li><li>4th column ((:,4)) contains values of the thickness of the thermally-active layer influencing surface refreezing (m)</li><li>5th column ((:,5)) contains values of the contains values of the Degree day factor for the melting of ice (mm/PDD)</li><li>6th column ((:,6)) contains values of the contains values of the Degree day factor for the melting of snow (mm/PDD)</li><li>7th column ((:,7)) contains values of the applied Sub-shelf melt parameterisation: Quadratic-local Antarctic slope parameterisation (1) - PICO model (2) - Plume model (3) - ISMIP6 Nonlocal quadratic parameterisation (4) - ISMIP6 Nonlocal quadratic parameterisation including dependency on local slope (5)</li><li>8th column ((:,8)) contains values of the effective ice-ocean heat flux: [0.1 x 10^-5 - 10 x 10^-5] m/s for gammaT* in PICO - [1 x 10^-4 - 10 x 10^-4] for Cd^1/2Gamma_TS in Plume - [1 x 10^-4 - 10 x 10^-4] for K in Quadratic-local Antarctic slope parameterisation - [1 x 10^4 - 4 x 10^4] m/yr for gamma0 in ISMIP6 Nonlocal quadratic parameterisation - [1 x 10^6 - 4 x 10^6] m/yr for gamma0 in ISMIP6 Nonlocal quadratic parameterisation with slope dependency</li><li>9th column ((:,9)) contains values of the CMIP6 climate model applied for climate forcing: MRI-ESM2-0 (1) - UKESM1-0-LL (2) - CESM2-WACCM (3) - IPSL-CM6A-LR (4)<br><br>'LHval' and 'LHS' contain the absolute values and the values of the parameters scaled linearly between 0 and 1 (0: minimum value, 1:maximum value) of the nine parameters, respectively.<br> </li></ul></li><li>'HIST_ENSEMBLE_DATA.mat' contains the following variables describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the historical period (1950-2014).<ul><li>H_ensemble: 4D matrix of dimension [X, Y, snap_time, ensemble member] with ice thickness field (in meters) for the 100 ensemble members at different years (snap_time). X and Y represent spatial coordinates on a grid.</li><li>MASK_ensemble: 4D matrix of dimension [X, Y, snap_time, ensemble member] with grounded mask field (in meters) for the 100 ensemble members at different years (snap_time). X and Y represent spatial coordinates on a grid. <br>It distinguishes grounded ice (1: grounded) from ocean or floating ice (0: ocean/floating).</li><li>mbcomp_ensemble: 3D matrix of dimension [time, mbcomp, ensemble member] with timeseries (yearly values at years time) of various mass balance components for the 100 ensemble members (in gigatons per year, Gt/yr). <br>The components mbcomp include the following ice-sheet aggregated and grounded ice sheet components:<br> (1) Ice-sheet aggregated surface mass balance<br> (2) Ice-sheet aggregated accumulation<br> (3) Ice-sheet aggregated surface melt<br> (4) Ice-sheet aggregated runoff<br> (5) Ice-sheet aggregated rain<br> (6) sub-shelf melt<br> (7) dynamic ice loss (calving)<br> (8) surface mass balance over the grounded ice sheet<br> (9) accumulation over the grounded ice sheet<br> (10) surface melt over the grounded ice sheet<br> (11) runoff over the grounded ice sheet<br> (12) rain over the grounded ice sheet <br> (13) Net mass balance (rate of HAF change)</li><li>SLC_ensemble: 2D matrix of dimension [ensemble member, time] with timeseries (yearly values at years time) of the ice-sheet sea-level contribution (in m) <br> </li></ul></li><li>'HIST_ENSEMBLE_DATA_NO_ELEVATION_FEEDBACK.mat': same as 'HIST_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the historical period (1950-2014) when neglecting the melt-elevation feedback.<br> </li><li>'HIST_ENSEMBLE_DATA_HYDROFRAC.mat': same as 'HIST_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the historical period (1950-2014) when including surface melt-driven hydrofracturing of the ice shelves (estimated following Pollard et al., 2015).<br> </li><li>'CONTROL_ENSEMBLE_DATA.mat': contains the variables H_ensemble, MASK_ensemble, mbcomp_ensemble and SLC_ensemble (as in 'HIST_ENSEMBLE_DATA') describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 when considering constant present-day conditions as of the year 2015.<br> </li><li>'SSP126_ENSEMBLE_DATA.mat': contains the variables H_ensemble, MASK_ensemble, mbcomp_ensemble and SLC_ensemble (as in 'HIST_ENSEMBLE_DATA') describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under a SSP1-2.6 scenario.<br> </li><li>'SSP585_ENSEMBLE_DATA.mat': contains the variables H_ensemble, MASK_ensemble, mbcomp_ensemble and SLC_ensemble (as in 'HIST_ENSEMBLE_DATA') describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under a SSP5-8.5 scenario. It also contains the variable Runoff_ensemble, a 4D matrix of dimension [X, Y, snap_time, ensemble member] with surface runoff field (in m/yr i.e.) for the 100 ensemble members at different years (snap_time). X and Y represent spatial coordinates on a grid, as used in Fig. 7.<br> </li><li>'SSP585_ENSEMBLE_DATA_NO_ELEVATION_FEEDBACK.mat': same as 'SSP585_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when neglecting the melt-elevation feedback.<br> </li><li>'SSP585_ENSEMBLE_DATA_HYDROFRAC.mat': same as 'SSP585_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when including surface melt-driven hydrofracturing of the ice shelves (estimated following Pollard et al., 2015).<br> </li><li>'SSP585_ENSEMBLE_DATA_ATM_ONLY.mat': same as 'SSP585_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when considering constant oceanic present-day conditions as of the year 2015.<br> </li><li>'SSP585_ENSEMBLE_DATA_NO_ELEVATION_FEEDBACK_ATM_ONLY.mat': same as 'SSP585_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when neglecting the melt-elevation feedback and considering constant oceanic present-day conditions as of the year 2015.<br> </li><li>'SSP585_ENSEMBLE_DATA_OCEAN_ONLY.mat': same as 'SSP585_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario considering constant atmospheric present-day conditions as of the year 2015.<br> </li><li>'HIST_ENSEMBLE_DATA_BASIN.mat' contains the following variables describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the historical period (1950-2014) integrated over 27 drainage basins (http://imbie.org/imbie-2016/drainage-basins/).<ul><li>SLC_ensemble_basin: 3D matrix of dimension [basin, ensemble member, time] with timeseries (yearly values at years time) of the ice-sheet sea-level contribution (in m) by basin</li><li>mbcomp_ensemble_basin: 4D matrix of dimension [basin, time, mbcomp, ensemble member] with timeseries (yearly values at years time) of various mass balance components for the 100 ensemble members (in gigatons per year, Gt/yr) by basin. The components mbcomp include the same ice-sheet aggregated and grounded ice-sheet components as in 'HIST_ENSEMBLE_DATA.mat'.<br> </li></ul></li><li>'HIST_ENSEMBLE_DATA_BASIN_NO_ELEVATION_DATA.mat': same as 'HIST_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the historical period (1950-2014) when neglecting the melt-elevation feedback.<br> </li><li>'HIST_ENSEMBLE_DATA_BASIN_HYDROFRAC.mat': same as 'HIST_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the historical period (1950-2014) when including surface melt-driven hydrofracturing of the ice shelves (estimated following Pollard et al., 2015).<br> </li><li>'SSP126_ENSEMBLE_DATA_BASIN.mat': contains the variables SLC_ensemble_basin and mbcomp_ensemble_basin (as in 'HIST_ENSEMBLE_DATA°BASIN') describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under a SSP1-2.6 scenario.<br> </li><li>'SSP585_ENSEMBLE_DATA_BASIN.mat': contains the variables SLC_ensemble_basin and mbcomp_ensemble_basin (as in 'HIST_ENSEMBLE_DATA') describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under a SSP5-8.5 scenario.<br> </li><li>'SSP585_ENSEMBLE_DATA_BASIN_NO_ELEVATION_FEEDBACK.mat': same as 'SSP585_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under a SSP5-8.5 scenario when neglecting the melt-elevation feedback.<br> </li><li>'SSP585_ENSEMBLE_DATA_BASIN_HYDROFRAC.mat': same as 'SSP585_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when including surface melt-driven hydrofracturing of the ice shelves (estimated following Pollard et al., 2015).<br> </li><li>'SSP585_ENSEMBLE_DATA_BASIN_ATM_ONLY.mat': same as 'SSP585_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when considering constant oceanic present-day conditions as of the year 2015.<br> </li><li>'SSP585_ENSEMBLE_DATA_BASIN_NO_ELEVATION_FEEDBACK_ATM_ONLY.mat': same as 'SSP585_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when neglecting the melt-elevation feedback and considering constant oceanic present-day conditions as of the year 2015.<br> </li><li>'SSP585_ENSEMBLE_DATA_BASIN_OCEAN_ONLY.mat': same as 'SSP585_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario considering constant atmospheric present-day conditions as of the year 2015.<br> </li><li>'GCM_SSPXXX_mean_aTs.mat': Timeseries of the regionally-averaged (between 90–60°S) annual near-surface (2-m) air temperature anomaly (°C) projected by the climate model 'GCM' from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) between 2015 and 2300 under the SSPXXX emission scenario, compared to the 1995-2014 reference period. SSPXXX may be 'SSP126' and 'SSP585' and GCM may be 'MRI-ESM2-0', 'CESM2-WACCM', 'IPSL-CM6A-LR', or 'UKESM1-0-LL'.<br> </li><li>'CALIBRATION DATA.mat': values ('val'), uncertainty ('sigma'), beginning ('year1') and end ('year2') of the average time period of the 12 regionally and temporally aggregated IMBIE data used in the Bayesian calibration (Table 2 in this study, coming from Table 2 from Otosaka et al., 2023)<br> </li><li>'INIT_MAR_aNorESM1-M_1950.mat' and 'INIT_RACMO_aNorESM1-M_1950.mat': Ice-sheet initial states at year 1950 obtained with the 1995-2014 atmospheric climatology from MARv3.11(Kittel eta l.,2021) or RACMOv2.3p2 (van Wessem et al., 2018), respectively, adjusted with a 1945-1955 anomaly from NorESM1-M. H is the ice thickness (in meters), B is the bedrock topography (in meters), and u is the surface velocity (in m/yr). These files were provided as input files to Kori-ULB to produce the projections. More info on the input files and their variables can be found here: https://github.com/FrankPat/Kori-ULB.</li></ul><p>----------------------------------------------------------<br>MATLAB FUNCTIONS USED IN SCRIPTS: <br>----------------------------------------------------------</p><p>- imagescn: imagesc with transparent NaNs, by Chad Greene (2023), downloaded from MATLAB Central File Exchange (https://www.mathworks.com/matlabcentral/fileexchange/61293-imagescn), <br>- brewermap: provides all ColorBrewer colorschemes for MATLAB, by Stephen23. Downloaded from https://github.com/DrosteEffect/BrewerMap.<br>- crameri: returns perceptually-uniform scientific colormaps created by Fabio Crameri (requires CrameriColourMaps8.0.mat)</p><p>----------------------------------------------------------------------------------<br>EXTERNAL DATA NOT CONTAINED IN THIS REPOSITORY:<br>----------------------------------------------------------------------------------</p><p>- BedMachine data used for the present-day grounding lines in Figures 2 and 7: It is BedMachine v2 (Morlighem et al., 2020) and can be found here: https://nsidc.org/data/nsidc-0756/versions/2.<br>- The delineation of the 27 Zwally Basins used to identify and separate the West and East Antarctic ice sheets and the Antarctic Peninsula can be found at http://imbie.org/imbie-2016/drainage-basins/<br>- Outputs from MAR(CNRM-CM6-1) and MAR(CESM2) used in Figures 7 and S10. The data can be downloaded at 10.5281/zenodo.4529004 and 10.5281/zenodo.4529002, respectively. It was then interpolated to the 16-km grid used by Kori-ULB.<br>- CESM2-WACCM outputs used in Figure 7 were downloaded from the CMIP6 search interface (https://esgf-node.llnl.gov/search/cmip6/) and interpolated to the 16-km grid used by Kori-ULB.<br>- The CMIP6 forcing data used in this study (and plotted in Figures S6 and S7) are accessible through the CMIP6 search interface (https://esgf-node.llnl.gov/search/cmip6/). They have been interpolated to the interpolated to the 16-km grid used by Kori-ULB.</p><p>---------------------<br>REFERENCES: <br>---------------------</p><p>Kittel, C., Amory, C., Agosta, C., Jourdain, N. C., Hofer, S., Delhasse, A., Doutreloup, S., Huot, P.-V., Lang, C., Fichefet, T., and Fettweis, X.: Diverging future surface mass balance between the Antarctic ice shelves and grounded ice sheet, The Cryosphere, 15, 1215–1236, https://doi.org/10.5194/tc-15-1215-2021, 2021.</p><p>Morlighem, M., Rignot, E., Binder, T. et al. Deep glacial troughs and stabilizing ridges unveiled beneath the margins of the Antarctic ice sheet. Nat. Geosci. 13, 132–137 (2020). https://doi.org/10.1038/s41561-019-0510-8</p><p>Otosaka, I. N., Shepherd, A., Ivins, E. R., Schlegel, N.-J., Amory, C., van den Broeke, M. R., Horwath, M., Joughin, I., King, M. D., Krinner, G., Nowicki, S., Payne, A. J., Rignot, E., Scambos, T., Simon, K. M., Smith, B. E., Sørensen, L. S., Velicogna, I., Whitehouse, P. L., A, G., Agosta, C., Ahlstrøm, A. P., Blazquez, A., Colgan, W., Engdahl, M. E., Fettweis, X., Forsberg, R., Gallée, H., Gardner, A., Gilbert, L., Gourmelen, N., Groh, A., Gunter, B. C., Harig, C., Helm, V., Khan, S. A., Kittel, C., Konrad, H., Langen, P. L., Lecavalier, B. S., Liang, C.-C., Loomis, B. D., McMillan, M., Melini, D., Mernild, S. H., Mottram, R., Mouginot, J., Nilsson, J., Noël, B., Pattle, M. E., Peltier, W. R., Pie, N., Roca, M., Sasgen, I., Save, H. V., Seo, K.-W., Scheuchl, B., Schrama, E. J. O., Schröder, L., Simonsen, S. B., Slater, T., Spada, G., Sutterley, T. C., Vishwakarma, B. D., van Wessem, J. M., Wiese, D., van der Wal, W., and Wouters, B.: Mass balance of the Greenland and Antarctic ice sheets from 1992 to 2020, Earth Syst. Sci. Data, 15, 1597–1616, https://doi.org/10.5194/essd-15-1597-2023, 2023.</p><p>Pollard, D., DeConto, R. M., and Alley, R. B.: Potential Antarctic Ice Sheet retreat driven by hydrofracturing and ice cliff failure, Earth and Planetary Science Letters, 412, 112–121, https://doi.org/10.1016/j.epsl.2014.12.035, 2015.<br> <br>van Wessem, J. M., van de Berg, W. J., Noël, B. P. Y., van Meijgaard, E., Amory, C., Birnbaum, G., Jakobs, C. L., Krüger, K., Lenaerts, J. T. M., Lhermitte, S., Ligtenberg, S. R. M., Medley, B., Reijmer, C. H., van Tricht, K., Trusel, L. D., van Ulft, L. H., Wouters, B., Wuite, J., and van den Broeke, M. R.: Modelling the climate and surface mass balance of polar ice sheets using RACMO2 – Part 2: Antarctica (1979–2016), The Cryosphere, 12, 1479–1498, https://doi.org/10.5194/tc-12-1479-2018, 2018.</p>
Example of boundary files used with the Los Alamos Sea Ice Model
<p>The zip archive available here contains two example files of sea ice time-varying boundaries for usage with the Los Alamos Sea Ice Model.</p>
multi-model metrics of daily sea-ice concentrations from CMIP6 models
<p>Multi-model metrics of daily mean sea-ice concentrations from 9 CMIP6 models:</p> <ul> <li>ACCESS-CM2</li> <li>CanESM5</li> <li>CNRM-ESM2.1</li> <li>EC-Earth3</li> <li>EC-Earth3-Veg</li> <li>IPSL-CM6A-LR</li> <li>MIROC6</li> <li>MRI-ESM2</li> <li>NorESM2-LM</li> </ul> <p> </p> <p>Metrics are mean, median, standard deviation, minimum and maximum sea-ice concentrations.</p>
Linear Kinematic Feature detected and tracked in sea-ice deformation simulationed by all models participating in the Sea Ice Rheology Experiment and from RGPS
<p>Linear Kinematic Features (LKFs) detected and tracked in sea-ice deformation fields simulated by sea-ice models participating in the Sea Ice Rheology Experiment (SIREx), a model intercomparison project of the Forum of Arctic Modeling and Observational Synthesis (FAMOS). These data are the basis of the feature-based evaluation of sea-ice deformation in Hutter et al., Sea Ice Rheology Experiment (SIREx), Part II: Evaluating linear kinematic features in high-resolution sea-ice simulations, Journal of Geophysical Research: Oceans (2022). This paper also provides further details on the parameters of the LKF extraction.</p> <p>The LKF data sets in this archive are stored in a csv-files for each year (1997 and/or 2008), which use semi-colons as delimiters. Each row corresponds to a pixel that was identified as LKF and the following information for this pixels is stored: Start Year, Start Month, Start Day, End Year, End Month, End Day, LKF No., Parent LKF No., lon, lat, ind_x, ind_y, divergence rate, shear rate. All pixels belonging to the same LKF have the same LKF number. Tracked LKFs are linked by the parent LKF number, where "0" denotes LKFs that newly formed. Detailed information on all variables is provided in the additional notes.</p>
MPAS-Albany Land Ice model simulations of Humboldt Glacier, North Greenland, from 2007–2100
<p>This dataset contains model input and output in netCDF format, model code, and analysis scripts for simulations of Humboldt Glacier, North Greenland, through the 21st century (Hillebrand et al., 2022) using the MPAS-Albany Land Ice model (Hoffman et al., 2018). We calibrate parameters controlling basal traction, iceberg calving, and submarine melt against observations from 2007–2017. We then explore the glacier’s sensitivity to climate forcing, iceberg calving, and basal conditions in an ensemble of 24 simulations from 2007–2100. We further explore its sensitivity to uncertainties in ice-shelf melt, bed topography, and calving rate limits in targeted sensitivity experiments. Input files include surface mass balance, ocean thermal forcing, and subglacial runoff forcings provided by ISMIP6 (Nowicki et al., 2020; Slater et al., 2020). Output includes basal traction optimization solutions for the year 2007; annual 2D ice speed, basal shear and driving stresses, and geometry; annual 3D temperature; and grounded, floating, and global mass budgets at every timestep.</p> <p>References:</p> <p>Hillebrand, T. R., Hoffman, M. J., Perego, M., Price, S. F., and Howat, I. M. (2022): The contribution of Humboldt Glacier, northern Greenland, to sea-level rise through 2100 constrained by recent observations of speedup and retreat, The Cryosphere, 16, 4679–4700, <a href="https://doi.org/10.5194/tc-16-4679-2022">https://doi.org/10.5194/tc-16-4679-2022</a>.</p> <p>Hoffman, M. J., Perego, M., Price, S. F., Lipscomb, W. H., Zhang, T., Jacobsen, D., et al. (2018). MPAS-Albany Land Ice (MALI): a variable-resolution ice sheet model for Earth system modeling using Voronoi grids. <em>Geoscientific Model Development</em>, <em>11</em>(9), 3747–3780.<a href="https://doi.org/10.5194/gmd-11-3747-2018"> https://doi.org/10.5194/gmd-11-3747-2018</a></p> <p>Nowicki, S., Goelzer, H., Seroussi, H., Payne, A. J., Lipscomb, W. H., Abe-Ouchi, A., et al. (2020). Experimental protocol for sea level projections from ISMIP6 stand-alone ice sheet models. <em>The Cryosphere</em>, <em>14</em>(7), 2331–2368.<a href="https://doi.org/10.5194/tc-14-2331-2020"> https://doi.org/10.5194/tc-14-2331-2020</a></p> <p>Slater, D. A., Felikson, D., Straneo, F., Goelzer, H., Little, C. M., Morlighem, M., et al. (2020). Twenty-first century ocean forcing of the Greenland ice sheet for modelling of sea level contribution. <em>The Cryosphere</em>, <em>14</em>(3), 985–1008.<a href="https://doi.org/10.5194/tc-14-985-2020"> https://doi.org/10.5194/tc-14-985-2020</a></p>
Ice core and model data for Moseid et al. 2022
<p>These datasets are used in the publication "Using ice cores to evaluate CMIP6 aerosol concentrations over the historical era" with the authors Kine Onsum Moseid, Michael Schulz, Anja Eichler, Margit<br> Schwikowski, Joseph R. McConnell, Dirk Olivi ́e, Alison S. Criscitiello, Karl J. Kreutz, and Michel Legrand.</p> <p>The paper is currently in review when this data is published.</p> <p>The excel sheet dataset contains sulfate and black carbon records from 15 ice cores as presented in the paper. </p> <p>One zip file contain the part of the data from NorESM2-LM experiments as described in the paper. Another dataset will be published to compliment this dataset. </p>
Data accompanying "A new brittle rheology and numerical framework for large-scale sea-ice models"
<p>Data accompanying "A new brittle rheology and numerical framework for<br> large-scale sea-ice models" by E. Olason et al, accepted for publication in<br> Journal of Advances in Modelling Earth Systems (2022).</p> <p>Files:<br> * CS2SMOS.tar.bz2: Contains Cryosat2/SMOS data, post-precessed and used to<br> produce figures comparing modelled thickness to observations.<br> * deformation_maps_demo.ipynb: An example jupyter notebook to read pairs.npz<br> * OlasonEtAl_BBM.tar.bz2: Thickness fields from the MEB run used to produce<br> figure 1 (netCDF).<br> * OlasonEtAl_MEB.tar.bz2: Thickness fields from the BBM run used to produce<br> figure 8 (netCDF).<br> * OlasonEtAl_mEVP.tar.bz2: Thickness fields from the mEVP run used to produce<br> figure 8 (netCDF).<br> * pairs.npz: Displacement pairs derived from the model's Lagrangian mesh used<br> to produce figures 3, 4, and 5 (numpy data file).<br> * Winter2006_7_BBM.nc.bz2: Thickness, concentration, and velocity fields from<br> the BBM run for the winter 2006-7 widely used in the paper (netCDF).<br> * Winter2006_7_mEVP.nc.bz2: Thickness, concentration, and velocity fields from<br> the mEVP run for the winter 2006-7 used for comparison in the paper<br> (netCDF).</p> <p> </p>
A simple model for daily basin-wide thermodynamic sea ice thickness growth retrieval: Data
<p>Data for:</p> <p>Anheuser, J., Liu, Y., and Key, J.: A daily basin-wide sea ice thickness retrieval methodology: Stefan's Law Integrated Conducted Energy (SLICE), The Cryosphere Discuss. [preprint], <a href="https://doi.org/10.5194/tc-2021-333">https://doi.org/10.5194/tc-2021-333</a>, in review, 2021.</p> <p> </p> <p>Scripts for producing data and figures can be found at:</p> <p>https://doi.org/10.5281/zenodo.6561431</p> <p> </p> <p> </p>
Model output data for Smith et al., "Effects of increasing the category resolution of the sea ice thickness distribution in a coupled climate model on Arctic and Antarctic sea ice"
<p>Model output data for Smith et al., "Effects of increasing the category resolution of the sea ice thickness distribution in a coupled climate model on Arctic and Antarctic sea ice", in review in Journal of Geophysical Research-Oceans, 2022. Details on CESM model settings and run setups can be found within the manuscript. </p>
CMIP6 model sea-ice concentration data
<p>Sea-ice concentration data from Climate System Models that participated in the sixth Coupled Model Intercomparison Project (CMIP6). <br> <br> Historical (1850-2014) and future projections (2015-2100) of sea-ice concentration were downloaded on the native model grid of 29 models, after which this data was regridded to a regular 1x1 degree latitude by longitude grid for multi-model comparison and calculation of statistics (mean, median, etc.).</p> <p>Future projections include the SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios.</p> <p>Models included in the scenarios are shown in the excel spreadsheet.</p> <p> </p>
Output from model simulations of a turbulent ice shelf-ocean boundary current
<p><em>This dataset contains output from 2 LES and 19 MITgcm simulations of an idealised configuration of the ice shelf-ocean boundary current. Core fields are provided such as velocity and density and these are given as ice-plane averaged. The output was generated to make an inter-model comparison of the representation of dynamical processes at the ice shelf-ocean boundary. The two LES configurations differ in their sub-grid-scale parameterisation. The MITgcm simulations investigate the sensitivity to various parameter changes including: resolution, diffusivity coefficients, advection scheme and melt-parameterisation. All configurations are outlined in Patmore et al. (2022).</em></p>
Deep potential model and datasets used in the publication "Plastic Deformation of Superionic Water Ices"
<p>The file training_data_for_deepmd.zip contains the training set and deep potential model for superionic ices used in the publication "Plastic Deformation of Superionic Water Ices". The deep potential (DP) neural-network model was generated using the DeepMD-kit package. The training set was generated using AIMD/CP2K calculations for both undeformed and deformed states of the fcc superionic phase at temperatures of 2500, 3000, 3500 and 4000 K. </p> <p> </p> <p>The file cp2k_shear.zip contains instructions to modify the CP2K code to calculate the shear vs strain deformation in continuous mode. </p> <p> </p> <p>The file lammps_dislocation.zip contains the files needed to run the dislocation mobility calculation. </p> <p> </p>
Ngozumpa Glacier Gokyo ice cliffs photographic surface model
<p>Data files of a photographic surface model of three ice cliffs exposed on the surface of the Ngozumpa Glacier ( 27°57′N, 85°42′E), Nepal, near the village of Gokyo.</p> <p>Details of how the data were produced:</p> <ul> <li>1160 full resolution JPEG images were used to make the difgital surface model (DSM)</li> <li>Photos were taken using Nikon D5000 with 100mm Tamron macro lens on 4th April 2016</li> <li>7 ground control points (GCPs) within the captured scene were used to scale and georeference the DSM</li> <li>GCPs were measured using a Trimble XH6000 with Tornado antenna as rover and a Trimble Geo7X with Zephyr antenna as a local base station</li> <li>Agisoft Photoscan (v1.26) was used to make the DSM using a SfM-MVS workflow</li> <li>Point pairs appearing in only one image pair and havoing a reprojection error grreater than 0.5 pixels were removed</li> <li>Model optimization was done using default parameters of f, b1, b2, cx, cy, k1-4, p1 and p2 and, as the lens distortion plot showed no extreme skewing or distortion, these optimization settings were accepted.</li> <li>The dense point cloud was generated using the ‘High’ quality setting with ‘Aggressive’ point filtering.</li> <li>The dense point cloud was then used directly to generate the DSM and orthomosaic at resolutions of 0.046 and 0.023 m respectively.</li> </ul> <p>Description of files:</p> <ul> <li><strong>GokyoIceCliffs_POINTCLOUD.txt:</strong> dense point cloud including the whole scene covered by the photographs. Format: x,y,z, text file, WGS 84 / UTM zone 45N.</li> <li><strong>GokyoIceCliffs_DSM_0.04.tif:</strong> 0.046 m resolution digital terrain model derived from the dense point cloud covering the area of interest including the 3 sampled ice cliffs. Format: TIFF WGS 84 / UTM zone 45N.</li> <li><strong>GokyoIceCliffs_ORTHO_0.04.tif: </strong>0.023 m resolution orthophoto covering the area of interest including the 3 sampled ice cliffs. Format: TIFF WGS 84 / UTM zone 45N.</li> </ul> <p> </p>
Model outputs from "Holocene thinning in central Greenland controlled by the Northeast Greenland Ice Stream"
<p>Two-dimensional outputs of the best Yelmo model simulation within the ensemble presented in the manuscript by Tabone et al., 2024, titled "Holocene Thinning in Central Greenland Controlled by the Northeast Greenland Ice Stream," published in Nature Communications. For more information, please contact the authors and read the paper. </p>
DALROMS-NWA12 v1.0, a coupled circulation-sea ice-biogeochemistry model for the northwest North Atlantic: input files (2 of 3)
<p>DALROMS-NWA12 v1.0 is a coupled circulation-sea ice-biogeochemistry modelling system based on ROMS, CICE, and MCT. The model domain covers the North Atlantic Ocean from ~81 deg W to ~39 deg W and ~33.5 deg N to 76 deg N. This record includes the files necessary for nudging the simulated temperature and salinity towards Copernicus GLORYS12V1 reanalysis values in a simulation from 1 September to 31 December 2013.</p> <p>The remaining input files for this period are available at <a href="https://doi.org/10.5281/zenodo.12752190" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12752190</a> and <a href="https://doi.org/10.5281/zenodo.12735153" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12735153</a>.</p> <p>Model codes, scripts for compiling the model, and sample CPP header files and runtime parameter files (namelists) for phyiscs-only simulations are available at <a href="https://doi.org/10.5281/zenodo.12752091" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12752091</a>. CPP header and runtime parameter files for the biogeochemistry module are available upon request.</p> <p>Sample output files (from the physics and biogeochemistry modules) are available at <a href="https://doi.org/10.5281/zenodo.12744506" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12744506</a> and <a href="https://doi.org/10.5281/zenodo.12746262" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12746262</a>.</p>
DALROMS-NWA12 v1.0, a coupled circulation-sea ice-biogeochemistry model for the northwest North Atlantic: input files (1 of 3)
<p>DALROMS-NWA12 v1.0 is a coupled circulation-sea ice-biogeochemistry modelling system based on ROMS, CICE, and MCT. The model domain covers the North Atlantic Ocean from ~81 deg W to ~39 deg W and ~33.5 deg N to 76 deg N. This record includes most of the input files necessary for a physics-only simulation from 1 September to 31 December 2013. The remaining input files for this period, which should be placed in the directory <code>sponge/</code> within the directory tree contained in this record, are available at <a href="https://doi.org/10.5281/zenodo.12734049" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12734049</a> and <a href="https://doi.org/10.5281/zenodo.12735153" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12735153</a>. Input files for the biogeochemistry module are available upon request.</p> <p>Model codes, scripts for compiling the model, and sample CPP header files and runtime parameter files (namelists) for physics-only simulations are available at <a href="https://doi.org/10.5281/zenodo.12752091" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12752091</a>. CPP header and runtime parameter files for the biogeochemistry module are available upon request.</p> <p>Sample output files (from the physics and biogeochemistry modules) are available at <a href="https://doi.org/10.5281/zenodo.12744506" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12744506</a> and <a href="https://doi.org/10.5281/zenodo.12746262" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12746262</a>.</p>
ScienceDex guides
Understand access before you commit
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.