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14 results for “Ice shelves”

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zenodo48/100

Large-eddy simulation investigating the role of double-diffusive convection in basal melting of Antarctic ice shelves: model output

Model output used in the publication: M. G. Rosevear, B. Gayen, B. K. Galton-Fenzi, The role of double-diffusive convection in the basal melting of Antarctic ice shelves. Proc. Natl. Acad. Sci. (2021) https://doi.org/10.1073/pnas.207541118 See README.md for a description of the data.

opencc-by-4.0Nov 2020View details →

zenodo48/100

Ensemble of ice shelf basal melt rates and ocean properties for tipped-over continental shelves

Summary: This dataset contains the reference and tipped states from several model configurations developed at the <a href="https://www.awi.de/en/">Alfred Wegener Institute (AWI)</a> and the <a href="https://www.ige-grenoble.fr/?lang=en">Institut des Géosciences de l’Environnement (IGE)</a>. They were gathered here in the context of the <a href="https://www.tipaccs.eu">TiPACCs European project</a> and constitute a useful ensemble of reference and tipped ocean–ice-shelf simulations that can be used to feed ice-sheet simulations or to train melt parameterizations. The simulations produced by AWI are based on the <a href="https://fesom.de">FESOM</a> global ocean–sea-ice model using either Z- or Sigma- coordinates and all show a cold-to-warm tipping point for Filchner-Ronne Ice Shelf. The two sets of simulations produced by IGE are based on the <a href="https://www.nemo-ocean.eu">NEMO</a> ocean–sea-ice model. They include a global configuration showing a cold-to-warm tipping point for Ross Ice Shelf, and regional Amundsen Sea configuration showing a warm-to-warmer transition (likely not a proper tipping point).  The files include 3-dimensional and sea-floor ocean temperatures and salinities, ice-shelf melt rates, as well as topographic and grid data. All variables are interpolated onto the common 8km stereographic grid that was used to provide ocean forcing in ISMIP6 (<a href="https://doi.org/10.5194/tc-14-2331-2020">Nowicki et al. 2020</a>). We provide the reference state and the anomaly, so that the tipped state is: <ul> <li>Tipped = Reference + Anomaly</li> </ul> To have an overview of the reference and tipped states, have a look at these figures: <ul> <li>figure_ref_and_anomalies_1.pdf</li> <li> figure_ref_and_anomalies_2.pdf </li> <li> figure_seafloor_temp_zooms.pdf </li> </ul>   _______________________________________________ Detailed Data Description:   <ul> <li>reference_high_FESOM_sigma_AWI_TiPACCs.nc <ul> <li>contact: Ralph Timmermann <a href="mailto:ralph.timmermann@awi.de">ralph.timmermann@awi.de</a>, Verena Haid <a href="mailto:verena.haid@awi.de">verena.haid@awi.de</a></li> <li>model: FESOM1.4, sigma-coordinates (global with refined grid around Antarctica)</li> <li>atmospheric forcing: HadCM3 20C</li> <li>provided average: 1990-1999 (10-year mean)</li> <li>more: <a href="https://doi.org/10.1007/s10236-013-0642-0">Timmermann and Hellmer (2013)</a></li> </ul> </li> </ul>   <ul> <li>reference_low_FESOM_sigma_AWI_TiPACCs.nc <ul> <li>contact: Ralph Timmermann <a href="mailto:ralph.timmermann@awi.de">ralph.timmermann@awi.de</a>, Verena Haid <a href="mailto:verena.haid@awi.de">verena.haid@awi.de</a></li> <li>model: FESOM1.4, sigma-coordinates (global with refined grid around Antarctica)</li> <li>atmospheric forcing: HadCM3 20C</li> <li>provided average: 1990-1999 (10-year mean)</li> <li>more: <a href="https://doi.org/10.5194/os-13-765-2017">Timmermann and Goeller (2017)</a></li> </ul> </li> </ul>   <ul> <li>reference_FESOM_z_AWI_TiPACCs.nc <ul> <li>contact: Verena Haid <a href="mailto:verena.haid@awi.de">verena.haid@awi.de</a></li> <li>model: FESOM1.4, Z-coordinates (global with refined grid around Antarctica)</li> <li>atmospheric forcing: ERA Interim</li> <li>provided average: 2008-2017 (10-year mean), i.e. model year 30-39</li> <li>more: same mesh as <a href="https://doi.org/10.5194/tc-13-2317-2019">Gürses et al. (2019)</a></li> </ul> </li> </ul>   <ul> <li>reference_NEMO4_eORCA025.L121_IGE_TiPACCs.nc <ul> <li>contact: Pierre Mathiot <a href="mailto:pierre.mathiot@univ-grenoble-alpes.fr">pierre.mathiot@univ-grenoble-alpes.fr</a></li> <li>model: NEMO-4.0, eORCA025.L121 (Global, 1/4°, 121 vertical levels)</li> <li>atmospheric forcing: JRA55do</li> <li>provided average: 2nd cycle of 1989-1998 (10-year mean); we first run 1979-2018, and we redo 1979-1998 starting from the 2018 state.</li> <li>more: <a href="https://pmathiot.github.io/NEMOCFG/docs/build/html/simu_eORCA025_OPM021.html">https://pmathiot.github.io/NEMOCFG/docs/build/html/simu_eORCA025_OPM021.html</a></li> </ul> </li> </ul>   <ul> <li>reference_NEMO3_AMUXL12.L75_IGE_TiPACCs.nc <ul> <li>contact: Nicolas Jourdain <a href="mailto:nicolas.jourdain@univ-grenoble-alpes.fr">nicolas.jourdain@univ-grenoble-alpes.fr</a></li> <li>model: NEMO-3.6, AMUXL12.L75 (Amundsen, 1/12°, 75 vertical levels)</li> <li>atmospheric forcing: MAR (<a href="https://doi.org/10.5194/tc-14-229-2020">Donat-Magnin et al. 2020</a>)</li> <li>provided average: 1989-2009 (21-year mean)</li> <li>more: similar model set-up as <a href="https://doi.org/10.1016/j.ocemod.2018.11.001">Jourdain et al. (2019)</a>.</li> </ul> </li> </ul>   <ul> <li>anomaly_high_FESOM_sigma_AWI_TiPACCs.nc <ul> <li>continuation of reference_high_FESOM_sigma_AWI_TiPACCs.nc</li> <li>forced with HadCM3 A1B</li> <li>provided average: 2190-2199 (10-year mean)</li> </ul> </li> </ul>   <ul> <li>anomaly_low_FESOM_sigma_AWI_TiPACCs.nc <ul> <li>continuation of reference_low_FESOM_sigma_AWI_TiPACCs.nc</li> <li>forced with HadCM3 A1B</li> <li>provided average: 2190-2199 (10-year mean)</li> </ul> </li> </ul>   <ul> <li>anomaly_high_FESOM_z_AWI_TiPACCs.nc <ul> <li>same model set-up as reference_FESOM_z_AWI_TiPACCs.nc</li> <li>atmospheric forcing south of 60°S HadCM3 A1B starting 2050, otherwise ERA Interim starting 1979</li> <li>provided average: model year 69-78 (10-year mean), i.e. 2008-2017 of 2nd 39yr-cycle</li> </ul> </li> </ul>   <ul> <li>anomaly_medium_FESOM_z_AWI_TiPACCs.nc <ul> <li>same model set-up as reference_FESOM_z_AWI_TiPACCs.nc</li> <li>atmospheric forcing: ERA Interim modified with a strong imprint of the seasonal cycle of HadCM3 A1B 2070-2089</li> <li>provided average: model year 69-78 (10-year mean), i.e. 2008-2017 of 2nd 39yr-cycle</li> </ul> </li> </ul>   <ul> <li>anomaly_low_FESOM_z_AWI_TiPACCs.nc <ul> <li>same model set-up as reference_FESOM_z_AWI_TiPACCs.nc</li> <li>atmospheric forcing: manipulated ERA Interim with prolongued summer and shorter, milder winter south of 50°S, additional modification of winds in Weddell Sea region</li> <li>provided average: model year 108-117 (10-year mean), i.e. 2008-2017 of 3rd 39yr-cycle</li> </ul> </li> </ul>   <ul> <li>anomaly_NEMO4_eORCA025.L121_IGE_TiPACCs.nc <ul> <li>similar to reference_NEMO4_eORCA025.L121_IGE_TiPACCs.nc</li> <li>perturbation of the model parameters: Different iceberg distribution and different sea-ice–ocean drag and snow conductivity on sea-ice, leading to less sea-ice production in the eastern Ross Sea.</li> <li>More: <a href="https://pmathiot.github.io/NEMOCFG/docs/build/html/simu_eORCA025_OPM020.html">https://pmathiot.github.io/NEMOCFG/docs/build/html/simu_eORCA025_OPM020.html</a></li> </ul> </li> </ul>   <ul> <li>anomaly_NEMO3_AMUXL12.L75_IGE_TiPACCs.nc <ul> <li>similar to reference_NEMO3_AMUXL12.L75_IGE_TiPACCs.nc</li> <li>perturbation of atmospheric forcing: MAR forced by the CMIP5 multi-model anomaly under the RCP8.5 scenario (<a href="https://doi.org/10.5194/tc-15-571-2021">Donat-Magnin et al. 2021</a>).</li> <li>provided average: 2080-2100 (21-year average)</li> </ul> </li> </ul>

opencc-by-4.0Jul 2021View details →

zenodo40/100

UKESM1.0-ice simulation output used as test data in Burgard et al., Emulating present and future simulations of melt rates at the base of Antarctic ice shelves with neural networks

These files contain NEMO ocean model output and domain definitions for the Southern Ocean from UKESM1.0-ice simulations described in section 6.3.2 of Smith et al. "Coupling the U.K. Earth System Model to Dynamic Models of the Greenland and Antarctic Ice Sheets" , Journal of Advances in Modeling Earth Systems, 2021 Files labelled "bf663" are the UKESM simulation referred to in that section as "constant 1970 greenhouse gas and other forcings". Files labelled bi646 are the UKESM simulation referred to in that section as "instantaneously quadrupled 1970 CO2 concentrations". They were used as test data for the performance of neural networks in Burgard et al., "Emulating present and future simulations of melt rates at the base of Antarctic ice shelves with neural networks", Journal of Advances in Modeling Earth Systems 2023.

opencc-by-4.0May 2023View details →

zenodo36/100

Data and scripts to reproduce figures from "Ocean warming threatens the viability of 60% of Antarctic ice shelves"

This is the formatted data related to the paper "Ocean warming threatens the viability of 60% of Antarctic ice shelves". Main data used to produce the main figures: > bayesian_weights_davison_varying_combined_2300_withoutGISS.nc: Bayesian weights used in the computation of weighted likelihoods and averages considering all simulations going to 2300 - used in the main study > all_fluxes_br_withoutGISS.nc: all fluxes forming the ice-shelf mass balance propagated across the different dimensions of uncertainty (CMIP models, basal melt parameterisations, bed plasticities) > hydrofracturing_limits_new.nc: File containing the percentiles of reaching the hydrofracturing criterion Files needed to compute the weights: > area_isf_greene22.nc: ice-shelf area used in Davison et al. 23 (from <a href="https://doi.org/10.1038/s41586-022-05037-w">Greene et al. 2022</a>) > varying_conditions_davison23.nc: Ice-shelf mass budget fluxes from 1997 on from <a href="https://doi.org/10.1126/sciadv.adi0186">Davison et al. 2023</a> > steadystate_davison23.nc: Ice-shelf mass budget fluxes from the steady state from <a href="https://doi.org/10.1126/sciadv.adi0186">Davison et al. 2023</a> Geometric information: > gridarea_ISMIP6_AIS_4000m_grid.nc: File containing area of grid cells > Mask_Iceshelf_4km_IMBIE_withNisf.nc: Mask used to define the location of the different ice shelves, based on <a href="https://zenodo.org/records/15863352">Caillet et al. 2025 </a> > BedMachine_4km_slope_info_bedrock_draft_latlon_oneFRIS.nc: Geometric information needed for neural network parameterisation, inferred using the <a href="https://github.com/ClimateClara/multimelt">multimelt package</a> > BedMachinev2_4km_isf_masks_and_info_and_distance_oneFRIS.nc: Geometric information needed for different computations, inferred using the <a href="https://github.com/ClimateClara/multimelt">multimelt package</a> > ano_choice_NEMOorISMIP_withoutGISS.nc: file recording which T and S profiles should be taken, either ISMIP climatology or NEMO hindcast, depending on their difference to the reference mass balance Data for analysis only until 2100: > bayesian_weights_davison_varying_combined_withoutGISS.nc: Bayesian weights used in the computation of weighted likelihoods and averages considering all simulations going to 2100 (see Extended Data Fig. 5) Additional files needed for the additional analysis on other plausible geometries:  > ElmerIce_4km_2100isf_masks_and_info_and_distance_oneFRIS.nc: Geometric information needed for different computations for the 2100 ice-sheet geometry > ElmerIce_4km_2100_slope_info_bedrock_draft_latlon_oneFRIS.nc: Geometric information needed for neural network parameterisation for the 2100 ice-sheet geometry > ElmerIce_4km_2150isf_masks_and_info_and_distance_oneFRIS.nc: Geometric information needed for different computations for the 2150 ice-sheet geometry > ElmerIce_4km_2150_slope_info_bedrock_draft_latlon_oneFRIS.nc: Geometric information needed for neural network parameterisation for the 2150 ice-sheet geometry > all_fluxes_br_withoutGISS_ElmerIcegeo2100.nc: all fluxes forming the ice-shelf mass balance propagated across the different dimensions of uncertainty (CMIP models, basal melt parameterisations, bed plasticities) for the 2100 ice-sheet geometry > all_fluxes_br_withoutGISS_ElmerIcegeo2150.nc: all fluxes forming the ice-shelf mass balance propagated across the different dimensions of uncertainty (CMIP models, basal melt parameterisations, bed plasticities) for the 2150 ice-sheet geometry The scripts are explained in the associated README in scripts_paper_iceshelf_viability.zip. You can also find them here: https://github.com/ClimateClara/scripts_paper_iceshelf_viability Here is a summary of the README: - Figure 2 and 3 were done with /notebooks_for_figures/timeseries_nb_viable_isf_withoutGISS_withhydrofrac_calving0.ipynb and /notebooks_for_figures/2D_subplots_viability_proba_withoutGISS_calving0.ipynb - Figure 4 was done with /notebooks_for_figures/2D_subplots_viability_proba_withoutGISS_calving0.ipynb - Figure 5 was done with /notebooks_for_figures/timeseries_nb_viable_isf_withoutGISS_withhydrofrac_calving0.ipynb and notebooks_for_figures/2D_subplots_viability_proba_onlyhydrofrac.ipynb - Extended Data Figures 1 to 4 were done with /notebooks_for_figures/plot_mass_fluxes.ipynb - Extended Data Figure 5 was done with /notebooks_for_figures/timeseries_nb_viable_isf_withoutGISS_withhydrofrac_calving0.ipynb - Extended Data Figure 6 was done with /notebooks_for_figures/timeseries_nb_viable_isf_withoutGISS_withhydrofrac.ipynb and /notebooks_for_figures/2D_subplots_viability_proba.ipynb - Extended Data Figure 7 was done with /notebooks_for_figures/2D_subplots_viability_proba.ipynb - Extended Data Figure 8 was done with /notebooks_for_figures/timeseries_nb_viable_isf_withoutGISS_withhydrofrac_ElmerIcegeometries.ipynb and /notebooks_for_figures/2D_subplots_viability_proba_withoutGISS_ElmerIcegeometries.ipynb - Extended Data Figures 10 to 12 were done with /notebooks_for_figures/histo_weights_new.ipynb In the folder 'notebooks_for_datapreparation', you will find a few scripts to prepare the data. These are not as detailed and not meant to be run out of the box but permit to give insight into the practical application of the methods described in the paper. For potential inspiration of similar work :) For the hydrofracturing calculations we refer to `Jourdain et al. 2025 <https://doi.org/10.5194/tc-19-1641-2025>`_ and the associated scripts: https://doi.org/10.5281/zenodo.13756240 and https://doi.org/10.5281/zenodo.15003864.

opencc-by-4.0Sep 2024View details →

zenodo36/100

Chlorophyll production in the Amundsen Sea boosts heat flux to atmosphere and weakens heat flux to ice shelves -> Model outputs

This repository contains MITgcm outputs associated with the paper "Chlorophyll production in the Amundsen Sea boosts heat flux to atmosphere and weakens heat flux to ice shelves", submitted to the Journal of Geophysical Research: Oceans.  The outputs are presented in netCDF format and come from two simulations - GREEN, with chlorophyll, and BLUE, without chlorophyll affecting shortwave heating. <ul> <li>Temperature and salinity <ul> <li>green_temp.nc <ul> <li>3-dimensional monthly fields of temperature in the GREEN experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: deg.C</li> </ul> </li> <li>green_salt.nc <ul> <li>3-dimensional monthly fields of salinity in the GREEN experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: g/kg</li> </ul> </li> <li>green_ohc.nc <ul> <li>3-dimensional monthly fields of ocean heat content trend in the GREEN experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: deg.C/day</li> </ul> </li> <li>blue_temp.nc <ul> <li>3-dimensional monthly fields of temperature in the BLUE experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: deg.C</li> </ul> </li> <li>blue_salt.nc <ul> <li>3-dimensional monthly fields of salinity in the BLUE experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: g/kg</li> </ul> </li> <li>blue_ohc.nc <ul> <li>3-dimensional monthly fields of ocean heat content trend in the BLUE experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: deg.C/day</li> </ul> </li> </ul> </li> <li>Sea ice <ul> <li>ice_green.nc <ul> <li>2-dimensional monthly fields of sea ice concentration in the GREEN experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>range: 0 -> 1</li> </ul> </li> <li>sit_green.nc <ul> <li>2-dimensional monthly fields of sea ice effective thickness in the GREEN experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: m</li> </ul> </li> <li>ice_blue.nc <ul> <li>2-dimensional monthly fields of sea ice concentration in the BLUE experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>range: 0 -> 1</li> </ul> </li> <li> sit_blue.nc <ul> <li>2-dimensional monthly fields of sea ice effective thickness in the BLUE experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: m</li> </ul> </li> </ul> </li> <li>Ice shelves <ul> <li>green_meltrate.nc <ul> <li>2-dimensional monthly fields of ice shelf basal melt in the GREEN experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: kg/m^2/s</li> </ul> </li> <li>blue_meltrate.nc <ul> <li>2-dimensional monthly fields of ice shelf basal melt in the BLUE experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: kg/m^2/s</li> </ul> </li> </ul> </li> <li>Surface heat fluxes <ul> <li>tflux_green.nc <ul> <li>2-dimensional monthly fields of total downward surface heat flux in the GREEN experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>latent_green.nc <ul> <li>2-dimensional monthly fields of downward latent heat flux in the GREEN experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>sensible_green.nc <ul> <li>2-dimensional monthly fields of downward sensible heat flux in the GREEN experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>longwave_green.nc <ul> <li>2-dimensional monthly fields of upward longwave heat flux in the GREEN experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>shortwave_green.nc <ul> <li>2-dimensional monthly fields of upward shortwave heat flux in the GREEN experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>tflux_blue.nc <ul> <li>2-dimensional monthly fields of total downward surface heat flux in the BLUE experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>latent_blue.nc <ul> <li>2-dimensional monthly fields of downward latent heat flux in the BLUE experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>sensible_blue.nc <ul> <li>2-dimensional monthly fields of downward sensible heat flux in the BLUE experiment </li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>longwave_blue.nc <ul> <li>2-dimensional monthly fields of upward longwave heat flux in the BLUE experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>shortwave_blue.nc <ul> <li>2-dimensional monthly fields of upward shortwave heat flux in the BLUE experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> </ul> </li> <li>BLING <ul> <li>chlorophyll.nc <ul> <li>2-dimensional monthly fields of surface chlorophyll in the GREEN experiment</li> <li>01.01.2003 -> 31.12.2014</li> <li>units: mg/m^3</li> </ul> </li> <li>euphotic_depth.nc <ul> <li>2-dimensional monthly fields of euphotic depth in the GREEN experiment</li> <li>01.01.2003 -> 31.12.2014</li> <li>units: m</li> </ul> </li> </ul> </li> </ul>

opencc-by-4.0Mar 2024View details →

zenodo36/100

Data: Clouds drive differences in future surface melt over the Antarctic ice shelves (Kittel et al., 2022)

Outputs used in: Kittel, C., Amory, C., Hofer, S., Agosta, C., Jourdain, N. C., Gilbert, E., Le Toumelin, L., Gallée, H., and Fettweis, X.: Clouds drive differences in future surface melt over the Antarctic ice shelves, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-263, accepted, 2021. <ul> <li>MAR outputs with summer values of melt, surface energy budget components, and cloud properties over the Antarctic ice sheet (1980--2100)</li> <li>Grid file used in MAR simulations</li> </ul> If you need other variables or output frequencies from MAR, write me (c2kittel@gmail.com) and I will be glad to help you. I will also be happy to share the scripts I have developed to analyse the outputs and make the figures in this paper if needed. Please cite the paper if you use these MAR outputs. Data usage notice: 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 that contained both informations related to MAR. In order to document MAR scientific impact and enable ongoing support of the model, users are likely encouraged to contact C. Kittel to add their works in the list of MAR-related publications.  "We thank C. Kittel and the MAR team which make available the model outputs, as well agencies (F.R.S - FNRS, CÉCI, and the Walloon Region) that provided computational resources for MAR simulations. " You should also refer to and cite the following paper: Kittel, C., Amory, C., Hofer, S., Agosta, C., Jourdain, N. C., Gilbert, E., Le Toumelin, L., Gallée, H., and Fettweis, X.: Clouds drive differences in future surface melt over the Antarctic ice shelves, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-263, accepted, 2021.

opencc-by-4.0Mar 2022View details →

zenodo36/100

An assessment of basal melt parameterisations for Antarctic ice shelves

This dataset contains the data and scripts for the publication "An assessment of basal melt parameterisations for Antarctic ice shelves" in The Cryosphere. Before going into details, here is a reminder that the NEMO runs are called 'OPM+number'. These are the corresponding names given in the manuscript: OPM006=HIGHGETZ, OPM016=WARMROSS, OPM018=COLDAMU and OPM021=REALISTIC. The zipping has been made so that if you download and unzip everything you will have the following file structure: =============== raw/ <ul> <li>Ant_MeltingRate.v2.2.nc (from RAW_other.zip): 2D observational estimates of melt rates updated from Rignot et al. (2013) by Jérémie Mouginot (used in Fig. B3)</li> <li>DUTRIEUX_2014/ (from RAW_other.zip): observational estimates temperature and salinity profiles for Pine Island Ice Shelf (inferred from Dutrieux et al., 2014 with 'find_click_position_T_from_article.py', used to produce 'T_S_profiles-dutrieux_2014_PIGL.nc') used for Fig. 8</li> <li>MASK_METADATA/ (from RAW_other.zip): contains txt and csv files (prepared by N. Jourdain) needed for the masks, and 'IPCC_cryo_div.txt' used for the maps in Fig. 5</li> <li>grid_eORCA025_CDO.nc (from RAW_other.zip): grid info needed to interpolate eORCA025 grid to stereographic (provided by Fabien Gillet-Chaulet)</li> <li>NEMO_main/NEMO_eORCA025.L121_OPM0*_ANT_STEREO (from RAW_nemo_OPM0*.zip): NEMO simulation output of OPM0* run, cut at 60°S on eORCA025 grid, contains: <ul> <li>variables_of_interest: variables_of_interest_*_Ant.nc: variables of interest for the study</li> <li>cavity_melt_*_Ant.nc: ice-shelf melt</li> <li>eORCA025.L121-OPM0*_mesh_mask.nc: geometric constants and masks</li> </ul> </li> <li>NEMO_appendix/NEMO_OPM0* (from RAW_nemo_appendix.zip): NEMO simulation output of OPM0* run used for the figures in Appendix B.</li> </ul> The NEMO simulations were conducted by Pierre Mathiot. =============== interim/ <ul> <li>ANTARCTICA_IS_MASKS/ (from INTERIM_ANTARCTICA_IS_MASKS.zip): contains 'nemo_5km_isf_masks_and_info_and_distance_new_oneFRIS.nc' for each NEMO run and for BedMachine, which contains masks and geometric information needed for the application of the parameterisation and computing plume and box characteristics => produced with 'preprocessing/isf_mask_NEMO.ipynb' and 'preprocessing/isf_mask_BedMachine.ipynb'</li> <li>BOXES/ (from INTERIM_BOXES.zip): contains 'nemo_5km_boxes_1D_oneFRIS.nc' and 'nemo_5km_boxes_2D_oneFRIS.nc' for each NEMO run and for BedMachine, which contains the variables needed to apply the box parameterisation => produced with 'preprocessing/isf_mask_NEMO.ipynb' and 'preprocessing/isf_mask_BedMachine.ipynb'</li> <li>PLUMES/ (from INTERIM_PLUMES.zip): contains 'nemo_5km_plume_characteristics_oneFRIS.nc' for each NEMO run and for BedMachine, which contains the variables needed to apply the plume parameterisation => produced with 'preprocessing/isf_mask_NEMO.ipynb' and 'preprocessing/isf_mask_BedMachine.ipynb'</li> <li>SIMPLE/ (from INTERIM_SIMPLE.zip) <ul> <li>nemo_5km_06161821_oneFRIS/: contains netcdf-files summarising the tuned parameters resulting from the cross-validation over ice shelves (CVshelves), the cross-validation over time (CVtime), the tuning over all sample (ALL) and the bootstrap tuning (BT and clusterbootstrap*) => summary of results from running the scripts in the folder 'tuning' - for simple parameterisations via 'tuning_cluster_ALL_CV_BT.ipynb' and for the others via 'run_generalized_tuning_from_bash_crossval.py', 'run_generalized_tuning_script.sh', 'group_CV_parameters.ipynb', 'group_BT_parameters.ipynb'</li> <li> nemo_5km_OPM0*: 'thermal_forcing_term_for_linreg_corrected_oneFRIS.nc', contains the term used for the tuning through linear regression of the simple parameterisations => produced via 'prepare_2D_thermal_forcing_simple.ipynb' and 'prepare_1D_thermal_forcing_term_simple_for_linreg.ipynb'</li> </ul> </li> <li>NEMO_eORCA025.L121_OPM*_ANT_STEREO/ (from INTERIM_geometry_interp_OPM0*.zip): for each NEMO run, <ul> <li>corrected_draft_bathy_isf.nc: file containing ice draft and bathymetry corrected by ice draft concentration to account for the biased draft and bathymetry at the grounding line resulting from the interpolation from the native NEMO grid to the stereographic grid (values under ice shelf and NaNs over land => produced in 'data_formatting/custom_lsmask.ipynb'</li> <li>custom_lsmask_Ant_stereo_clean.nc: land-sea mask giving 0 = ocean, 1 = shelf, 2 = land => produced in 'data_formatting/custom_lsmask.ipynb'</li> <li>isfdraft_conc_Ant_stereo.nc: ice-shelf concentration resulting from the interpolation from the native NEMO grid to the stereographic grid => produced in 'data_formatting/custom_lsmask.ipynb'</li> <li>other_mask_vars_Ant_stereo.nc: contains other variables used for the masks => produced in 'data_formatting/custom_lsmask.ipynb'</li> </ul> </li> <li>T_S_PROF/ (from INTERIM_T_S_PROF.zip) <ul> <li>dutrieux_2014/: observational estimates temperature and salinity profiles for Pine Island Ice Shelf in one netcdf => produced in 'pre_processing/T_S_profiles_Dutrieux14.ipynb'</li> <li>info_chunks.txt: contains corresponding NEMO run and start and end year for each time block used in cross-validation</li> <li>T_S_mean_prof_corrected_km_contshelf_and_offshore_1980-2018_oneFRIS.nc for each NEMO run, which contains temperature and salinity profiles averaged over 5 different regions in front of each ice shelf => produced in 'pre_processing/T_S_profile_formatting_with_conversion.ipynb' and 'pre_processing/T_S_profiles_front.ipynb'</li> </ul> </li> </ul> =============== processed/MELT_RATE/ <ul> <li>BedMachine_for_comparison (from PROCESSED_BedMachine_for_comparison.zip): contains 'melt_rates_PIGL_dutrieux_time_mean_pattern.nc', which is the mean melt pattern of the melt parameterised using Dutrieux2014 observational temperature and salinity estimates => produced in 'apply_params/apply_param_PIGL_dutrieux_BedMachine.ipynb'</li> <li>nemo_5km_OPM* (from PROCESSED_nemo_5km_OPM0*.zip): for each NEMO run <ul> <li>eval_metrics_1D*.nc : files containing 'melt_1D_Gt_per_y' and 'melt_1D_mean_myr_box1' for all parameterisations using parameters from the cross-validation over shelves (CVshelves), the cross-validation over time (CVtime), the original parameters (orig), and from Favier et al. 2019 (favier) and Jourdain et al. 2020 (lipscomb)  => produced in 'apply_params/evalmetrics_results_CV.ipynb'</li> <li>diff_melt_param_ref_box1_*.nc: files containing the difference between parameterised and reference melt in box1 for each point to create the left panel of Fig. F1 => produced in 'apply_pointbypointRMSE_box1_forFigF1.ipynb'</li> <li>melt_rates_1D_NEMO_oneFRIS.nc, melt_rates_2D_NEMO.nc, melt_rates_box1_NEMO_oneFRIS.nc: reference melt rates and integrated melt => produced in 'prepare_reference_melt_file.ipynb'</li> </ul> </li> </ul> additionally, only in nemo_5km_OPM021: <ul> <li>melt_rates_2D_NEMO_timmean.nc, melt_rates_2D_boxes_timmean_oneFRIS.nc, melt_rates_2D_plumes_timmean_oneFRIS.nc, melt_rates_2D_simple_timmean_oneFRIS.nc: files containing mean patterns used in Fig. 5 and Fig. => produced in 'prepare_data_Figures_5_6.ipynb'</li> </ul> =============== ATTENTION! EXTERNAL DATA NOT CONTAINED IN THIS REPOSITORY: <ul> <li>Observational estimates of melt rates for Pine Island Ice Shelf (from Shean et al., 2019, for Fig. 8a) => This data is currently available upon request from D. Shean and will be soon made open access as well. The DOI will be shared here at that point</li> <li>BedMachine data used for Figure 8: It is BedMachine v2 (Morlighem, 2020) and can be found here: <a href="https://nsidc.org/data/nsidc-0756/versions/2">https://nsidc.org/data/nsidc-0756/versions/2</a>.</li> <li>World Ocean Atlas data for comparisons in Appendix B. The data was downloaded here: <a href="https://www.ncei.noaa.gov/access/world-ocean-atlas-2018/bin/woa18.pl">https://www.ncei.noaa.gov/access/world-ocean-atlas-2018/bin/woa18.pl</a>. It was then interpolated to the eORCA025 grid and converted to conservative temperature with TEOS10.</li> </ul> ===================== The explanation around the scripts can be found in README.rst with the scripts in SCRIPTS.zip. Note that these are the scripts needed to produce the results in the paper. You can also find them on Github: <a href="https://github.com/ClimateClara/scripts_paper_assessment_basal_melt_param">https://github.com/ClimateClara/scripts_paper_assessment_basal_melt_param</a>, find the most up-to-date version of the package 'multimelt' here: <a href="https://github.com/ClimateClara/multimelt">https://github.com/ClimateClara/multimelt</a> and a version you can install via pip here: <a href="https://github.com/ClimateClara/multimelt">https://pypi.org/project/multimelt/ </a> Finally, if anything is unclear, check out the "Methods" section of the paper: <a href="https://doi.org/10.5194/tc-2022-32">https://doi.org/10.5194/tc-2022-32</a>

opencc-by-4.0Nov 2022View details →

zenodo32/100

Emulating present and future simulations of melt rates at the base of Antarctic ice shelves with neural networks

This dataset contains the data and scripts for the publication "<a href="https://doi.org/10.1029/2023MS003829">Emulating present and future simulations of melt rates at the base of Antarctic ice shelves with neural networks</a>" in Journal of Advances in Modeling Earth Systems. Before going into details, here is a reminder that the NEMO runs for the training dataset are called 'OPM+number'. These are the corresponding names given in the manuscript: OPM006=HIGHGETZ, OPM016=WARMROSS, OPM018=COLDAMU and OPM021=REALISTIC. For the testing dataset: 'bf663' is the REPEAT1970 run and 'bi646' is the 4xCO2 run. Most of the formatting and preprocessing of the training data has been made for <a href="https://tc.copernicus.org/articles/16/4931/2022/">Burgard et al. 2022</a>. The raw and to-some-degree processed data can therefore be found here: <a href="https://doi.org/10.5281/zenodo.7308352">https://doi.org/10.5281/zenodo.7308352</a>.  The raw data for the testing dataset is from <a href="https://doi.org/10.1029/2021MS002520">Smith et al. 2021</a>, you can find it here: <a href="doi.org/10.5281/zenodo.7886986">https://doi.org/10.5281/zenodo.7886986</a> The following folders and files can be found here: =============== raw/ Some geometrical files needed for initial data formatting and masking. =============== interim/ <ul> <li>ANTARCTICA_IS_MASKS/ (from INTERIM_ANTARCTICA_IS_MASKS.zip): contains <ul> <li>masks and geometric information for the testing dataset to be included in the input file of the neural network and for the classic parameterisations.</li> <li>local bedrock and ice meridional and zonal slopes</li> </ul> </li> <li>BOXES/ (from INTERIM_BOXES.zip): contains variables needed to apply the box parameterisation for the testing dataset</li> <li>PLUMES/ (from INTERIM_PLUMES.zip): contains the variables needed to apply the plume parameterisation for the testing dataset</li> <li>SMITH_bf663/ and SMITH_bi646/ (from INTERIM_SMITH*.zip): for testing dataset, <ul> <li>corrected_draft_bathy_isf.nc: file containing ice draft and bathymetry corrected by ice draft concentration to account for the biased draft and bathymetry at the grounding line resulting from the interpolation from the native NEMO grid to the stereographic grid (values under ice shelf and NaNs over land</li> <li>custom_lsmask_Ant_stereo_clean.nc: land-sea mask giving 0 = ocean, 1 = shelf, 2 = land</li> <li>isfdraft_conc_Ant_stereo.nc: ice-shelf concentration resulting from the interpolation from the native NEMO grid to the stereographic grid</li> <li>other_mask_vars_Ant_stereo.nc: contains other variables used for the masks</li> <li>the reference melt: 1D containing integrated melt, 2D containing melt fields, box1 containing melt near the grounding line</li> </ul> </li> <li>T_S_PROF/ (from INTERIM_T_S_PROF.zip) <ul> <li>Mean profiles used as input for traditional parameterisations</li> <li>T and S 2D fields, extrapolated from the mean profiles to the local ice draft depth (needed as input for the neural network)</li> <li>Fields of mean and standard deviation T and S for all points (needed as input for the neural network)</li> </ul> </li> <li>NN_MODELS/ (from INTERIM_NN_MODELS.zip) contains all neural networks trained for this paper (for the cross validation and over the whole dataset for testing)</li> <li>INPUT_DATA/ (from INTERIM_INPUT_DATA.zip) contains all input csv files containing the input datasets for the different training and testing iterations. Also contains the metrics to normalise the input. For the cross-validation, the input csv files are not included because they are too large. However, they can be reconstructed from the individual files for ice shelves and time blocks. The metrics to normalise the data during the cross-validation are included in EXTRAPOLATED_ISFDRAFT_CHUNKS_CV!</li> </ul> =============== processed/MELT_RATE/ Contains resulting melt rates <ul> <li>CV_ISF : Cross-validation results over ice shelves</li> <li>CV_TBLOCKS : Cross-validation results over time</li> <li>SMITH_bf663 : Neural network results for REPEAT1970</li> <li>SMITH_bf663_CLASSIC  : "Traditional" parameterisation results for REPEAT1970</li> <li>SMITH_bi646 : Neural network results for 4xCO2</li> <li>SMITH_bi646_CLASSIC: "Traditional" parameterisation results for 4xCO2</li> </ul> ===================== The explanation around the scripts can be found in README.rst with the scripts in scripts_paper_simpleNN_basal_melt.zip. Note that these are the scripts needed to produce the results in the paper. You can also find them on Github: <a href="https://github.com/ClimateClara/https://github.com/ClimateClara/scripts_paper_simpleNN_basal_melt">https://github.com/ClimateClara/scripts_paper_simpleNN_basal_melt</a>, find the most up-to-date version of the package 'multimelt' here: <a href="https://github.com/ClimateClara/multimelt">https://github.com/ClimateClara/multimelt</a> and a version you can install via pip here: <a href="https://github.com/ClimateClara/multimelt">https://pypi.org/project/multimelt/</a> Finally, if anything is unclear, check out the "Methods" section of the paper: <a href="https://doi.org/10.1029/2023MS003829">https://doi.org/10.1029/2023MS003829</a>

opencc-by-4.0Nov 2023View details →

zenodo32/100

Data and code for: "Annual mass budget of Antarctic ice shelves from 1997 to 2021"

All code required to reproduce the results presented in Davison et al. Annual mass budget of Antarctic ice shelves from 1997 to 2021. Also provided are the ice shelf masks and 1000x1000 m basal melt rates.

opencc-by-4.0Dec 2022View details →

dryad32/100

Vertical ocean heat transport near Antarctic ice shelves: data and processing code

Open the record for dataset details and reuse information.

publicDec 2024View details →

dryad32/100

The tale of two ice shelves: Zachariae Isstrøm and Nioghalvfjerdsfjorden, Northeast Greenland

Open the record for dataset details and reuse information.

publicNov 2020View details →

zenodo28/100

SNOWPACK simulations forced by MERRA-2 for Antarctic Ice Shelves

This repository contains the SNOWPACK simulations used in the manuscript: Banwell, A., Wever, N., Dunmire, D. and Picard, G. (2023): Quantifying Antarctic-wide ice-shelf surface melt volume using microwave and firn model data: 1980 to 2021. Geophys. Res. Lett. (accepted). For each MERRA-2 grid point that is on an ice shelf, the zip file of the corresponding simulation is named: <latitude>_<longitude>_ICESHELVES.zip Each zip file then contains the following files: <ul> <li><code>output/-72.000_69.375_ICESHELVES.ini</code>: This file contains the model settings as used for the simulation</li> <li><code>output/-72.000_69.375_ICESHELVES.pro</code>: This file contains the detailed, layered SNOWPACK output, with for each layer, for example, temperature, density, liquid water content, grain size, etc. Can be visualized using <a href="https://niviz.org">niViz</a>.</li> <li><code>output/-72.000_69.375_ICESHELVES.smet</code>: This file contains the SNOWPACK time series output, like surface sublimation, snow surface temperature, latent heat flux, etc. Can be visualized using <a href="https://niviz.org">niViz</a>.</li> <li><code>output/-72.000_69.375_ICESHELVES.sno</code>: This file contains the SNOWPACK model state at the end of the simulation.</li> <li><code>output/-72.000_69.375_ICESHELVES.snobackup</code>: This file contains the SNOWPACK model state from the last backup point during the simulation.</li> <li><code>output/-72.000_69.375_ICESHELVES.haz</code>: This file contains very specific SNOWPACK model output and was ignored in the study associated with this dataset.</li> <li><code>output/-72.000_69.375_ICESHELVES.hazbackup</code>: This file contains very specific SNOWPACK model output from the last backup point during the simulation and was ignored in the study associated with this dataset.</li> </ul>   Also available are statistics derived from the SNOWPACK model output using the code published at <a href="https://doi.org/10.5281/zenodo.7957639">doi: 10.5281/zenodo.7957639</a> (<a href="https://github.com/nwever/SNOWPACK_iceshelves_melt">GitHub repository</a>):   <code>statistics.zip</code> ========== Contains statistics calculated from the SNOWPACK output. Statistics per region are stored in the folders <code>stats622</code> (for yearly statistics) and <code>stats_per_day622</code> (for daily statistics). Statistics per ice shelf are stored in the folders <code>stats_iceshelves622</code> (for yearly statistics) and <code>stats_iceshelves_per_day622</code> (for daily statistics). For each ice shelf and region, the following files are available: <ul> <li>melt_RX.txt and melt_SX.txt: sum of melt amount (kg/m2) for region RX or ice shelf SX, respectively.</li> <li>meltdays_Amm_RX.txt and meltdays_Amm_SX.txt: number of days exceeding A mm of melt for region RX or ice shelf SX, respectively.</li> <li>mswater_Amm_RX.txt and mswater_Amm_SX.txt: number of days with total firn column water content exceeding A kg/m2 for region RX or ice shelf SX, respectively.</li> <li>mswaterA_B_Cmm_RX.txt and mswaterA_B_Cmm_SX.txt: number of days with more than C kg/m2 amount of liquid water in the firn between A and B meters below the surface for region RX or ice shelf SX, respectively.</li> </ul> Each of the files report the year, respectively the date, in the first column, followed by the statistics for each grid point in the region/ice shelf, followed by the mean and standard deviation over all grid points in the respective region/ice shelf listed in the preceding columns. The first line is a header. To find out the sequence by which MERRA-2 points appear in the output columns, please refer to the list in the file <code>list_points.txt</code>. This list is constructed as follows. First there is a header indicating that what follows are regions. Then, each line contains a comma-delimited list of the region number, latitude, and longitude, respectively. The first line for a region is reported in the column with header <code>1</code> in the statistics file, the second line in the column with header <code>2</code>, and so on. After all regions are listed, there is again a header line denoting that what follows are the ice shelves. Similar to the section for the regions, each following line contains a comma-delimited list of the ice shelf number, latitude, and longitude, respectively. The numbering of regions and ice shelves is listed in the tables below.   <code>forcingstatistics.zip</code> =============== Contains monthly statistics of the forcing variables TA (air temperature, K), QI (specific humidity, -), ILWR (incoming longwave radiation W/m2), ISWR (incoming shortwave radiation, W/m2), PSUM (precipitation, kg/m2/h), and VW (wind speed, m/s). The statistics for each region and ice shelf are stored in <code>monthlystats</code> and <code>monthlystats_iceshelves</code>, respectively. Each file name contains the forcing variable and the region/ice shelf number. The numbering of regions and ice shelves can be found in the tables below. Each file then contains the month (expressed as YYYYMM), followed by means of the specific variable for each MERRA-2 grid point in the region/ice shelves (in the same sequence as for the statistics files discussed above), followed by the mean and standard deviation over all MERRA-2 grid points listed in the preceding columns.       <table> <caption>Regions</caption> <tbody> <tr> <td>Number</td> <td> </td> </tr> <tr> <td>R1</td> <td>Amery</td> </tr> <tr> <td>R2</td> <td>Amundsen</td> </tr> <tr> <td>R3</td> <td>Dronning</td> </tr> <tr> <td>R4</td> <td>Peninsula</td> </tr> <tr> <td>R5</td> <td>Ronne</td> </tr> <tr> <td>R6</td> <td>Ross</td> </tr> <tr> <td>R7</td> <td>Victoria</td> </tr> <tr> <td>R8</td> <td>Wilkes</td> </tr> </tbody> </table> <table> <caption>   Ice shelves </caption> <tbody> <tr> <td>S115</td> <td>Amery</td> </tr> <tr> <td>S102</td> <td>West</td> </tr> <tr> <td>S104</td> <td>Publications</td> </tr> <tr> <td>S53</td> <td>Venable</td> </tr> <tr> <td>S110</td> <td>Abbot</td> </tr> <tr> <td>S54</td> <td>Cosgrove</td> </tr> <tr> <td>S55</td> <td>PIG</td> </tr> <tr> <td>S56</td> <td>Thwaites</td> </tr> <tr> <td>S57</td> <td>Crosson</td> </tr> <tr> <td>S58</td> <td>Dotson</td> </tr> <tr> <td>S60</td> <td>Getz</td> </tr> <tr> <td>S64</td> <td>Nickerson</td> </tr> <tr> <td>S65</td> <td>Sulzberger</td> </tr> <tr> <td>S36</td> <td>Brunt</td> </tr> <tr> <td>S37</td> <td>Riiser-Larsen</td> </tr> <tr> <td>S38</td> <td>Quar</td> </tr> <tr> <td>S39</td> <td>Ekstrom</td> </tr> <tr> <td>S124</td> <td>Atka</td> </tr> <tr> <td>S125</td> <td>Jelbaert</td> </tr> <tr> <td>S121</td> <td>Fimbil</td> </tr> <tr> <td>S123</td> <td>Vigrid</td> </tr> <tr> <td>S122</td> <td>Nivlisen</td> </tr> <tr> <td>S120</td> <td>Lazarez</td> </tr> <tr> <td>S119</td> <td>Borchgrevink</td> </tr> <tr> <td>S118</td> <td>Baudouin</td> </tr> <tr> <td>S158</td> <td>Prince Herald</td> </tr> <tr> <td>S51</td> <td>Scar Inlet</td> </tr> <tr> <td>S8</td> <td>Larsen C</td> </tr> <tr> <td>S6</td> <td>Larsen D</td> </tr> <tr> <td>S7</td> <td>Larsen E</td> </tr> <tr> <td>S45</td> <td>N GVIIS</td> </tr> <tr> <td>S44</td> <td>S GVIIS</td> </tr> <tr> <td>S46</td> <td>Bach</td> </tr> <tr> <td>S52</td> <td>Stange</td> </tr> <tr> <td>S50</td> <td>Wilkins</td> </tr> <tr> <td>S34</td> <td>Ronne</td> </tr> <tr> <td>S35</td> <td>Filchner</td> </tr> <tr> <td>S70</td> <td>Ross West</td> </tr> <tr> <td>S71</td> <td>Ross East</td> </tr> <tr> <td>S91</td> <td>Mertz</td> </tr> <tr> <td>S162</td> <td>Ninnis</td> </tr> <tr> <td>S90</td> <td>Cook</td> </tr> <tr> <td>S86</td> <td>Rennick</td> </tr> <tr> <td>S83</td> <td>Mariner</td> </tr> <tr> <td>S73</td> <td>Drygalski</td> </tr> <tr> <td>S74</td> <td>Nansen</td> </tr> <tr> <td>S114</td> <td>Dibble</td> </tr> <tr> <td>S100</td> <td>Shackleton</td> </tr> <tr> <td>S24</td> <td>Tracy Tremenchus</td> </tr> <tr> <td>S99</td> <td>Conger Glenzer</td> </tr> <tr> <td>S159</td> <td>Vincennes Bay</td> </tr> <tr> <td>S98</td> <td>Totten</td> </tr> <tr> <td>S157</td> <td>Moscow uni</td> </tr> <tr> <td>S156</td> <td>Holmes</td> </tr> </tbody> </table>

opencc-by-4.0May 2023View details →

nasa28/100

Images of Antarctic Ice Shelves, Version 2

Images of Antarctic Ice Shelves is a data set created as part of an effort to closely monitor major ice streams and outlet glaciers along the Antarctic coast, with a focus on the ice shelves considered the most susceptible to rapid change due to climatic warming and those that occasionally calve major icebergs. This monitoring effort began using AVHRR 1 Km images from several NOAA polar-orbiting satellites, and later included MODIS images from both Terra and Aqua. The scenes are derived from either the visible or the thermal channels. AVHRR images are available from August 1989 to October 2001, while MODIS images are available from March 2000 to present. New images processed from the MODIS Level 1B Calibrated Radiances are provided every month.

restrictednotspecifiedApr 2025View details →

nasa20/100

ICESat-2 L4 Grounding Zone for Antarctic Ice Shelves V001

This data set provides an Antarctic ice shelf grounding zone geolocation product, including the landward limit of ice flexure caused by ocean tidal movement (Point F), the seaward limit of ice flexure (Point H), and the break in surface slope (Point Ib) based on the ATLAS/ICESat-2 ATL06 Land Ice Height data set acquired between March 2019 and September 2020. The grounding zone estimates were derived from automated techniques using ICESat-2 repeat tracks.

restrictednotspecifiedMar 2025View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

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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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record