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65 results for “Ice melting”
Supplementary material for 'Reorganisation of subglacial drainage processes during rapid melting of the Fennoscandian Ice Sheet'
<p>Supplementary material for 'Reorganisation of subglacial drainage processes during rapid melting of the Fennoscandian Ice Sheet'</p> <p>This repository two zipped directories: Default_results_repository includes ISSM md (model) files, saved in the .mat MATLAB format for every model submission reported on in the corresponding manuscript. For the default model runs, the result .outbin files are also include for the ISSM ice sheet spinup, the GlaDS steady state run, and the GlaDS transient forcing run. For all other models runs, the included .mat files are executable such that running the included models will reproduce the rest of the results discussed in the paper.</p> <p>Manu_scripts includes the input geophysical data, shapefiles on Murtoo field locations from Ahokangas et al. (2021), glacial landforms shapefile data from Palmu et al. (2021), example input scripts used to produce the results discussed in the manuscript, and functions to plot those results are included. Note, the example scripts (A_ISSM_ice_spinup, B_GlaDS_seady_state, C_GlaDS_transient) will not run without modification to the specific users cluster and storage setup and are instead intended as guides to use for one's own model setup. </p> <p> For our modelling we used the Ice-sheet and Sea-level System Model (Larour et al., 2012) revision 27448 available from: https://issm.jpl.nasa.gov/ (last accessed on 06-09-2023). . </p> <p> </p>
Data for The importance of cloud phase when assessing surface melting in an offline coupled firn model over Ross Ice shelf, West Antarctica
<p>This is the data used in the paper "The importance of cloud phase when assessing surface melting in an offline coupled firn model over Ross Ice shelf, West Antarctica"</p>
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
<p>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</p> <p>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 CO<sub>2</sub> concentrations".</p> <p>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.</p>
McMurdo Dry Valleys Major Ion Concentrations for Glacier Ice, Snow, and Melt Water Samples 1993-1997
The chemistry of various glaciers (Canada, Commonwealth, Howard, Suess, Taylor) in Taylor Valley was measured for the following analytes between 1993 and 1997: Alkalinity, Ca, Cl, F, K, Mg, Na, NO3, Si, and SO4.
Main output data used in "Coupling the regional climate MAR model with the ice sheet model PISM mitigates the melt-elevation positive feedback" (Delhasse et al., 2024)
<p>Outputs used in:</p> <p><em>Delhasse, A., Beckmann, J., Kittel, C., and Fettweis, X.: Coupling MAR (Modèle Atmosphérique Régional) with PISM (Parallel Ice Sheet Model) mitigates the positive melt–elevation feedback, The Cryosphere, 18, 633–651, https://doi.org/10.5194/tc-18-633-2024, 2024.</em></p> <p>MAR-PISM coupling experiments outputs over 1991-2200. The main experiments are:</p> <ul> <li>MAPI-2w: 2-way coupling, consideration <em>online</em> of the melt-elevation feedback (evolving topography in MAR).</li> <li>MAPI-1w: 1-way coupling, consideration of the melt-elevation feedback only with the <em>offline</em> correction (Franco <em>et al.</em>, 2012) of the MAR outputs (fixed topography in MAR).</li> <li>MAPI-0w: 0-way coupling, no consideration of the melt-elevation feedback (fixed topography in MAR and no correction during interpolation).</li> </ul> <p>MAR files contain yearly SMB (surface mass balance) and ST (surface temperature) interpolated (with correction) on the PISM-4.5km grid. Gradients used for the correction of the melt-elevation feedback are also given for both variables. SMB and ST are the two required MAR fields to couple MAR with PISM. </p> <p>PISM files contain yearly ice thickness (THK) and ice mask (MASK) as simulated by PISM for each of the three experiments. </p> <p>The MAR code used in this dataset is tagged as v3.11.3 on https://gitlab.com/Mar-Group/MARv3# (last access: 23 January 2024) (MARTeam, 2024). The PISM code used is tagged as PISMv1.2.2 on <a href="https://github.com/pism/pism/releases/tag/v1.2.2" target="_blank" rel="noopener noreferrer">https://github.com/pism/pism/releases/tag/v1.2.2</a> (last access: 23 January 2024). Other coupling scripts are also available upon request by email (<a href="mailto:alison.delhasse@uliege.be" target="_blank" rel="noopener noreferrer">alison.delhasse@uliege.be</a>).</p> <p>If you need other variables from MAR or PISM, send us an email (alison.delhasse@uliege.be, johanna.beckmann@monash.edu) and we will be glad to help you. We will also be happy to share the scripts we have developed to analyse the outputs and make the figures in this paper if needed. Please cite the paper if you use these MAR-PISM outputs.<br><br>Data usage notice:</p> <p>If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgments should be similar to the one below that contains information related to MAR and PISM. To document MAR scientific impact and enable ongoing support of the model, users are likely encouraged to contact me to add their works to the list of MAR-related publications. </p> <p>"We thank A. Delhasse and J. Beckmann, as well as the MAR and PISM teams which make available the model outputs. We also thank agencies (F.R.S - FNRS, CÉCI, and the Walloon Region) that provided computational resources for MAR-PISM simulations. "</p> <p>You should also refer to and cite the following paper in its latest version:</p> <p><em>Delhasse, A., Beckmann, J., Kittel, C., and Fettweis, X.: Coupling MAR (Modèle Atmosphérique Régional) with PISM (Parallel Ice Sheet Model) mitigates the positive melt–elevation feedback, The Cryosphere, 18, 633–651, https://doi.org/10.5194/tc-18-633-2024, 2024.</em></p> <p>Reference</p> <p><em>Franco, B., Fettweis, X., Lang, C., and Erpicum, M.: Impact of spatial resolution on the modelling of the Greenland ice sheet surface mass balance between 1990–2010, using the regional climate model MAR, The Cryosphere, 6, 695–711, https://doi.org/10.5194/tc-6-695-2012, 2012.</em></p> <p><em>MARTeam: MARv3.11, GitLab [data set], <a href="https://gitlab.com/Mar-Group/MARv3" target="_blank" rel="noopener">https://gitlab.com/Mar-Group/MARv3#</a> (last access: 28 May 2022), 2021.</em></p>
Circum-Antarctic data used in "Tipping point behaviour of ice-sheet grounding-zone melting due to ocean water intrusion" by Bradley and Hewitt
<p>The file 'Antarctica-data.mat' contains the following fields:</p><p>'x' [units: m] x position of grid points</p><p>'y' [units m] y position of grid points</p><p>'tf_max' [units: C] maximum thermal forcing from Adusumilli et al. 2020 (doi: https://doi.org/10.1038/s41561-020-0616-z)</p><p>'H' [units: m] ice thickness from Bedmachine V3</p><p>'B' [units: m] bed elevation from Bedmachine V3</p><p>'mask' [units: n/a] Bedmachine V3 mask</p><p>'isedge' [units: n/a] Logical array with 1 corresponding to edges of ice shelves and 0 otherwise</p><p>'isgl' [units: n/a] Logical array with 1 corresponding to grounding line points and 0 otherwise</p><p>'isfront' [units: n/a] Logical array with 1 corresponding to ice fronts and 0 otherwise</p><p>'vx' [units: m/a] Ice velocity in the x-direction from ITS_LIVE 240m mosaic</p><p>'vy' [units: m/a] Ice velocity in the y-direction from ITS_LIVE 240m mosaic</p>
Improving surface melt estimation over the Antarctic Ice Sheet using deep learning: a proof of concept over the Larsen Ice Shelf
<p>Hu, Z., Kuipers Munneke, P., Lhermitte, S., Izeboud, M., and van den Broeke, M.: Improving Surface Melt Estimation over Antarctica Using Deep Learning: A Proof-of-Concept over the Larsen Ice Shelf, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-102, in review, 2021.</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>(1) MLP_model_surface_melt_corr.h5</strong> is the developed MLP model used for correcting RACMO2 surface melt.</p> <p><strong>(2) RACMO2_surface_melt_corr_MLP_AWS14.xlsx </strong>corrected surface melt [mm w.e. per day] from RACMO2 at AWS 14 during austral summers 2001 - 2016. The model inputs are (1) the simulated albedo, (2) the albedo difference between the observed and simulated albedo, (3) air temperature at 2m, (4) incoming shortwave radiation, (5) downwelling longwave radiation, (6) simulated surface melt, (7) Boolean melt flag, (8) surface melt difference to the previous day, and (9) record date as day of the year.</p> <p><strong>(3) RACMO2_surface_melt_corr_MLP_AWS17.xlsx </strong>The same as point 2 but for AWS 17</p> <p><strong>(4) RACMO2_surface_melt_corr_MLP_AWS18.xlsx </strong>The same as point 2 but for AWS 18</p> <p>Note: Data 2-4 are corrected RACMO2 simulations of surface melt at the pixels in RACMO2 27 km grid corresponding to AWS 14, 17, and 18 locations. They are not AWS observations.</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>Related data set:</strong></p> <p>MODIS/Terra Surface Reflectance Daily L2G Global 1 km and 500 m SIN Grid product is available via the Land Processes Distributed Active Archive Center (LP DAAC) (https://doi.org/10.5067/MODIS/MOD09GA.006, last access: 3 December 2021). MODIS/Terra+Aqua Albedo Daily L3 Global 500 m SIN Grid product is also available via LP DAAC (https://doi.org/10.5067/MODIS/MCD43A3.006, last access: 3 December 2021). Sentinel-1 images are provided by the European Space Agency (ESA) (https://sentinel.esa.int/web/sentinel/sentinel-data-access, last access: 3 December 2021). Automatic weather station observations from AWS 14, 17, and 18 are available via https://doi.pangaea.de/10.1594/PANGAEA.910473 (last access: 3 December 2021). RACMO2 simulations (https://www.projects.science.uu.nl/iceclimate/models/antarctica.php#2-1, last access: 3 December 2021) are provided by van Wessem et al. (2018) which are available on request to the original authors.</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>You should also refer to and cite the following paper:</strong></p> <p>Hu, Z., Kuipers Munneke, P., Lhermitte, S., Izeboud, M., and van den Broeke, M.: Improving Surface Melt Estimation over Antarctica Using Deep Learning: A Proof-of-Concept over the Larsen Ice Shelf, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-102, in review, 2021.</p>
Data: Clouds drive differences in future surface melt over the Antarctic ice shelves (Kittel et al., 2022)
<p>Outputs used in:</p> <p><em>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.</em></p> <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> <p>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.<br> <br> Data usage notice:</p> <p>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. </p> <p>"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. "</p> <p>You should also refer to and cite the following paper:</p> <p><em>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.</em></p>
Datasets for "A Detection of the Sea Level Fingerprint of Greenland Ice Sheet Melt" Coulson et al., 2022, Science
<p>Sea surface height (SSH) change altimetry-derived datasets and model predictions for:<br> "A Detection of the Sea Level Fingerprint of Greenland Ice Sheet Melt", Submitted to Science January 2022. Sophie Coulson, Sönke Dangendorf, Jerry X. Mitrovica, Mark E. Tamisiea, Linda Pan, David T. Sandwell.<br> Email: slcoulson@lanl.gov, sdangendorf1@tulane.edu</p> <p>See README for file details.</p>
Data set of 1D model runs, CTRL and ICE runs, associated with "Underestimation of oceanic carbon uptake in the Arctic Ocean: Ice melt as predictor of the sea ice carbon pump"
<p>Dataset of one-dimensional runs for investigation on the sea ice carbon pump. Associated with Sect. 3.1 and 4.1 of manuscript "Underestimation of oceanic carbon uptake in the Arctic Ocean: Ice melt as predictor of the sea ice carbon pump".</p>
Model data for probabilistic projections of the Amery Ice Shelf catchment, Antarctica, under high ice-shelf basal melt conditions
<p>Model data for probabilistic projections of the Amery Ice Shelf catchment, <br>Antarctica, under high ice-shelf basal melt conditions<br>======================================================================</p> <p>This archive contains ice-sheet model output and statistical models <br>used in the manuscript:<br>"Probabilistic projections of the Amery Ice Shelf catchment, Antarctica, under<br>high ice-shelf basal melt conditions" by:<br>Sanket Jantre, Matthew Hoffman, Nathan M. Urban, Trevor Hillebrand, Mauro<br>Perego, Stephen Price, John D. Jakeman</p> <p>OVERVIEW<br>--------</p> <p>Statistical models and corresponding datasets are archived in the file<br>Amery_UQ_Study_Statistical_Models.zip. That file contains a README that<br>describes its contents.<br>Contact for statistical models:<br>Sanket Jantre, Brookhaven National Laboratory, sjantre@bnl.gov</p> <p>The remaining files in this archive contain output from ice-sheet model <br>simulations using the MPAS-Albany Land Ice (MALI) model <br>(Hoffman et al. 2018, https://github.com/MALI-Dev/E3SM)<br>applied to a regional domain of the Amery Ice Shelf catchment<br>of Antarctica with mesh resolution varying from 4 to 20 km.<br>The contents of these files are described below.<br>Contact for MALI simulations:<br>Matthew Hoffman, Los Alamos National Laboratory, mhoffman@lanl.gov</p> <p>ENSEMBLES<br>---------</p> <p>The ensembles consist of all or a subset of 200 model runs with values for 6<br>parameters sampled from a Sobol' sequence over a specified range.</p> <p>Parameters and Sampled Ranges:<br>1. Ice stiffness scaling factor, Cφ: (0.8, 1.2)<br>2. Basal friction scaling factor, Cμ: (0.8, 1.2)<br>3. Basal slip exponent, q: (0.1, 0.333)<br>4. Calving yield stress, σmax: (80, 180) kPa<br>5. Ice-shelf melt coefficient, γ0: (9620, 471000) m yr−1<br>6. Ice-shelf basal melt rate, m: (12, 58) Gt yr−1</p> <p>SCENARIOS<br>---------</p> <p>The archive contains output from 4 scenarios described in the manuscript:</p> <p>Historical relaxation (RELX): For each ensemble member, we conducted a 50 year<br>relaxation from the initial condition using historical climate forcing to<br>integrate out fast transient behavior. For surface mass balance, we applied a<br>1995–2017 climatological average from RACMO2.3p1 (Van Wessem et al., 2014; van<br>den Broeke, 2019). The ocean thermal forcing was the observation-based<br>climatology compiled for ISMIP6-Antarctica, which uses data from 1995–2018<br>(Jourdain et al., 2020; Nowicki et al., 2020). The 50 year relaxation duration<br>was chosen as the most rapid adjustments occur in the first few decades of<br>integration, while the long term adjustment to a fully steady state takes<br>thousands of years. Relaxation to full steady state would require<br>substantially more computing resources than our entire set of ensembles and<br>also leads to the complication of different runs having potentially very<br>different initial states. Future improvements to model initialization that<br>account for surface elevation change (Perego et al., 2014) may reduce model<br>drift and adjust this requirement. The final model state in each run at the<br>end of RELX was given the nominal date of January 1, 2015, and all three<br>projection ensembles were branched from these states.</p> <p>Control projection (CTRL): The CTRL projection ensemble were an extension of<br>the RELX configurations, continuing the same surface mass balance and ocean<br>thermal forcing from January 1, 2015, to January 1, 2300. This ensemble was<br>used to assess model drift relative to the forced response of the climate<br>scenarios. </p> <p>SSP1-2.6 projection (SSP1): Our SSP1 projection used annual surface mass<br>balance and ocean thermal forcing derived from a UKESM SSP1-2.6 climate<br>scenario (expAE10 from Seroussi et al., 2024). Surface mass balance and ocean<br>thermal forcing were applied as anomalies relative to the climatological mean<br>forcings in RELX/CTRL to avoid issues related to climate model bias and abrupt<br>changes in forcing. This ensemble was also run from January 1, 2015, to<br>January 1, 2300.</p> <p>SSP5-8.5 projection (SSP5) Our SSP5 projection used the UKESM SSP5-8.5<br>projection forcings (expAE05 from Seroussi et al., 2024), again applied as<br>anomalies and from 2015 to 2300.</p> <p><br>OUTPUT<br>------</p> <p>Each ensemble directory contains at the base level:<br>* branch_ensemble.cfg - configuration file for the ensemble_generator test<br> case (https://mpas-dev.github.io/compass/latest/users_guide/landice/test_groups/ensemble_generator.html) <br> of COMPASS (Configuration Of Model for Prediction Across Scales Setups, https://github.com/MPAS-Dev/compass)<br> used to set up the ensemble.<br>* mesh_vars.nc - netCDF file containing the mesh variables. This file is the<br> same for all ensembles. These fields need to be appended to an output file to<br> visualize in, e.g., Paraview. This can be done with the command:<br> ncks -A mesh_vars.nc output.nc</p> <p>Each ensemble contains subdirectories for each run numbered 000-199. Each run<br>directory contains the files:<br>* globalStats.nc - scalar metrics at every time step<br>* output.nc - spatial fields at 10 year intervals<br>* run_info.cfg - summary of parameter values</p> <p>Notes:<br>* The SSP1 and SSP5 ensembles contain a subset of the full 200 runs<br> because some runs were filtered out as being unnecessary during model<br> calibration (see manuscript).<br>* The RELX ensemble is run for 50 years with an arbitrary start year of 2000.<br> The CTRL, SSP1, and SSP5 ensembles are started in 2015 from the final state<br> of the corresponding RELX run (nominally 2050). Because spatial data has<br> been saved in this archive at 10 year intervals, the first output for the<br> three projection ensembles is 2020. To get the initial condition for the<br> projection ensembles, use the 2050 state from RELX for the corresponding<br> model run.<br>* Spatial field output at 1 year intervals, as well as run input, log, and<br> restart files are available from the corresponding contact. The complete<br> run data is about 2 TB.</p> <p><br>CITATION<br>--------<br>If you find these data useful, please cite the DOI for this <br>archive (10.5281/zenodo.11166628), as well as our manuscript<br>submitted to The Cryosphere.</p> <p>This archive is approved by Los Alamos National Laboratory<br>for public release under LA-UR-24-24969; distribution is unlimited.</p> <p> </p> <p>REFERENCES<br>----------</p> <p>Hoffman, M. J., Perego, M., Price, S. F., Lipscomb, W. H., Zhang, T.,<br>Jacobsen, D., Tezaur, I., Salinger, A. G., Tuminaro, R., and Bertagna, L.:<br>MPAS-Albany Land Ice (MALI): a variable-resolution ice sheet model for Earth<br>system modeling using Voronoi grids, Geoscientific Model Development, 11,<br>3747–3780, 2018.</p> <p>Jourdain, N. C., Asay-Davis, X., Hattermann, T., Straneo, F., Seroussi, H.,<br>Little, C. M., and Nowicki, S.: A protocol for calculating basal melt rates in<br>the ISMIP6 Antarctic ice sheet projections, The Cryosphere, 14, 3111–3134,<br>https://doi.org/10.5194/tc-14-3111-2020, 2020.</p> <p>Nowicki, S., Goelzer, H., Seroussi, H., Payne, A. J., Lipscomb, W. H.,<br>Abe-Ouchi, A., Agosta, C., Alexander, P., Asay-Davis, X. S., Barthel, A.,<br>Bracegirdle, T. J., Cullather, R., Felikson, D., Fettweis, X., Gregory, J. M.,<br>Hattermann, T., Jourdain, N. C., Kuipers Munneke, P., Larour, E., Little, C.<br>M., Morlighem, M., Nias, I., Shepherd, A., Simon, E., Slater, D., Smith, R.<br>S., Straneo, F., Trusel, L. D., van den Broeke, M. R., and van de Wal, R.:<br>Experimental protocol for sea level projections from ISMIP6 stand-alone ice<br>sheet models, The Cryosphere, 14, 2331–2368,<br>https://doi.org/10.5194/tc-14-2331-2020, 2020.</p> <p>Perego, M., Price, S., and Stadler, G.: Optimal Initial Conditions for<br>Coupling Ice Sheet Models to Earth System Models, Journal of Geophysical<br>Research: Earth Surface, 119, 1894–1917, https://doi.org/10.1002/2014JF003181,<br>2014.</p> <p>Seroussi, H., et al. 2024. ISMIP6 Projections 2300 Antarctica Protocol.<br>https://theghub.org/groups/ismip6/wiki/ISMIP6-Projections2300-Antarctica. </p> <p>van den Broeke, M.: RACMO2.3p1 annual surface mass balance Antarctica<br>(1979-2014), PANGAEA - Data Publisher for Earth & Environmental Science,<br>https://doi.org/10.1594/PANGAEA.896940, 2019.</p> <p>van Wessem, J., Reijmer, C., Morlighem, M., Mouginot, J., Rignot, E., Medley,<br>B., Joughin, I., Wouters, B., Depoorter, M., Bamber, J., Lenaerts, J., Van De<br>Berg, W., Van Den Broeke, M., and Van Meijgaard, E.: Improved Representation<br>of East Antarctic Surface Mass Balance in a Regional Atmospheric Climate<br>Model, Journal of Glaciology, 60, 761–770,<br>https://doi.org/10.3189/2014JoG14J051, 2014.</p>
An assessment of basal melt parameterisations for Antarctic ice shelves
<p>This dataset contains the data and scripts for the publication "An assessment of basal melt parameterisations for Antarctic ice shelves" in <em>The Cryosphere</em>.</p> <p>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.</p> <p>The zipping has been made so that if you download and unzip everything you will have the following file structure:</p> <p>===============<br> <strong>raw/</strong></p> <ul> <li><strong>Ant_MeltingRate.v2.2.nc</strong> <em>(from RAW_other.zip):</em> 2D observational estimates of melt rates updated from Rignot et al. (2013) by Jérémie Mouginot (used in Fig. B3)</li> <li><strong>DUTRIEUX_2014/</strong> <em>(from RAW_other.zip)</em>: 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><strong>MASK_METADATA/</strong> <em>(from RAW_other.zip)</em>: 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><strong>grid_eORCA025_CDO.nc</strong> <em>(from RAW_other.zip)</em>: grid info needed to interpolate eORCA025 grid to stereographic (provided by Fabien Gillet-Chaulet)</li> <li><strong>NEMO_main/NEMO_eORCA025.L121_OPM0*_ANT_STEREO</strong><strong> </strong><em>(from RAW_nemo_OPM0*.zip)</em>: 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><strong>NEMO_appendix/NEMO_OPM0* </strong><em>(from RAW_nemo_appendix.zip)</em>: NEMO simulation output of OPM0* run used for the figures in Appendix B.</li> </ul> <p>The NEMO simulations were conducted by Pierre Mathiot.</p> <p>===============<br> <strong>interim/</strong></p> <ul> <li><strong>ANTARCTICA_IS_MASKS</strong>/ (<em>from INTERIM_ANTARCTICA_IS_MASKS.zip</em>): 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<br> => produced with 'preprocessing/isf_mask_NEMO.ipynb' and 'preprocessing/isf_mask_BedMachine.ipynb'</li> <li><strong>BOXES/</strong> (<em>from INTERIM_BOXES.zip</em>): 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<br> => produced with 'preprocessing/isf_mask_NEMO.ipynb' and 'preprocessing/isf_mask_BedMachine.ipynb'</li> <li><strong>PLUMES/ </strong>(<em>from INTERIM_PLUMES.zip</em>): contains 'nemo_5km_plume_characteristics_oneFRIS.nc' for each NEMO run and for BedMachine, which contains the variables needed to apply the plume parameterisation<br> => produced with 'preprocessing/isf_mask_NEMO.ipynb' and 'preprocessing/isf_mask_BedMachine.ipynb'</li> <li><strong>SIMPLE</strong>/ (<em>from INTERIM_SIMPLE.zip</em>) <ul> <li><strong>nemo_5km_06161821_oneFRIS/</strong>: 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*)<br> => 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> <strong>nemo_5km_OPM0*</strong>: 'thermal_forcing_term_for_linreg_corrected_oneFRIS.nc', contains the term used for the tuning through linear regression of the simple parameterisations<br> => produced via 'prepare_2D_thermal_forcing_simple.ipynb' and 'prepare_1D_thermal_forcing_term_simple_for_linreg.ipynb'</li> </ul> </li> <li><strong>NEMO_eORCA025.L121_OPM*_ANT_STEREO/ </strong>(<em>from INTERIM_geometry_interp_OPM0*.zip</em>): for each NEMO run, <ul> <li><strong>corrected_draft_bathy_isf.nc</strong>: 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<br> => produced in 'data_formatting/custom_lsmask.ipynb'</li> <li><strong>custom_lsmask_Ant_stereo_clean.nc</strong>: land-sea mask giving 0 = ocean, 1 = shelf, 2 = land<br> => produced in 'data_formatting/custom_lsmask.ipynb'</li> <li><strong>isfdraft_conc_Ant_stereo.nc</strong>: ice-shelf concentration resulting from the interpolation from the native NEMO grid to the stereographic grid<br> => produced in 'data_formatting/custom_lsmask.ipynb'</li> <li><strong>other_mask_vars_Ant_stereo.nc</strong>: contains other variables used for the masks<br> => produced in 'data_formatting/custom_lsmask.ipynb'</li> </ul> </li> <li><strong>T_S_PROF/ </strong>(<em>from INTERIM_T_S_PROF.zip</em>) <ul> <li><strong>dutrieux_2014</strong>/: observational estimates temperature and salinity profiles for Pine Island Ice Shelf in one netcdf<br> => produced in 'pre_processing/T_S_profiles_Dutrieux14.ipynb'</li> <li><strong>info_chunks.txt</strong>: contains corresponding NEMO run and start and end year for each time block used in cross-validation</li> <li><strong>T_S_mean_prof_corrected_km_contshelf_and_offshore_1980-2018_oneFRIS.nc</strong> for each NEMO run, which contains temperature and salinity profiles averaged over 5 different regions in front of each ice shelf<br> => produced in 'pre_processing/T_S_profile_formatting_with_conversion.ipynb' and 'pre_processing/T_S_profiles_front.ipynb'</li> </ul> </li> </ul> <p>===============<br> <strong>processed/MELT_RATE/</strong></p> <ul> <li><strong>BedMachine_for_comparison</strong> <em>(from PROCESSED_BedMachine_for_comparison.zip)</em>: 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<br> => produced in 'apply_params/apply_param_PIGL_dutrieux_BedMachine.ipynb'</li> <li><strong>nemo_5km_OPM*</strong> <em>(from PROCESSED_nemo_5km_OPM0*.zip)</em>: for each NEMO run <ul> <li><strong>eval_metrics_1D*.nc</strong> : 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)<br> => produced in 'apply_params/evalmetrics_results_CV.ipynb'</li> <li><strong>diff_melt_param_ref_box1_*.nc</strong>: files containing the difference between parameterised and reference melt in box1 for each point to create the left panel of Fig. F1<br> => produced in 'apply_pointbypointRMSE_box1_forFigF1.ipynb'</li> <li><strong>melt_rates_1D_NEMO_oneFRIS.nc</strong>, <strong>melt_rates_2D_NEMO.nc</strong>,<strong> melt_rates_box1_NEMO_oneFRIS.nc</strong>: reference melt rates and integrated melt<br> => produced in 'prepare_reference_melt_file.ipynb'</li> </ul> </li> </ul> <p><em>additionally, only in nemo_5km_OPM021:</em></p> <ul> <li><strong>melt_rates_2D_NEMO_timmean.nc</strong>, <strong>melt_rates_2D_boxes_timmean_oneFRIS.nc</strong>, <strong>melt_rates_2D_plumes_timmean_oneFRIS.nc</strong>, <strong>melt_rates_2D_simple_timmean_oneFRIS.nc</strong>: files containing mean patterns used in Fig. 5 and Fig.<br> => produced in 'prepare_data_Figures_5_6.ipynb'</li> </ul> <p>===============<br> <strong>ATTENTION! EXTERNAL DATA NOT CONTAINED IN THIS REPOSITORY:</strong></p> <ul> <li>Observational estimates of melt rates for Pine Island Ice Shelf (from Shean et al., 2019, for Fig. 8a)<br> => 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> <p>=====================</p> <p>The explanation around the scripts can be found in README.rst with the scripts in <strong>SCRIPTS.zip</strong>.<br> <em>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></em>, <em>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></em></p> <p>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></p>
2020 Multiyear Ice Region Summer Melt Data
<p>Sentinel-2 image classification and ICESat-2 melt pond depth results from:</p> <p>Buckley, E. M., Farrell, S. L., Herzfeld, U. C., Webster, M., Trantow, T., Baney, O. N., Duncan, K., Han, H., Lawson, M. (2023). Observing the Evolution of Summer Melt on Multiyear Sea Ice with ICESat-2 and Sentinel-2 [Manuscript in Preparation].</p>
Data for "Gullies on Mars could have formed by melting of water ice during periods of high obliquity."
<p>Code, movies and climate model outputs for "Gullies on Mars could have formed by melting of water ice during periods of high obliquity" by Dickson et al. Science, 2023.</p>
Role of breakage, melt and floe size distribution on sea ice summer transition
<p>Marginal ice zones are composed of individual sea ice floes, whose breakage and melt determine its summer behavior. These processes are not well represented by global or regional climate models due to the continuum approximations used for sea ice. Here, we use a Discrete Element Model (DEM) coupled to a slab thermodynamic ocean to investigate how breakage and melt processes impact the decay of summer sea ice. The DEM is calibrated using MODIS satellite imagery and reanalysis data within the Arctic Ocean’s Baffin Bay during June-July 2018. The sensitivity of the sea ice decay is evaluated by varying the solar heating, ice/ocean heat exchange parameter, and a prescribed floe breakage rate. For the parameter regime that best fits observations, the ratios of mass loss of resolved floes (>2 km) due to breakage versus melt is 0.47, and oceanic versus solar melt is 0.46. The rate at which resolved floes lose mass is most sensitive to the breakage rate, as compared to the solar and oceanic melt parameters. The number decay of the largest floes (>21 km) is controlled by breakage, while decay of smaller floes (2 - 21 km) depends strongly on lateral melt. Inferences from this exploration of parameter space may help develop more accurate parameterizations of the floe size distribution evolution in climate models.</p>
Replication data for: Future response of Antarctic continental shelf temperatures to ice shelf basal melting and calving
<p>Replication data for: Future response of Antarctic continental shelf temperatures to ice shelf basal melting and calving</p> <p>This manuscript is in press at Geophysical Research Letters.</p> <p><a href="https://zenodo.org/api/files/e04b47c6-d136-4833-b8c6-a79f5b05eb65/SSP585FW_ensemble.tar.gz?versionId=6f33274b-a436-4850-99a8-9dac047b812b">SSP585FW_ensemble.tar.gz</a> contains data needed to replicate the figures and analysis in the manuscript.</p>
Ice-shelf melting around Antarctica
Open the record for dataset details and reuse information.
Radar dataset for 'Two layers of melting ice particles within a single radar bright band' study
<p>Radar dataset for 'Two layers of melting ice particles within a single radar bright band' study.</p> <p>Site: The University of Helsinki Station for Measuring Ecosystem-Atmosphere Relation II (SMEAR II), Hyytiälä, Southern Finland (61.845N, 24.287E).</p> <p>Time: April 18th - 19th, 2018 UTC.</p> <p>Instruments: Vertically pointing W- and C- band radars (HYDRA-W and -C). In addition to reflectivity, LDR, Doppler velocity and spectrum width that are observed by both radars, HYDRA-W records Doppler spectra. HYDRA-C operates with the maximum unambiguous Doppler velocity of 4.98 m/s. The settings of the HYDRA-W observations depend on the range. Below 996 m, the range resolution is 25.5 m and Doppler spectra are computed by using a 1024-point FFT with the Nyquist velocity of 10.24 m/s. For the heights between 996 m and 3577 m, the range resolution is kept the same, while the Doppler spectrum is computed using 512-point FFT with the Nyquist velocity of 5.12 m/s. For the heights above 3577 m, the range resolution is decreased to 34 m, while the Doppler measurements are done using the same settings. The temporal resolution of HYDRA-C and HYDRA-W measurements are 1.37 and 3.35 s, respectively. </p>
Greenland ice sheet surface melt projections using artifical neural networks
<p>Surface melt projections for the Greenland ice sheet using artificial neural networks.</p> <p>Organized as follows:</p> <p><scenario>/<variable>/<model>/<ensemble_number>.nc</p>
Data from: Ground ice melt in the high Arctic leads to greater ecological heterogeneity
1. The polar desert biome of the Canadian high Arctic Archipelago is currently experiencing some of the greatest mean annual air temperature increases on the planet, threatening the stability of ecosystems residing above temperature-sensitive permafrost. 2. Ice wedges are the most widespread form of ground ice, occurring in up to 25% of the world's terrestrial near-surface, and their melting (thermokarst) may catalyze a suite of biotic and ecological changes, facilitating major ecosystem shifts. 3. These unknown ecosystem shifts raise serious questions as to how permafrost stability, vegetation diversity, and edaphic conditions will change with a warming high Arctic. Ecosystem and thermokarst processes tend to be examined independently, limiting our understanding of a coupled system whereby the effect of climate change on one will affect the outcome of the other. 4. Using in-depth, comprehensive field observations and a space-for-time approach, we investigate the highly structured landscape that has emerged due to the thermokarst-induced partitioning of microhabitats. We examine differences in vegetation diversity, community composition, and soil conditions on the Fosheim Peninsula, Ellesmere Island, Nunavut. We hypothesize that: (i) greater ice wedge subsidence results in increased vegetation cover due to elevated soil moisture, thereby decreasing the seasonal depth of thaw and restricting groundwater outflow; (ii) thermokarst processes result in altered vegetation richness, turnover, and dispersion, with greater microhabitat diversity at the landscape scale; (iii) shifts in hydrology and plant community structure alter soil chemistry. 5. We found that the disturbance caused by melting ice wedges catalyzes a suite of environmental and biotic effects: topographical changes, a new hydrological balance, significant species richness and turnover changes, and distinct soil chemistries. Thermokarst areas favour a subset of species unique from the polar desert and are characterized by greater species turnover (β-diversity) across the landscape. 6. Synthesis. Our findings suggest that projected increases of thermokarst in the polar desert will lead to the increased partitioning of microhabitats, creating a more heterogeneous high arctic landscape through diverging vegetation communities and edaphic conditions, resulting in a wetland-like biome in the high Arctic that could replace much of the ice-rich polar desert.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.