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61 results for “Ice shelf”
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>
MITgcm model setup and output for "What determines the shape of the Pine-Island-like ice shelf?"
<p><strong>(Contents)</strong><br> Here it contains<br> <br> jim_run3 = CTRL (Similar to Jordan et al., 2018 but with smaller ocean)</p> <p>melt/run/ = IOCTRL, M(all)V(dyn)U(dyn)</p> <p>melt2/ = M( changing, see below ) V( dyn ) U( 0 )<br> melt2/run/ = M(all)V(dyn)U(0)<br> melt2/run2/ = M(20)V(dyn)U(0)<br> melt2/run3/ = M(GL10)V(dyn)U(0)<br> melt2/run4/ = M(GL20)V(dyn)U(0)</p> <p><br> melt3/ = M ( changing, see below ) V( changing ) U( 0 )<br> melt3/run5/ = M(all)V(2000)U(0)<br> melt3/run10/ = M(20)V(2000)U(0)</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> Input melt data for running ice only experiments<br> #data.streamice#<br> meltCTRL.bin = M(all) ... melt rate data of CTRL for every time steps <br> meltCTRL2.bin = M(GL10)<br> meltCTRL3.bin = M(20 m/a)<br> meltCTRL4.bin = M(GL20)</p> <p>Input velocity data for running ice only experiments <br> melt3/<br> uvel_ext4.bin = U(0 m/a)<br> vvel_ext5.bin = V(2000 m/a)</p> <p><strong>(How to compile and run)</strong><br> mkdir build<br> cd build/<br> module load intel/2021.2.0<br> module load impi/2021.2.0<br> export LANG=en_US.UTF-8<br> export LC_ALL=en_US.utf8<br> ../../../tools/genmake2 -of ../../../tools/build_options/linux_amd64_ifort+mpi_ice_nas_tokyo3 -mpi -mods ../code/<br> make depend<br> export LANG=en_US.UTF-8<br> export LC_ALL=en_US.utf8<br> make -j 16</p> <p> mkdir ../run<br> cd ../run/<br> cp ../build/mitgcmuv .<br> cp ../input/* .<br> pjsub job*.pbs<br> <br> <strong>Other important links</strong><br> https://github.com/hgu784/MITgcm_67s<br> <br> For more info please send an email to Yoshihiro.Nakayama@lowtem.hokudai.ac.jp</p>
Icequake-magnitude scaling relationship along a rift within the Ross Ice Shelf, Antarctica
<p>This data set contains the icequake catalog used for Huang et al. (2022).</p>
Data for manuscript 'The response of Ross Sea shelf water properties to enhanced Amundsen Sea ice shelf melting'
<p>Data for manuscript 'The response of Ross Sea shelf water properties to enhanced Amundsen Sea ice shelf melting'.</p> <p>This repository contains the average data for the last 5 years for each sensitivity experiment used for analysis and processed data used to generate the figures in the manuscript.</p>
Thwaites Glacier thins and retreats fastest where ice-shelf channels intersect its grounding zone, dataset+code
<p>Thwaites Glacier thins and retreats fastest where ice-shelf channels intersect its grounding zone, updated dataset+code submitted for publication in The Cryosphere. Dataset includes all data produced in this study, including velocity maps derived from speckle tracking of Sentinel 1 images, maps of rates of ice shelf change and the annual mosaic digital surface models from which they were derived, maps of the basal conditions at Thwaites glacier, shapefiles and masks of the annual hydrostatic boundary (grounding line proxy position), and shapefiles of all hydrostatic boundary features, ice shelf basal channels and surface depressions, intermediate polygons used to filter the features, and digital surface model strips with registration data included as attributes. </p>
Amery Ice Shelf Grounding Line Datapoints Extraction from Airborne Ice-penetrating Radar
<p>We present a new grounding line product for Amery Ice Shelf - the ice-penetrating radar-derived grounding line points. The 137 grounding line points were identified by 53 survey lines from 2017 to 2020 and classified into three categories. The 'Class 1' points are extracted by significant echo reflection changes between ice-bed and ice-seawater interfaces along survey lines with continuous signal and have the highest accuracy. The 'Class2' and 'Class 3' points were derived from survey lines with 'fuzzy region' with lower accuracy. The mean interval of radar-derived points is 16.4 km. The 'best case' radar-derived positions (Class 1) and those from satellite data reveals a mean separation of 1.00±1.16 km. Two products are available: (1) ice thickness data of Amery Ice Shelf from ice-penetrating radar lines; (2) radar-derived grounding line position (137 points). The ice thickness was calculated by ice surface and bottom signal and the unit is m. The radar-derived grounding line position have six fields, latitude, longitude, id, ice thickness, date (for collecting the radar data) and Class (the category of point).</p>
Data supporting 'Modeling Antarctic ice shelf basal melt patterns using the one-Layer Antarctic model for Dynamical Downscaling of Ice--ocean Exchanges (LADDIE v1.0)'
<p>This data set contains the data produced for the paper 'Modeling Antarctic ice shelf basal melt patterns using the one-Layer Antarctic model for Dynamical Downscaling of Ice--ocean Exchanges (LADDIE v1.0)'</p> <p>The data set contains output from LADDIE simulations, including basal melt rates.</p> <p>The main simulations used in the man text are:</p> <p>- Crosson-Dotson: <a href="https://zenodo.org/api/files/0640a922-97a9-4ced-ad3d-dab66f05c696/CrossDots_0.5_tanh_Tdeep0.4_ztcl-500_050.nc">CrossDots_0.5_tanh_Tdeep0.4_ztcl-500_050.nc </a><br> - Filchner-Ronne: <a href="https://zenodo.org/api/files/0640a922-97a9-4ced-ad3d-dab66f05c696/FRIS_1.0_linear_S134.8_T1-2.3_720.nc">FRIS_1.0_linear_S134.8_T1-2.3_720.nc </a><br> </p> <p>The additional simulations included in the Appendix are:</p> <p>- 3D forcing of Crosson-Dotson: <a href="https://zenodo.org/api/files/0640a922-97a9-4ced-ad3d-dab66f05c696/CrossDots_0.5_mitgcm_2003_2008_100.nc">CrossDots_0.5_mitgcm_2003_2008_100.nc </a><br> - Tuning of Crosson-Dotson: XX_YY_ZZ.nc, where XX is the resolution in km, YY is the value for Cd,top, and ZZ is the value for Dmin<br> - Pine Island Ice Shelf: <a href="https://zenodo.org/api/files/0640a922-97a9-4ced-ad3d-dab66f05c696/PIG.nc">PIG.nc </a><br> </p>
Data from: Late spring nitrate distributions beneath the ice-covered northeastern Chukchi Shelf
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Data for: Grounding zone of Amery Ice Shelf, Antarctica, from differential synthetic-aperture radar interferometry
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Assets for "Simulation-Based Inference of Surface Accumulation and Basal Melt Rates of an Antarctic Ice shelf from Isochronal Layers"
<p>Experimental data reported in "Simulation-Based Inference of Surface Accumulation and Basal Melt Rates of an Antarctic Ice shelf from Isochronal Layers".</p><p>Contains processed simulation data for both synthetic and Ekström Ice Shelf experiments. Selected best layers for each Internal Radar Horizons in the respective simulated datasets are contained in "all_layers_final.p", and the respective mass balance parameters for those simulations in "all_mbs_final.p".</p><p>Trained Neural Posterior Estimator (NPE) models for each IRH are found in "inference.p" files, and posterior predictive simulations in "post_predictive.p" files for each posterior respectively. Code to visualise and plot this data is found in https://github.com/mackelab/sbi-ice.</p>
Diverse impacts of sea ice and ice shelf melting on phytoplankton communities in the Cosmonaut Sea, East Antarctica
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Evolutionary changes in the fast ice along the front of the Scar Inlet Ice Shelf from 2005 to 2019
<p>Evolutionary changes in the fast ice along the front of the Scar Inlet Ice Shelf from 2005 to 2019</p>
Summertime productivity and carbon export potential in the Weddell Sea, with a focus on the waters adjacent to Larsen C Ice Shelf
<p>The data stored here are part of the manuscript entitled "Summertime productivity and carbon export potential in the Weddell Sea, with a focus on the waters adjacent to Larsen C Ice Shelf" published in European Geosciences Union: Biogeosciences. https://doi.org/10.5194/bg-2021-122</p> <p>The spreadsheet includes oceanographic data, namely CTD data, nutrient concentrations, phytoplankton nitrogen (N) and carbon (C) uptake rates, and phytoplankton by microscopy and flow cytometry. </p>
Getz Ice Shelf Model Setup and Results
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SUMER Antarctic Ice-shelf Buttressing, Version 1
This data set, part of the French National Research Agency’s project on Survey and Modelling of East Antarctica (SUMER), consists of high-resolution information about ice-shelf buttressing for the whole of Antarctica. Buttressing is inferred from known ice geometry and ice motion with the Elmer/Ice ice flow model. Input sources are Bedmap2, MEaSUREs surface ice velocities, and the MEaSUREs grounding-line positions.
MetROMS model output for manuscript "Dynamic response to ice shelf basal meltwater relevant to explain observed sea ice trends near the Antarctic continental shelf"
<p>This dataset contains output of the MetROMS ocean/sea-ice/ice-shelf model.</p><p>The output was used for analyses of the manuscript:</p><p>Huneke W. G. C., Hobbs W.R., Klocker A., Naughten K. A., 2023, Dynamic response to ice shelf basal meltwater relevant to explain observed sea ice trends near the Antarctic continental shelf, Geophysical Research Letters, 50, e2023GL105435, https://doi.org/10.1029/2023GL105435</p><p>The Github repository https://github.com/wghuneke/MetROMS_BasalMelt_Perturbation/tree/main contains analysis (python) scripts for processing and visualising the model output.</p><p>Contact wilma.huneke@anu.edu.au if you need further information.</p>
MITgcm model setup and output for "Submesoscale variability and basal melting in ice shelf cavities of the Amundsen Sea"
<p>Here, it contains the results of the high-res eastern AMS simulation. A detailed description of the model configuration and model output is provided by Nakayama et al.,2019.</p><p>Nakayama, Yoshihiro, Georgy Manucharyan, Hong Zhang, Pierre Dutrieux, Hector S. Torres, Patrice Klein, Helene Seroussi, Michael Schodlok, Eric Rignot, and Dimitris Menemenlis. "Pathways of ocean heat towards Pine Island and Thwaites grounding lines." <i>Scientific reports</i> 9, no. 1 (2019): 16649.</p><p>Due to space limitations, please access NASA data for all other model daily outputs (Registration is required). https://ecco.jpl.nasa.gov/drive/files/ECCO2/High_res_PIG/AMS_200m. </p><p>Contents can be downloaded easily using wget (see link below).<br>https://ecco-group.org/docs/wget_download_multiple_files_and_directories.pdf</p><p>(Contents)<br>code.zip (code to run this simulation)<br>input_ctrl.zip (input file required for this simulation <br>input_nomelt.zip (input file required for this simulation <br>results_ctrl.zip (days 30 and 60))<br>results_nomelt.zip (days 30 and 60))<br>Caution: You need to download heavier input files to rerun this simulation from ECCO-Drive. <br><br>(How to build and run)<br>mkdir build<br>./../../tools/genmake2 -of ../../../tools/build_options/linux_amd64_ifort+mpi_ice_nas -mpi -mods ../code/<br>make depend<br>make -j 16<br>cd ..<br>mkdir test<br>cd test<br>ln -sf ../input/* .<br>ln -sf /nobackup/hzhang1/forcing/era_xx_it33/ .<br>cp ../build/mitgcm_uv .<br>qsub run8_sandy_tracer_init_cont_2.pbs</p>
Dataset for Barotropic tides in MPAS-Ocean (E3SM V2): impact of ice shelf cavities (GMD, 2023)
<p>This is the data set containing initial condition and parameter files for the results in our paper.</p>
MEaSUREs ITS_LIVE Antarctic Quarterly 1920 m Ice Shelf Height Change and Basal Melt Rates, 1992-2017 V001
This ITS_LIVE data set, part of the Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, includes quarterly estimates of Antarctic ice shelf surface elevation, thickness, basal melt rate, surface mass balance, firn air content, and associated errors, from 17 March 1992 through 16 December 2017 at 1920 m resolution. The data were generated from four European Space Agency (ESA) satellite radar altimetry missions—ERS-1, ERS-2, Envisat, and CryoSat-2—using a novel data fusion approach and the Glacier Energy and Mass Balance model (GEMB).
Accelerating ice mass loss across Arctic Russia in response to Atlantification of the Eurasian Arctic Shelf Seas
<p>This file contains three datasets of geophysical data compiled from satellite observations made over the archipelagos of Novaya Zemlya and Severnaya Zemlya in the Russian Arctic between 2010 and 2018:</p> <p>- maps of surface elevation change (dh) over the entire glaciated area of the two regions, and rasterised ice masks (source RGI 6.0) of both land- and marine-terminating glaciers and ice caps; </p> <p>- time series of surface elevation change (dh) at 90-day time steps over single glacier and ice cap basins, and over larger areas (control domains) defined as follows: B1N, B2N, B3N, B4N, K1N, K2N, K3N (Novaya Zemlya), and K1S, K2S, L1S, L2S, L3S (Severnaya Zemlya);</p> <p>- climate forcing (T2m, SST, SIC and SOTF1-3) averaged over each of the 12 aforementioned control areas. The climate data is presented as time series, means and longer term trends of seasonal (90-days) anomalies with respect to a pre-defined baseline period.</p> <p>A more complete description of these datasets will be provided in the publication with the same name (currently in review).</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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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.
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