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223 results for “model ice”
Data from: Prediction model for aerodynamic coefficients of iced quad bundle conductors based on machine learning method
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Getz Ice Shelf Model Setup and Results
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Initial condition and forcing files for the circulation and sea ice MITgcm model for the West Antarctic Peninsula
<p>This dataset contains the initial condition, grid and forcing files used to run a circulation and sea ice model for the West Antarctic Peninsula (WAP), in hindcast experiments that simulated the period between 1980-2014. The files include glacial runoff, atmospheric forcings, boundary conditions, shelf ice files and grid. </p>
Output of CAM simulations performed for study "Impact of cloud physics on the Greenland Ice Sheet near-surface climate: a study with the Community Atmosphere Model"
<p>Output of CAM simulations performed for study "Impact of cloud physics on the Greenland Ice Sheet near-surface climate: a study with the Community Atmosphere Model" in JGR-Atmospheres (2020). </p> <p>Output are NetCDF files containing annual means (named 'yearmean', 2007-2013), or multi-annual monthly means ('ymonmean', 2007-2012) of various variables that are of interest and/or used for analysis in this study. The file name starts with the variable name. Fields are global, at a resolution of 0.9 x 1.25 degrees latitude/longitude.</p> <p>The test simulations are named (as discussed in the paper):</p> <p>cam4_clm5<br> cam5_clm5<br> cam6_noicenucl_clm5<br> cam6_noclubb_clm5<br> cam6_mg1_clm5<br> cam6</p>
Finite Volume Community Ocean Model-based Arctic Ocean Forecast System: A Comprehensive Assessment of Sea Ice Forecast Results without Data Assimilation
<p>The dataset contains sea ice forecast results from the Finite Volume Community Ocean Model-based Arctic Ocean Forecast System (FVCOM-AOFS) for the period 2019-2020. The output result of the system is presented as daily mean values.</p><p>Each netCDF file within the dataset includes the following variables: lon, lat, lonc, latc, aice, vice, uuice, and vvice. Specifically, the variables of lon and lat represent the longitude and latitude of the unstructured triangular grid. The variables of lonc and latc represent the longitude and latitude of the unstructured triangular cell. The variables of aice and vice indicate sea ice concentration and sea ice thickness. The variables of uuice and vvice indicate eastward and northward sea ice drift velocity. The scalars including aice and vice use lon and lat coordinates, and the vectors including uuice and vvice use lonc and latc coordinates. </p>
Supplementary material for "The comparative role of physical system processes in Hudson Strait ice stream cycling: a comprehensive model-based test of Heinrich event hypotheses"
<p>This supplementary material for "The comparative role of physical system processes in Hudson Strait ice stream cycling: a comprehensive model-based test of Heinrich event hypotheses" includes two animations showing a full Hudson Strait surge cycle with the default GSM heat flux.</p>
Model simulation data used in "Impacts of ice-nucleating particles on cirrus clouds and radiation derived from global model simulations with MADE3 in EMAC" (Beer et al., Atmos. Chem. Phys., 2024)
<p>This dataset contains the namelist setup and the output of the EMAC global model simulations analysed and discussed in Beer et al. (<i>Atmos. Chem. Phys.</i>, 2024). For details see the README.md file and Table 2 in the paper.</p>
Post-processed output for paper "Spatial and interannual variability of the Antarctic Slope Current in an eddying ocean-sea ice model"
<p>This dataset contains processed output of the ACCESS-OM2-01 global ocean-sea ice model.</p> <p>The processed output was used for analyses of the paper:</p> <p>Huneke W. G. C., Morrison A. K., Hogg A. McC. Spatial and interannual variability of the Antarctic Slope Current in an eddying ocean-sea ice model, 2022, Journal of Physical Oceanography, doi:10.1175/JPO-D-21-0143.1</p> <p>The Github repository github.com/wghuneke/ASC_SpatialTemporalVariability contains Jupyter notebooks and instructions for how to produce these files from the raw model output.</p> <p>Contact wilma.huneke@anu.edu.au if you want access to the ACCESS-OM2-01 raw model output itself.</p>
Sea ice model outputs developed in "Modelling Antarctic sea ice variability using a brittle rheology"
<p>Output files from the BBM, mEVP and mEVP+ runs developed in the paper entlited "Modelling Antarctic sea ice variability using a brittle rheology". </p> <p>Paper abstract:</p> <p>Sea ice is a composite solid material that sustains large fracture features at scales from metres to kilometres which can play an important role in coupled atmosphere-ocean processes. To model these features, brittle sea ice physics, via the Brittle-Bingham-Maxwell (BBM) rheology, has been implemented in the Lagrangian neXt generation Sea Ice Model (neXtSIM). In Arctic-only simulations the BBM rheology has shown a capacity to represent observationally consistent sea ice fracture patterns and breakup across a wide range of time and length scales. Still, it has not been tested whether this approach is suitable for the modelling of Antarctic sea ice. Here, we introduce a new 50-km grid-spacing Antarctic configuration of neXtSIM, neXtSIM-Ant, using the BBM rheology. We evaluate this simulation against observations of sea ice extent, drift, and thickness and compare it with identically-forced neXtSIM simulations that use the standard modified Elastic-Visco-Plastic (mEVP) rheology. In general, using BBM results in thicker sea ice and an improved representation of sea ice drift than mEVP. We suggest that this is related to short-duration breakup events caused by Antarctic storms that are not captured in the viscous-plastic model. </p>
Numerical model setup for studying the energetics of the ocean current under sea ice
<p>This dataset contains model code and input files for studying the energetics of the ocean current under sea ice. The model used in this study is the Massachusetts Institute of Technology general circulation model (MITgcm). Original source code is the MITgcm_c66m.</p> <p>code: contains code configuration for Exp-PD, Exp-D, Exp-P, and Exp-PD100.</p> <p>code_F: contains code configuration for Exp-PF.</p> <p>input_PD: contains parameter settings and input files for the base experiment Exp-PD (initial perturbation + ice-ocean drag).</p> <p>input_D: contains parameter settings and input files for the sensitivity experiment Exp-D (no initial perturbation).</p> <p>input_P: contains parameter settings and input files for the sensitivity experiment Exp-P (no ice-ocean drag).</p> <p>input_PD100: contains parameter settings and input files for the sensitivity experiment Exp-PD100 (a restart of Exp-P from day 100 and is run for 100 days with the ice-ocean drag).</p> <p>input_F: contains parameter settings and input files for the sensitivity experiment Exp-PF (no sea ice model; Forcings include surface stress, shortwave radiation, and heat and freshwater fluxes diagnosed from Exp-PD).</p>
Data for "The Stochastic Ice-Sheet and Sea-Level System Model v1.0 (StISSM v1.0)" by Verjans et al.
<p>Results and scripts to reproduce figures of The Stochastic Ice-Sheet and Sea-Level System Model v1.0 (StISSM v1.0)</p> <p>Input files, preprocessing, run control and postprocessing scripts for all simulations are also provided.</p> <p>See readme.txt for details.</p> <p>Update 22 November 2022: use v2 of code files for updated version of StISSM</p> <p>by Verjans et al.</p>
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>
Contribution to High Asia Runoff from Ice and Snow (CHARIS) Melt Model Output, 2001 - 2014, Version 1
This data set contains input and output data for temperature index (TI) model runs completed for the Contributions to High Asia Runoff from Ice and Snow (CHARIS) project at NSIDC in 2018 and 2019. The input data are the area of snow on land, snow on ice, and exposed glacier ice as well as surface air temperature. These inputs are used to model the volumes of melt runoff from the snow on land, snow on ice, and exposed glacier ice in certain areas of High Mountain Asia.
Sea-ice Thickness and Draft Statistics from Submarine ULS, Moored ULS, and a Coupled Model, Version 1
This data set consists of estimates of mean values of sea-ice thickness and sea-ice draft in meters computed from three different input data sets: sea ice draft from Upward Looking Sonar (ULS) mounted on submarines, sea ice draft from ULS mounted on bottom-anchored moorings, and a simulated ice-thickness model. The data span from 1979 to 2004 and cover two regions centered around the North Pole.
Experimental data of manuscript "A new DL sea ice detection model OceanTDLx for SAR image"
<p>Experimental data of manuscript.</p>
MEaSUREs Greenland Ice Mapping Project (GIMP) Digital Elevation Model V001
This data set consists of an enhanced resolution digital elevation model (DEM) for the Greenland Ice Sheet. It was constructed by combining ASTER and SPOT 5 DEMs over the ice sheet periphery and margin with AVHRR photoclinometry for the interior and far north, and calibrating the data to approximate mean ICESat/GLAS elevations from 2003 to 2009. See <a href="http://nsidc.org/data/measures/gimp">Greenland Ice Mapping Project (GIMP)</a> for related data.
MEaSUREs Greenland Ice Mapping Project (GrIMP) Digital Elevation Model from GeoEye and WorldView Imagery V002
This data set consists of an enhanced resolution digital elevation model (DEM) for the Greenland Ice Sheet, derived from sub-meter resolution, panchromatic stereoscopic imagery collected by the GeoEye-1, WorldView-1, -2, and -3 satellites operated by Maxar Technologies. The DEM was created from in-track image pairs (i.e., both images collected minutes apart along the same orbital pass) and cross-track images (i.e., from different orbits) within the in-track imaging geometry and maximum time separation criteria. The DEM is registered to ATLAS/ICESat-2 L3A Land Ice Height, Version 5 (ATL06, V5) data collected in the summers of 2019 and 2020. See <a href="http://nsidc.org/data/measures/gimp">Greenland Ice Mapping Project (GrIMP)</a> for related data
NASA 3D Models: Aeronomy of Ice
AIM is a two-year mission to study Polar Mesospheric Clouds (PMCs), the Earth's highest clouds, which form an icy membrane 50 miles (80.4 km) above the surface at the edge of space.
"Basal conditions of Denman Glacier from glacier hydrology and ice dynamics modeling" datasets
<p>Data for hydrology and inversions in "Basal conditions of Denman Glacier from glacier hydrology and ice dynamics modeling"</p> <p>Denman_hydrology.mat - hydrology outputs from GlaDS.</p> <p>SchoofNoiseX.mat - stressbalance models with perturbed meshes, Schoof friction law, and GlaDS effective pressure extrapolated to the ISSM domain</p> <p>SchoofCapX.mat - stressbalance models with Schoof friction law, and GlaDS effective pressure extrapolated to the ISSM domain capped at X% of overburden</p> <p>SchoofPrescribed.mat - stressbalance model with Schoof friction law and prescribed effective pressure</p> <p>BuddPrescribed.mat - stressbalance model with Budd friction law and prescribed effective pressure</p> <p>SchoofPrescribedExtrapolation.mat - stressbalance model with Schoof friction law, GlaDS effective pressure for the GlaDS domain and prescribed effective pressure for the rest of the grounded domain</p> <p>BuddPrescribedRigidity.mat - stressbalance model with Budd friction law and prescribed effective pressure, ice rigidity is inverted for after the Budd friction coefficient</p> <p>SchoofPrescribedRigidity.mat - stressbalance model with Schoof friction law and prescribed effective pressure, ice rigidity is inverted for after the Schoof friction coefficient</p> <p>BuddMain - The main stressbalance model with Budd friction law and inverted rigidity used in the paper</p> <p>SchoofMain - The main stressbalance model with Schoof friction law and inverted rigidity used in the paper</p> <p>Buddm3 - stressbalance model with Budd friction law m=1/3 and GlaDS effective pressure</p> <p>Buddm3Prescribed - stressbalance model with Budd friction law m=1/3 and prescribed effective pressure</p> <p>SchoofCmax0X - stressbalance model with Schoof friction law C_max=0.X and GlaDS effective pressure</p> <p>SchoofCmax0XPrescribed - stressbalance model with Schoof friction law C_max=0.X and GlaDS effective pressure</p> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.