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223 results for “model ice”
DALROMS-NWA12 v1.0, a coupled circulation-sea ice-biogeochemistry model for the northwest North Atlantic: output files (2 of 2)
<p>DALROMS-NWA12 v1.0 is a coupled circulation-sea ice-biogeochemistry modelling system based on ROMS, CICE, and MCT. The model domain covers the North Atlantic Ocean from ~81 deg W to ~39 deg W and ~33.5 deg N to 76 deg N. This record includes daily-mean output of all (ocean circulation, sea ice, and biogeochemistry) modules for September 2015. Similar files for September 2013 are available at <a href="https://doi.org/10.5281/zenodo.12744506" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12744506</a>.</p> <p>Model codes, scripts for compiling the model, and sample CPP header files and runtime parameter files (namelists) for physics-only simulations are available at <a href="https://doi.org/10.5281/zenodo.12752091" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12752091</a>. CPP header and runtime parameter files for the biogeochemistry module are available upon request.</p> <p>Sample input files for physics-only simulations are available at <a href="https://doi.org/10.5281/zenodo.12752190" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12752190</a>, <a href="https://doi.org/10.5281/zenodo.12734049" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12734049</a>, and <a href="https://doi.org/10.5281/zenodo.12735153" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12735153</a>. Input files for the biogeochemistry module are available upon request.</p>
Sensitivity of Arctic Clouds to Ice Microphysical Processes in the NorESM2 Climate Model
<div> <div> <div> <p>Thermodynamic and microphysical data (matlab files) for NorESM2 simulations presented in the article "Sensitivity of Arctic Clouds to Ice Microphysical Processes in the NorESM2 Climate Model" </p> </div> </div> </div>
Model results for `Surface pond energy absorption across four Himalayan glaciers accounts for 1/8 of total catchment ice loss'
<p>Model setup (setup.mat) and outputs (allkeyres.mat, postproc.mat) for 5000 runs of Monte Carlo supraglacial pond energy-balance modelling in the Langtang catchment of Nepal. The full set of results are included for the median model run (run_..._n1645.zip).</p> <p>Also included are flux gate results for calculation of emergence velocity (fgates...zip).</p>
Compressive Strength of Model Ice Measurements
<p>This data package contain a detailed measurement report and the measurements data from the mechanical property measurements conducted in HSVA Large Ice Tank.<br>This work is part of HYDRALAB+ Adaptation for Climate Change – project that was supported by the European Community's Horizon 2020 Programme through the <br>grant to the budget of the Integrated Infrastructure Initiative HYDRALAB+, Contract no. 654110. The measurements are a part of the deliverable D9.5 Collection of Benchmark data sets of Joint Research Activities (JRA) 2 – COMPLEX (Cross disciplinary Observations of Morphodynamics and Protective structures, Linked to Ecology and eXtreme events) within Hydralab+ project.</p> <p>The focus of the tests was to provide compressive strength measurement data when the target flexural strength and aspect ratio (length to width ratio) is varied. The Young's modulus, flexural strength, salinity, and density are reported along previous lines. Additionally, the measurement data, raw and filtered, from the compressive strength measurements is provided. The provided measurement data was saved in ASCII format in .txt files. The files are named according to the number of the test, 001.txt etc. The first column in the files is the time stamp in seconds. The second column is the raw unfiltered data in Newtons. The third column is the filtered measurement data in Newtons.</p>
Secondary ice production parameterization output - COSMO model
<p>Secondary ice production via processes like rime splintering, frozen droplet shattering, and breakup upon ice hydrometeor collision have been proposed to explain discrepancies between in-cloud ice crystal and ice-nucleating particle numbers. To understand the impact of this kind of additional ice number generation on surface precipitation, we present one of the first studies to implement frozen droplet shattering and ice-ice collisional breakup parameterizations in a larger-scale model. We simulate a cold frontal rainband from the Aerosol Properties, PRocesses, And InfluenceS on the Earth's Climate campaign and investigate the impact of the new parameterizations on the simulated ice crystal number concentrations (ICNC) and precipitation. Near the convective regions of the rainband, contributions to ICNC can be as large from secondary production as from primary nucleation, but ICNCs greater than 50 L-1 remain underestimated by the model. Addition of the secondary production parameterizations also clearly intensifies the differences in both accumulated precipitation and precipitation rate between the convective towers and non-convective gap regions. We suggest, then, that secondary ice production parameterizations be included in large-scale models on the basis of large hydrometeor concentration and convective activity criteria.</p>
Sea ice model fields shown in paper titled "E3SMv0-HiLAT: A Modified Climate System Model Targeted for the Study of High Latitude Processes
<p>These files contain the full climatology, averaged over years 234-253 of the E3SMv0-HiLAT model preindustrial simulation, as generated by the CESM diagnostic package. Files are in netcdf format, with fields described within the file (and subsequently compressed).</p>
Ice-sheet model simulation ensembles (produced Fall 2018)
<p>Ice-sheet model simulation ensembles over the last interglacial and a future high emissions scenario (RCP8.5), and varied over two model parameters (CREVLIQ and CLIFVMAX). The data was pickled as a pandas dataframe with python 3, and can be retrieved by loading using pickle with the same version.</p>
Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6-LIM3 global ocean–sea ice model
<p>Model output and observational data and scripts corresponding to the manuscript "Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6-LIM3 global ocean–sea ice model"</p> <p><strong>Abstract. </strong></p> <p>This study assesses the impact of different sea ice thickness distribution (ITD) configurations on the sea ice concentration (SIC) variability in ocean-standalone NEMO3.6-LIM3 simulations. Three ITD configurations with different numbers of sea ice thickness categories and boundaries are evaluated against three different satellite products (hereafter referred to as “data”). Typical model and data interannual SIC variability is characterized by k-means clustering both in the Arctic and Antarctica between 1979 and 2014 in two seasons, January–March and August–October, which show the largest coherence across clusters in individual months. Analysis in the Arctic is done before and after detrending the series with a 2nd degree polynomial to separate interannual from longer-term variability.</p> <p>Before detrending, winter clusters capture SIC response to atmospheric variability at both poles and summer cluster a positive and negative trend in the Arctic and Antarctic SIC respectively. After detrending, Arctic clusters reflect SIC response to interannual atmospheric variability predominantly. Model–data cluster comparison suggests that no specific ITD configuration or category number increases realism of the simulated Arctic and Antarctic SIC variability in winter. In the Arctic summer, more thin-ice categories decrease model–data agreement without detrending but increase agreement after detrending. Overall, a single-category configuration agrees the worst with data.</p> <p>Direct model–data comparison of SIC anomaly fields shows that more thick-ice categories improve winter SIC variability realism in Central Arctic regions with very thick ice. By contrast, more thin-ice categories reduce model–data agreement in the Central Arctic in summer, due to an overly large simulated sea ice volume.</p> <p>In summary, whereas better resolving thin ice in NEMO3.6-LIM3 can hamper model realism in the Arctic but improve it in Antarctica, more thick-ice categories increase realism in the Arctic winter. And although the single-category configuration performs the worst overall, no optimal configuration is identified. Our results suggest that no clear benefit is obtained from increasing the number of sea ice thickness categories beyond the current usual standard of 5 categories in NEMO3.6-LIM3.</p>
Interannual Salinity Variability on the Ross Sea Continental Shelf in a Regional Ocean-Sea Ice-Ice Shelf Model
<p>This data is only used for paper submitted to the JPO entitled 'Interannual Salinity Variability on the Ross Sea Continental Shelf in a Regional Ocean-Sea Ice-Ice Shelf Model'.</p>
Dataset from the Towing Tank Test of the B.I.O. Hespérides ship model, conducted at the CEHINAV Towing Tank, in the presence of simulated broken ice
<p>This data set presents a collection of measurements carried out at CEHINAV Towing Tank. The colletion includes several rar files described below:</p> <ul> <li><strong>Towing_test.rar</strong>: Raw data (ASCII files) containing the following information: <ul> <li>Time (Chanel 1)</li> <li>Velocity (Chanel 2)</li> <li>Resistance (Chanel 4)</li> <li>Laser position bow (Chanel 5)</li> <li>Laser position stern (Chanel 6)</li> <li>Laser position longitudinal (Chanel 7)</li> <li>Absolute carriage position (Chanel 8)</li> </ul> </li> <li><strong>Towed_self_propulsion</strong>: Raw data (ASCII files) containing the followig information: <ul> <li>Time (Chanel 1)</li> <li>Velocity (Chanel 2)</li> <li>RPM engine (Chanel 3)</li> <li>Resistance (Chanel 4)</li> <li>Laser position bow (Chanel 5)</li> <li>Laser position stern (Chanel 6)</li> <li>Laser position longitudinal (Chanel 7)</li> <li>Thrust (Chanel 8)</li> <li>Torque (Chanel 9)</li> <li>Absolute carriage position (Chanel 11)</li> </ul> </li> <li><strong>Figures_coverage</strong>: PNG files with images of block coverage tested and files containing the relative position during the test.</li> <li><strong>HESPERIDES_model.msd</strong>: Maxurf file with the geometry of the model tested.</li> </ul> <p> </p> <p>The tests were conducted in february and november of 2020.</p>
Data for "ISMIP6-based Antarctic Projections to 2100: simulations with the BISICLES ice sheet model"
<p>Data to accompany:</p> <p>O’Neill, J.F., Edwards, T.L., Martin, D.F., Shafer, C., Cornford, S.L., Seroussi, H.L., Nowicki, S., Adhikari, M., Gregoire, L.J.. (2024). "ISMIP6-based Antarctic Projections to 2100: simulations with the BISICLES ice sheet model in the Cryosphere". <em>The Cryosphere</em>. DOI: 10.5194/egusphere-2024-441 (preprint)</p> <p>Zipped directories called ismip6_<em>expname</em>_8km containing NetCDFs of output data from each experiment, on an 8 km EPSG3031 polar stereographic common grid for ISMIP6. Variable names are the same as those used for ISMIP6 i.e: land ice mass (lim), land ice mass above floatation (limnsw), floating area (iareaf), grounded area (iareag), thickness (lithk), x component of mean velocity (xvelmean), y component of mean velocity (yvelmean), basal mass flux (libmassbffl), acabf (surface mass balance), sftflf (floating ice mask), sftgrf (grounded ice mask), sftgif (ice mask), dlithkdt (ice thickness imbalance), base (elevation at base of ice sheet) and orog (surface elevation of ice sheet). These latter two are only included for the experiments plotted in Figure 11 in "ISMIP6-based Antarctic Projections to 2100: simulations with the BISICLES ice sheet model in the Cryosphere".</p> <p> </p> <p>Also included are csv data for summary variables, masked regionally, and by sectors detailed in the main paper. Please contact J ONeill with any questions or requests. </p>
MITgcm model setup and output for "Impact of subglacial freshwater discharge on Pine Island Ice Shelf"
<p>Here, it contains the results of the regional PIG simulation. Model grid is lat-lon similar to Nakayama et al., 2019 roughly 200 m in the region. See Nakayama et al., submitted to GRL for detail. </p> <p>For complete model output (10*Qsg and 50*Qsg cases), please access NASA data (Registration is required).<br> https://ecco.jpl.nasa.gov/drive/files/ECCO2/High_res_PIG/PIG_only_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><strong>(Contents)</strong><br> code.zip (code to run this simulation)<br> input.zip (input file required for this simulation)<br> latlon_run23.zip (CTRL)<br> latlon_run24.zip (Qsg case)<br> latlon_run25.zip (2*Qsg case)<br> <br> <strong>(How to build and run)</strong><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 ..</p> <p>mkdir test<br> cd test<br> ln -sf ../input/* .<br> ln -sf /nobackup/hzhang1/forcing/era_xx_it50/ .<br> cp ../build/mitgcm_uv .<br> qsub latlon_run23.sh</p>
A new sampling capability for uncertainty quantification in the Ice-sheet and Sea-level System Model v4.19 using Gaussian Markov random fields -- Datasets and results
<p>Data archives for test experiments (Section 3) and Pine Island Glacier application (Section 4) from the manuscript "Kevin Bulthuis and Eric Larour, A new sampling capability for uncertainty quantification in the Ice-sheet and Sea-Level System Model v4.19 using Gaussian Markov random fields"</p> <p>Source code is available at https://doi.org/10.5281/zenodo.5532775.</p>
Antarctic Ice Sheet simulations driven by CMIP6 climate models under historical and SSP5-8.5 scenarios
<p><strong>Antarctic Ice Sheet simulations driven by CMIP6 climate models under historical and SSP5-8.5 scenarios</strong></p> <p>This dataset contains output ice sheet model runs forced by climate boundary conditions provided by CMIP6 climate model output. Each experiment set is archived in separate compressed tar.gz files. </p> <p>Description of the experiment sets, including the model setup, key parameters, climate forcings, and their main objectives are documented in Table 1 of Li, DeConto, Pollard (2023) Climate model differences contribute deep uncertainty in future Antarctic ice loss, Science Advances.</p> <p>Two kinds of output are included in each ice sheet run: fort.22 files contain time series of several key variables for the Antarctic Ice Sheet (area, volume, sea-level equivalent, etc.); fort.92.nc files contain 2D and 3D fields such as ice thickness and velocity at specific time slices.</p> <p> </p> <p> </p>
Multi-proxy agreement on Atlantic circulation dynamics since the last ice age: Model output data
<p>This dataset contains model output for the simulations presented in: <em>"Multi-proxy agreement on Atlantic circulation dynamics since the last ice age"</em>.</p> <p> </p>
200-years ice core bromine reconstruction at Dome C (Antarctica): observational and modelling results
<p>Bromine enrichment (Br<sub>enr</sub>) has been proposed as an ice core proxy for past sea-ice reconstruction. Understanding the processes that influence bromine preservation in the ice is crucial to achieve a reliable interpretation of ice core signals and to potentially relate them to past sea ice variability. Here, we present a 210 years bromine record that sheds light on the main processes controlling bromine preservation in the snow and ice at Dome C, East Antarctic plateau. Using observations alongside a modelling approach, we demonstrate that the bromine signal is preserved at Dome C and it is not affected by the strong variations in ultraviolet radiation reaching the Antarctic plateau due to the stratospheric ozone hole. Based on this, we investigate whether the Dome C Br<sub>enr</sub> record can be used as an effective tracer of past Antarctic sea ice. Due to the limited time window covered by satellite measurements and the low sea-ice variability observed during the last 30 years in East Antarctica, we cannot fully validate Br<sub>enr </sub>as an effective proxy for past sea-ice reconstructions at Dome C.</p> <p>In this database, annual averaged data for Br, Na and Br<sub>enr</sub> for the shallow ice core are provided.</p> <p> </p>
Videos of Functional Regression Model for the Antarctic Sea Ice Edge
<p><a href="https://zenodo.org/api/files/198831a6-6078-449d-a532-8564977b425c/ObservedConcentration.mp4">ObservedConcentration.mp4</a> - Daily observed Antarctic sea ice concentration as provided by NSIDC</p> <p><a href="https://zenodo.org/api/files/198831a6-6078-449d-a532-8564977b425c/ObservedOutlines.mp4">ObservedOutlines.mp4</a> - Daily observed Antarctic sea ice edge based on the above concentrations. The edge is defined as the 15% sea ice concentration isoline</p> <p><a href="https://zenodo.org/api/files/198831a6-6078-449d-a532-8564977b425c/model_outline_daily_lon_lat.mp4">model_outline_daily_lon_lat.mp4</a> - Daily observed Antarctic sea ice edge (black) with modeled sea ice edge (blue) and 95% prediction intervals (dashed lines) in geographic projection</p> <p><a href="https://zenodo.org/api/files/198831a6-6078-449d-a532-8564977b425c/model_outline_daily_stereographic.mp4">model_outline_daily_stereographic.mp4</a> - Daily observed Antarctic sea ice edge (black) with modeled sea ice edge (blue) and 95% prediction intervals (dashed lines) in stereographic projection</p> <p> </p>
Model output from "The chance of freezing – a conceptional study to parameterize temperature-dependent freezing by including randomness of ice-nucleating particle concentrations"
<p>Model output from "The chance of freezing – a conceptional study to parameterize temperature-dependent freezing by including randomness of ice-nucleating particle concentrations", accepted for publication in Atmospheric Chemistry and Physics, 2023, same authors.<br> The simulations were done using MIMICA version4 (Savre at el., 2014) and the data includes all model output presented in the publication.</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>
Supplementary data for 'Results of the third Marine Ice Sheet Model Intercomparison Project (MISMIP+)'
<p>Datasets and model datasheets provided to the third Marine Ice Sheet Model Intercomparison Project (MISMIP+)</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.