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223 results for “model ice”
First application of artificial neural networks to estimate 21st century Greenland ice sheet surface melt: scripts and models
<p>In this repository you will find the models and the scripts used to generate the journal article: "First application of artificial neural networks to estimate 21st century Greenland ice sheet surface melt."</p> <p>The model.tar contains the script for making a model, in addition to the models used in the journal artcile.</p> <p>The proc.tar contains the scripts used for processing of the CMIP6 data.</p> <p>The plots.tar contains scripts for generating the plots in the journal article, as well as the supplementary information.</p>
Last glacial cycle simulations forced by PMIP3 climate with a matrix and index method using a 3D thermodynamical ice-sheet model IMAU-ICE
<p>IMAU-ICE 2.0 model output of the ice evolution during the last glacial cycle at a 10 ka temporal resolution, as described in Scherrenberg at al., 2023.</p>
Preliminary DOI/Repository of ALPINE3D and SNOWPACK data of the submitted paper "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model"
<p>There are 2 zip folders in this repository.</p> <p>"a3d_jgr.zip" contains a folder structure that must be kept as it is in order to run the simulation in the current configuration.<br> The setup contains both input and output data as well as the model configuration as used in the submitted manuscript <br> "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model".</p> <p>The zip file contains 3 main folders:</p> <ul> <li>base_setup_files</li> <li>a3d_jgr_alpha1</li> <li> a3d_jgr_alpha3</li> </ul> <p>The "base_setup_files" contains all input files that are necessary to run the reference (R) simulation ("a3d_jgr_alpha1" folder) and the comparison "C" scenario ("a3d_jgr_alpha3") folder. In the a3d_jgr_alpha1 and a3d_jgr_alpha3 folders you find the corresponding outputs as used in the paper, as well as the settings used - which only differ by the changed "SCHMIDT_DRIFT_FUDGE" value that is found in each a3d_jgr_alphax/setup/io.ini file. The input data is already linked accordingly in each io.ini file.</p> <p>a3d_jgr_alpha1 also contains the detailed snow profiles for each point along the transects.</p> <p>To reproduce the results, download and compile the source code for the adjusted ALPINE3D model first, which can be obtained from https://gitlabext.wsl.ch/snow-models/alpine3d.git under the "alpine3d_mosaic" branch. After installing, you can run the provided model setup uploaded here.</p> <p>_________________________________________________________________________________________________________<br> <br> "SNOWPACK_JGR.zip" contains both input and output data for SNOWPACK as well as the model configuration as used in the submitted manuscript "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model".</p> <p>The zip file contains 2 main folders: </p> <ul> <li>SNOWPACK_JGR_ALPHA1</li> <li>SNOWPACK_JGR_ALPHA3</li> </ul> <p>In the SNOWPACK_JGR_ALPHA1 (reference "SP_R" simulation) and SNOWPACK_JGR_ALPHA3 (comparison "SP_C" scenario) folders you find the corresponding inputs, outputs and configuration as used in the paper, as well as the settings used - which only differ by the changed "SCHMIDT_DRIFT_FUDGE" value that is found in each SNOWPACK_JGR_ALPHA/setup/io.ini file. The input data is already linked accordingly in each io.ini file.</p> <p>To reproduce the results, download and compile the source code for the adjusted SNOWPACK model first, which can be obtained from https://gitlabext.wsl.ch/snow-models/snowpack.git under the "snowpack_mosaic" branch. After installing, you can run the provided model setup uploaded here.</p>
Data accompanying the article "Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020"
<p>The .zip file contains temporal-spatial averaged metrics for evaluating simulations against observed ice thickness, concentration, volume, and drift. These quantities are presented in the manuscript "Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020"</p> <p>Subfolders are named by the experiment IDs, including metrics obtained from the relevant experimental results and observations.</p> <p>In case information is missing, do not hesitate to contact chengsukun@hotmail.com</p> <p>We thank Pavel Sakov for helpful discussions and improvement regarding the EnKF-C code and Jiping Xie for contributing the TOPAZ interface to sea ice observations. We are grateful for the support from Timothy Williams and Anton Korosov regarding the environments of neXtSIM and its analysis tools. The work is funded by the DASIM-II grant from ONR (grant nos. N00014-18-1-2493 and N00014-18-1-2204). Alberto Carrassi, Christopher K. R. T. Jones, Ali Aydo ̆gdu, and Pierre Rampal acknowledge the support of the project SASIP funded by Schmidt Futures – a philanthropic initiative that seeks to improve societal outcomes through the development of emerging science and technologies. Sukun Cheng and Laurent Bertino were co-funded by the FOCUS project from the Research Council of Norway (grant no. 301450), and Alberto Carrassi and Yumeng Chen are also supported by the UK National Centre for Earth Observation (grant no. NCEO02004). Computations were carried out on the Norwegian Supercomputing InfrastructureSigma2 (grants nn2993k for computing and NS2993K for data storage)</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>
Response of water isotopes in precipitation to a collapse of the West Antarctic Ice Sheet in high-resolution simulations with the Weather Research and Forecasting Model
<p>This archive includes data and ipython notebooks to create the figures for the manuscript "Response of water isotopes in precipitation to a collapse of the West Antarctic Ice Sheet in high-resolution simulations with the Weather Research and Forecasting Model" submitted to Journal of Climate in August 2022.</p> <p>Model output from WRFwiso and iCAM is in data.zip (saved as monthly means)</p> <p>Notebooks and python modules are in scripts.zip</p> <p>Required python packages (all included in environment.yml):</p> <ul> <li>numpy</li> <li>matplotlib</li> <li>netcdf4</li> <li>basemap</li> <li>scipy</li> <li>wrf-python</li> <li>windspharm</li> <li>metpy</li> <li>intergrid</li> <li>cmocean</li> </ul> <p>Version 1 is the original upload from the first submission.</p> <p>Version 2 includes small updates of the data and scripts from the revision.</p>
Data for "Impact of time-dependent data assimilation on ice flow model initialization and projections: a case study of Kjer Glacier, Greenland"
<p>Data for the main figures for "Impact of time-dependent data assimilation on ice flow model initialization: A case study of Kjer Glacier, Greenland"</p>
Dataset from: Relative Sea-Level Sensitivity in the Eurasian Region to Earth and Ice-Sheet Model Uncertainty During the Last Interglacial
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Data from: Present-day genetic structure of Atlantic salmon (Salmo salar) in Icelandic rivers and ice-cap retreat models
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Supporting model output for article "Relative Impact of Sea Ice and Temperature Changes on Arctic Marine Production"
<p>The following is a summary of processed model output data from a series of HiLAT model runs. A description of the model runs and visualization of model output and analysis can be found in the accompanying manuscript.</p> <p>Gibson, G.A., Weijer, W., Jeffery, N. and Wang, S (2020) Relative impact of sea ice and temperature changes on Arctic marine production. Journal of Geophysical Research: Biogeosciences. DOI: 10.1029/2019JG005343</p> <p>The model outputs and accompanying research was supported by the National Science Foundation 173886 and by DOE | SC |Biological and Environmental Research (BER).</p> <p>There are 4 versions of most output file types, corresponding to the 4 experiments that were conducted.</p> <p>These experiments are:</p> <p>i) the baseline model simulation (Base);</p> <p>ii) a moderate ice reduction experiment (S1);</p> <p>iii) a dramatic ice reduction experiment (S2); and</p> <p>iv) an atmospheric warming scenario (RCP45).</p> <p>Each of the 4 experiments is described in detail in Gibson et al (2020).</p> <p>Many variables in the output files are regional averages where model regions are indicated by a number *note - for aesthetics, the numbering used in the model output files differs slightly from the numbering used in the accompanying manuscript. </p> <p>Bering Sea =1</p> <p>Chukchi Sea =2 </p> <p>East Siberian Sea =3</p> <p>Laptev Sea =4 </p> <p>Beaufort Sea =5</p> <p>Barents Sea =6</p> <p>Canadian =7 </p> <p>Eurasian Basins =8 </p> <p>Nordic Seas =9</p> <p>Labrador Sea=10</p> <p>Kara Sea =11</p> <p> </p> <p><strong>Grid </strong></p> <p>gx1v6_polar_mask.10.12.18.nc This file is the original grid file used for all model runs with the addition of Arctic_Region and Antarctic_Polar_Front variables which indicates which grid cells are in the model regions described above.</p> <p> </p> <p><strong>Forcing</strong></p> <p>ncep_RCP45 ncep.t_10.T62.*.nc where * is 1948-2009 These files contain the modified atmospheric forcing used for the RCP45 model experiment. The unmodified NCEP forcing is freely publically available elsewhere to not duplicated here</p> <p> </p> <p><strong>Ice </strong></p> <p>Ice-Regional-Monthly-177-186-*.nc where * indicates model run - Base, S1, S2 or RCP45. These file contain the monthly averaged ice variables (ice area, ice algae production) for each model region for the last ten years of the model run.</p> <p> </p> <p>cice-monthly-177-186-*.nc where * indicates model run - Base, S1, S2 or RCP45. These file are the monthly averaged output filed from the cice model. Variables are averaged over each model analysis region.</p> <p> </p> <p>Ice_snow_depth_region_Avg-137-186-*.nc where * indicates model run - Base, S1, S2 or RCP45. These file contain the regionally averaged ice and snow depth for each analysis region.</p> <p> </p> <p>IceVol_Base-137-186.nc Contains ice variable file for the baseline model run. Model contains ice variables hi and aice for every grid cell of the model domain. Ice volume was computed as hi * Tarea (from grid file).</p> <p> </p> <p>Annual-SummerAvg-IceArea-177-186_*.nc Where * indicates model run - Base, S1, S2 or RCP45. Each of these files contains the annual average ice area averaged over Summer months (JuneAugust) for last 10 years of model run (200-2009) for each grid cell in the HiLAT model</p> <p> </p> <p><strong>Ocean variables </strong></p> <p>TempNitrate TEMP_NO-177-186.nc This file contains model estimates of temperature and nitrate for the baseline model run for model years 177-186 (2000-2009, the last en years of the model run) for the upper five model layers (500, 1500, 2500, 3500, 4500 cm).</p> <p> </p> <p><strong>CO2flux </strong></p> <p>AnnAvgAllCflux*.nc</p> <p>Where * indicates model run - Base, S1, S2 or RCP45. Each of these files contains the annual average CO2 Flux for last 10 years of model run (200-2009) for each grid cell of the HiLAT model.</p> <p> </p> <p>CO2flux_region_avg-177-186-*.nc Where * indicates model run - Base, S1, S2 or RCP45. Each of these files contains the annual average CO2 Flux for last 10 years of model run (2000-2009) for each region of the HiLAT model.</p> <p> </p> <p><strong>Ocean Production </strong></p> <p>OceanProd_Year-147-156-*.nc OceanProd_Year-157-166-*.nc OceanProd_Year-167-176-*.nc OceanProd_Year-177-186-*.nc Where * indicates model run - Base, S1, S2 or RCP45. These file contain the annual average vertically integrated ocean phytoplankton production for each grid cell. Each file contains 10 model years is the sum of diatoms, diazotrophs, phaeocystis, and smaller phytoplankton.</p> <p>Region_production_by_phyto_group-diat_ps-137-186-*.nc Where * indicates model run - Base, S1, S2 or RCP45. These file contain the annual average vertically integrated ocean phytoplankton production for each region. Each file contains group specific production for diatoms, diazotrophs, phaeocystis, and smaller phytoplankton.</p> <p>OceanProd_Daily-177-186-BASE.nc This file contains the daily average vertically integrated ocean phytoplankton production for each grid cell. The file contains 10 model years. Production is the sum of all four phytoplankton groups - diatoms, diazotrophs, phaeocystis, and smaller phytoplankton.</p> <p> </p> <p><strong>PAR </strong></p> <p>PAR-MonthyAvg-177-168-*.nc</p> <p>Photosynthetically Active Radiation in the ocean. Where * indicates model run - Base, S1, S2 or RCP45.</p>
Mapping the age of ice of Gauligletscher combining surface radionuclide contamination and ice flow modeling: Radionuclide data
<p>The data corresponds to 239-Pu and 236-U concentrations of 220 samples of ice collected along 5 flowlines at the surface of Gauligletscher in July 2019. Details about the data sampling and analysis can be found in the paper "Mapping the age of ice of Gauligletscher combining surface radionuclide contamination and ice flow modeling", by G. Jouvet, S. Röllin, H. Sahli, J. Corcho, L. Gnägi, L. Compagno, D. Sidler, M. Schwikowski, A. Bauder, and M. Funk. The Cryosphere, 2020.</p>
Contributions of the Liquid and Ice Phases to Global Surface Precipitation: Observations and Global Climate Modeling
This study is the first to reach a global view of the precipitation process partitioning, using a combination of satellite and global climate modeling data. The pathways investigated are (1) precipitating ice (ice/snow/graupel) that forms above the freezing level and melts to produce rain (S) followed by additional condensation and collection as the melted precipitating ice falls to the surface (R); (2) growth completely through condensation and collection (coalescence), warm rain (W); and (3) precipitating ice (primarily snow) that falls to the surface (SS). To quantify the amounts, data from satellite-based radar measurements—CloudSat, GPM, and TRMM—are used, as well as climate model simulations from the Community Atmosphere Model (CAM) and the UK Met Office Unified Model (UM).
DALROMS-NWA12 v1.0, a coupled circulation-sea ice-biogeochemistry model for the northwest North Atlantic: input files (3 of 3)
<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 the files necessary for nudging the currents towards Copernicus GLORYS12V1 reanalysis values in a simulation from 1 September to 31 December 2013.</p> <p>The remaining input files for this period are available at <a href="https://doi.org/10.5281/zenodo.12752190" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12752190</a> and <a href="https://doi.org/10.5281/zenodo.12734049" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12734049</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 output files (from the physics and biogeochemistry modules) are available at <a href="https://doi.org/10.5281/zenodo.12744506" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12744506</a> and <a href="https://doi.org/10.5281/zenodo.12746262" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12746262</a>.</p>
DALROMS-NWA12 v1.0, a coupled circulation-sea ice-biogeochemistry model for the northwest North Atlantic: output files (1 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 2013, when the simulation was initialized. Similar files for January 2015 are available at <a href="https://doi.org/10.5281/zenodo.12746262" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12746262</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>
Model input data, simulation output data and processed data presented in "Modelling the three-dimensional, diagnostic anisotropy field of an ice rise"
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Model files and output seismograms for Seismic Wavefield Modelling of Enceladus: challenges and opportunities presented by a 3D ice shell (Dapré & Irving)
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Phase Field model of ice crystallization in sugar solutions
<p>Phase Field model of ice crystallization in sugar solutions</p>
Experiments on the snowfall, temperature, and humidity to the Arctic summer snowstorm using ocean-ice couple model (POP2-CICE5) with JRA55-do and MERRA2 forcing
<p><span>In the Arctic, short-lived summer snowstorms can provide snow cover that can increase surface reflectivity and heat capacity. Despite their potential importance, little research has been done to understand the impact of summer snowstorms on basin-scale Arctic sea ice cover. Our observational analysis shows that a summer snowstorm event is accompanied by cyclonic ice drift, increases in surface albedo and surface air cooling that can persist for up to ~2 weeks, dampening sea ice loss. Specifically, multiple snowstorm events in a summer, on average, results in net increase in sea ice extent of ~0.2×106 km<sup>2</sup> by early September. Experiments with a sophisticated ice-ocean model framework indicate that the initial expansion of sea ice extent is driven by cyclonic wind-driven ice drifts driving sea ice southwards and increasing albedo around the summer ice edge, however the thermal effects from the associated snowfall and atmospheric conditions result in a stronger overall impact on basin-averaged sea ice extent at seasonal scales.</span></p> <p><span>Additional model experiments were carried out to isolate the physical processes contributing to the thermal response of Arctic sea ice to summer snowstorms. Our results show the impact of surface air cooling on sea ice extent is about 3.5 times larger than the snowfall/albedo response. However, our simulated albedo response is weaker than the observed response, likely due to the negligible difference in surface albedo between old snow and freshly fallen snow – a limiting factor in our analysis and a topic worthy of future focus.</span></p>
Are neural network potentials trained on liquid states transferable to crystal nucleation? A test on ice nucleation in the mW water model
<p>Dataset used to train the neural network potential for reproducing mW nucleation.</p>
Experiments on the snowfall, temperature, and humidity to the Arctic summer snowstorm using ocean-ice couple model (POP2-CICE5) with JRA55-do and MERRA2 forcing
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