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8 results for “Sea-ice simulation”
FESOM2 simulations with increasing sea-ice model complexity under different atmospheric forcings
<p><strong>Introduction</strong></p> <p>This dataset has been compiled in support of the paper "Impact of sea-ice model complexity on the performance of an unstructured sea-ice/ocean model under different atmospheric forcings" by Zampieri et al., submitted to the Journal of Advances in Modeling Earth Systems (JAMES) published by the American Geophysical Union (AGU).</p> <p><strong>Scientific description of the dataset</strong></p> <p>The dataset contains the results of sea-ice simulations performed with the Finite-volumE Sea ice-Ocean Model version 2 (FESOM2), based on six model configurations: C1-E, C1-N, C2-E, C2-N, C3-E, and C3-N. As described in the paper, the complexity of the sea-ice model increases from the setup C1 to C3. The suffix -E and -N indicate respectively the ERA5 and NCEP atmospheric forcings used as boundary conditions for the FESOM2 model. As two iterations of the Green's function approach for the optimization of the parameter space have been performed, each configuration features three separate simulations: a control run (cnt), a first-round of optimization (opt_1), and a second and final round of optimization (opt_2). The parameter optimization is based on various sea-ice observations retrieved over the period 2002–2015. In total, 18 simulations compose the dataset (6 configurations x 3 realizations). The following 2D monthly-averaged variables are provided: the sea-ice concentration, the sea-ice thickness, the meridional and zonal components of the sea-ice velocity, and the snow thickness on top of the sea ice. The fields are defined on a global unstructured mesh denominated "CORE2", which is also included in the database.</p> <p><strong>Technical description of the dataset</strong></p> <p>As an unstructured model output is not widely diffused in the sea-ice community, we include here some suggestions for handling and analyzing the simulation results.</p> <p>The files can be interpolated to a regular grid using the following <strong><a href="https://code.mpimet.mpg.de/projects/cdo">CDO</a></strong> commands:</p> <ol> <li>Add grid description to model file: <strong><em>cdo setgrid,CORE2_mesh.nc var.fesom.yyyy.nc temp.nc</em></strong></li> <li>Interpolate to regular grid: <strong>cdo remapycon,r360x180 temp.nc var.fesom.interpolated.yyyy.nc</strong></li> </ol> <p>Furthermore, the python package<strong> <a href="https://code.mpimet.mpg.de/projects/cdo">pyfesom2</a></strong> can be used for plotting the unstructured model data and for interpolating it to a regular grid. The R package <strong><a href="https://github.com/FESOM/spheRlab">spheRlab</a></strong> can be used for plotting the model data directly on its unstructured grid and for performing further analysis. More information can be found on the <strong><a href="https://fesom.de/cmip6/work-with-awi-cm-unstructured-data/">FESOM website</a></strong>.</p> <p>The following naming convention is adopted for the model variables:</p> <ul> <li><strong><em>a_ice</em></strong> → sea-ice concentration</li> <li><strong><em>m_ice</em></strong> → sea-ice volume per unit area of ice</li> <li><strong><em>m_snow </em></strong>→ snow-volume per unit area of ice</li> <li><strong><em>vice</em></strong> → meridional component of the sea-ice velocity</li> <li><strong><em>uice</em></strong> → zonal component of the sea-ice velocity</li> </ul> <p>Three types of simulation are included:</p> <ul> <li><strong>cnt </strong>→ control run before the parameters optimization (2000–2019)</li> <li><strong>opt_1 </strong>→ after the first iteration of the parameter optimization method (2000–2015)</li> <li><strong>opt_2</strong> → after the second iteration of the parameter optimization method (2000–2019)</li> </ul> <p>Do not hesitate to contact the corresponding author (lorenzo.zampieri@awi.de) for additional information about the data processing and for any other issue with this dataset.</p> <p> </p> <p> </p>
Dataset and neural network weights to the paper: "Generative diffusion for regional surrogate models from sea-ice simulations"
<p>All the needed code and data to reproduce the results from the paper: "Generative diffusion for regional surrogate models from sea-ice simulations".<br>While most of the code is a frozen clone of the original <a href="https://github.com/cerea-daml/diffusion-nextsim-regional">Repository</a>, this capsule also includes the dataset and neural network weights to train and apply the surrogate models.</p> <p>The <strong>dataset</strong> for training and evaluation can be found at <em>data/nextsim</em>, which includes three different Zarr folders for training/validation/testing. The dataset is based on neXtSIM simulation data and ERA5 forcing data and extracted from the <a href="https://ige-meom-opendap.univ-grenoble-alpes.fr/thredds/catalog/meomopendap/extract/catalog.html">SASIP shared data OpenDAP server</a>:</p> <ul> <li>The neXtSIM simulations were performed by Gauillaume Boutin and published in the paper "<a href="https://doi.org/10.5194/tc-17-617-2023">Arctic sea ice mass balance in a new coupled ice–ocean model using a brittle rheology framework</a>" (Boutin et al., 2023) and available as Zenodo <a href="../records/7277523">dataset</a> (Boutin et al., 2022).</li> <li>The forcing data is based on the ERA5 reanalysis dataset published in the paper: "<a href="https://doi.org/10.1002/qj.3803">The ERA5 global reanalysis</a>" (Hersbach et al., 2020) and available as dataset from the Copernicus Climate Change Service (C3S, Copernicus Climate Change Service, 2023). The here used forcing data is based on the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels">hourly reanalysis data on single levels</a> and interpolated with nearest neighbors to the curvilinear grid as used in the output from the neXtSIM simulations. <strong>Disclaimer:</strong> The results contain modified Copernicus Climate Change Service information, 2023. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.</li> </ul> <p>The <strong>neural network weights</strong> are included under <em>data/models </em>and split into weights for the deterministic models and the diffusion models.<br>These neural network weights have been used to generate the results presented in the paper.</p> <p>In this capsule, the <em>notebooks</em> folder includes also the figures used within the paper and additional trajectory data used in the qualitative analysis of the paper.</p> <p>Generally, we recommend to just download the <em>data.tar.gz </em>file and use otherwise the original <a href="https://github.com/cerea-daml/diffusion-nextsim-regional">Repository</a>, since the here included code can be outdated. We further refer to the repository for additional information.</p> <p> </p> <p>Contained in this capsule:</p> <ul> <li>configs.tar.gz: The configuration files for the experiments.</li> <li>data.tar.gz: The dataset and neural network weights.</li> <li>diffusion_nextsim.tar.gz: The main code for the neural network etc.</li> <li>environment.yaml: The anaconda environment file, can be used to install the needed packages.</li> <li>notebooks.tar.gz: The notebooks that were used to create the figures in the paper. The figures from the paper and the data from the qualitative analysis are included as well.</li> <li>readme.md: The readme file from the repository.</li> <li>scripts.tar.gz: The scripts used for the experiments.</li> <li>setup.py: the file to install the <em>diffusion_nextsim</em> package in a python environment.</li> </ul> <p>References:</p> <p>Guillaume Boutin, Heather Regan, Einar Ólason, Laurent Brodeau, Claude Talandier, Camille Lique, & Pierre Rampal. (2022). Data accompanying the article "Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework" (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7277523</p> <p>Boutin, G., Ólason, E., Rampal, P., Regan, H., Lique, C., Talandier, C., Brodeau, L., and Ricker, R.: Arctic sea ice mass balance in a new coupled ice–ocean model using a brittle rheology framework, The Cryosphere, 17, 617–638, https://doi.org/10.5194/tc-17-617-2023, 2023.</p> <p>Copernicus Climate Change Service (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: <a href="https://doi.org/10.24381/cds.adbb2d47">10.24381/cds.adbb2d47</a>.</p> <p>Hersbach H, Bell B, Berrisford P, et al. The ERA5 global reanalysis. <em>Q J R Meteorol Soc</em>. 2020; 146: 1999–2049. <a href="https://doi.org/10.1002/qj.3803">https://doi.org/10.1002/qj.3803</a></p> <p> </p>
Linear Kinematic Feature detected and tracked in sea-ice deformation simulationed by all models participating in the Sea Ice Rheology Experiment and from RGPS
<p>Linear Kinematic Features (LKFs) detected and tracked in sea-ice deformation fields simulated by sea-ice models participating in the Sea Ice Rheology Experiment (SIREx), a model intercomparison project of the Forum of Arctic Modeling and Observational Synthesis (FAMOS). These data are the basis of the feature-based evaluation of sea-ice deformation in Hutter et al., Sea Ice Rheology Experiment (SIREx), Part II: Evaluating linear kinematic features in high-resolution sea-ice simulations, Journal of Geophysical Research: Oceans (2022). This paper also provides further details on the parameters of the LKF extraction.</p> <p>The LKF data sets in this archive are stored in a csv-files for each year (1997 and/or 2008), which use semi-colons as delimiters. Each row corresponds to a pixel that was identified as LKF and the following information for this pixels is stored: Start Year, Start Month, Start Day, End Year, End Month, End Day, LKF No., Parent LKF No., lon, lat, ind_x, ind_y, divergence rate, shear rate. All pixels belonging to the same LKF have the same LKF number. Tracked LKFs are linked by the parent LKF number, where "0" denotes LKFs that newly formed. Detailed information on all variables is provided in the additional notes.</p>
On the importance of representing snow over sea-ice for simulating the Arctic boundary layer
<p>Correctly representing the snow on sea-ice in coupled numerical weather prediction models has great potential to improve weather forecast and climate monitoring applications, such as climate reanalyses, which are usually produced using such systems. In this study two different methodologies to account for the effect of the snowpack accumulating over the sea-ice in 5-day global forecasts are compared. </p> <p>This dataset contains the high-resolution (dx=9km) model data for the different simulations, with and without the snow over sea-ice, co-located with observations at the locations of the SHEBA and N-ICE2015 field campaigns. The dataset also contains the daily averaged skin temperature, for the different simulations (interpolated on 0.25x0.25) performed over the extended time period, 2015-01-01 to 2015-02-28. </p>
HadGEM3-GC31-MM year-long ENSO, QBO, and sea-ice simulations
<p>HadGEM3-GC31-MM year-long (June to May) simulations of two ENSO, two QBO, and two sea-ice states. The ENSO states are neutral ENSO and El Niño, the QBO states are QBO westerly and easterly, and the sea-ice states are present day and future sea-ice (following the<a href="https://gmd.copernicus.org/articles/12/1139/2019/"> PAMIP protocol</a>). There are 150 ensemble members per experiment. HadGEM3 is run in an atmosphere only configuration. The variables making up this dataset are zonal wind (ua), sea level pressure (psl), surface temperature (ts), precipitation (pr), atmospheric temperature (ta), geopotential height (zg), vertical Eliassen-Palm flux (epfz), meridional Eliassen-Palm flux (epfy) and Eliassen-Palm flux divergence (epfd). Zonal means are given for ua, ta, zg, epfz, epfy, and epfd. Both daily and monthly version of zonal wind are provided.</p> <p>This data is uploaded to support the paper "Nonlinear response of the extratropics to tropical climate variability" published in Geophysical Research Letters and in support of the paper "Interdependent extratropical atmospheric responses to Arctic sea-ice loss, QBO and ENSO" submitted to Journal of Climate.</p>
Data & figures: Comparison between Large-Scale Observed and Simulated Antarctic Sea-Ice Variability Response to Changes in Atmospheric and Oceanic Circulation
<p>These are the model data, key figures, and Python code generated during the project titled “Comparison between Large-Scale Observed and Simulated Antarctic Sea-Ice Variability Response to Changes in Atmospheric and Oceanic Circulation." This project was undertaken during a 3-month research scholarship at the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, funded by the Helmholtz Visiting Researcher Grant, a program promoted by the Helmholtz Information and Data Science Academy (HIDA). Statistical methods pertain to the coupling of sea surface temperature and Antarctic sea-ice interactions. These methods can be applied to observations, reanalysis, and earth system model data</p>
SI3 sea-ice hourly output for SI3-BBM and SI3-default Pan-Arctic simulations (Brodeau et al., 2024, final version)
<p>These netCDF files contain the simulated SI3 sea-ice hourly output for experiments SI3-BBM and SI3-EVP that are discussed in the following paper:</p> <p><em>Implementation of a brittle sea-ice rheology in an Eulerian, finite-difference, C-grid modeling framework: </em><br><em>Impact on the simulated deformation of sea-ice in the Arctic</em></p> <p>by Laurent Brodeau, Pierre Rampal, Einar Ólason and Véronique Dansereau, in Geoscientific Model Development (GMD), 2024.</p> <p> </p> <p>More specifically, they contain:</p> <ul> <li>hourly sea-ice velocity (u,v) vector (m/s)</li> <li>hourly sea-ice concentration</li> <li>hourly sea-ice volume per area (m)</li> <li>hourly sea-ice damage</li> <li> </li> </ul> <p>File <code>NANUK4_ICE-BBM2412_1h_19961201_19970420_icemod.nc4</code> contains data for experiment "SI3-BBM"</p> <p>File <code>NANUK4_ICE-EVP2403_1h_19961201_19970420_icemod.nc4</code> contains data for experiment "SI3-default"</p> <p>File <code>mesh_mask_NANUK4_L31_4.2_1stLev.nc</code> contains the metrics of the horizontal grid of the model (NEMO regional Arctic configuration named NANUK4).</p>
CMIP6 models rarely simulate Antarctic winter sea-ice anomalies as large as observed in 2023
<p><span>Contains processed data to create all figures and tables from the publication Diamond et al. (2024) </span><span>CMIP6 models rarely simulate Antarctic winter sea-ice anomalies as large as observed in 2023; Geophysical Research Letters </span><span>[Full reference available on publication]</span></p> <p><span> </span></p> <p><span>Directory structure and associated contents listed below. Folders contain all data required to make corresponding figure, apart from where indicated. Full description of analysis in Diamond et al. (2024). Unless otherwise indicated, 'SIE' refers to August Antarctic SIE.</span></p> <p><span> </span></p> <p><span>F1/</span></p> <p><span>f1a_obs_ts.csv: Timeseries of 1979-2023 SIE </span></p> <p><span>f1b_all_instances.csv: All instances (labelled by model, simulation, and simulation year) where an anomaly of magnitude SIE_2023 was detected. SIE timeseries from years -20 to +20 about each anomaly.</span></p> <p><span>f1b_quartiles.csv: Upper and lower quartiles, mean, and median, computed over all instances.</span></p> <p><span> </span></p> <p><span>F2/</span></p> <p><span>F2_a.csv: 5--95% range of SIE variability dataset, for all model simulations, and observations.</span></p> <p><span>F2_b.csv: T_aug23, computed using ECDFs, for all model simulations, and multi-model ensemble.</span></p> <p><span>F2_c.csv: T_aug23, computed using GEVs, for all model simulations, and multi-model ensemble.</span></p> <p><span> </span></p> <p><span>F3/</span></p> <p><span>Data for all figures is already provided elsewhere:</span></p> <p><span>F3a: for observations in SF3/SF3_obs_dataset.csv, and for simulations in SF3/SF3_simulations.csv.</span></p> <p><span>F3b: for multi-model ensemble T_aug23 with associated errors in SF6/SF6_all.csv</span></p> <p><span>F3c: for GEV_estimated T_aug23 for all simulations and models in SF6/SF6_all.csv; and<span> </span>5--95% ranges of SIE variability datasets in F2/F2_a.csv</span></p> <p><span> </span></p> <p><span>~</span></p> <p><span>SF1/</span></p> <p><span>SF1_all.csv: SIE timeseries for all model simulations, and associated moving averages.</span></p> <p><span>SF2/</span></p> <p><span>sf2_all.csv: SIE decadal means and ranges for all model simulations.</span></p> <p><span>sf2_quartiles.csv: taken over all simulations above - mean decadal mean in each bin; and lower quartile, median and upper quartile of the decadal ranges in each bin.</span></p> <p><span>SF3/</span></p> <p><span>SF3_obs_dataset.csv: SIE variability dataset for 1979-2023 observations.</span></p> <p><span>SF3_simulations.csv: SIE variability datasets for all model simulations</span></p> <p><span>SF4/</span></p> <p><span>F4_a.csv: p_aug23, computed using ECDFs, for all model simulations, and multi-model ensemble.</span></p> <p><span>F4_b.csv: p_aug22, computed using ECDFs, for all model simulations, and multi-model ensemble.</span></p> <p><span>F4_c.csv: T_aug23, computed using GEVs, for all model simulations, and multi-model ensemble.</span></p> <p><span>F4_d.csv: for all model simulations: p-value that simulation SIE variability dataset is drawn from same probability distribution as observations. Computed using Kolmogorov-Smirnoff 2-sample test.</span></p> <p><span>SF5/</span></p> <p><span>Data for both SF5a and b are already provided in F1/f1b_all_instances.csv.</span></p> <p><span>SF6/</span></p> <p><span>SF6_all.csv: for all model simulations, and multi-model ensemble, p_aug23 and T_aug23, with lower and upper errors (computed from 5--95% confidence interval using bootstrapping)</span></p> <p><span>SF7/</span></p> <p><span>Data for SF7a is already provided elsewhere: for observations in SF3/SF3_obs_dataset.csv, and for simulations in SF3/SF3_simulations.csv.</span></p> <p><span>SF7_all.csv: as for SF6_all.csv, but instead using subset of models.</span></p>
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