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126 results for “Arctic sea ice”
Surface feature height, spacing and form drag coefficients over Arctic sea ice from Operation IceBridge, 2009-2015
<p>Data used to estimate the neutral form drag coefficient over Arctic sea ice using high-resolution IceBridge laser (ATM) data, from Petty et al., (2017). The data here include the raw 1D linear profiling (along the edge of the ATM swath) data of surface feature data: e.g. 1km_xyres2m_20cm/1D/[year]/ and also 10 km mean along-track surface feature and form drag estimates: 1km_xyres2m_20cm/1D/ATMO/.</p> <p>The code to generate these data can be found here: https://github.com/akpetty/ibdrag2017. The Python pickle files have been converted to CSV here to aid data ingestion. 2D surface feature data using the same input data can be found here: https://zenodo.org/record/6617715</p> <p>The following raw IceBridge datasets are used to create these data:</p> <ul> <li>L1B ATM data: <a href="https://nsidc.org/data/docs/daac/icebridge/ilatm1b/">https://nsidc.org/data/docs/daac/icebridge/ilatm1b/</a>.</li> <li>L1B DMS imagery: <a href="http://nsidc.org/data/iodms1b%7D">http://nsidc.org/data/iodms1b}</a>.</li> <li>IceBridge IDCSI4 and quick-look sea ice thickness data: <a href="http://nsidcorg/data/docs/daac/icebridge/evaluation_products/sea">http://nsidcorg/data/docs/daac/icebridge/evaluation_products/sea</a>-ice-freeboard-snowdepth-thickness-quicklook-index.html and <a href="http://nsidc.org/data/idcsi4.html">http://nsidc.org/data/idcsi4.html</a>.</li> </ul> <p><strong>References</strong></p> <p>Petty, A. A., M. C. Tsamados, N. T. Kurtz, S. L. Farrell, T. Newman, J. P. Harbeck, D. L. Feltham, and J. A. Richter-Menge (2016), Characterizing Arctic sea ice topography using high-resolution IceBridge data, The Cryosphere, 10(3), 1161–1179, doi:10.5194/tc-10-1161-2016.</p> <p>Petty, A. A., M. C. Tsamados, N. T. Kurtz (2017), Atmospheric form drag over Arctic sea ice using remotely sensed ice topography observations, J. Geophys. Res. Earth Surf., 122, doi:10.1002/2017JF004209.</p>
Data set of 1D model runs, CTRL and ICE runs, associated with "Underestimation of oceanic carbon uptake in the Arctic Ocean: Ice melt as predictor of the sea ice carbon pump"
<p>Dataset of one-dimensional runs for investigation on the sea ice carbon pump. Associated with Sect. 3.1 and 4.1 of manuscript "Underestimation of oceanic carbon uptake in the Arctic Ocean: Ice melt as predictor of the sea ice carbon pump".</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>
Arctic sea ice concentration data record in 6.25 km polar stereographic grid from three-year Landsat-8 imagery
<p>This dataset consists of true-like sea ice concentration (SIC) data records over the Arctic Ocean, which was derived from the 30 m resolution imagery from the Operational Land Imager (OLI) onboard Landsat-8. Each SIC map are given in a 6.25 km polar stereographic grid, and are catalogued into one of the twelve sub-regions (Baffin Bay and Labarador Seas, Barents Sea, Beaufort Sea, Bering Sea, Canadian Archipelago, Central Arctic, Chukchi Sea, East Greenland Sea, East Siberian Sea, Hudson Bay, Kara Sea, Laptev Sea) of the Arctic Ocean. This dataset also contains the number of Landsat-8 pixels used in the calculation of each SIC values, the coastal mask, and the sub-region mask.</p> <p>The naming convention for the files is "sic_landsat08_{sub-region name}.nc".</p> <p>The dataset is in netCDF format and is compliant with the CF 1.8 and ACDD 1.3 convention for netCDF files. The description of the variables along with some key global attributes of the data are provide below.</p> <table> <tbody> <tr> <td><strong>Global Attribute</strong></td> <td><strong>Meaning</strong></td> <td><strong> Attributes</strong></td> <td> </td> </tr> <tr> <td>title</td> <td>Title of the dataset</td> <td><strong> </strong></td> <td><strong> </strong></td> </tr> <tr> <td>sub_region</td> <td>Name of the sub-region for each dataset</td> <td><strong> </strong></td> <td><strong> </strong></td> </tr> <tr> <td><strong>Variable Name</strong></td> <td><strong>Meaning</strong></td> <td><strong>Attributes</strong></td> <td><strong>Dimension</strong></td> </tr> <tr> <td>time</td> <td>Reference time of satellite image</td> <td>seconds since 1981-01-01 00:00:00Z</td> <td>[time]</td> </tr> <tr> <td>lon</td> <td>Longitude</td> <td> </td> <td>[Y, X]</td> </tr> <tr> <td>lat</td> <td>Latitude</td> <td> </td> <td>[Y, X]</td> </tr> <tr> <td>X</td> <td>x coordinate of projection</td> <td> </td> <td>[X]</td> </tr> <tr> <td>Y</td> <td>y coordinate of projection</td> <td> </td> <td>[Y]</td> </tr> <tr> <td>sea_ice_concentration</td> <td>Estimated fractional sea ice area from Landsat-8 measurements</td> <td>_FillValue : -99</td> <td>[time, Y, X]</td> </tr> <tr> <td>sample_size</td> <td>Number of Landsat-8 pixels used to estimate the sea ice concentration</td> <td>_FillValue : 0</td> <td>[time, Y, X]</td> </tr> <tr> <td>coastal_mask</td> <td>Open-sea/Coastal Flag</td> <td>[Open_sea, Coast] = [0, 1]</td> <td>[Y, X]</td> </tr> <tr> <td>sub_region_mask</td> <td>Sub-region Flag</td> <td>[inside_sub_region, outside_sub_region] = [0, 1]</td> <td>[Y, X]</td> </tr> <tr> <td>cloud_contamination_category</td> <td>Qualitatively assessed cloud contamination for Landsat-8 Level 1 Collection 2 data used to produce SIC</td> <td>[underestimated_cloud_cover, overestimated_cloud_cover, correctly_estimated_cloud_cover_for_clear_sky, correctly_estimated_cloud_cover_for_cloudy_sky] = [1, 2, 3, 4]</td> <td>[time]</td> </tr> <tr> <td>source_name</td> <td>Filename of original Landsta-8 Level 1 Collection 2 data used to produce SIC</td> <td> </td> <td>[time]</td> </tr> </tbody> </table> <p> </p>
Scripts for "Evaluation and Attribution of a Warm Winter Bias Over Arctic Sea Ice in a Climate Model"
<p>The scripts used to generate the main figures and results of the work entitled ''Evaluation and Attribution of a Warm Winter Bias Over Arctic Sea Ice in a Climate Model'' by Michalezyk et al., submitted for publication in JAMES - AGU in 2024.</p> <p>If you have any questions, please contact Nicolas MICHALEZYK : nicolas.michalezyk@locean.ipsl.fr</p>
Atmospheric and sea ice model fields from the perturbed parameter ensemble E3SMv0-HILAT used to examine emergent relationships among climate variables in the Arctic
<p>These files contain time series of several sea ice ad atmospheric fields produced in an ensemble of perturbed parameter simulations using the E3SMv0-HiLAT model. The time series are used to produced seasonal means, which are used to examine emerging relationships in the ensemble discussed in our manuscript, as of September 2019, in review at JGR</p>
The prediction data analyzed in "Seasonal Arctic sea ice prediction using a newly developed fully coupled regional model with the assimilation of satellite sea ice observations"
<p>The outputs of seasonal predictions with the new modeling system analyzed in the article including:</p> <p>Sea ice concentration (SIC)</p> <p>Sea ice thickness (SIT)</p> <p>Sea surface temperature (SST)</p> <p>Near surface air temperature (T2) </p>
Aerial images of sea ice during the Oden_AO2018 campaign (Arctic Ocean 2018: MOCCHA - ACAS - ICE) Central Arctic / North Pole drift station in 2018 on IB ODEN
<p>35 oblique aerial images of sea ice (.jpg) were obtained during helicopter and drone flights during the Oden_AO2018 campaign (Arctic Ocean 2018: MOCCHA - ACAS - ICE) Central Arctic / North Pole drift station in 2018 on IB ODEN. The images display the drifting, melt-pond covered multi-year ice (MYI) floe close to the geographic North Pole in autumn 2018 on which the IB ODEN was anchored to. The images were taken on 14, 16, 28 August and 13 September. The images document the evolution of melt ponds between their fully developed stage in late summer towards their autumn characteristics with a refrozen surface and finally a snow cover.</p> <p>For details, please see the respective publication Anhaus, P., Katlein, C., Nicolaus, M., Hoppmann, M., and Haas, C.: From Bright Windows to Dark Spots: The Evolution of Melt Pond Optical Properties during Refreezing. Currently under review in Geophysical Research Letters. Preprint available <a href="https://doi.org/10.1002/essoar.10507628.1">doi:10.1002/essoar.10507628.1</a></p> <p>For the cruise report, please see Leck, C., Matrai, P. A., Perttu, A.-M., and Gårdfeldt, K. (2019): Expedition report: SWEDARTIC Arctic Ocean 2018. Luleå: Swedish Polar Research Secretariat. <a href="https://nbn-resolving.org/urn:nbn:se:polar:diva-8405">urn:nbn:se:polar:diva-8405</a></p>
Shortwave radiation budget under Arctic Sea ice in Earth System models
<p>Contains data and scripts of the study " Improving the representation of shortwave radiation budget under Arctic Sea ice in Earth System models using observations", submitted to Journal of Geophysical Research - Oceans. </p>
Datasets for "Arctic sea ice loss weakens Northern Hemisphere summertime storminess due to ocean coupling"
<p>The datasets contain post-processed model outputs and reanalysis data for creating figures in the paper. The data are npz files, which can be easily accessed using Python 3 and numpy package.</p>
The sensitivity of primary productivity in Disko Bay, a coastal Arctic ecosystem to changes in freshwater discharge and sea ice cover
<p>Data for figure 4 in <a href="https://doi.org/10.5194/egusphere-2022-916">https://doi.org/10.5194/egusphere-2022-916</a></p>
Modeled wintertime sea ice drift, sea ice thickness, dynamic sea surface height and sea ice drift budget terms in the Arctic
<p>Modeled wintertime sea ice drift, sea ice thickness, dynamic sea surface height and sea ice drift budget terms in the Arctic between 1981 and 2020.</p>
Preliminary data of drifting snow mass flux from the lower SPC at MOSAiC (2020-01-26 to 2020-02-04) for the submitted paper "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model"
<p>Preliminary data of lower SPC massflux from MOSAiC, for the time period 2020-01-26 -- 2020-02-04.</p> <p>1-h averaged time series of mass flux (kg/m²/h) to compare with the ALPINE3D simulation results.</p> <p>Will soon be replaced with a DOI / Repositiry at the Arctic Data Centre from BAS.</p>
Data for "Observed winter Barents Kara Sea ice variations induce prominent sub-decadal variability and a multidecadal trend in the Warm Arctic Cold Eurasia pattern"
<p>Here the model experiment data used to create the figures in the article "Observed winter Barents Kara Sea ice variations induce prominent sub-decadal variability and a multi-decadal trend in the Warm Arctic Cold Eurasia pattern" are provided.</p>
The simulated outputs analyzed in the article: "Understanding the influences of ocean waves on Arctic sea ice simulation: a modeling study with an atmosphere-ocean-wave-sea ice coupled model"
<p>In Ice-mass_[experiment] files, they include daily-averaged sea ice concentration and sea ice mass/area budgets.</p> <p>In Flux_[experiment] files, they include daily-averaged net ice surface flux, net shortwave/longwave radiation at the ice surface, latent/sensible heat flux at the ice surface, conductive heat flux at the top ice layer, and ice-ocean heat flux. </p>
Radiative transfer model and datasets for Li et al. (2023), 'Wintertime low-level clouds over sea ice cool the Arctic climate system'
<p>Source code for the radiative transfer model (RAPRAD) and cloud radiative flux data used in the study Li et al. (2022).</p>
Daily-resolution pan-Arctic sea ice thickness
<p>This dataset contains daily-resolution pan-Arctic sea ice thickness estimates, created following the methodology outlined in <strong>Stroeve</strong><a href="https://doi.org/10.1029/2022GL100696"><strong> </strong></a><strong>et al. (2024): "Mapping potential timing of ice algal blooms from satellite"</strong></p> <p>Please refer to this publication for a full description of the data and cite this publication when using these data.</p> <p>Variables: Sea Ice Thickness (m), Longitude, Latitude, Day</p> <p>Dates are stated in the file names</p> <p>IS2 data is available year-round</p> <p>CS2 and CS2S3 data is available for winter seasons only, with each winter season running from 1 October - 30 April of the specified years</p> <p>Days where no data is available are filled with NaNs</p>
Data supporting 'The Response of Midlatitude Surface Temperature Persistence to Arctic Sea-Ice Loss' by Neil T Lewis, William J M Seviour, Hannah E Roberts-Straw, and James A Screen.
<p>Data supporting Lewis et al., 2023. The Response of Midlatitude Surface Temperature Persistence to Arctic Sea-Ice Loss. Submitted to Geophysical Research Letters.</p><p>All model output is contained within the folder data/. All data is in NetCDF format.</p><p>The folder data/PAMIP/ contains output from coupled AOGCMs that contributed piArcSIC and futArcSIC timeslice runs to PAMIP. The AOGCMS present are: HadGEM3-GC31-MM, IPSL-CM6A-LR, CESM2-WACCM6, and CESM-WACCM-SC. For each model + run, two data files are included. One contains the autocorrelation of surface temperature, at 5, 10, and 15 day lags. The second contains the frequency and duration of persistent extremes (as defined in Lewis et al., 2023).</p><p>Additional output is included in data/PAMIP/ from extended pdSIC-ext and futArcSIC-ext experiments run using CNRM-CM6-1. For each run, a file containing the autocorrelation of surface temperature (as above) is included.</p><p>The folder data/CMIP/ contains output from CMIP6 historical/SSP585 runs using three of the models listed above: HadGEM3-GC31-MM, IPSL-CM6A-LR, and CESM2-WACCM6. For each model, 'pre-industrial' and 'future' output is available. Output in these files was computed from 30-year time-periods, subsampled from the historical/SSP585 runs, selected so that the 30-year average sea-ice area matched that in the corresponding PAMIP runs above. For each model and time period, two data files are included. One contains the autocorrelation of surface temperature, at 5, 10, and 15 day lags. The second contains the frequency and duration of persistent extremes (as defined in Lewis et al., 2023).</p><p>Output is also included in data/CMIP/ from CNRM-CM6-1 'present day' and 'future' time periods, selected to match the sea-ice area in the CNRM -ext PAMIP runs. For this model, output files contain the autocorrelation of surface temperaure. </p>
Use of sea ice by arctic terns Sterna paradisaea in Antarctica and impacts of climate change
Open the record for dataset details and reuse information.
Model code and output of CSIBv4 pan-Arctic sea ice-ocean DMS simulation
<p>here i deposit the model source code and output of the manuscript "Spatio-temporal variability in modelled bottom-ice and sea-surface dimethylsulfide concentrations and fluxes in the Arctic during 1979-2015" by Hayashida et al. (2020).</p> <p><strong>See "readme.txt" for information.</strong></p> <p>For question, contact Hakase Hayashida.</p>
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