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552 results for “Sea ice”
Sea ice and wave breaking patterns during the Terra Nova Bay Polynya event on September 19, 2019, based on very high resolution satellite imagery
<p>Results of the WorldView-2 (WV2) panchromatic image analysis, described in Herman and Bradtke 2023 "Fetch-limited, strongly forced wind waves in waters with frazil and grease ice — spectral modelling and satellite observations in an Antarctic coastal polynya". The image, which cover part of the polynya in Terra Nova Bay, was taken on September 19, 2019 at 21:22 UTC. The spatial resolution of source data is 0.5 m.</p> <p>The zip file contains:</p> <ol> <li> “AOI” folder with the analyzed area outline in SHAPEFILE format</li> <li>“whitecaps” folder with breaking wave signatures (polygons) in SHAPEFILE format</li> <li>Sea ice (1) - water (2) mask in GeoTIFF format (0 means NoData)</li> </ol> <p>Data are provided in WGS 1984 / UTM Zone 58S projection (EPSG:32758)</p>
Dataset: Inferring Inherent Optical Properties of Sea Ice Using 360-Degree Camera Radiance Measurements
<p>New types of compact 360-degree cameras have recently appeared on the consumer technology market. Some of these allow users to access raw imagery, offering sensor-level data that can be directly exploited for absolute light quantification. This paves the way for easy-to-use, inexpensive and accessible radiance cameras that can be operated in a wide range of natural environments. </p> <p>This dataset presents the angular radiance distributions measured with the Insta360 ONE 360-degree camera in sea ice. We report vertical profiles of the light field structure at two sites reprensentative of distinct sea ice types: High Arctic multi-year ice and Chaleur Bay (Quebec, Canada) landfast first-year ice. </p> <p>This repository contains the radiometric data stored in <strong>Hierarchical Data Format (HDF5, h5)</strong> under the following names: </p> <ul> <li><strong><a href="https://zenodo.org/api/records/14263256/draft/files/oden-08312018-imf-fluo.h5/content" target="_blank" rel="noopener noreferrer">oden-08312018-imf-fluo.h5</a></strong></li> <li><strong><a href="https://zenodo.org/api/records/14263256/draft/files/baiedeschaleurs-03232022-imf-fluo.h5/content" target="_blank" rel="noopener noreferrer">baiedeschaleurs-03232022-imf-fluo.h5</a></strong></li> </ul> <p>The High Arctic dataset (<strong>oden-08312018-imf-fluo.h5</strong>) contains only one station, while the Chaleur Bay (<strong>baiedeschaleurs-03232022-imf-fluo.h5</strong>) has four that can be accessed using these tags: "station_1", "station_2", "station_3", "station_4". The radiance measurements at each depth are reported as 2-dimensionals arrays with the azimuth directions (0-359°, 1° resolution) as columns and the zenith directions (0-180°, 1° resolution) as lines. The routines (coded in python) for the data processing can be found in the following <a href="https://github.com/RaphaelLarouche/radiance_camera_insta360/tree/master_v01" target="_blank" rel="noopener">Github repository</a> (master_v01) or the <a href="https://zenodo.org/records/4660994" target="_blank" rel="noopener">Zenodo stored version</a>. </p> <p>The methodologies to carefully calibrated the 360-degree camera for radiometry purpose are described in this <a href="https://doi.org/10.1364/AO.524122" target="_blank" rel="noopener">pulibcation</a> and the raw calibration data can be found in this Zenodo <a href="https://zenodo.org/records/10278731" target="_blank" rel="noopener">repository</a>. </p> <p>Additionnal information on the fieldwork and the data analysis are described in the <a href="https://doi.org/10.31223/X5V955" target="_blank" rel="noopener">preprint</a>.</p>
A monthly 5 km Arctic sea ice thickness product from 1995 to 2023 using multiple radar altimetry data
<p>Arctic sea ice is of great importance to the regional and global climate change study. Satellite observations have demonstrated that the Arctic sea ice extent has been declined for the last four decades. However, long-term variations of the Arctic sea ice thickness (SIT) are less focused as SIT cannot be measured directly by satellite-based instruments. Here, we presented a monthly Arctic SIT product based on multiple radar altimetry observations from ERS-2, Envisat, and CryoSat-2. To ensure the accuracy of the SIT retrievals, we proposed a novel data processing procedure including leads detection, freeboard conversion to thickness and inter-mission bias correction. Finally, we were able to generate the monthly SIT estimates for the Arctic Ocean from October 1995 to December 2023. The thickness estimates are posted on a 5 km resolution polar stereographic grid. The variations of the Arctic SIT are analyzed in terms of the spatial and temporal distributions. We also compared our SIT estimates with observations from upward looking sonars (ULSs) and airborne laser altimetry from Operation IceBridge (OIB), as well as seven publicly released Arctic SIT products. The validation results demonstrate that our SIT product features equivalent accuracy with existing products. The accuracy of our products is about 0.4 m during Envisat period, and is within 0.2 m during CryoSat-2 period.</p>
RESICE - Reusability-targeted Enriched Sea Ice Core Database - Part A
<div> <div>RESICE is described in detail in the article <em>Reusability-targeted enrichment of sea ice core data</em> published on 2025-03-20 in Scientific Data (DOI: <a href="https://doi.org/10.1038/s41597-025-04665-x" target="_blank" rel="noopener">10.1038/s41597-025-04665-x</a>). This is Part A of RESICE. RESICE_PartA.csv contains all data including profile data (several rows per core), RESICE_PartA_cores.csv provides all data excuding profile data (one row per core) and sources_PartA.csv provides a list of all sources. The database including its <a title="RE-SICE Part B" href="https://www.doi.org/10.5281/zenodo.14744942" target="_blank" rel="noopener">Part B</a> is described in the <a title="RE-SICE General Information" href="https://www.doi.org/10.5281/zenodo.14744912" target="_blank" rel="noopener">general information. </a>Part A and Part B had to be separated due to different licenses of the orginal data sources.</div> </div>
RESICE - Reusability-targeted Enriched Sea Ice Core Database - General Information
<div> <div>RESICE is described in detail in the article <em>Reusability-targeted enrichment of sea ice core data</em> published on 2025-03-20 in Scientific Data (DOI: <a href="https://doi.org/10.1038/s41597-025-04665-x" target="_blank" rel="noopener">10.1038/s41597-025-04665-x</a>).</div> <div> </div> <div>A large number of sea ice core data sets are available that have been acquired by research groups around the world and published in different data repositories. The structure of sea ice core data differs substantially across repositories and entries regarding combinations of content, level and quality of description, label names, formats, units, etc. Here, we have compiled sea ice core data and metadata available in data sets (DS) into a tabular database. Additionally, we have added data and metadata from articles (A) and expedition reports (ER). We have enriched the database with metadata from instrument manuals (IM) and controlled terminologies (CT) such as the <a title="SIN" href="https://library.wmo.int/idurl/4/41953" target="_blank" rel="noopener"><em>Sea Ice Nomenclature</em></a> (SIN) from the World Meteorological Organization (WMO) and the <a title="SeaVoX Polygons" href="https://doi.org/10.14284/590" target="_blank" rel="noopener"><em>Polygon data set of water body extent from the SeaVoX Salt and Fresh Water Body Gazetteer</em></a> by the British Oceanographic Data Centre (BODC). We grouped the type of sources into primary sources (DS), secondary sources (A, ER), and tertiary sources (IM, CT). RESICE enhances reusability of the included sea ice core data through enrichment. RESICE provides a comprehensive resource for sea ice modeling applications that aim at using information from compiled sea ice core data. Some examples are calibration and validation of physics-based process models addressing the generation and evolution of sea ice or the training of data-driven models that rely on harmonized training data. As data and metadata are combined from many sources, each entry in the data set needs to be traceable to the original source and its corresponding DOI or URL. Where appropriate, we refer to the original excerpt, figure or table of the original source or comment on inconsistencies or required changes to transparently communicate the entries origin. This is the general information on the database. Please find <a title="RESICE Part A" href="https://www.doi.org/10.5281/zenodo.14745035" target="_blank" rel="noopener">Part A</a> of the database that can be reused under license CC-BY, and <a href="https://www.doi.org/10.5281/zenodo.14744942">Part B</a> of the database that can be reused under license CC-BY-SA. RESICE can be interactively viewed, analyzed and plotted in the <a title="MOSAiC webODV" href="https://mvre.webodv.cloud.awi.de/DataExploration/id/DVevtE7c">MOSAiC webODV</a> instance. RESICE can be reproduced and extended with the <a title="pyresice Python package" href="https://doi.org/10.5281/zenodo.11198658" target="_blank" rel="noopener">pyresice</a> Python package available on <a title="pyresice gitLab" href="https://git.rwth-aachen.de/mbd/pyresice/" target="_blank" rel="noopener">gitLab</a>.</div> </div>
Example of boundary files used with the Los Alamos Sea Ice Model
<p>The zip archive available here contains two example files of sea ice time-varying boundaries for usage with the Los Alamos Sea Ice Model.</p>
Numerical experiments of ENSO and Arctic sea ice
<p>The data named "CTRL_40yrs_output.nc", “EXP_ALL_40yrs_R.nc”, “EXP_SIC_40yrs.nc”, “EXP_SST_40yrs_R.nc” are the output of last 40 model years for CTRL, EXP_ALL, EXP_SIC, and EXP_SST, which are run for totally 60 model years based on CAM5.</p>
multi-model metrics of daily sea-ice concentrations from CMIP6 models
<p>Multi-model metrics of daily mean sea-ice concentrations from 9 CMIP6 models:</p> <ul> <li>ACCESS-CM2</li> <li>CanESM5</li> <li>CNRM-ESM2.1</li> <li>EC-Earth3</li> <li>EC-Earth3-Veg</li> <li>IPSL-CM6A-LR</li> <li>MIROC6</li> <li>MRI-ESM2</li> <li>NorESM2-LM</li> </ul> <p> </p> <p>Metrics are mean, median, standard deviation, minimum and maximum sea-ice concentrations.</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>
Photophysiological response of autumn phytoplankton in the Antarctic Sea-Ice Zone
<p>The datasets in this repository are part of the manuscript entitled "<strong>Photophysiological response of autumn phytoplankton in the Antarctic Sea-Ice Zone</strong>".</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>
Data accompanying "A new brittle rheology and numerical framework for large-scale sea-ice models"
<p>Data accompanying "A new brittle rheology and numerical framework for<br> large-scale sea-ice models" by E. Olason et al, accepted for publication in<br> Journal of Advances in Modelling Earth Systems (2022).</p> <p>Files:<br> * CS2SMOS.tar.bz2: Contains Cryosat2/SMOS data, post-precessed and used to<br> produce figures comparing modelled thickness to observations.<br> * deformation_maps_demo.ipynb: An example jupyter notebook to read pairs.npz<br> * OlasonEtAl_BBM.tar.bz2: Thickness fields from the MEB run used to produce<br> figure 1 (netCDF).<br> * OlasonEtAl_MEB.tar.bz2: Thickness fields from the BBM run used to produce<br> figure 8 (netCDF).<br> * OlasonEtAl_mEVP.tar.bz2: Thickness fields from the mEVP run used to produce<br> figure 8 (netCDF).<br> * pairs.npz: Displacement pairs derived from the model's Lagrangian mesh used<br> to produce figures 3, 4, and 5 (numpy data file).<br> * Winter2006_7_BBM.nc.bz2: Thickness, concentration, and velocity fields from<br> the BBM run for the winter 2006-7 widely used in the paper (netCDF).<br> * Winter2006_7_mEVP.nc.bz2: Thickness, concentration, and velocity fields from<br> the mEVP run for the winter 2006-7 used for comparison in the paper<br> (netCDF).</p> <p> </p>
MODIS Sea ice leads detections using a U-Net
<p>Sea ice leads are long and narrow sea ice fractures. Despite accounting for a small fraction of the Arctic surface area, leads play a critical role in the energy flux between the ocean and atmosphere. As the volume of sea ice in the Arctic has declined over recent decades, it is increasingly important to monitor the corresponding changes in sea ice leads. An approach described in Hoffman et al. 2021 uses artificial intelligence (AI) to detect sea ice leads using satellite thermal infrared window data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The AI used to detect sea ice leads in satellite imagery is a particular kind of convolutional neural network, a U-Net. The originally published dataset included only a small case study of results. Here, the dataset is expanded to include the daily detection of leads since 2002 for the season between November through April.</p>
Crystal size and texture data of sea ice from the Antarctic Marginal Ice Zone collected in Winter 2019
<p>This dataset details the crystal size and texture data from the sea ice cores collected during the SCALE Winter 2019 voyage to the Antarctic Marginal Ice Zone. Each sample collected has a series of accompanying photograph figures from which the crystal size and texture are based. </p>
Crystal size and texture data of sea ice from the Antarctic Marginal Ice Zone collected in Spring 2019
<p>This dataset details the crystal size and texture data from the sea ice cores collected during the SCALE Spring 2019 voyage to the Antarctic Marginal Ice Zone. Each sample collected has a series of accompanying photograph figures from which the crystal size and texture are based. </p>
A simple model for daily basin-wide thermodynamic sea ice thickness growth retrieval: Data
<p>Data for:</p> <p>Anheuser, J., Liu, Y., and Key, J.: A daily basin-wide sea ice thickness retrieval methodology: Stefan's Law Integrated Conducted Energy (SLICE), The Cryosphere Discuss. [preprint], <a href="https://doi.org/10.5194/tc-2021-333">https://doi.org/10.5194/tc-2021-333</a>, in review, 2021.</p> <p> </p> <p>Scripts for producing data and figures can be found at:</p> <p>https://doi.org/10.5281/zenodo.6561431</p> <p> </p> <p> </p>
Results of ultrasound measurements of sea-ice cores sampled during the Southern oCean seAsonal Experiment (SCALE) winter cruise in 2019
<p>This dataset details the results of testing sea ice cores collected during the SCALE 2019 Winter Cruise using ultrasound techniques. </p>
Data for Directional Surface Wave Spectra And Sea Ice Structure from ICEsat-2 Altimetry
<p>This is data used for <em>Directional Surface Wave Spectra And Sea Ice Structure from ICEsat-2 Altimetry</em> in the Cryosphere.</p> <p>The code that reproduces this data can be found at</p> <pre>10.5281/zenodo.6908645</pre> <p>See README.md for further instructions.</p> <p> </p>
CICE6 standalone sea ice surface temperature output
<p>CICE6 standalone sea ice concentration and sea ice surface temperature data for July-August and October-November 2019. JRA55-do 1.4.0 (2010-2018) and JRA55-do 1.5.0 (2019) was used for atmospheric forcing, ocean forcing was taken from an ACCESS-OM2 (1deg_jra55_iaf_omip2_cycle6) output. A wave propagation and attenuation model (Meylan et al., 2014) was implemented into CICE6 to enable wave forcing from WaveWatch III (CAWCR). </p>
VIIRS Sea ice leads detections using a U-Net
<p>Sea ice leads are long and narrow sea ice fractures. Despite accounting for a small fraction of the Arctic surface area, leads play a critical role in the energy flux between the ocean and atmosphere. As the volume of sea ice in the Arctic has declined over recent decades, it is increasingly important to monitor the corresponding changes in sea ice leads. An approach described in Hoffman et al. 2021 uses artificial intelligence (AI) to detect sea ice leads using satellite thermal infrared window data from the Visible Infrared Imaging Radiometer Suite (VIIRS). The AI used to detect sea ice leads in satellite imagery is a particular kind of convolutional neural network, a U-Net. The originally published dataset included only a small case study of results. Here, the dataset is expanded to include the daily detection of leads since 2011 for the season between November through April.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.