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Satellite-derived monthly Arctic winter sea ice thickness, snow depth, freeboards, ice draft, and bulk ice density (2011-2022) and validation datasets
<h1><strong>[Description]</strong></h1> <p>This dataset is curated for a manuscript published in Earth and Space Science by Hoyeon Shi and his colleagues in April 2024. </p> <blockquote> <p>Shi, H., Tonboe, R., Lee, M., Dybkjær, G., Sohn, J., Singha, S., & Baordo, F. (2024). A Simple and Robust CryoSat-2 Radar Freeboard Correction Method Dedicated to TFMRA50 for the Arctic Winter Snow Depth and Sea Ice Thickness Retrieval. <em>Earth and Space Science</em>, <em>11</em>(10), e2024EA003715. https://doi.org/10.1029/2024EA003715</p> </blockquote> <p>Here, version 2 is uploaded, corresponding to the revised manuscript during the revision. The main changes compared to version 1 are:<br> 1) Update of the CryoSat-2 radar freeboard dataset (from v2p4 to v2p6)<br> 2) Update of the coefficients for the radar freeboard correction equations<br> 3) Extension of the retrieval period for the CS2IS2 method (April is now included)<br> 4) Removal of OIB data points used for the regression from the validation datasets<br> 5) Inclusion of the Fram Strait mooring dataset in the validation dataset</p> <p>It consists of three directories, each described below.</p> <h2><strong>01_retrieval_results</strong></h2> <p>This directory includes CryoSat-2-based monthly fields of Arctic sea ice thickness, snow depth, total freeboard, ice freeboards, ice draft, and bulk sea ice density for the winter months of the 2011-2022 period (January-March for alpha method and January-April for CS2IS2 method). Those variables are obtained using six combinations of two retrieval methods and three radar freeboard correction methods.</p> <p><em>Retrieval methods</em></p> <ul> <li>alpha method: A simultaneous retrieval method based on Shi et al. (2020) and Shi et al. (2023), combining CryoSat-2, AVHRR, and AMSR data</li> <li>CS2IS2 method: A simultaneous retrieval method based on Kwok and Marcus (2018) and Kwok et al. (2020), combining CryoSat-2 and ICESat-2 data</li> </ul> <p><em>Radar freeboard correction methods</em></p> <ul> <li>Wave speed correction method: Mallet et al. (2020)</li> <li>Empirical correction method: An empirical correction derived from the CS2_OIB_matchup data, using snow depth as a predictor</li> <li>Bias correction method: An empirical correction derived from the CS2_OIB_matchup data, doing bias correction</li> </ul> <p>The datasets used for generating this dataset are as follows:</p> <ul> <li>CryoSat-2 <br>- AWI CryoSat-2 sea ice thickness v2p6 (doi: <a href="https://doi.org/10.5281/zenodo.10044554" target="_blank" rel="noopener">10.5281/zenodo.10044554</a>)</li> <li>ICESat-2<br>- NSIDC ATL20 dataset (doi: <a href="https://doi.org/10.5067/ATLAS/ATL20.004" target="_blank" rel="noopener">10.5067/ATLAS/ATL20.004</a>)</li> <li>AVHRR<br>- Copernicus Marine Service's surface temperature datasets (doi: <a href="https://doi.org/10.48670/MOI-00130" target="_blank" rel="noopener">10.48670/MOI-00130</a>, doi: <a href="https://doi.org/10.48670/MOI-00123" target="_blank" rel="noopener">10.48670/MOI-00123</a>)</li> <li>AMSR<br>- JAXA AMSR-E 6.9 GHz brightness temperature (doi: <a href="https://doi.org/10.57746/EO.01gs73ayng11rpwk7n54aynyj1" target="_blank" rel="noopener">10.57746/EO.01gs73ayng11rpwk7n54aynyj1</a>)<br>- JAXA AMSR2 6.9 GHz brightness temperature (doi: <a href="https://doi.org/10.57746/EO.01gs73b1nzeh3g66jr4p04mr0j" target="_blank" rel="noopener">10.57746/EO.01gs73b1nzeh3g66jr4p04mr0j</a>)</li> <li>Auxiliary data<br>- Sea ice concentration: OSI SAF (doi: <a href="https://doi.org/10.15770/EUM_SAF_OSI_0013" target="_blank" rel="noopener">10.15770/EUM_SAF_OSI_0013</a>, doi: <a href="https://doi.org/10.15770/EUM_SAF_OSI_0014" target="_blank" rel="noopener">10.15770/EUM_SAF_OSI_0014</a>)<br>- Sea ice type: OSI SAF (doi: <a href="https://doi.org/10.15770/EUM_SAF_OSI_NRT_2006" target="_blank" rel="noopener">10.15770/EUM_SAF_OSI_NRT_2006</a>)</li> </ul> <p>The naming convention is 'RetrievalMethod_CorrectionMethod_yyyymm.bin'. The 'RetrievalMethod' is either 'alpha' or 'CS2IS2', and the 'CorrectionMethod' is either 'WaveSpeed,' 'Empirical,' or 'BiasCorrection.' The data format is a 32-bit floating point array in the shape of 6 x 448 x 304 (25 km polar stereographic grid). The first dimension indicates the variables (in the order of snow depth (0), sea ice thickness (1), ice freeboard (2), total freeboard (3), sea ice draft (4), and bulk sea ice density (5)). For example, to read the sea ice thickness of January 2020 based on the alpha method with an empirical correction, you may write this Python command:</p> <p><code>import numpy as np</code><br><code>data = np.fromfile('alpha_Empirical_202001.bin', dtype=np.float32).reshape(6,448,304)</code><br><code>hi = data[1,:,:]</code></p> <p>The unit of thickness-related variable is cm, and the unit of density is kg/m3. The 25 km polar stereographic grid information is available on the NSIDC website (doi: <a href="https://doi.org/10.5067/N6INPBT8Y104" target="_blank" rel="noopener">10.5067/N6INPBT8Y104</a>).</p> <h2><strong>02_valdiation data </strong></h2> <p>This directory includes reference data used for quality assessment of retrievals. There are three sub-directories:</p> <p>'Mooring_draft_psn25_monthly' includes sea ice draft measurements from the moorings in the Beaufort Sea (https://www2.whoi.edu/site/beaufortgyre/data/mooring-data/), Fram Strait (doi: <a href="https://doi.org/10.21334/npolar.2022.b94cb848" target="_blank" rel="noopener">10.21334/npolar.2022.b94cb848</a>), and the Laptev Sea (doi: <a href="https://doi.org/10.1594/PANGAEA.912927" target="_blank" rel="noopener">10.1594/PANGAEA.912927</a>, doi: <a href="https://doi.org/10.1594/PANGAEA.899275" target="_blank" rel="noopener">10.1594/PANGAEA.899275</a>).</p> <p>'OIB_SD_psn25_monthly' and 'OIB_TFB_psn25_monthly' include airborne snow depth and total freeboard measurements from NASA's Operation IceBridge campaign (doi: <a href="https://doi.org/10.5067/G519SHCKWQV6" target="_blank" rel="noopener">10.5067/G519SHCKWQV6</a>, doi: <a href="https://doi.org/10.5067/GRIXZ91DE0L9" target="_blank" rel="noopener">10.5067/GRIXZ91DE0L9</a>).</p> <p>Original data were processed to become monthly gridded data to make a comparison with satellite retrievals. The OIB data points used for the regression were excluded when processing the monthly gridded data. The naming convention of each file is 'Var_yyyymm.bin,' where 'Var' is the variable name (SD: snow depth, TFB: total freeboard, Di: ice draft). For example, you can use the following code to read the OIB snow depth in March 2014.</p> <p><code>import numpy as np</code><br><code>hs = np.fromfile('SD_201403.bin', dtype=np.float32).reshape(448,304)</code></p> <h2><strong>03_CS2_OIB_matchup</strong></h2> <p>This directory includes a match-up of AWI's CryoSat-2 L2P track data and OIB track data. The matching was done by resampling two high-resolution data on a coarser-resolution common grid (25 km polar stereographic grid) using a drop-in-a-bucket resampling method. The file format is CSV, and it is straightforward to understand when it is opened.</p> <h1><strong>[Abbreviations]</strong></h1> <p>AMSR: Advanced Microwave Scanning Radiometer<br>AVHRR: Advanced Very High Resolution Radiometer<br>AWI: Alfred Wegener Institute<br>CS2: CryoSat-2<br>JAXA: Japan Aerospace Exploration Agency<br>NASA: National Aeronautics and Space Administration<br>NSIDC: National Snow and Ice Data Center<br>OIB: Operation IceBridge<br>OSI SAF: Ocean and Sea Ice Satellite Application Facility</p> <p> </p>
Audiovisual Vignettes of Sea Ice Ridging in the Beaufort Sea in 2007
<p>This presents footage demonstrating the scales of sea ice motion involved in creating ridges under varied degrees of compression and shear. Sound heard in these vignettes is associated with frictional dissipation of kinetic energy during vertical ice displacement. Images shown were recorded during April 2-15, 2007 UTC, as part of the field campaign: Sea Ice Experiment - Dynamic Nature of the Arctic (SEDNA). Footage and photographs presented in this vignette were taken by Andrew Roberts with the assistance of Jennifer Hutchings and Cathleen Geiger. Funding for SEDNA was provided by the National Science Foundation, grant number OPP ARC 0612527. An overview of the SEDNA field campaign is given in: Hutchings, J. K. et al. (2008), Role of Ice Dynamics in the Sea Ice Mass Balance, <em>Eos Trans. AGU</em>, <em>89</em>(50), doi:10.1029/2008EO500003. </p> <p>[Version 2 includes minor corrections and additions to text in Version 1]</p>
SPICES – Sea ice edge maps from the Fram Strait
<p>Sea ice edge maps derived from Sentinel-1 SAR dual polarisation EW images using a Support Vector Machine (SVM) algorithm. Thisalgorithm is based on a SVM approach, and in addition uses texture calculation and principal component analysis (PCA) to classify sea ice types (Korosov et al., 2016). The main steps of the algorithms include:<br> (1) pre-processing of the raw SAR data, <br> (2) calculation of texture features, <br> (3) unsupervised pre-classification of the image using PCA and k-means cluster analysis to reduce the number of ice classes, <br> (4) expert re-classification of the image into the pre-calculated classes, <br> (5) training of the SVM using input from the previous step, and <br> (6) classifying the full image into the reduced number of classes using the trained SVM. <br> To generate an ice edge product, the SVM algorithm is used with only two classes: sea ice and open water. </p> <p>Korosov, A., N. Zakhvatkina, A. Vesman, A. Mushta, and S. Muckenhuber, Sea ice classification algorithm for Sentinel-1 images, Poster at ESA Living Planet Symposium 2016, Prague, Czech Republic, 9-13 may, 2016.</p> <p> </p> <p> </p>
SPICES – Sea ice type maps from Fram Strait, Barents and Kara Sea
<p>Sea ice type classification of Sentinel-1 SAR dual polarization EW images. </p> <p>Prior to classification, Sentinel-1 SAR images have been pre-processed to remove noise [1]. The procedure of classification is comprised of the following steps: 1. Haralick texture features are computed, 2. Principal Component Analysis (PCA) is applied, 3. K-means clustering is used to group the data into 15 clusters using factor scores of the PCA as input, 4. The 15 clusters are analysed by an ice expert and classified as open water and different types of sea ice.</p> <p> </p> <p>[1] J.-W. Park, A. A. Korosov, M. Babiker, S. Sandven, J.-S. Won, Efficient Thermal Noise Removal for Sentinel-1 TOPSAR Cross-Polarization Channel, IEEE Transactions on Geoscience and Remote Sensing, 2018; 56, 3, DOI:10.1109/TGRS.2017.2765248.</p>
SPICES – Sea ice edge maps from the Kara Sea
<p>Sea ice edge maps derived from Sentinel-1 SAR dual polarisation EW images using a Support Vector Machine (SVM) algorithm. This algorithm is based on a SVM approach, and in addition uses texture calculation and principal component analysis (PCA) to classify sea ice types (Korosov et al., 2016). The main steps of the algorithms include: (1) pre-processing of the raw SAR data, (2) calculation of texture features, (3) unsupervised pre-classification of the image using PCA and k-means cluster analysis to reduce the number of ice classes, (4) expert re-classification of the image into the pre-calculated classes, (5) training of the SVM using input from the previous step, and (6) classifying the full image into the reduced number of classes using the trained SVM. To generate an ice edge product, the SVM algorithm is used with only two classes: sea ice and open water. </p> <p>Korosov, A., N. Zakhvatkina, A. Vesman, A. Mushta, and S. Muckenhuber, Sea ice classification algorithm for Sentinel-1 images, Poster at ESA Living Planet Symposium 2016, Prague, Czech Republic, 9-13 may, 2016.</p>
CMI: Sea ice properties from mesocosm oil-in-ice experiments at University of Alaska Fairbanks, 2014-15
<p>Sea ice core properties (salinity) and raw ice temperature data measurement collected during the CMI mesocosm experiment aiming at assessing the impacts of crude oil on ice biota lead at UAF in 2014 and 2015.</p>
MOSIDEO: Sea ice properties and oil concentrations measured during the HSVA experiment during MOSIDEO
<p>Physical ice properties (porosity, permeability and brine volume fraction), and oil concentration measured on collected ice cores during the experiments.</p> <ul> <li>Oil concentrations are measured using a UV-fluorescence meter TD500TM (Turner Designs Hydrocarbon Instruments, Inc.)</li> <li>Ice temperature measured in-situ with thermocouple strings</li> <li>Porosity and permeability fields are computed from ice and temperature profiles using semi-empirical equations (Cox and Weeks, 1983; Golden et al., 2009). The oil intake and pore space saturation in oil (oil saturation) are derived from acoustic data of the oil/water and oil/ice interface position.</li> </ul>
Outputs of the next generation sea ice model (neXtSIM) for winter 2006 - 2007 saved for comparison with RGPS.
<p>NeXtSIM was run from 1 December 2006 to 15 April 2007 with the following parameters:</p> <p><code>[mesh]</code><br><code>filename=small_arctic_10km.msh</code></p> <p><code>[simul]</code><br><code>duration=150</code><br><code>time_init=2006-11-15</code><br><code>timestep=900</code></p> <p><code>[dynamics]</code><br><code>compression_factor=13800</code><br><code>C_lab=2675000</code><br><code>nu0=0.301</code><br><code>tan_phi=0.624</code><br><code>substeps=90</code><br><code>time_relaxation_damage=15</code><br><code>use_temperature_dependent_healing=true</code></p> <p><code>[output]</code><br><code>exporter_path=/cluster/work/users/akorosov/music/sa10free_mat00</code><br><code>output_per_day=4</code><br><code>variables=M_VT</code><br><code>variables=Concentration</code><br><code>variables=Thickness</code></p> <p><code>[setup]</code><br><code>atmosphere-type=era5</code><br><code>ice-type=topaz_osisaf_icesat</code><br><code>ocean-type=topaz</code><br><code>bathymetry-type=etopo</code><br><code>dynamics-type=bbm</code></p> <p><code>[thermo]</code><br><code>diffusivity_sss=0</code><br><code>diffusivity_sst=0</code><br><code>h_young_max=0.3</code><br><code>newice_type=1</code><br><code>hnull=0.5</code></p> <p><code>[debugging]</code><br><code>check_fields_fast=false</code></p> <p>The outputs (binary snapshots at every 3 hours) were then merged with RGPS data from the same period using this notebook:</p> <p>https://github.com/nansencenter/music_nextsim_tuning_paper/blob/main/02_process_nextsim.ipynb</p> <p> </p>
Macroscopic, histological and stereological image dataset of Megrim (Lepidorhombus whiffiagonis) ovaries from the ICES Celtic Seas, south of Greater North sea or Bay of Biscay Ecoregions
<p><strong>Contents: </strong></p> <p>This dataset contains the macroscopic and histological images of the ovaries of 202 Megrim (female, <em>Lepidorhombus whiffiagonis</em>, Walbaum, 1792) collected from the ICES Celtic Seas, south Greater North sea or Bay of Biscay Ecoregions (Eco) in November 2019 (n=25; Eco=7h & 7j), November 2020 (n=14, Eco=7h & 7j), December 2020 (n=1, Eco=7h), May 2021 (n=15, Eco=7h & 7g), June 2021 (n=15, Eco=7h), July 2021 (n=15, Eco=7h & 7e), October 2021 (n=15, Eco=7g & 7f), October 2021 (n=6, Eco=8a & 8b & 8c), November 2021 (n=6, Eco=8a & 8b), November 2021 (n=15, Eco=7j), December 2021 (n=15, Eco=7e & 7g), January 2022 (n=15, Eco=7f), February 2022 (n=15, Eco=7g), March 2022 (n=15, Eco=7g) and May 2022 (n=15, Eco=7g).</p> <p> </p> <p><strong>Images:</strong></p> <ul> <li><strong>Macroscopic_pictures.zip: </strong>archive in zip format of 549 pictures (.JPG; 2Mo-8Mo; JPG; 350pp) from 202 female megrim dissected during this study. Each photo was taken with a digital camera (no flash). For each individual, up to three pictures were taken when possible (Le Meleder <em>et al.</em>, 2022) with : <ul> <li>one picture of the entire fish with its abdominal cavity open with the ovaries in view</li> <li>one picture of the whole fish with the ovaries outside of the abdominal cavity</li> <li>one picture of the ovaries</li> <li>the name of the picture is the same as the fish's ID number.</li> </ul> </li> <li><strong>Histology_slides.zip :</strong> archive in zip format containing the ovarian histological slides digitized using an Aperio CS (Scan Scope Console software, v.10.2.0.2352), x20 lens. The whole slide images (.svs) are of the 461 histological slides acquired during this study. </li> </ul> <p> </p> <p><strong>Data:</strong></p> <ul> <li><strong>Readings.zip :</strong> archive in zip format containing the stereology reading results of the ovarian histological slides. In this folder, three directories are available. <ul> <li><strong>Calibration</strong> : Reading results of 3 different agents, with the first and last readings, as well as the QuPath scripts used.</li> <li><strong>Homogeneity</strong> : Reading results for 102 histological slides used to check the cellular homogeneity inter- and intra-gonad. These 102 slides belong to 17 fish, with three histological samples taken in the anterior (1), median (2) and posterior (3) sections of the left (G) and right (D) ovaries. A QuPath folder is also present, containing the scripts used.</li> <li><strong>Total </strong>: Reading results for 202 ovarian histological slides of the median position of either the left or right ovary. One median slide was read per sampled fish. A QuPath folder is also present, containing the scripts used.</li> </ul> </li> <li><strong>Macro_WHI_read_me.txt</strong> : a text file (.txt) listing the acronyms used in the <strong>Macro_WHI.xlsx</strong> file, as well as their meaning.</li> <li><strong>Macro_WHI.xlsx</strong> : Excel file (.xlsx) containing measurements of macroscopic parameters for all 202 fish sampled during this study. The information contained in this table is as follows: <ul> <li>Fish_id: identification of the fish. This id is identical to the name given to the pictures of the full ovaries (<strong>Macroscopic_pictures_Data</strong>)</li> <li>ICES _Division: International Council for the Exploration of the Sea (ICES) division where the fish was sampled in the Food and agricultural Organization of the United nations (FAO) fishing area 27</li> <li>ICES_statistical_rectangle : Statistical rectangle where the fish was sampled within the FAO fishing area 27</li> <li>Date: date the fish was caught (dd/mm/yyyy)</li> <li>Total_fish_length: total length of the fish (cm)</li> <li>Ungutted_fish_weight: total weight of the fish (g)</li> <li>Otolith_ID: unique identification number given to each sampled fish through the Imagine (Ellebode <em>et al.</em>, 2022) software used by IFREMER</li> <li>Parasite: presence (Y) or absence (N) of parasite in or on the fish</li> <li>age: age (in years) of the fish after analysis of the fish's otolith. The IFREMER laboratory of Boulogne-sur-Mer (FRANCE) executed this analysis</li> <li>Visual_maturity : visually estimated maturity, after observation macroscopic criteria of the fish's gonad with the naked eye, following the WKASMSF (ICES, 2018) scale</li> <li>Liver_weight: liver weight (g)</li> <li>Droite_gonad_weight : gonad weight (g) of right ovary</li> <li>Gauche_gonad_weight : gonad weight (g) of left ovary</li> <li>Sections: number of cross sections sampled for the individual</li> </ul> </li> <li><strong>Stereo_WHI_read_me.txt</strong> : a text file (.txt) listing the acronyms used in the <strong>Stereo_WHI.csv</strong> file, as well as their meaning.</li> <li><strong>Stereo_WHI.csv</strong> : a text data file (.csv) of the stereology count results of 287 slides read during this study. Among these slides, 102 were read to test the homogeneity distribution of different cell types found throughout each ovary (17 fish with 6 histological sections : a median, an anterior and a posterior histological section, for both ovaries), slides were read by multiple agents for calibration purposes (see <strong>Calibration</strong> folder for reading results of the 3 agents). Finally, 202 median histological ovarian slides were read. The information contained in this table is as follows: <ul> <li>cell_type: structure identified for one sample point (for the abbreviations, see Heude-Berthelin <em>et al.</em> 2023)</li> <li>idpt: identification number of the sampling point</li> <li>id: unique complex identification number of the sampling point generated by combining the x and y coordinates</li> <li>x: x coordinate of the sampling point</li> <li>y: y coordinate of the sampling point</li> <li>reading: Indicates if the reading data was used to test cellular homogeneity (Homogeneity) or to the sexual maturity phase</li> <li>slideid: identification number of the digitized histological slide that was used for the stereological count. Shares the same 12 first characters with <strong>Fish_id</strong></li> </ul> </li> </ul> <p> </p> <p><strong>Contact :</strong></p> <p>This dataset was established under the MATO (MATurité Objectif des poissons par l'histologie quantitative) project, during the PhD of Carine Sauger (October 2021-2023), financed by France Fillière Pêche (FFP/2020/AM/MF/109), under the supervision of IFREMER (Institut Français de Recherche pour l'Exploitation de la Mer) and BOREA (Biologie des Organismes et Ecosystèmes Aquatiques), and with the collaboration of a research facility from the University of Caen-Normandie : CMABIO3 (Centre de Microscopie Appliquée à la Biologie). For any enquiries, please contact: carine.sauger@gmail.com or laurent.dubroca@ifremer.fr</p>
NASA Eulerian Snow On Sea Ice Model Version 1.1 (NESOSIMv1.1) data: 1980 - 2024
<p><strong>Repository updates</strong></p> <p><em>Update on Sep 12th 2024: The repository now includes NESOSIM v1.1 output from September 1st 2022 to April 30th 2023 and September 1st 2023 to April 30th 2024 </em></p> <p><em>Update on Sep 5th 2022: </em>The repository now includes NESOSIM v1.1 output from September 1st 2021 to April 30th 2022</p> <p><em>Update on June 7th 2022: </em>The repository now includes NESOSIM v1.1 output from September 1st 2021 to March 31st 2022</p> <p><em>Update on March 8th 2021: </em>The gridded forcing files are now available in the gridded_forcings.zip file. Data are stored as Python pickles which can be easily read in by the core NESOSIM source code. </p> <p><em>Update on March 8th 2021: </em>The repository now includes zip files of gridded forcing (snowfall, winds, ice drift, ice concentration, initial conditions) as well as gridded Operation IceBridge snow depths. </p> <p><em>Update on January 30th 2021: </em>The repository now also includes a NESOSIM v1.1. daily gridded snow climatology using the mean (np.nanmean) of all data available between September 1 2010 and April 30 2020.</p> <p><strong>Overview</strong></p> <p>NESOSIM is a three-dimensional, two-layer (vertical), Eulerian snow on sea ice budget model developed with the primary aim of producing daily estimates of the depth and density of snow on sea ice across the polar oceans through the winter accumulation season, generally September through April (Petty et al., 2018).</p> <p>This repository contains model output from September 1st 1980 to April 30th 2021 [and September 1st 2021 to March 31st 2022 as of June 7th 2022] based on the NESOSIM v1.1 code release which is available on GitHub (https://github.com/akpetty/NESOSIM/tree/v1.1) and archived through Zenodo (10.5281/zenodo.4448355). More information about changes between the v1.0 and v1.1 model framework can be found in those links.</p> <p>A preprint is now available in <em>The Cryosphere Discuss</em> explaining these upgrades and their impacts on ICESat-2 winter Arctic sea ice thickness estimates (Petty et al., 2022). </p> <p><strong>Data production:</strong></p> <p>Data are re-initialized at the end of summer each year (September 1st) using summer near-surface air temperature-scaled initial snow depths and run through until the end of April of the following year. The 1987-1988 winter is missing due to the lack of passive-microwave derived ice concentration data available during this period. Daily data are generated on a 100 km x 100 km North Polar Stereographic grid (EPSG: 3413) across the entire Arctic Ocean including the peripheral seas.</p> <p><strong>Forcings:</strong></p> <p><em>NB: Recent year runs often require the use of near-real-time data products, so the underlying forcings used in this v1.1 release can change in time, as noted below:</em></p> <ul> <li>Snowfall: European Center for Medium Range Weather Forecasts (ECMWF) ERA5 (https://cds. climate.copernicus.eu, September 1 1980 onwards2) + CloudSat scaling (Cabaj et al., 2020).</li> <li>Near-surface winds: ECMWF ERA5 (https://cds. climate.copernicus.eu, September 1 1980 onwards).</li> <li>Sea ice drift: NSIDC Polar Pathfinder v4 (https://nsidc.org/data/nsidc-0116, September 1 1980 to April 30 2019), OSI SAF merged (https://osi-saf.eumetsat.int/products/osi-405-c, September 1 2019 onwards).</li> <li>Sea ice concentration: Final v3 NSIDC Climate Data Record (https://nsidc.org/data/g02202/versions/3/, September 1 1980 to December 31 2020), and near-real-time v2 NSIDC Climate Data Record (https://nsidc.org/data/g10016, January 1 2021 onwards).</li> <li>Near-surface air temperature (to derive temperature-scaled initial conditions): ECMWF ERA5 (https://cds. climate.copernicus.eu, September 1 1980 onwards).</li> </ul> <p><em>The forcings used to generate each winter dataset are described in a new 'forcings' variable in each NetCDF file. </em></p> <p><strong>Operation IceBridge snow depths:</strong></p> <p>The repository now also includes the gridded Operation IceBridge snow depths we used for calibration purposes, as described in Petty et al., (2022). The data contained within <em>gridded_oib_snowdepths.zip</em> includes the daily gridded data on the NESOSIM v1.1 100 km domain, ordered by day of collection. Data are stored as Python pickles and text files and include estimates derived from the following snow depth algorithms: SRLD (2009-2015): snow radar layer detection, JPL (2009-2015): Jet Propulsion Laboratory, GSFC (2009-2015): Goddard Space Flight Center, NSIDC (2009-2012): archived NASA GSFC data on the NSIDC, QL (2013-2019): NSIDC quick-look data based on the GSFC algorithm. MEDIAN (2010-2015): consensus snow depth from median of GSFC, JPL and SRLD. </p> <p><strong>References:</strong></p> <p>Cabaj, A., P. J. Kushner, C. G. Fletcher, S. Howell, A. Petty (2020), Constraining reanalysis snowfall over the Arctic Ocean using CloudSat observations, Geophysical Research Letters, 47, doi:10.1029/2019GL086426.</p> <p>Petty, A. A., M. Webster, L. N. Boisvert, T. Markus (2018), The NASA Eulerian Snow on Sea Ice Model (NESOSIM) v1.0: Initial model development and analysis, Geosci. Model Dev., doi: 10.5194/gmd-11-4577-2018.</p> <p>Petty A. A., N. Keeney, A. Cabaj, P. Kushner, M. Bagnardi (2023), Winter Arctic sea ice thickness from ICESat-2: upgrades to freeboard and snow loading estimates and an assessment of the first three winters of data collection, The Cryosphere, 17, 127–156, doi: 10.5194/tc-17-127-2023.</p>
PMW Sea Ice Thickness - from SSM/I and SSMIS CDR
<p>Sea Ice Thickness is retrieved from brightness temperatures acquired by the SSM/I and SSMIS passive microwave radiometers from 1992 to 2020 for the months Oct-Mar. The algorithm uses the statistical relationships observed between passive microwave observations and SIT to train a Multi-Layered Perceptron (MLP) to reproduce ICESat-2 SIT from brightness temperatures at 19 and 37 GHz. The MLP has been trained on the polar winter 2018-2019 where it showed good performance when compared to the CryoSat-2 satellite retrieval and the Operation Ice Bridge airborne measurements.<br>More details in:</p> <p>Soriot, C., Prigent, C., Jimenez, C., & Frappart, F. (2023). Arctic sea ice thickness estimation from passive microwave satellite observations between 1.4 and 36 GHz. <em>Earth and Space Science</em>, <em>10</em>(2), e2022EA002542</p> <p>Soriot, C., Vancoppenolle, M., Prigent, C., Jimenez, C., & Frappart, F. (2024). Winter arctic sea ice volume decline: uncertainties reduced using passive microwave-based sea ice thickness. <em>Scientific Reports</em>, <em>14</em>(1), 21000.</p> <p> </p>
High-resolution sea ice drift and deformation example data derived from Sentinel-1 in the Arctic Ocean during MOSAiC
<p>This data set contains two high-resolution sea ice drift and deformation fields from 30/31 December 2019 and 20/21 June 2021. They were acquired in the Transpolar Drift along the drift track of the research campaign "Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC). Drift fields were calculated from Sentinel-1, HH polarization SAR images acquired in enhanced wide mode. These had a pixel resolution of 50 m in Polar Stereographic North projection (latitude of true scale: 70 N, center longitude: 45 W). We used an ice tracking algorithm introduced by Thomas et al. (2008, 2011) and modified by Hollands and Dierking (2011) to derive drift from sequential pairs. The time step between two sequential images is approximately one day. The resulting drift data set was defined on a regular grid with a spatial resolution of 700 m. Outliers in the velocity data were reduced by a 3x3 point running median filter covering an area of 2.1x2.1 km. For the deformation estimates, we calculated deformation using a linear approximation based on Green's Theorem that relates the double integral over a plane to the line integral along a simple curve surrounding the plane. We discretized the curve applying the trapezoid method that linearly interpolates velocity between the vertices of the grid cells. This work contains modified Copernicus Sentinel data (2020)</p> <p>Related publications:</p> <p><strong>von Albedyll, L., Haas, C., and Dierking, W.</strong>: Linking sea ice deformation to ice thickness redistribution using high-resolution satellite and airborne observations, The Cryosphere, 15, 2167–2186, <a href="https://doi.org/10.5194/tc-15-2167-2021">https://doi.org/10.5194/tc-15-2167-2021</a>, 2021.</p> <p><strong>Hollands, Thomas; Dierking, Wolfgang (2011):</strong> Performance of a multiscale correlation algorithm for the estimation of sea-ice drift from SAR images: initial results. <em>Annals of Glaciology</em>, <strong>52(57)</strong>, 311-317, <a href="https://doi.org/10.3189/172756411795931462">https://doi.org/10.3189/172756411795931462</a></p> <p><strong>Thomas, Mani; Geiger, Cathleen A; Kambhamettu, Chandra (2008):</strong> High resolution (400 m) motion characterization of sea ice using ERS-1 SAR imagery. <em>Cold Regions Science and Technology</em>, <strong>52(2)</strong>, 207-223, <a href="https://doi.org/10.1016/j.coldregions.2007.06.006">https://doi.org/10.1016/j.coldregions.2007.06.006</a></p> <p><strong>Thomas, Mani; Kambhamettu, Chandra; Geiger, Cathleen A (2011):</strong> Motion Tracking of Discontinuous Sea Ice. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, <strong>49(12)</strong>, 5064-5079, <a href="https://doi.org/10.1109/TGRS.2011.2158005">https://doi.org/10.1109/TGRS.2011.2158005</a></p>
Remote and Local Processes Controlling Decadal Sea Ice Variability in the Weddell Sea
<p>These datasets are based on the 270-yr simulation results of CTR and SAOWED experiments, which include annual average of atmospheric and ocean variables used to make figures in a paper by Morioka and Behera (2021).</p>
Supporting data: "RECEIPT D7.3: Future climate scenarios: sea level rise and sea ice extent"
<p>This is the supporting data for deliverable D7.3 from work package 7 ( Sea level rise, infrastructure and coastal flooding ) of the RECEIPT H2020 project (No 820712).</p> <p>Deliverable 7.3 describes the development of future SLR (sea level rise) and sea ice extent scenarios. Each SLR contributor (e.g. thermal expansion, instability of Antarctic and Greenland ice sheets, melting glaciers, ocean circulation and land water storage) are included in the assessment (KNMI, Task 7.4). Sea ice extent is derived from CMIP5/6.</p> <p>This dataset is composed of three compressed files:</p> <p>cmip5_zos_zostoga_v2.zip and cmip6_zos_zostoga_v2.zip: Netcdf files of ocean thermal expansion and ocean dynamics computed from zos and zostoga data from the ESGF nodes.</p> <p>data_RECEIPT_D73.zip: Netcdf files of three sea level scenarios. Data is provided globally but scenarios are designed for the European coast.</p>
Development of a diffuse reflectance probe for in situ measurement of inherent optical properties in sea ice
<p>Included are the data presented in the publication entitled: <em>Development of a diffuse reflectance probe for in situ measurement of inherent optical properties in sea ice</em> accepted for publication in The Cryosphere Journal (2021). The data set includes Data and codes:</p> <p>1. Data (duplicated in .xlsx and .mat):</p> <p> </p> <p>1.1 Sites coordinates- (figure 5) -Geolocalisation of both sea ice sampling sites visited for this study (1 and 4)</p> <p> </p> <p>1.2 cumu_sg- (figure 6)- cumulative signal vs depth vs source-detector distance vs scattering coefficient obtained with Monte Carlo simulations</p> <p> —cumu_sg- cumulative signal (%)</p> <p> — depth (mm)</p> <p> —standard deviation on depth where signal is cumulated</p> <p> —ddet (mm)- radial distance between source and detection point </p> <p> — b (m^-1)-scattering coefficient</p> <p> </p> <p>1.3 validation-(figure 7)- Error on IOPs vs IOP value estimated measuring on microspheres solutions </p> <p> </p> <p>—vf (-)- microspheres volume fraction (in water)</p> <p> —a_theo (m^-1) - theoretical value of the absorption coefficient</p> <p> — mean_error_a(%) - error between theoretical value and measured value</p> <p> —std_error_a_x (%) - standard deviation on theoretical value (based on the standard deviation on microspheres diameter)</p> <p> —std_error_a_y (%) -standard deviation on error_a </p> <p> —rb_theo (m^-1) - theoretical value of the reduced scattering coefficient</p> <p> —mean_error_rb(%) - error between theoretical value and measured value</p> <p> —std_error_rb_x (%) - standard deviation on theoretical value (based on the standard deviation on microspheres diameter)</p> <p> —std_error_rb_y (%)) -standard deviation on error_rb </p> <p> —gamma_theo (-) - theoretical value of gamma</p> <p> —mean_error_gamma (%) - standard deviation on theoretical value (based on the standard deviation on microspheres diameter)</p> <p> —std_error_gamma (%) - standard deviation on error_gamma</p> <p> </p> <p>-1.4 T-S-(figure 8)- Vertical profiles of temperature and bulk salinity of sampled sea ice available at both snow covered site 1 and bare ice site 4</p> <p> </p> <p> —T (celsius) - ice temperature</p> <p> —S_si (ppt) - ice bulk salinity</p> <p> —depth (cm)</p> <p> </p> <p>1.5 Rmes-(figure 9)-Vertical profiles of spatially resolved diffuse Reflectance in sea ice using different covers to shade available at both snow covered site 1 and bare ice site 4</p> <p> </p> <p> —Rmes (-) - spatially resolved diffuse Reflectance</p> <p> —Rmes_nbg (-) - spatially resolved diffuse Reflectance with no background sunlight subtraction in calculation of Rmes</p> <p> —dmes (mm) - distance between source and detecting fibre (named rho in the paper)</p> <p> —depth (cm)</p> <p> — cover - cover used to shade from the sun: te=tent,nc= no cover, ta=tarp</p> <p> </p> <p>1.6 IOPprofiles-(figure 9)-Vertical profiles of reduced scattering coefficient in sea ice using different covers to shade available at both snow covered site 1 (ice+snow) and bare ice site 4</p> <p> </p> <p> —infferedrb (m^-1) - reduced scattering coefficient</p> <p> —infferedrb_nbg (m^-1) - reduced scattering coefficient with no background sunlight subtraction in calculation of Rmes</p> <p> —cr1 (binary)— criteria determining if the measurement is kept or not</p> <p> —depth (cm)- depth from the surface . **watch out** at site 1 , the measurments start from the surface of the snow. Substract 24 cm to get measurement from surface of the ice.</p> <p> — cover - cover used to shade from the sun: te=tent,nc= no cover, ta=tarp</p> <p> </p> <p>2. Code (written in .m with MATLAB_R2018b ®) :</p> <p> </p> <p>2.1 inversion algorithm—(figure 9 ) — used to find rb from Rmes (dmes) vertical profiles in sea ice</p> <p> </p> <p>— Main_vprofiles_Rtorb-qik2019_article.m - Main script of the inversion alorithm to get rb from Rmes (dmes)</p> <p>—importfiledata.m-subfunction to import data from .csv </p> <p>—importfiledatamay8.m-subfunction to import data from .csv (specific to may 8th because file was corrupted)</p> <p>—interp1lookup_HR_enlarged_bin10.mat - lookup table of Reflectance vs dmes vs a vs b’ vs gamma used in the inversion</p> <p>—calibjune6_ha_interp1_indcalib2.mat - calibration factor with microspheres as a reference</p> <p>—site1_c20-picture of the ice core taken at site 1</p> <p>—site4_c20-picture of the ice core taken at site 4</p> <p>—may8th+othertests_fixed.csv-raw data from may 8 (site1)</p> <p>—may9day3.csv-raw data from may 9 (site4)</p> <p> </p> <p> </p>
Combined SMOS and SMAP sea ice thickness Arctic
<p>This data set contains Arctic sea ice thicknesses derived from L-band passive microwave brightness temperatures. For this purpose, brightness temperatures at 40° incidence angle from the SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) satellites were combined to a homogenised data set (see also related data sets). Sea ice thicknesses were then derived using the algorithm described in Tian-Kunze et al. (2014).</p> <p>Data are generally produced for the freeze-up period from 15 October to 15 April. This data set contains data from on 1 April 2015 (first available SMAP data) to 15 April 2015 and for the winter seasons 2015/16 to 2017/18.</p> <p>A detailed description of the data set can be found Schmitt and Kaleschke (2018) and in the document <em>1_Documentation_Combined_SIT.pdf</em>.</p> <p><strong>Please note</strong>: Sea ice thicknesses from L-band are most suitable for thin ice and reach saturation for thicker sea ice (above 0.5 m - 1 m, depending on ice salinity and temperature). Thickness values with a saturation ratio of 100 % should be discarded (depending on the application)!</p> <p>Version 1.0 of this data set is based on SMOS version v620 and SMAP version 3 data.</p> <p>The files contain the following data fields:<br> <strong>sea_ice_thickness</strong> - sea ice thickness with post-processing to account for the thickness distribution<br> <strong>plane_layer_thickness</strong> - sea ice thickness using the plane layer assumption<br> <strong>thickness_uncertainty_upper</strong> - upper limit of the sea ice thickness uncertainty interval<br> <strong>thickness_uncertainty_lower</strong> - lower limit of the sea ice thickness uncertainty interval<br> <strong>saturation_ratio</strong> - ratio of plane layer thickness and maximum retrievable thickness (%)<br> <strong>TB_intensity</strong> - brightness temperature intensity combined from SMOS and SMAP at 40° incidence angle</p> <p>The grid coordinates are provided as a separate file <em>Latlon_e12.5.nc</em></p>
Data accompanying the article "Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework"
<p><em>Amonthly_files.tar.gz</em> contains the gridded monthly averaged quantities used in the manuscript Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework" for each year between 2000 and 2018.</p> <p>Files containing "simba" in their name contain quantities related to the sea ice mass balance (volume of melt/growth...)</p> <p>Files containing "icemod" in their name contain other quantities related to sea ice properties (thickness, concentration...)</p> <p>In case information is missing, do not hesitate to contact guillaume.boutin@nersc.no , heather.regan@nersc.no or einar.olason@nersc.no</p> <p>This research has been funded by the Norwegian Research Council (Nansen Legacy: grant no. 27673, FRASIL: grant no. 263044, and ARIA: grant no. 302934), JPI Climate and JPI Oceans (MEDLEY project, under agreement with the Norwegian Research Council, grant no 316730), and by Copernicus Marine Environment Monitoring Service (CMEMS) WIzARd project. CMEMS is implemented by Mercator Ocean in the framework of a delegation agreement with the European Union<br> Copernicus Marine Environment Monitoring Services (contract no.<br> 69), and the European Space Agency through the Cryosphere Virtual Laboratory (CVL, grant no. 4000128808/19/I-NS).</p>
Antarctic Sea Ice Thickness Estimates from CryoSat-2: 2010-2021
<p><strong>*Preliminary data associated with manuscript submission to The Cryosphere*</strong></p> <p>Gridded estimates of Antarctic sea ice physical properties derived from CryoSat-2 Baseline-D SAR and SARIn data spanning July 2010 through August 2021. Data are generated using the CryoSat-2 Waveform-Fitting method for Antarctic sea ice (CS2WFA). Files include:</p> <ul> <li>CS2WFA_25km_YYYYMM.nc: Monthly mean values gridded on the NSIDC 25km polar stereographic grid. Each file represents 1 month of data (134 files total).</li> </ul> <p>Each monthly file contains:</p> <ul> <li>latitude, longitude, and time (in months since January 2000)</li> <li>Grids of retrieved snow depth, snow freeboard, ice freeboard, and sea ice concentration</li> <li>Grids of seasonal snow, sea ice, and seawater density</li> <li>Grids of sea ice thickness and estimated thickness uncertainty, as well as thickness estimated using a 70% threshold retracker and using a zero-ice-freeboard assumption.</li> </ul>
Modeled dynamic and thermodynamic sea ice growth in the Arctic 1980-2019 from NAOSIM
<p>This data set is related to the paper "Evidence for an Increasing Role of Ocean Heat in Arctic Winter Sea Ice Growth" by Ricker et al. (2021). Please refer to this study for further details.</p> <p>Ricker, R., Kauker, F., Schweiger, A., Hendricks, S., Zhang, J., & Paul, S. (2021). Evidence for an Increasing Role of Ocean Heat in Arctic Winter Sea Ice Growth, Journal of Climate, 34(13), 5215-5227. Retrieved Nov 24, 2022, from https://journals.ametsoc.org/view/journals/clim/34/13/JCLI-D-20-0848.1.xml</p>
ERA5 Dataset used for sea-ice forecasting with IceNet
<p>Monthly 2 metre temperature and other variables used for running IceNet. The data is from ECMWF ERA5 between January 2019 to December 2021.</p> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.