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126 results for “Arctic sea ice”
Earliest snowmelt estimation dates for Arctic sea ice (2003)
<p>Earliest snowmelt estimation dates calculated for the year 2003 are provided using sea ice brightness temperatures from AMSR-E (Cavalieri et al., 2014) and DMSP SSM/I-SSMIS (Meier et al., 2019), as well as simulated sea ice brightness temperatures from the CESM2 JRA-55 (Danabasoglu et al., 2020; Kobayashi et al., 2015; Tsujino et al., 2018), which were created using the Arctic Ocean Observation Operator (ARC3O; Burgard et al, 2020a,b). Scripts and README files are provided for preparing the model data to act as input to ARC3O. </p>
Arctic sea ice velocity in summer from AMSR2 (2013-2023)
<p>Sea ice drift in summer plays a key role in Arctic sea ice mass balance and navigation safety of the Arctic Passage. Resulted from surface melt over sea ice and atmospheric water vapor, previous passive microwave sea ice velocity data present relatively poor quality in summer than in winter. Here, based on an improved sea ice velocity retrieval method, we produced daily Arctic sea ice velocity data during summertime (May 1st to September 30th) from 2013 to 2023. These sea ice velocity data are derived from the daily gridded AMSR2 brightness temperature (TB) at 36.5 GHz channel distributed by the University of Bremen using the continuous maximum cross-correlation algorithm. We used the polarization difference of TB to track the displacement of the sea ice templates. The size of templates is 11×11 pixels, and the spatial spacing between adjacent templates is five pixels. The time interval of this data is 24 h, and the spatial resolution is 62.5 km. Outliers were identified and discarded by surface wind (10-m wind derived from ERA5 atmospheric reanalysis) and surrounding sea ice velocity vectors. Vectors over open water areas were discarded by sea ice concentration with 6.25 km distributed by the University of Bremen.</p>
ICESat-2 monthly gridded winter Arctic sea ice thickness
<p>Monthly gridded (winter only) Arctic sea ice thickness estimates from ICESat-2 derived using ATL10 freeboards (https://nsidc.org/data/atl10) together with snow depth and density estimates from the NASA Eulerian Snow on Sea Ice Model (NESOSIM, https://github.com/akpetty/NESOSIM). Along-track data (from the three strong beams) are binned to the 25 km x 25 km NSIDC polar stereographic projection (EPSG:3411). The full processing chain is described in Petty et al., (2020) (code available at https://github.com/akpetty/ICESat-2-sea-ice-thickness) including several updates as detailed below.</p> <p>Temporal range: November 2018 - April 2019, October 2019 to April 2020.</p> <p>Data: A single netCDF file is included for each month. Variables include:</p> <ul> <li>Sea ice freeboard (from ATL10)</li> <li>Snow depth (redistributed NESOSIM)</li> <li>Snow density (redistributed NESOSIM)</li> <li>Bulk sea ice density</li> <li>Sea ice type (from OSI SAF)</li> <li>Sea ice thickness uncertainty</li> <li>Mean day of month in a given grid cell</li> <li>Number of freeboard segments in a given grid cell.</li> </ul> <p>A summary of the differences between the version 1 and version 2 winter Arctic sea ice thickness estimates are being presented at AGU 2020 and prepared for publication.</p> <p>Key changes from version 1 (Petty et al., 2020) to version 2 include:</p> <ul> <li>Use of release 003 ATL10 freeboards. A detailed assessment of the freeboard changes from release 002 to release 003 is provided in Kwok et al., (2020).</li> <li>Upgrade to NESOSIM v1.1: CloudSat scaling of ERA5 snowfall, a new atmospheric wind loss term, calibration against recent OIB snow depths, an extended Arctic Ocean domain and various bug fixes (<a href="https://github.com/akpetty/NESOSIM">https://github.com/akpetty/NESOSIM</a>).</li> <li>Use of all three strong beams (instead of just strong beam #1).</li> </ul> <p>The data have also been made available on a Google Cloud bucket to enable rapid data analysis from any cloud-based analytics platform: <em>gs://sea-ice-thickness-data/v2/</em></p>
Supporting Data for: McKenna et al. (2018), Arctic sea-ice loss in different regions leads to contrasting Northern Hemisphere impacts
<p>This is a dataset of output from version 4 of the Reading Intermediate Global Circulation Model (IGCM4) that was used in the article: </p> <p>McKenna, C. M., Bracegirdle, T. J., Shuckburgh, E. F., Haynes, P. H., & Joshi, M. M. (2018). Arctic sea ice loss in different regions leads to contrasting Northern Hemisphere impacts. <em>Geophysical Research Letters</em>, 45, 945-954. <a href="https://doi.org/10.1002/2017GL076433">https://doi.org/10.1002/2017GL076433</a></p> <p> </p> <p>Files required to setup the IGCM4 simulations are given in the directory 'IGCM4_setup'.</p> <p>All other directories contain netcdf files of timeseries of various monthly mean fields for each IGCM4 simulation (see paper for details on these simulations). The available variables are:</p> <ul> <li>ua: zonal winds</li> <li>zg: geopotential height</li> <li>ts: surface temperature</li> <li>hfls, hfss, rlds, rlus: surface heatfluxes</li> <li>Flat, Fz, divF: Eliassen-Palm flux vectors and their divergence (only for months November-February)</li> </ul> <p>The ua and zg variables are given for different pressure levels indicated in the filenames (e.g., ua500 is ua at 500 hPa). ua is additionally given in terms of the zonal mean with latitude and pressure. zg is additionally given in terms of longitude and pressure, averaged over latitudes between 60N-80N. All files follow CF conventions in terms of metadata, variable names, etc. </p> <p>Note that the CTL, ATL, PAC, and ATLandPAC simulations were all run continuously in time (i.e., every year starts from the end of the previous year). The 0.5ATL and 0.5PAC simulations, however, were run for 300 years in three separate 100-year chunks (i.e., the initial conditions used to start each 100-year chunk were different). The three 100-year chunks have been appended together in the netcdf files. </p>
ICESat-2 Arctic Sea Ice Surface Topography from the University of Maryland-Ridge Detection Algorithm: April 2019, 2020, and 2021
<p>This dataset is derived from the ICESat-2 (IS-2) Global Geolocated Photon Height Product (ATL03) using the University of Maryland-Ridge Detection Algorithm (UMD-RDA). The UMD-RDA is applied to ATL03 on a per-shot basis, nominally resulting in elevation measurements at IS-2's maximum along-track resolution of ~0.7 m. From these elevation measurements, the UMD-RDA can measure various sea ice parameters including, but not limited to, individual ridge crests and their respective sail heights, the distance between ridges, and sea ice surface roughness.</p> <p><strong>********Changes in Version 2********</strong></p> <p><em>Version 2 includes a column for time (seconds since 2018-01-01) in all parameter files in addition to longitude, latitude, and parameter value.</em></p> <p><em>The full resolution UMD-RDA derived elevation data was too large to host here, but is available upon request. If you need a particular track or segment for your research please contact me with your request by email: kd</em><em>uncan at umd dot edu</em></p>
Arctic sea ice radar freeboard from ERS-1, ERS-2, Envisat and CryoSat-2
<p>This dataset presents a radar freeboard time series from 1993 to 2021 for Arctic sea ice. Envisat, ERS-2 and ERS-1 radar freeboards have been estimated using CryoSat-2 as a reference, they are "SAR-like" estimations as they have been calibrated on CS-2 SAR TFMRA50 radar freeboard. </p>
Model data from GRL paper: "Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone"
<p>This folder includes monthly model data (experiments using SC-WACCM4 and E3SMv1) of temperature (TEMP) and sea level pressure (SLP) that were used in the Geophysical Research Letters paper "<strong>Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone</strong>", # 2020GL088583. See also for additional information/data: <a href="https://zenodo.org/record/3066448">https://zenodo.org/record/3066448</a></p> <p>Labe, Z., Peings, Y., & Magnusdottir, G. (2020). Warm Arctic , cold Siberia pattern : role of full Arctic amplification versus sea ice loss alone. <em>Geophysical Research Letters</em>, 1–26. <a href="https://doi.org/10.1029/2020GL088583">https://doi.org/10.1029/2020GL088583</a></p> <p><a href="https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020GL088583">[Paper]</a><a href="https://sites.uci.edu/zlabe/arctic-amplification/">[Plain Language Summary]</a><a href="https://github.com/zmlabe/AA">[GitHub]</a></p>
Data archive and code for "Predicting September Arctic Sea Ice: A Multi-Model Seasonal Skill Comparison"
<p>This upload contains data and code related to the paper "Predicting September Arctic Sea Ice: A Multi-Model Seasonal Skill Comparison" by M. Bushuk, S. Ali, D. Bailey, Q. Bao, L. Batte, U. S. Bhatt, E. Blanchard-Wrigglesworth, E. Blockley, G. Cawley, J. Chi, F. Counillon, P. Goulet Coulombe, R. Cullather, F. X. Diebold, A. Dirkson, E. Exarchou, M. Gobel, W. Gregory, V. Guemas, L. Hamilton, B. He, S. Horvath, M. Ionita, J. E. Kay, E. Kim, N. Kimura, D. Kondrashov, Z. M. Labe, W. Lee, Y. J. Lee, C. Li, X. Li, Y. Lin, Y. Liu, W. Maslowski, F. Massonnet, W. N. Meier, W. J. Merryfield, H. Myint, J. C. Acosta Navarro, A. Petty, F. Qiao, D. Schroder, A. Schweiger, Q. Shu, M. Sigmond, M. Steele, J. Stroeve, N. Sun, S. Tietsche, M. Tsamados, K. Wang, J. Wang, W. Wang, Y. Wang, Y. Wang, J. Williams, Q. Yang, X. Yuan, J. Zhang, and Y. Zhang, published in the Bulletin of the American Meteorological Society, DOI: https://doi.org/10.1175/BAMS-D-23-0163.1.</p> <p>See README.txt for a description of the datasets and code.</p>
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>
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>
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>
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>
Model outputs for the article "Modelling the evolution of Arctic multiyear sea ice over 2000–2018"
<p><em>icemod_monthly.tar.gz </em>contains the gridded monthly averaged quantities used in the manuscript "Modelling the evolution of Arctic multiyear sea ice over 2000-2018" for each year between 2000 and 2018.</p> <p>Multiyear ice variables are conc_myi (concentration of multiyear ice in a grid cell) and thick_myi (cell average thickness of multiyear ice in a grid cell, in metres), along with source and sink terms (units per day) for multiyear concentration (dci_mlt_myi, dci_ridge_myi and dci_rplnt_myi, for melt, ridging and replenishment) and volume (dvi_mlt_myi and dvi_rplnt_myi, for melt and replenishment).</p> <p><em>transports_monthly_sections.zip </em>contains the transports of multiyear ice through the sections defining each region in Figure 8 of the paper. MYIsiaXport indicates multiyear ice area transport, while myiXport indicates multiyear ice volume transport.</p> <p>In case information is missing, do not hesitate to contact heather.regan@nersc.no, guillaume.boutin@nersc.no, or einar.olason@nersc.no.</p>
Arctic Sea Ice Freeze Onsest
<p>The product contains yearly maps of early freeze onset and freeze onset for the sea ice surface based on the improved PMW algorithm.</p> <p>The data were derived using brightness temperature observations from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2), Sea Ice Concentration (SIC) observations using the Enhanced NASA Team (NT2) algorithm, and sea ice age dataset.</p> <p>They are gridded on the NSIDC northern hemisphere polar stereographic grid at 12.5 km, with a time range of 2003 to 2021 (2011 and 2012 missing).</p>
Wintertime Arctic warm air intrusion detection algorithm for satellite sea ice concentration analysis
<p>This Dataset is related to the Article <em>Relevance of warm air intrusions for Arctic satellite sea ice concentration time </em>series in <em>The Cryosphere</em> (https://doi.org/10.5194/tc-2023-69).</p> <p>Provided are the core detection algorithm and a minimal working example as well as a list of all detected warm air intrusions between November 1979 and April 2020 (monthly data).</p>
Nueva serie de extensión del hielo marino ártico en septiembre entre 1935 y 2014 - A new time series of September Arctic sea ice extent: 1935-2014
<p>Archivo CSV - Presentamos una nueva serie de extensión del hielo marino ártico en el mes de septiembre desde 1935 hasta 2014 que incluye datos para el sector siberiano no utilizados hasta ahora en las series que cubren el conjunto del Ártico. La nueva serie ha sido ajustada para ser consistente con los datos de satélite</p> <p>CSV file - We present a new time series of September Arctic sea ice extent from 1935 to 2014 that includes data for the Siberian sector (AARI operational charts) not used previously in the Arctic wide existing time series (Walsh, HadISST). The new record has been adjusted to be consistent with the satellite data.</p> <p> </p>
Nueva serie de extensión del hielo marino ártico en septiembre entre 1935 y 2014 - A new time series of September Arctic sea ice extent: 1935-2014
<p>Archivo NetCDF- Presentamos una nueva serie de datos raster con la extensión del hielo marino ártico en el mes de septiembre desde 1935 hasta 2014 que incluye datos para el sector siberiano no utilizados hasta ahora en las series que cubren el conjunto del Ártico. La nueva serie ha sido ajustada para ser consistente con los datos de satélite.</p> <p>NetCDF file - We present a new gridded September Arctic sea ice extent dataset from 1935 to 2014 that includes data for the Siberian sector (AARI operational charts) not used previously in the Arctic wide existing time series (Walsh, HadISST). The new record has been adjusted to be consistent with the satellite data.</p> <p> </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>
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>
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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.