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36 results for “snow on sea ice”
Sea ice thickness, snow depth, and sea ice freeboard in lagoon sites along the Alaska Beaufort Sea coast, 2019-ongoing
Physical parameters related to snow and sea ice have implications for lagoon circulation, sea-air heat exchange, and underwater light regimes. To understand these relationships and their greater effect on ecosystem function, the Beaufort Lagoon Ecosystem LTER (BLE LTER) uses in situ methods to assess snow depth, ice freeboard, and ice thickness in select water bodies across the Beaufort Sea coast (Elson Lagoon, Simpson Lagoon, Kaktovik Lagoon, Jago Lagoon, and Stefansson Sound). Sea ice thickness is the distance from sea ice bottom to top, not including snow. Freeboard, determined in the same drilled hole, is the distance from the surface of the water to the top of the ice, not including snow cover. These measurements are made annually, close to maximum ice thickness (typically April).
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>
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>
Simulated Sea Ice and Snow Thickness along the MOSAIC drift trajectory, from AWI-CM-1 and AWI-CM-3 nudged simulations.
<p>Sea ice thickness and snow (on sea ice) thickness from nudged simulations performed using the coupled climate models AWI-CM-1 (zonal wavenumber truncated at 20) and AWI-CM-3 (T20 truncation; Pithan et al., 2023) with a 1h relaxation time. The model data is collocated to the drift trajectory of the Multidisciplinary Drifting Observatory for the Study of the Arctic Climate (MOSAIC) across the Arctic Ocean, from 01 September 2019 until 31 August 2020. The collocation is done daily, by finding all model grid cells within the area covered by the distributed network of snow and sea ice measuring instruments deployed and maintained during MOSAIC. The sea ice and snow thickness is then spatially averaged for each day. </p><p>Data is provided in three .nc files for each model and variable (m_ice for sea ice thickness, m_snow for snow thickness) representing ensemble members 1 to 3.</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>
Supporting data for "Arctic sea ice response to flooding of the snow layer in future warming scenarios"
<p>Supporting data for "Arctic sea ice response to flooding of the snow layer in future warming scenarios" submitted to Earth's Future in April 2021</p> <p>Contains model output from both the Icepack and CCSM4 experiments from the paper. File descriptions for the Icepack and CCSM4 data are contained in the files README_icepack and README_CCSM respectively.</p>
Multi-frequency altimetry snow depth product over Arctic sea ice
<p>Satellite altimetry can be used to estimate sea ice thickness, an essential variable to better understand and forecast the dynamic ice cover. Nevertheless, some sources of uncertainty remain, and one of the most important concerns the snow depth, a key parameter to convert the measured ice freeboard into sea ice thickness.</p> <p>Snow depth can be estimated using different altimeter frequencies with different snow penetration capabilities. We have developed a monthly snow depth product based on the differences between CryoSat-2 SAR Ku and IceSat-2 laser altimeters covering the period 2018-2021 with a spatial resolution of 25 km. </p> <p> </p>
Monthly total freeboard and snow depth over Arctic sea ice from AMSR-E&2 and AVHRR measurements (2003-2020)
<p><strong>[Data description]</strong></p> <p>Monthly total freeboard (Ft), snow depth (hs), and snow depth uncertainty over Arctic sea ice data for January-February-March months of the 2003-2020 period produced by Lee and Shi et al. (2021, manuscript under review) are provided.</p> <p>Both variables are derived from satellite passive infrared and microwave measurements: Total freeboard was obtained from AMSR-E&2 measurements and snow depth was estimated from the AMSR and AVHRR measurements.</p> <p>The uploaded file titled "monthly averaged total freeboard and snow depth (JFM 2003-2020).zip" contains two directories: one for total freeboard and the other for snow depth. Naming convention is "variable_yyyymm.bin" and data format is 32-bit floating point array in shape of 304 x 448 (25 km polar stereographic grid).</p> <p>Here we provide an example Python code to read monthly snow depth of January 2003 using numpy.<br> import numpy as np<br> hs = np.fromfile('hs_200301.bin', dtype=np.float32).reshape(448,304)</p> <p>Geocoordinate tools for the 25 km polar stereographic grid are available at NSIDC website (https://nsidc.org/data/polar-stereo/tools_geo_pixel.html)<br> <br> <strong>[Abbreviations]</strong></p> <p>AMSR: Advanced Microwave Scanning Radiometer<br> AVHRR: Advanced Very High Resolution Radiometer<br> NSIDC: National Snow and Ice Data Center</p>
Sea ice, snow and melt pond example data from MOSAiC transect observations
<p>This data set contains in-situ observation of sea ice, snow and melt pond properties from two days in winter (January 23, 2020) and summer (July 7, 2020) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. It originates from two sensors:</p> <ol> <li>Broad-band electromagnetic induction sensor (Geophex GEM-2) measuring the combined thickness of the sea ice and snow layers</li> <li>A GPS snow depth probe (Snow-Hydro MagnaProbe) measuring the thickness of the snow layer and melt ponds depth during summer</li> </ol> <p>Both sensors were operated coincidently along transect loops while different loops were used in both days. This data set is a subset of similar weekly activities between October 2019 and September 2020. This publication intends to provide a preview of the data properties and approximate changes between the winter and summer periods. It also has to be noted, that the final data of the EM induction sensor might differ from this release, which is based on a quick-look processing directly after data acquisition.</p> <p><em>GEM-2 data files</em></p> <p>The file format of GEM-2 data is a text file with comma-separated values. Notable parameters are:</p> <ul> <li>‘time’: UTC time</li> <li>‘longitude’: Longitude in degrees east (fill value: 0.0)</li> <li>‘latitude’: Latitude in degrees north (fill value: 0.0)</li> <li>‘`f{frequency}Hz_hcp_{i:Inphase|q:Quadrature}`: total (ice + snow) thickness of the sea ice and snow layers in meter for different channels*</li> </ul> <p>*The channels correspond to the real (Inphase) or imaginary (Quadrature) part of the EM signal at a given frequency. The variable name in the csv file is to be read as `f{frequency}Hz_hcp_{i:Inphase|q:Quadrature}`. It is recommended to use the Inphase component of the 18.325 kHz frequency (variable `f18325Hz_hcp_i`) for analysis.</p> <p><em>MagnaProbe data files</em></p> <p>The file format of MagnaProbe data is a text file with comma-separated values. Notable parameters are:</p> <ul> <li>`timestamp`: Timestamp</li> <li>`longitude_a`: longitude degree in degrees east</li> <li>`longitude_b`: longitude minute</li> <li>`latitude_a`: latitude degree in degrees north</li> <li>`latitude_b`: latitude minute</li> <li>`depthCm`: Snow thickness or melt ponds depth in cm</li> <li>`flag`: flag value indicating the type or measurement*</li> </ul> <p>Flag values are:</p> <ul> <li>-1 : melt pond</li> <li>1 : snow or surface scattering layer depth,</li> <li>2 : mixed surface type when pond water pools at the base of a melting</li> </ul> <p>The filenames follow the naming convention of <sensor>-mosaic-transect-<date>-<device-operations-id>.csv with the device operation id as a unique identifier of the sensor raw data within the MOSAiC project.</p>
Prior and posterior uncertainties of sea ice volume and snow volume
Prior and posterior uncertainties of sea ice volume (SIV, columns 4-6) and snow volume (SNV, columns 7-9) respectively for three regions in km3. Column 1 indicates observation, column 2 indicates uncertainty range ("product" refers to uncertainty specification provided with product), column 3 indicates uncertainty range of additional hypothetical snow product ("–" means no snow product is used). In each of columns 4-9 the lowest uncertainty range is highlighted in bold face font. The two bottom rows give estimates for the uncertainty due to model error, i.e. the residual uncertainty with optimal control vector.
Dataset for "Estimating Contribution of Sea Ice and Land Snow to Climate Sensitivity"
<p>The reproducible dataset for "Estimating Contribution of Sea Ice and Land Snow to Climate Sensitivity".</p>
Snow phenological parameters based on Northern Hemisphere EASE Grid 2.0 Weekly Snow and Sea Ice Extent (NHSCE) from 1972-2019
<p>This dataset include three parts to obtain Figures 1 and 2 in the study of "<strong>Review of snow phenology variation in the Northern Hemisphere and its relationship with climate and vegetation</strong>". The first one is the annual spatial distribution maps of the Snow Cover Onset Date(SCOD), Snow Cover End Date(SCED) and the Snow Cover Days(SCD). The second one is the annual average SCOD, SCED and SCD. The three one is the annual statistics of the SCOD, SCED and SCD. The snow phenological parameters are calculated based on Northern Hemisphere EASE Grid 2.0 Weekly Snow and Sea Ice Extent (NHSCE).</p>
Preliminary data of drifting snow mass flux from the lower SPC at MOSAiC (2020-01-26 to 2020-02-04) for the submitted paper "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model"
<p>Preliminary data of lower SPC massflux from MOSAiC, for the time period 2020-01-26 -- 2020-02-04.</p> <p>1-h averaged time series of mass flux (kg/m²/h) to compare with the ALPINE3D simulation results.</p> <p>Will soon be replaced with a DOI / Repositiry at the Arctic Data Centre from BAS.</p>
Snow and sea ice observations during the field phase of MOSAiC
<p>Measurements of discrete and continous snow and sea ice measurements during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. The tables contain the observational dates from various methods as described in Nicolaus et al. (2021). The dates refer to Figure 3 in this publication.</p> <p> </p> <p>Reference:</p> <p>Nicolaus M, Perovich D, Spreen G, Granskog M, Albedyll L, Angelopoulos M, Anhaus P, Arndt S, Belter H, Bessonov V, Birnbaum G, Brauchle J, Calmer R, Cardellach E, Cheng B, Clemens-Sewall D, Dadic R, Damm E, Boer G, Demir O, Dethloff K, Divine D, Fong A, Fons S, Frey M, Fuchs N, Gabarró C, Gerland S, Goessling H, Gradinger R, Haapala J, Haas C, Hamilton J, Hannula H-R, Hendricks S, Herber A, Heuzé C, Hoppmann M, Høyland K, Huntemann M, Hutchings J, Hwang B, Itkin P, Jacobi H-W, Jaggi M, Jutila A, Kaleschke L, Katlein C, Kolabutin N, Krampe D, Kristensen S, Krumpen T, Kurtz N, Lampert A, Lange B, Lei R, Light B, Linhardt F, Liston G, Loose B, Macfarlane A, Mahmud M, Matero I, Maus S, Morgenstern A, Naderpour R, Nandan V, Niubom A, Oggier M, Oppelt N, Pätzold F, Perron C, Petrovsky T, Pirazzini R, Polashenski C, Rabe B, Raphael I, Regnery J, Rex M, Ricker R, Riemann-Campe K, Rinke A, Rohde J, Salganik E, Scharien R, Schiller M, Schneebeli M, Semmling M, Shimanchuk E, Shupe M, Smith M, Smolyanitsky V, Sokolov V, Stanton T, Stroeve J, Thielke L, Timofeeva A, Tonboe R, Tavri A, Tsamados M, Wagner D, Watkins D, Webster M, Wendisch M. 2021. Overview of the MOSAiC expedition – Snow and sea ice. Elementa Science of the Anthropocene 9. doi:10.1525/elementa.2021.000046.</p> <p> </p> <p> </p>
Data for Modeling Snow on Sea Ice using Physics Guided Machine Learning
Open the record for dataset details and reuse information.
Snow particle number, aerosol concentration and 10 meter windspeed data from MOSAiC, N-ICE, Weddel Sea expeditions and chemistry transport model data (p-TOMCAT) .
<p>The folder contains data for the MOSAiC, N-ICE and Weddell sea expedition for Snow particle counter measurements. Coarse aerosol measurements from the MOSAiC expedition are also included. Simulation data from a chemistry transport model (p-TOMCAT) is also available. This version includes both .mat and .nc files</p>
Preliminary DOI/Repository of ALPINE3D and SNOWPACK data of the submitted paper "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model"
<p>There are 2 zip folders in this repository.</p> <p>"a3d_jgr.zip" contains a folder structure that must be kept as it is in order to run the simulation in the current configuration.<br> The setup contains both input and output data as well as the model configuration as used in the submitted manuscript <br> "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model".</p> <p>The zip file contains 3 main folders:</p> <ul> <li>base_setup_files</li> <li>a3d_jgr_alpha1</li> <li> a3d_jgr_alpha3</li> </ul> <p>The "base_setup_files" contains all input files that are necessary to run the reference (R) simulation ("a3d_jgr_alpha1" folder) and the comparison "C" scenario ("a3d_jgr_alpha3") folder. In the a3d_jgr_alpha1 and a3d_jgr_alpha3 folders you find the corresponding outputs as used in the paper, as well as the settings used - which only differ by the changed "SCHMIDT_DRIFT_FUDGE" value that is found in each a3d_jgr_alphax/setup/io.ini file. The input data is already linked accordingly in each io.ini file.</p> <p>a3d_jgr_alpha1 also contains the detailed snow profiles for each point along the transects.</p> <p>To reproduce the results, download and compile the source code for the adjusted ALPINE3D model first, which can be obtained from https://gitlabext.wsl.ch/snow-models/alpine3d.git under the "alpine3d_mosaic" branch. After installing, you can run the provided model setup uploaded here.</p> <p>_________________________________________________________________________________________________________<br> <br> "SNOWPACK_JGR.zip" contains both input and output data for SNOWPACK as well as the model configuration as used in the submitted manuscript "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model".</p> <p>The zip file contains 2 main folders: </p> <ul> <li>SNOWPACK_JGR_ALPHA1</li> <li>SNOWPACK_JGR_ALPHA3</li> </ul> <p>In the SNOWPACK_JGR_ALPHA1 (reference "SP_R" simulation) and SNOWPACK_JGR_ALPHA3 (comparison "SP_C" scenario) folders you find the corresponding inputs, outputs and configuration as used in the paper, as well as the settings used - which only differ by the changed "SCHMIDT_DRIFT_FUDGE" value that is found in each SNOWPACK_JGR_ALPHA/setup/io.ini file. The input data is already linked accordingly in each io.ini file.</p> <p>To reproduce the results, download and compile the source code for the adjusted SNOWPACK model first, which can be obtained from https://gitlabext.wsl.ch/snow-models/snowpack.git under the "snowpack_mosaic" branch. After installing, you can run the provided model setup uploaded here.</p>
ECCO Sea-Ice and Snow Concentration and Thickness - Daily Mean 0.5 Degree (Version 4 Release 4)
This dataset contains daily-averaged sea-ice and snow concentration and thickness interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.
ECCO Sea-Ice and Snow Concentration and Thickness - Daily Mean llc90 Grid (Version 4 Release 4)
This dataset provides daily-averaged sea-ice and snow concentration, thickness, and pressure loading on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.
ECCO Sea-Ice and Snow Concentration and Thickness - Monthly Mean llc90 Grid (Version 4 Release 4)
This dataset provides monthly-averaged sea-ice and snow concentration, thickness, and pressure loading on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.
ScienceDex guides
Understand access before you commit
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.