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79 results for “ice thickness”
Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6-LIM3 global ocean–sea ice model
<p>Model output and observational data and scripts corresponding to the manuscript "Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6-LIM3 global ocean–sea ice model"</p> <p><strong>Abstract. </strong></p> <p>This study assesses the impact of different sea ice thickness distribution (ITD) configurations on the sea ice concentration (SIC) variability in ocean-standalone NEMO3.6-LIM3 simulations. Three ITD configurations with different numbers of sea ice thickness categories and boundaries are evaluated against three different satellite products (hereafter referred to as “data”). Typical model and data interannual SIC variability is characterized by k-means clustering both in the Arctic and Antarctica between 1979 and 2014 in two seasons, January–March and August–October, which show the largest coherence across clusters in individual months. Analysis in the Arctic is done before and after detrending the series with a 2nd degree polynomial to separate interannual from longer-term variability.</p> <p>Before detrending, winter clusters capture SIC response to atmospheric variability at both poles and summer cluster a positive and negative trend in the Arctic and Antarctic SIC respectively. After detrending, Arctic clusters reflect SIC response to interannual atmospheric variability predominantly. Model–data cluster comparison suggests that no specific ITD configuration or category number increases realism of the simulated Arctic and Antarctic SIC variability in winter. In the Arctic summer, more thin-ice categories decrease model–data agreement without detrending but increase agreement after detrending. Overall, a single-category configuration agrees the worst with data.</p> <p>Direct model–data comparison of SIC anomaly fields shows that more thick-ice categories improve winter SIC variability realism in Central Arctic regions with very thick ice. By contrast, more thin-ice categories reduce model–data agreement in the Central Arctic in summer, due to an overly large simulated sea ice volume.</p> <p>In summary, whereas better resolving thin ice in NEMO3.6-LIM3 can hamper model realism in the Arctic but improve it in Antarctica, more thick-ice categories increase realism in the Arctic winter. And although the single-category configuration performs the worst overall, no optimal configuration is identified. Our results suggest that no clear benefit is obtained from increasing the number of sea ice thickness categories beyond the current usual standard of 5 categories in NEMO3.6-LIM3.</p>
Supplementary Data for "The state and fate of lake ice thickness in the Northern Hemisphere"
<p>This dataset consists of four files including (1) Lake ice thickness of 16 large lakes measured by satellite altimeters for 1992-2019 (Altimetric LIT for 16 large lakes.xlsx); (2) Daily lake ice thickness and lake surface snow depth of 1,313 lakes with an area > 50 km<sup>2</sup> in the Northern Hemisphere modeled by a one-dimensional remote sensing lake ice model for 2003-2018 (in NetCDF format); (3) Future lake ice thickness and surface snow depth for 2071-2099 modeled by the lake ice model with a modified ice growth module (table S1.xlsx); (4) A lookup table containing lake IDs, names, locations, and areas. This daily lake ice and snow thickness dataset could provide a benchmark for the estimation of global lake ice and snow mass, thereby improving our understanding of the ecological and economical significance of freshwater ice as well as its response to climate change.</p>
Surface elevation (2012-2020) and ice thickness (2014-2020) datasets measured at Artesonraju Glacier, Cordillera Blanca, Perú
<p>We present a set of data of interpreted ice thickness and ice surface elevation of Artesonraju glacier.<br> The ice thickness was obtained by means of Ground Penetrating Radar (GPR) which was measured by Instituto Nacional de Investigación en Glaciares y Ecosistemas de Montaña (INAIGEM) and Autoridad Nacional de Agua (ANA). The years of ice thickness records include 2013, 2014, 2015, 2017, 2018, and 2020. On the other hand, the surface elevation points were obtained by means of automated total stations and mass balance stakes, integrally measured by ANA. The years of measurements are 2012, 2014, 2015, 2017, 2018, 2019, and 2020.<br> The results from GPR data show a maximum depth of 235±18 m and a decreasing mean depth of ranging from 134±18 m in 2013 to 110±18 m in 2020. Additionally, we estimate a mean ice thickness change rate of 4.2±3.2 m yr<sup>-1</sup> between 2012 and 2020 with GPR data alone, which is in agreement with the elevation change in the same period. The latter was estimated with the more accurate surface elevation data, yielding a change rate of -3.2±0.2 m yr<sup>-1</sup>, and hence, confirming a negative glacier mass balance. The datasets can be valuable for further analysis when combined with other data types, and as input for glacier dynamics modeling, ice volume estimations, and GLOF risk assessment.</p>
Animated E3SM V1 High Resolution Labrador Sea Ice Thickness and Concentration with mid-20th Century Atmospheric Constituents
<p>This animated GIF file visualizes daily grid-cell mean sea ice thickness and concentration for the Labrador Sea region from version 1 of the Energy Exascale Earth System Model (E3SM) using 25km Atmosphere/Land and 8-16km Sea Ice-Ocean resolutions as described in the manuscript "The DOE E3SM coupled model version 1: Description 1 and results at high resolution". River routing is resolved at 0.125˚. This fully coupled simulation was spun-up for 6 years, and then proceeded for 50 subsequent years using HighResMIP protocols for continuuous 1950s atmospheric constituents. This animation shows sea thickness evolution in each frame for five model years 46 to 50, inclusive, with rendered transparency determined from sea ice concentration to demonstrate the influence of ocean eddies around the southern tip of Greenland. The coastline is the true model boundary. The animation is best viewed using a web browser.</p>
Animated E3SM V1 High Resolution Full Coupled Sea Ice Thickness and Extent with mid-20th Century Atmospheric Constituents
<p>This animated GIF file visualizes daily grid-cell mean sea ice thickness from version 1 of the Energy Exascale Earth System Model (E3SM) using 25km Atmosphere/Land and 8-16km Sea Ice-Ocean resolutions as described in the manuscript "The DOE E3SM coupled mo del version 1: Description 1 and results at high resolution". River routing is resolved at 0.125˚. This fully coupled simulation was spun-up for 6 years, and then proceeded for 50 subsequent years using HighResMIP protocols for continuuous 1950s atmospheric constituents. The animation shows both pan-Arctic and Southern Ocean daily sea thickness evolution in each frame for model years 46 to 55, truncated at 15% sea ice concentration, and is best viewed from within a web browser.</p>
Animated E3SM V1 High Resolution Mertz Polynya Sea Ice Thickness and Concentration with mid-20th Century Atmospheric Constituents
<p>This animated GIF file visualizes daily grid-cell mean sea ice thickness and concentration for the Mertz Glacier Polynya region of the East Antarctic coast from version 1 of the Energy Exascale Earth System Model (E3SM) using 25km Atmosphere/Land and 8-16km Sea Ice-Ocean resolutions as described in the manuscript "The DOE E3SM coupled model version 1: Description 1 and results at high resolution". River routing is resolved at 0.125˚. This fully coupled simulation was spun-up for 6 years, and then proceeded for 50 subsequent years using HighResMIP protocols for continuuous 1950s atmospheric constituents. This animation shows sea thickness evolution in each frame for ten model years 46 to 55, inclusive, with shading transparency determined by sea ice concentration, and grid cell outlines dissappearing where there is less than 0.1% sea ice concentration. The coastline is the true model boundary. This animation is best viewed using a web browser.</p>
Annual Dynamic Ice Thickness Change Rate, Greenland 1993-2017
<p>This data set includes annual dynamic ice sheet thickness change rate time series reconstructed at 16,136 locations on the Greenland Ice Sheet. Time series were derived from laser altimetry data collected from 1993 to 2017 by using the Surface Elevation Reconstruction and Change (SERAC) approach (Schenk and Csatho, 2012). Ice thickness changes caused by surface processes were modeled by RACMO 2.3p2 (Noël et al., 2019) and subtracted from the total elevation change to obtain ice thickness changes due to ice dynamics. Annual rates were computed from polynomial fit (max. 3<sup>rd</sup>, order) or ALPS (Shekhar et al., 2021).</p> <p> </p> <p><strong>Parameters:</strong></p> <table> <thead> <tr> <th scope="col">Name</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>PointID</td> <td>Unique ID assign to location</td> </tr> <tr> <td>X</td> <td>X coordinate of time series given in meter, UTM projection (zone N24, EPSG: 32624)</td> </tr> <tr> <td>Y</td> <td>Y coordinate of time series given in meter, UTM projection (zone N24, EPSG: 32624)</td> </tr> <tr> <td>ObsInt</td> <td>Length of time series (month, from first to last observational epoch used)</td> </tr> <tr> <td>Hrange_flag</td> <td>Set according to the range of thickness change (from original time series before outlier removal). Hrange_flag=0, range of thickness change less than 5 m, annual rates computed with polynomial modeling (max 3<sup>rd</sup> order/cubic), Hrange_flag=1, range of thickness change more than 5 m, annual rates computed with ALPS.</td> </tr> <tr> <td>POL9394</td> <td>Annual rate from polynomial interpretation for balance year 1993/94 (September 1 1993 – August 31, 1994), all years until balance year 2016/17.</td> </tr> <tr> <td>ALPS9394</td> <td>Annual rate from ALPS modeling for balance year 1993/94 (September 1 1993 – August 31, 1994), all years until balance year 2015/16.</td> </tr> </tbody> </table> <p> </p> <p><strong>Laser altimetry used:</strong></p> <ul> <li>ATM/ATM: ATM airborne laser altimetry (Studinger et al., 2018)</li> <li>LVIS/LVIS: LVIS airborne laser altimetry (Blair and Hofton, 2019)</li> <li>ICESat/L2A-L2F: ICESat satellite laser altimetry (Zwally et al., 2014)</li> </ul> <p> </p> <p><strong>References:</strong></p> <ol> <li>Blair, J. B. and Hofton, M. (2019). IceBridge LVIS L2 Geolocated Surface Elevation Product, Version 2 [Greenland]. Boulder, Colorado USA. <em>NASA National Snow and Ice Data Center Distributed Active Archive Center. </em>https://doi.org/10.5067/E9E9QSRNLYTK. Date Accessed 11-08-2022.</li> <li>Noël, B. P. Y., van de Berg, W. J., Lharmitte, S., and van den Broeke, M. R. 2019. Rapid Ablation Zone Expansion Amplifies North Greenland Mass Loss, <em>Science Advances</em>, <strong>5</strong>(9), <a href="https://doi.org/10.1126/sciadv.aaw0123">DOI: 10.1126/sciadv.aaw0123</a></li> <li>Schenk, T. and Csatho, B. (2012). A New Methodology for Detecting Ice Sheet Surface Elevation Changes From Laser Altimetry Data. <em>IEEE Transactions on</em> <em>Geoscience and Remote Sensing, </em><strong>50,</strong> 3302–3316.</li> <li>Shekhar, P., Csathó, B., Schenk, T., Roberts, C. & Patra, A. K. (2021). ALPS: A Unified Framework for Modeling Time Series of Land Ice Changes. <em>IEEE Transactions on</em> <em>Geoscience and Remote Sensing,</em><strong> 59</strong>, 6466–6481.</li> <li>Studinger, M. (2018). IceBridge ATM L2 Icessn Elevation, Slope, and Roughness, Version 2 [Greenland]. Boulder, Colorado USA. <em>NASA National Snow and Ice Data Center Distributed Active Archive Center. </em>https://doi.org/10.5067/CPRXXK3F39RV. Date Accessed 11-08-2022.</li> <li>Zwally, H. J., Schutz, R., Dimarzio, J.., and Hancock. D. (2014). GLAS/ICESat L1B Global Elevation Data (HDF5), Version 34 [Greenland]. Boulder, Colorado USA. <em>NASA National Snow and Ice Data Center Distributed Active Archive Center. </em>https://doi.org/10.5067/ICESAT/GLAS/DATA109. Date Accessed 11-08-2022.</li> </ol>
A climatology of thermodynamic vs. dynamic Arctic wintertime sea ice thickness effects during the CryoSat-2 era: Data
<p>Data for:</p> <p>Anheuser, J., Liu, Y., and Key, J.: A climatology of thermodynamic vs. dynamic Arctic wintertime sea ice thickness effects during the CryoSat-2 era, submitted to: The Cryosphere. 2022</p> <p>Code can be found at:</p> <pre>https://doi.org/10.5281/zenodo.7987926</pre>
Simulated top-of-atmosphere (120 km) downward and upward solar and thermal-infrared irradiances and ice cloud optical thickness; calculated solar, TIR and net cloud radiative effect. Simulated with ice crystal properties for aggregates, droxtals, and plates based on Yang (2013).
<p>This dataset consists of three .nc files for ice crystal shapes of aggregates, plates, and droxtals. The files include ice cloud optical thickness <span class="math-tex">\(\tau\)</span> (550nm), the simulated upward and downward irradiances <span class="math-tex">\(F\)</span> at the top-of-atmosphere (with and without the presence of the ice cloud), and the calculated ice cloud radiative effect <span class="math-tex">\(\Delta F\)</span> (solar [0.3-3.5 <span class="math-tex">\(\mu\)</span>m], thermal-infrared [3.5-75 <span class="math-tex">\(\mu\)</span>m], and net). The data set allows the user to extract <span class="math-tex">\(\Delta F\)</span> values for their parameter combinations. The available cloudy and cloud-free irradiances further allow to calculate the cirrus radiative effect (RE) by scaling the 'cloudy' RE with the required cloud cover. This serves as a first-approximation because, as 3D effects are neglected.</p>
Monthly averaged fast-ice thickness in McMurdo Sound from a sea-ice monitoring station
<p>Updated monthly averaged fast-ice thickness on level first year fast ice in McMurdo Sound, Antarctica from a sea-ice monitoring station. Fast-ice thickness is derived from temperature measurements and manual drill hole measurements, and linearly interpolated to produce mid-month values. Do not use the original version (SIMS_thickness.xlsx) as an error has been discovered. SIMS_thickness_V2.xlsx is the corrected dataset. A description of the stations and the methods used can be found in Gough et al., 2012 and Richter et al. 2022.</p> <p>Gough, A. J., Mahoney, A. R., Langhorne, P. J., Williams, M. J. M., Robinson, N. J., & Haskell, T. G. (2012). Signatures of supercooling: McMurdo Sound platelet ice. Journal of Glaciology, 58 (207), 38–50. doi:10.3189/2012jog10j218</p> <p>Richter, M. E., Leonard, G. H., Smith, I. J., Langhorne, P. J., Mahoney, A. R., & Parry, M. (2022). Accuracy and precision when deriving sea-ice thickness from thermistor strings: a comparison of methods. Journal of Glaciology, 1–20. doi:10.1017/jog.2022.108</p> <p> </p>
Data and code from: Sea ice perturbation and mass starvation of Thick-billed Murres
Open the record for dataset details and reuse information.
Ice thickness measurements from the Ice Storm Experiment (ISE) at the Hubbard Brook Experimental Forest
An ice storm simulation was performed at the Hubbard Brook Experimental Forest to evaluate impacts of these extreme weather events on northern hardwood forests. Water was pumped from the main branch of Hubbard Brook and sprayed above the forest canopy in subfreezing conditions so that it rained down and froze on contact with trees. The experiment consisted of five treatments, including a control (no ice) and three target levels of radial ice accretion: low (6.4 mm), mid (12.7 mm), and high (19.0 mm). Two of the mid-level treatment plots (midx2) were iced in back-to-back years to evaluate impacts of consecutive storms. The ice accretion values reported are measurements made on passive collectors using both digital calipers and a water volume approach. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Modeled Ice thickness Distribution of Glaciers in Chandra Basin, Western Himalayas, India
<p>The glacier ice thickness distribution data provided here was generated using an optimally parameterized GlabTop2_IITB [Glacier Bed Topography Indian Institute of Technology Bombay (IITB) version] model with high-resolution DEM as an input. This research work is under publication in the Journal of Mountain Science. The study reports modeled ice thickness distribution and total ice volume of selected 65 glaciers (>0.5 km<sup>2</sup>) of Chandra basin, located in Western Himalayas. </p> <p> </p>
Lake ice thickness dataset
<p>This dataset consist of three products including: (1) Lake ice thickness of 11 large lakes measured by satellite altimeters for 2002-2019; (2) Lake ice thickness and lake surface snow depth of 1260 lakes with an area > 50 km<sup>2</sup> in the Northern Hemisphere modeled by a one-dimensional remote sensing lake ice model for 2003-2018; (3) Future lake ice thickness and surface snow depth for 2091-2099 modeled by the lake ice model with a modified ice growth module. This daily lake ice and snow thickness dataset could provide a benchmark for the estimation of global lake ice and snow mass, thereby improving our understanding of the ecological and economical significance of fresh water ice as well as its response to climate change.</p>
Glacier_inventory_debris_cover_ice_thickness_dataset_Chandra_Bhaga_basin_Himalaya
<p>This is a multitemporal glacier dataset over ChandraBhaga basin in western Himalaya which includes glacier inventory (for 251 glaciers with area > 0.5 km<sup>2</sup>) for year 1993, 2000, 2010 and 2019 (.shp files); debris cover for year 1993 and 2019 (.tif files) and ice thickness (.tif files). Details of files are included in data_description.docx. These datasets form part of manuscript submitted to Earth Systems and Science Data.</p>
Ice thickness and surface velocities for the manuscritpt Mass balance and stability of ice tongues in the Western Ross Sea
<p>The data set contains:</p> <p>ICESat-2 derived point ice thickness for 9 ice tongues in csv format</p> <p>Surface average velocities derived from the ASF Vertex platform on demand HYP3 autoRIFT velocity product for 9 ice tongues.</p>
AGF212 2024: GPR ice thickness measurements of the glaciers Tellbreen and Blekumbreen (Svalbard)
Open the record for dataset details and reuse information.
Database for Towards ice thickness inversion: an evaluation of global DEMs in the glacierized Tibetan Plateau
<p>x, latitude of ICESat-2 point</p> <p>y, longitute of ICESat-2 point</p> <p>icesat-2, elevation from icesat-2</p> <p>sigma-icesat-2, error of elevation from icesat-2</p> <p>date-icesat2, acquiring date of icesat-2 point</p> <p>dhdx, along track slope of icesat-2 data</p> <p>dhdx_sigma, error of along track slope of icesat-2 data</p> <p>elevation range min, the minimum elevation of glaciers where icesat-2 data is located</p> <p>elevation range med, the median elevation of glaciers where icesat-2 data is located</p> <p>elevation range max, the maximum elevation of glaciers where icesat-2 data is located</p> <p>dh, glacier surface elevation change from Shean et al. (2020)</p> <p>aw3d30, elevation from aw3d30 in EGM96 geoid</p> <p>srtmgl1, elevation from srtmgl1 in EGM96 geoid</p> <p>tandem, elevation from TanDEM-X in WGS84 ellipsoid</p> <p>srtmv41, elevation from srtmv41 in EGM96 geoid</p> <p>nasadem, elevation from NASADEM in WGS84 ellipsoid</p> <p>merit, elevation from merit in EGM96 geoid</p> <p>aw3d30slp, slope from aw3d30</p> <p>srtmgl1slp, slope from srtmgl1</p> <p>tandemslp, slope from tandem</p> <p>srtmv41slp, slope from srtmv41</p> <p>nasademslp, slope from nasadem</p> <p>meritslp, slope from merit</p> <p>aw3d30asp, aspect from aw3d30</p> <p>srtmgl1asp, aspect from srtmgl1</p> <p>tandemasp, aspect from tandem</p> <p>srtmv41asp, aspect from srtmv41</p> <p>nasademasp, aspect from nasadem</p> <p>meritasp, aspect from merit</p> <p>The elevation should be <strong>converted</strong> using geoidheight function in MATLAB</p> <p> </p> <p> </p> <p> </p>
Modeled wintertime sea ice drift, sea ice thickness, dynamic sea surface height and sea ice drift budget terms in the Arctic
<p>Modeled wintertime sea ice drift, sea ice thickness, dynamic sea surface height and sea ice drift budget terms in the Arctic between 1981 and 2020.</p>
Daily-resolution pan-Arctic sea ice thickness
<p>This dataset contains daily-resolution pan-Arctic sea ice thickness estimates, created following the methodology outlined in <strong>Stroeve</strong><a href="https://doi.org/10.1029/2022GL100696"><strong> </strong></a><strong>et al. (2024): "Mapping potential timing of ice algal blooms from satellite"</strong></p> <p>Please refer to this publication for a full description of the data and cite this publication when using these data.</p> <p>Variables: Sea Ice Thickness (m), Longitude, Latitude, Day</p> <p>Dates are stated in the file names</p> <p>IS2 data is available year-round</p> <p>CS2 and CS2S3 data is available for winter seasons only, with each winter season running from 1 October - 30 April of the specified years</p> <p>Days where no data is available are filled with NaNs</p>
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