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42 results for “ice dynamics”
Dataset: Environmental drivers of under-ice phytoplankton bloom dynamics in the Arctic Ocean
<p>This dataset is linked to this manuscript entitled "Environmental drivers of under-ice phytoplankton bloom dynamics in the Arctic Ocean" published in Elementa: Science of the Anthropocene (<a href="http://doi.org/10.1525/elementa.430">http://doi.org/10.1525/elementa.430</a>). Please find the abstract below:</p> <p>The decline of sea-ice thickness, area, and volume due to the transition from multi-year to first-year sea ice improves the under-ice light environment for pelagic Arctic ecosystems. One unexpected and direct consequence of this transition, the proliferation of under-ice phytoplankton blooms (UIBs), challenges the paradigm that waters beneath the ice pack harbor little planktonic life. Little is known about the diversity and spatial distribution of UIBs in the Arctic Ocean, or the environmental drivers behind their timing, magnitude, and species composition. Here, we compiled a unique and comprehensive dataset from seven major research projects in the Arctic Ocean (11 expeditions, covering the spring sea-ice-covered period to summer ice-free conditions) to identify the environmental drivers responsible for initiating and shaping the magnitude and assemblage structure of UIBs. The temporal dynamics behind UIB formation related to the ways that snow and sea-ice conditions impact the under-ice light field. In particular, the onset of snowmelt significantly increased under-ice light availability (> 0.1–0.2 mol photons m<sup>–2</sup> d<sup>–1</sup>), marking the concomitant termination of the sea-ice algal bloom and initiation of UIBs. At the pan-Arctic scale, bloom magnitude (expressed as maximum chlorophyll <em>a </em>concentration) was predicted best by winter water Si(OH)<sub>4</sub> and PO<sub>4</sub><sup>3–</sup> concentrations, as well as Si(OH)<sub>4</sub>:NO<sub>3</sub><sup>–</sup> and PO<sub>4</sub><sup>3–</sup>:NO<sub>3</sub><sup>–</sup><sub> </sub>drawdown ratios, but not NO<sub>3</sub><sup>–</sup> concentration. Two main phytoplankton assemblages dominated UIBs (diatoms or <em>Phaeocystis</em>), driven primarily by the winter nitrate:silicate (NO<sub>3</sub><sup>–</sup>:Si(OH)<sub>4</sub>) ratio and the under-ice light climate. <em>Phaeocystis</em> co-dominated in low Si(OH)<sub>4</sub> (i.e., NO<sub>3</sub>:Si(OH)<sub>4</sub> molar ratios > 1) waters, while diatoms contributed the bulk of UIB biomass when Si(OH)<sub>4</sub> was high (i.e., NO<sub>3</sub>:Si(OH)<sub>4</sub> molar ratios < 1). The implications of such differences in UIB composition could have important ramifications for Arctic biogeochemical cycles, and ultimately impact carbon flow to higher trophic levels and the deep ocean.</p>
Data used in the manuscript entitled "Turbulent heat flux dynamics along the Dotson and Getz ice-shelf fronts (Amundsen Sea, Antarctica)"
<p>Data files used in the analysis in the manuscript entitled "Turbulent heat flux dynamics along the Dotson and Getz ice-shelf fronts (Amundsen Sea, Antarctica)".</p> <p>Data were collected during the RV NB Palmer NBP2202 cruise, during the 2022 TARSAN campagine in the Amundsen Sea.</p> <p>Underway data provides daily files from the underway and meteorology sensors in JGOFS format. CTD data collected from the cruise. Information about sensors and data formats is included in the data report.</p> <p>Glider data was processed through the UEA Seaglider Toolbox (https://bitbucket.org/bastienqueste/uea-seaglider-toolbox/src/toolbox/) and is provided in Matlab format.</p> <p> </p> <p>Manuscript abstract:</p> <p>In coastal polynyas, where sea–ice formation occurs, it is crucial to have accurate estimates of heat fluxes in order to predict future rates of sea–ice formation. The Amundsen Sea Polynya is the fourth largest coastal polynya around Antarctica, yet remains poorly observed because of its remoteness. Consequently, we rely on models and reanalysis that are unvalidated to study the effect of atmospheric forcing on polynya dynamics. We use summer ship-board data from the NBP22/02 cruise to understand the turbulent heat flux dynamics in the Amundsen Sea Polynya and evaluate our ability to represent these dynamics in ERA5. We show that cold and dry air outbreaks from Antarctica enhance air–sea temperature and humidity gradients, triggering episodic heat loss events. The heat loss is larger along the ice shelves, and it is also where the ERA5 turbulent heat flux exhibits the largest biases, underestimating the flux by up to 141~W~m$^{-2}$ due to its coarse resolution and misrepresentation of ice-shelf location. By reconstructing a turbulent heat flux product from ERA5 variables using a nearest neighbour approach to obtain sea surface temperature, we decrease the bias to 107 W m$^{-2}$. Using a 1D-model, we show that the mean co-located ERA5 heat loss underestimation of -28~W~m$^{-2}$ led to an overestimation of the summer evolution of sea surface temperature (heat content) by +0.76~°C (+8.2e+07~J) over 35-days. By obtaining the reconstructed flux, the reduced heat loss bias (12 W~m$^{-2}$) reduced the seasonal bias in sea surface temperature (heat content) to -0.17~°C (-3.30e+07~J) over the 35-days. This study shows that caution should be applied when retrieving ERA5 turbulent flux along the ice shelves, and that a reconstructed flux using ERA5 variables shows better accuracy.</p> <p> </p> <p> </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>
Dynamic strength characterisation of wet granular ice layers
<p>Dataset for the research paper "Dynamic strength characterisation of wet granular ice layers" submitted to the International Journal of Impact Engineering.</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>
Multi-proxy agreement on Atlantic circulation dynamics since the last ice age: Model output data
<p>This dataset contains model output for the simulations presented in: <em>"Multi-proxy agreement on Atlantic circulation dynamics since the last ice age"</em>.</p> <p> </p>
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>
Measurements of N cycling dynamics in the soils of the Ice Storm Experiment (ISE) plots at the Hubbard Brook Experimental Forest
As the climate changes, it is thought that ice storms will begin to occur with increasing frequency. This project evaluates the damage and changes ice storms cause to northern hardwood forests in nutrient cycling. Ten 20x30 meter plots were established in a predominately sugar maple stand, with 4 icing treatments and 2 control plots. This data set includes soil nitrate (NO3-) and ammonium (NH4+) concentrations, microbial biomass carbon (C ) and nitrogen (N) content, microbial respiration, potential nitrification and N mineralization rates determined via lab incubation and in situ field incubations, denitrification potential, and organic matter content monitored in the Ice Storm Experiment plots at the Hubbard Brook Experimental Forest from 2015-2017.
Full-field numerical simulations of temperate ice viscoplastic deformation and dynamic recrystallization [Data set]
<p>This data set corresponds to the scientific article Llorens, M.-G., Griera, A., Bons, P.D., Gomez-Rivas, E., Weikusat, I., Prior, D., Kerch, J. and Lebensohn, R.A. Seismic anisotropy of temperate ice in polar ice sheets. Journal of Geophysical Research: Earth Surface.</p> <p>This data set contains (i) the output files with the crystal orientation and phase data of each simulation presented in the article (run using the open-source software platform ELLE; Bons et al., 2008; Piazolo et al., 2019), and (ii) a code to plot the crystallographic orientation density function (ODF) using the open-source code MTEX (Mainprice et al., 2015). For the visualization of seismic wave velocities the information contained in these output files can be loaded (in radians) to the software package AEH-EBSD Analysis Toolbox (Naus-Thijseen, 2011; Vel et al., 2016). The output files are provided for time steps 50 (shear strain of 1), 100 (shear strain of 2), 200 (shear strain of 4), 300 (shear strain of 6) and 400 (shear strain of 8).</p> <p>Each file has eight columns and multiple rows. Each row stores the output data for an unode of the model, and the model has 256x256 unodes. The first three columns correspond to the three Euler angles (<em>α</em>, <em>β</em>, <em>γ</em>) in degrees, the fourth and fifth column are the <em>x</em> and <em>y</em> coordinates of each unode, the sixth and seventh columns contain attributes not used in these simulations and column eight shows the phase number corresponding to each unode (1 for solid ice and 2 for water).</p> <p>Three simulations are presented and analyzed in this article, and thus their results stored in this data set:</p> <p>Simulation 1: purely solid ice (melt fraction <em>ϕ</em>=0)</p> <p>Simulation 2: ice including 5% water (melt fraction <em>ϕ</em>=5)</p> <p>Simulation 3: ice including 15% water (melt fraction <em>ϕ</em>=15)</p>
XMCD data and XAS spectra, and simulation data for Clocked Dynamics in Artificial Spin Ice
<p><strong>Experimental data</strong></p><p>Raw data of XMCD images collected at ALBA Synchrotron between 8th to 12th of September 2022. The data are used to create the experimental magnetization curves and magnetic contrast images in the paper <i>Clocked dynamics in artificial spin ice. </i>Additionally, XAS spectra of the Fe L3 edge obtained prior to imaging are included.</p><p>Folder numbers starting at 225 through to 320 contain the data for the unipolar clocking experiment. The folder "001_XAS_Fe" contains the XAS spectrum of the sample taken prior to this series. <br>Folder numbers starting at 166 through to 217 contain the data for the bipolar clocking experiment. The folder "013_XAS_Fe_L3_CN" contains the XAS spectrum of the sample prior to this series.</p><p><strong>Simulation data</strong></p><p>The resulting data from flatspin simulations that are plotted as magnetization curves in the paper <i>Clocked dynamics in artificial spin ice. </i></p><p>The folder "flatspin-unipolar" contains the data for the unipolar clocking experiment.<br>The folder "flatspin-bipolar" contains the data for the bipolar clocking experiment.</p>
Results of "Comparison of ice dynamics using full-Stokes and Blatter-Pattyn approximation: application to the Northeast Greenland Ice Stream"
<p>This archive provides ice sheet model output produced as part of the publication:</p> <p>Rückamp, M., Kleiner, T., and Humbert, A.: Comparison of ice dynamics using full-Stokes and Blatter–Pattyn approximation: application to the Northeast Greenland Ice Stream, The Cryosphere, 16, 1675–1696, https://doi.org/10.5194/tc-16-1675-2022, 2022.</p> <p>About the data:<br> The archive contains 2 zipped tar-files for the two 2 different regions 'icestream' and 'outlet'. Each tar-file contains ascii files for the different conducted scenarios. An exemplary filename looks like:<br> FSvsBPlike_icestream_res12800m_E1_SlidingM1_isFS_P1P1GLS_BCstrong.txt</p> <p>'FSvsBPlike' indicates the general study<br> 'icestream' indicates the region (either icestream or outlet)<br> 'res12800m' indicates the horizontal resolution (ranges from 12800 to 100 meter)<br> 'E1' indicates the enhancement factor used (available values E=0.1, 1, 3 and 6)<br> 'SlidingM1' indicates the employed sliding exponent (available values m=1 and 3)<br> 'isFS' indicates that the full-Stokes equation was employed (either isFS or isBPlike)<br> 'P1P1GLS' describes the employed finite element discretization (either P1P1GLS or P2P1)<br> 'BCstrong' indicates that the strong basal boundary implementation was used (either BCstrong or BCweak)<br> <br> An exemplary file structure looks as follows:<br> % Creation date: 03-May-2022<br> % Institution: AWI (Bremerhaven-Germany), BADW (Munich-Germany)<br> % Correspondence: Martin Rueckamp (martin.rueckamp@badw.de), Angelika Humbert (angelika.humbert@awi.de)<br> % Source: COMSOL version 5.6 (www.comsol.com)<br> % Citation: Rückamp et al. (2022): https://tc.copernicus.org/preprints/tc-2021-193/<br> % Projection: EPSG:3413 (proj4 string: +init=epsg:3413)<br> x(m) y(m) z_s(m) z_b(m) vx_s(m/a) vy_s(m/a) vz_s(m/a) p_s(Pa) vx_b(m/a) vy_b(m/a) vz_b(m/a) p_b(Pa)<br> 301099.83 -1256559.78 1840.83 205.40 -0.031 0.028 -5.456 -157729.374 -2.312 2.076 0.012 14684584.911<br> ...<br> <br> The fields x, y contain x- and y coordinates of the grid nodes in EPSG:3413 projection. The fields z_s and z_b are the ice surface and ice base, respectively. The fields vx, vy, and vz are the ice velocities in horizontal (x,y) and vertical (z) direction. The field p is the pressure. The subscripts _s and _b indicate the surface and ice base, respectively.</p>
Global annual mean Absolute Dynamic Topography of permanently ice-free Oceans 1993 to 2020
<p>The text, data and figure disclosed are part of the manuscript “Intensification of the South Pacific Subtropical Gyre Circulation (1993–2020) Revealed by Satellite Altimetry” by Wolfgang Schneider, Freddy Hernández-Vaca, José Garcés-Vargas, and Iván Pérez-Santos, which was submitted to the Journal of Geophysical Research: Oceans</p> <p> </p> <p>The time series presented below were generated using E.U. Copernicus Marine Service Information, namely “Absolute Dynamic Topography” which was retrieved from the following Copernicus Marine Environment Monitoring Service (CMEMS) ftp site.</p> <p> </p> <p>ftp://my.cmems-du.eu/Core/SEALEVEL_GLO_PHY_L4_MY_008_047/cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.25deg_P1D/1993/01/</p> <p> </p>
Modeling the impacts of Antarctic Sea Ice Decline: Responses of Atmospheric Dynamics
Open the record for dataset details and reuse information.
Climate and ice sheet dynamics in Patagonia throughout Marine Isotope Stages 3 and 2
<p>This dataset contains the modelled ice thickness over Patagonia during the global Last Glacial Maximum at 4 km resolution by using SICOPOLIS forced by the results from PMIP models. It also contains the modelled output of the transient simulations performed at 8 km resolution forced by MPI-ESM1-2-LR and different cores.</p>
Graph neural network emulator for modeling of ice dynamics and calving in the Helheim Glacier, Greenland
<p>These files include the following codes and datasets for developing graph neural network (GNN) emulators for the Ice-sheet and Sea-level System Model (ISSM) for modeling ice sheet dynamics and calving in the Helheim Glacier, Greenland.</p> <ul> <li>ISSM_DGL_Helheim.py: Python file for training GNN models</li> <li>ISSM_CNN_Helheim.py: Python file for training convolutional neural network (CNN) models</li> <li>*.mat: Datasets of the ISSM transient simulation results</li> </ul>
A new parameterisation for homogeneous ice nucleation driven by highly variable dynamical forcings
<p>This archive contains the data associated with the preprint of the article <em>"A New Parameterization for Homogeneous Ice Nucleation Driven by Highly Variable Dynamical Forcings."</em> The collection includes datasets used to construct the initial conditions for forcing an air parcel model with ice physics, outputs produced by the parcel model, and data necessary for generating the plots presented in the paper. A README file is provided, offering a detailed explanation of the contents and structure of the archive,</p>
Data used in "Morphosedimentary Response of an Urban Beach System to Interannual Variability in Shorefast Ice Dynamics"
<p>This dataset includes digital surface models (DSM) and orthomosaics of Tibbits Cove beach located along the St. Lawrence Fluvial Estuary (Québec, Canada). Surveys were conducted using a UAV before, during and after the 2021 and 2022 winter season. It also includes videos acquired with fixed cameras for these periods. For questions, please contact jean-francois.bernier@ggr.ulaval.ca.</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>
Impacts of bed topography resolution on sea-level rise projections from coupled subglacial hydrology and ice dynamics for Thwaites Glacier, Antarctica
Open the record for dataset details and reuse information.
Collective magnetic dynamics in artificial spin ice probed by AC susceptibility
<p>A collection of data presented and analysed in a manuscript under the same title and author list, posted on arXiv.</p>
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