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552 results for “Sea ice”
Acoustic recording under landfast sea ice near glacier
<p>A hydrophone was deployed in February 2022 underneath landfast sea ice in Tempelfjorden, Svalbard. The hydrophone was located approximately 2 km from the glacier. Several major events were recorded by vibrations sensors on the ice next to the hydrophone. Source triangulation identified that the events were coming from the glacier wall. This dataset includes the recording of one of the events (with the event starting at about 0:14), and a sample thereof.</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>
Sea ice core biogeochemical data collected during the 2019 SCALE Winter Cruise
<p><strong>Title: </strong>Biogeochemical profiles of sea ice cores sampled during the Southern oCean seAsonal Experiment (SCALE) winter cruise in 2019.</p> <p> </p> <p><strong>Authors:</strong> Riesna R. Audh, Siobhan Johnson, Mark Hambrock, Hazel Little, Joshua Mirkin, Emmanuel Omatuku, Benjamin Hall, Tokoloho Rampai, Keith MacHutchon, Sebastian Skatulla, Sarah E. Fawcett, Marcello Vichi</p> <p> </p> <p><strong>Data Description:</strong></p> <p> </p> <p><strong>Abstract</strong></p> <p>Biogeochemical profiles of sea ice cores extracted from in situ sea ice floes and lifted pancakes were measured in the Atlantic sector of the Antarctic Marginal Ice Zone during the Southern oCean seAsonal Experiment (SCALE) winter cruise in 2019 (<a href="http://www.scale.org.za">www.scale.org.za</a>) aboard the SA Agulhas II.</p> <p> </p> <p>A total of four sea ice cores (cores) were sampled during the cruise. Two cores were collected overboard on a consolidated floe that was accessed via a personnel carrier suspended by the ship’s forward crane. Two cores were collected from a pancake that was lifted aboard the ship via a net that was attached to the ship’s aft crane and placed on the helideck for sampling. Profiles were obtained by cutting the cores using a bandsaw in a cold laboratory at -10 °C. The cores were cut into approximately 0.05 m segments, starting from the bottom of the core. These segments were allowed to melt in the dark in an insulated box. The meltwater was filtered for chlorophyll measurements (Welschmeyer, 1994) and the filtrate was analysed for oxygen isotopes (Walker and others, 2015), ammonium (Holmes et al., 1999), phosphate, nitrate, nitrite and silicate (using a SEAL AA500 segmented flow autoanalyser). These values are reported at the depth of the top of the segment in the core in μM. In order to facilitate comparison with the seawater concentrations below the ice, the in-ice nutrients (including NH4+) were salinity normalised using the equation of Fripiat and others (2017):</p> <p> </p> <p><em>C</em><em>norm</em><em> = C</em>SwS<em> </em><em> </em></p> <p> </p> <p>Where C is the measured bulk concentration, Sw is the salinity of the seawater, and S is the corresponding measured bulk salinity of the ice segment.</p> <p> </p> <p>Although sampling of the core occurred from the bottom of the core to the top of the core, the data are reported as the top of the core (snow/ice interface) being 0 m (depth=0 m). </p> <p> </p> <p><strong>This research has been funded by the National Research Foundation of South Africa (NRF)</strong></p> <p><br> </p> <p><strong>Cruise:</strong> VOY-038 (SCALE2019-WINTER) (URL: https://scale.org.za/)</p> <p><strong>Station(s):</strong> VOY-038-MIZ3A</p> <p>VOY-038-MIZ1D</p> <p><strong>Position(s):</strong> -58.13783 S; 0.00442 W</p> <p>-56.8017 S; 0.30262 E</p> <p><strong>Date/Time:</strong> 2019-07-27/10:38:00</p> <p>2019-07-28/09:15:00</p> <p><strong>Method(s):</strong> Overboard coring</p> <p>Pancake lifting via aft crane, on deck coring</p> <p><strong>Parameters:</strong><strong> </strong>Station Number (Station)</p> <p>Date/Time of station (Date/Time)</p> <p>Latitude of station (Latitude)</p> <p>Longitude of station (Longitude)</p> <p>Ice type (Ice Type)</p> <p>Core ID(Core), Pancake identifier A/B/C/D</p> <p>Oxygen isotopes (d18O)</p> <p>Chlorophyll (Chl-a)</p> <p>Ammonium (NH4)</p> <p>Nitrate + Nitrite (NO3+NO2)</p> <p>Nitrite (NO2)</p> <p>Phosphate (PO4)</p> <p>Silicate (Si)</p> <p>Nitrate (NO3)</p> <p>Salinity of the ice segment from physical cores (IceSalinity)</p> <p>Standard deviation of the salinity average from physical cores (IceSalinityStdev)</p> <p>Seawater salinity from CTD (SeawaterSalinity)</p> <p>Salinity normalised nitrate+nitrite (N03+N02_Avg_SalinityNormalised)</p> <p>Salinity normalised ammonium (NH4_SalinityNormalised)</p> <p>Salinity normalised nitrite (NO2_SalinityNormalised)</p> <p>Salinity normalised phosphate (PO4_SalinityNormalised)</p> <p>Salinity normalised silicate (Si_SalinityNormalised)</p> <p>Salinity normalised nitrate (NO3_SalinityNormalised)</p> <p><br> </p> <p> </p> <p><strong>Keywords: </strong>sea ice cores, Antarctica, pancake ice, sea ice, biogeochemistry, winter</p> <p> </p> <p><strong>References</strong><strong>:</strong></p> <p> </p> <p>Fripiat, F., Meiners, K.M., Vancoppenolle, M., Papadimitriou, S., Thomas, D.N., Ackley, S.F., Arrigo, K.R., Carnat, G., Cozzi, S., Delille, B. and Dieckmann, G.S., 2017. Macro-nutrient concentrations in Antarctic pack ice: Overall patterns and overlooked processes. Elementa: Science of the Anthropocene, 5. </p> <p> </p> <p>Holmes, R.M., Aminot, A., Kérouel, R., Hooker, B.A. and Peterson, B.J., 1999. A simple and precise method for measuring ammonium in marine and freshwater ecosystems. Canadian Journal of Fisheries and Aquatic Sciences, 56(10), pp.1801-1808. </p> <p> </p> <p>Walker, S.A., Azetsu‐Scott, K., Normandeau, C., Kelley, D.E., Friedrich, R., Newton, R., Schlosser, P., McKay, J.L., Abdi, W., Kerrigan, E. and Craig, S.E., 2016. Oxygen isotope measurements of seawater (18O/16O): A comparison of cavity ring‐down spectroscopy (CRDS) and isotope ratio mass spectrometry (IRMS). Limnology and Oceanography: Methods, 14(1), pp.31-38. </p> <p> </p> <p>Welschmeyer, N., 1994. A method for the determination of chlorophyll a in the presence of chlorophyll b and pheopigments. Limnology and Oceanography, 39, pp.1985-1992. </p> <p> </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>
BISICLES ice-sheet model for the Amundsen Sea Embayment, Antarctica : ensemble simulations to 2050
<p>BISICLES ice-sheet model simulations for the Amundsen Sea Embayment. Full details of the model set-up and ensemble design are described in the attached manuscript which has been accepted for publication in Journal of Glaciology.<br> In brief, a 213-member ensemble of simulations was created by varying four different model parameters. The parameters are the u0 value in a regularised Coulomb friction law, the rate of imposed thinning of floating ice (∂h/∂t(Ωf)), and scaling factors for sliding and viscosity coefficients (<em>C</em> and ϕ) between 0.9 and 1.1. We attach a summary text file of results, as well as NetCDF files of simulated variables land ice thickness and u and v components of velocity.<br> <strong>ASE2050_bisicles.csv </strong>contains annual (2007 to 2050, columns 5 to 48) sea level equivalent (mm) mass losses of ice from the Pine Island and Thwaites Glacier catchment basins. The parameters, given in columns 1 to 4, respectively, are the u0 (m/a), the rate of imposed thinning of floating ice (m/a), and the scaling factors for sliding and viscosity coefficients.<br> The NetCDF files in <strong>ASE_BISICLES.tar.gz</strong> contain annual (2007 to 2050) simulated output variables for the Amundsen Sea region at a spatial resolution of 1 km, with one file per ensemble member. The variables follow the ISMIP6 naming protocol:<br> (https://www.climate-cryosphere.org/wiki/index.php?title=ISMIP6-Projections-Antarctica#A2.3_Model_output_variables_and_README_file).<br> We include state variables lithk, uvelmean, and vvelmean. Each file is named according to the variable, the simulation parameters, and the resultant 2050 SLE value of ice loss (mm). For example, <strong>lithk_ASE_BISICLES.uj_20.dhfdt_5.C_0.90.phi_0.90.slr_43.06.nc </strong>is the land ice thickness data for simulation u0=20 m/a, ∂h/∂t(Ωf) = 5 m/a, C scaled by 0.9, ϕ scaled by 0.9, and a final SLE of 43.06 mm.</p> <p> </p>
Climatological maps of sea ice variability for the Antarctic marginal ice zone
<p>Monthly climatologies of a statistical indicator of sea ice variability computed from daily maps of sea ice concentration (SIC) from satellites (Vichi, 2022). The sigma_SIA indicator is based on the standard deviation of SIC daily anomalies computed over<br> the monthly time scales, using a monthly climatology over the period 1989-2019 as the baseline. The analysis starts from year 1989 since before that period data were every second day.</p> <p><strong>Please use the most updated version of this dataset and cite the main DOI unless required</strong></p> <p>The following files have been derived from NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 4 (<a href="https://doi.org/10.7265/efmz-2t65">https://doi.org/10.7265/efmz-2t65</a> and used in Vichi (2022). The code to produce them is available at the following repository <a href="https://github.com/mvichi/antarcticMIZ">https://github.com/mvichi/antarcticMIZ</a> (<a href="https://doi.org/10.25375/uct.21103363">DOI:10.25375/uct.21103363</a>) </p> <ol> <li><strong>NSIDC_cdr_clim_m_sigmaSIA.nc</strong>: Climatological monthly sigma_SIA (climatological 'pooled' monthly std of daily anomaly) from NOAA/NSIDC CDR V4 Southern Hemisphere Sea Ice Concentration</li> <li><strong>NSIDC_cdr_clim_m_maskMIZ.nc</strong>: Climatological mask of the MIZ extent using a sigma_SIA>0.1 criterion</li> <li><strong>NSIDC_cdr_clim_m_sigmaSIA_exceedance.nc</strong>: Climatological probability maps of exceeding sigma_SIA>0.1 and sigma_SIA>0.2</li> <li><strong>NSIDC_cdr_m_sigmaSIA.nc</strong>: Monthly sigma_SIA from NOAA/NSIDC CDR V4 Southern Hemisphere Sea Ice Concentration. Please note that file 1 above is not the climatology obtained from this file.</li> </ol> <p>NetCDF files 1-3 contain 12 monthly arrays with the climatological sigma_SIA computed over the period 1989-2019. Please note that the indicator in these file is in % of SIC, while in the original paper it is presented in fractional units.</p> <p>File 4 has 396 records for all the months in the analyzed period. The units are fractional as in Vichi (2022).</p> <p><strong>Reference</strong></p> <p>Vichi, M.: An indicator of sea ice variability for the Antarctic marginal ice zone, The Cryosphere, 16, 4087–4106, <a href="https://doi.org/10.5194/tc-16-4087-2022">https://doi.org/10.5194/tc-16-4087-2022</a>, 2022.</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>
Perturbed Parameters for ICEPACK-DART Study Titled "Exploring Bounded Non-parametric Ensemble Filter Impacts on Sea Ice Data Assimilation"
<p>The file contains the values of the two perturbed CICE parameters that were used in the study titled "Exploring Bounded Non-parametric Ensemble Filter Impacts on Sea Ice Data Assimilation." The tw perturbed parameters are the standard deviation of the dry snow grain radius (Rsnow), and the thermal conductivity of snow (Ksnow). There are 80 values since the ensemble used in the study had 80 members.</p>
The Antarctic sea ice reconstruction (CMST-South) based on the optimized Data Assimilation System for the Southern Ocean
<p>The wealth of historical sea ice concentration (SIC) observations, coupled with their extensive spatial coverage, renders them indispensable for the reconstruction of long-term Antarctic sea ice variability. However, recent studies have pointed out the presence of significant uncertainties in certain aspects of Antarctic sea ice reanalyses obtained from assimilating SIC. Notably, while previous studies on ocean data assimilation have already demonstrated the significance of optimizing model-dependent parameters for assimilating oceanic observations, this aspect has received limited attention in current sea ice data assimilation studies. As a result, whether optimizing model-dependent parameters can enhance the effectiveness of assimilating SIC remains an open question. Thus, we address this gap by refining the model-dependent parameters of Data Assimilation System for the Southern Ocean (DASSO), including the development of a latitude-dependent localization scheme and the objective estimation of observation error variance of SIC which takes into account both measurement errors and representation errors.</p> <p>Here, the monthly anomalies in Antarctic sea ice extent and volume (1980 -2018) are uploaded which is produced by the optimized Data Assimilation System for the Southern Ocean (DASSO) with assimilating SIC. Besides, a 13-month moving mean is applied to monthly anomalies to focus on the low-frequency variability of Antarctic sea ice.</p>
PROTECT-SLR BISICLES ice-sheet model simulations for Amundsen Sea Embayment to 2050
<p>BISICLES ice-sheet model results. The NetCDF files in ASE_BISICLES.tar.gz contain simulated output variables for the Amundsen Sea Embayment sector of the West Antarctic Ice Sheet at a spatial resolution of 1 km. The model start date is 2007 and the outputs are yearly to 2052. Each of the 30 simulations is a result of a different combination of model parameters. The parameters are the u<sub>0</sub> value in a regularized Coulomb friction law, the rate of imposed thinning of floating ice, and scaling factors for sliding and viscosity coefficients between 0.9 and 1.1. The final part of each dataset name gives the sea-level equivalent (SLE) of loss of ice above floatation within Pine Island and Thwaites Glacier catchment basins. Each output was randomly selected from a 2 cm 2050 SLE band of a histogram of a large ensemble of simulations.</p> <p>See the pdf report included for further details.</p>
Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3
<p>This is the underlying dataset of the publication "Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3" by Gürses et al. (in press), Geoscientific Model Development. In addition to unstructured mesh information, it contains the results of ocean biogeochemistry in the Regulated Ecosystem Model version 3 (REcoM3) coupled to the ocean and sea ice model FESOM2.1. The model simulations cover the period 1958 to 2021 and are forced with observed atmospheric CO<sub>2</sub> and JRA55-do atmospheric reanalyses. Three simulations are provided:</p> <p><strong>simulation A:</strong> with varying climate forcing conditions and varying atmospheric CO<sub>2</sub></p> <p><strong>simulation B:</strong> with constant climate forcing conditions and constant atmospheric CO<sub>2</sub></p> <p><strong>simulation D:</strong> with varying climate forcing conditions and constant atmospheric CO<sub>2</sub></p> <p>The following 2D/3D monthly-averaged fields (data period is given in parentheses) are provided on the native model grid:</p> <ul> <li><strong>Alk:</strong> Alkalinity (2012-2021)</li> <li><strong>CO2f:</strong> Air-Sea CO<sub>2</sub> flux (1800-2021)</li> <li><strong>DFe: </strong>Dissolved Iron concentration (2012-2021)</li> <li><strong>DIN:</strong> Dissolved Inorganic Nitrogen concentration (2012-2021)</li> <li><strong>DIC:</strong> Dissolved Inorganic Carbon concentration (1800, 1994-2021)</li> <li><strong>DSi:</strong> Dissolved Inorganic Silicon concentration (2012-2021)</li> <li><strong>DiaChl:</strong> Chlorophyll a concentration of diatoms (2012-2021)</li> <li><strong>DetC:</strong> Carbon concentration in slow-sinking detritus (2012-2021)</li> <li><strong>DetCalc: </strong>Calcite concentration in slow-sinking detritus (2012-2021)</li> <li><strong>DetSi: </strong>Silicon concentration in slow-sinking detritus (2012-2021)</li> <li><strong>idetz2c:</strong> Carbon concentration in fast-sinking detritus (2012-2021)</li> <li><strong>idetz2calc:</strong> Calcite concentration in fast-sinking detritus (2012-2021)</li> <li><strong>idetz2si:</strong> Silicon concentration in fast-sinking detritus (2012-2021)</li> <li><strong>HetC:</strong> Small zooplankton carbon biomass (2012-2021)</li> <li><strong>MLD:</strong> Mixed Layer Depth (2012-2021)</li> <li><strong>NPPn:</strong> Net Primary Production of small pyhtoplankton (2012-2021)</li> <li><strong>NPPd:</strong> Net Primary Production of diatoms (2012-2021)</li> <li><strong>O2:</strong> Dissolved Oxygen concentration (2012-2021)</li> <li><strong>PhyChl:</strong> Chlorophyll a concentration of small phytoplankton (2012-2021)</li> <li><strong>Zoo2C:</strong> Macrozooplankton carbon biomass (2012-2021)</li> <li><strong>pCO2s:</strong> Partial pressure of carbon dioxide of the surface ocean (1970-2021)</li> <li><strong>salt:</strong> Salinity (2012-2021)</li> <li><strong>temp:</strong> Temperature (2012-2021)</li> <li><strong>w:</strong> Vertical velocity (2012-2021)</li> </ul> <p>Please contact the corresponding author (ozgur.gurses@awi.de) for further information.</p>
Experiment output data from a seamless sea ice prediction system based on AWI-CM 1.1
<p>The data provide experiment output of the seamless sea ice prediction system based on AWI-CM 1.1.</p> <p>Exp_C_T_SIT_2007-2012.nc is the monthly sea ice thickness analysis averaged over from 2007 to 2012 for Exp_C_T.</p> <p>Exp_twin_SIC_SIT.tar.bz is the sea ice concentration and sea ice thickness analysis for Exp_twin.</p> <p>Exp_CTD_T_atm_T2m_n63grid.nc is the averaged 2m atmosphere temperature from 2007-2018 on N63 grid for Exp_CTD_T.<br> Exp_CTD_T_atm_u10_n63grid.nc is the averaged 10m wind velocity (u-component) from 2007-2018 on N63 grid for Exp_CTD_T.<br> Exp_CTD_T_atm_v10_n63grid.nc is the averaged 10m wind velocity (v-component) from 2007-2018 on N63 grid for Exp_CTD_T.<br> Exp_CTD_T_SALT_2007-2018_monmean.nc is the averaged ocean salinity from 2007-2018 for Exp_CTD_T. </p> <p>Exp_CTD_T_TEMP_2007-2018_monmean.nc is the averaged ocean temperature from 2007-2018 for Exp_CTD_T.<br> Exp_CTD_T_SIC_SIT_SIV.tar.bz is the analysis of sea ice concentration, sea ice thickness, and sea ice drift from 2007-2018 for Exp_CTD_T.<br> Exp_CTD_T_SIV_INC.nc is the sea ice drift increment from 2007-2018 for Exp_CTD_T.<br> Exp_CTD_T_TEMP_FCST_2007-2018_monmean.nc is the monthly mean ocean temperature forecast averaged over 2007-2018 for Exp_CTD_T.<br> Exp_CTD_T_UEL_2014-2018_mean.nc is the mean oceanic velocity (u-component) averaged over 2014-2018 for Exp_CTD_T.<br> Exp_CTD_T_VEL_2014-2018_mean.nc is the mean oceanic velocity (v-component) averaged over 2014-2018 for Exp_CTD_T.</p> <p><br> Exp_CTRL_atm_T2m_n63grid.nc is the averaged 2m atmosphere temperature from 2007-2018 on N63 grid for Exp_CTRL.<br> Exp_CTRL_atm_u10_n63grid.nc is the averaged 10m wind velocity (u-component) from 2007-2018 on N63 grid for Exp_CTRL.<br> Exp_CTRL_atm_v10_n63grid.nc is the averaged 10m wind velocity (v-component) from 2007-2018 on N63 grid for Exp_CTRL.<br> Exp_CTRL_SALT_2007-2018_monmean.nc is the averaged ocean salinity from 2007-2018 for Exp_CTRL. <br> Exp_CTRL_SIC_SIT.tar.bz is the sea ice concentration and sea ice thickness simulation from 1997-2018 for Exp_CTRL.<br> Exp_CTRL_SIV.tar.bz is sea ice drift simulation from 1997-2018 for Exp_CTRL.<br> Exp_CTRL_TEMP_2007-2018_monmean.nc is the averaged ocean temperature from 2007-2018 for Exp_CTRL. <br> Exp_CTRL_UEL_2014-2018_mean.nc is the averaged oceanic velocity (u-component) from 2014-2018 for Exp_CTRL. <br> Exp_CTRL_VEL_2014-2018_mean.nc is the averaged oceanic velocity (v-component) from 2014-2018 for Exp_CTRL. </p> <p>fesom.initial.mesh.diag.nc contains the area and volume of the CORE-II mesh.<br> mesh_core2.tar.bz contains the detailed CORE-II mesh information.<br> </p>
Dataset for "Brief communication: On calculating the sea-level contribution in marine ice-sheet models"
<p>This archive provides the data in Figures 3 and S1 of the following publication:</p> <p>Goelzer, H., Coulon, V., Pattyn, F., de Boer, B., and van de Wal, R.: Brief communication: On calculating the sea-level contribution in marine ice-sheet models , The Cryosphere, 14, 833–840, https://doi.org/10.5194/tc-14-833-2020, 2020.</p>
Multi-scale sea ice kinematics modeling dataset with a tripolar grid hierarchy (TS grids) in CESM
<p>1. We design a new tripolar grid generation method for global ocean-sea ice modeling. The generated grid is orthogonal and compatible with ocean and sea ice models. A hierarchy of ocean-sea ice model grid is constructed and incorporated in CESM by using the grid generation method. The generated tripolar grids are regarded as TS045, TS015 and TS005, with the nominal resolution of 0.45<sup>o </sup>(800x560), 0.15<sup>o</sup> (2400x1680) and 0.05<sup>o </sup>(7200x5040), respectively. The resolution range for the grid hierarchy covers climate modeling to sub-mesoscale capable for ocean modeling.</p> <p>2. Atmosphere forced simulations based on CESM D-type experiments are carried out for TS grid. Both TS045 and TS015 experiments start with no sea ice, integrate for 42 years. TS005 starts from the equilibrium state of TS015 result (36 year), runs for another 7 years.</p> <p>3. TS0*.grid files include necessary grid information, such as grid latitude, longitude, etc. TS0*.kmt files denote the deepest level at each grid location. Bilinear interpolators from TS grid to atmosphere T62 grid are provided with map*.nc files. The model outputs at year 42 are provided with T*.nc files. The last two kinds of netcdf files are compressed with Linux command "gzip".</p>
Dataset for "The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6"
<p>This data set provides processed model output of ISMIP6 Greenland projections as documented and analysed in the following publication:</p> <p>Heiko Goelzer, Sophie Nowicki, Anthony Payne, Eric Larour, Helene Seroussi, William H. Lipscomb, Jonathan Gregory, Ayako Abe-Ouchi, Andy Shepherd, Erika Simon, Cecile Agosta, Patrick Alexander, Andy Aschwanden, Alice Barthel, Reinhard Calov, Christopher Chambers, Youngmin Choi, Joshua Cuzzone, Christophe Dumas, Tamsin Edwards, Denis Felikson, Xavier Fettweis, Nicholas R. Golledge, Ralf Greve, Angelika Humbert, Philippe Huybrechts, Sebastien Le clec'h, Victoria Lee, Gunter Leguy, Chris Little, Daniel P. Lowry, Mathieu Morlighem, Isabel Nias, Aurelien Quiquet, Martin Rückamp, Nicole-Jeanne Schlegel, Donald Slater, Robin Smith, Fiamma Straneo, Lev Tarasov, Roderik van de Wal, and Michiel van den Broeke: The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6 , The Cryosphere, 2020. doi:10.5194/tc-2019-319</p> <p>About the data:<br> - The results are based on model output regridded conservatively to a 5x5 km regular ISMIP6 grid unless this is already the native grid. <br> - The results are calculated over the ice-covered area of Greenland, map projection error corrected, ice sheet model specific densities taken into account.<br> - The contribution of peripheral glaciers and ice caps has been removed, by considering their area-coverage in each grid cell.<br> - The results for the projections 'exp*' are all calculated as differences to the control experiment ctrl_proj (suffix cr in filename for control removed).<br> - Results for ctrl_proj and historical are un-corrected (no suffix cr in filename).</p> <p><br> Directory structure:<br> versionid<br> groupname1<br> modelname1<br> expid<br> scalars_mm_cr_GIS_groupname1_modelname1_expid.nc<br> scalars_rm_cr_GIS_groupname1_modelname1_expid.nc<br> scalars_zm_cr_GIS_groupname1_modelname1_expid.nc<br> ...</p> <p>Variables per file:</p> <p>scalars_mm_cr_GIS ----------------- Greenland wide numbers </p> <p>oarea - assumed ocean area [m2]<br> rhof - model specific freshwater density [kg m-3]<br> rhoi - model specific ice density [kg m-3]<br> rhow - model specific ocean water density [kg m-3]</p> <p>time - time, typically in days since X<br> iarea - Fraction of grid cell covered by land ice [1]<br> iareagr - Fraction of grid cell covered by grounded ice sheet<br> iareafl - Fraction of grid cell covered by ice sheet flowing over seawater</p> <p>ivol - ice volume [m3]<br> ivolgr - grounded ice volume [m3]<br> ivolfl - floating ice volume [m3]<br> ivaf - ice volume above flotation [m3]</p> <p>lim - ice mass [kg]<br> limgr - grounded ice mass [kg]<br> limfl - floating ice mass [kg]<br> limaf - ice mass above flotation [kg]</p> <p>sle - sea-level equivalent mass [m] !! decreases with mass loss !! <br> smb - spatially integrated surface mass balance anomaly [kg s-1]</p> <p><br> scalars_rm_cr_GIS ----------------- IMBIE2-Rignot basins xx=[no,ne,se,sw,cw,nw]</p> <p>oarea - assumed ocean area [m2]<br> rhof - model specific freshwater density [kg m-3]<br> rhoi - model specific ice density [kg m-3]<br> rhow - model specific ocean water density [kg m-3]</p> <p>time - time, typically in days since X<br> ivaf_xx - ice volume above flotation [m3]<br> smb_xx - spatially integrated surface mass balance anomaly [kg s-1]<br> limaf_xx - ice mass above flotation [kg]<br> sle_xx - sea-level equivalent mass [m] !! decreases with mass loss !! </p> <p><br> scalars_zm_cr_GIS ----------------- IMBIE2-Zwally basins xx=[z11,z12,z13,z14,z21,z22,z31,z32,z33,z41,z42,z43,z50,z61,z62,z71,z72,z81,z82]</p> <p>oarea - assumed ocean area [m2]<br> rhof - model specific freshwater density [kg m-3]<br> rhoi - model specific ice density [kg m-3]<br> rhow - model specific ocean water density [kg m-3]</p> <p>time - time, typically in days since X<br> ivaf_xx - ice volume above flotation [m3]<br> smb_xx - spatially integrated surface mass balance anomaly [kg s-1]<br> limaf_xx - ice mass above flotation [kg]<br> sle_xx - sea-level equivalent mass [m] !! decreases with mass loss !! </p> <p> </p> <p>Data usage notice:<br> If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgements should have language similar to the below.</p> <p>“We thank the Climate and Cryosphere (CliC) effort, which provided support for ISMIP6 through sponsoring of workshops, hosting the ISMIP6 website and wiki, and promoted ISMIP6. We acknowledge the World Climate Research Programme, which, through it's Working Group on Coupled Modelling, coordinated and promoted CMIP5 and CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the CMIP data and providing access, the University at Buffalo for ISMIP6 data distribution and upload, and the multiple funding agencies who support CMIP5 and CMIP6 and ESGF. We thank the ISMIP6 steering committee, the ISMIP6 model selection group and ISMIP6 dataset preparation group for their continuous engagement in defining ISMIP6."</p> <p>You should also refer to and cite the following papers:</p> <p>Heiko Goelzer, Sophie Nowicki, Anthony Payne, Eric Larour, Helene Seroussi, William H. Lipscomb, Jonathan Gregory, Ayako Abe-Ouchi, Andy Shepherd, Erika Simon, Cecile Agosta, Patrick Alexander, Andy Aschwanden, Alice Barthel, Reinhard Calov, Christopher Chambers, Youngmin Choi, Joshua Cuzzone, Christophe Dumas, Tamsin Edwards, Denis Felikson, Xavier Fettweis, Nicholas R. Golledge, Ralf Greve, Angelika Humbert, Philippe Huybrechts, Sebastien Le clec'h, Victoria Lee, Gunter Leguy, Chris Little, Daniel P. Lowry, Mathieu Morlighem, Isabel Nias, Aurelien Quiquet, Martin Rückamp, Nicole-Jeanne Schlegel, Donald Slater, Robin Smith, Fiamma Straneo, Lev Tarasov, Roderik van de Wal, and Michiel van den Broeke: The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6 , The Cryosphere, 2020. doi:10.5194/tc-2019-319</p> <p>Sophie Nowicki, Antony Payne, Heiko Goelzer, Helene Seroussi, William Lipscomb, Ayako Abe-Ouchi, Cecile Agosta, Patrick Alexander, Xylar Asay-Davis, Alice Barthel, Thomas Bracegirdle, Richard Cullather, Denis Felikson, Xavier Fettweis, Jonathan Gregory, Tore Hatterman, Nicolas Jourdain, Peter Kuipers Munneke, Eric Larour, Christopher Little, Mathieu Morlinghem, Isabel Nias, Andrew Shepherd, Erika Simon, Donald Slater, Robin Smith, Fiammetta Straneo, Luke Trusel, Michiel van den Broeke, and Roderik van de Wal: Experimental protocol for sea level projections from ISMIP6 standalone ice sheet models, The Cryosphere, doi:10.5194/tc-2019-322, 2020.</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>
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>
Primary Sea Ice Edge from Satellite Passive Microwave Observations
<p>These are matlab output files with smoothed and unsmoothed primary ice edges around Antarctica. The primary ice edge is defined as the northernmost contour of 15% sea ice concentration, and defines the outer boundary of sea ice extent. The brightness data come from three satellites; SSM/I, AMSR-E, and AMSR2 and are converted to sea ice concentrations with the NASA Team 2 algorithm and the ARTIST algorithm.</p>
Beneath the Antarctic sea-ice: Fine-scale analysis of Weddell seal (Leptonychotes weddellii) behaviour and predator-prey interactions, using micro-sonar data in Terre Adélie
<p>In this study, we tried to assess:<br>i) whether and how female Weddell seals feed (frequency, depth, duration) during lactation,<br>ii) what is their utilization of a limited foraging area (benthic or pelagic dives) as they are spatially constrained by the presence of their pup, and<br>iii) how can we characterize their foraging dives and the approach/catching phases using new tools providing a more detailed description of their behaviour.<br>Sonar tags were deployed on three breeding female Weddell seals in Terre Adélie (East Antarctica) in November 2019, to study animals' movements and dives at high resolution (3D acceleration, magnetometry, time and depth and GPS location), as well as information on prey and predator-prey interactions using acoustic data.</p>
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