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12 results for “Antarctic margins”
A data repository for: Changing phytoplankton phenology in the marginal ice zone west of the Antarctic Peninsula
<p>This data repository is a permanent archive of the results presented in the associated publication (Turner et al. 2024, Marine Ecology Progress Series, <a href="https://doi.org/10.3354/meps14567">https://doi.org/10.3354/meps14567</a>). The objective of this study was to investigate phytoplankton phenology patterns west of the Antarctic Peninsula using satellite ocean color remote sensing data. This dataset extends from 80<sup>o</sup>W to 55<sup>o</sup>W longitude and from 70<sup>o</sup>S to 60<sup>o</sup>S latitude. The data span the time period September 1997 through August 2022. This dataset includes the data and code used to create the figures in the publication. The data in this repository include chlorophyll-a concentration (Chl-a), dates of phytoplankton bloom start date and phytoplankton bloom peak date, photosynthetically active radiation (PAR), sea surface temperature (SST), wind speed, and dates of sea ice retreat and advance. Downloaded spatially-subsetted data files are included as netCDF files (extension .nc) compressed into .zip archives. Additional files used to perform the analyses and make the figures are included as MATLAB scripts and MATLAB data files (extensions .m and .mat, respectively). </p> <p>Recommended citation:</p> <p>Turner, Jessica S., (2024) A data repository for: Changing phytoplankton phenology in the marginal ice zone west of the Antarctic Peninsula. Zenodo. https://doi.org/10.5281/zenodo.10790613</p>
Machine learning-based quality assessment of Antarctic margins salinity - code, data and figures
<p>The submission contains the data, functions and code needed to reproduce the figures in Sohail et al., 2025</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>
Crystal size and texture data of sea ice from the Antarctic Marginal Ice Zone collected in Winter 2019
<p>This dataset details the crystal size and texture data from the sea ice cores collected during the SCALE Winter 2019 voyage to the Antarctic Marginal Ice Zone. Each sample collected has a series of accompanying photograph figures from which the crystal size and texture are based. </p>
Crystal size and texture data of sea ice from the Antarctic Marginal Ice Zone collected in Spring 2019
<p>This dataset details the crystal size and texture data from the sea ice cores collected during the SCALE Spring 2019 voyage to the Antarctic Marginal Ice Zone. Each sample collected has a series of accompanying photograph figures from which the crystal size and texture are based. </p>
Direct observations of wave-sea ice interactions in the Antarctic Marginal Ice Zone
<p>These are the data used in the analysis and creation of figures in S. Wahlgren, L.C. Biddle, J. Thomson and S. Swart: <em><strong>Direct observations of wave-sea ice interactions in the Antarctic Marginal Ice Zone</strong> </em>[Submitted]. Preprint: https://doi.org/10.22541/essoar.168201718.84053333/v1</p> <p>Abstract:</p> <p>Wave energy propagating into the Antarctic marginal ice zone affects the quality and extent of the sea ice, and wave propagation is therefore an important factor for understanding and predicting changes in sea ice cover. Sea ice is notoriously hard to model and in-situ observations of wave activity in the Antarctic marginal ice zone are scarce, due to the extreme conditions of the region. Here, we provide new in-situ data from two drifting Surface Wave Instrument Float with Tracking (SWIFT) buoys deployed in the Weddell Sea in the austral winter and spring in 2019. The buoy location ranges from open water to more than 200 km into the sea ice. We estimate the attenuation of swell with wave periods 8-18 s, and find an attenuation coefficient <em>α = </em>4·10<sup>-6</sup> to 7·10<sup>-5</sup> m<sup>-1</sup> in spring, and approximately five-fold larger in winter. The attenuation coefficients show a power law frequency dependence, with power coefficient 3.3 in spring and 4 in winter. The in-situ data also shows a change in wave direction, where wave direction tends to be more perpendicular to the ice edge farther into the sea ice. A possible explanation for this might be a change in the dispersion relation caused by changing sea ice composition. These observations can help shed further light on the influence of sea ice on waves propagating into the Marginal Ice Zone, aiding development of coupled wave-sea ice models.</p> <p><strong>Related code: </strong> The accompanying code can be found at <strong> </strong>https://github.com/stinawahlgren/roammiz-wave-seaice-interactions. SWIFT buoy data has been processed using https://github.com/SASlabgroup/SWIFT-codes.</p>
Sea-ice, primary productivity and ocean temperatures at the Antarctic marginal zone during late Pleistocene
<p>These data in excel refer to all proxies used to reconstruct late Quaternary sea ice and productivity in in cape Adare, Ross Sea, Antarctica, for the publication in Quaternary Science Reviews, for open access </p>
Biomarker indices and concentrations and biomarker-based temperature estimates from the Iberian Margin core MD95-2042, composite atmospheric temperature record from Greenland, and stacks of delta 18Oice and atmospheric temperature records from three Antarctic sites
<p>Core MD95-2042 alkenone and GDGT data: This dataset provides the following information for core MD95-2042: depth, age, summed OH-GDGT, iGDGT, and di-unsaturated and tri-unsaturated C<sub>37</sub> alkenone concentrations, OH-GDGT-based, iGDGT-based, and alkenone-based paleothermometric indices, GDGT-2/GDGT-3 ratio, and biomarker-based sea surface temperature (SST) and 0‐ to 200‐m sea temperature (subT; gamma function probability distribution for target temperatures with a = 4.5 and b = 15) estimates. Sediment samples were taken every 5 cm from core MD95-2042 and homogenized before lipid extraction. The lipid extracts were splitted into two fractions: one for alkenone analysis by gas chromatography coupled to a flame ionization detector, and the other for GDGT analysis by high-performance liquid chromatography coupled to mass spectrometry. All GDGT analyses were done in duplicate. The 1σ analytical uncertainties from 37 replicate analyses of the core catcher sample from core MD95-2042 are 0.007 (0.4 °C) for RI-OH, 0.008 (0.2 °C) for RI-OH′, 0.003 (0.2 °C) for TEX<sub>86</sub>, 0.238 for GDGT-2/GDGT-3, and 0.010 (0.26 °C) for U<sup>K′</sup><sub>37</sub>. RI-OH′-SST estimates are from the following global calibration: SST = (RI-OH′ + 0.029)/0.0422 (Fietz et al., 2020). RI-OH-SST estimates are from the following global calibration: SST = (RI-OH − 1.11)/0.018 (Lü et al., 2015). TEX<sub>86</sub><sup>H</sup>-SST estimates are from the following regional paleocalibration: SST = 68.4 × TEX<sub>86</sub><sup>H</sup> + 33.0 (Darfeuil et al., 2016). U<sup>K′</sup><sub>37</sub>-SST estimates are from the following global calibration: SST = 29.876 × U<sup>K′</sup><sub>37</sub> − 1.334 (Conte et al., 2006). Bayesian calibrations were also used for TEX<sub>86</sub>-SST and TEX<sub>86</sub>-subT estimates (BAYSPAR; Tierney & Tingley, 2014, 2015) and for U<sup>K′</sup><sub>37</sub>-SST estimates (BAYSPLINE; Tierney & Tingley, 2018). Alkenone data covering the 160–70 and 70–0 ka BP periods are from Davtian et al. (2021) and Darfeuil et al. (2016), respectively. GDGT data covering the 160–45 ka BP period are from Davtian et al. (2021). The age model of core MD95-2042 for the 160–43 and 43–0 ka BP periods was obtained by tuning to Chinese speleothems (Cheng et al., 2016) and by recalibrating existing <sup>14</sup>C ages with the Marine20 calibration curve (Heaton et al., 2020), respectively. MIS, Marine Isotope Stage; GDGT, glycerol dialkyl glycerol tetraether; and N/A, not available.</p> <p>Greenland atmospheric temperature record: This dataset consists in a composite Greenland atmospheric temperature record, which was built with the following records: the GISP2 atmospheric temperature record by Kobashi et al. (2017) for the 10–0 ka BP period, the NGRIP atmospheric temperature record by Kindler et al. (2014) for the 120–10 ka BP period, and the NEEM atmospheric temperature record by NEEM community members (2013) for the 129–120 ka BP period. The NEEM temperature anomalies obtained by NEEM community members (2013) were shifted by –31 °C to obtain absolute air temperatures. The employed age model is the one of Davtian and Bard (2023) for Greenland and Antarctic ice-core records.</p> <p>Antarctic δ<sup>18</sup>O<sub>ice</sub> and atmospheric temperature stacks: This dataset consists in two stacks of three Antarctic records (EDC, EDML, and WD), one for δ<sup>18</sup>O<sub>ice</sub> and the other for atmospheric temperature: both stacks are provided with their stacking uncertainties. To build the Antarctic δ<sup>18</sup>O<sub>ice</sub> stack, the Antarctic δ<sup>18</sup>O<sub>ice</sub> records were resampled every 10 years before centering to zero means and normalization to unit standard deviations over the 140–0 ka BP period (68–0 ka BP for WD). To optimize the continuity between the portions with and without the WD ice core, the Antarctic δ<sup>18</sup>O<sub>ice</sub> records were centered to zero means over the 68–67 ka BP period. The resulting Antarctic δ<sup>18</sup>O<sub>ice</sub> records were then averaged and stacking uncertainties were calculated as the pooled standard deviation of the stacked Antarctic δ<sup>18</sup>O<sub>ice</sub> records divided by the square root of the number of stacked Antarctic δ<sup>18</sup>O<sub>ice</sub> records. The final Antarctic δ<sup>18</sup>O<sub>ice</sub> stack, expressed in ‰, has the same standard deviation as the δ<sup>18</sup>O<sub>ice</sub> record from EDML over the 140–0 ka BP period, and has a zero mean over the 1–0 ka BP. The Antarctic atmospheric temperature stack was built like the Antarctic δ<sup>18</sup>O<sub>ice</sub> stack, except that the Antarctic δ<sup>18</sup>O<sub>ice</sub> records were corrected for seawater δ<sup>18</sup>O<sub>ice</sub> variations before conversion into atmospheric temperature. The employed age model is the one of Davtian and Bard (2023) for Greenland and Antarctic ice-core records.</p>
Data for Haid et al.: On the drivers of regime shifts in Antarctic marginal seas... Part I
<p>Data behind the figures in the article; 4D potential temperature and salinity in Weddell region for reference experiments REF and H20C, and reversal experiments R_ERA and R_H20C.</p>
FIGURE 4. Chitonaster cataphracus USNM 1122200 A. Abactinal surface. USNM 1122198. B. Oral surface. C. Dorsolateral surface showing marginal plate series. D in Taxonomy of high-latitude Goniasteridae (Subantarctic & Antarctic): one new genus, and three new species with an overview and key to taxa
FIGURE 4. Chitonaster cataphracus USNM 1122200 A. Abactinal surface. USNM 1122198. B. Oral surface. C. Dorsolateral surface showing marginal plate series. D. Abactinal plate surface on arm showing irregular marginal plate series. E. Close-up of abactinal plates showing absence of papulae. Scale Bar= 0.5 cm.
Data for Haid et al.: On the drivers of regime shifts in Antarctic marginal seas... Part II
<p>4D potential temperature and salinity in Weddell region for perturbation experiments SA_G, SA_S, SA_W, SUMMER_S and SUMMER_S+SAw_W</p>
FIGURE 14. Crossaster penicillatus USNM 1122950. A. Abactinal surface. B. Closeup showing abactinal paxillae. C. Actinal surface. D. Abactinal-lateral view showing marginal paxillae. E. Actinal intermediate and adambulacral spination. F in New Genera, Species, and observations on the biology of Antarctic Valvatida (Asteroidea)
FIGURE 14. Crossaster penicillatus USNM 1122950. A. Abactinal surface. B. Closeup showing abactinal paxillae. C. Actinal surface. D. Abactinal-lateral view showing marginal paxillae. E. Actinal intermediate and adambulacral spination. F. Closeup of adambulacral spination showing furrow and subambulacral spines.
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